Backtest any Indicator v5Happy Trade,
here you get the opportunity to backtest any of your indicators like a strategy without converting them into a strategy. You can choose to go long or go short and detailed time filters. Further more you can set the take profit and stop loss, initial capital, quantity per trade and set the exchange fees. You get an overall result table and even a detailed, scroll-able table with all trades. In the Image 1 you see the provided info tables about all Trades and the Result Summary. Further more every trade is marked by a background color, Labels and Levels. An opening Label with the trade direction and trade number. A closing Label again with the trade number, the trades profit in % and the total amount of $ after all past trades. A green line for the take profit level and a red line for the stop loss.
Image 1
Example
For this description we choose the Stochastic RSI indicator from TradingView as it is. In Image 2 is shown the performance of it with decent settings.
Timeframe=45, BTCUSD, 2023-08-01 - 2023-10-20
Stoch RSI: k=30, d=40, RSI-length=140, stoch-length=140
Backtest any Indicator: input signal=Stoch RSI, goLong, take profit=9.1%, stop loss=2.5%, start capital=1000$, qty=5%, fee=0.1%, no Session Filter
Image 2
Usage
1) You need to know the name of the boolean (or integer) variable of your indicator which hold the buy condition. Lets say that this boolean variable is called BUY. If this BUY variable is not plotted on the chart you simply add the following code line at the end of your pine script.
For boolean (true/false) BUY variables use this:
plot(BUY ? 1:0,'Your buy condition hold in that variable BUY',display = display.data_window)
And in case your script's BUY variable is an integer or float then use instate the following code line:
plot(BUY ,'Your buy condition hold in that variable BUY',display = display.data_window)
2) Probably the name of this BUY variable in your indicator is not BUY. Simply replace in the code line above the BUY with the name of your script's trade condition variable.
3) Save your changed Indicator script.
4) Then add this 'Backtest any Indicator' script to the chart ...
5) and go to the settings of it. Choose under "Settings -> Buy Signal" your Indicator. So in the example above choose .
The form is usually: ' : BUY'. Then you see something like Image 2
6) Decide which trade direction the BUY signal should trigger. A go Long or a go Short by set the hook or not.
Now you have a backtest of your Indicator without converting it into a strategy. You may change the setting of your Indicator to the best results and setup the following strategy settings like Time- and Session Filter, Stop Loss, Take Profit etc. More of it below in the section Settings Menu.
Appereance
In the Image 2 you see on the right side the List of Trades . To scroll down you go into the settings again and decrease the scroll value. So you can see all trades that have happened before. In case there is an open trade you will find it at the last position of the list.
Every Long trade is green back grounded while Short trades are red.
Every trade begins with a label that show goLong or goShort and its number. And ends with another label again with its number, Profit in % and the resulting total amount of cash.
If activated you further see the Take Profit as a green line and the Stop Loss as a orange line. In the settings you can set their percentage above or below the entry price.
You also see the Result Summary below. Here you find the usual stats of a strategy of all closed trades. The profit after total amount of fees , amount of trades, Profit Factor and the total amount of fees .
Settings Menu
In the settings menu you will find the following high-lighted sections. Most of the settings have a question mark on their right side. Move over it with the cursor to read specific explanation.
Input Signal of your Indicator: Under Buy you set the trade signal of your Indicator. And under Target you set the value when a trade should happen. In the Example with the Stochastic RSI above we used 20. Below you can set the trade direction, let it be go short when hooked or go long when unhooked.
Trade Settings & List of Trades: Take Profit set the target price of any trade. Stop Loss set the price to step out when a trade goes the wrong direction. Check mark the List of Trades to see any single trade with their stats. In case that there are more trades as fits in the list you can scroll down the list by decrease the value Scroll .
Time Filter: You can set a Start Time or deactivate it by leave it unhooked. The same with End Time .
Session Filter: here you can choose to activate it on weekly base. Which days of the week should be trading and those without. And also on daily base from which time on and until trade are possible. Outside of all times and sessions there will be no new trades if activated.
Invest Settings: here you can choose the amount of cash to start with. The Quantity percentage define for every trade how much of the cash should be invested and the Fee percentage which have to be payed every trade. Open position and closing position.
Other Announcements
This Backtest script don't use the strategy functions of TradingView. It is programmed as an indicator. All trades get executed at candle closing. This script use the functionality "Indicator-on-Indicator" from TradingView.
Conclusion
So now it is your turn, take your promising indicators and connect it to that Backtest script. With it you get a fast impression of how successful your indicator will trade. You don't have to relay on coders who maybe add cheating code lines. Further more you can check with the Time Filter under which market condition you indicator perform the best or not so well. Also with the Session Filter you can sort out repeating good market conditions for your indicator. Even you can check with the GoShort XOR GoLong check mark the trade signals of you indicator in opposite trade direction with one click. And compare your indicators under the same conditions and get the results just after 2 clicks. Thanks to the in-build fee setting you get an impression how much a 0.1% fee cost you in total.
Cheers
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Backtest [OptAlgo]This backtest script is designed to convert ideas or indicators into backtest results. The script creates buy/sell signals by comparing price sources against fixed values or other imported plots using many comparison methods. It has many features including multiple exit systems: TP/SL, custom plot-based stops and more. It supports full trading automation through webhook alerts with live signal processing.
🔢 Signal Creation System
→ Values Group : Compare price sources against fixed numerical values
→ Plots Group : Compare two different price sources/indicators against each other
→ Flexible Comparisons : 15+ comparison methods (equal, crossover, rising...)
→ Signal Types : Long, Short, Close All, Block signals, and combination signals
→ Merge Rules : Minimum condition requirements for signal activation
🔀 Advanced Signal Logic
→ Counter Signals : Choose between reversing positions or closing them
→ Signal Inversion : Flip all buy/sell signals with one toggle
→ External Signal Import : Import coded signals (1=Long, -1=Short, 0=Close)
→ Day Blocker : Enable/disable trading on specific weekdays
→ Session Control : Limit trading to specific market sessions
⚙️ Strategy Settings
→ Position Sides : All Ways, Long Only, or Short Only modes
→ Signal Control : Individual enable/disable for long and short signals
→ Counter Signal Mode : Reverse Open Position vs Close Open Position
→ Signal Reversal : Global signal inversion capability
🔰 Risk Management (Limiter Settings)
→ Leverage Control : Leverage with liquidation warnings
→ Drawdown Limit : Auto-halt strategy at specified drawdown percentage
→ Tradable Ratio : Use portion of available balance (0.01-1.0)
→ Contract Limit : Cap maximum contract size regardless of balance
🎯 TP/SL System
→ Fixed TP/SL : Set percentage-based take profit and stop loss
→ Custom Plot Stops : Use any indicator/plot as dynamic stop loss
→ ATR-Based Exits : Volatility-adjusted TP/SL using Average True Range
→ Realistic Protection : Prevents unrealistic TP/SL prices in live trading
→ Stop Modes : Instant (Sudden) vs Candle Close execution
→ ATR Stop Loss : Override fixed SL with volatility-based calculations
→ ATR Take Profit : Dynamic TP based on market volatility
→ Trailing Options : Safe, Normal, or Aggressive trailing methods
→ Calculation Modes : Normal, Volume-weighted, or Limited (with max %) options
→ Volume Integration : ATR levels adjust based on volume influx
🤖 Automation & Alerts
→ Webhook Integration : Send JSON alerts for automated execution
→ Live Signals : Real-time signal processing (every tick vs bar close)
→ Strategy Key : Unique identifier for automated systems
→ Early Entry : Send alerts X seconds before candle close
→ Fast Execution : Prevent signal lag in automated trading
🐞 Development Tools
→ Alert Plotting : Visualize signals directly on chart (disable for live alerts)
→ Professional Mode : Remove UI controls for faster calculation
→ Debug : Metrics are plotted in data window.
📊 Key Advantages
→ Multi-Condition Logic : Combine multiple indicators with flexible rules
→ Risk-First Design : Built-in drawdown and leverage protection
→ Automation Ready : Full webhook and alert system integration
⚠️ Important Warnings
→ High leverage combined with high SL may adjust to liquidation price
→ Use consistent leverage across all strategies on same trading isolated margin pair
→ Live signals require "Calculate on every tick" enabled in settings
→ Disable alert plotting when creating actual alerts to prevent latency
Strategy - Backtest Uber SSL Channel / SSL Indicator [UTS]Backtesting of Uber SSL Channel / SSL Indicator
Backtest with focus win/loss profitability. Formula: profitability = win / (win+loss)
Do not put too much weight on trade PNL as the value is not necessary correct.
For example: on SL or TP hit an open position is marked as to be closed but executed on the open a new candle, thus leads to incorrect PNL.
Default equity 50k
Default 2% Risk per trade
Default currency USD
Define backtest interval precisely by month, year, day
ATR (len: 14, smooth: SMA)
ATR based Stop-Loss, if hit trade will be closed and considered as loss
ATR based Take-Profit, if hit trade will be closed and considered as win
If TP or SL is hit trade is closed and of course considered as win/loss
---
DM me / Tip (see Signature) or Subscribe for access
Strategy - Backtest Uber ASH - Absolute Strength Histogram [UTS]Backtesting of Uber ASH - Absolute Strength Histogram
Backtest with focus win/loss profitability. Formula: profitability = win / (win+loss)
Do not put too much weight on trade PNL as the value is not necessary correct.
For example: on SL or TP hit an open position is marked as to be closed but executed on the open a new candle, thus leads to incorrect PNL.
Default equity 50k
Default 2% Risk per trade
Default currency USD
Define backtest interval precisely by month, year, day
ATR (len: 14, smooth: SMA)
ATR based Stop-Loss, if hit trade will be closed and considered as loss
ATR based Take-Profit, if hit trade will be closed and considered as win
If TP or SL is hit trade is closed and of course considered as win/loss
---
DM me / Tip (see Signature) or Subscribe for access
Backtest History Setup 1.0Script of strategy component to setup the backtext lookback. You setup the maximum days back in the history, which will be used for backtest.
Supertrend Oscillators w/ MAs Backtest [loxx]Backtest for the following indicator:
Features
-3 selectable TPs with ATR
-Adjustable ATR and TPs levels
-Show shorts shorts or longs
-date ranges
Instructions:
-When you select the % per TP you must make sure they add up to 100% between the ones selected or no data will show
-Used to backtest corresponding indicator linked above
Future updates:
-Add lines for TPs and SL
Backtest Custom HiLo StrategyThis script implements the HiLo trend follower strategy for backtesting the HiLo indicator. It comes with some custom options. Among them is the Type option that allows to choose between the moving averages for the highs and lows (HiLo), or highest and lowest values for those moving averages, respectively (HiLo Activator).
AI ONE SETUP | ML | Buy/sell | BACKTEST📖 Indicator User Guide
**In a nutshell:** This indicator is developed using AI and machine BYBIT:WLDUSDT.P learning, trained on hundreds of charts across various cryptocurrencies. It searches for reversal/pullback signals, filters them by trend, plots trades, and backtests them with TP/SL (fixed or dynamic).
The core principle is a one-take approach. Below is everything you need to understand and how to use it:
🚀 **What the Indicator Does**
* It displays **Buy / Sell signals** directly on the chart.
* It automatically calculates the **Take Profit (TP)** and **Stop Loss (SL)** levels.
* It can independently **backtest** trades over a selected period and display statistics (profit, number of trades, win rate).
⚙️ **Main Settings**
🔹 **1. Signals**
* **Show Signals** – Enable/disable the display of entry points.
* → Keep it **enabled** ✅ to see the Buy/Sell labels. (They are enabled by default).
🔹 **2. TP/SL**
* **TP/SL Method** – The method for calculating profit targets and stop losses.
* **Fixed** → Fixed percentages.
* **Dynamic** → Adaptive levels based on the volatility of each specific chart.
👉 **For beginners:** Start with **Dynamic**.
* **TP Percent** – The profit percentage.
* **SL Percent** – The loss percentage (stop loss level).
💡 **What happens if you choose Fixed?** Here, you set the take profit and stop loss sizes yourself! Example: TP = 2%, SL = 1%. This means that on a Buy signal, the indicator will automatically calculate the exit price at +2% (TP) or –1% (SL).
🔹 **3. Parameters for Dynamic TP/SL** (if you use Dynamic)
* The indicator will **automatically select** the take profit and stop loss zones.
* You can choose the **risk level**:
* **Lowest** – Lowest risk (smaller stop loss and take profit percentages).
* **Highest** – Higher potential profit, but a wider stop loss.
🔹 **4. Backtest**
* **Backtest Period** – You can select the start and end dates to test the strategy.
* → For example, the last 1-2 months.
* → The indicator will display statistics in a table: **profit, number of trades, win rate**.
🔹 **5. Where does it perform best?**
On **liquid coins**. Avoid low-liquidity assets with sharp moves and non-volatile charts.
👉🏻 **For setup assistance, contact us on TELEGRAM:**
t.me
Or write to us via private messages.
Miyagi BacktesterMiyagi: The attempt at mastering something for the best results.
Miyagi indicators combine multiple trigger conditions and place them in one toolbox for traders to easily use, produce alerts, backtest, reduce risk and increase profitability.
The Miyagi Backtester is a standalone backtester which is to be applied to the chart after the Miyagi indicator to be backtested.
The backtester can only backtest one script at a time, and is meant to backtest ONCE PER BAR CLOSE entries.
It is currently not possible to backtest ONCE PER BAR entries.
The backtester will allow users to all Miyagi Indicators using DCA strategies to show returns over a selectable time period.
The backtester allows leverage, and as such users should be aware of the Maximum Amount for Bot Usage and Leverage Required Calculations.
The DCA Selector switch will allow users to backtest with, or without DCA.
Static DCA is used within the backtester and allows users to see DCA Statistics on closed trades.
How to use the Miyagi Backtester
Step 1: Apply the Miyagi Indicator of Choice to backtest (4in1/10in1/Strend).
DATE AND TIME RANGE:
-Date and time range to backtest.
TRADE:
-Entry source to backtest. Please select the "Outbound Entry Signal Sender"
-Trade Direction to backtest. This can be helpful to backtest according to your strategy (long or short).
-Take Profit % to backtest. This is the percent take profit to backtest. Slippage can be accounted for on the "Properties" tab.
-Stoploss % to backtest. This is the percent stoploss to backtest.
DCA:
DCA Checkbox: Enable the DCA Checkbox to backtest with DCA. Disable it to backtest without DCA.
Leverage: Input the Leverage you will trade with.
Base Order Size (% Equity): This is the Base order (BO) size to backtest in % of equity.
Safety Order Size (% Equity): This is the Safety order (SO) size to backtest in % of equity.
Number of DCA Orders: This is the maximum amount of DCA orders to place, or total DCA orders.
Price Deviation (% from initial order): This is the percent at which the first safety is placed.
Safety Order Step Scale: This is the scale at which is applied to the deviation for the step calculation to determine next SO placement.
Safety Order Volume Scale: This is the scale at which is applied to the safety orders for the volume calculation to determine SO Volume.
Real world DCA Example:
The process is as follows.
Base Order: This is your initial order size, $100 used for Base Order
Safety Order: This is your first safety order size, which is placed at the deviation. $100 Safety Order, it is good to keep the same size as your BO for your scaling to be effective.
Price deviation: This is the deviation at which your first Safety order is placed. 0.3-0.75% used by most of our members.
Safety Order Volume Scale: This is the scale at which is applied to the safety orders for the volume calculation. Scale of 2 used, which means that SO2 = (SO1) * 2, or $200. This scaling is typical for all following orders and as such SO3 = (SO2) *2, or $400.
Safety Order Step Scale: This is the scale at which is applied to the deviation for the step calculation. This is similar to the volume scale however the last order percentage is added.
Scale of 2 used, which means that SO2 % = ((Deviation) * 2) + (SO1%). (0.5% *2) + (0.5) = 1.5%.
This scaling is typical for all following orders except that the prior deviation is used and as such SO3 = ((Prior%) * 2) + (Deviation). (1.5% * 2) +(0.5%) or 3.5%.
Total SO Number: The calculations will continue going until the last SO. It is helpful to understand the amount of SO’s and scaling determines how efficient your DCA is.
Backtester Outputs include:
Net Profit to display net profit
Daily Net Profit to estimate
Percent Profitable which shows ratio of winning trades to losing trades.
Total Trades
Winning Trades
Losing Trades (only applicable if stoploss is used)
Buy & Hold Return (of the backtested asset) to compare if the strategy used beats buy & hold return.
Avg Trade Time is very helpful to see average trade time.
Max Trade Time is very helpful to see the maximum trade time.
Total Backtested Time will return total backtested time.
Initial Capital which is taken from the Properties tab.
Max amount for Bot Usage which can be helpful to see bot usage.
Leverage Required will show you the leverage required to sustain the DCA configuration.
Total SO Deviation will allow users to see the drop coverage their DCA provides.
Max Spent which is a % of total account spent on one trade.
Max Drawdown which displays the maximum drawdown of any trade.
Max % distance from entry shows the maximum distance price went away from entry prior to the trade closing.
Max SO Used which shows the maximum number of SO's used on a single trade
Avg SO Used which shows the average number of SO's used in all closed trades.
Deals closing with BO Only calculation will show how many trades are closed without DCA.
Deals closing with 1-7 SOs calculation will show how many trades are closed with DCA, and allow for fine-tuning.
Happy Trading!
This script will be effective to backtest and produce the best settings for each timeframe and pair across all STP Scripts.
This will take a lot of the manual work out of backtesting for our users while improving profit potential.
Happy Trading!
GKD-BT Multi-Ticker Full GKD Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker Full GKD Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Multi-Ticker Full GKD Backtest
The Multi-Ticker Full GKD Backtest is a Full GKD backtest that allows traders to test single GKD-C Confirmation indicator filtered by a GKD-B Multi-Ticker Baseline, GKD-V Volatility/Volume, and GKD-C Confirmation 2 indicator across 1-10 tickers. In addition. this module adds on various other long and short signls that fall outside the normal GKD standard long and short signals. These additional signals are formed using the GKD-B Multi-Ticker Baseline, GKD-V Volatility/Volume, GKD-C Confirmation 2, and GKD-C Continuation indicators. The purpose of this backtest is to enable traders to quickly evaluate a Baseline, Volatility/Volume, Confirmation 2, and Continuation indicators filtered GKD-C Confirmation 1 indicator across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting threshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import 1-10 tickers into the GKD-B Multi-Ticker Baseline indicator
2. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-B Multi-Ticker Baseline indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
3. Select the "Multi-ticker" option in the GKD-V Volatility/Volume indicator
4. Import 1-10 tickers into the GKD-V Volatility/Volume indicator
5. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-V Volatility/Volume indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
6. Select the "Multi-ticker" option in the GKD-C Confirmation 1 indicator.
7. Import 1-10 tickers into the GKD-C Confirmation 1 indicator.
8. Import the same 1-10 indicators into the GKD-BT Multi-Ticker Full GKD Backtest.
9. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation 1 indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
10. Import 1-10 tickers into the GKD-C Confirmation 2 indicator.
11. Import the same 1-10 indicators into the GKD-BT Multi-Ticker Full GKD Backtest.
12. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation 2 indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
13. Import 1-10 tickers into the GKD-C Continuation indicator.
14. Import the same 1-10 indicators into the GKD-BT Multi-Ticker Full GKD Backtest.
15. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Continuation indicator into the GKD-BT Multi-Ticker Full GKD Backtest.
16. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
17. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C" indicator.
Activate Baseline: Activates the GKD-B Multi-Ticker Baseline.
Activate Goldie Locks Zone Minimum Threshold: Activates the inner Goldie Locks Zone from the GKD-B Multi-Ticker Baseline
Activate Goldie Locks Zone Maximum Threshold: Activates the outer Goldie Locks Zone from the GKD-B Multi-Ticker Baseline
Activate Volatility/Volume: Activates the GKD-V Volatility/Volume indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker Full GKD Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Transform as shown on the chart above
Confirmation 2: uf2018 as shown on the chart above
Continuation: Coppock Curve as shown on the chart above
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Multi-Ticker SCC Backtest
GKD-BT Multi-Ticker SCS Backtest
GKD-BT Multi-Ticker SCSC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker SCSC Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Multi-Ticker SCSC Backtest
The Multi-Ticker SCSC Backtest is a Solo Confirmation Super Complex backtest that allows traders to test single GKD-C Confirmation indicator filtered by both a GKD-B Multi-Ticker Baseline and GKD-V Volatility/Volume indicator across 1-10 tickers. In addition. this module adds on various other long and short signls that fall outside the normal GKD standard long and short signals. These additional signals are formed using the GKD-B Multi-Ticker Baseline, GKD-V Volatility/Volume, and GKD-C Continuation indicators. The purpose of this backtest is to enable traders to quickly evaluate a Baseline, Volatility/Volume, and Continuation indicators filtered GKD-C Confirmation 1 indicator across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting threshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import 1-10 tickers into the GKD-B Multi-Ticker Baseline indicator
2. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-B Multi-Ticker Baseline indicator into the GKD-BT Multi-Ticker SCSC Backtest.
3. Select the "Multi-ticker" option in the GKD-V Volatility/Volume indicator
4. Import 1-10 tickers into the GKD-V Volatility/Volume indicator
5. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-V Volatility/Volume indicator into the GKD-BT Multi-Ticker SCSC Backtest.
6. Select the "Multi-ticker" option in the GKD-C Confirmation indicator.
7. Import 1-10 tickers into the GKD-C Confirmation indicator.
8. Import the same 1-10 indicators into the GKD-BT Multi-Ticker SCSC Backtest.
9. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation indicator into the GKD-BT Multi-Ticker SCSC Backtest.
10. Import 1-10 tickers into the GKD-C Continuation indicator.
11. Import the same 1-10 indicators into the GKD-BT Multi-Ticker SCSC Backtest.
12. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Continuation indicator into the GKD-BT Multi-Ticker SCSC Backtest.
13. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
14. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C Confirmation" indicator.
Import GKD-C Continuation: Imports the "GKD-C Continuation" indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker SCSC Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Transform as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve as shown on the chart above
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Multi-Ticker SCC Backtest
GKD-BT Multi-Ticker SCS Backtest
GKD-BT Multi-Ticker SCC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker SCC Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
The Multi-Ticker SCC Backtest is a Solo Confirmation Complex backtest that allows traders to test single GKD-C confirmation indicator filtered by both a GKD-B Multi-Ticker Baseline and GKD-V Volatility/Volume indicator across 1-10 tickers. The purpose of this backtest is to enable traders to quickly evaluate a Baseline and Volatility/Volume filtered GKD-C Confirmation indicator across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting threshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import 1-10 tickers into the GKD-B Multi-Ticker Baseline indicator
2. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-B Multi-Ticker Baseline indicator into the GKD-BT Multi-Ticker SCC Backtest.
3. Select the "Multi-ticker" option in the GKD-V Volatility/Volume indicator
4. Import 1-10 tickers into the GKD-V Volatility/Volume indicator
5. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-V Volatility/Volume indicator into the GKD-BT Multi-Ticker SCC Backtest.
6. Select the "Multi-ticker" option in the GKD-C Confirmation indicator.
7. Import 1-10 tickers into the GKD-C Confirmation indicator.
8. Import the same 1-10 indicators into the GKD-BT Multi-Ticker SCC Backtest.
9. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation indicator into the GKD-BT Multi-Ticker SCC Backtest.
10. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
11. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C" indicator.
Activate Baseline: Activates the GKD-B Multi-Ticker Baseline.
Activate Goldie Locks Zone Minimum Threshold: Activates the inner Goldie Locks Zone from the GKD-B Multi-Ticker Baseline
Activate Goldie Locks Zone Maximum Threshold: Activates the outer Goldie Locks Zone from the GKD-B Multi-Ticker Baseline
Activate Volatility/Volume: Activates the GKD-V Volatility/Volume indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker SCC Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: uf2018
Continuation: Vortex
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Multi-Ticker SCS Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker SCS Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
The Multi-Ticker SCS Backtest is a Solo Confirmation Simple backtest that allows traders to test single GKD-C confirmation indicators across 1-10 tickers. The purpose of this backtest is to enable traders to quickly evaluate GKD-C across hundreds of tickers within 30-60 minutes.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting treshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Select the "Multi-ticker" option in the GKD-C Confirmation indicator.
2. Import 1-10 tickers into the GKD-C Confirmation indicator.
3. Import the same 1-10 indicators into the GKD-BT Multi-Ticker SCS Backtest.
4. Import the value "Input into NEW GKD-BT Multi-ticker Backtest" from the GKD-C Confirmation indicator into the GKD-BT Multi-Ticker SCS Backtest.
5. When importing tickers, ensure that you import the same type of tickers for all 1-10 tickers. For example, test only FX or Cryptocurrency or Stocks. Do not combine different tradable asset types.
6. Make sure that your chart is set to a ticker that corresponds to the tradable asset type. For cryptocurrency testing, set the chart to BTCUSDT. For Forex testing, set the chart to EURUSD.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-C: Imports the "GKD-C" source, which provides signals or data for the backtest.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker SCS Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Kase Peak Oscillator
Confirmation 2: uf2018
Continuation: Vortex
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Optimizer SCC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Optimizer SCC Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Optimizer SCC Backtest
The Optimizer SCC Backtest is a Solo Confirmation Complex backtest that allows traders to test single GKD-C Confirmation indicator with GKD-B Baseline and GKD-V Volatility/Volume filtering across 10 varying inputs. The purpose of this backtest is to enable traders to optimize a GKD-C indicator given varying inputs.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting treshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline indicator into the GKD-BT Optimizer SCC Backtest.
2. Import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume indicator into the GKD-BT Optimizer SCC Backtest.
3. Select the "Optimizer" option in the GKD-C Confirmation indicator
4. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Signals" into the GKD-C Indicator Signals dropdown
5. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Start" into the GKD-C Indicator Start dropdown
6. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Skip" into the GKD-C Indicator Skip dropdown
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C Confirmation" indicator.
Import GKD-C Continuation: Imports the "GKD-C Continuation" indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Optimizer Full GKD Backtest as shown on the chart above
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Transofrm as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Optimizer SCC Backtest
GKD-BT Optimizer SCC Backtest
GKD-BT Optimizer SCC Backtest
GKD-C GKD-BT Optimizer Full GKD Backtest
GKD-BT Multi-Ticker CC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Multi-Ticker CC Backtest is a backtest module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Multi-Ticker CC Backtest
This backtest allows you to test GKD-C Confirmation 1 and GKD-C Confirmation 2 indicators together without the hassle of adding additional confluence indicators. The backtest includes 1 take profit and 1 SL and various types of volatility. The backtest results on the chart are using 10% equity of 1 million total equity and $5 commission per trade.
To use this indicator:
1. Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 1 indicator into the GKD-BT Multi-Ticker CC Backtest.
2. Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 2 indicator into the GKD-BT Multi-Ticker CC Backtest.
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C Confirmation" indicator.
Import GKD-C Continuation: Imports the "GKD-C Continuation" indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker CC Backtest as shown on the chart above
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Transform as shown on the chart above
Confirmation 2: uf2018 as shown on the chart above
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Multi-Ticker SCC Backtest
GKD-BT Multi-Ticker SCS Backtest
GKD-BT Multi-Ticker SCS Backtest
GKD-BT Multi-Ticker Full GKD Backtest
LuxAlgo - Backtester (S&O)The S&O Backtester is an innovative strategy script that encompasses features + optimization methods from our Signals & Overlays™ toolkit and combines them into one easy-to-use script for backtesting the most detailed trading strategies possible.
Our Signals & Overlays™ toolkit is notorious for its signal optimization methods such as the 'Optimal Sensitivity' displayed in its dashboard which provides optimization backtesting of the Sensitivity parameter for the Confirmation & Contrarian Signals.
This strategy script allows even more detailed & precise backtests than anything available previously in the Signals & Overlays™ toolkit; including External Source inputs allowing users to use any indicator including our other paid toolkits for take profit & stop loss customization to develop strategies, along with 10+ pre-built filters directly Signals & Overlays™' features.
🔶 Features
Full Sensitivity optimization within the dashboard to find the Best Win rates or Best Profits.
Counter Trade Mode to reverse signals in undesirable market conditions (may introduce higher drawdowns)
Built-in filters for Confirmation Signals w/ Indicator Overlays from Signals & Overlays™.
Built-in Confirmation exit points are available within the settings & on by default.
External Source Input to filter signals or set custom Take Profits & Stop Losses.
Optimization Matrix dashboard option showing all possible permutations of Sensitivity.
Option to Maximize for Winrate or Best Profit.
🔶 Settings
Sensitivity signal optimizations for the Confirmation Signals algorithm
Buy & Sell conditions filters with Indicator Overlays & External Source
Take Profit exit signals option
External Source for Take Profit & Stop Loss
Sensitivity ranges
Backtest window default at 2,000 bars
External source
Dashboard locations
🔶 Usage
Backtests are not necessarily indicative of future results, although a trader may want to use a strategy script to have a deeper understanding of how their strategy responds to varying market conditions, or to use as a tool for identifying possible flaws in a strategy that could potentially be indicative of good or bad performance in the future.
A strategy script can also be useful in terms of it's ability to generate more complete & configurable alerts, giving users the option to integrate with external processes.
In the chart below we are using default settings and built-in optimization parameters to generate the highest win rate.
Results like the above will vary & finding a strategy with a high win rate does not necessarily mean it will persist into the future, however, some indications of a well-optimized strategy are:
A high number of closed trades (100+) with a consistently green equity curve
An equity curve that outperforms buy & hold
A low % max drawdown compared to the Net Profit %.
Profit factor around 1.5 or above
In the chart below we are using the Trend Catcher feature from Signals & Overlays™ as a filter for standard Confirmation Signals + exits on a higher timeframe.
By filtering bullish signals only when the Trend Catcher is bullish, as well as bearish signals for when the Trend Catcher is bearish, we have a highly profitable strategy created directly from our flagship features.
While the Signals & Overlays features being used as built-in filters can generate interesting backtests, the provided External Sources can allow for even more creativity when creating strategies. This feature allows you to use many indicators from TradingView as filters or to trigger take-profit/stop-loss events, even if they aren't from LuxAlgo.
The chart below shows the HyperWave Oscillator from our Oscillator Matrix™ being used for take-profit exit conditions, exiting a long position on a profit when crossing 80, and exiting a short position when crossing 20.
🔶 Counter Trade Mode
Our thesis has always firmly remained to use Confirmation Signals within Signals & Overlays™ as a supportive tool to find trends & use as extra confirmation within strategies.
We included the counter-trade mode as a logical way to use the Confirmation signals as direct entries for longs & shorts within more contrarian trading strategies. Many traders can relate to using a trend-following indicator and having the market not respect its conditions for entries.
This mode directly benefits a trader who is aware that market conditions are generally not-so-perfect trends all the time. Acknowledging this, allows the user to use this to their advantage by introducing countertrend following conditions as direct entries, which tend to perform very well in ranging markets.
The big downfall of using counter-trade mode is the potential for very large max-drawdowns during trending market conditions. We suggest for making a strategy to consider introducing stop-loss conditions that can efficiently minimize max-drawdowns during the process of backtesting your creations.
Sensitivity Optimization
Within the Signals & Overlays™ toolkit, we allow users to adjust the Confirmation Signals with a Sensitivity parameter.
We believe the Sensitivity paramter is the most realistic way to generate the most actionable Confirmation Signals that can navigate various market conditions, and the Confirmation Signals algorithm was designed specifically with this in mind.
This script takes this parameter and backtests it internally to generate the most profitable value to display on the dashboard located in the top right of the chart, as well as an optimization table if users enable it to visualize it's backtesting.
In the image below, we can see the optimization table showing permutations of settings within the user-selected Sensitivity range.
The suggested best setting is given at the current time for the backtesting window that's customizable within the indicator. Optimized settings for technical indicators are not indicative of future results and the best settings are highly likely / guaranteed to change over time.
Optimizing signal settings has become a popular activity amongst technical analysts, however, the real-time beneficial applications of optimizing settings are limited & best described as complicated (even with forward testing).
🔶 Strategy Properties (Important)
We strongly recommend all users to ensure they adjust the Properties within the script settings to be in line with their accounts & trading platforms of choice to ensure results from strategies built are realistic.
🔶 How to access
You can see the Author's Instructions below to learn how to get access on our website.
GKD-BT Optimizer SCS Backtest [Loxx]The Giga Kaleidoscope GKD-BT Optimizer SCS Backtest is a backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ Giga Kaleidoscope GKD-BT Optimizer SCS Backtest
The Optimizer SCS Backtest is a Solo Confirmation Simple backtest that allows traders to test single GKD-C confirmation indicators across 10 varying inputs. The purpose of this backtest is to enable traders to optimize a GKD-C indicator given varying inputs.
The backtest module supports testing with 1 take profit and 1 stop loss. It also offers the option to limit testing to a specific date range, allowing simulated forward testing using historical data. This backtest module only includes standard long and short signals. Additionally, users can choose to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. Traders can also select a highlighting treshold for Total Percent Wins and Percent Profitable, and Profit Factor.
To use this indicator:
1. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Signals" into the GKD-C Indicator Signals dropdown
1. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Start" into the GKD-C Indicator Start dropdown
1. Import a GKD-C indicator "Input into NEW GKD-BT Optimizer Backtest Skip" into the GKD-C Indicator Skip dropdown
This backtest includes the following metrics:
1. Net profit: Overall profit or loss achieved.
2. Total Closed Trades: Total number of closed trades, both winning and losing.
3. Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
4. Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
5. Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
6. Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
7. Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
Summary of notable settings:
Input Tickers separated by commas: Allows the user to input tickers separated by commas, specifying the symbols or tickers of financial instruments used in the backtest. The tickers should follow the format "EXCHANGE:TICKER" (e.g., "NASDAQ:AAPL, NYSE:MSFT").
Import GKD-B Baseline: Imports the "GKD-B Multi-Ticker Baseline" indicator.
Import GKD-V Volatility/Volume: Imports the "GKD-V Volatility/Volume" indicator.
Import GKD-C Confirmation: Imports the "GKD-C Confirmation" indicator.
Import GKD-C Continuation: Imports the "GKD-C Continuation" indicator.
Initial Capital: Represents the starting account balance for the backtest, denominated in the base currency of the trading account.
Order Size: Determines the quantity of contracts traded in each trade.
Order Type: Specifies the type of order used in the backtest, either "Contracts" or "% Equity."
Commission: Represents the commission per order or transaction cost incurred in each trade.
**the backtest data rendered to the chart above uses $5 commission per trade and 10% equity per trade with $1 million initial capital. Each backtest result for each ticker assumes these same inputs. The results are NOT cumulative, they are separate and isolate per ticker and trading side, long or short**
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Optimizer Full GKD Backtest as shown on the chart above
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Transofrm as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-BT Optimizer SCC Backtest
GKD-BT Optimizer SCS Backtest
GKD-BT Optimizer SCS Backtest
GKD-C GKD-BT Optimizer Full GKD Backtest
GKD-BT Optimizer SCSC Backtest [Loxx]The Giga Kaleidoscope GKD-BT Optimizer SCSC Backtest (Solo Confirmation Super Complex) is a Backtest module included in AlgxTrading's "Giga Kaleidoscope Modularized Trading System." (see the section Giga Kaleidoscope (GKD) Modularized Trading System below for an explanation of the GKD trading system)
**the backtest data rendered to the chart above and all screenshots below use $5 commission per trade and 10% equity per trade with $1 million initial capital**
█ GKD-BT Optimizer SCSC Backtest
The GKD-BT Optimizer SCSC Backtest is a comprehensive backtesting module designed to optimize the combination of key GKD indicators within AlgxTrading's "Giga Kaleidoscope Modularized Trading System." This module facilitates precise strategy refinement by allowing traders to configure and optimize the following critical GKD indicators:
GKD-B Baseline
GKD-V Volatility/Volume
GKD-C Confirmation 1
GKD-C Continuation
Each indicator is equipped with an "Optimizer" mode, enabling dynamic feedback and iterative improvements directly into the backtesting environment. This integrated approach ensures that each component contributes effectively to the overall strategy, providing a robust framework for achieving optimized trading outcomes.
The GKD-BT Optimizer supports granular test configurations including a single take profit and stop loss setting, and allows for targeted testing within specified date ranges to simulate forward testing with historical data. This feature is essential for evaluating the resilience and effectiveness of trading strategies under various market conditions.
Furthermore, the module is designed with user-centric features such as:
Customizable Trading Panel: Displays critical backtest results and trade statistics, which can be shown or hidden as per user preference.
Highlighting Thresholds: Users can set thresholds for Total Percent Wins, Percent Profitable, and Profit Factor, which helps in quickly identifying the most relevant metrics for analysis.
The detailed setup ensures that traders can not only adjust their strategies based on historical performance but also fine-tune their approach to meet specific trading objectives.
🔶 To configure this indicator: ***all GKD indicators listed below are all included in the AlgxTrading trading system package***
1. Add GKD-C Confirmation, GKD-B Baseline, GKD-V Volatility/Volume, and GKD-C Continuation to your chart
2. In the GKD-B Baseline indicator, change "Baseline Type" to "Optimizer"
3. In the GKD-V Volatility/Volume indicator, change "Volatility/Volume Type" to "Optimizer"
4. In the GKD-C Confirmation 1 indicator, change "Confirmation Type" to "Optimizer"
5. In the GKD-C Continuation indicator, change "Confirmation Type" to "Optimizer"
An example of steps 2-5. In the screenshot example below, we change the value "Confirmation Type" in the GKD-C Fisher Transform indicator to "Optimizer"
6. In the GKD-BT Optimizer SCSC Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline indicator into the field "Import GKD-B Baseline indicator"
7. In the GKD-BT Optimizer SCSC Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume indicator into the field "Import GKD-V Volatility/Volume indicator"
8. In the GKD-BT Optimizer SCSC Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 1 indicator into the field "Import GKD-C Confirmation 1 indicator"
9. In the GKD-BT Optimizer SCSC Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Continuation indicator into the field "Import GKD-C Continuation indicator"
An example of steps 6-9. In the screenshot example below, we import the value "Input into NEW GKD-BT Backtest" from the GKD-C Fisher Transform indicator into the GKD-BT Optimizer SCSC Backtest
10. Decide which of the 5 indicators you wish to optimize in first in the GKD-BT Optimizer SCSC Backtest. Change the value of the import from "Input into NEW GKD-BT Backtest" to "Input into NEW GKD-BT Optimizer Signals"
An example of step 10. In the screenshot example below, we chose to optimize the Confirmation 1 indicator, the GKD-C Fisher Transform. We change the value of the field "Import GKD-C Confirmation 1 indicator" from "Input into NEW GKD-BT Backtest" to "Input into NEW GKD-BT Optimizer Signals"
11. In the GKD-BT Optimizer SCSC Backtest and under the "Optimization Settings", use the dropdown menu "Optimization Indicator" to select the type of indicator you selected from step 12 above: "Baseline", "Volatility/Volume", "Confirmation 1", or "Continuation"
12. In the GKD-BT Optimizer SCSC Backtest and under the "Optimization Settings", import the value "Input into NEW GKD-BT Optimizer Start" from the indicator you selected to optimize in step 12 above into the field "Import Optimization Indicator Start"
13. In the GKD-BT Optimizer SCSC Backtest and under the "Optimization Settings", import the value "Input into NEW GKD-BT Optimizer Skip" from the indicator you selected to optimize in step 12 above into the field "Import Optimization Indicator Skip"
An example of step 11. In the screenshot example below, we select "Confirmation 1" from the "Optimization Indicator" dropdown menu
An example of steps 12 and 13. In the screenshot example below, we import "Import Optimization Indicator Start" and "Import Optimization Indicator Skip" from the GKD-C Fisher Transform indicator into their respective fields
🔶 This backtest includes the following metrics
Net profit: Overall profit or loss achieved.
Total Closed Trades: Total number of closed trades, both winning and losing.
Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
🔶 Summary of notable settings not already explained above
🔹 Backtest Properties
These settings define the financial and logistical parameters of the trading simulation, including:
Initial Capital: Specifies the starting balance for the backtest, setting the baseline for measuring profitability and loss.
Order Size: Determines the size of trades, which can be fixed or a percentage of the equity, affecting risk and return.
Order Type: Chooses between fixed contract sizes or a percentage-based order size, allowing for static or dynamic trading volumes.
Commission per Order: Accounts for trading costs, subtracting these from profits to provide a more accurate net performance result.
🔹 Signal Qualifiers
This group of settings establishes criteria related to the strategy's Baseline, and Volatility/Volume indicators in relation to the GKD-C Confirmation 1 indicator, which is crucial for validating trade signals. These include:
Maximum Allowable Post Signal Baseline Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the trend position of the Baseline, then should the Baseline "catch-up" to the long/short trend of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
Maximum Allowable Post Signal Volatility/Volume Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the position of the Volatility/Volume, then should the Volatility/Volume "catch-up" with the long/short of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
🔹 Signal Settings
Signal Options: These settings allow users to toggle the visibility of different types of entries based on the strategy criteria, such as standard entries, baseline entries, and continuation entries.
Standard Entry Rules Settings: Detailed criteria for standard entries can be customized here, including conditions on baseline agreement, price within specific zones, and agreement with other confirmation indicators.
1-Candle Rule Standard Entry Rules Settings: Similar to standard entries, but with a focus on conditions that must be met within a one-candle timeframe.
Baseline Entry Rules Settings: Specifies rules for entries based on the baseline, including conditions on confirmation agreement and price zones.
Volatility/Volume Entry Rules Settings: This includes settings for entries based on volatility or volume conditions, with specific rules on confirmation agreement and baseline agreement.
Continuation Entry Rules Settings: This group outlines the conditions for continuation entries, focusing on agreement with baseline and confirmation indicators since the entry signal trigger.
🔹 Volatility Settings
Volatility PnL Settings: Parameters for defining the type of volatility measure to use, its period, and multipliers for profit and stop levels.
Volatility Types Included
Standard Deviation of Logarithmic Returns: Quantifies asset volatility using the standard deviation applied to logarithmic returns, capturing symmetric price movements and financial returns' compound nature.
Exponential Weighted Moving Average (EWMA) for Volatility: Focuses on recent market information by applying exponentially decreasing weights to squared logarithmic returns, offering a dynamic view of market volatility.
Roger-Satchell Volatility Measure: Estimates asset volatility by analyzing the high, low, open, and close prices, providing a nuanced view of intraday volatility and market dynamics.
Close-to-Close Volatility Measure: Calculates volatility based on the closing prices of stocks, offering a streamlined but limited perspective on market behavior.
Parkinson Volatility Measure: Enhances volatility estimation by including high and low prices of the trading day, capturing a more accurate reflection of intraday market movements.
Garman-Klass Volatility Measure: Incorporates open, high, low, and close prices for a comprehensive daily volatility measure, capturing significant price movements and market activity.
Yang-Zhang Volatility Measure: Offers an efficient estimation of stock market volatility by combining overnight and intraday price movements, capturing opening jumps and overall market dynamics.
Garman-Klass-Yang-Zhang Volatility Measure: Merges the benefits of Garman-Klass and Yang-Zhang measures, providing a fuller picture of market volatility including opening market reactions.
Pseudo GARCH(2,2) Volatility Model: Mimics a GARCH(2,2) process using exponential moving averages of squared returns, highlighting volatility shocks and their future impact.
ER-Adaptive Average True Range (ATR): Adjusts the ATR period length based on market efficiency, offering a volatility measure that adapts to changing market conditions.
Adaptive Deviation: Dynamically adjusts its calculation period to offer a nuanced measure of volatility that responds to the market's intrinsic rhythms.
Median Absolute Deviation (MAD): Provides a robust measure of statistical variability, focusing on deviations from the median price, offering resilience against outliers.
Mean Absolute Deviation (MAD): Measures the average magnitude of deviations from the mean price, facilitating a straightforward understanding of volatility.
ATR (Average True Range): Finds the average of true ranges over a specified period, indicating the expected price movement and market volatility.
True Range Double (TRD): Offers a nuanced view of volatility by considering a broader range of price movements, identifying significant market sentiment shifts.
🔹 Other Settings
Backtest Dates: Users can specify the timeframe for the backtest, including start and end dates, as well as the acceptable entry time window.
Volatility Inputs: Additional settings related to volatility calculations, such as static percent, internal filter period for median absolute deviation, and parameters for specific volatility models.
UI Options: Settings to customize the user interface, including table activation, date panel visibility, and aesthetics like color and text size.
Export Options: Allows users to select the type of data to export from the backtest, focusing on metrics like net profit, total closed trades, and average profit per trade.
█ Giga Kaleidoscope (GKD) Modularized Trading System
The GKD Trading System is a comprehensive, algorithmic trading framework from AlgxTrading, designed to optimize trading strategies across various market conditions. It employs a modular approach, incorporating elements such as volatility assessment, trend identification through a baseline, multiple confirmation strategies for signal accuracy, and volume analysis. Key components also include specialized strategies for entry and exit, enabling precise trade execution. The system allows for extensive backtesting, providing traders with the ability to evaluate the effectiveness of their strategies using historical data. Aimed at reducing setup time, the GKD system empowers traders to focus more on strategy refinement and execution, leveraging a wide array of technical indicators for informed decision-making.
🔶 Core components of a GKD Algorithmic Trading System
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system. The GKD algorithm is built on the principles of trend, momentum, and volatility. There are eight core components in the GKD trading algorithm:
🔹 Volatility - In the GKD trading system, volatility is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. There are 17+ different types of volatility available in the GKD system including Average True Range (ATR), True Range Double (TRD), Close-to-Close, Garman-Klass, and more.
🔹 Baseline (GKD-B) - The baseline is essentially a moving average and is used to determine the overall direction of the market. The baseline in the GKD trading system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other GKD indicators.
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards or price is above the baseline, then only long trades are taken, and if the baseline is sloping downwards or price is below the baseline, then only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
🔹 Confirmation 1, Confirmation 2, Continuation (GKD-C) - The GKD trading system incorporates technical confirmation indicators for the generation of its primary long and short signals, essential for its operation.
The GKD trading system distinguishes three specific categories. The first category, Confirmation 1 , encompasses technical indicators designed to identify trends and generate explicit trading signals. The second category, Confirmation 2 , a technical indicator used to identify trends; this type of indicator is primarily used to filter the Confirmation 1 indicator signals; however, this type of confirmation indicator also generates signals*. Lastly, the Continuation category includes technical indicators used in conjunction with Confirmation 1 and Confirmation 2 to generate a special type of trading signal called a "Continuation"
In a full GKD trading system all three categories generate signals. (see the section “GKD Trading System Signals” below)
🔹 Volatility/Volume (GKD-V) - Volatility/Volume indicators are used to measure the amount of buying and selling activity in a market. They are based on the trading Volatility/Volume of the market, and can provide information about the strength of the trend. In the GKD trading system, Volatility/Volume indicators are used to confirm trading signals generated by the various other GKD indicators. In the GKD trading system, Volatility is a proxy for Volume and vice versa.
Volatility/Volume indicators reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by GKD-C confirmation and GKD-B baseline indicators.
🔹 Exit (GKD-E) - The exit indicator in the GKD system is an indicator that is deemed effective at identifying optimal exit points. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
🔹 Backtest (GKD-BT) - The GKD-BT backtest indicators link all other GKD-C, GKD-B, GKD-E, GKD-V, and GKD-M components together to create a GKD trading system. GKD-BT backtests generate signals (see the section “GKD Trading System Signals” below) from the confluence of various GKD indicators that are imported into the GKD-BT backtest. Backtest types include: GKD-BT solo and full GKD backtest strategies used for a single ticker; GKD-BT optimizers used to optimize a single indicator or the full GKD trading system; GKD-BT Multi-ticker used to backtest a single indicator or the full GKD trading system across up to ten tickers; GKD-BT exotic backtests like CC, Baseline, and Giga Stacks used to test confluence between GKD components to then be injected into a core GKD-BT Multi-ticker backtest or single ticker strategy.
🔹 Metamorphosis (GKD-M) ** - The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, GKD-E, or GKD-V slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
*see the section “GKD Trading System Signals” below
**not a required component of the GKD algorithm
🔶 What does the application of the GKD trading system look like?
Example trading system:
Volatility: Average True Range (ATR) (selectable in all backtests and other related GKD indicators)
GKD-B Baseline: GKD-B Multi-Ticker Baseline using Hull Moving Average
GKD-C Confirmation 1 : GKD-C Advance Trend Pressure
GKD-C Confirmation 2: GKD-C Dorsey Inertia
GKD-C Continuation: GKD-C Stochastic of RSX
GKD-V Volatility/Volume: GKD-V Damiani Volatmeter
GKD-E Exit: GKD-E MFI
GKD-BT Backtest: GKD-BT Multi-Ticker Full GKD Backtest
GKD-M Metamorphosis: GKD-M Baseline Optimizer
**all indicators mentioned above are included in the same AlgxTrading package**
Each module is passed to a GKD-BT backtest module. In the backtest module, all components are combined to formulate trading signals and statistical output. This chaining of indicators requires that each module conform to AlgxTrading's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the various indictor types in the GKD algorithm.
🔶 GKD Trading System Signals
Standard Entry requires a sequence of conditions including a confirmation signal from GKD-C, baseline agreement, price criteria related to the Goldie Locks Zone, and concurrence from a second confirmation and volatility/volume indicators.
1-Candle Standard Entry introduces a two-phase process where initial conditions must be met, followed by a retraction in price and additional confirmations in the subsequent candle, including baseline, confirmations 1 and 2, and volatility/volume criteria.
Baseline Entry focuses on signals generated by the GKD-B Baseline, requiring agreement from confirmation signals, specific price conditions within the Goldie Locks Zone, and a timing condition related to the confirmation 1 signal.
1-Candle Baseline Entry mirrors the baseline entry but adds a requirement for a price retraction and subsequent confirmations in the following candle, maintaining the focus on the baseline's guidance.
Volatility/Volume Entry is predicated on signals from volatility/volume indicators, requiring support from confirmations, price criteria within the Goldie Locks Zone, baseline agreement, and a timing condition for the confirmation 1 signal.
1-Candle Volatility/Volume Entry adapts the volatility/volume entry to include a phase of initial signal and agreement, followed by a retracement phase that seeks further agreement from the system's components in the subsequent candle.
Confirmation 2 Entry is based on the second confirmation signal, requiring the first confirmation's agreement, specific price criteria, agreement from volatility/volume indicators, and baseline, with a timing condition for the confirmation 1 signal.
1-Candle Confirmation 2 Entry adds a retracement requirement to the confirmation 2 entry, necessitating additional agreements from the system's components in the candle following the signal.
PullBack Entry initiates with a baseline signal and agreement from the first confirmation, with a price condition related to volatility. It then looks for price to return within the Goldie Locks Zone and seeks further agreement from the system's components in the subsequent candle.
Continuation Entry allows for the continuation of an active position, based on a previously triggered entry strategy. It requires that the baseline hasn't crossed since the initial trigger, alongside ongoing agreements from confirmations and the baseline.
█ Conclusion
The GKD-BT Optimizer SCSC Backtest is a critical tool within the Giga Kaleidoscope Modularized Trading System, designed for precise strategy refinement and evaluation within the GKD framework. It enables the optimization and testing of various trading indicators and strategies under different market conditions. The module's design facilitates detailed analysis of individual trading components' performance, allowing for the optimization of indicators like Baseline, Volatility/Volume, Confirmation, and Continuation. This optimization process aids traders in identifying the most effective configurations, thereby enhancing trading outcomes and strategy efficiency within the GKD ecosystem.
█ How to Access
You can see the Author's Instructions below to learn how to get access.
GKD-BT Optimizer Full GKD Backtest [Loxx]The Giga Kaleidoscope GKD-BT Optimizer Full GKD Backtest is a Backtest module included in AlgxTrading's "Giga Kaleidoscope Modularized Trading System." (see the section Giga Kaleidoscope (GKD) Modularized Trading System below for an explanation of the GKD trading system)
**the backtest data rendered to the chart above and all screenshots below use $5 commission per trade and 10% equity per trade with $1 million initial capital**
█ GKD-BT Optimizer Full GKD Backtest
The GKD-BT Optimizer Full GKD Backtest is a comprehensive backtesting module designed to optimize the combination of key GKD indicators within AlgxTrading's "Giga Kaleidoscope Modularized Trading System." This module facilitates precise strategy refinement by allowing traders to configure and optimize the following critical GKD indicators:
GKD-B Baseline
GKD-V Volatility/Volume
GKD-C Confirmation 1
GKD-C Confirmation 2
GKD-C Continuation
Each indicator is equipped with an "Optimizer" mode, enabling dynamic feedback and iterative improvements directly into the backtesting environment. This integrated approach ensures that each component contributes effectively to the overall strategy, providing a robust framework for achieving optimized trading outcomes.
The GKD-BT Optimizer supports granular test configurations including a single take profit and stop loss setting, and allows for targeted testing within specified date ranges to simulate forward testing with historical data. This feature is essential for evaluating the resilience and effectiveness of trading strategies under various market conditions.
Furthermore, the module is designed with user-centric features such as:
Customizable Trading Panel: Displays critical backtest results and trade statistics, which can be shown or hidden as per user preference.
Highlighting Thresholds: Users can set thresholds for Total Percent Wins, Percent Profitable, and Profit Factor, which helps in quickly identifying the most relevant metrics for analysis.
The detailed setup ensures that traders can not only adjust their strategies based on historical performance but also fine-tune their approach to meet specific trading objectives.
🔶 To configure this indicator: ***all GKD indicators listed below are all included in the AlgxTrading trading system package***
1. Add GKD-C Confirmation, GKD-B Baseline, GKD-V Volatility/Volume, GKD-C Confirmation 2, and GKD-C Continuation to your chart
2. In the GKD-B Baseline indicator, change "Baseline Type" to "Optimizer"
3. In the GKD-V Volatility/Volume indicator, change "Volatility/Volume Type" to "Optimizer"
4. In the GKD-C Confirmation 1 indicator, change "Confirmation Type" to "Optimizer"
5. In the GKD-C Confirmation 2 indicator, change "Confirmation Type" to "Optimizer"
6. In the GKD-C Continuation indicator, change "Confirmation Type" to "Optimizer"
An example of steps 2-6. In the screenshot example below, we change the value "Confirmation Type" in the GKD-C Fisher Transform indicator to "Optimizer"
7. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline indicator into the field "Import GKD-B Baseline indicator"
8. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume indicator into the field "Import GKD-V Volatility/Volume indicator"
9. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 1 indicator into the field "Import GKD-C Confirmation 1 indicator"
10. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 2 indicator into the field "Import GKD-C Confirmation 2 indicator"
11. In the GKD-BT Optimizer Full GKD Backtest, import the value "Input into NEW GKD-BT Backtest" from the GKD-C Continuation indicator into the field "Import GKD-C Continuation indicator"
An example of steps 7-11. In the screenshot example below, we import the value "Input into NEW GKD-BT Backtest" from the GKD-C Coppock Curve indicator into the GKD-BT Optimizer Full GKD Backtest
12. Decide which of the 5 indicators you wish to optimize in first in the GKD-BT Optimizer Full GKD Backtest. Change the value of the import from "Input into NEW GKD-BT Backtest" to "Input into NEW GKD-BT Optimizer Signals"
An example of step 12. In the screenshot example below, we chose to optimize the Confirmation 1 indicator, the GKD-C Fisher Transform. We change the value of the field "Import GKD-C Confirmation 1 indicator" from "Input into NEW GKD-BT Backtest" to "Input into NEW GKD-BT Optimizer Signals"
13. In the GKD-BT Optimizer Full GKD Backtest and under the "Optimization Settings", use the dropdown menu "Optimization Indicator" to select the type of indicator you selected from step 12 above: "Baseline", "Volatility/Volume", "Confirmation 1", "Confirmation 2", or "Continuation"
14. In the GKD-BT Optimizer Full GKD Backtest and under the "Optimization Settings", import the value "Input into NEW GKD-BT Optimizer Start" from the indicator you selected to optimize in step 12 above into the field "Import Optimization Indicator Start"
15. In the GKD-BT Optimizer Full GKD Backtest and under the "Optimization Settings", import the value "Input into NEW GKD-BT Optimizer Skip" from the indicator you selected to optimize in step 12 above into the field "Import Optimization Indicator Skip"
An example of step 13. In the screenshot example below, we select "Confirmation 1" from the "Optimization Indicator" dropdown menu
An example of steps 14 and 15. In the screenshot example below, we import "Import Optimization Indicator Start" and "Import Optimization Indicator Skip" from the GKD-C Fisher Transform indicator into their respective fields
🔶 This backtest includes the following metrics
Net profit: Overall profit or loss achieved.
Total Closed Trades: Total number of closed trades, both winning and losing.
Total Percent Wins: Total wins, whether long or short, for the selected time interval regardless of commissions and other profit-modifying addons.
Percent Profitable: Total wins, whether long or short, that are also profitable, taking commissions into account.
Profit Factor: The ratio of gross profits to gross losses, indicating how much money the strategy made for every unit of money it lost.
Average Profit per Trade: The average gain or loss per trade, calculated by dividing the net profit by the total number of closed trades.
Average Number of Bars in Trade: The average number of bars that elapsed during trades for all closed trades.
🔶 Summary of notable settings not already explained above
🔹 Backtest Properties
These settings define the financial and logistical parameters of the trading simulation, including:
Initial Capital: Specifies the starting balance for the backtest, setting the baseline for measuring profitability and loss.
Order Size: Determines the size of trades, which can be fixed or a percentage of the equity, affecting risk and return.
Order Type: Chooses between fixed contract sizes or a percentage-based order size, allowing for static or dynamic trading volumes.
Commission per Order: Accounts for trading costs, subtracting these from profits to provide a more accurate net performance result.
🔹 Signal Qualifiers
This group of settings establishes criteria related to the strategy's Baseline, Volatility/Volume, and Confirmation 2 indicators in relation to the GKD-C Confirmation 1 indicator, which is crucial for validating trade signals. These include:
Maximum Allowable Post Signal Baseline Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the trend position of the Baseline, then should the Baseline "catch-up" to the long/short trend of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
Maximum Allowable Post Signal Volatility/Volume Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the position of the Volatility/Volume, then should the Volatility/Volume "catch-up" with the long/short of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
Maximum Allowable Post Signal Confirmation 2 Cross Bars Back: Sets the maximum number of bars that can elapse after a signal generated by a GKD-C Confirmation 1 indicator triggers. If the GKD-C Confirmation 1 indicator generates a long/short signal that doesn't yet agree with the trend position of the Confirmation 2, then should the Confirmation 2 "catch-up" to the long/short trend of the GKD-C Confirmation 1 indicator within the number of bars specified by this setting, then a signal is generated.
🔹 Signal Settings
Signal Options: These settings allow users to toggle the visibility of different types of entries based on the strategy criteria, such as standard entries, baseline entries, and continuation entries.
Standard Entry Rules Settings: Detailed criteria for standard entries can be customized here, including conditions on baseline agreement, price within specific zones, and agreement with other confirmation indicators.
1-Candle Rule Standard Entry Rules Settings: Similar to standard entries, but with a focus on conditions that must be met within a one-candle timeframe.
Baseline Entry Rules Settings: Specifies rules for entries based on the baseline, including conditions on confirmation agreement and price zones.
Volatility/Volume Entry Rules Settings: This includes settings for entries based on volatility or volume conditions, with specific rules on confirmation agreement and baseline agreement.
Confirmation 2 Entry Rules Settings: Settings here define the rules for entries based on a second confirmation indicator, detailing the required agreements and conditions.
Continuation Entry Rules Settings: This group outlines the conditions for continuation entries, focusing on agreement with baseline and confirmation indicators since the entry signal trigger.
🔹 Volatility Settings
Volatility PnL Settings: Parameters for defining the type of volatility measure to use, its period, and multipliers for profit and stop levels.
Volatility Types Included
Standard Deviation of Logarithmic Returns: Quantifies asset volatility using the standard deviation applied to logarithmic returns, capturing symmetric price movements and financial returns' compound nature.
Exponential Weighted Moving Average (EWMA) for Volatility: Focuses on recent market information by applying exponentially decreasing weights to squared logarithmic returns, offering a dynamic view of market volatility.
Roger-Satchell Volatility Measure: Estimates asset volatility by analyzing the high, low, open, and close prices, providing a nuanced view of intraday volatility and market dynamics.
Close-to-Close Volatility Measure: Calculates volatility based on the closing prices of stocks, offering a streamlined but limited perspective on market behavior.
Parkinson Volatility Measure: Enhances volatility estimation by including high and low prices of the trading day, capturing a more accurate reflection of intraday market movements.
Garman-Klass Volatility Measure: Incorporates open, high, low, and close prices for a comprehensive daily volatility measure, capturing significant price movements and market activity.
Yang-Zhang Volatility Measure: Offers an efficient estimation of stock market volatility by combining overnight and intraday price movements, capturing opening jumps and overall market dynamics.
Garman-Klass-Yang-Zhang Volatility Measure: Merges the benefits of Garman-Klass and Yang-Zhang measures, providing a fuller picture of market volatility including opening market reactions.
Pseudo GARCH(2,2) Volatility Model: Mimics a GARCH(2,2) process using exponential moving averages of squared returns, highlighting volatility shocks and their future impact.
ER-Adaptive Average True Range (ATR): Adjusts the ATR period length based on market efficiency, offering a volatility measure that adapts to changing market conditions.
Adaptive Deviation: Dynamically adjusts its calculation period to offer a nuanced measure of volatility that responds to the market's intrinsic rhythms.
Median Absolute Deviation (MAD): Provides a robust measure of statistical variability, focusing on deviations from the median price, offering resilience against outliers.
Mean Absolute Deviation (MAD): Measures the average magnitude of deviations from the mean price, facilitating a straightforward understanding of volatility.
ATR (Average True Range): Finds the average of true ranges over a specified period, indicating the expected price movement and market volatility.
True Range Double (TRD): Offers a nuanced view of volatility by considering a broader range of price movements, identifying significant market sentiment shifts.
🔹 Other Settings
Backtest Dates: Users can specify the timeframe for the backtest, including start and end dates, as well as the acceptable entry time window.
Volatility Inputs: Additional settings related to volatility calculations, such as static percent, internal filter period for median absolute deviation, and parameters for specific volatility models.
UI Options: Settings to customize the user interface, including table activation, date panel visibility, and aesthetics like color and text size.
Export Options: Allows users to select the type of data to export from the backtest, focusing on metrics like net profit, total closed trades, and average profit per trade.
█ Giga Kaleidoscope (GKD) Modularized Trading System
The GKD Trading System is a comprehensive, algorithmic trading framework from AlgxTrading, designed to optimize trading strategies across various market conditions. It employs a modular approach, incorporating elements such as volatility assessment, trend identification through a baseline, multiple confirmation strategies for signal accuracy, and volume analysis. Key components also include specialized strategies for entry and exit, enabling precise trade execution. The system allows for extensive backtesting, providing traders with the ability to evaluate the effectiveness of their strategies using historical data. Aimed at reducing setup time, the GKD system empowers traders to focus more on strategy refinement and execution, leveraging a wide array of technical indicators for informed decision-making.
🔶 Core components of a GKD Algorithmic Trading System
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system. The GKD algorithm is built on the principles of trend, momentum, and volatility. There are eight core components in the GKD trading algorithm:
🔹 Volatility - In the GKD trading system, volatility is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. There are 17+ different types of volatility available in the GKD system including Average True Range (ATR), True Range Double (TRD), Close-to-Close, Garman-Klass, and more.
🔹 Baseline (GKD-B) - The baseline is essentially a moving average and is used to determine the overall direction of the market. The baseline in the GKD trading system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other GKD indicators.
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards or price is above the baseline, then only long trades are taken, and if the baseline is sloping downwards or price is below the baseline, then only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
🔹 Confirmation 1, Confirmation 2, Continuation (GKD-C) - The GKD trading system incorporates technical confirmation indicators for the generation of its primary long and short signals, essential for its operation.
The GKD trading system distinguishes three specific categories. The first category, Confirmation 1 , encompasses technical indicators designed to identify trends and generate explicit trading signals. The second category, Confirmation 2 , a technical indicator used to identify trends; this type of indicator is primarily used to filter the Confirmation 1 indicator signals; however, this type of confirmation indicator also generates signals*. Lastly, the Continuation category includes technical indicators used in conjunction with Confirmation 1 and Confirmation 2 to generate a special type of trading signal called a "Continuation"
In a full GKD trading system all three categories generate signals. (see the section “GKD Trading System Signals” below)
🔹 Volatility/Volume (GKD-V) - Volatility/Volume indicators are used to measure the amount of buying and selling activity in a market. They are based on the trading Volatility/Volume of the market, and can provide information about the strength of the trend. In the GKD trading system, Volatility/Volume indicators are used to confirm trading signals generated by the various other GKD indicators. In the GKD trading system, Volatility is a proxy for Volume and vice versa.
Volatility/Volume indicators reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by GKD-C confirmation and GKD-B baseline indicators.
🔹 Exit (GKD-E) - The exit indicator in the GKD system is an indicator that is deemed effective at identifying optimal exit points. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
🔹 Backtest (GKD-BT) - The GKD-BT backtest indicators link all other GKD-C, GKD-B, GKD-E, GKD-V, and GKD-M components together to create a GKD trading system. GKD-BT backtests generate signals (see the section “GKD Trading System Signals” below) from the confluence of various GKD indicators that are imported into the GKD-BT backtest. Backtest types include: GKD-BT solo and full GKD backtest strategies used for a single ticker; GKD-BT optimizers used to optimize a single indicator or the full GKD trading system; GKD-BT Multi-ticker used to backtest a single indicator or the full GKD trading system across up to ten tickers; GKD-BT exotic backtests like CC, Baseline, and Giga Stacks used to test confluence between GKD components to then be injected into a core GKD-BT Multi-ticker backtest or single ticker strategy.
🔹 Metamorphosis (GKD-M) ** - The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, GKD-E, or GKD-V slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
*see the section “GKD Trading System Signals” below
**not a required component of the GKD algorithm
🔶 What does the application of the GKD trading system look like?
Example trading system:
Volatility: Average True Range (ATR) (selectable in all backtests and other related GKD indicators)
GKD-B Baseline: GKD-B Multi-Ticker Baseline using Hull Moving Average
GKD-C Confirmation 1 : GKD-C Advance Trend Pressure
GKD-C Confirmation 2: GKD-C Dorsey Inertia
GKD-C Continuation: GKD-C Stochastic of RSX
GKD-V Volatility/Volume: GKD-V Damiani Volatmeter
GKD-E Exit: GKD-E MFI
GKD-BT Backtest: GKD-BT Multi-Ticker Full GKD Backtest
GKD-M Metamorphosis: GKD-M Baseline Optimizer
**all indicators mentioned above are included in the same AlgxTrading package**
Each module is passed to a GKD-BT backtest module. In the backtest module, all components are combined to formulate trading signals and statistical output. This chaining of indicators requires that each module conform to AlgxTrading's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the various indictor types in the GKD algorithm.
🔶 GKD Trading System Signals
Standard Entry requires a sequence of conditions including a confirmation signal from GKD-C, baseline agreement, price criteria related to the Goldie Locks Zone, and concurrence from a second confirmation and volatility/volume indicators.
1-Candle Standard Entry introduces a two-phase process where initial conditions must be met, followed by a retraction in price and additional confirmations in the subsequent candle, including baseline, confirmations 1 and 2, and volatility/volume criteria.
Baseline Entry focuses on signals generated by the GKD-B Baseline, requiring agreement from confirmation signals, specific price conditions within the Goldie Locks Zone, and a timing condition related to the confirmation 1 signal.
1-Candle Baseline Entry mirrors the baseline entry but adds a requirement for a price retraction and subsequent confirmations in the following candle, maintaining the focus on the baseline's guidance.
Volatility/Volume Entry is predicated on signals from volatility/volume indicators, requiring support from confirmations, price criteria within the Goldie Locks Zone, baseline agreement, and a timing condition for the confirmation 1 signal.
1-Candle Volatility/Volume Entry adapts the volatility/volume entry to include a phase of initial signal and agreement, followed by a retracement phase that seeks further agreement from the system's components in the subsequent candle.
Confirmation 2 Entry is based on the second confirmation signal, requiring the first confirmation's agreement, specific price criteria, agreement from volatility/volume indicators, and baseline, with a timing condition for the confirmation 1 signal.
1-Candle Confirmation 2 Entry adds a retracement requirement to the confirmation 2 entry, necessitating additional agreements from the system's components in the candle following the signal.
PullBack Entry initiates with a baseline signal and agreement from the first confirmation, with a price condition related to volatility. It then looks for price to return within the Goldie Locks Zone and seeks further agreement from the system's components in the subsequent candle.
Continuation Entry allows for the continuation of an active position, based on a previously triggered entry strategy. It requires that the baseline hasn't crossed since the initial trigger, alongside ongoing agreements from confirmations and the baseline.
█ Conclusion
The GKD-BT Optimizer Full GKD Backtest is a critical tool within the Giga Kaleidoscope Modularized Trading System, designed for precise strategy refinement and evaluation within the GKD framework. It enables the optimization and testing of various trading indicators and strategies under different market conditions. The module's design facilitates detailed analysis of individual trading components' performance, allowing for the optimization of indicators like Baseline, Volatility/Volume, Confirmation, and Continuation. This optimization process aids traders in identifying the most effective configurations, thereby enhancing trading outcomes and strategy efficiency within the GKD ecosystem.
█ How to Access
You can see the Author's Instructions below to learn how to get access.
Easy Backtester PROWHAT IS EasyBacktester ?
EasyBacktester is a tool that helps you backtest trading strategies built by yourself with an included strategy builder and a multitude of options.
From within the parameters of the tool, you can specifically pick your entry settings across 12 most common indicators, such as "RSI", "MACD", "Moving Averages" etc... Then you can immediately visualise your setup's Stop-loss & Take Profit, your expected Profits & Loss and a lot of other statistics for your entry strategy. Once you are satisfied with your entry strategy, you are given a set of tools to optimize your setup using stop-loss rules, take profits rules, partial profits, trailing-stops, entry timing...
WHY IS THIS TOOL DIFFERENT ?
EasyBacktester is a backtesting engine with no coding skills required. TradingView allows for "Strategy Scripting" using PineScript, which is not an option for non-coder audience. EasyBacktester fills this gap and allow non-coders to get an idea of how their trading strategies may perform using mouse clicks only.
Some similar attempts have been made on TradingView, allowing some limited options, but none have the same capabilities EasyBacktester offers, for instance, as of April 2022 these features have not been seen in any other TradingView tools:
- partial take profits
- leverage simulation
- a multitude of trailing stop-loss possibilities including trail triggers and trail parameters
- visualisation of entries including stop-loss, take profits, partial take profits, and trailing stops. One can now visualize such complex setups.
- visualisation of Profits & Loss
- time in trade
- wait strategy after a signal: for example, when RSI is oversold, "WAIT until price retraces 100% of the original signal" amongst other possibilities
QUICK START GUIDE:
STEP 1: DEFINE YOUR SIGNAL STRATEGY
From the settings of the tool, find the "SIGNALS STRATEGY" section.
Select a type of entry you wish to simulate, for example "LONGs", and activate the checkbox right before "Simulate".
Right below, you will find 4 signal builder for you to play with and pick your strategy accordingly.
For example, to simulate a signal when RSI is oversold, follow these steps:
- On the 1st multiple choice box, select "RSI"
- On the 2nd multiple choice box, select "is below..."
- On the 3rd multiple choice box, select "OverSold level"
Don't forget to activate this rule by checking the checkbox in front of it.
After this first step, one should immediately see the chart affected with some plots. The dots represents the signal entry defined by the rule we just created, and the red/green boxes visually represent trades that could have been taken with this signal which, in this example, occurs "when RSI is below oversold level". Note that all specific parameters for RSI including its specific "oversold level" is customisable at the end of the tools settings along with all other indicators settings.
STEP 2: STATISTICS
By default, the "APPEARANCE" section only plots potential entry signals (materialized by dots) and actual entry boxes (materialized by red/green boxes).
But the user can easily add other precious statistics to the chart, and obviously the most important one for backtesting: Profits & Loss (P&L).
In the "STATISTICS" section please check the "P&L" box to see appear a chart of the simulated P&L for our example. You should immediately see a new graph below the chart representing the evolution of the P&L after each entry.
Other statistics are available to the user, including: Equity, Number of Trades, Time in Position, Number of trades Won, Number of trades Lost, Number of trades Stopped.
Play around with those to see them plotted on your chart.
STEP 3: OPTIMIZE YOUR ENTRY
Under the "ENTRY STRATEGY" section, one can pick how to enter AFTER the signal, which provides the user with an extensive flexibility to pick its timing.
Here there are a various set of choices offered, ranging from the default "Market Order at Next Candle Open", to "Limit Order: at signal's candle open" or even "Stop-Buy: at break of last candle high". As its name suggests, this option allows you to actually wait before randomly enter in trade.
It is important to also note that the user can totally prevent entry if the conditions are not filled after a customizable number of candles represented in "Max bars to wait for entry" (default being 1, meaning the engine will wait the condition to be filled during only 1 candle)
STEP 4: MANAGE YOUR RISK
Under the "RISK MANAGEMENT" section, the user is given a series of options to set the amount (s)he would like to risk.
This is extremely important to set, and is the result of a combination of customizable options including:
- the Initial Capital of the account
- the amount to risk per trade, and HOW to risk it: some fixed % the initial equity or adjust the stop-loss to the desired risk ?
- use of leverage or not
- initial stop-loss, as well as minimum and maximum
- trailing stop-loss: what should trigger the trailing ? and by how much should the engine trail ?
STEP 5: HAVE AN EXIT PLAN
Under the "EXIT STRATEGY", the user can define how to exit the trade.
For instance, here again a lot of options are given:
- Take Profit: exit at some level of profits defined by a multiple of the stop-loss, or a multiple of the ATR, or some % or points
- Partial Profit taking before exit
- Panic close position after some time spent on the trade
STEP 6: FURTHER OPTIMIZATIONS
Under the sections "Commissions" & "Calendar & Sessions", one can simulate real trading conditions by including commissions fees as well as filtering actual dates and trading sessions. These sections are straightforward for any user to use.
SETP 7: INDICATORS SETTINGS
Since EasyBacktester uses a predefined set of indicators to get started, those indicators are also customizable in the last section of the settings. Here, one can easily customize RSI periodicity, MACD lengths, Moving averages types & lengths, ATR, etc...
STEP 8: GOING FURTHER
This is only a start to give users an overview of how various options affect their trading performance. But of course, each trader has its special recipe and specific detailed setup that is not possible to embed in a single tool. For advanced simulation, EasyBacktester provides plug & play connectors for advanced users. Namely, there are 3 connectors:
- signal connector
- trail trigger connector
- exit connector
Each of these connectors are an opportunity to customize the engine signals, trail trigger and exit choices with the user's own options. This case does require a little bit of coding, but it can easily be implemented by copy-pasting existing resources or with a slight help of a professional. In fact, the only conditions to build a proper connector is to export a plot with the numbers 1 (for signals), 2 (for trigger trails) and 3 (for exits). Here is an example of custom SIGNAL connector compatible with EasyBacktester, to produce a signal when last RSI was below 30 and current RSI reads above 30:
============================================================
//@version=5
indicator("My custom RSI signal")
// when previous RSI 14 was below 30 and current RSI 14 is above 30, set "custom_signal" to 1, otherwise set "custom_signal" to 0
custom_signal = ta.rsi(close, 14) < 30 and ta.rsi(close, 14) > 30 ? 1 : 0
// Export a plot of "custom_signal", but do not display it
plot(custom_signal, title="my signal", display=display.none)
============================================================
Once this indicator has been built, the user only needs to connect it with EasyBacktester as follow:
1. Open a desired chart, and add both EasyBacktester indicator as well as the custom "My custom signal" we just created above.
2. Open EasyBacktester's settings, and in the first option, there is "Connect signals source" which by default is set to "close". In the multiple choices, find your custom signal which should be named something like "My custom RSI signal: my signal", generally speaking the name is built like this " : ".
3. Now the custom code is connected to EasyBacktester, but we need to indicate the engine we actually want to use it as custom signal.
4. Under the "SIGNALS STRATEGY" section, where we generally build signals rules, there is special rule for this specific connection named "Use external source as entry signal". Just check the checkbox to activate it and see how the chart took our custom signal into consideration.
That's it for the overview of EasyBacktester. Thank you for reading and happy trading :)
FreedX Backtest█ Our strategy template empowers TradingView users to effortlessly backtest any indicator, enhancing their trading strategy's effectiveness. In addition, users can create automated webhook alerts from the template. This document details our template's features and how to utilize them effectively.
█ TRADE DATE SETTINGS
The Trading Date Settings feature in our TradingView script allows you to refine their backtesting parameters by specifying trading dates and hours. This feature enhances the accuracy of the backtest by aligning it with specific time frames and days, ensuring that the strategy is tested under relevant market conditions.
Features:
⚙️ Enable Trading Between Specific Dates:
🎯 Purpose:
→ Allows you to limit the backtesting of their strategy to a specific date range.
💡 How to Use:
→ Input the Start Date and End Date for the backtest period.
→ The script will execute the strategy only within this specified date range.
⚙️ Enable Trading Between Specific Hours:
🎯 Purpose:
→ Allows you to limit the backtesting of their strategy to a specific hour range.
💡 How to Use:
→ Input the start and end hour for in Trading Session section.
→ The script will execute the strategy only within this specified hour range.
⚙️ Enable Trading on Specified Days of the Week:
🎯 Purpose:
→ Gives you the option to conduct backtesting on selected days of the week, tailoring the strategy to particular market behaviours that may occur on these days.
💡 How to Use:
→ Select the days of the week for the backtest.
→ The script will activate the trading strategy only on these chosen days.
█ BUY/SELL TRIGGER SETTINGS
The Buy/Sell Trigger Settings feature is designed to provide users with flexibility in defining the conditions for 'LONG' and 'SHORT' signals based on various indicator types. This customization is crucial for tailoring strategies to different trading styles and market conditions.
Features:
⚙️ Single-Line Plotted Indicators :
🎯 Purpose:
→ Enables you to select a single-line plotted indicator as a source for backtesting. You can define specific levels to trigger 'LONG' or 'SHORT' signals.
💡 How to Use:
→ Choose a Single-Line Plotted indicator as the source.
→ Set the top and bottom levels for the indicator.
→ The script triggers 'LONG' signals at the bottom level and 'SHORT' signals at the top level.
⚙️ Two-Line Plotted Indicators :
🎯 Purpose:
→ Allows backtesting with two-line cross plot sources. Signals are generated based on the crossover of these lines.
💡 How to Use:
→ Select two lines as 'Source 1' and 'Source 2' for the indicator.
→ The script triggers a 'LONG' signal when 'Source 1' crosses above 'Source 2'.
→ Conversely, a 'SHORT' signal is triggered when 'Source 2' crosses above 'Source 1'.
⚙️ Custom Signals :
🎯 Purpose:
→ This setting enables users to define their own criteria for LONG, SHORT, and CLOSE signals based on custom indicator outputs.
💡 How to Use:
→ Select the custom source for your signals.
→ Define the output values that correspond to each signal type (e.g., “1” for 'LONG', “-1” for SHORT, and “0” for CLOSE).
→ The script will trigger signals according to these custom-defined values.
█ TP/SL SETTINGS
The TP/SL (Take Profit/Stop Loss) Settings feature is designed to give users control over their profit securing and risk mitigation strategies. This feature allows for setting custom TP and SL levels, which can be critical in managing trades effectively.
Features:
Custom TP/SL Levels for Long/Short Signals:
🎯 Purpose:
→ Enables users to set specific percentage levels for Take Profit and Stop Loss on long and short signals.
💡 How to Use:
→ In the TP/SL Settings, input the desired percentage for Take Profit (TP) and Stop Loss (SL).
→ For example, to secure a profit at a 10% price increase on LONG signals, set the “Long TP Percentage” to “10”.
█ STRATEGY SETTINGS
Strategy Settings provide a range of options to customize the trading strategy. These settings include leverage, drawdown limits, position direction changes, and more, allowing users to tailor their strategy to their risk tolerance and market view.
Features:
⚙️ Enable Leverage :
🎯 Purpose:
→ Allows users to apply leverage to their trades.
☢️ Caution:
→ High leverage can significantly increase the risk of liquidation.
→ High leverage and a high stop-loss price may override your fixed stoploss percentage, adjusting the stop-loss to the liquidation price.
💡 How to Use:
→ Set the desired leverage ratio in the Strategy Settings.
⚙️ Enable Drawdown Limit:
🎯 Purpose:
→ Sets a maximum drawdown limit, automatically halting the strategy if this limit is reached, thereby controlling risk.
💡 How to Use:
→ Input the maximum drawdown limit (default: 100, min: 0, max: 100).
⚙️ Enable Reverse Position:
🎯 Purpose:
→ Automatically closes a current position and opens a new one in the opposite direction upon detecting a signal for a market trend change.
🎯 Example:
→ If a LONG signal is received while in a SHORT position, the script will close the SHORT position and open a LONG position.
💡 How to Use:
→ Activate this feature in the Strategy Settings.
⚙️ Enable Spot Mode:
🎯 Purpose:
→ Disables short orders, using short signals only for closing long positions.
💡 How to Use:
→ Select the 'Spot Mode' option in the Strategy Settings.
⚙️ Enable Invert Signals:
🎯 Purpose:
→ Inverts all indicator signals, changing LONG signals to SHORT and vice versa.
💡 How to Use:
→ Opt for the 'Invert Signals' feature in the Strategy Settings.
⚙️ Enable Trailing Stop:
🎯 Purpose:
→ Triggers a trailing stop order on the exchange instead of a standard stop market order.
☢️ Caution:
→ The backtesting of this feature on TradingView may not accurately reflect actual strategy performance due to discrepancies between TradingView and exchange mechanisms.
💡 How to Use:
→ Select 'Trailing Stop' in the Strategy Settings.
█ ADVANCED STRATEGY SETTINGS
Advanced Strategy Settings offer sophisticated methods for managing Stop Loss (SL) and Take Profit (TP) using the Average True Range (ATR). These settings are ideal for traders who want to incorporate volatility into their exit strategies.
Features:
⚙️ Enable ATR Stop Loss:
🎯 Purpose:
→ Automatically sets the Stop Loss price using the Average True Range at the time of entry.
💡 How to Use:
→ Activate 'ATR Stop Loss' to have the SL price calculated based on the current ATR.
⚙️ Enable ATR Take Profit:
🎯 Purpose:
→ Sets the Take Profit price based on the Average True Range at the time of entry.
💡 How to Use:
→ Choose 'ATR Take Profit' for TP price determination using ATR.
⚙️ Enable ATR Trailing Stop:
🎯 Purpose:
→ Dynamically updates the Stop Loss price with each new bar, according to the Average True Range.
💡 How to Use:
→ Activate 'ATR Trailing Stop'.
→ Set the ATR Period to define the number of bars for ATR calculation.
→ Adjust the ATR SL Multiplier to determine the stop loss distance.
→ Modify the ATR TP Multiplier for setting the take profit distance.
█ TREND FILTERING SETTINGS
Trend Filtering Settings are designed to align trading strategies with the prevailing market trend, enhancing the precision of trade entries and exits. These settings utilize moving averages for trend analysis and decision-making.
Features:
⚙️ Enable Trend Filtering:
🎯 Purpose:
→ Limits trades based on moving average trends, blocking short trades in an uptrend and vice versa.
💡 How to Use:
→ Enable 'Trend Filtering'.
→ Set Fast and Slow MA Lengths for trend analysis.
→ Select the Timeframe for moving averages.
→ Choose the Moving Average Type for trend filtering.
🎯 Note:
→ Be cautious with timeframe selections; lower timeframes than the base may cause inconsistencies.
⚙️ Enable Exit on Trend Reversal:
🎯 Purpose:
→ Automatically closes a position when a market trend reversal is detected.
💡 How to Use:
→ Turn on 'Exit on Trend Reversal' in the settings.
⚙️ Enable Trend Drawing On Chart:
🎯 Purpose:
→ Visually represents the trend filter directly on the chart for easy reference.
💡 How to Use:
→ Activate 'Trend Drawing On Chart' to see the trend filter overlaid on the trading chart.
█ AUTOMATED ALERT SETTINGS
Automated Alert Settings are designed to integrate your TradingView script with webhook alerts. These settings allow for enhanced strategy execution and management.
Features:
Enable Webhook Alerts:
🎯 Purpose:
→ Trigger BUY, SELL, CHANGE_DIRECTION or MOVE_STOP_LOSS .
💡 How to Use:
→ Enable 'Webhook Alerts' in the settings.
→ Enter your Strategy ID.
→ Optionally, activate 'Override Allocation Percentage' to bypass the preset allocation percentage.
☢️ Caution:
→ Overriding the allocation percentage may result in trade entry errors due to misalignment between entry cost and available balance.
█ DEBUGGING SETTINGS
Debugging Settings are crucial for users who want to analyze and optimize their strategies. These settings provide tools for visualizing alerts on charts and accessing detailed data outputs.
Features:
⚙️ Enable Alert Plotting:
🎯 Purpose:
→ Allows users to visualize trading alerts directly on the chart, aiding in strategy analysis and refinement.
💡 How to Use:
→ Activate 'Alert Plotting' to draw alerts on the chart.
☢️ Caution:
→ It is recommended to disable this feature when creating actual trading alerts, as it can cause latency in signal processing.
⚙️ Enable Debugger Mode:
🎯 Purpose:
→ Facilitates strategy debugging by providing detailed data output in the TradingView Data Window.
💡 How to Use:
→ Turn on 'Debugger Mode' to access real-time data and metrics relevant to your strategy.
█ ADDITIONAL SETTINGS
⚙️ Enable Bar Magnifier
⚙️ Enable Using standard OHLC
GKD-BT Giga Confirmation Stack Backtest [Loxx]Giga Kaleidoscope GKD-BT Giga Confirmation Stack Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Giga Confirmation Stack Backtest
The Giga Confirmation Stack Backtest module allows users to perform backtesting on Long and Short signals from the confluence between GKD-C Confirmation 1 and GKD-C Confirmation 2 indicators. This module encompasses two types of backtests: Trading and Full. The Trading backtest permits users to evaluate individual trades, whether Long or Short, one at a time. Conversely, the Full backtest allows users to analyze either Longs or Shorts separately by toggling between them in the settings, enabling the examination of results for each signal type. The Trading backtest emulates actual trading conditions, while the Full backtest assesses all signals, regardless of being Long or Short.
Additionally, this backtest module provides the option to test using indicators with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed.
Take profit 2: 25% of the trade is removed.
Take profit 3: 25% of the trade is removed.
Stop loss: 100% of the trade is removed.
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest module also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
To utilize this strategy, follow these steps:
1. Adjust the "Confirmation Type" in the GKD-C Confirmation 1 Indicator to "GKD New."
2. GKD-C Confirmation 1 Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 1 module into the GKD-BT Giga Confirmation Stack Backtest module setting named "Import GKD-C Confirmation 1."
3. Adjust the "Confirmation Type" in the GKD-C Confirmation 2 Indicator to "GKD New."
4. GKD-C Confirmation 2 Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation 2 module into the GKD-BT Giga Confirmation Stack Backtest module setting named "Import GKD-C Confirmation 2."
█ Giga Confirmation Stack Backtest Entries
Entries are generated from the confluence of a GKD-C Confirmation 1 and GKD-C Confirmation 2 indicators. The Confirmation 1 gives the signal and the Confirmation 2 indicator filters or "approves" the the Confirmation 1 signal. If Confirmation 1 gives a long signal and Confirmation 2 shows a downtrend, then the long signal is rejected. If Confirmation 1 gives a long signal and Confirmation 2 shows an uptrend, then the long signal is approved and sent to the backtest execution engine.
█ Volatility Types Included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Confiramtion Stack Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Transform as shown on the chart above
Confirmation 2: uf2018 as shown on the chart above
Continuation: Vortex
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
