Crypto Stablecoin Supply - Indicator [presentTrading]█ Introduction and How it is Different
The "Stablecoin Supply - Indicator" differentiates itself by focusing on the aggregate supply of major stablecoins—USDT, USDC, and DAI—rather than traditional price-based metrics. Its premise is that fluctuations in the total supply of these stablecoins can serve as leading indicators for broader market movements, offering traders a unique vantage point to anticipate shifts in market sentiment.
BTCUSD 6h for recent bull market
BTCUSD 8h
█ Strategy, How it Works: Detailed Explanation
🔶 Data Collection
The strategy begins with the collection of the closing supply for USDT, USDC, and DAI stablecoins. This data is fetched using a specified timeframe (**`tfInput`**), allowing for flexibility in analysis periods.
🔶 Supply Calculation
The individual supplies of USDT, USDC, and DAI are then aggregated to determine the total stablecoin supply within the market at any given time. This combined figure serves as the foundation for the subsequent statistical analysis.
🔶 Z-Score Computation
The heart of the indicator's strategy lies in the computation of the Z-Score, which is a statistical measure used to identify how far a data point is from the mean, relative to the standard deviation. The formula for the Z-Score is:
Z = (X - μ) / σ
Where:
- Z is the Z-Score
- X is the current total stablecoin supply (TotalStablecoinClose)
- μ (mu) is the mean of the total stablecoin supply over a specified length (len)
- σ (sigma) is the standard deviation of the total stablecoin supply over the same length
A moving average of the Z-Score (**`zScore_ma`**) is calculated over a short period (defaulted to 3) to smooth out the volatility and provide a clearer signal.
🔶 Signal Interpretation
The Z-Score itself is plotted, with its color indicating its relation to a defined threshold (0.382), serving as a direct visual cue for market sentiment. Zones are also highlighted to show when the Z-Score is within certain extreme ranges, suggesting overbought or oversold conditions.
Bull -> Bear
█ Trade Direction
- **Entry Threshold**: A Z-Score crossing above 0.382 suggests an increase in stablecoin supply relative to its historical average, potentially indicating bullish market sentiment or incoming capital flow into cryptocurrencies.
- **Exit Threshold**: Conversely, a Z-Score dropping below -0.382 may signal a reduction in stablecoin supply, hinting at bearish sentiment or capital withdrawal.
█ Usage
Traders can leverage the "Stablecoin Supply - Indicator" to gain insights into the underlying market dynamics that are not immediately apparent through price analysis alone. It is particularly useful for identifying potential shifts in market sentiment before they are reflected in price movements. By integrating this indicator with other technical analysis tools, traders can develop a more rounded and informed trading strategy.
█ Default Settings
- Timeframe Input (`tfInput`): Allows users to specify the timeframe for data collection, adding flexibility to the analysis.
- Z-Score Length (`len`): Set to 252 by default, representing the period over which the mean and standard deviation of the stablecoin supply are calculated.
- Color Coding: Uses distinct colors (green for bullish, red for bearish) to indicate the Z-Score's position relative to its thresholds, enhancing visual clarity.
- Extreme Range Fill: Highlights areas between defined high and low Z-Score thresholds with distinct colors to indicate potential overbought or oversold conditions.
By integrating considerations of stablecoin supply into the analytical framework, the "Stablecoin Supply - Indicator" offers a novel perspective on cryptocurrency market dynamics, enabling traders to make more nuanced and informed decisions.
"the strat"に関するスクリプトを検索
AI SuperTrend x Pivot Percentile - Strategy [PresentTrading]█ Introduction and How it is Different
The AI SuperTrend x Pivot Percentile strategy is a sophisticated trading approach that integrates AI-driven analysis with traditional technical indicators. Combining the AI SuperTrend with the Pivot Percentile strategy highlights several key advantages:
1. Enhanced Accuracy in Trend Prediction: The AI SuperTrend utilizes K-Nearest Neighbors (KNN) algorithm for trend prediction, improving accuracy by considering historical data patterns. This is complemented by the Pivot Percentile analysis which provides additional context on trend strength.
2. Comprehensive Market Analysis: The integration offers a multi-faceted approach to market analysis, combining AI insights with traditional technical indicators. This dual approach captures a broader range of market dynamics.
BTC 6H L/S Performance
Local
█ Strategy: How it Works - Detailed Explanation
🔶 AI-Enhanced SuperTrend Indicators
1. SuperTrend Calculation:
- The SuperTrend indicator is calculated using a moving average and the Average True Range (ATR). The basic formula is:
- Upper Band = Moving Average + (Multiplier × ATR)
- Lower Band = Moving Average - (Multiplier × ATR)
- The moving average type (SMA, EMA, WMA, RMA, VWMA) and the length of the moving average and ATR are adjustable parameters.
- The direction of the trend is determined based on the position of the closing price in relation to these bands.
2. AI Integration with K-Nearest Neighbors (KNN):
- The KNN algorithm is applied to predict trend direction. It uses historical price data and SuperTrend values to classify the current trend as bullish or bearish.
- The algorithm calculates the 'distance' between the current data point and historical points. The 'k' nearest data points (neighbors) are identified based on this distance.
- A weighted average of these neighbors' trends (bullish or bearish) is calculated to predict the current trend.
For more please check: Multi-TF AI SuperTrend with ADX - Strategy
🔶 Pivot Percentile Analysis
1. Percentile Calculation:
- This involves calculating the percentile ranks for high and low prices over a set of predefined lengths.
- The percentile function is typically defined as:
- Percentile = Value at (P/100) × (N + 1)th position
- Where P is the desired percentile, and N is the number of data points.
2. Trend Strength Evaluation:
- The calculated percentiles for highs and lows are used to determine the strength of bullish and bearish trends.
- For instance, a high percentile rank in the high prices may indicate a strong bullish trend, and vice versa for bearish trends.
For more please check: Pivot Percentile Trend - Strategy
🔶 Strategy Integration
1. Combining SuperTrend and Pivot Percentile:
- The strategy synthesizes the insights from both AI-enhanced SuperTrend and Pivot Percentile analysis.
- It compares the trend direction indicated by the SuperTrend with the strength of the trend as suggested by the Pivot Percentile analysis.
2. Signal Generation:
- A trading signal is generated when both the AI-enhanced SuperTrend and the Pivot Percentile analysis agree on the trend direction.
- For instance, a bullish signal is generated when both the SuperTrend is bullish, and the Pivot Percentile analysis shows strength in bullish trends.
🔶 Risk Management and Filters
- ADX and DMI Filter: The strategy uses the Average Directional Index (ADX) and the Directional Movement Index (DMI) as filters to assess the trend's strength and direction.
- Dynamic Trailing Stop Loss: Based on the SuperTrend indicator, the strategy dynamically adjusts stop-loss levels to manage risk effectively.
This strategy stands out for its ability to combine real-time AI analysis with established technical indicators, offering traders a nuanced and responsive tool for navigating complex market conditions. The equations and algorithms involved are pivotal in accurately identifying market trends and potential trade opportunities.
█ Usage
To effectively use this strategy, traders should:
1. Understand the AI and Pivot Percentile Indicators: A clear grasp of how these indicators work will enable traders to make informed decisions.
2. Interpret the Signals Accurately: The strategy provides bullish, bearish, and neutral signals. Traders should align these signals with their market analysis and trading goals.
3. Monitor Market Conditions: Given that this strategy is sensitive to market dynamics, continuous monitoring is crucial for timely decision-making.
4. Adjust Settings as Needed: Traders should feel free to tweak the input parameters to suit their trading preferences and to respond to changing market conditions.
█Default Settings and Their Impact on Performance
1. Trading Direction (Default: "Both")
Effect: Determines whether the strategy will take long positions, short positions, or both. Adjusting this setting can align the strategy with the trader's market outlook or risk preference.
2. AI Settings (Neighbors: 3, Data Points: 24)
Neighbors: The number of nearest neighbors in the KNN algorithm. A higher number might smooth out noise but could miss subtle, recent changes. A lower number makes the model more sensitive to recent data but may increase noise.
Data Points: Defines the amount of historical data considered. More data points provide a broader context but may dilute recent trends' impact.
3. SuperTrend Settings (Length: 10, Factor: 3.0, MA Source: "WMA")
Length: Affects the sensitivity of the SuperTrend indicator. A longer length results in a smoother, less sensitive indicator, ideal for long-term trends.
Factor: Determines the bandwidth of the SuperTrend. A higher factor creates wider bands, capturing larger price movements but potentially missing short-term signals.
MA Source: The type of moving average used (e.g., WMA - Weighted Moving Average). Different MA types can affect the trend indicator's responsiveness and smoothness.
4. AI Trend Prediction Settings (Price Trend: 10, Prediction Trend: 80)
Price Trend and Prediction Trend Lengths: These settings define the lengths of weighted moving averages for price and SuperTrend, impacting the responsiveness and smoothness of the AI's trend predictions.
5. Pivot Percentile Settings (Length: 10)
Length: Influences the calculation of pivot percentiles. A shorter length makes the percentile more responsive to recent price changes, while a longer length offers a broader view of price trends.
6. ADX and DMI Settings (ADX Length: 14, Time Frame: 'D')
ADX Length: Defines the period for the Average Directional Index calculation. A longer period results in a smoother ADX line.
Time Frame: Sets the time frame for the ADX and DMI calculations, affecting the sensitivity to market changes.
7. Commission, Slippage, and Initial Capital
These settings relate to transaction costs and initial investment, directly impacting net profitability and strategy feasibility.
Four WMA Strategy with TP and SLBasically I read a research paper on how they used different moving averages for long entries and short entries, and it kind of dawned on me that I always used the same one for long entry or exit, or even swing trading. So I smashed this together to see what would happen.
The strategy combines the use of four different WMAs for identifying trade entry points, along with a predefined take profit (TP) and stop loss (SL) for risk management. Here's a detailed description of its features and how it operates:
Main Features
1. **WMAs as the Core Indicator**:
- The strategy uses four WMAs with different lengths. Two WMAs (`longM1` and `longM2`) are used for long entry signals, and the other two (`shortM1` and `shortM2`) for short entry signals.
- The lengths of these WMAs are adjustable through input parameters.
2. **Trade Entry Conditions**:
- A long entry is signaled when the shorter WMA crosses under the longer WMA .
- Conversely, a short entry is signaled when the shorter WMA crosses under the longer WMA.
3. **Take Profit and Stop Loss**:
- The strategy includes a take profit and stop loss mechanism.
- The TP and SL levels are set as a percentage of the entry price, with the percentage values being adjustable through input parameters.
4. **Visual Representation**:
- The WMAs are plotted on the chart for visual aid, each with a distinct color for easy identification.
How It Works
- The strategy continuously monitors the crossing of WMAs to detect potential entry points for long and short positions.
- Upon detecting a long or short condition, it automatically enters a trade and sets the corresponding TP and SL levels based on the current price and the specified percentages.
- The strategy then actively manages the trade, exiting the position when either the TP or SL level is reached.
Drawbacks
- **Overreliance on WMAs**: The strategy heavily relies on WMAs for trade signals. While WMAs are useful for identifying trends, they might not always provide timely entry and exit signals.
- **Market Conditions**: It may not perform well in highly volatile or sideways markets where WMA crossovers could lead to false signals.
- **Risk Management**: The fixed percentage for TP and SL might not be suitable for all market conditions. Traders might need to adjust these values frequently based on market volatility and their risk tolerance.
Apparently I need to emphasize to use brains when using indicators and setting them up to achieve the results you can or want. Also risk of 12% is considered very high so I lowered the numbers to 5%, which tanked the profits, try adjusting them on your own. Check the properties settings for more info on comission and slippage.
Conclusion
The "Four WMA Strategy with TP and SL" is suitable for traders who prefer a moving average-based approach to trading, combined with a straightforward mechanism for risk management through take profit and stop loss. However, like all strategies, it should be used with an understanding of its limitations and ideally tested thoroughly in various market conditions before applying it to live trading.
SandTigerSandTiger is an auto-counting tool that counts naturally occurring events in a price series. This version has been reduced to 377 lines of code and should run faster than previous versions. Although not shown here, I highly recommend running my 'ELB' script with SandTiger. ELB is an 'event locator' and will mark all points that SandTiger numbers - giving you visual cues as to where these points are located. ELB also displays support/resistance levels.
SandTiger is designed to be used with MAGENTA - a counting system for Forex and other markets.
MAGENTA is a free and open framework for understanding and explaining price movement in financial markets. Any materials associated with MAGENTA are strictly for educational purposes only.
SandTiger tracks Component Values, Dyads, and Sum Table Values (STV's) over straight and curved trends, allowing a trader to discern where directional shifts are likely to occur.
SandTiger requires just 3 things to function accurately:
1) A correct starting point (this will typically be an obvious trend turn high or low in a series of price moves).
2) A 'push 1' count ('push 1' runs from the starting point to the event prior to the first terminal of the first FCT or Fractured Counter-Trend).
3) A 'high prime' value (the high prime count runs from the starting point through to the second terminal of the first FCT with no skips).
FRAMEWORK OVERVIEW: 'Component' values are filtered from the prime set (including the half prime and further reductions). Once we have the comp table we add the values to get a 'total'. With the 'total' we divide and multiply by two to get two additional values. 'Derivatives' are based on various calculations using these three values.
We're looking for 'total/2' to count into either itself, 'total', 'total*2', or a derivative. Comp counts are in Tx form and counted from trend start. If the trend doesn't turn on a comp value it will likely turn on a Dyad or STV value. If that also doesn't happen it's likely you have a 'curved' trend/sequence that will turn on one of the above after moving away from its high/low. This can also be traded using SandTiger's 'Seg Terminals' skip option.
Sum tables and Dyad values are drawn from the 'primes' and Dyads use the 'push1' value as well. In a structural trend, primes are gotten by counting pushpulls 1 & 2 in 'Ti' form. Comps, Sum table values, and Dyads are equivalent, sequences can turn on either value type belonging to the 1st or 2nd prime set. Both STV's and Dyads are counted in 'Tx' form (except where count-through signals occur).
Types and antitypes correlate and are associated with a 12-count 'cycle.' (Ti = 'Terminals Included'; Tx = 'Terminals eXcluded'; both refer to FCT terminals)
THE STRATEGY:
For Structures: Trade Comps, Dyads, and STV's from sets 1 (all) and 2 (Dyads and STV's only) in the 'main' segment then on the 'carry-over' by skipping segment terminals. If a PC or cycle caps the sequence, trade that as well.
For NSM's: Trade movements that flash a signal prior to the end of the initial cycle. The mark will be the push1 value. Twelve will be the 'high prime.' Skip interrupts and trade carry-over values.
The first version of SandTiger was conceived/planned/authored by Erek A.D. and coded by Erek A.D. and @SimpleCryptoLife beginning in August 2022 and finishing in Dec. 2022
The current version was written and developed July 3, 2023 and has been refined and upgraded by Erek A.D. through Jan. 2024...
Pairs strategyHello, Tradingview community,
I am been playing with this idea that nowadays trading instruments are interconnected and when one goes too far "out of order" it should return to the mean.
So, here's a relatively simple idea.
This is a LONG-ONLY strategy.
Buy when your traded instrument's last bar closes down, and the comparing instrument closes up.
Sell when close is higher than the previous bar's high.
Best results I found with medium timeframes: 45min, 120min, 180min.
Also, feel free to test non-typical timeframes such as 59min, 119min, 179min, etc.
My reasoning for medium timeframes would be, that they are big enough to avoid "market noise"
of smaller timeframes + commissions & slippage is less negligible, and small enough to avoid exposure of higher timeframes, although, I haven't tested D timeframe and above.
The best results, I found were with instruments that aren't directly correlated. I mostly tested equities and equity futures, so for equity indexes, equity index futures, or large-cap stocks, NASDAQ:SMH , NASDAQ:NVDA , EURUSD, and Crude Oil would be a good candidate for comparing symbols.
When testing either futures or stocks, please adjust the commission for each asset, for stocks I use % equity, so it compounds over time, whereas, for futures, I use 1 contract all the time.
Here's NASDAQ:MSFT on 119min chart
Here's AMEX:SPY on 59min chart using NASDAQ:NVDA as comparison
Here's CME_MINI:ES1! on 179min chart using NYMEX:CL1! as comparison
To change comparison symbol just insert your symbol between the brackets on both fields down here.
SymbolClose = request.security("YOUR SYMBOL HERE", timeframe.period, close)
SymbolOpen = request.security("YOUR SYMBOL HERE", timeframe.period, open)
Since I am still relatively new to testing, hence, I am publishing this idea, so you can point out some crucial things I may have missed.
Thanks,
Enjoy the strategy!
Threshold counterOVERVIEW
The "Threshold Counter" is a tool for quantifying occurrences of closing prices of an asset that align with specified criteria and is a flexible and visual approach to studying price action.
A user-definable target threshold can be set and a comparator (<, =, >, and so on) can be selected. The indicator counts values on the main chart meeting these conditions, over a user-defined `lookback` period.
KEY FEATURES
User definable threshold: target value with optional upper bound can be specified
Versatile Comparisons: Choose from "=", ">=", ">", "<=", "<", "between", and "between (inclusive)" for diverse analysis.
Historical Analysis: Assess occurrences over a customisable period.
Visual Representation: Displays instances graphically on the chart with customisable colours.
Summary: Provides a summary label for a quick understanding of the analysed data.
USE-CASES
Pattern Recognition: Identify patterns or trends based on user-defined price criteria.
Threshold Analysis: Quantify occurrences of prices crossing or staying within a specified range.
Strategy Testing: Evaluate historical performance of strategies relying on specific price conditions.
Behavioural Insights: Gain insights into price behaviour by counting occurrences of interest.
The "Threshold Counter" indicator offers a flexible and visual approach to studying price action, which may aid in making decisions based on historical data.
IMPORTANT CONSIDERATIONS
Period selection: The effectiveness of the analysis may be influenced by the choice of the lookback period. Consider an appropriate duration based on the strategy or pattern being analysed.
Comparator Selection: Comparison operator selection will obviously affect the results. There are two range operators of `between` and `between (inclusive)`. The latter will add closing prices that exactly meet the threshold and upper bound. The former does not.
Visualisation: Interpretation of the visual representation is colour-coded.
Red is threshold condition is not met.
Green is threshold condition is met.
Aqua is outside of the lookback period.
User Discretion: This script relies on historical data and should be used with caution. Past performance is not indicative of future results.
Supplementary Analysis: Trading decisions should not rely solely on this script. Users should exercise judgment and consider market conditions.
Scale In : Scale OutScale In : Scale Out strategy is an adaptation and extension of dollar-cost-averaging.
As the name implies it not only scales in - allocates a given percentage of available capital to buy at each bar - it also scales out - sells a given percentage of holdings at each bar when a target profit level is reached.
The strategy can potentially mitigate risks associated with market timing.
Although dollar-cost-averaging is often recommended as a strategy for building a position, the management of taking and retaining profits is not often addressed. This strategy demonstrates the potential benefits of managing both the building and (full or partial) liquidation of an investment.
We do not provide any mechanism for managing stop losses. We assume a scale in/out strategy will typically be applied to investing in assets with a high conviction thesis based on criteria external to the strategy. If the strategy does not perform, then the thesis may need to be re-evaluated, and the position liquidated. Even in this case, scaling out should still be considered.
Mean Reversion with Incremental Entry by HedgerLabsThe "Mean Reversion with Incremental Entry" strategy, designed by HedgerLabs, is an advanced TradingView strategy script focusing on the mean reversion technique in financial markets. This strategy is engineered for traders who prefer a systematic approach with an emphasis on incremental entries based on price movements relative to a moving average.
Key Features:
Moving Average Based Strategy: Central to this strategy is the simple moving average (SMA), around which all trade entries and exits revolve. Traders can customize the MA length, making it flexible for various trading styles and timeframes.
Incremental Entry Mechanism: Unique to this strategy is the incremental entry system. The strategy initiates an initial trade when the price deviates from the MA by a specified percentage. Subsequent entries are made at incremental steps, defined by the trader, as the price moves further away from the MA. This method can potentially capitalize on increasing market volatility.
Dynamic Position Management: The strategy intelligently manages positions by entering long when the price is below the MA and short when above, allowing for adaptive positioning in different market conditions.
Automated Exit Logic: Exit points are determined when the price touches the MA, aiming to close positions at potential reversal points for optimized trade outcomes.
Continuous Market Analysis: With 'calc_on_every_tick' enabled, the strategy constantly evaluates market conditions, ensuring prompt reaction to price movements.
Usage Scenario:
This strategy is particularly beneficial in markets exhibiting mean-reverting behavior. It is suitable for traders focusing on swing trading or those who prefer to scale into positions during periods of high volatility.
Disclaimer:
Please remember that this strategy is for informational and educational purposes only and is not intended as financial or investment advice. Trading in financial markets carries risks, including the potential loss of capital. We advise doing your own research and consulting with a financial expert before making any investment decisions.
EMA Hafeezullah ReversalTitle: Enhanced EMA Breakout Strategy for Buy and Sell Signals
Description:
This script is an enhanced version of the traditional EMA (Exponential Moving Average) Breakout strategy, designed to provide clear buy and sell signals on price charts. The strategy revolves around a 5-period EMA, which helps traders identify potential breakout points in the market.
How It Works:
EMA Calculation: The script calculates a 5-period EMA, which smooths out price movements to identify the underlying trend.
Buy Signal Logic: A buy signal is generated when the previous candle closes below the EMA, and the current high is greater than the previous high. This indicates potential bullish momentum as the price breaks above the EMA.
Sell Signal Logic: A sell signal is triggered when the previous candle closes above the EMA, and the current close is lower than the previous low. This suggests bearish momentum as the price breaks below the EMA.
Cooldown Period: To avoid frequent signals and potential false breakouts, the script imposes a cooldown period. A new signal can only be generated if a certain number of bars (defined by cooldownBars) have passed since the last signal.
Signal Visualization: Buy signals are marked with green upward triangles below the bars, and sell signals with red downward triangles above the bars.
EMA Visualization: The 5-period EMA is plotted for reference, providing a visual representation of the current trend and potential breakout points.
Usage:
Ideal for intraday and short-term trading.
Can be applied to various asset classes including stocks, forex, and cryptocurrencies.
Best used in conjunction with other technical analysis tools for confirmation and to determine exit points.
Pine Script Version: The script is written in Pine Script version 5.
Originality and Usefulness:
This script stands out due to its simplicity and effectiveness in identifying breakout points. The addition of a cooldown period helps filter out noise and increases the reliability of the signals. It's a valuable tool for traders focusing on momentum and breakout strategies.
BreakoutTrendFollowingINFO:
The "BreakoutTrendFollowing" indicator is a comprehensive trading system designed for trend-following in various market environments. It combines multiple technical indicators, including Moving Averages (MA), MACD, and RSI,
along with volume analysis and breakout detection from consolidation, to identify potential entry points in trending markets. This strategy is particularly effective for assets that exhibit strong trends and significant price movements.
Note that using the consolidation filter reduces the amount of entries the strategy detects significantly, and needs to be used if we want to have an increased confidence in the trend via breakout.
However, the strategy can be easily transformed to various only trend-following strategies, by applying different filters and configurations.
The indicator can be used to connect to the Signal input of the TTS (TempalteTradingStrategy) by jason5480 in order to backtest it, thus effectively turning it into a strategy (instructions below in TTS CONNECTIVITY section)
DETAILS:
The strategy's core is built upon several key components:
Moving Average (MA): Used to determine the general trend direction. The strategy checks if the price is above the selected MA type and length.
MACD Filter: Analyzes the relationship between two moving averages to confirm the trend's momentum.
Consolidation Detection: Identifies periods of price consolidation and triggers trades on breakouts from these ranges.
Volume Analysis: Assesses trading volume to confirm the strength and validity of the breakout.
RSI: Used to avoid overbought conditions, ensuring trades are entered in favorable market situations.
Wick filters: make sure there is not a long wick that indicates selling pressure from above
The strategy generates buy signals when several conditions are met concurrently (each one of them can be individually enabled/disabled)"
The price is above the selected MA.
A breakout occurs from a configurable consolidation range.
The MACD line is above the signal line, indicating bullish momentum.
The RSI is below the overbought threshold.
There's an increase in trading volume, confirming the breakout's strength.
Currently the strategy fires SL signals, as the approach is to check for loss of momentum - i.e. crossunder of the MACD line and signal line, but that is to everyone to determine the exit conditions.
The buy and SL signals are set on the chart using green or orange triangles on the below/above the price action.
SETTINGS:
Users can customize various parameters, including MA type and period, MACD settings, consolidation length, and volume increase percentage. The strategy is equipped with alert conditions for both entry (buy signals) and exit (set stop loss) points, facilitating both manual and automated trading.
Each one of the technical indicators, as well as the consilidation range and breakout/wick settings can be configured and enabled/disabled individually.
Please thoroughly review the available settings of the script, but here is an outline of the most important ones:
Use bar wicks (instead of open/close) - the ref_high/low will be taken based on the bar wicks, rather than the open/close when determining the breakout and MA
Enter position only on green candles - additional filters to make sure that we enter only on strong momentum
MA Filter: (enable, source, type, length) - general settings for MA filter to be checked against the stock price (close or upper wick)
MACD Filter: (enable, source, Osc MA type, Signal MA type, Fast MA length, Slow MA length, Low MACD Hist) - detailed settings for fine MACD tuning
Consolidation:
Consolidation Type: we have two different ways of detecting the consolidation, note the types below.
CONSOLIDATION_BASIC - consolidation areas by looking for the pivot point of a trend and counts the number of bars that have not broken the consolidation high/low levels.
CONSOLIDATIO_RANGE_PERCENT - identifies consolidation by comparing the range between the highest and lowest price points over a specified period.
So in summary the CONSOLIDATIO_RANGE_PERCENT uses a percentage-based range to define consolidation, while CONSOLIDATION_BASIC uses a count of bars within a high-low range to establish consolidation.
Thus the former is more focused on the tightness of the price range, whereas the latter emphasizes the duration of the consolidation phase.
The CONSOLIDATIO_RANGE_PERCENT might be more sensitive to recent price movements and suitable for shorter-term analysis, while CONSOLIDATION_BASIC could be better for identifying longer-term consolidation patterns.
Min consolidation length - applicable for CONSOLIDATION_BASIC case, the min number of bars for the price to be in the range to consider consolidation
Consolidation Loopback period - applicable for CONSOLIDATION_BASIC case, the loopback number of bars to look for consolidation
Consolidation Range percent - applicable for CONSOLIDATIO_RANGE_PERCENT, the percent between the high and low in the range to consider consolidation
Plot consolidation - enables plotting of the consolidation (only for debug purposes)
Breakout: (enable, low, high) - the definition of the breakout from the previous consolidation range, the price should be between to determine the breakout as successfull
Upper wick: (enable, percent) - defines the percent of the upper wick compared to the whole candle to allow breakout (if the wick is too big part of the candle we can consider entering the position riskier)
RSI: (enable, length, overbought) - general settings for RSI TA
Volume (enbale, percentage increase, average volume filter en, loopback bars) - percentage of increase of the volume to consider for a breakout. There are two modes - percentage increase compared to the previous bar, or percentage against the average volume for the last loopback bars.
Note that there are many different configuration that you can play with, and I believe this is the strength of the strategy, as it can provide a single solution for different cases and scenarios.
My advice is to try and play with the different options for different markets based on the approach you want to implement and try turning features on/off and tuning them further.
TTS SETTINGS (NEEDED IF USED TO BACKTEST WITH TTS):
The TempalteTradingStrategy is a strategy script developed in Pine by jason5480, which I recommend for quick turn-around of testing different ideas on a proven and tested framework
I cannot give enough credit to the developer for the efforts put in building of the infrastructure, so I advice everyone that wants to use it first to get familiar with the concept and by checking
by checking jason5480's profile www.tradingview.com
The TTS itself is extremely functional and have a lot of properties, so its functionality is beyond the scope of the current script -
Again, I strongly recommend to be thoroughly explored by everyone that plans on using it.
In the nutshell it is a script that can be feed with buy/sell signals from an external indicator script and based on many configuration options it can determine how to execute the trades.
The TTS has many settings that can be applied, so below I will cover only the ones that differ from the default ones, at least according to my testing - do your own research, you may find something even better :)
The current/latest version that I've been using as of writing and testing this script is TTSv48
Settings which differ from the default ones:
Deal Conditions Mode - External (take enter/exit conditions from an external script)
🔌Signal 🛈➡ - BreakoutTrendFollowing: 🔌Signal to TTS (this is the output from the indicator script, according to the TTS convention)
Order Type - STOP (perform stop order)
Distance Method - HHLL (HigherHighLowerLow - in order to set the SL according to the strategy definition from above)
The next are just personal preferences, you can feel free to experiment according to your trading style
Take Profit Targets - 0 (either 100% in or out, no incremental stepping in or out of positions)
Dist Mul|Len Long/Short- 10 (make sure that we don't close on profitable trades by any reason)
Quantity Method - EQUITY (personal backtesting preference is to consider each backtest as a separate portfolio, so determine the position size by 100% of the allocated equity size)
Equity % - 100 (note above)
COSTAR Strategy [SS]A little late posting this but here it is, as promised!
This is the companion to the COSTAR indicator.
What it does:
It creates a co-integration paired relationship with a separate, cointegrated ticker. It then plots out the expected range based on the value of the cointegrated pair. When the current ticker is below the value of its co-integrated partner, it becomes a "Buy" and should be longed. When it becomes overvalued in comparison, it becomes a "Sell" and should be shorted.
The example above is with BA and USO, which have a strong inverse relationship.
How it works:
I made the strategy version a bit more intuitive. Instead of you selecting the parameters for your model, it will autoselect the ideal parameters based on your desired co-integrated pair. You simply enter the ticker you want to compare against, and it will sort through the values at various lags to find significance and stationarity. It will then create a model and plot the model out for you on your chart, as you can see above.
The premise of the strategy:
The premise of the strategy is as stated before. You long when the ticker is undervalued in comparison to its co-integrated pair, and short when it is overvalued. The conditions for entry are simply a co-integrated pair being over the expected range (short) or below the expected range (long).
The condition to exit is a "re-integration", or a crossover of the expected value of the ticker (the centreline).
What if it can't find a relationship?
In some instances, the indicator will not be able to determine a co-integrated relationship, owning to a lack of stationarity between the data. When this happens, you will get the following error:
The indicator provides you with prompts, such as switching the timeframe or trying an alternative ticker. In the case displayed above, if we simply switch to the 1 hour timeframe, we have a viable model with great backtest results:
You can toggle in the settings menu the various parameters, such as timeframe, fills and displays.
And that is the strategy in a nutshell, be sure to check out its partner indicator, COSTAR, for more information on the premise of using co-integrated models for trading. And let me know your questions below!
Safe trades everyone!
CAPM Calculator [TrendX_]CAPM calculator is a powerful tool that helps find the cost of equity, which is the minimum return that shareholders require to invest in a company.
With the CAPM calculator, you can assess how well your trading strategy performs compared to the market. The goal of your strategy is to earn higher returns than what you would get by investing in the market with the same level of risk. This is called the risk-adjusted cost of capital, and it represents the minimum return that you should accept for your investment.
USAGE
A simple way to measure this is to compare the Compound annual growth rate (CAGR) of the trading strategy with the “Compound CAPM”, which is the CAGR of investing in the market with the same beta as the strategy.
If the trading strategy has a higher CAGR than the “Compound CAPM”, it means that it has outperformed the market on a risk-adjusted basis.
This is a sign of an effective trading strategy.
DISCLAIMER
The results achieved in the past are not all reliable sources of what will happen in the future. There are many factors and uncertainties that can affect the outcome of any endeavor, and no one can guarantee or predict with certainty what will occur.
Therefore, you should always exercise caution and judgment when making decisions based on past performance.
Backtester UtilityLook ahead bias is the most evil bias responsible for overestimation of the performance of the trading system.
As the Bar replay feature is only available to paid users which is a great tool for manual testing of the trading system. Leaving other users prone to the evil of Look ahead bias.
So that I have developed this indicator which will help users to manually backtest the strategies.
This indicator hides the price action after specified date and time.
Here are the steps for using the indicator.
1) Hide your chart manually.
2) Plot the indicator.
3) Change the input of time and date after which you want to hide price action.
4) Change the script according to your trading strategy.
5) Enjoy the free of cost manual backtesting.
Good trading buddies !
Note : This post is only for educational purpose , it does not contain any financial advise.
RMI Trend Sync - Strategy [presentTrading]█ Introduction and How It Is Different
The "RMI Trend Sync - Strategy " combines the strength of the Relative Momentum Index (RMI) with the dynamic nature of the Supertrend indicator. This strategy diverges from traditional methodologies by incorporating a dual analytical framework, leveraging both momentum and trend indicators to offer a more holistic market perspective. The integration of the RMI provides an enhanced understanding of market momentum, while the Super Trend indicator offers clear insights into the end of market trends, making this strategy particularly effective in diverse market conditions.
BTC 4h long/short performance
█ Strategy: How It Works - Detailed Explanation
- Understanding the Relative Momentum Index (RMI)
The Relative Momentum Index (RMI) is an adaptation of the traditional Relative Strength Index (RSI), designed to measure the momentum of price movements over a specified period. While RSI focuses on the speed and change of price movements, RMI incorporates the direction and magnitude of those movements, offering a more nuanced view of market momentum.
- Principle of RMI
Calculation Method: RMI is calculated by first determining the average gain and average loss over a given period (Length). It differs from RSI in that it uses the price change (close-to-close) rather than absolute gains or losses. The average gain is divided by the average loss, and this ratio is then normalized to fit within a 0-100 scale.
- Momentum Analysis in the Strategy
Thresholds for Decision Making: The strategy uses predetermined thresholds (pmom for positive momentum and nmom for negative momentum) to trigger trading decisions. When RMI crosses above the positive threshold and other conditions align (e.g., a bullish trend), it signals a potential long entry. Similarly, crossing below the negative threshold in a bearish trend may trigger a short entry.
- Super Trend and Trend Analysis
The Super Trend indicator is calculated based on a higher time frame, providing a broader view of the market trend. This indicator uses the Average True Range (ATR) to adapt to market volatility, making it an effective tool for identifying trend reversals.
The strategy employs a Volume Weighted Moving Average (VWMA) alongside the Super Trend, enhancing its capability to identify significant trend shifts.
ETH 4hr long/short performance
█ Trade Direction
The strategy offers flexibility in selecting the trading direction: long, short, or both. This versatility allows traders to adapt to their market outlook and risk tolerance, whether looking to capitalize on bullish trends, bearish trends, or a combination of both.
█ Usage
To effectively use the "RMI Trend Sync" strategy, traders should first set their preferred trading direction and adjust the RMI and Super Trend parameters according to their risk appetite and trading goals.
The strategy is designed to adapt to various market conditions, making it suitable for different asset classes and time frames.
█ Default Settings
RMI Settings: Length: 21, Positive Momentum Threshold: 70, Negative Momentum Threshold: 30
Super Trend Settings: Length: 10, Higher Time Frame: 480 minutes, Super Trend Factor: 3.5, MA Source: WMA
Visual Settings: Display Range MA: True, Bullish Color: #00bcd4, Bearish Color: #ff5252
Additional Settings: Band Length: 30, RWMA Length: 20
Zemog Channels[Zemogtrading]Channels Strategy
User Description:
This Channels strategy is a powerful technical analysis tool that empowers traders with a comprehensive view of the market's support and resistance levels. Designed for both beginners and experienced traders, this strategy brings a systematic and adaptable approach to chart analysis.
Default Parameters:
Swing Length (SL): 45
Higher Timeframe: Daily (D)
Multiplier for Level 2: 3.5
Multiplier for Level 3: 12
How It Works:
Swing Analysis: The Swing Length (SL) parameter allows users to fine-tune the sensitivity of the strategy. A higher SL value provides a more smoothed-out analysis, ideal for a broader market perspective, while a lower value enhances responsiveness to short-term price movements.
Higher Timeframe Insights: The Channels fetches high and low prices from a user-specified higher timeframe (default: Daily). This ensures that the strategy is well-informed by significant price levels from a broader market context.
Dynamic ATR Calculation: The Average True Range (ATR) adapts dynamically to changing market conditions. This ensures that support and resistance levels adjust in real-time based on the prevailing volatility, providing traders with adaptive insights.
Smoothed Support and Resistance: Utilizing a Smoothed Moving Average (SMA), the strategy calculates support and resistance levels based on high and low prices from the higher timeframe. This smoothing effect enhances clarity in identifying key levels, facilitating more informed trading decisions.
Additional Levels: The Channels introduces Level 2 and Level 3 support and resistance zones. Users can customize multipliers for these levels, allowing for the identification of secondary zones for potential market reversals.
Visualization: The strategy vividly plots support and resistance levels on the chart. Green lines indicate support, red lines denote resistance, and yellow lines represent additional support at Level 3.
Using Channels is a versatile tool that equips traders with a deeper understanding of crucial market levels. By seamlessly integrating swing analysis, higher timeframe data, and adaptive calculations, this strategy offers a holistic and user-friendly approach to technical analysis.
hamster-bot MRS 2 (simplified version) MRS - Mean Reversion Strategy (Countertrend) (Envelope strategy)
This script does not claim to be unique and does not mislead anyone. Even the unattractive backtest result is attached. The source code is open. The idea has been described many times in various sources. But at the same time, their collection in one place provides unique opportunities.
Published by popular demand and for ease of use. so that users can track the development of the script and can offer their ideas in the comments. Otherwise, you have to communicate in several telegram chats.
Representative of the family of counter-trend strategies. The basis of the strategy is Mean reversion . You can also read about the Envelope strategy .
Mean reversion , or reversion to the mean, is a theory used in finance that suggests that asset price volatility and historical returns eventually will revert to the long-run mean or average level of the entire dataset.
The strategy is very simple. Has very few settings. Good for beginners to get acquainted with algorithmic trading. A simple adjustment will help avoid overfitting. There are many variations of this strategy, but for understanding it is better to start with this implementation.
Principle of operation.
1)
A conventional MA is being built. (fuchsia line). A limit order is placed on this line to close the position.
2)
(green line) A limit order is placed on this line to open a long position
3)
(red line) A limit order is placed on this line to open a short position
Attention!
Please note that a limit order is used. Conclude that the strategy has a limited capacity. And the results obtained on low-liquid instruments will be too high in the tester. On real auctions there will be a different result.
Note for testing the strategy in the spot market:
When testing in the spot market, do not include both long and short at the same time. It is recommended to test only the long mode on the spot. Short mode for more advanced users.
Settings:
Available types of moving averages:
SMA
EMA
TEMA - triple exponential moving average
DEMA - Double Exponential Moving Average
ZLEMA - Zero lag exponential moving average
WMA - weighted moving average
Hma - Hull Moving Average
Thma - Triple Exponential Hull Moving Average
Ehma - Exponential Hull Moving Average
H - MA built based on highs for n candles | ta.highest(len)
L - MA built based on lows for n candles | ta.lowest(len)
DMA - Donchian Moving Average
A Kalman filter can be applied to all MA
The peculiarity of the strategy is a large selection of MA and the possibility of shifting lines. You can set up a reverse trending strategy on the Donchian channel for example.
Use Long - enable/disable opening a Long position
Use Short - enable/disable opening a Short position
Lot Long, % - % allocated from the deposit for opening a Long position. In the spot market, do not use % greater than 100%
Lot Short, % - allocated % of the deposit for opening a Short position
Start date - the beginning of the testing period
End date - the end of the testing period (Example: only August 2020 can be tested)
Mul - multiplier. Used to offset lines. Example:
Mul = 0.99 is shift -1%
Mul = 1.01 is shift +1%
Non-strict recommendations:
1) Test the SPOT market on crypto exchanges. (The countertrend strategy has liquidation risk on futures)
2) Symbols altcoin/bitcoin or altcoin/altcoin. Example: ETH/BTC or DOGE/ETH
3) Timeframe is usually 1 hour
If the script passes moderation, I will supplement it by adding separate settings for closing long and short positions according to their MA
