Daily Bollinger Band StrategyOverview of the Daily Bollinger Band Strategy
1. Strategy Overview and Features
This strategy is a tool for backtesting a trading method that uses Bollinger Bands. It is *not* a tool for automated trading.
1-1. Main Display Items
The main chart displays the Bollinger Bands and the 200-day moving average.
It also shows the entry and exit points along with the position size (in units of 100 shares).
1-2. Summary of Trading Rules
For long (buy) strategies, the trade enters when the price crosses above the +1σ line of the Bollinger Bands, aiming to ride an upward trend. The position is exited when the price crosses below the middle band.
For short (sell) strategies, the trade enters when the price crosses below the -1σ line of the Bollinger Bands, aiming to ride a downward trend. The position is exited when the price crosses above the middle band.
1-3. Strategic Enhancements
The strategy uses the slope of the 200-day moving average to determine the trend direction and enter trades accordingly. This improves the win rate and payoff ratio.
Additionally, to reduce the probability of ruin, the risk per trade is limited to 1.0% of capital, and position sizing is adjusted using ATR (a volatility indicator).
2. Trading Rules
2-1. Chart Type
Only daily charts are used.
2-2. Indicators Used
(1) Bollinger Bands** (used for entry and exit signals)
- Period: Fixed at 80 days
- Upper and lower bands: Fixed at ±1σ
(2) Moving Average** (used to determine trend direction)
- Period: Fixed at 200 days
- Trend direction is judged based on whether the difference from the previous day is positive (upward) or negative (downward)
2-3. Buy Rules
Setup:
- Price crosses above the +1σ line from below
- Both the middle band and 200-day moving average are upward sloping
Entry:
- Buy at the next day’s market open using a market order
Exit:
- If the price crosses below the middle band, sell at the next day’s open using a market order
2-4. Sell Rules
Setup:
- Price crosses below the -1σ line from above
- Both the middle band and 200-day moving average are downward sloping
Entry:
- Sell at the next day’s market open using a market order
Exit:
- If the price crosses above the middle band, buy back at the next day’s open using a market order
2-5. Risk Management Rules
- Risk per trade: 1.0% of total capital (acceptable loss = capital × 1.0%)
- Position size: Acceptable loss ÷ 2ATR (rounded down to the nearest unit of 100 shares)
2-6. Other Notes
- No brokerage fees
- No pyramiding
- No partial exits
- No reverse positions (no “stop-and-reverse” trades)
3. Strategy Parameters
The following settings can be specified:
3-1. Period Settings
- Start date: Set the start date for the backtest period
- Stop date: Set the end date for the backtest period
3-2. Display of Trend and Signals
- Show trend: When checked, the background color of the bars is light red for an uptrend and light blue for a downtrend
- Show signal: When checked, entry and exit signals are displayed (note: signals are executed at the next day’s open, so there is a one-day lag in the display)
3-3. Capital Management Settings
- Funds: Capital available for trading (in JPY)
- Risk rate: Specify what percentage of the capital to risk per trade
Settings in the “Properties” tab are not used in this strategy.
4. Backtest Results (Example)
Here are the backtest results conducted by the author:
- Target Stocks: All components of the Nikkei 225
- Test Period: January 4, 2000 – December 30, 2024
- Data Points: 12,886
- Win Rate: 33.45%
- Net Profit: ¥82,132,380
- Payoff Ratio: 2.450
- Expected Value: ¥6,373.8
- Risk Rate: 1.0%
- Probability of Ruin: 0.00%
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デイリー・ボリンジャーバンド・ストラテジーの概要
1. ストラテジーの概要と特徴
このストラテジーは、ボリンジャーバンドを使ったトレード手法のバックテストを行うツールです。自動売買を行うツールではありません。
1-1. 主な表示項目
メインチャートにボリンジャーバンドと 200日移動平均線を表示します。
また、エントリーと手仕舞いのタイミングと数量（100株単位）も表示されます。
1-2. トレードルールの概要
買い戦略の場合、ボリンジャーバンドの +1σ 超えでエントリーして上昇トレンドに乗り、ミドルバンドを割ったら決済します。
売り戦略の場合、ボリンジャーバンドの -1σ 割りでエントリーして下降トレンドに乗り、ミドルバンドを上抜けたら決済します。
1-3. ストラテジーの工夫点
200日移動平均線の傾きを見てトレンド方向にエントリーをしています。こうして勝率とペイオフレシオの成績を向上しています。
また、破産確率を抑えるために、リスク資金比率を 1.0% にして、ATR（ボラティリティ指標） を使って注文数を調整しています。
2. 売買ルール
2-1. 使用するチャート
日足チャートに限定します
2-2. 使用する指標
(1) ボリンジャーバンド（仕掛けと手仕舞いのシグナルに使用）
期間は80日に固定
上下バンドは ±1σ に固定
(2) 移動平均線（トレンドの方向を見るために使用）
期間は200日に固定
移動平均の値の前日との差がプラスのとき上向き、マイナスのとき下向きと判断
2-3. 買いのルール
セットアップ：ボリンジャーバンドの +1σ を価格が下から上に交差 かつ ミドルバンドと 200日移動平均線が上向き
仕掛け：翌日の寄り付きに成行で買う
手仕舞い：ボリンジャーバンドのミドルバンドを価格が上から下に交差したら、翌日の寄り付きに成行で売る
2-4. 売りのルール
セットアップ：ボリンジャーバンドの -1σ を価格が上から下に交差 かつ ミドルバンドと 200日移動平均線が下向き
仕掛け：翌日の寄り付きに成行で売る
手仕舞い：ボリンジャーバンドのミドルバンドを価格が下から上に交差したら、翌日の寄り付きに成行で買い戻す
2-5. 資金管理のルール
リスク資金比率：資産の 1.0%（許容損失 = 資産 × 1.0%）
注文数：許容損失 ÷ 2ATR（単元株数未満は切り捨て）
2-6. その他
仲介手数料：なし
ピラミッディング：なし
分割決済：なし
ドテン：しない
3. ストラテジーのパラメーター
次の項目が指定できます。
3-1. 期間の設定
Staer date : バックテストの検証期間の開始日を指定します
Stop date : バックテストの検証期間の終了日を指定します
3-2. トレンドとシグナルの表示
Show trend : チェックを入れると、バーの背景色が、トレンドが上昇のときは薄い赤で、下落のときは薄い青で表示されます
Show signal : チェックを入れると、エントリーと手仕舞いのシグナルを表示します（シグナルの出た翌日の寄り付きに売買をするので表示に1日のずれがあります）
3-3. 資金管理用の設定
Funds : トレード用の資金(円)
Risk rate : 許容損失を資金の何%にするかで指定します
「プロパティタブ」で設定する値は、このストラテジーでは有効ではありません。
4. バックテストの結果（例）
作者がバックテストを実施した結果をお知らせします。
対象銘柄：日経225構成銘柄すべて
対象期間：2000年1月4日～2024年12月30日
データ件数：12,886
勝率：33.45%
純利益：82,132,380
ペイオフレシオ：2.450
期待値：6,373.8
リスク資金比率：1.0%
破産確率：0.00%
"跨境通12月4日地天板"に関するスクリプトを検索
Ichimoku Cloud Auto TF🧠 Timeframe Breakdown for Ichimoku Cloud Auto TF
Each timeframe in this indicator is carefully calibrated to reflect meaningful Ichimoku behavior relative to its scale. Here's how each one is structured and what it's best used for:
⏱️ 1 Minute (1m)
Tenkan / Kijun / Span B: 5 / 15 / 45
Use: Scalping fast price action.
Logic: Quick reaction to short-term momentum. Best for highly active traders or bots.
⏱️ 2 Minutes (2m)
Tenkan / Kijun / Span B: 6 / 18 / 54
Use: Slightly smoother than 1m, still ideal for scalping with a little more stability.
⏱️ 5 Minutes (5m)
Tenkan / Kijun / Span B: 8 / 24 / 72
Use: Intraday setups, quick trend capture.
Logic: Balanced between reactivity and noise reduction.
⏱️ 15 Minutes (15m)
Tenkan / Kijun / Span B: 9 / 27 / 81
Use: Short-term swing and intraday entries with higher reliability.
⏱️ 30 Minutes (30m)
Tenkan / Kijun / Span B: 10 / 30 / 90
Use: Intra-swing entries or confirmation of 5m/15m signals.
🕐 1 Hour (1H)
Tenkan / Kijun / Span B: 12 / 36 / 108
Use: Ideal for swing trading setups.
Logic: Anchored to Daily reference (1H × 24 ≈ 1D).
🕐 2 Hours (2H)
Tenkan / Kijun / Span B: 14 / 42 / 126
Use: High-precision swing setups with better context.
🕒 3 Hours (3H)
Tenkan / Kijun / Span B: 15 / 45 / 135
Use: Great compromise between short and mid-term vision.
🕓 4 Hours (4H)
Tenkan / Kijun / Span B: 18 / 52 / 156
Use: Position traders & intraday swing confirmation.
Logic: Designed to echo the structure of 1D Ichimoku but on smaller scale.
📅 1 Day (1D)
Tenkan / Kijun / Span B: 9 / 26 / 52
Use: Classic Ichimoku settings.
Logic: Standard used globally for technical analysis. Suitable for swing and position trading.
📆 1 Week (1W)
Tenkan / Kijun / Span B: 12 / 24 / 120
Use: Long-term position trading & institutional swing confirmation.
Logic: Expanded ratios for broader perspective and noise filtering.
🗓️ 1 Month (1M)
Tenkan / Kijun / Span B: 6 / 12 / 24
Use: Macro-level trend visualization and investment planning.
Logic: Condensed but stable structure to handle longer data cycles.
📌 Summary
This indicator adapts Ichimoku settings dynamically to your chart's timeframe, maintaining logical ratios between Tenkan, Kijun, and Span B. This ensures each timeframe remains responsive yet meaningful for its respective market context.
IWMA - DolphinTradeBot1️⃣ WHAT IS IT ?
▪️ The Inverted Weighted Moving Average (IWMA) is the reversed version of WMA, where older prices receive higher weights, while recent prices receive lower weights. As a result, IWMA focuses more on past price movements while reducing sensitivity to new prices.
2️⃣ HOW IS IT WORK ?
🔍 To understand the IWMA(Inverted Weighted Moving Average) indicator, let's first look at how WMA (Weighted Moving Average) is calculated.
LET’S SAY WE SELECTED A LENGTH OF 5, AND OUR CURRENT CLOSING VALUES ARE .
▪️ WMA Calculation Method
When calculating WMA, the most recent price gets the highest weight, while the oldest price gets the lowest weight.
The Calculation is ;
( 10 ×1)+( 12 ×2)+( 21 ×3)+( 24 ×4)+( 38 ×5) = 10+24+63+96+190 = 383
1+2+3+4+5 = 15
WMA = 383/15 ≈ 25.53
WMA = ta.wma(close,5) = 25.53
▪️ IWMA Calculation Method
The Inverted Weighted Moving Average (IWMA) is the reversed version of WMA, where older prices receive higher weights, while recent prices receive lower weights. As a result, IWMA focuses more on past price movements while reducing sensitivity to new prices.
The Calculation is ;
( 10 ×5)+( 12 ×4)+( 21 ×3)+( 24 ×2)+( 38 ×1) = 50+48+63+48+38 = 247
1+2+3+4+5 = 15
IWMA = 247/15 ≈ 16.46
IWMA = iwma(close,5) = 16.46
3️⃣ SETTINGS
in the indicator's settings, you can change the length and source used for calculation.
With the default settings, when you first add the indicator, only the iwma will be visible. However, to observe how much it differs from the normal wma calculation, you can enable the "show wma" option to see both indicators with the same settings or you can enable the Show Signals to see IWMA and WMA crossover signals .
4️⃣ 💡 SOME IDEAS
You can use the indicator for support and resistance level analysis or trend analysis and reversal detection with short and long moving averages like regular moving averages.
Another option is to consider whether the iwma is above or below the normal wma or to evaluate the crossovers between wma and iwma.
Multi-timeframe Difference Forecast (MTD)Description:
The Multi-timeframe Difference Forecast indicator projects potential future price levels by comparing open prices across multiple timeframe pairs. It uses 12 predefined timeframe pairs where each pair consists of a lower and a higher timeframe. For each pair, the indicator calculates a forecast value by adding the difference between the lower timeframe’s open and the higher timeframe’s open to the current bar’s close. These forecast values are then plotted as points into the future and connected by blue line segments, forming a continuous projection line on your chart.
How It Works:
Timeframe Pairs:
The indicator defines 12 pairs. For example:
Pair 1: Lower timeframe = 15 minutes; Higher timeframe = 150 minutes
Pair 2: Lower timeframe = 30 minutes; Higher timeframe = 165 minutes
⋮
Pair 12: Lower timeframe = 180 minutes; Higher timeframe = 720 minutes
Forecast Calculation:
For each pair, the forecast is computed as:
forecast = close + (lower timeframe open - higher timeframe open)
This produces a series of forecast values that are then plotted on the chart.
Time Offset:
Each forecast point is offset into the future by a number of bars calculated as the ratio between the lower timeframe’s duration (in seconds) and the current chart’s timeframe (in seconds). This adjustment helps align the forecast points correctly on the time axis.
Visualization:
The indicator draws blue lines (width = 2) connecting the current price to each forecast point sequentially, forming a polyline that visually represents the projected price trajectory.
How to Use:
Overlay on Chart:
Apply this indicator to any chart, and it will automatically overlay the forecast line on your current price chart.
Timeframe Flexibility:
The calculations adjust to the chart’s timeframe, so you can use it on various timeframes without needing to change the code.
Interpretation:
The forecast line is intended to provide a visual estimate of potential future price movement based on historical open price differences. It is meant to serve as an additional analytical tool rather than a standalone trading signal.
Disclaimer:
This script is provided for educational and informational purposes only and should not be construed as financial or trading advice. Trading involves significant risk, and past performance is not indicative of future results. You should perform your own analysis and consult with a qualified professional before making any trading decisions. Use this indicator at your own risk.
Multi-indicator Signal Builder [Skyrexio]Overview
Multi-Indicator Signal Builder is a versatile, all-in-one script designed to streamline your trading workflow by combining multiple popular technical indicators under a single roof. It features a single-entry, single-exit logic, intrabar stop-loss/take-profit handling, an optional time filter, a visually accessible condition table, and a built-in statistics label. Traders can choose any combination of 12+ indicators (RSI, Ultimate Oscillator, Bollinger %B, Moving Averages, ADX, Stochastic, MACD, PSAR, MFI, CCI, Heikin Ashi, and a “TV Screener” placeholder) to form entry or exit conditions. This script aims to simplify strategy creation and analysis, making it a powerful toolkit for technical traders.
Indicators Overview
1. RSI (Relative Strength Index)
Measures recent price changes to evaluate overbought or oversold conditions on a 0–100 scale.
2. Ultimate Oscillator (UO)
Uses weighted averages of three different timeframes, aiming to confirm price momentum while avoiding false divergences.
3. Bollinger %B
Expresses price relative to Bollinger Bands, indicating whether price is near the upper band (overbought) or lower band (oversold).
4. Moving Average (MA)
Smooths price data over a specified period. The script supports both SMA and EMA to help identify trend direction and potential crossovers.
5. ADX (Average Directional Index)
Gauges the strength of a trend (0–100). Higher ADX signals stronger momentum, while lower ADX indicates a weaker trend.
6. Stochastic
Compares a closing price to a price range over a given period to identify momentum shifts and potential reversals.
7. MACD (Moving Average Convergence/Divergence)
Tracks the difference between two EMAs plus a signal line, commonly used to spot momentum flips through crossovers.
8. PSAR (Parabolic SAR)
Plots a trailing stop-and-reverse dot that moves with the trend. Often used to signal potential reversals when price crosses PSAR.
9. MFI (Money Flow Index)
Similar to RSI but incorporates volume data. A reading above 80 can suggest overbought conditions, while below 20 may indicate oversold.
10. CCI (Commodity Channel Index)
Identifies cyclical trends or overbought/oversold levels by comparing current price to an average price over a set timeframe.
11. Heikin Ashi
A type of candlestick charting that filters out market noise. The script uses a streak-based approach (multiple consecutive bullish or bearish bars) to gauge mini-trends.
12. TV Screener
A placeholder condition designed to integrate external buy/sell logic (like a TradingView “Buy” or “Sell” rating). Users can override or reference external signals if desired.
Unique Features
1. Multi-Indicator Entry and Exit
You can selectively enable any subset of 12+ classic indicators, each with customizable parameters and conditions. A position opens only if all enabled entry conditions are met, and it closes only when all enabled exit conditions are satisfied, helping reduce false triggers.
2. Single-Entry / Single-Exit with Intrabar SL/TP
The script supports a single position at a time. Once a position is open, it monitors intrabar to see if the price hits your stop-loss or take-profit levels before the bar closes, making results more realistic for fast-moving markets.
3. Time Window Filter
Users may specify a start/end date range during which trades are allowed, making it convenient to focus on specific market cycles for backtesting or live trading.
4. Condition Table and Statistics
A table at the bottom of the chart lists all active entry/exit indicators. Upon each closed trade, an integrated statistics label displays net profit, total trades, win/loss count, average and median PnL, etc.
5. Seamless Alerts and Automation
Configure alerts in TradingView using “Any alert() function call.”
The script sends JSON alert messages you can route to your own webhook.
The indicator can be integrated with Skyrexio alert bots to automate execution on major cryptocurrency exchanges
6. Optional MA/PSAR Plots
For added visual clarity, optionally plot the chosen moving averages or PSAR on the chart to confirm signals without stacking multiple indicators.
Methodology
1. Multi-Indicator Entry Logic
When multiple entry indicators are enabled (e.g., RSI + Stochastic + MACD), the script requires all signals to align before generating an entry. Each indicator can be set for crossovers, crossunders, thresholds (above/below), etc. This “AND” logic aims to filter out low-confidence triggers.
2. Single-Entry Intrabar SL/TP
One Position At a Time: Once an entry signal triggers, a trade opens at the bar’s close.
Intrabar Checks: Stop-loss and take-profit levels (if enabled) are monitored on every tick. If either is reached, the position closes immediately, without waiting for the bar to end.
3. Exit Logic
All Conditions Must Agree: If the trade is still open (SL/TP not triggered), then all enabled exit indicators must confirm a closure before the script exits on the bar’s close.
4. Time Filter
Optional Trading Window: You can activate a date/time range to constrain entries and exits strictly to that interval.
Justification of Methodology
Indicator Confluence: Combining multiple tools (RSI, MACD, etc.) can reduce noise and false signals.
Intrabar SL/TP: Capturing real-time spikes or dips provides a more precise reflection of typical live trading scenarios.
Single-Entry Model: Straightforward for both manual and automated tracking (especially important in bridging to bots).
Custom Date Range: Helps refine backtesting for specific market conditions or to avoid known irregular data periods.
How to Use
1. Add the Script to Your Chart
In TradingView, open Indicators , search for “Multi-indicator Signal Builder”.
Click to add it to your chart.
2. Configure Inputs
Time Filter: Set a start and end date for trades.
Alerts Messages: Input any JSON or text payload needed by your external service or bot.
Entry Conditions: Enable and configure any indicators (e.g., RSI, MACD) for a confluence-based entry.
Close Conditions: Enable exit indicators, along with optional SL (negative %) and TP (positive %) levels.
3. Set Up Alerts
In TradingView, select “Create Alert” → Condition = “Any alert() function call” → choose this script.
Entry Alert: Triggers on the script’s entry signal.
Close Alert: Triggers on the script’s close signal (or if SL/TP is hit).
Skyrexio Alert Bots: You can route these alerts via webhook to Skyrexio alert bots to automate order execution on major crypto exchanges (or any other supported broker).
4. Visual Reference
A condition table at the bottom summarizes active signals.
Statistics Label updates automatically as trades are closed, showing PnL stats and distribution metrics.
Backtesting Guidelines
Symbol/Timeframe: Works on multiple assets and timeframes; always do thorough testing.
Realistic Costs: Adjust commissions and potential slippage to match typical exchange conditions.
Risk Management: If using the built-in stop-loss/take-profit, set percentages that reflect your personal risk tolerance.
Longer Test Horizons: Verify performance across diverse market cycles to gauge reliability.
Example of statistic calculation
Test Period: 2023-01-01 to 2025-12-31
Initial Capital: $1,000
Commission: 0.1%, Slippage ~5 ticks
Trade Count: 468 (varies by strategy conditions)
Win rate: 76% (varies by strategy conditions)
Net Profit: +96.17% (varies by strategy conditions)
Disclaimer
This indicator is provided strictly for informational and educational purposes .
It does not constitute financial or trading advice.
Past performance never guarantees future results.
Always test thoroughly in demo environments before using real capital.
Enjoy exploring the Multi-Indicator Signal Builder! Experiment with different indicator combinations and adjust parameters to align with your trading preferences, whether you trade manually or link your alerts to external automation services. Happy trading and stay safe!
Scalping trading system based on 4 ema linesScalping Trading System Based on 4 EMA Lines
Overview:
This is a scalping trading strategy built on signals from 4 EMA moving averages: EMA(8), EMA(12), EMA(24) and EMA(72).
Conditions:
- Time frame: H1 (1 hour).
- Trading assets: Applicable to major currency pairs with high volatility
- Risk management: Use a maximum of 1-2% of capital for each transaction. The order holding time can be from a few hours to a few days, depending on the price fluctuation amplitude.
Trading rules:
Determine the main trend:
Uptrend: EMA(8), EMA(12) and EMA(24) are above EMA(72).
Downtrend: EMA(8), EMA(12) and EMA(24) are below EMA(72).
Trade in the direction of the main trend** (buy in an uptrend and sell in a downtrend).
Entry conditions:
- Only trade in a clearly trending market.
Uptrend:
- Wait for the price to correct to the EMA(24).
- Enter a buy order when the price closes above the EMA(24).
- Place a stop loss below the bottom of the EMA(24) candle that has just been swept.
Downtrend:
- Wait for the price to correct to the EMA(24).
- Enter a sell order when the price closes below the EMA(24).
- Place a stop loss above the top of the EMA(24) candle that has just been swept.
Take profit and order management:
- Take profit when the price moves 20 to 40 pips in the direction of the trade.
Use Trailing Stop to optimize profits instead of setting a fixed Take Profit.
Note:
- Do not trade within 30 minutes before and after the announcement of important economic news, as the price may fluctuate abnormally.
Additional filters:
To increase the success rate and reduce noise, this strategy uses additional conditions:
1. The price is calculated only when the candle closes (no repaint).
2. When sweeping through EMA(24), the price needs to close above EMA(24).
3. The closing price must be higher than 50% of the candle's length.
4. **The bottom of the candle sweeping through EMA(24) must be lower than the bottom of the previous candle (liquidity sweep).
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Alert function:
When the EMA(24) sweep conditions are met, the system will trigger an alert if you have set it up.
- Entry point: The closing price of the candle sweeping through EMA(24).
- Stop Loss:
- Buy Order: Place at the bottom of the sweep candle.
- Sell Order: Place at the top of the sweep candle.
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Note:
This strategy is designed to help traders identify profitable trading opportunities based on trends. However, no strategy is 100% guaranteed to be successful. Please test it thoroughly on a demo account before using it.
Year-over-Year % Change for PCEPILFEHello, traders!
This indicator is specifically for FRED:PCEPILFE , which is a 'Personal Consumption Expenditures (PCE) Index excluding food and energy.'
What this indicator does is compare the monthly data to that of the same month last year to see how it has changed over the year. This comparison method is widely known as YoY(Year-over-Year).
While I made this indicator to use for FRED:PCEPILFE , you may use it for different charts as long as they show monthly data.
FRED:PCEPILFE is one of the main measures of inflation the Federal Reserve uses.
You can see the YoY % change of the PCE Index excluding food and energy in the official website for the Bureau of Labor Statistics, but unfortunately, I couldn't find one in TradingView.
So instead, I decided to make my own indicator showing the changes using FRED:PCEPILFE .
The code is very simple: it compares the data to the data 12 points ago because 12 points would mean 12 months in this chart. We then multiply the result by 100 for percentage.
Doing so, we compare the current month to the same month of the previous year.
Because I am only interested in the YoY % Change of the index, I pulled the indicator all the way up, covering the original chart data entirely. (Or you could achieve the same by simply moving your indicator to the pane above. But this way, the original chart data is also visible.)
I hope this indicator helps you with your analysis. Feel free to ask questions if have any!
God bless!
ICT Digital open Daily DividersDescription for "ICT Digital Open Daily Dividers" TradingView Indicator
Overview
The "ICT Digital Open Daily Dividers" is a versatile and comprehensive TradingView Pine Script indicator designed for traders who utilize Institutional Order Flow methodologies, particularly in ICT (Inner Circle Trader) trading. This indicator provides a structured visual framework to assist traders in identifying key daily market sessions, critical opening prices, and distinguishing different trading days, especially focusing on the Sunday open, which is a crucial element in the ICT trading strategy.
Core Functionalities
Daily Vertical Lines: The script plots vertical lines at the start of each trading day, which helps to demarcate daily trading sessions. These lines are customizable, allowing traders to choose their color, style (solid, dashed, or dotted), and width. This feature helps in visually segmenting each trading day, making it easier to analyze daily price action patterns.
Sunday Open Differentiation: Unlike many other daily divider indicators, this script uniquely provides the option to highlight the Sunday open at 6 PM EST with distinct lines. This feature is especially valuable for ICT traders who consider the Sunday open as a critical reference point for weekly analysis. The color, style, and width of the Sunday open lines can be set separately, providing a clear visual distinction from regular weekday separators.
12 AM Open Toggle: For markets that are influenced by midnight opens, the indicator includes an option to shift the daily open line to 12 AM instead of the default 6 PM. This flexibility allows traders to adapt the indicator to different market dynamics or trading strategies.
Timezone Customization: The indicator allows traders to set the timezone for the open lines, ensuring that the vertical lines align accurately with the trader’s specific market hours, whether they follow New York time or any other timezone.
Session Time Filters: The script can hide or show specific trading session markers, such as the New York session open and close, which are pivotal for ICT traders. These markers help in focusing on the most active and liquid trading times.
Customizable Style Settings: The script includes comprehensive styling options for the plotted lines and session markers, allowing traders to personalize their charts to suit their visual preferences and improve clarity.
Day of the Week Labels: The indicator can plot labels for each day of the week, providing a quick reference to the day’s price action. This feature is particularly useful in reviewing weekly trading patterns and performance.
Use in ICT Trading
In ICT trading, the concept of the "open" is fundamental. The "ICT Digital Open Daily Dividers" indicator serves multiple purposes:
Market Structure Identification: By clearly marking daily opens, traders can easily identify market structure changes such as breakouts, retracements, or consolidations around these key levels.
Reference Points: The Sunday open is often a key level in ICT analysis, serving as a benchmark for assessing market direction for the upcoming week. This indicator’s ability to plot Sunday opens separately makes it uniquely suited for ICT strategies.
Time-based Analysis: ICT methodology often involves analyzing the market at specific times of the day. This indicator supports such analysis by marking significant session opens and closes.
Uniqueness and Advantages
The "ICT Digital Open Daily Dividers" stands out from other similar indicators due to its specialized features:
Sunday Open Highlighting: Few indicators offer the capability to specifically mark the Sunday open with distinct styling options.
Flexibility in Time Adjustments: With options to adjust the open time to either 6 PM or 12 AM, this indicator caters to a broader range of trading strategies and market conditions.
Enhanced Visualization: The wide range of customization options ensures that traders can tailor the indicator to their specific needs, enhancing the usability and visual clarity of their charts.
Compliance with TradingView's Pine Script Community Guidelines
The description adheres to TradingView's guidelines by being comprehensive, clear, and informative. It highlights the utility of the script, its unique features, and its application in trading strategies without making exaggerated claims about performance or profitability. The detailed customization options and unique functionalities are emphasized to differentiate this script from other standard daily divider indicators.
Rolling Window Geometric Brownian Motion Projections📊 Rolling GBM Projections + EV & Adjustable Confidence Bands
Overview
The Rolling GBM Projections + EV & Adjustable Confidence Bands indicator provides traders with a robust, dynamic tool to model and project future price movements using Geometric Brownian Motion (GBM). By combining GBM-based simulations, expected value (EV) calculations, and customizable confidence bands, this indicator offers valuable insights for decision-making and risk management.
Key Features
Rolling GBM Projections: Simulate potential future price paths based on drift (μμ) and volatility (σσ).
Expected Value (EV) Line: Represents the average projection of simulated price paths.
Confidence Bands: Define ranges where the price is expected to remain, adjustable from 51% to 99%.
Simulation Lines: Visualize individual GBM paths for detailed analysis.
EV of EV Line: A smoothed trend of the EV, offering additional clarity on price dynamics.
Customizable Lookback Periods: Adjust the rolling lookback periods for drift and volatility calculations.
Mathematical Foundation
1. Geometric Brownian Motion (GBM)
GBM is a mathematical model used to simulate the random movement of asset prices, described by the following stochastic differential equation:
dSt=μStdt+σStdWt
dSt=μStdt+σStdWt
Where:
StSt: Price at time tt
μμ: Drift term (expected return)
σσ: Volatility (standard deviation of returns)
dWtdWt: Wiener process (standard Brownian motion)
2. Drift (μμ) and Volatility (σσ)
Drift (μμ): Represents the average logarithmic return of the asset. Calculated using a simple moving average (SMA) over a rolling lookback period.
μ=SMA(ln(St/St−1),Lookback Drift)
μ=SMA(ln(St/St−1),Lookback Drift)
Volatility (σσ): Measures the standard deviation of logarithmic returns over a rolling lookback period.
σ=STD(ln(St/St−1),Lookback Volatility)
σ=STD(ln(St/St−1),Lookback Volatility)
3. Price Simulation Using GBM
The GBM formula for simulating future prices is:
St+Δt=St×e(μ−12σ2)Δt+σϵΔt
St+Δt=St×e(μ−21σ2)Δt+σϵΔt
Where:
ϵϵ: Random variable from a standard normal distribution (N(0,1)N(0,1)).
4. Confidence Bands
Confidence bands are determined using the Z-score corresponding to a user-defined confidence percentage (CC):
Upper Band=EV+Z⋅σ
Upper Band=EV+Z⋅σ
Lower Band=EV−Z⋅σ
Lower Band=EV−Z⋅σ
The Z-score is computed using an inverse normal distribution function, approximating the relationship between confidence and standard deviations.
Methodology
Rolling Drift and Volatility:
Drift and volatility are calculated using logarithmic returns over user-defined rolling lookback periods (default: μ=20μ=20, σ=16σ=16).
Drift defines the overall directional tendency, while volatility determines the randomness and variability of price movements.
Simulations:
Multiple GBM paths (default: 30) are generated for a specified number of projection candles (default: 12).
Each path is influenced by the current drift and volatility, incorporating random shocks to simulate real-world price dynamics.
Expected Value (EV):
The EV is calculated as the average of all simulated paths for each projection step, offering a statistical mean of potential price outcomes.
Confidence Bands:
The upper and lower bounds of the confidence bands are derived using the Z-score corresponding to the selected confidence percentage (e.g., 68%, 95%).
EV of EV:
A running average of the EV values, providing a smoothed perspective of price trends over the projection horizon.
Indicator Functionality
User Inputs:
Drift Lookback (Bars): Define the number of bars for rolling drift calculation (default: 20).
Volatility Lookback (Bars): Define the number of bars for rolling volatility calculation (default: 16).
Projection Candles (Bars): Set the number of bars to project future prices (default: 12).
Number of Simulations: Specify the number of GBM paths to simulate (default: 30).
Confidence Percentage: Input the desired confidence level for bands (default: 68%, adjustable from 51% to 99%).
Visualization Components:
Simulation Lines (Blue): Display individual GBM paths to visualize potential price scenarios.
Expected Value (EV) Line (Orange): Highlight the mean projection of all simulated paths.
Confidence Bands (Green & Red): Show the upper and lower confidence limits.
EV of EV Line (Orange Dashed): Provide a smoothed trendline of the EV values.
Current Price (White): Overlay the real-time price for context.
Display Toggles:
Enable or disable components (e.g., simulation lines, EV line, confidence bands) based on preference.
Practical Applications
Risk Management:
Utilize confidence bands to set stop-loss levels and manage trade risk effectively.
Use narrower confidence intervals (e.g., 50%) for aggressive strategies or wider intervals (e.g., 95%) for conservative approaches.
Trend Analysis:
Observe the EV and EV of EV lines to identify overarching trends and potential reversals.
Scenario Planning:
Analyze simulation lines to explore potential outcomes under varying market conditions.
Statistical Insights:
Leverage confidence bands to understand the statistical likelihood of price movements.
How to Use
Add the Indicator:
Copy the script into the TradingView Pine Editor, save it, and apply it to your chart.
Customize Settings:
Adjust the lookback periods for drift and volatility.
Define the number of projection candles and simulations.
Set the confidence percentage to tailor the bands to your strategy.
Interpret the Visualization:
Use the EV and confidence bands to guide trade entry, exit, and position sizing decisions.
Combine with other indicators for a holistic trading strategy.
Disclaimer
This indicator is a mathematical and statistical tool. It does not guarantee future performance.
Use it in conjunction with other forms of analysis and always trade responsibly.
Happy Trading! 🚀
Historical High/Lows Statistical Analysis(More Timeframe interval options coming in the future)
Indicator Description
The Hourly and Weekly High/Low (H/L) Analysis indicator provides a powerful tool for tracking the most frequent high and low points during different periods, specifically on an hourly basis and a weekly basis, broken down by the days of the week (DOTW). This indicator is particularly useful for traders seeking to understand historical behavior and patterns of high/low occurrences across both hourly intervals and weekly days, helping them make more informed decisions based on historical data.
With its customizable options, this indicator is versatile and applicable to a variety of trading strategies, ranging from intraday to swing trading. It is designed to meet the needs of both novice and experienced traders.
Key Features
Hourly High/Low Analysis:
Tracks and displays the frequency of hourly high and low occurrences across a user-defined date range.
Enables traders to identify which hours of the day are historically more likely to set highs or lows, offering valuable insights into intraday price action.
Customizable options for:
Hourly session start and end times.
22-hour session support for futures traders.
Hourly label formatting (e.g., 12-hour or 24-hour format).
Table position, size, and design flexibility.
Weekly High/Low Analysis by Day of the Week (DOTW):
Captures weekly high and low occurrences for each day of the week.
Allows traders to evaluate which days are most likely to produce highs or lows during the week, providing insights into weekly price movement tendencies.
Displays the aggregated counts of highs and lows for each day in a clean, customizable table format.
Options for hiding specific days (e.g., weekends) and customizing table appearance.
User-Friendly Table Display:
Both hourly and weekly data are displayed in separate tables, ensuring clarity and non-interference.
Tables can be positioned on the chart according to user preferences and are designed to be visually appealing yet highly informative.
Customizable Date Range:
Users can specify a start and end date for the analysis, allowing them to focus on specific periods of interest.
Possible Uses
Intraday Traders (Hourly Analysis):
Analyze hourly price action to determine which hours are more likely to produce highs or lows.
Identify intraday trading opportunities during statistically significant time intervals.
Use hourly insights to time entries and exits more effectively.
Swing Traders (Weekly DOTW Analysis):
Evaluate weekly price patterns by identifying which days of the week are more likely to set highs or lows.
Plan trades around days that historically exhibit strong movements or price reversals.
Futures and Forex Traders:
Use the 22-hour session feature to exclude the CME break or other session-specific gaps from analysis.
Combine hourly and DOTW insights to optimize strategies for continuous markets.
Data-Driven Trading Strategies:
Use historical high/low data to test and refine trading strategies.
Quantify market tendencies and evaluate whether observed patterns align with your strategy's assumptions.
How the Indicator Works
Hourly H/L Analysis:
The indicator calculates the highest and lowest prices for each hour in the specified date range.
Each hourly high and low occurrence is recorded and aggregated into a table, with counts displayed for all 24 hours.
Users can toggle the visibility of empty cells (hours with no high/low occurrences) and adjust the table's design to suit their preferences.
Supports both 12-hour (AM/PM) and 24-hour formats.
Weekly H/L DOTW Analysis:
The indicator tracks the highest and lowest prices for each day of the week during the user-specified date range.
Highs and lows are identified for the entire week, and the specific days when they occur are recorded.
Counts for each day are aggregated and displayed in a table, with a "Totals" column summarizing the overall occurrences.
The analysis resets weekly, ensuring accurate tracking of high/low days.
Code Breakdown:
Data Aggregation:
The script uses arrays to store counts of high/low occurrences for both hourly and weekly intervals.
Daily data is fetched using the request.security() function, ensuring consistent results regardless of the chart's timeframe.
Weekly Reset Mechanism:
Weekly high/low values are reset at the start of a new week (Monday) to ensure accurate weekly tracking.
A processing flag ensures that weekly data is counted only once at the end of the week (Sunday).
Table Visualization:
Tables are created using the table.new() function, with customizable styles and positions.
Header rows, data rows, and totals are dynamically populated based on the aggregated data.
User Inputs:
Customization options include text colors, background colors, table positioning, label formatting, and date ranges.
Code Explanation
The script is structured into two main sections:
Hourly H/L Analysis:
This section captures and aggregates high/low occurrences for each hour of the day.
The logic is session-aware, allowing users to define custom session times (e.g., 22-hour futures sessions).
Data is displayed in a clean table format with hourly labels.
Weekly H/L DOTW Analysis:
This section tracks weekly highs and lows by day of the week.
Highs and lows are identified for each week, and counts are updated only once per week to prevent duplication.
A user-friendly table displays the counts for each day of the week, along with totals.
Both sections are completely independent of each other to avoid interference. This ensures that enabling or disabling one section does not impact the functionality of the other.
Customization Options
For Hourly Analysis:
Toggle hourly table visibility.
Choose session start and end times.
Select hourly label format (12-hour or 24-hour).
Customize table appearance (colors, position, text size).
For Weekly DOTW Analysis:
Toggle DOTW table visibility.
Choose which days to include (e.g., hide weekends).
Customize table appearance (colors, position, text size).
Select values format (percentages or occurrences).
Conclusion
The Hourly and Weekly H/L Analysis indicator is a versatile tool designed to empower traders with data-driven insights into intraday and weekly market tendencies. Its highly customizable design ensures compatibility with various trading styles and instruments, making it an essential addition to any trader's toolkit.
With its focus on accuracy, clarity, and customization, this indicator adheres to TradingView's guidelines, ensuring a robust and valuable user experience.
faiz MACDMACD: Moving Average Convergence Divergence
The Moving Average Convergence Divergence (MACD) is a popular momentum indicator used in technical analysis to gauge the strength, direction, and potential reversal points of a trend in a financial asset's price movement. Developed by Gerald Appel in the late 1970s, MACD is particularly favored by traders for its ability to capture both trend-following and momentum aspects of price behavior.
Components of the MACD
The MACD is derived from two exponential moving averages (EMAs) of a security's price:
MACD Line: This is the difference between the 12-day and 26-day EMAs. The shorter 12-day EMA reacts more quickly to price changes, while the 26-day EMA smooths out price fluctuations, offering a longer-term perspective.
Formula: MACD Line = 12-day EMA - 26-day EMA
Signal Line: This is the 1-day EMA of the MACD Line itself. The signal line is used to generate buy and sell signals when it crosses the MACD line.
Formula: Signal Line = 1-day EMA of the MACD Line
MACD Histogram: The histogram represents the difference between the MACD Line and the Signal Line. It is displayed as bars that oscillate above and below a zero line, helping to visualize the convergence or divergence between the two lines.
Formula: Histogram = MACD Line - Signal Line
Interpretation of MACD
The MACD indicator is used to identify potential buy and sell signals based on the following observations:
MACD Line and Signal Line Crossovers:
Bullish Crossover: A buy signal occurs when the MACD Line crosses above the Signal Line. This suggests that the momentum is shifting in favor of the bulls, indicating a potential upward price movement.
Bearish Crossover: A sell signal occurs when the MACD Line crosses below the Signal Line. This suggests a bearish trend may be emerging, signaling a potential downward movement.
Divergence:
Bullish Divergence: Occurs when the price of the asset is making new lows, but the MACD is forming higher lows. This suggests that the downward momentum is weakening and a potential reversal to the upside may be imminent.
Bearish Divergence: Occurs when the price is making new highs, but the MACD is forming lower highs. This suggests that the upward momentum is weakening and a reversal to the downside may occur.
Only use it in timeframe m1, and solely use for XAUUSD pair.
Advisable to use it as a confirmation with other indicator such as
BBMA, SMC, SUPPORT RESISTANCE, SUPPLY AND DEMAND.
how to use :
MA 5 Crossing above MA9, will generate BUY signals
MA 5 Crossing below MA9, will generate SELL signals
Trade at your own SKILLS.
I dont mind people using this script for free.
All I want is just prayer for me and my family success.
Thank You and Have a nice and pleasant day :-)
Universal Ratio Trend Matrix [InvestorUnknown]The Universal Ratio Trend Matrix is designed for trend analysis on asset/asset ratios, supporting up to 40 different assets. Its primary purpose is to help identify which assets are outperforming others within a selection, providing a broad overview of market trends through a matrix of ratios. The indicator automatically expands the matrix based on the number of assets chosen, simplifying the process of comparing multiple assets in terms of performance.
Key features include the ability to choose from a narrow selection of indicators to perform the ratio trend analysis, allowing users to apply well-defined metrics to their comparison.
Drawback: Due to the computational intensity involved in calculating ratios across many assets, the indicator has a limitation related to loading speed. TradingView has time limits for calculations, and for users on the basic (free) plan, this could result in frequent errors due to exceeded time limits. To use the indicator effectively, users with any paid plans should run it on timeframes higher than 8h (the lowest timeframe on which it managed to load with 40 assets), as lower timeframes may not reliably load.
Indicators:
RSI_raw: Simple function to calculate the Relative Strength Index (RSI) of a source (asset price).
RSI_sma: Calculates RSI followed by a Simple Moving Average (SMA).
RSI_ema: Calculates RSI followed by an Exponential Moving Average (EMA).
CCI: Calculates the Commodity Channel Index (CCI).
Fisher: Implements the Fisher Transform to normalize prices.
Utility Functions:
f_remove_exchange_name: Strips the exchange name from asset tickers (e.g., "INDEX:BTCUSD" to "BTCUSD").
f_remove_exchange_name(simple string name) =>
string parts = str.split(name, ":")
string result = array.size(parts) > 1 ? array.get(parts, 1) : name
result
f_get_price: Retrieves the closing price of a given asset ticker using request.security().
f_constant_src: Checks if the source data is constant by comparing multiple consecutive values.
Inputs:
General settings allow users to select the number of tickers for analysis (used_assets) and choose the trend indicator (RSI, CCI, Fisher, etc.).
Table settings customize how trend scores are displayed in terms of text size, header visibility, highlighting options, and top-performing asset identification.
The script includes inputs for up to 40 assets, allowing the user to select various cryptocurrencies (e.g., BTCUSD, ETHUSD, SOLUSD) or other assets for trend analysis.
Price Arrays:
Price values for each asset are stored in variables (price_a1 to price_a40) initialized as na. These prices are updated only for the number of assets specified by the user (used_assets).
Trend scores for each asset are stored in separate arrays
// declare price variables as "na"
var float price_a1 = na, var float price_a2 = na, var float price_a3 = na, var float price_a4 = na, var float price_a5 = na
var float price_a6 = na, var float price_a7 = na, var float price_a8 = na, var float price_a9 = na, var float price_a10 = na
var float price_a11 = na, var float price_a12 = na, var float price_a13 = na, var float price_a14 = na, var float price_a15 = na
var float price_a16 = na, var float price_a17 = na, var float price_a18 = na, var float price_a19 = na, var float price_a20 = na
var float price_a21 = na, var float price_a22 = na, var float price_a23 = na, var float price_a24 = na, var float price_a25 = na
var float price_a26 = na, var float price_a27 = na, var float price_a28 = na, var float price_a29 = na, var float price_a30 = na
var float price_a31 = na, var float price_a32 = na, var float price_a33 = na, var float price_a34 = na, var float price_a35 = na
var float price_a36 = na, var float price_a37 = na, var float price_a38 = na, var float price_a39 = na, var float price_a40 = na
// create "empty" arrays to store trend scores
var a1_array = array.new_int(40, 0), var a2_array = array.new_int(40, 0), var a3_array = array.new_int(40, 0), var a4_array = array.new_int(40, 0)
var a5_array = array.new_int(40, 0), var a6_array = array.new_int(40, 0), var a7_array = array.new_int(40, 0), var a8_array = array.new_int(40, 0)
var a9_array = array.new_int(40, 0), var a10_array = array.new_int(40, 0), var a11_array = array.new_int(40, 0), var a12_array = array.new_int(40, 0)
var a13_array = array.new_int(40, 0), var a14_array = array.new_int(40, 0), var a15_array = array.new_int(40, 0), var a16_array = array.new_int(40, 0)
var a17_array = array.new_int(40, 0), var a18_array = array.new_int(40, 0), var a19_array = array.new_int(40, 0), var a20_array = array.new_int(40, 0)
var a21_array = array.new_int(40, 0), var a22_array = array.new_int(40, 0), var a23_array = array.new_int(40, 0), var a24_array = array.new_int(40, 0)
var a25_array = array.new_int(40, 0), var a26_array = array.new_int(40, 0), var a27_array = array.new_int(40, 0), var a28_array = array.new_int(40, 0)
var a29_array = array.new_int(40, 0), var a30_array = array.new_int(40, 0), var a31_array = array.new_int(40, 0), var a32_array = array.new_int(40, 0)
var a33_array = array.new_int(40, 0), var a34_array = array.new_int(40, 0), var a35_array = array.new_int(40, 0), var a36_array = array.new_int(40, 0)
var a37_array = array.new_int(40, 0), var a38_array = array.new_int(40, 0), var a39_array = array.new_int(40, 0), var a40_array = array.new_int(40, 0)
f_get_price(simple string ticker) =>
request.security(ticker, "", close)
// Prices for each USED asset
f_get_asset_price(asset_number, ticker) =>
if (used_assets >= asset_number)
f_get_price(ticker)
else
na
// overwrite empty variables with the prices if "used_assets" is greater or equal to the asset number
if barstate.isconfirmed // use barstate.isconfirmed to avoid "na prices" and calculation errors that result in empty cells in the table
price_a1 := f_get_asset_price(1, asset1), price_a2 := f_get_asset_price(2, asset2), price_a3 := f_get_asset_price(3, asset3), price_a4 := f_get_asset_price(4, asset4)
price_a5 := f_get_asset_price(5, asset5), price_a6 := f_get_asset_price(6, asset6), price_a7 := f_get_asset_price(7, asset7), price_a8 := f_get_asset_price(8, asset8)
price_a9 := f_get_asset_price(9, asset9), price_a10 := f_get_asset_price(10, asset10), price_a11 := f_get_asset_price(11, asset11), price_a12 := f_get_asset_price(12, asset12)
price_a13 := f_get_asset_price(13, asset13), price_a14 := f_get_asset_price(14, asset14), price_a15 := f_get_asset_price(15, asset15), price_a16 := f_get_asset_price(16, asset16)
price_a17 := f_get_asset_price(17, asset17), price_a18 := f_get_asset_price(18, asset18), price_a19 := f_get_asset_price(19, asset19), price_a20 := f_get_asset_price(20, asset20)
price_a21 := f_get_asset_price(21, asset21), price_a22 := f_get_asset_price(22, asset22), price_a23 := f_get_asset_price(23, asset23), price_a24 := f_get_asset_price(24, asset24)
price_a25 := f_get_asset_price(25, asset25), price_a26 := f_get_asset_price(26, asset26), price_a27 := f_get_asset_price(27, asset27), price_a28 := f_get_asset_price(28, asset28)
price_a29 := f_get_asset_price(29, asset29), price_a30 := f_get_asset_price(30, asset30), price_a31 := f_get_asset_price(31, asset31), price_a32 := f_get_asset_price(32, asset32)
price_a33 := f_get_asset_price(33, asset33), price_a34 := f_get_asset_price(34, asset34), price_a35 := f_get_asset_price(35, asset35), price_a36 := f_get_asset_price(36, asset36)
price_a37 := f_get_asset_price(37, asset37), price_a38 := f_get_asset_price(38, asset38), price_a39 := f_get_asset_price(39, asset39), price_a40 := f_get_asset_price(40, asset40)
Universal Indicator Calculation (f_calc_score):
This function allows switching between different trend indicators (RSI, CCI, Fisher) for flexibility.
It uses a switch-case structure to calculate the indicator score, where a positive trend is denoted by 1 and a negative trend by 0. Each indicator has its own logic to determine whether the asset is trending up or down.
// use switch to allow "universality" in indicator selection
f_calc_score(source, trend_indicator, int_1, int_2) =>
int score = na
if (not f_constant_src(source)) and source > 0.0 // Skip if you are using the same assets for ratio (for example BTC/BTC)
x = switch trend_indicator
"RSI (Raw)" => RSI_raw(source, int_1)
"RSI (SMA)" => RSI_sma(source, int_1, int_2)
"RSI (EMA)" => RSI_ema(source, int_1, int_2)
"CCI" => CCI(source, int_1)
"Fisher" => Fisher(source, int_1)
y = switch trend_indicator
"RSI (Raw)" => x > 50 ? 1 : 0
"RSI (SMA)" => x > 50 ? 1 : 0
"RSI (EMA)" => x > 50 ? 1 : 0
"CCI" => x > 0 ? 1 : 0
"Fisher" => x > x ? 1 : 0
score := y
else
score := 0
score
Array Setting Function (f_array_set):
This function populates an array with scores calculated for each asset based on a base price (p_base) divided by the prices of the individual assets.
It processes multiple assets (up to 40), calling the f_calc_score function for each.
// function to set values into the arrays
f_array_set(a_array, p_base) =>
array.set(a_array, 0, f_calc_score(p_base / price_a1, trend_indicator, int_1, int_2))
array.set(a_array, 1, f_calc_score(p_base / price_a2, trend_indicator, int_1, int_2))
array.set(a_array, 2, f_calc_score(p_base / price_a3, trend_indicator, int_1, int_2))
array.set(a_array, 3, f_calc_score(p_base / price_a4, trend_indicator, int_1, int_2))
array.set(a_array, 4, f_calc_score(p_base / price_a5, trend_indicator, int_1, int_2))
array.set(a_array, 5, f_calc_score(p_base / price_a6, trend_indicator, int_1, int_2))
array.set(a_array, 6, f_calc_score(p_base / price_a7, trend_indicator, int_1, int_2))
array.set(a_array, 7, f_calc_score(p_base / price_a8, trend_indicator, int_1, int_2))
array.set(a_array, 8, f_calc_score(p_base / price_a9, trend_indicator, int_1, int_2))
array.set(a_array, 9, f_calc_score(p_base / price_a10, trend_indicator, int_1, int_2))
array.set(a_array, 10, f_calc_score(p_base / price_a11, trend_indicator, int_1, int_2))
array.set(a_array, 11, f_calc_score(p_base / price_a12, trend_indicator, int_1, int_2))
array.set(a_array, 12, f_calc_score(p_base / price_a13, trend_indicator, int_1, int_2))
array.set(a_array, 13, f_calc_score(p_base / price_a14, trend_indicator, int_1, int_2))
array.set(a_array, 14, f_calc_score(p_base / price_a15, trend_indicator, int_1, int_2))
array.set(a_array, 15, f_calc_score(p_base / price_a16, trend_indicator, int_1, int_2))
array.set(a_array, 16, f_calc_score(p_base / price_a17, trend_indicator, int_1, int_2))
array.set(a_array, 17, f_calc_score(p_base / price_a18, trend_indicator, int_1, int_2))
array.set(a_array, 18, f_calc_score(p_base / price_a19, trend_indicator, int_1, int_2))
array.set(a_array, 19, f_calc_score(p_base / price_a20, trend_indicator, int_1, int_2))
array.set(a_array, 20, f_calc_score(p_base / price_a21, trend_indicator, int_1, int_2))
array.set(a_array, 21, f_calc_score(p_base / price_a22, trend_indicator, int_1, int_2))
array.set(a_array, 22, f_calc_score(p_base / price_a23, trend_indicator, int_1, int_2))
array.set(a_array, 23, f_calc_score(p_base / price_a24, trend_indicator, int_1, int_2))
array.set(a_array, 24, f_calc_score(p_base / price_a25, trend_indicator, int_1, int_2))
array.set(a_array, 25, f_calc_score(p_base / price_a26, trend_indicator, int_1, int_2))
array.set(a_array, 26, f_calc_score(p_base / price_a27, trend_indicator, int_1, int_2))
array.set(a_array, 27, f_calc_score(p_base / price_a28, trend_indicator, int_1, int_2))
array.set(a_array, 28, f_calc_score(p_base / price_a29, trend_indicator, int_1, int_2))
array.set(a_array, 29, f_calc_score(p_base / price_a30, trend_indicator, int_1, int_2))
array.set(a_array, 30, f_calc_score(p_base / price_a31, trend_indicator, int_1, int_2))
array.set(a_array, 31, f_calc_score(p_base / price_a32, trend_indicator, int_1, int_2))
array.set(a_array, 32, f_calc_score(p_base / price_a33, trend_indicator, int_1, int_2))
array.set(a_array, 33, f_calc_score(p_base / price_a34, trend_indicator, int_1, int_2))
array.set(a_array, 34, f_calc_score(p_base / price_a35, trend_indicator, int_1, int_2))
array.set(a_array, 35, f_calc_score(p_base / price_a36, trend_indicator, int_1, int_2))
array.set(a_array, 36, f_calc_score(p_base / price_a37, trend_indicator, int_1, int_2))
array.set(a_array, 37, f_calc_score(p_base / price_a38, trend_indicator, int_1, int_2))
array.set(a_array, 38, f_calc_score(p_base / price_a39, trend_indicator, int_1, int_2))
array.set(a_array, 39, f_calc_score(p_base / price_a40, trend_indicator, int_1, int_2))
a_array
Conditional Array Setting (f_arrayset):
This function checks if the number of used assets is greater than or equal to a specified number before populating the arrays.
// only set values into arrays for USED assets
f_arrayset(asset_number, a_array, p_base) =>
if (used_assets >= asset_number)
f_array_set(a_array, p_base)
else
na
Main Logic
The main logic initializes arrays to store scores for each asset. Each array corresponds to one asset's performance score.
Setting Trend Values: The code calls f_arrayset for each asset, populating the respective arrays with calculated scores based on the asset prices.
Combining Arrays: A combined_array is created to hold all the scores from individual asset arrays. This array facilitates further analysis, allowing for an overview of the performance scores of all assets at once.
// create a combined array (work-around since pinescript doesn't support having array of arrays)
var combined_array = array.new_int(40 * 40, 0)
if barstate.islast
for i = 0 to 39
array.set(combined_array, i, array.get(a1_array, i))
array.set(combined_array, i + (40 * 1), array.get(a2_array, i))
array.set(combined_array, i + (40 * 2), array.get(a3_array, i))
array.set(combined_array, i + (40 * 3), array.get(a4_array, i))
array.set(combined_array, i + (40 * 4), array.get(a5_array, i))
array.set(combined_array, i + (40 * 5), array.get(a6_array, i))
array.set(combined_array, i + (40 * 6), array.get(a7_array, i))
array.set(combined_array, i + (40 * 7), array.get(a8_array, i))
array.set(combined_array, i + (40 * 8), array.get(a9_array, i))
array.set(combined_array, i + (40 * 9), array.get(a10_array, i))
array.set(combined_array, i + (40 * 10), array.get(a11_array, i))
array.set(combined_array, i + (40 * 11), array.get(a12_array, i))
array.set(combined_array, i + (40 * 12), array.get(a13_array, i))
array.set(combined_array, i + (40 * 13), array.get(a14_array, i))
array.set(combined_array, i + (40 * 14), array.get(a15_array, i))
array.set(combined_array, i + (40 * 15), array.get(a16_array, i))
array.set(combined_array, i + (40 * 16), array.get(a17_array, i))
array.set(combined_array, i + (40 * 17), array.get(a18_array, i))
array.set(combined_array, i + (40 * 18), array.get(a19_array, i))
array.set(combined_array, i + (40 * 19), array.get(a20_array, i))
array.set(combined_array, i + (40 * 20), array.get(a21_array, i))
array.set(combined_array, i + (40 * 21), array.get(a22_array, i))
array.set(combined_array, i + (40 * 22), array.get(a23_array, i))
array.set(combined_array, i + (40 * 23), array.get(a24_array, i))
array.set(combined_array, i + (40 * 24), array.get(a25_array, i))
array.set(combined_array, i + (40 * 25), array.get(a26_array, i))
array.set(combined_array, i + (40 * 26), array.get(a27_array, i))
array.set(combined_array, i + (40 * 27), array.get(a28_array, i))
array.set(combined_array, i + (40 * 28), array.get(a29_array, i))
array.set(combined_array, i + (40 * 29), array.get(a30_array, i))
array.set(combined_array, i + (40 * 30), array.get(a31_array, i))
array.set(combined_array, i + (40 * 31), array.get(a32_array, i))
array.set(combined_array, i + (40 * 32), array.get(a33_array, i))
array.set(combined_array, i + (40 * 33), array.get(a34_array, i))
array.set(combined_array, i + (40 * 34), array.get(a35_array, i))
array.set(combined_array, i + (40 * 35), array.get(a36_array, i))
array.set(combined_array, i + (40 * 36), array.get(a37_array, i))
array.set(combined_array, i + (40 * 37), array.get(a38_array, i))
array.set(combined_array, i + (40 * 38), array.get(a39_array, i))
array.set(combined_array, i + (40 * 39), array.get(a40_array, i))
Calculating Sums: A separate array_sums is created to store the total score for each asset by summing the values of their respective score arrays. This allows for easy comparison of overall performance.
Ranking Assets: The final part of the code ranks the assets based on their total scores stored in array_sums. It assigns a rank to each asset, where the asset with the highest score receives the highest rank.
// create array for asset RANK based on array.sum
var ranks = array.new_int(used_assets, 0)
// for loop that calculates the rank of each asset
if barstate.islast
for i = 0 to (used_assets - 1)
int rank = 1
for x = 0 to (used_assets - 1)
if i != x
if array.get(array_sums, i) < array.get(array_sums, x)
rank := rank + 1
array.set(ranks, i, rank)
Dynamic Table Creation
Initialization: The table is initialized with a base structure that includes headers for asset names, scores, and ranks. The headers are set to remain constant, ensuring clarity for users as they interpret the displayed data.
Data Population: As scores are calculated for each asset, the corresponding values are dynamically inserted into the table. This is achieved through a loop that iterates over the scores and ranks stored in the combined_array and array_sums, respectively.
Automatic Extending Mechanism
Variable Asset Count: The code checks the number of assets defined by the user. Instead of hardcoding the number of rows in the table, it uses a variable to determine the extent of the data that needs to be displayed. This allows the table to expand or contract based on the number of assets being analyzed.
Dynamic Row Generation: Within the loop that populates the table, the code appends new rows for each asset based on the current asset count. The structure of each row includes the asset name, its score, and its rank, ensuring that the table remains consistent regardless of how many assets are involved.
// Automatically extending table based on the number of used assets
var table table = table.new(position.bottom_center, 50, 50, color.new(color.black, 100), color.white, 3, color.white, 1)
if barstate.islast
if not hide_head
table.cell(table, 0, 0, "Universal Ratio Trend Matrix", text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.merge_cells(table, 0, 0, used_assets + 3, 0)
if not hide_inps
table.cell(table, 0, 1,
text = "Inputs: You are using " + str.tostring(trend_indicator) + ", which takes: " + str.tostring(f_get_input(trend_indicator)),
text_color = color.white, text_size = fontSize), table.merge_cells(table, 0, 1, used_assets + 3, 1)
table.cell(table, 0, 2, "Assets", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, 2, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.cell(table, 0, x + 3, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = f_asset_col(array.get(ranks, x)), text_size = fontSize)
for r = 0 to (used_assets - 1)
for c = 0 to (used_assets - 1)
table.cell(table, c + 1, r + 3, text = str.tostring(array.get(combined_array, c + (r * 40))),
text_color = hl_type == "Text" ? f_get_col(array.get(combined_array, c + (r * 40))) : color.white, text_size = fontSize,
bgcolor = hl_type == "Background" ? f_get_col(array.get(combined_array, c + (r * 40))) : na)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, x + 3, "", bgcolor = #010c3b)
table.cell(table, used_assets + 1, 2, "", bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 1, x + 3, "==>", text_color = color.white)
table.cell(table, used_assets + 2, 2, "SUM", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
table.cell(table, used_assets + 3, 2, "RANK", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 2, x + 3,
text = str.tostring(array.get(array_sums, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_sum(array.get(array_sums, x), array.get(ranks, x)))
table.cell(table, used_assets + 3, x + 3,
text = str.tostring(array.get(ranks, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_rank(array.get(ranks, x)))
ETH Signal 15m
This strategy uses the Supertrend indicator combined with RSI to generate buy and sell signals, with stop loss (SL) and take profit (TP) conditions based on ATR (Average True Range). Below is a detailed explanation of each part:
1. General Information BINANCE:ETHUSDT.P
Strategy Name: "ETH Signal 15m"
Designed for use on the 15-minute time frame for the ETH pair.
Default capital allocation is 15% of total equity for each trade.
2. Backtest Period
start_time and end_time: Define the start and end time of the backtest period.
start_time = 2024-08-01: Start date of the backtest.
end_time = 2054-01-01: End date of the backtest.
The strategy will only run when the current time falls within this specified range.
3. Supertrend Indicator
Supertrend is a trend-following indicator that provides buy or sell signals based on the direction of price changes.
factor = 2.76: The multiplier used in the Supertrend calculation (increasing this value makes the Supertrend less sensitive to price movements).
atrPeriod = 12: Number of periods used to calculate ATR.
Output:
direction: Determines the buy/sell direction based on Supertrend.
If direction decreases, it signals a buy (Long).
If direction increases, it signals a sell (Short).
4. RSI Indicator
RSI (Relative Strength Index) is a momentum indicator, often used to identify overbought or oversold conditions.
rsiLength = 12: Number of periods used to calculate RSI.
rsiOverbought = 70: RSI level considered overbought.
rsiOversold = 30: RSI level considered oversold.
5. Entry Conditions
Long Entry:
Supertrend gives a buy signal (ta.change(direction) < 0).
RSI must be below the overbought level (rsi < rsiOverbought).
Short Entry:
Supertrend gives a sell signal (ta.change(direction) > 0).
RSI must be above the oversold level (rsi > rsiOversold).
The strategy will only execute trades if the current time is within the backtest period (in_date_range).
6. Stop Loss (SL) and Take Profit (TP) Conditions
ATR (Average True Range) is used to calculate the distance for Stop Loss and Take Profit based on price volatility.
atr = ta.atr(atrPeriod): ATR is calculated using 12 periods.
Stop Loss and Take Profit are calculated as follows:
Long Trade:
Stop Loss: Set at close - 4 * atr (current price minus 4 times the ATR).
Take Profit: Set at close + 2 * atr (current price plus 2 times the ATR).
Short Trade:
Stop Loss: Set at close + 4 * atr (current price plus 4 times the ATR).
Take Profit: Set at close - 2.237 * atr (current price minus 2.237 times the ATR).
Summary:
This strategy enters a Long trade when the Supertrend indicates an upward trend and RSI is not in the overbought region. Conversely, a Short trade is entered when Supertrend signals a downtrend, and RSI is not oversold.
The trade is exited when the price reaches the Stop Loss or Take Profit levels, which are determined based on price volatility (ATR).
Disclaimer:
The content provided in this strategy is for informational and educational purposes only. It is not intended as financial, investment, or trading advice. Trading in cryptocurrency, stocks, or any financial markets involves significant risk, and you may lose more than your initial investment. Past performance is not indicative of future results, and no guarantee of profit can be made. You should consult with a professional financial advisor before making any investment decisions. The creator of this strategy is not responsible for any financial losses or damages incurred as a result of following this strategy. All trades are executed at your own risk.
Larry Conners Vix Reversal II Strategy (approx.)This Pine Script™ strategy is a modified version of the original Larry Connors VIX Reversal II Strategy, designed for short-term trading in market indices like the S&P 500. The strategy utilizes the Relative Strength Index (RSI) of the VIX (Volatility Index) to identify potential overbought or oversold market conditions. The logic is based on the assumption that extreme levels of market volatility often precede reversals in price.
How the Strategy Works
The strategy calculates the RSI of the VIX using a 25-period lookback window. The RSI is a momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is often used to identify overbought and oversold conditions in assets.
Overbought Signal: When the RSI of the VIX rises above 61, it signals a potential overbought condition in the market. The strategy looks for a RSI downtick (i.e., when RSI starts to fall after reaching this level) as a trigger to enter a long position.
Oversold Signal: Conversely, when the RSI of the VIX drops below 42, the market is considered oversold. A RSI uptick (i.e., when RSI starts to rise after hitting this level) serves as a signal to enter a short position.
The strategy holds the position for a minimum of 7 days and a maximum of 12 days, after which it exits automatically.
Larry Connors: Background
Larry Connors is a prominent figure in quantitative trading, specializing in short-term market strategies. He is the co-author of several influential books on trading, such as Street Smarts (1995), co-written with Linda Raschke, and How Markets Really Work. Connors' work focuses on developing rules-based systems using volatility indicators like the VIX and oscillators such as RSI to exploit mean-reversion patterns in financial markets.
Risks of the Strategy
While the Larry Connors VIX Reversal II Strategy can capture reversals in volatile market environments, it also carries significant risks:
Over-Optimization: This modified version adjusts RSI levels and holding periods to fit recent market data. If market conditions change, the strategy might no longer be effective, leading to false signals.
Drawdowns in Trending Markets: This is a mean-reversion strategy, designed to profit when markets return to a previous mean. However, in strongly trending markets, especially during extended bull or bear phases, the strategy might generate losses due to early entries or exits.
Volatility Risk: Since this strategy is linked to the VIX, an instrument that reflects market volatility, large spikes in volatility can lead to unexpected, fast-moving market conditions, potentially leading to larger-than-expected losses.
Scientific Literature and Supporting Research
The use of RSI and VIX in trading strategies has been widely discussed in academic research. RSI is one of the most studied momentum oscillators, and numerous studies show that it can capture mean-reversion effects in various markets, including equities and derivatives.
Wong et al. (2003) investigated the effectiveness of technical trading rules such as RSI, finding that it has predictive power in certain market conditions, particularly in mean-reverting markets .
The VIX, often referred to as the “fear index,” reflects market expectations of volatility and has been a focal point in research exploring volatility-based strategies. Whaley (2000) extensively reviewed the predictive power of VIX, noting that extreme VIX readings often correlate with turning points in the stock market .
Modified Version of Original Strategy
This script is a modified version of Larry Connors' original VIX Reversal II strategy. The key differences include:
Adjusted RSI period to 25 (instead of 2 or 4 commonly used in Connors’ other work).
Overbought and oversold levels modified to 61 and 42, respectively.
Specific holding period (7 to 12 days) is predefined to reduce holding risk.
These modifications aim to adapt the strategy to different market environments, potentially enhancing performance under specific volatility conditions. However, as with any system, constant evaluation and testing in live markets are crucial.
References
Wong, W. K., Manzur, M., & Chew, B. K. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Applied Financial Economics, 13(7), 543-551.
Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
True Day Open1. *nyTime*: Converts the current time to the New York timezone.
2. *nyHour and nyMinute*: Extracts the hour and minute of the current candle in the New York timezone.
3. *isNyMidnightCandle*: A boolean variable that checks if the current candle is the 12:00 AM candle in New York.
4. *bgcolor*: Colors the background of the 12:00 AM candle blue.
5. *plotshape*: Optionally, you can mark the 12:00 AM candle with a blue label above the bar for better visibility.
You can copy and paste this code into the Pine Editor on TradingView and apply it to your chart. Make sure your chart is set to the 5-minute timeframe.
Dual Chain StrategyDual Chain Strategy - Technical Overview
How It Works:
The Dual Chain Strategy is a unique approach to trading that utilizes Exponential Moving Averages (EMAs) across different timeframes, creating two distinct "chains" of trading signals. These chains can work independently or together, capturing both long-term trends and short-term price movements.
Chain 1 (Longer-Term Focus):
Entry Signal: The entry signal for Chain 1 is generated when the closing price crosses above the EMA calculated on a weekly timeframe. This suggests the start of a bullish trend and prompts a long position.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Exit Signal: The exit signal is triggered when the closing price crosses below the EMA on a daily timeframe, indicating a potential bearish reversal.
exitLongChain1 = enableChain1 and ta.crossunder(src1, exitEMA1)
Parameters: Chain 1's EMA length is set to 10 periods by default, with the flexibility for user adjustment to match various trading scenarios.
Chain 2 (Shorter-Term Focus):
Entry Signal: Chain 2 generates an entry signal when the closing price crosses above the EMA on a 12-hour timeframe. This setup is designed to capture quicker, shorter-term movements.
bullishChain2 = enableChain2 and ta.crossover(src2, entryEMA2)
Exit Signal: The exit signal occurs when the closing price falls below the EMA on a 9-hour timeframe, indicating the end of the shorter-term trend.
exitLongChain2 = enableChain2 and ta.crossunder(src2, exitEMA2)
Parameters: Chain 2's EMA length is set to 9 periods by default, and can be customized to better align with specific market conditions or trading strategies.
Key Features:
Dual EMA Chains: The strategy's originality shines through its dual-chain configuration, allowing traders to monitor and react to both long-term and short-term market trends. This approach is particularly powerful as it combines the strengths of trend-following with the agility of momentum trading.
Timeframe Flexibility: Users can modify the timeframes for both chains, ensuring the strategy can be tailored to different market conditions and individual trading styles. This flexibility makes it versatile for various assets and trading environments.
Independent Trade Logic: Each chain operates independently, with its own set of entry and exit rules. This allows for simultaneous or separate execution of trades based on the signals from either or both chains, providing a robust trading system that can handle different market phases.
Backtesting Period: The strategy includes a configurable backtesting period, enabling thorough performance assessment over a historical range. This feature is crucial for understanding how the strategy would have performed under different market conditions.
time_cond = time >= startDate and time <= finishDate
What It Does:
The Dual Chain Strategy offers traders a distinctive trading tool that merges two separate EMA-based systems into one cohesive framework. By integrating both long-term and short-term perspectives, the strategy enhances the ability to adapt to changing market conditions. The originality of this script lies in its innovative dual-chain design, providing traders with a unique edge by allowing them to capitalize on both significant trends and smaller, faster price movements.
Whether you aim to capture extended market trends or take advantage of more immediate price action, the Dual Chain Strategy provides a comprehensive solution with a high degree of customization and strategic depth. Its flexibility and originality make it a valuable tool for traders seeking to refine their approach to market analysis and execution.
How to Use the Dual Chain Strategy
Step 1: Access the Strategy
Add the Script: Start by adding the Dual Chain Strategy to your TradingView chart. You can do this by searching for the script by name or using the link provided.
Select the Asset: Apply the strategy to your preferred trading pair or asset, such as #BTCUSD, to see how it performs.
Step 2: Configure the Settings
Enable/Disable Chains:
The strategy is designed with two independent chains. You can choose to enable or disable each chain depending on your trading style and the market conditions.
enableChain1 = input.bool(true, title='Enable Chain 1')
enableChain2 = input.bool(true, title='Enable Chain 2')
By default, both chains are enabled. If you prefer to focus only on longer-term trends, you might disable Chain 2, or vice versa if you prefer shorter-term trades.
Set EMA Lengths:
Adjust the EMA lengths for each chain to match your trading preferences.
Chain 1: The default EMA length is 10 periods. This chain uses a weekly timeframe for entry signals and a daily timeframe for exits.
len1 = input.int(10, minval=1, title='Length Chain 1 EMA', group="Chain 1")
Chain 2: The default EMA length is 9 periods. This chain uses a 12-hour timeframe for entries and a 9-hour timeframe for exits.
len2 = input.int(9, minval=1, title='Length Chain 2 EMA', group="Chain 2")
Customize Timeframes:
You can customize the timeframes used for entry and exit signals for both chains.
Chain 1:
Entry Timeframe: Weekly
Exit Timeframe: Daily
tf1_entry = input.timeframe("W", title='Chain 1 Entry Timeframe', group="Chain 1")
tf1_exit = input.timeframe("D", title='Chain 1 Exit Timeframe', group="Chain 1")
Chain 2:
Entry Timeframe: 12 Hours
Exit Timeframe: 9 Hours
tf2_entry = input.timeframe("720", title='Chain 2 Entry Timeframe (12H)', group="Chain 2")
tf2_exit = input.timeframe("540", title='Chain 2 Exit Timeframe (9H)', group="Chain 2")
Set the Backtesting Period:
Define the period over which you want to backtest the strategy. This allows you to see how the strategy would have performed historically.
startDate = input.time(timestamp('2015-07-27'), title="StartDate")
finishDate = input.time(timestamp('2026-01-01'), title="FinishDate")
Step 3: Analyze the Signals
Understand the Entry and Exit Signals:
Buy Signals: When the price crosses above the entry EMA, the strategy generates a buy signal.
bullishChain1 = enableChain1 and ta.crossover(src1, entryEMA1)
Sell Signals: When the price crosses below the exit EMA, the strategy generates a sell signal.
bearishChain2 = enableChain2 and ta.crossunder(src2, entryEMA2)
Review the Visual Indicators:
The strategy plots buy and sell signals on the chart with labels for easy identification:
BUY C1/C2 for buy signals from Chain 1 and Chain 2.
SELL C1/C2 for sell signals from Chain 1 and Chain 2.
This visual aid helps you quickly understand when and why trades are being executed.
Step 4: Optimize the Strategy
Backtest Results:
Review the strategy’s performance over the backtesting period. Look at key metrics like net profit, drawdown, and trade statistics to evaluate its effectiveness.
Adjust the EMA lengths, timeframes, and other settings to see how changes affect the strategy’s performance.
Customize for Live Trading:
Once satisfied with the backtest results, you can apply the strategy settings to live trading. Remember to continuously monitor and adjust as needed based on market conditions.
Step 5: Implement Risk Management
Use Realistic Position Sizing:
Keep your risk exposure per trade within a comfortable range, typically between 1-2% of your trading capital.
Set Alerts:
Set up alerts for buy and sell signals, so you don’t miss trading opportunities.
Paper Trade First:
Consider running the strategy in a paper trading account to understand its behavior in real market conditions before committing real capital.
This dual-layered approach offers a distinct advantage: it enables the strategy to adapt to varying market conditions by capturing both broad trends and immediate price action without one chain's activity impacting the other's decision-making process. The independence of these chains in executing transactions adds a level of sophistication and flexibility that is rarely seen in more conventional trading systems, making the Dual Chain Strategy not just unique, but a powerful tool for traders seeking to navigate complex market environments.
WaveTrend With Divs & RSI(STOCH) Divs by WeloTradesWaveTrend with Divergences & RSI(STOCH) Divergences by WeloTrades
Overview
The "WaveTrend With Divergences & RSI(STOCH) Divergences" is an advanced Pine Script™ indicator designed for TradingView, offering a multi-dimensional analysis of market conditions. This script integrates several technical indicators—WaveTrend, Money Flow Index (MFI), RSI, and Stochastic RSI—into a cohesive tool that identifies both regular and hidden divergences across these indicators. These divergences can indicate potential market reversals and provide critical trading opportunities.
This indicator is not just a simple combination of popular tools; it offers extensive customization options, organized data presentation, and valuable trading signals that are easy to interpret. Whether you're a day trader or a long-term investor, this script enhances your ability to make informed decisions.
Originality and Usefulness
The originality of this script lies in its integration and the synergy it creates among the indicators used. Rather than merely combining multiple indicators, this script allows them to work together, enhancing each other's strengths. For example, by identifying divergences across WaveTrend, RSI, and Stochastic RSI simultaneously, the script provides multiple layers of confirmation, which reduces the likelihood of false signals and increases the reliability of trading signals.
The usefulness of this script is apparent in its ability to offer a consolidated view of market dynamics. It not only simplifies the analytical process by combining different indicators but also provides deeper insights through its divergence detection features. This comprehensive approach is designed to help traders identify potential market reversals, confirm trends, and ultimately make more informed trading decisions.
How the Components Work Together
1. Cross-Validation of Signals
WaveTrend: This indicator is primarily used to identify overbought and oversold conditions, as well as potential buy and sell signals. WaveTrend's ability to smooth price data and reduce noise makes it a reliable tool for identifying trend reversals.
RSI & Stochastic RSI: These momentum oscillators are used to measure the speed and change of price movements. While RSI identifies general overbought and oversold conditions, Stochastic RSI offers a more granular view by tracking the RSI’s level relative to its high-low range over a period of time. When these indicators align with WaveTrend signals, it adds a layer of confirmation that enhances the reliability of the signals.
Money Flow Index (MFI): This volume-weighted indicator assesses the inflow and outflow of money in an asset, giving insights into buying and selling pressure. By analyzing the MFI alongside WaveTrend and RSI indicators, the script can cross-validate signals, ensuring that buy or sell signals are supported by actual market volume.
Example Bullish scenario:
When a bullish divergence is detected on the RSI and confirmed by a corresponding bullish signal on the WaveTrend, along with an increasing Money Flow Index, the probability of a successful trade setup increases. This cross-validation minimizes the risk of acting on false signals, which might occur when relying on a single indicator.
Example Bearish scenario:
When a bearish divergence is detected on the RSI and confirmed by a corresponding bearish signal on the WaveTrend, along with an decreasing Money Flow Index, the probability of a successful trade setup increases. This cross-validation minimizes the risk of acting on false signals, which might occur when relying on a single indicator.
2. Divergence Detection and Market Reversals
Regular Divergences: Occur when the price action and an indicator (like RSI or WaveTrend) move in opposite directions. Regular bullish divergence signals a potential upward reversal when the price makes a lower low while the indicator makes a higher low. Conversely, regular bearish divergence suggests a downward reversal when the price makes a higher high, but the indicator makes a lower high.
Hidden Divergences: These occur when the price action and indicator move in the same direction, but with different momentum. Hidden bullish divergence suggests the continuation of an uptrend, while hidden bearish divergence suggests the continuation of a downtrend. By detecting these divergences across multiple indicators, the script identifies potential trend reversals or continuations with greater accuracy.
Example: The script might detect a regular bullish divergence on the WaveTrend while simultaneously identifying a hidden bullish divergence on the RSI. This combination suggests that while a trend reversal is possible, the overall market sentiment remains bullish, providing a nuanced view of the market.
A Regular Bullish Divergence Example:
A Hidden Bullish Divergence Example:
A Regular Bearish Divergence Example:
A Hidden Bearish Divergence Example:
3. Trend Strength and Sentiment Analysis
WaveTrend: Measures the strength and direction of the trend. By identifying the extremes of market sentiment (overbought and oversold levels), WaveTrend provides early signals for potential reversals.
Money Flow Index (MFI): Assesses the underlying sentiment by analyzing the flow of money. A rising MFI during an uptrend confirms strong buying pressure, while a falling MFI during a downtrend confirms selling pressure. This helps traders assess whether a trend is likely to continue or reverse.
RSI & Stochastic RSI: Offer a momentum-based perspective on the trend’s strength. High RSI or Stochastic RSI values indicate that the asset may be overbought, suggesting a potential reversal. Conversely, low values indicate oversold conditions, signaling a possible upward reversal.
Example:
During a strong uptrend, the WaveTrend & RSI's might signal overbought conditions, suggesting caution. If the MFI also shows decreasing buying pressure and the RSI reaches extreme levels, these indicators together suggest that the trend might be weakening, and a reversal could be imminent.
Example:
During a strong downtrend, the WaveTrend & RSI's might signal oversold conditions, suggesting caution. If the MFI also shows increasing buying pressure and the RSI reaches extreme levels, these indicators together suggest that the trend might be weakening, and a reversal could be imminent.
Conclusion
The "WaveTrend With Divergences & RSI(STOCH) Divergences" script offers a powerful, integrated approach to technical analysis by combining trend, momentum, and sentiment indicators into a single tool. Its unique value lies in the cross-validation of signals, the ability to detect divergences, and the comprehensive view it provides of market conditions. By offering traders multiple layers of analysis and customization options, this script is designed to enhance trading decisions, reduce false signals, and provide clearer insights into market dynamics.
WAVETREND
Display of WaveTrend:
Display of WaveTrend Setting:
WaveTrend Indicator Explanation
The WaveTrend indicator helps identify overbought and oversold conditions, as well as potential buy and sell signals. Its flexibility allows traders to adapt it to various strategies, making it a versatile tool in technical analysis.
WaveTrend Input Settings:
WT MA Source: Default: HLC3
What it is: The data source used for calculating the WaveTrend Moving Average.
What it does: Determines the input data to smooth price action and filter noise.
Example: Using HLC3 (average of High, Low, Close) provides a smoother data representation compared to using just the closing price.
Length (WT MA Length): Default: 3
What it is: The period used to calculate the Moving Average.
What it does: Adjusts the sensitivity of the WaveTrend indicator, where shorter lengths respond more quickly to price changes.
Example: A length of 3 is ideal for short-term analysis, providing quick reactions to price movements.
WT Channel Length & Average: Default: WT Channel Length = 9, Average = 12
What it is: Lengths used to calculate the WaveTrend channel and its average.
What it does: Smooths out the WaveTrend further, reducing false signals by averaging over a set period.
Example: Higher values reduce noise and help in identifying more reliable trends.
Channel: Style, Width, and Color:
What it is: Customization options for the WaveTrend channel's appearance.
What it does: Adjusts how the channel is displayed, including line style, width, and color.
Example: Choosing an area style with a distinct color can make the WaveTrend indicator clearly visible on the chart.
WT Buy & Sell Signals:
What it is: Settings to enable and customize buy and sell signals based on WaveTrend.
What it does: Allows for the display of buy/sell signals and customization of their shapes and colors.
When it gives a Buy Signal: Generated when the WaveTrend line crosses below an oversold level and then rises back, indicating a potential upward price movement.
When it gives a Sell Signal: Triggered when the WaveTrend line crosses above an overbought level and then declines, suggesting a possible downward trend.
Example: The script identifies these signals based on mean reversion principles, where prices tend to revert to the mean after reaching extremes. Traders can use these signals to time their entries and exits effectively.
WAVETREND OVERBOUGTH AND OVERSOLD LEVELS
Display of WaveTrend with Overbought & Oversold Levels:
Display of WaveTrend Overbought & Oversold Levels Settings:
WaveTrend Overbought & Oversold Levels Explanation
WT OB & OS Levels: Default: OB Level 1 = 53, OB Level 2 = 60, OS Level 1 = -53, OS Level 2 = -60
What it is: The default overbought and oversold levels used by the WaveTrend indicator to signal potential market reversals.
What it does: When the WaveTrend crosses above the OB levels, it indicates an overbought condition, potentially signaling a reversal or selling opportunity. Conversely, when it crosses below the OS levels, it indicates an oversold condition, potentially signaling a reversal or buying opportunity.
Example: A trader might use these levels to time entry or exit points, such as selling when the WaveTrend crosses into the overbought zone or buying when it crosses into the oversold zone.
Show OB/OS Levels: Default: True
What it is: Toggle options to show or hide the overbought and oversold levels on your chart.
What it does: When enabled, these levels will be visually represented on your chart, helping you to easily identify when the market reaches these critical thresholds.
Example: Displaying these levels can help you quickly see when the WaveTrend is approaching or has crossed into overbought or oversold territory, allowing for more informed trading decisions.
Line Style, Width, and Color for OB/OS Levels:
What it is: Options to customize the appearance of the OB and OS levels on your chart, including line style (solid, dotted, dashed), line width, and color.
What it does: These settings allow you to adjust how prominently these levels are displayed on your chart, which can help you better visualize and respond to overbought or oversold conditions.
Example: Setting a thicker, dashed line in a contrasting color can make these levels stand out more clearly, aiding in quick visual identification.
Example of Use:
Scenario: A trader wants to identify potential selling points when the market is overbought. They set the OB levels at 53 and 60, choosing a solid, red line style to make these levels clear on their chart. As the WaveTrend crosses above 53, they monitor for further price action, and upon crossing 60, they consider initiating a sell order.
WAVETREND DIVERGENCES
Display of WaveTrend Divergence:
Display of WaveTrend Divergence Setting:
WaveTrend Divergence Indicator Explanation
The WaveTrend Divergence feature helps identify potential reversal points in the market by highlighting divergences between the price and the WaveTrend indicator. Divergences can signal a shift in market momentum, indicating a possible trend reversal. This component allows traders to visualize and customize divergence detection on their charts.
WaveTrend Divergence Input Settings:
Potential Reversal Range: Default: 28
What it is: The number of bars to look back when detecting potential tops and bottoms.
What it does: Sets the range for identifying possible reversal points based on historical data.
Example: A setting of 28 looks back across the last 28 bars to find reversal points, offering a balance between responsiveness and reliability.
Reversal Minimum LVL OB & OS: Default: OB = 35, OS = -35
What it is: The minimum overbought and oversold levels required for detecting potential reversals.
What it does: Adjusts the thresholds that trigger a reversal signal based on the WaveTrend indicator.
Example: A higher OB level reduces the sensitivity to overbought conditions, potentially filtering out false reversal signals.
Lookback Bar Left & Right: Default: Left = 10, Right = 1
What it is: The number of bars to the left and right used to confirm a top or bottom.
What it does: Helps determine the position of peaks and troughs in the price action.
Example: A larger left lookback captures more extended price action before the peak, while a smaller right lookback focuses on the immediate past.
Lookback Range Min & Max: Default: Min = 5, Max = 60
What it is: The minimum and maximum range for the lookback period when identifying divergences.
What it does: Fine-tunes the detection of divergences by controlling the range over which the indicator looks back.
Example: A wider range increases the chances of detecting divergences across different market conditions.
R.Div Minimum LVL OB & OS: Default: OB = 53, OS = -53
What it is: The threshold levels for detecting regular divergences.
What it does: Adjusts the sensitivity of the regular divergence detection.
Example: Higher thresholds make the detection more conservative, identifying only stronger divergence signals.
H.Div Minimum LVL OB & OS: Default: OB = 20, OS = -20
What it is: The threshold levels for detecting hidden divergences.
What it does: Similar to regular divergence settings but for hidden divergences, which can indicate potential reversals that are less obvious.
Example: Lower thresholds make the hidden divergence detection more sensitive, capturing subtler market shifts.
Divergence Label Options:
What it is: Options to display and customize labels for regular and hidden divergences.
What it does: Allows users to visually differentiate between regular and hidden divergences using customizable labels and colors.
Example: Using different colors and symbols for regular (R) and hidden (H) divergences makes it easier to interpret signals on the chart.
Text Size and Color:
What it is: Customization options for the size and color of divergence labels.
What it does: Adjusts the readability and visibility of divergence labels on the chart.
Example: Larger text size may be preferred for charts with a lot of data, ensuring divergence labels stand out clearly.
FAST & SLOW MONEY FLOW INDEX
Display of Fast & Slow Money Flow:
Display of Fast & Slow Money Flow Setting:
Fast Money Flow Indicator Explanation
The Fast Money Flow indicator helps traders identify the flow of money into and out of an asset over a shorter time frame. By tracking the volume-weighted average of price movements, it provides insights into buying and selling pressure in the market, which can be crucial for making timely trading decisions.
Fast Money Flow Input Settings:
Fast Money Flow: Length: Default: 9
What it is: The period used for calculating the Fast Money Flow.
What it does: Determines the sensitivity of the Money Flow calculation. A shorter length makes the indicator more responsive to recent price changes, while a longer length provides a smoother signal.
Example: A length of 9 is suitable for traders looking to capture quick shifts in market sentiment over a short period.
Fast MFI Area Multiplier: Default: 5
What it is: A multiplier applied to the Money Flow area calculation.
What it does: Adjusts the size of the Money Flow area on the chart, effectively amplifying or reducing the visual impact of the indicator.
Example: A higher multiplier can make the Money Flow more prominent on the chart, aiding in the quick identification of significant money flow changes.
Y Position (Y Pos): Default: 0
What it is: The vertical position adjustment for the Fast Money Flow plot on the chart.
What it does: Allows you to move the Money Flow plot up or down on the chart to avoid overlap with other indicators.
Example: Adjusting the Y Position can be useful if you have multiple indicators on the chart and need to maintain clarity.
Fast MFI Style, Width, and Color:
What it is: Customization options for how the Fast Money Flow is displayed on the chart.
What it does: Enables you to choose between different plot styles (line or area), set the line width, and select colors for positive and negative money flow.
Example: Using different colors for positive (green) and negative (red) money flow helps to visually distinguish between periods of buying and selling pressure.
Slow Money Flow Indicator Explanation
The Slow Money Flow indicator tracks the flow of money into and out of an asset over a longer time frame. It provides a broader perspective on market sentiment, smoothing out short-term fluctuations and highlighting longer-term trends.
Slow Money Flow Input Settings:
Slow Money Flow: Length: Default: 12
What it is: The period used for calculating the Slow Money Flow.
What it does: A longer period smooths out short-term fluctuations, providing a clearer view of the overall money flow trend.
Example: A length of 12 is often used by traders looking to identify sustained trends rather than short-term volatility.
Slow MFI Area Multiplier: Default: 5
What it is: A multiplier applied to the Slow Money Flow area calculation.
What it does: Adjusts the size of the Money Flow area on the chart, helping to emphasize the indicator’s significance.
Example: Increasing the multiplier can help highlight the Money Flow in markets with less volatile price action.
Y Position (Y Pos): Default: 0
What it is: The vertical position adjustment for the Slow Money Flow plot on the chart.
What it does: Allows for vertical repositioning of the Money Flow plot to maintain chart clarity when used with other indicators.
Example: Adjusting the Y Position ensures that the Slow Money Flow indicator does not overlap with other key indicators on the chart.
Slow MFI Style, Width, and Color:
What it is: Customization options for the visual display of the Slow Money Flow on the chart.
What it does: Allows you to choose the plot style (line or area), set the line width, and select colors to differentiate positive and negative money flow.
Example: Customizing the colors for the Slow Money Flow allows traders to quickly distinguish between buying and selling trends in the market.
RSI
Display of RSI:
Display of RSI Setting:
RSI Indicator Explanation
The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. It is typically used to identify overbought or oversold conditions in the market, providing traders with potential signals for buying or selling.
RSI Input Settings:
RSI Source: Default: Close
What it is: The data source used for calculating the RSI.
What it does: Determines which price data (e.g., close, open) is used in the RSI calculation, affecting how the indicator reflects market conditions.
Example: Using the closing price is standard practice, as it reflects the final agreed-upon price for a given time period.
MA Type (Moving Average Type): Default: SMA
What it is: The type of moving average applied to the RSI for smoothing purposes.
What it does: Changes the smoothing technique of the RSI, impacting how quickly the indicator responds to price movements.
Example: Using an Exponential Moving Average (EMA) will make the RSI more sensitive to recent price changes compared to a Simple Moving Average (SMA).
RSI Length: Default: 14
What it is: The period over which the RSI is calculated.
What it does: Adjusts the sensitivity of the RSI. A shorter length (e.g., 7) makes the RSI more responsive to recent price changes, while a longer length (e.g., 21) smooths out the indicator, reducing the number of signals.
Example: A 14-period RSI is commonly used for identifying overbought and oversold conditions, providing a balance between sensitivity and reliability.
RSI Plot Style, Width, and Color:
What it is: Options to customize the appearance of the RSI line on the chart.
What it does: Allows you to adjust the visual representation of the RSI, including the line width and color.
Example: Setting a thicker line width and a bright color like yellow can make the RSI more visible on the chart, aiding in quick analysis.
Display of RSI with RSI Moving Average:
RSI Moving Average Explanation
The RSI Moving Average adds a smoothing layer to the RSI, helping to filter out noise and provide clearer signals. It is particularly useful for confirming trend strength and identifying potential reversals.
RSI Moving Average Input Settings:
MA Length: Default: 14
What it is: The period over which the Moving Average is calculated on the RSI.
What it does: Adjusts the smoothing of the RSI, helping to reduce false signals and provide a clearer trend indication.
Example: A 14-period moving average on the RSI can smooth out short-term fluctuations, making it easier to spot genuine overbought or oversold conditions.
MA Plot Style, Width, and Color:
What it is: Customization options for how the RSI Moving Average is displayed on the chart.
What it does: Allows you to adjust the line width and color, helping to differentiate the Moving Average from the main RSI line.
Example: Using a contrasting color for the RSI Moving Average (e.g., magenta) can help it stand out against the main RSI line, making it easier to interpret the indicator.
STOCHASTIC RSI
Display of Stochastic RSI:
Display of Stochastic RSI Setting:
Stochastic RSI Indicator Explanation
The Stochastic RSI (Stoch RSI) is a momentum oscillator that measures the level of the RSI relative to its high-low range over a set period of time. It is used to identify overbought and oversold conditions, providing potential buy and sell signals based on momentum shifts.
Stochastic RSI Input Settings:
Stochastic RSI Length: Default: 14
What it is: The period over which the Stochastic RSI is calculated.
What it does: Adjusts the sensitivity of the Stochastic RSI. A shorter length makes the indicator more responsive to recent price changes, while a longer length smooths out the fluctuations, reducing noise.
Example: A length of 14 is commonly used to identify momentum shifts over a medium-term period, providing a balanced view of potential overbought or oversold conditions.
Display of Stochastic RSI %K Line:
Stochastic RSI %K Line Explanation
The %K line in the Stochastic RSI is the main line that tracks the momentum of the RSI over the chosen period. It is the faster-moving component of the Stochastic RSI, often used to identify entry and exit points.
Stochastic RSI %K Input Settings:
%K Length: Default: 3
What it is: The period used for smoothing the %K line of the Stochastic RSI.
What it does: Smoothing the %K line helps reduce noise and provides a clearer signal for potential market reversals.
Example: A smoothing length of 3 is common, offering a balance between responsiveness and noise reduction, making it easier to spot significant momentum shifts.
%K Plot Style, Width, and Color:
What it is: Customization options for the visual representation of the %K line.
What it does: Allows you to adjust the appearance of the %K line on the chart, including line width and color, to fit your visual preferences.
Example: Setting a blue color and a medium width for the %K line makes it stand out clearly on the chart, helping to identify key points of momentum change.
%K Fill Color (Above):
What it is: The fill color that appears above the %K line on the chart.
What it does: Adds visual clarity by shading the area above the %K line, making it easier to interpret the direction and strength of momentum.
Example: Using a light blue fill color above the %K line can help emphasize bullish momentum, making it visually prominent.
Display of Stochastic RSI %D Line:
Stochastic RSI %D Line Explanation
The %D line in the Stochastic RSI is a moving average of the %K line and acts as a signal line. It is slower-moving compared to the %K line and is often used to confirm signals or identify potential reversals when it crosses the %K line.
Stochastic RSI %D Input Settings:
%D Length: Default: 3
What it is: The period used for smoothing the %D line of the Stochastic RSI.
What it does: Smooths out the %D line, making it less sensitive to short-term fluctuations and more reliable for identifying significant market signals.
Example: A length of 3 is often used to provide a smoothed signal line that can help confirm trends or reversals indicated by the %K line.
%D Plot Style, Width, and Color:
What it is: Customization options for the visual representation of the %D line.
What it does: Allows you to adjust the appearance of the %D line on the chart, including line width and color, to match your preferences.
Example: Setting an orange color and a thicker line width for the %D line can help differentiate it from the %K line, making crossover points easier to spot.
%D Fill Color (Below):
What it is: The fill color that appears below the %D line on the chart.
What it does: Adds visual clarity by shading the area below the %D line, making it easier to interpret bearish momentum.
Example: Using a light orange fill color below the %D line can highlight bearish conditions, making it visually easier to identify.
RSI & STOCHASTIC RSI OVERBOUGHT AND OVERSOLD LEVELS
Display of RSI & Stochastic with Overbought & Oversold Levels:
Display of RSI & Stochastic Overbought & Oversold Settings:
RSI & Stochastic Overbought & Oversold Levels Explanation
The Overbought (OB) and Oversold (OS) levels for RSI and Stochastic RSI indicators are key thresholds that help traders identify potential reversal points in the market. These levels are used to determine when an asset is likely overbought or oversold, which can signal a potential trend reversal.
RSI & Stochastic Overbought & Oversold Input Settings:
RSI & Stochastic Level 1 Overbought (OB) & Oversold (OS): Default: OB Level = 170, OS Level = 130
What it is: The first set of thresholds for determining overbought and oversold conditions for both RSI and Stochastic RSI indicators.
What it does: When the RSI or Stochastic RSI crosses above the overbought level, it suggests that the asset might be overbought, potentially signaling a sell opportunity. Conversely, when these indicators drop below the oversold level, it suggests the asset might be oversold, potentially signaling a buy opportunity.
Example: If the RSI crosses above 170, traders might look for signs of a potential trend reversal to the downside, while a cross below 130 might indicate a reversal to the upside.
RSI & Stochastic Level 2 Overbought (OB) & Oversold (OS): Default: OB Level = 180, OS Level = 120
What it is: The second set of thresholds for determining overbought and oversold conditions for both RSI and Stochastic RSI indicators.
What it does: These levels provide an additional set of reference points, allowing traders to differentiate between varying degrees of overbought and oversold conditions, potentially leading to more refined trading decisions.
Example: When the RSI crosses above 180, it might indicate an extreme overbought condition, which could be a stronger signal for a sell, while a cross below 120 might indicate an extreme oversold condition, which could be a stronger signal for a buy.
RSI & Stochastic Overbought (OB) Band Customization:
OB Level 1: Width, Style, and Color:
What it is: Customization options for the visual appearance of the first overbought band on the chart.
What it does: Allows you to set the line width, style (solid, dotted, dashed), and color for the first overbought band, enhancing its visibility on the chart.
Example: A dashed red line with medium width can clearly indicate the first overbought level, helping traders quickly identify when this threshold is crossed.
OB Level 2: Width, Style, and Color:
What it is: Customization options for the visual appearance of the second overbought band on the chart.
What it does: Allows you to set the line width, style, and color for the second overbought band, providing a clear distinction from the first band.
Example: A dashed red line with a slightly thicker width can represent a more significant overbought level, making it easier to differentiate from the first level.
RSI & Stochastic Oversold (OS) Band Customization:
OS Level 1: Width, Style, and Color:
What it is: Customization options for the visual appearance of the first oversold band on the chart.
What it does: Allows you to set the line width, style (solid, dotted, dashed), and color for the first oversold band, making it visually prominent.
Example: A dashed green line with medium width can highlight the first oversold level, helping traders identify potential buying opportunities.
OS Level 2: Width, Style, and Color:
What it is: Customization options for the visual appearance of the second oversold band on the chart.
What it does: Allows you to set the line width, style, and color for the second oversold band, providing an additional visual cue for extreme oversold conditions.
Example: A dashed green line with a thicker width can represent a more significant oversold level, offering a stronger visual cue for potential buying opportunities.
RSI DIVERGENCES
Display of RSI Divergence Labels:
Display of RSI Divergence Settings:
RSI Divergence Lookback Explanation
The RSI Divergence settings allow traders to customize the parameters for detecting divergences between the RSI (Relative Strength Index) and price action. Divergences occur when the price moves in the opposite direction to the RSI, potentially signaling a trend reversal. These settings help refine the accuracy of divergence detection by adjusting the lookback period and range. ( NOTE: This setting only imply to the RSI. This doesn't effect the STOCHASTIC RSI. )
RSI Divergence Lookback Input Settings:
Lookback Left: Default: 10
What it is: The number of bars to look back from the current bar to detect a potential divergence.
What it does: Defines the left-side lookback period for identifying pivot points in the RSI, which are used to spot divergences. A longer lookback period may capture more significant trends but could also miss shorter-term divergences.
Example: A setting of 10 bars means the script will consider pivot points up to 10 bars before the current bar to check for divergence patterns.
Lookback Right: Default: 1
What it is: The number of bars to look forward from the current bar to complete the divergence pattern.
What it does: Defines the right-side lookback period for confirming a potential divergence. This setting helps ensure that the identified divergence is valid by allowing the script to check subsequent bars for confirmation.
Example: A setting of 1 bar means the script will look at the next bar to confirm the divergence pattern, ensuring that the signal is reliable.
Lookback Range Min: Default: 5
What it is: The minimum range of bars required to detect a valid divergence.
What it does: Sets a lower bound on the range of bars considered for divergence detection. A lower minimum range might capture more frequent but possibly less significant divergences.
Example: Setting the minimum range to 5 ensures that only divergences spanning at least 5 bars are considered, filtering out very short-term patterns.
Lookback Range Max: Default: 60
What it is: The maximum range of bars within which a divergence can be detected.
What it does: Sets an upper bound on the range of bars considered for divergence detection. A larger maximum range might capture more significant divergences but could also include less relevant long-term patterns.
Example: Setting the maximum range to 60 bars allows the script to detect divergences over a longer timeframe, capturing more extended divergence patterns that could indicate major trend reversals.
RSI Divergence Explanation
RSI divergences occur when the RSI indicator and price action move in opposite directions, signaling potential trend reversals. This section of the settings allows traders to customize the appearance and detection of both regular and hidden bullish and bearish divergences.
RSI Divergence Input Settings:
R. Bullish Div Label: Default: True
What it is: An option to display labels for regular bullish divergences.
What it does: Enables or disables the visibility of labels that mark regular bullish divergences, where the price makes a lower low while the RSI makes a higher low, indicating a potential upward reversal.
Example: A trader might use this to spot buying opportunities in a downtrend when a bullish divergence suggests the trend may be reversing.
Bullish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of regular bullish divergence labels.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: Selecting a green label color and a distinct line width makes bullish divergences easily recognizable on your chart.
R. Bearish Div Label: Default: True
What it is: An option to display labels for regular bearish divergences.
What it does: Enables or disables the visibility of labels that mark regular bearish divergences, where the price makes a higher high while the RSI makes a lower high, indicating a potential downward reversal.
Example: A trader might use this to spot selling opportunities in an uptrend when a bearish divergence suggests the trend may be reversing.
Bearish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of regular bearish divergence labels.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: Choosing a red label color and a specific line width makes bearish divergences clearly stand out on your chart.
H. Bullish Div Label: Default: False
What it is: An option to display labels for hidden bullish divergences.
What it does: Enables or disables the visibility of labels that mark hidden bullish divergences, where the price makes a higher low while the RSI makes a lower low, indicating potential continuation of an uptrend.
Example: A trader might use this to confirm an existing uptrend when a hidden bullish divergence signals continued buying strength.
Hidden Bullish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of hidden bullish divergence labels.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: A softer green color with a thinner line width might be chosen to subtly indicate hidden bullish divergences, keeping the chart clean while providing useful information.
H. Bearish Div Label: Default: False
What it is: An option to display labels for hidden bearish divergences.
What it does: Enables or disables the visibility of labels that mark hidden bearish divergences, where the price makes a lower high while the RSI makes a higher high, indicating potential continuation of a downtrend.
Example: A trader might use this to confirm an existing downtrend when a hidden bearish divergence signals continued selling pressure.
Hidden Bearish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of hidden bearish divergence labels.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: A muted red color with a thinner line width might be selected to indicate hidden bearish divergences without overwhelming the chart.
Divergence Text Size and Color: Default: S (Small)
What it is: Settings to adjust the size and color of text labels for RSI divergences.
What it does: Allows you to customize the size and color of text labels that display the divergence information on the chart.
Example: Choosing a small text size with a bright white color can make divergence labels easily readable without taking up too much space on the chart.
STOCHASTIC DIVERGENCES
Display of Stochastic RSI Divergence Labels:
Display of Stochastic RSI Divergence Settings:
Stochastic RSI Divergence Explanation
Stochastic RSI divergences occur when the Stochastic RSI indicator and price action move in opposite directions, signaling potential trend reversals. These settings allow traders to customize the detection and visual representation of both regular and hidden bullish and bearish divergences in the Stochastic RSI.
Stochastic RSI Divergence Input Settings:
R. Bullish Div Label: Default: True
What it is: An option to display labels for regular bullish divergences in the Stochastic RSI.
What it does: Enables or disables the visibility of labels that mark regular bullish divergences, where the price makes a lower low while the Stochastic RSI makes a higher low, indicating a potential upward reversal.
Example: A trader might use this to spot buying opportunities in a downtrend when a bullish divergence in the Stochastic RSI suggests the trend may be reversing.
Bullish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of regular bullish divergence labels in the Stochastic RSI.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: Selecting a blue label color and a distinct line width makes bullish divergences in the Stochastic RSI easily recognizable on your chart.
R. Bearish Div Label: Default: True
What it is: An option to display labels for regular bearish divergences in the Stochastic RSI.
What it does: Enables or disables the visibility of labels that mark regular bearish divergences, where the price makes a higher high while the Stochastic RSI makes a lower high, indicating a potential downward reversal.
Example: A trader might use this to spot selling opportunities in an uptrend when a bearish divergence in the Stochastic RSI suggests the trend may be reversing.
Bearish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of regular bearish divergence labels in the Stochastic RSI.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: Choosing an orange label color and a specific line width makes bearish divergences in the Stochastic RSI clearly stand out on your chart.
H. Bullish Div Label: Default: False
What it is: An option to display labels for hidden bullish divergences in the Stochastic RSI.
What it does: Enables or disables the visibility of labels that mark hidden bullish divergences, where the price makes a higher low while the Stochastic RSI makes a lower low, indicating potential continuation of an uptrend.
Example: A trader might use this to confirm an existing uptrend when a hidden bullish divergence in the Stochastic RSI signals continued buying strength.
Hidden Bullish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of hidden bullish divergence labels in the Stochastic RSI.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: A softer blue color with a thinner line width might be chosen to subtly indicate hidden bullish divergences, keeping the chart clean while providing useful information.
H. Bearish Div Label: Default: False
What it is: An option to display labels for hidden bearish divergences in the Stochastic RSI.
What it does: Enables or disables the visibility of labels that mark hidden bearish divergences, where the price makes a lower high while the Stochastic RSI makes a higher high, indicating potential continuation of a downtrend.
Example: A trader might use this to confirm an existing downtrend when a hidden bearish divergence in the Stochastic RSI signals continued selling pressure.
Hidden Bearish Label Color, Line Width, and Line Color:
What it is: Settings to customize the appearance of hidden bearish divergence labels in the Stochastic RSI.
What it does: Allows you to choose the color of the labels, adjust the width of the divergence lines, and select the color for these lines.
Example: A muted orange color with a thinner line width might be selected to indicate hidden bearish divergences without overwhelming the chart.
Divergence Text Size and Color: Default: S (Small)
What it is: Settings to adjust the size and color of text labels for Stochastic RSI divergences.
What it does: Allows you to customize the size and color of text labels that display the divergence information on the chart.
Example: Choosing a small text size with a bright white color can make divergence labels easily readable without taking up too much space on the chart.
Alert System:
Custom Alerts for Divergences and Reversals:
What it is: The script includes customizable alert conditions to notify you of detected divergences or potential reversals based on WaveTrend, RSI, and Stochastic RSI.
What it does: Helps you stay informed of key market movements without constantly monitoring the charts, enabling timely decisions.
Example: Setting an alert for regular bearish divergence on the WaveTrend could notify you of a potential sell opportunity as soon as it is detected.
How to Use Alerts:
Set up custom alerts in TradingView based on these conditions to be notified of potential trading opportunities. Alerts are triggered when the indicator detects conditions that match the selected criteria, such as divergences or potential reversals.
By following the detailed guidelines and examples above, you can effectively use and customize this powerful indicator to suit your trading strategy.
For further understanding and customization, refer to the input settings within the script and adjust them to match your trading style and preferences.
How Components Work Together
Synergy and Cross-Validation: The indicator combines multiple layers of analysis to validate trading signals. For example, a WaveTrend buy signal that coincides with a bullish divergence in RSI and positive fast money flow is likely to be more reliable than any single indicator’s signal. This cross-validation reduces the likelihood of false signals and enhances decision-making.
Comprehensive Market Analysis: Each component plays a role in analyzing different aspects of the market. WaveTrend focuses on trend strength, Money Flow indicators assess market sentiment, while RSI and Stochastic RSI offer detailed views of price momentum and potential reversals.
Ideal For
Traders who require a reliable, multifaceted tool for detecting market trends and reversals.
Investors seeking a deeper understanding of market dynamics across different timeframes and conditions, whether in forex, equities, or cryptocurrency markets.
This script is designed to provide a comprehensive tool for technical analysis, combining multiple indicators and divergence detection into one versatile and customizable script. It is especially useful for traders who want to monitor various indicators simultaneously and look for convergence or divergence signals across different technical tools.
Acknowledgements
Special thanks to these amazing creators for inspiration and their creations:
I want to thank these amazing creators for creating there amazing indicators , that inspired me and also gave me a head start by making this indicator! Without their amazing indicators it wouldn't be possible!
vumanchu: VuManChu Cipher B Divergences.
MisterMoTa: RSI + Divergences + Alerts .
DevLucem: Plain Stochastic Divergence.
Note
This indicator is designed to be a powerful tool in your trading arsenal. However , it is essential to backtest and adjust the settings according to your trading strategy before applying it to live trading . If you have any questions or need further assistance, feel free to reach out.
Median Analyst ConsensusThe Median Analyst Consensus Indicator provides an unbiased, easy-to-interpret view of market sentiment by leveraging TradingView's comprehensive financial data library. This tool displays the median 12-month price target and the percentage difference from the current price directly on your charts.
Key Features
1. Accurate Market Sentiment: By consolidating analyst ratings and price targets from multiple reputable sources like Bloomberg, Refinitiv (formerly Thomson Reuters), S&P Capital IQ, and Morningstar, this indicator displays the median analyst consensus. Using the median ensures outlier ratings don't skew the overall sentiment, providing a more robust representation.
2. Simplicity at a Glance: View the median 12-month price target and percentage difference from the current price directly on your chart. No need to juggle multiple reports - key insights are surfaced within your normal trading workflow.
3. Data-Driven Transparency: If no analyst data is available for a particular asset, the indicator will not display, ensuring you only see reliable information. The number of contributing analysts is also shown for context.
Why the Median?
The median is favored over the mean to minimize the impact of outlier ratings that could distort the consensus view. By taking the middle value across all analyst projections, the median provides a more stable, outlier-resistant measure of market sentiment.
Powered by TradingView Data
This indicator taps into TradingView's financial data library, which aggregates analyst ratings, estimates, and recommendations from leading institutional data providers. TradingView sources this data from firms like FactSet, Bloomberg, Refinitiv, S&P Capital IQ, and Morningstar, ensuring a comprehensive and trusted view of analyst sentiment.
The library provides variables like:
syminfo.recommendations_buy
syminfo.recommendations_sell
syminfo.target_price_median
syminfo.recommendations_buy_strong
syminfo.recommendations_sell_strong
The indicator calculates and displays the median of these analyst inputs.
Usage
The indicator displays:
The median 12-month price target across analysts
The percentage difference between the price target and current price
The number of contributing analyst estimates
If no analyst data is available, the indicator does not display, ensuring full transparency.
The Median Analyst Consensus Indicator provides an unbiased, easy-to-interpret view of market sentiment by leveraging TradingView's comprehensive financial data library. This tool offers a new perspective on potential trade opportunities directly on your charts.
Disclaimer
While the data is sourced from reputable providers, analyst forecasts should not be construed as investment recommendations. This indicator aims to synthesize market opinions, but investment decisions are solely your responsibility. As with any analytical tool, you should conduct your own research and risk assessments before executing any trades.
Guppy Wave [UkutaLabs]█ OVERVIEW
The Guppy Wave Indicator is a collection of Moving Averages that provide insight on current market strength. This is done by plotting a series of 12 Moving Averages and analysing where each one is positioned relative to the others.
In doing this, this script is able to identify short-term moves and give an idea of the current strength and direction of the market.
The aim of this script is to simplify the trading experience of users by automatically displaying a series of useful Moving Averages to provide insight into short-term market strength.
█ USAGE
The Guppy Wave is generated using a series of 12 total Moving Averages composed of 6 Small-Period Moving Averages and 6 Large Period Moving Averages. By measuring the position of each moving average relative to the others, this script provides unique insight into the current strength of the market.
Rather than simply plotting 12 Moving Averages, a color gradient is instead drawn between the Moving Averages to make it easier to visualise the distribution of the Guppy Wave. The color of this gradient changes depending on whether the Small-Period Averages are above or below the Large-Period Averages, allowing traders to see current short-term market strength at a glance.
When the gradient fans out, this indicates a rapid short-term move. When the gradient is thin, this indicates that there is no dominant power in the market.
█ SETTINGS
• Moving Average Type: Determines the type of Moving Average that get plotted (EMA, SMA, WMA, VWMA, HMA, RMA)
• Moving Average Source: Determines the source price used to calculate Moving Averages (open, high, low, close, hl2, hlc3, ohlc4, hlcc4)
• Bearish Color: Determines the color of the gradient when Small-Period MAs are above Large-Period MAs.
• Bullish Color: Determines the color of the gradient when Small-Period MAs are below Large-Period MAs.
MACD by Take and TradeImproved version of MACD with asymmetrical BUY and SELL approaches.
This indicator is based on popular MACD one, but with some "tricks" designed to make it more applicable to the rapidly changing crypto market.
Key benefits:
Dynamic auto-adjusted threshold to filter out weak signals
Highlighted BUY/SELL signals with divergence (if a signal is accompanied by divergence, for example, price makes a new high while macd has a second high below the first, this signal is considered stronger and will be highlighted in a darker color)
Boost BUY signals on very slow market in accumulation phase
Not symmetric! It uses 2 different signal lines, which allows to obtain SELL signals earlier comparing to classic MACD approach
Classic concept of MACD
Classic MACD, in its simplest case, consists of two lines - macd line and signal line. Macd line is a difference between so-called "fast" and "slow" EMA lines (there are just a Exponential Moving Average lines with different windows: "12" for fast and "26" for slow). Signal line is just a smoothed "macd" line.
When macd line crosses signal line from bottom to up and intersection point < 0, this is "BUY" signal. And vise versa, when macd line crosses signal line from top to bottom, and intersection point > 0, this is "SELL" signal.
Parameters used in default configuration of classic MACD indicator:
Fast line: EMA-12
Slow line: EMA-26
Signal line: EMA-9
Problem of classic concept
Classic MACD indicator usually gives not bad "BUY" signals, especially if using them not for operational trading but for "investment" strategy. But "SELL" signalls usually generated too late. Simply because the market tends to fall much faster than it rises.
Possible solution (the main feature of our version of MACD)
To make indicator react faster on SELL condition, while still keeping it reliable for BUY signals, we decided to use two signal lines . Faster than default signal line (with window=6) for BUY signals and much faster than default (with window=2) for SELL signals.
This approach allowed us to receive sell signals earlier and exit deals on more favorable prices. Trade off of this change - is the number of SELL signals - there were more of them. However, this does not matter, since we receive the very first sell signal with a "very fast signal line" much earlier than with classic indicator settings.
Parameters we use in our improved MACD indicator:
Fast line: EMA-12
Slow line: EMA-24
Faster signal line: EMA-6
Much faster signal line: EMA-2
Removing noise (false triggerings)
Other drawback of classic MACD - it generates a lot of "weak" (false) signals. This signals are generated when macd crosses signal line much close to zero-line. And usually there are a lot of such intersections.
To remove this kind of noise, we added a trigger threshold, which by default is equal to 2.5% of the average asset price over a long period of time. Due to the link to the average price, this threshold automatically takes a specific value for each trading pair. Threshold 2.5% works perfect for all trading pairs for 1D timeframe. For other timeframes user can (and maybe will want) change it.
Boost weak BUY signals in a prolonged bear market
Signals on bearish stage are usually very weak, because there is no volatility, and no price impulse. And such signals will be filtered out as "noise" - see above. But this time is perfect time to buy! Therefore, we further boost the buy signals in a prolonged bear market so that they can pass through the filter and appear on the chart. Bearish period is the best time to invest!
Developed by Take and Trade. Enjoy using it!
Seek liquidityGuided by ICT tutoring, I create this versatile "Seek liquidity" indicator.
This indicator shows an easy way to view the Liquidity that has been Created - Eliminated - and what liquidity is left to eliminate.
Liquidity levels appear after the sessions are over, and the lines get stuck on the candle that eliminates them.
Timing session =
//---Asian
- 18:00-00:00
//---London
- 00:00-02:00
- 02:00-05:00
- 00:00-06:00
//---New York
- 06:00-12:00
- 09.30-12.00
//---Lunch
- 12:00-13:30
//---PM
- 1.30pm - 4.00pm
- 12:00-18:00
The user has the possibility to:
- Choose whether or not to view sessions
- Choose to show levels from previous sessions
- Choose to show today's session levels
- Choose whether to view the boxes
- Choose to view the division is open daily
The indicator should be used as ICT shows in its concepts, the indicator takes into consideration both the previous and today's Liquidity, and the session levels can be used for a reversal as in the example below:
Moving Average TransformThe MAT is essentially a different kind of smoothed moving average. It is made to filter out data sets that deviate from the specified absolute threshold and the result becomes a smoothing function. The goal here, inspired by time series analysis within mathematical study, is to eliminate data anomalies and generate a more accurate trendline.
Functionality:
This script calculates a filtered average by:
Determining the mean of the entire data series.
Initializing sum and count variables.
Iterating through the data to filter values that deviate from the mean beyond the threshold.
Calculating a filtered mean based on the filtered data.
The filtered mean is then passed through a moving average function, where various types of moving averages like SMA, EMA, DEMA, TEMA, and ALMA can be applied. Some popular averages such as the HMA were omitted due to their heavy dependency on weighing specific data points.
Some information from "Time Series Analysis" regarding deviations
Definition of Anomaly: An anomaly or outlier is a data point that differs significantly from other observations in the dataset. It can be caused by various reasons such as measurement errors, data entry errors, or genuine extreme observations.
Impact on Mean: The mean (or average) of a dataset is calculated by summing all the values and dividing by the number of values. Since the mean is sensitive to extreme values, even a single outlier can significantly skew the mean.
Example: Consider a simple time series dataset: . The value "150" is an anomaly in this context. If we calculate the mean with this outlier, it is (10 + 12 + 11 + 9 + 150) / 5 = 38.4. However, if we exclude the outlier, the mean becomes (10 + 12 + 11 + 9) / 4 = 10.5. The presence of the outlier has substantially increased the mean.
Accuracy and Representativeness: While the mean calculated without outliers might be more "accurate" in the sense of being more representative of the central tendency of the bulk of the data, it's essential to note that anomalies might convey important information about the system being studied. Blindly removing or ignoring them might lead to overlooking significant events or phenomena.
Approaches to Handle Anomalies?
Detection and Removal
Robust Statistics
Transformation
K's Pivot PointsPivot points are a popular technical analysis tool used by traders to identify potential levels of support and resistance in a given timeframe. Pivot points are derived from previous price action and are used to estimate potential price levels where an asset may experience a reversal, breakout, or significant price movement.
The calculation of pivot points involves a simple formula that takes into account the high, low, and close prices from the previous trading session or a specific period. The most commonly used pivot point calculation method is the "Standard" or "Classic" method. Here's the formula:
Pivot Point (P) = (High + Low + Close) / 3
In addition to the pivot point itself, several support and resistance levels are calculated based on the pivot point value.
K's Pivot Points try to enhance them by incorporating multiple elements and by applying a re-integration strategy to validate two events:
* Found_Support: This event represents a basing market that is bound to recover or at least shape a bounce.
* Found_Resistance: This event represents a toppish market that is bound to consolidate or at least shape a pause.
K's Pivot Points are calculated following these steps:
1. Calculate the highest of highs for the previous 24 periods (preferably hours).
2. Calculate the lowest of lows for the previous 24 periods (preferably hours).
3. Calculate a 24-period (preferably hours) moving average of the close price.
4. Calculate K's Pivot Point as the average between the three previous step.
5. To find the support, use this formula: Support = (Lowest K's pivot point of the last 12 periods * 2) - Step 1
6. To find the resistance, use this formula: Resistance = (Highest K's pivot point of the last 12 periods * 2) - Step 2
The re-integration strategy to find support and resistance areas is as follows:
* A support has been found if the market breaks the support and shapes a close above it afterwards.
* A resistance has been found if the market surpasses the resistance and shapes a close below it afterwards.
The lookback period (whether 24 and 12) can be modified but the default versions work well.
