Swift Trend Signals (MTF) [Atiloan]Swift Trend Signals (MTF) by Atiloan
Swift Trend Signals (MTF) is a trend-following indicator tool designed to assist traders in identifying potential trend reversals and entry points across multiple timeframes. The indicator utilizes a Zero-Lag Exponential Moving Average (ZLEMA) and volatility bands based on Average True Range (ATR) to provide quicker and more accurate signals with reduced lag.
Key Features:
Multi-Timeframe Support: The indicator shows trend signals from up to 5 different timeframes, allowing you to analyze market movement from multiple perspectives.
Zero-Lag EMA: The ZLEMA helps to reduce the lag seen in traditional moving averages, offering faster trend signals.
Volatility Bands: Customizable bands that show trend strength and provide clear buy and sell signals.
Color-Coded Trend Signals: Bullish trends are marked in green, and bearish trends in red, for easy visualization.
Entry and Exit Signals: The indicator provides buy and sell signals directly on the chart based on the ZLEMA and volatility bands.
Alerts: Alerts for trend changes and entry opportunities, so you never miss important market moves.
Customization Options:
Length: Adjustable look-back period for ZLEMA calculation.
Band Multiplier: Determines the thickness of the volatility bands, with larger values reducing market noise.
Timeframes: Allows you to set up to five different timeframes for trend analysis, including 5 minutes, 15 minutes, 1 hour, 4 hours, and daily.
Best for:
Trend Following: Ideal for traders looking to capitalize on market trends.
Entry Signals: Perfect for those seeking to precisely time their entries using multi-timeframe trend analysis.
Publication Type:
Public: Since this indicator is offered for free and is beneficial for a broad audience, it should be published as "Public" to allow all TradingView users to benefit from it.
Additional Notes:
No Unrealistic Claims: This indicator does not guarantee 100% accuracy or success rate. Avoid exaggerated statements like "100% win rate" or similar.
Safe Usage: The indicator is a tool to assist in decision-making and should not be the sole basis for trades. Always implement proper risk management strategies.
Strategy
거미줄 자동매매 250227Okay, let's break down this TradingView Pine Script code, '거미줄 자동매매 250227' (Spiderweb Auto-Trading 250227).
This script implements a grid trading strategy for long positions in the USDT market. The core idea is to place a series of buy limit orders at progressively lower prices below an initial entry point, aiming to lower the average entry price as the price drops. It then aims to exit the entire position when the price rises a certain percentage above the average entry price.
Here's a detailed breakdown:
1. Strategy Setup (`strategy` function):
`'거미줄 자동매매 250227'`: The name of the strategy.
`overlay = true`: Draws plots and labels directly on the main price chart.
`pyramiding = 15`: Allows up to 15 entries in the same direction (long). This is essential for grid trading, as it needs to open multiple buy orders.
`initial_capital = 600`: Sets the starting capital for backtesting to 600 USDT.
`currency = currency.USDT`: Specifies the account currency as USDT.
`margin_long/short = 0`: Doesn't define specific margin requirements (might imply spot trading logic or rely on exchange defaults if used live).
`calc_on_order_fills = false`: Strategy calculations happen on each bar's close, not just when orders fill.
2. Inputs (`input`):
Core Settings:
`lev`: Leverage (default 10x). Used to calculate position sizes.
`per1`: Percentage of total capital to allocate to the initial grid (default 80%).
`per2`: Percentage of the *remaining* capital (100 - `per1`) to use for the "semifinal" entry (default 50%). The rest goes to the "final" entry.
`len`: Lookback period (default 4 bars) to determine the initial `maxPrice`.
`range1`: The total percentage range downwards from `maxPrice` where the grid orders will be placed (default -10%, meaning 10% down).
`tp`: Take profit percentage above the average entry price (default 0.45%).
`semifinal`: Percentage drop from `maxPrice` to trigger the "semifinal" larger entry (default 12%).
`final`: Percentage drop from `maxPrice` to trigger the "final" larger entry (default 15%).
Trading Mechanics:
`trail`: Boolean (true/false) to enable an ATR-based trailing stop only if just the first grid order ("Buy 1") has filled (default true).
`atrPeriod`, `atrMult`: Settings for the ATR calculation if `trail` is true.
Rounding & Display:
`roundprice`, `round`: Decimal places for rounding price and quantity calculations.
`texts`, `label_style`: User interface preferences for text size and label appearance on the chart.
Time Filter:
`startTime`, `endTime`: Defines the date range for the backtest.
3. Calculations & Grid Setup:
`maxPrice`: The highest price point for the grid setup. Calculated as the lowest low of the previous `len` bars only if no trades are open . If trades are open, it uses the entry price of the very first order placed in the current sequence (`strategy.opentrades.entry_price(0)`).
`minPrice`: The lowest price point for the grid, calculated based on `maxPrice` and `range1`.
`totalCapital`: The amount of capital (considering leverage and `per1`) allocated for the main grid orders.
`coinRatios`: An array ` `. This defines the *relative* size ratio for each of the 11 grid orders. Later orders (at lower prices) will be progressively larger.
`totalRatio`: The sum of all ratios (66).
`positionSizes`: An array calculated based on `totalCapital` and `coinRatios`. It determines the actual quantity (size) for each of the 11 grid orders.
4. Order Placement Logic (`strategy.entry`):
Initial Grid Orders:
Runs only if within the specified time range and no position is currently open (`strategy.opentrades == 0`).
A loop places 11 limit buy orders (`Buy 1` to `Buy 11`).
Prices are calculated linearly between `maxPrice` and `minPrice`.
Order sizes are taken from the `positionSizes` array.
Semifinal & Final Entries:
Two additional, larger limit buy orders are placed simultaneously with the grid orders:
`semifinal entry`: At `maxPrice * (1 - semifinal / 100)`. Size is based on `per2`% of the capital *not* used by the main grid (`1 - per1`).
`final entry`: At `maxPrice * (1 - final / 100)`. Size is based on the remaining capital (`1 - per2`% of the unused portion).
5. Visualization (`line.new`, `label.new`, `plot`, `plotshape`, `plotchar`):
Grid Lines & Labels:
When a position is open (`strategy.opentrades > 0`), horizontal lines and labels are drawn for each of the 11 grid order prices and the "final" entry price.
Lines extend from the bar where the *first* entry occurred.
Labels show the price and planned size for each level.
Dynamic Coloring: If the price drops below a grid level, the corresponding line turns green, and the label color changes, visually indicating that the level has been reached or filled.
Plotted Lines:
`maxPrice` (initial high point for the grid).
`strategy.position_avg_price` (current average entry price of the open position, shown in red).
Target Profit Price (`strategy.position_avg_price * (1 + tp / 100)`, shown in green).
Markers:
A flag marks the `startTime`.
A rocket icon (`🚀`) appears below the bar where the `final entry` triggers.
A stop icon (`🛑`) appears below the bar where the `semifinal entry` triggers.
6. Exit Logic (`strategy.exit`, `strategy.entry` with `qty=0`):
Main Take Profit (`Full Exit`):
Uses `strategy.entry('Full Exit', strategy.short, qty = 0, limit = target2)`. This places a limit order to close the entire position (`qty=0`) at the calculated take profit level (`target2 = avgPrice * (1 + tp / 100)`). Note: Using `strategy.entry` with `strategy.short` and `qty=0` is a way to close a long position, though `strategy.exit` is often clearer. This exit seems intended to apply whenever any part of the grid position is open.
First Order Trailing Stop (`1st order Full Exit`):
Conditional : Only active if `trail` input is true AND the *last* order filled was "Buy 1" (meaning only the very first grid level was entered).
Uses `strategy.exit` with `trail_points` and `trail_offset` based on ATR values to implement a trailing stop loss/profit mechanism for this specific scenario.
This trailing stop order is cancelled (`strategy.cancel`) if any subsequent grid orders ("Buy 2", etc.) are filled.
Final/Semifinal Take Profit (`final Full Exit`):
Conditional : Only active if more than 11 entries have occurred (meaning either the "semifinal" or "final" entry must have triggered).
Uses `strategy.exit` to place a limit order to close the entire position at the take profit level (`target3 = avgPrice * (1 + tp / 100)`).
7. Information Display (Tables & UI Label):
`statsTable` (Top Right):
A comprehensive table displaying grouped information:
Market Info (Entry Point, Current Price)
Position Info (Avg Price, Target Price, Unrealized PNL $, Unrealized PNL %, Position Size, Position Value)
Strategy Performance (Realized PNL $, Realized PNL %, Initial/Total Balance, MDD, APY, Daily Profit %)
Trade Statistics (Trade Count, Wins/Losses, Win Rate, Cumulative Profit)
`buyAvgTable` (Bottom Left):
* Shows the *theoretical* entry price and average position price if trades were filled sequentially up to each `buy` level (buy1 to buy10). It uses hardcoded percentage drops (`buyper`, `avgper`) based on the initial `maxPrice` and `coinRatios`, not the dynamically changing actual average price.
`uiLabel` (Floating Label on Last Bar):
Updates only on the most recent bar (`barstate.islast`).
Provides real-time context when a position is open: Size, Avg Price, Current Price, Open PNL ($ and %), estimated % drop needed for the *next* theoretical buy (based on `ui_gridStep` input), % rise needed to hit TP, and estimated USDT profit at TP.
Shows "No Position" and basic balance/trade info otherwise.
In Summary:
This is a sophisticated long-only grid trading strategy. It aims to:
1. Define an entry range based on recent lows (`maxPrice`).
2. Place 11 scaled-in limit buy orders within a percentage range below `maxPrice`.
3. Place two additional, larger buy orders at deeper percentage drops (`semifinal`, `final`).
4. Calculate the average entry price as orders fill.
5. Exit the entire position for a small take profit (`tp`) above the average entry price.
6. Offer a conditional ATR trailing stop if only the first order fills.
7. Provide extensive visual feedback through lines, labels, icons, and detailed information tables/UI elements.
Keep in mind that grid strategies can perform well in ranging or slowly trending markets but can incur significant drawdowns if the price trends strongly against the position without sufficient retracements to hit the take profit. The leverage (`lev`) input significantly amplifies both potential profits and losses.
Reversal Strength Meter – Adib NooraniThe Reversal Strength Meter is an oscillator designed to identify potential reversal zones based on supply and demand dynamics. It uses smoothed stochastic logic to reduce noise and highlight areas where momentum may be weakening, signaling possible market turning points.
🔹 Smooth, noise-reduced stochastic oscillator
🔹 Custom zones to highlight potential supply and demand imbalances
🔹 Non-repainting, compatible across all timeframes and assets
🔹 Visual-only tool — intended to support discretionary trading decisions
This oscillator assists scalpers and intraday traders in tracking subtle shifts in momentum, helping them identify when a market may be preparing to reverse — always keeping in mind that trading is based on probabilities, not certainties.
📘 How to Use the Indicator Efficiently
For Reversal Trading:
Buy Setup
– When the blue line dips below the 20 level, wait for it to re-enter above 20.
– Look for reversal candlestick patterns (e.g., bullish engulfing, hammer, or morning star).
– Enter above the pattern’s high, with a stop loss below its low.
Sell Setup
– When the blue line rises above the 80 level, wait for it to re-enter below 80.
– Look for bearish candlestick patterns (e.g., bearish engulfing, inverted hammer, or evening star).
– Enter below the pattern’s low, with a stop loss above its high.
🛡 Risk Management Guidelines
Risk only 0.5% of your capital per trade
Book 50% profits at a 1:1 risk-reward ratio
Trail the remaining 50% using price action or other supporting indicators
Reversal Scalping Ribbon - Adib NooraniThe Reversal Scalping Ribbon is a trend-following overlay tool designed to visually identify potential reversal zones based on price extremes and dynamic volatility bands. It calculates adaptive upper and lower bands using price action and custom ATR logic, helping traders quickly assess market direction and possible turning points
🔹 Volatility-adjusted bands based on price highs/lows
🔹 Color-coded ribbons to indicate trend bias and potential reversal shifts
🔹 No repainting, works on all timeframes and assets
🔹 Visual-only display, no trade signals — supports discretion-based entries
This ribbon is designed for scalpers and intraday traders to spot reversal setups with clarity. It enhances your trading by showing real-time market bias without unnecessary distractions. By focusing on probabilities, it helps to improve decision-making in fast-paced environments
How to use the indicator efficiently
For Reversal Trading:
Buy: When price closes below the green ribbon with a red candle, then re-enters with a green candle. Enter above the high of the green candle with a stop loss below the lowest low of the recent green/red candles
Sell: When price closes above the red ribbon with a green candle, then re-enters with a red candle. Enter below the low of the red candle with a stop loss above the highest high of the recent red/green candles
Risk Management:
Limit risk to 0.5% of your capital per trade
Take 50% profit at a 1:1 risk-reward ratio
For the remaining 50%, trail using the lower edge of the green band for buys and the upper edge of the red band for sells
Gabriel's Price Action Strategy🧠 Gabriel's Price Action Strategy — Smart Signal Sequence with Dynamic Risk Control
Created by: OneWallStreetQuant
Strategy Type: Momentum-based Sequence Logic + Smart Volume & RSI Filters
Ideal For: Intraday scalping, swing trading, and momentum trend entries on stocks, forex, crypto, indices.
🚀 Overview
Gabriel's Price Action Strategy is a multi-layered, logic-driven trading system that combines:
✅ Candle Sequence Detection: Detects persistent bullish/bearish momentum using a smart configurable sequence of green/red candles.
✅ Structure Break Filtering: Prevents entries if recent price invalidates the momentum setup (e.g., a red candle breaks a bullish low).
✅ Custom Volume Engine: Integrates a hybrid tick-volume model using Negative/Positive Volume Index (NVI-PVI) to identify smart money flows.
✅ Advanced RSI Logic: Uses Jurik RSX for accurate oversold/overbought filtering.
✅ Optional MTF Trend Filter: Validates trend direction using a slope-based Jurik MA on higher timeframes.
✅ MPT-Based DMI Filter: Adds pyramid entries only during strong trend phases, based on Gain/Pain ratios and Ulcer-index smoothed ADX.
✅ Risk Management: ATR-based SL/TP and fully customizable trailing logic for both profit and stop-loss.
📈 Entry Logic
Trades are triggered only when:
A minimum number of recent candles are bullish/bearish (Min Green/Red Candles)
Structure has not been broken by opposite price action (optional)
Relative volume exceeds average (optional)
RSI is below overbought or above oversold (optional)
MTF slope is aligned with trend direction (optional)
💡 Key Features
Custom Candle Logic: Detects momentum shifts using a tunable lookback window (up to 50 bars).
Smart Volume Filtering: Volume is intelligently estimated using tick-based ranges and NVI-PVI deltas.
Risk Management Built-in: Set your ATR length, SL/TP multipliers, and dynamic trailing offsets with full control.
Scorecard System: A built-in scoring engine evaluates Win Rate, Drawdown, Sharpe Ratio, Recovery Factor, and Profit Factor — visualized on chart as a label.
Backtest-Friendly: Includes date range toggles, bar-magnifier support, and optimized execution on every tick.
📊 Strategy Scorecard (Label)
Automatically calculates:
✅ Total Trades
✅ Win Rate (%)
✅ Net Profit
✅ Profit Factor
✅ Expected Payoff
✅ Max & Avg Drawdown
✅ Recovery Factor
✅ Sharpe Ratio
✅ VaR (95%)
Plus, assigns a normalized score from 0 to 100 for evaluating overall robustness.
⚙️ Customization
Every module — from entry filters to pyramiding and trailing logic — is fully configurable:
Volume Filters ✅
RSI Filters ✅
Structure Break Checks ✅
HTF Jurik MA & Slope Threshold ✅
Multi-Timeframe Mode ✅
Backtest Score Visualization ✅
⚠️ Notes
Enable bar magnifier and calc on every tick for best accuracy.
On early bars, signal logic may delay until enough candles are available.
Best paired with assets showing directional volatility (SPY, BTC, ETH, Gold, etc.).
Ideally paired on trending timeframes such as M1, M5, M15, M30, 1HR, 4 Hourly, Daily, Weekly, Monthly, etc.
Moving Average Shift WaveTrend StrategyMoving Average Shift WaveTrend Strategy
🧭 Overview
The Moving Average Shift WaveTrend Strategy is a trend-following and momentum-based trading system designed to be overlayed on TradingView charts. It executes trades based on the confluence of multiple technical conditions—volatility, session timing, trend direction, and oscillator momentum—to deliver logical and systematic trade entries and exits.
🎯 Strategy Objectives
Enter trades aligned with the prevailing long-term trend
Exit trades on confirmed momentum reversals
Avoid false signals using session timing and volatility filters
Apply structured risk management with automatic TP, SL, and trailing stops
⚙️ Key Features
Selectable MA types: SMA, EMA, SMMA (RMA), WMA, VWMA
Dual-filter logic using a custom oscillator and moving averages
Session and volatility filters to eliminate low-quality setups
Trailing stop, configurable Take Profit / Stop Loss logic
“In-wave flag” prevents overtrading within the same trend wave
Visual clarity with color-shifting candles and entry/exit markers
📈 Trading Rules
✅ Long Entry Conditions:
Price is above the selected MA
Oscillator is positive and rising
200-period EMA indicates an uptrend
ATR exceeds its median value (sufficient volatility)
Entry occurs between 09:00–17:00 (exchange time)
Not currently in an active wave
🔻 Short Entry Conditions:
Price is below the selected MA
Oscillator is negative and falling
200-period EMA indicates a downtrend
All other long-entry conditions are inverted
❌ Exit Conditions:
Take Profit or Stop Loss is hit
Opposing signals from oscillator and MA
Trailing stop is triggered
🛡️ Risk Management Parameters
Pair: ETH/USD
Timeframe: 4H
Starting Capital: $3,000
Commission: 0.02%
Slippage: 2 pips
Risk per Trade: 2% of account equity (adjustable)
Total Trades: 224
Backtest Period: May 24, 2016 — April 7, 2025
Note: Risk parameters are fully customizable to suit your trading style and broker conditions.
🔧 Trading Parameters & Filters
Time Filter: Trades allowed only between 09:00–17:00 (exchange time)
Volatility Filter: ATR must be above its median value
Trend Filter: Long-term 200-period EMA
📊 Technical Settings
Moving Average
Type: SMA
Length: 40
Source: hl2
Oscillator
Length: 15
Threshold: 0.5
Risk Management
Take Profit: 1.5%
Stop Loss: 1.0%
Trailing Stop: 1.0%
👁️ Visual Support
MA and oscillator color changes indicate directional bias
Clear chart markers show entry and exit points
Trailing stops and risk controls are transparently managed
🚀 Strategy Improvements & Uniqueness
In-wave flag avoids repeated entries within the same trend phase
Filtering based on time, volatility, and trend ensures higher-quality trades
Dynamic high/low tracking allows precise trailing stop placement
Fully rule-based execution reduces emotional decision-making
💡 Inspirations & Attribution
This strategy is inspired by the excellent concept from:
ChartPrime – “Moving Average Shift”
It expands on the original idea with advanced trade filters and trailing logic.
Source reference:
📌 Summary
The Moving Average Shift WaveTrend Strategy offers a rule-based, reliable approach to trend trading. By combining trend and momentum filters with robust risk controls, it provides a consistent framework suitable for various market conditions and trading styles.
⚠️ Disclaimer
This script is for educational purposes only. Trading involves risk. Always use proper backtesting and risk evaluation before applying in live markets.
RSI Full [Titans_Invest]RSI Full
One of the most complete RSI indicators on the market.
While maintaining the classic RSI foundation, our indicator integrates multiple entry conditions to generate more accurate buy and sell signals.
All conditions are fully configurable, allowing complete customization to fit your trading strategy.
⯁ WHAT IS THE RSI❓
The Relative Strength Index (RSI) is a technical analysis indicator developed by J. Welles Wilder. It measures the magnitude of recent price movements to evaluate overbought or oversold conditions in a market. The RSI is an oscillator that ranges from 0 to 100 and is commonly used to identify potential reversal points, as well as the strength of a trend.
⯁ HOW TO USE THE RSI❓
The RSI is calculated based on average gains and losses over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and includes three main zones:
Overbought: When the RSI is above 70, indicating that the asset may be overbought.
Oversold: When the RSI is below 30, indicating that the asset may be oversold.
Neutral Zone: Between 30 and 70, where there is no clear signal of overbought or oversold conditions.
⯁ ENTRY CONDITIONS
The conditions below are fully flexible and allow for complete customization of the signal.
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🔹 CONDITIONS TO BUY 📈
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND/OR .
📈 RSI Conditions:
🔹 RSI > Upper
🔹 RSI < Upper
🔹 RSI > Lower
🔹 RSI < Lower
🔹 RSI > Middle
🔹 RSI < Middle
🔹 RSI > MA
🔹 RSI < MA
📈 MA Conditions:
🔹 MA > Upper
🔹 MA < Upper
🔹 MA > Lower
🔹 MA < Lower
📈 Crossovers:
🔹 RSI (Crossover) Upper
🔹 RSI (Crossunder) Upper
🔹 RSI (Crossover) Lower
🔹 RSI (Crossunder) Lower
🔹 RSI (Crossover) Middle
🔹 RSI (Crossunder) Middle
🔹 RSI (Crossover) MA
🔹 RSI (Crossunder) MA
🔹 MA (Crossover) Upper
🔹 MA (Crossunder) Upper
🔹 MA (Crossover) Lower
🔹 MA (Crossunder) Lower
📈 RSI Divergences:
🔹 RSI Divergence Bull
🔹 RSI Divergence Bear
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🔸 CONDITIONS TO SELL 📉
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• Signal Validity: The signal will remain valid for X bars .
• Signal Sequence: Configurable as AND/OR .
📉 RSI Conditions:
🔸 RSI > Upper
🔸 RSI < Upper
🔸 RSI > Lower
🔸 RSI < Lower
🔸 RSI > Middle
🔸 RSI < Middle
🔸 RSI > MA
🔸 RSI < MA
📉 MA Conditions:
🔸 MA > Upper
🔸 MA < Upper
🔸 MA > Lower
🔸 MA < Lower
📉 Crossovers:
🔸 RSI (Crossover) Upper
🔸 RSI (Crossunder) Upper
🔸 RSI (Crossover) Lower
🔸 RSI (Crossunder) Lower
🔸 RSI (Crossover) Middle
🔸 RSI (Crossunder) Middle
🔸 RSI (Crossover) MA
🔸 RSI (Crossunder) MA
🔸 MA (Crossover) Upper
🔸 MA (Crossunder) Upper
🔸 MA (Crossover) Lower
🔸 MA (Crossunder) Lower
📉 RSI Divergences:
🔸 RSI Divergence Bull
🔸 RSI Divergence Bear
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🤖 AUTOMATION 🤖
• You can automate the BUY and SELL signals of this indicator.
______________________________________________________
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⯁ UNIQUE FEATURES
______________________________________________________
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
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📜 SCRIPT : RSI Full
🎴 Art by : @Titans_Invest & @DiFlip
👨💻 Dev by : @Titans_Invest & @DiFlip
🎑 Titans Invest — The Wizards Without Gloves 🧤
✨ Enjoy the Spell!
______________________________________________________
o Mission 🗺
• Inspire Traders to manifest Magic in the Market.
o Vision 𐓏
• To elevate collective Energy 𐓷𐓏
ZVOL — Z-Score Volume Heatmapⓩ ZVOL transforms raw volume into a statistically calibrated heatmap using Z-score thresholds. Unlike classic volume indicators that rely on fixed MA comparisons, ZVOL calculates how many standard deviations each volume bar deviates from its mean. This makes the reading adaptive across timeframes and assets, in order to distinguish meaningful crowd behavior from random volatility.
📊 The core display is a five-zone histogram, each encoded by color and statistical depth. Optional background shading mirrors these zones across the entire pane, revealing subtle compression or structural rhythm shifts across time. By grounding the volume reading in volatility-adjusted context, ZVOL inhibits impulsive trading tactics by compelling the structure, not the sentiment, to dictate the signal.
🥵 Heatmap Coloration:
🌚 Suppressed volume — congestion, coiling phases
🩱 Stable flow — early trend or resting volume
🏀 High activity — emerging pressure
💔 Extreme — possible climax or institutional print
🎗️ A dynamic Fibonacci-based 21:34-period EMA ribbon overlays the histogram. The fill area inverts color on crossover, providing a real-time read on tempo, expansion, or divergence between price structure and crowd effort.
💡 LTF Usage Suggestions:
• Confirm breakout legs when orange or red zones align with range exits
• Fade overextended moves when red bars appear into resistance
• Watch for rising EMAs and orange volume to front-run impulsive moves
• Combine with volatility suppression (e.g. ATR) to catch compression → expansion transitions
🥂 Ideal Pairings:
• OBVX Conviction Bias — to confirm directional intent behind volume shifts
• SUPeR TReND 2.718 — for directional filters
• ATR Turbulence Ribbon — to detect compression phases
👥 The OBVX Conviction Bias adds a second dimension to ZVOL by revealing whether crowd effort is aligning with price direction or diverging beneath the surface. While ZVOL identifies statistical anomalies in raw volume, OBVX tracks directional commitment using cumulative volume and moving average cross logic. Use them together to spot fake-outs, anticipate structure-confirmed breakouts, or time pullbacks with volume-based conviction.
🔬 ZVOL isn’t just a volume filter — it’s a structural lens. It reveals when crowd effort is meaningful, when it's fading, and when something is about to shift. Designed for structure-aware traders who care about context, not noise.
7-Channel Trend Meter v3🔥 7-Channel Trend Meter – Ultimate Trend Confirmation Tool 💹
Purpose: Supplementary indicator used as confirmation
The 7-Channel Trend Meter offers an all-in-one confirmation system that combines 7 high-accuracy indicators into one easy-to-read visual tool. Say goodbye to guesswork and unnecessary tab-switching—just clear, actionable signals for smarter trades. Whether you're trading stocks, crypto, or forex, this indicator streamlines your decision-making process and enhances your strategy’s performance.
⚙️ What’s Inside The Box?
Here is each tool that the Trend Meter uses, and why/how they're used:
Average Directional Index: Confirms market strength ✅
Directional Movement Index: Confirms trend direction ✅
EMA Cross: Confirms reversals in trend through average price ✅
Relative Strength Index: Confirms trend through divergences ✅
Stochastic Oscillator: Confirms shifts in momentum ✅
Supertrend: Confirms trend-following using ATR calculations ✅
Volume Delta: Confirms buying/selling pressure weight by finding differences ✅
🧾 How To Read It:
🟨 Bar 1 – Market Strength Meter:
Light Gold 🟡: Strong market with trending conditions.
Dark Gold 🟤: Weakening market or consolidation—proceed with caution.
📊 Bars 2 to 7 – Trend Direction Confirmations:
🟩 Green: Bullish signal, uptrend likely.
🟥 Red: Bearish signal, downtrend likely.
💯 Why it's helpful to traders:
✅ 7 Confirmations in 1 View: No need to flip between multiple charts.
✅ Visual Clarity: Spot trends instantly with a quick glance.
✅ Perfect for Entry Confirmation: Confirm trade signals before pulling the trigger.
✅ Boosts Your Win Rate: Make data-backed decisions, not guesses.
✅ Works Across Multiple Markets: Stocks, crypto, forex—you name it 🌍.
🤔 "What's with the indicator mashup/How do these components work together? 🤔
The 7-Channel Trend Meter is designed as an original and useful tool that integrates multiple indicators to enhance trading decisions, rather than merely combining existing tools without logical coherence. This strategic mashup creates a comprehensive analysis framework that offers deeper insights into market conditions by capitalizing on each component's unique strengths. The careful integration of seven indicators creates a unified system that eliminates conflicting signals and enhances the decision-making process. Rather than simply merging indicators for the sake of it, the 7-Channel Trend Meter is designed to streamline trading strategies, making it a practical tool for traders across various markets. By leveraging the combined strengths of these indicators, traders can act with greater confidence, backed by comprehensive data rather than fragmented insights. Here’s how they synergistically work together:
Average Directional Index (ADX) and Directional Movement Index (DMI): The reason for this mashup is because ADX indicates the strength of the prevailing trend, while the DMI pinpoints its direction. Together, they equip traders with a dual framework that not only identifies whether to engage with a trend but also quantifies its strength, allowing for more decisive trading strategies.
EMA Cross: The reason for this addition to the mashup is because this tool signals potential trend reversals by identifying moving average crossovers. When combined with the ADX and DMI, traders can better differentiate between genuine trend shifts and market noise, leading to more accurate entries.
Relative Strength Index (RSI) and Stochastic Oscillator: The reason for this mashup is because by using both momentum indicators, traders gain a multifaceted view of market dynamics. The RSI assesses overbought or oversold conditions, while the Stochastic Oscillator confirms momentum shifts. When both agree with the trend signals from the DMI, it enhances the reliability of reversal or continuation strategies.
Supertrend: The reason for this addition to the mashup is because as a trailing stop based on market volatility, the Supertrend indicator works hand-in-hand with the ADX’s strength assessment, allowing traders to ride strong trends while managing risk. This cohesion prevents premature exits during minor pullbacks.
Volume Delta: The reason for this addition to the mashup is because integrating volume analysis helps validate signals from the price action indicators. Significant volume behind a price movement reinforces the likelihood of its continuation, ensuring that traders can act on well-supported signals.
🔍 How it does what it says it does 🔍
While the exact calculations remain proprietary, the following outlines how the components synergistically work to aid traders in making informed decisions:
Market Strength Assessment: Average Directional Index (ADX)
This component is used as confirmation by measuring the strength of the market trend on a scale from 0 to 100. A reading above 20 generally indicates a strong trend, while readings below 20 suggest sideways movement. The Trend Meter flags strong trends, effectively helping traders identify optimal conditions for entering positions.
Trend Direction Confirmation: Directional Movement Index (DMI)
This component is used as confirmation by distinguishing between bullish and bearish trends by evaluating price movements. This combination allows traders to confirm not only if a trend exists but also its direction, informing whether to buy or sell.
Trend Reversal Detection: Exponential Moving Average (EMA) Cross
This component is used as confirmation by calculating two EMAs (one shorter and one longer) to identify potential reversal points. When the shorter EMA crosses above the longer EMA, it signals a bullish reversal, and vice versa for bearish reversals. This helps traders pinpoint optimal entry or exit points.
Momentum Analysis: Relative Strength Index (RSI) and Stochastic Oscillator
These components are used as confirmation by providing insights into momentum. The RSI assesses the speed and change of price movements, indicating overbought or oversold conditions. The Stochastic Oscillator compares a particular closing price to a range of prices over a specified period. This helps identify whether momentum is slowing or speeding up, offering a clear view of potential reversal points. When both the RSI and Stochastic Oscillator converge on signals, it increases the reliability of those signals in trading decisions.
Volatility-Based Trend Following: Supertrend
This component is used as confirmation by utilizing Average True Range (ATR) calculations to help traders stay in momentum-driven trades by providing dynamic support and resistance levels that adapt to volatility. This enables better risk management while allowing traders to capture stronger trends.
Volume Confirmation: Volume Delta
This component is used as confirmation by analyzing buying and selling pressure by measuring the difference between buy and sell volumes, offering critical insights into market sentiment. Significant volume behind a price movement increases confidence in the sustainability of that move.
🧠 Pro Tip:
When all 7 bars line up in green or red, it’s time to take action: load up for a confirmed move or sit back and wait for market confirmation. Let the Trend Meter guide your strategy with precision.
Conclusion:
Integrate the 7-Channel Trend Meter as useful confirmation for your TradingView strategy and stop trading like the average retail trader. This tool eliminates the noise and helps you stay focused on high-confidence trades.
Adaptive Fibonacci Pullback System -FibonacciFluxAdaptive Fibonacci Pullback System (AFPS) - FibonacciFlux
This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Original concepts by FibonacciFlux.
Abstract
The Adaptive Fibonacci Pullback System (AFPS) presents a sophisticated, institutional-grade algorithmic strategy engineered for high-probability trend pullback entries. Developed by FibonacciFlux, AFPS uniquely integrates a proprietary Multi-Fibonacci Supertrend engine (0.618, 1.618, 2.618 ratios) for harmonic volatility assessment, an Adaptive Moving Average (AMA) Channel providing dynamic market context, and a synergistic Multi-Timeframe (MTF) filter suite (RSI, MACD, Volume). This strategy transcends simple indicator combinations through its strict, multi-stage confluence validation logic. Historical simulations suggest that specific MTF filter configurations can yield exceptional performance metrics, potentially achieving Profit Factors exceeding 2.6 , indicative of institutional-level potential, while maintaining controlled risk under realistic trading parameters (managed equity risk, commission, slippage).
4 hourly MTF filtering
1. Introduction: Elevating Pullback Trading with Adaptive Confluence
Traditional pullback strategies often struggle with noise, false signals, and adapting to changing market dynamics. AFPS addresses these challenges by introducing a novel framework grounded in Fibonacci principles and adaptive logic. Instead of relying on static levels or single confirmations, AFPS seeks high-probability pullback entries within established trends by validating signals through a rigorous confluence of:
Harmonic Volatility Context: Understanding the trend's stability and potential turning points using the unique Multi-Fibonacci Supertrend.
Adaptive Market Structure: Assessing the prevailing trend regime via the AMA Channel.
Multi-Dimensional Confirmation: Filtering signals with lower-timeframe Momentum (RSI), Trend Alignment (MACD), and Market Conviction (Volume) using the MTF suite.
The objective is to achieve superior signal quality and adaptability, moving beyond conventional pullback methodologies.
2. Core Methodology: Synergistic Integration
AFPS's effectiveness stems from the engineered synergy between its core components:
2.1. Multi-Fibonacci Supertrend Engine: Utilizes specific Fibonacci ratios (0.618, 1.618, 2.618) applied to ATR, creating a multi-layered volatility envelope potentially resonant with market harmonics. The averaged and EMA-smoothed result (`smoothed_supertrend`) provides a robust, dynamic trend baseline and context filter.
// Key Components: Multi-Fibonacci Supertrend & Smoothing
average_supertrend = (supertrend1 + supertrend2 + supertrend3) / 3
smoothed_supertrend = ta.ema(average_supertrend, st_smooth_length)
2.2. Adaptive Moving Average (AMA) Channel: Provides dynamic market context. The `ama_midline` serves as a key filter in the entry logic, confirming the broader trend bias relative to adaptive price action. Extended Fibonacci levels derived from the channel width offer potential dynamic S/R zones.
// Key Component: AMA Midline
ama_midline = (ama_high_band + ama_low_band) / 2
2.3. Multi-Timeframe (MTF) Filter Suite: An optional but powerful validation layer (RSI, MACD, Volume) assessed on a lower timeframe. Acts as a **validation cascade** – signals must pass all enabled filters simultaneously.
2.4. High-Confluence Entry Logic: The core innovation. A pullback entry requires a specific sequence and validation:
Price interaction with `average_supertrend` and recovery above/below `smoothed_supertrend`.
Price confirmation relative to the `ama_midline`.
Simultaneous validation by all enabled MTF filters.
// Simplified Long Entry Logic Example (incorporates key elements)
long_entry_condition = enable_long_positions and
(low < average_supertrend and close > smoothed_supertrend) and // Pullback & Recovery
(close > ama_midline and close > ama_midline) and // AMA Confirmation
(rsi_filter_long_ok and macd_filter_long_ok and volume_filter_ok) // MTF Validation
This strict, multi-stage confluence significantly elevates signal quality compared to simpler pullback approaches.
1hourly filtering
3. Realistic Implementation and Performance Potential
AFPS is designed for practical application, incorporating realistic defaults and highlighting performance potential with crucial context:
3.1. Realistic Default Strategy Settings:
The script includes responsible default parameters:
strategy('Adaptive Fibonacci Pullback System - FibonacciFlux', shorttitle = "AFPS", ...,
initial_capital = 10000, // Accessible capital
default_qty_type = strategy.percent_of_equity, // Equity-based risk
default_qty_value = 4, // Default 4% equity risk per initial trade
commission_type = strategy.commission.percent,
commission_value = 0.03, // Realistic commission
slippage = 2, // Realistic slippage
pyramiding = 2 // Limited pyramiding allowed
)
Note: The default 4% risk (`default_qty_value = 4`) requires careful user assessment and adjustment based on individual risk tolerance.
3.2. Historical Performance Insights & Institutional Potential:
Backtesting provides insights into historical behavior under specific conditions (always specify Asset/Timeframe/Dates when sharing results):
Default Performance Example: With defaults, historical tests might show characteristics like Overall PF ~1.38, Max DD ~1.16%, with potential Long/Short performance variance (e.g., Long PF 1.6+, Short PF < 1).
Optimized MTF Filter Performance: Crucially, historical simulations demonstrate that meticulous configuration of the MTF filters (particularly RSI and potentially others depending on market) can significantly enhance performance. Under specific, optimized MTF filter settings combined with appropriate risk management (e.g., 7.5% risk), historical tests have indicated the potential to achieve **Profit Factors exceeding 2.6**, alongside controlled drawdowns (e.g., ~1.32%). This level of performance, if consistently achievable (which requires ongoing adaptation), aligns with metrics often sought in institutional trading environments.
Disclaimer Reminder: These results are strictly historical simulations. Past performance does not guarantee future results. Achieving high performance requires careful parameter tuning, adaptation to changing markets, and robust risk management.
3.3. Emphasizing Risk Management:
Effective use of AFPS mandates active risk management. Utilize the built-in Stop Loss, Take Profit, and Trailing Stop features. The `pyramiding = 2` setting requires particularly diligent oversight. Do not rely solely on default settings.
4. Conclusion: Advancing Trend Pullback Strategies
The Adaptive Fibonacci Pullback System (AFPS) offers a sophisticated, theoretically grounded, and highly adaptable framework for identifying and executing high-probability trend pullback trades. Its unique blend of Fibonacci resonance, adaptive context, and multi-dimensional MTF filtering represents a significant advancement over conventional methods. While requiring thoughtful implementation and risk management, AFPS provides discerning traders with a powerful tool potentially capable of achieving institutional-level performance characteristics under optimized conditions.
Acknowledgments
Developed by FibonacciFlux. Inspired by principles of Fibonacci analysis, adaptive averaging, and multi-timeframe confirmation techniques explored within the trading community.
Disclaimer
Trading involves substantial risk. AFPS is an analytical tool, not a guarantee of profit. Past performance is not indicative of future results. Market conditions change. Users are solely responsible for their decisions and risk management. Thorough testing is essential. Deploy at your own considered risk.
Uptrick X PineIndicators: Z-Score Flow StrategyThis strategy is based on the Z-Score Flow Indicator developed by Uptrick. Full credit for the original concept and logic goes to Uptrick.
The Z-Score Flow Strategy combines statistical mean-reversion logic with trend filtering, RSI confirmation, and multi-mode trade execution, offering a flexible and structured approach to trading both reversals and trend continuations.
Core Concepts Behind Z-Score Flow
1. Z-Score Mean Reversion Logic
The Z-score measures how far current price deviates from its statistical mean, in standard deviations.
A high positive Z-score (e.g. > 2) suggests price is overbought and may revert downward.
A low negative Z-score (e.g. < -2) suggests price is oversold and may revert upward.
The strategy uses Z-score thresholds to trigger signals when price deviates far enough from its mean.
2. Trend Filtering with EMA
To prevent counter-trend entries, the strategy includes a trend filter based on a 50-period EMA:
Only allows long entries if price is below EMA (mean-reversion in downtrends).
Only allows short entries if price is above EMA (mean-reversion in uptrends).
3. RSI Confirmation and Lockout System
An RSI smoothing mechanism helps confirm signals and avoid whipsaws:
RSI must be below 30 and rising to allow buys.
RSI must be above 70 and falling to allow sells.
Once a signal occurs, it is "locked out" until RSI re-enters the neutral zone (30–70).
This avoids multiple signals in overextended zones and reduces overtrading.
Entry Signal Logic
A buy or sell is triggered when:
Z-score crosses below (buy) or above (sell) the threshold.
RSI smoothed condition is met (oversold and rising / overbought and falling).
The trend condition (EMA filter) aligns.
A cooldown period has passed since the last opposite trade.
This layered approach helps ensure signal quality and timing precision.
Trade Modes
The strategy includes three distinct trade modes to adapt to various market behaviors:
1. Standard Mode
Trades are opened using the Z-score + RSI + trend filter logic.
Each signal must pass all layered conditions.
2. Zero Cross Mode
Trades are based on the Z-score crossing zero.
This mode is useful in trend continuation setups, rather than mean reversion.
3. Trend Reversal Mode
Trades occur when the mean slope direction changes, i.e., basis line changes color.
Helps capture early trend shifts with less lag.
Each mode can be customized for long-only, short-only, or long & short execution.
Visual Components
1. Z-Score Mean Line
The basis (mean) line is colored based on slope direction.
Green = bullish slope, Purple = bearish slope, Gray = flat.
A wide shadow band underneath reflects current trend momentum.
2. Gradient Fill to Price
A gradient zone between price and the mean reflects:
Price above mean = bearish zone with purple overlay.
Price below mean = bullish zone with teal overlay.
This visual aid quickly reveals market positioning relative to equilibrium.
3. Signal Markers
"𝓤𝓹" labels appear for buy signals.
"𝓓𝓸𝔀𝓷" labels appear for sell signals.
These are colored and positioned according to trend context.
Customization Options
Z-Score Period & Thresholds: Define sensitivity to price deviations.
EMA Trend Filter Length: Filter entries with long-term bias.
RSI & Smoothing Periods: Fine-tune RSI confirmation conditions.
Cooldown Period: Prevent signal spam and enforce timing gaps.
Slope Index: Adjust how far back to compare mean slope.
Visual Settings: Toggle mean lines, gradients, and more.
Use Cases & Strategy Strengths
1. Mean-Reversion Trading
Ideal for catching pullbacks in trending markets or fading overextended price moves.
2. Trend Continuation or Reversal
With multiple trade modes, traders can choose between fading price extremes or trading slope momentum.
3. Signal Clarity and Risk Control
The combination of Z-score, RSI, EMA trend, and cooldown logic provides high-confidence signals with built-in filters.
Conclusion
The Z-Score Flow Strategy by Uptrick X PineIndicators is a versatile and structured trading system that:
Fuses statistical deviation (Z-score) with technical filters.
Provides both mean-reversion and trend-based entry logic.
Uses visual overlays and signal labels for clarity.
Prevents noise-driven trades via cooldown and lockout systems.
This strategy is well-suited for traders seeking a data-driven, multi-condition entry framework that can adapt to various market types.
Full credit for the original concept and indicator goes to Uptrick.
VIDYA Auto-Trading(Reversal Logic)Overview
This script is a dynamic trend-following strategy based on the Variable Index Dynamic Average (VIDYA). It adapts in real time to market volatility, aiming to enhance entry precision and optimize risk management.
⚠️ This strategy is intended for educational and research purposes. Past performance does not guarantee future results. All results are based on historical simulations using fixed parameters.
Strategy Objectives
The objective of this strategy is to respond swiftly to sudden price movements and trend reversals, providing consistent and reliable trade signals under historical testing conditions. It is designed to be intuitive and efficient for traders of all levels.
Key Features
Momentum Sensitivity via VIDYA: Reacts quickly to momentum shifts, allowing for accurate trend-following entries.
Volatility-Based ATR Bands: Automatically adjusts stop levels and entry conditions based on current market volatility.
Intuitive Trend Visualization: Uptrends are marked with green zones, and downtrends with red zones, giving traders clear visual guidance.
Trading Rules
Long Entry: Triggered when price crosses above the upper band. Any existing short position is closed.
Short Entry: Triggered when price crosses below the lower band. Any existing long position is closed.
Exit Conditions: Positions are reversed based on signal changes, using a position reversal strategy.
Risk Management Parameters
Market: ETHUSD(5M)
Account Size: $3,000 (reasonable approximation for individual traders)
Commission: 0.02%
Slippage: 2 pip
Risk per Trade: 5% of account equity (adjusted to comply with TradingView guidelines for realistic risk levels)
Number of Trades: 251 (based on backtest over the selected dataset)
⚠️ The risk per trade and other values can be customized. Users are encouraged to adapt these to their individual needs and broker conditions.
Trading Parameters & Considerations
VIDYA Length: 10
VIDYA Momentum: 20
Distance factor for upper/lower bands: 2
Source: close
Visual Support
Trend zones, entry points, and directional shifts are clearly plotted on the chart. These visual cues enhance the analytical experience and support faster decision-making.
Visual elements are designed to improve interpretability and are not intended as financial advice or trade signals.
Strategy Improvements & Uniqueness
Inspired by the public work of BigBeluga, this script evolves the original concept with meaningful enhancements. By combining VIDYA and ATR bands, it offers greater adaptability and practical value compared to conventional trend-following strategies.
This adaptation is original work and not a direct copy. Improvements are designed to enhance usability, risk control, and market responsiveness.
Summary
This strategy offers a responsive and adaptive approach to trend trading, built on momentum detection and volatility-adjusted risk management. It balances clarity, precision, and practicality—making it a powerful tool for traders seeking reliable trend signals.
⚠️ All results are based on historical data and are subject to change under different market conditions. This script does not guarantee profit and should be used with caution and proper risk management.
Triangular Hull Moving Average [BigBeluga X PineIndicators]This strategy is based on the original Triangular Hull Moving Average (THMA) + Volatility indicator by BigBeluga. Full credit for the concept and design goes to BigBeluga.
The strategy blends smoothed trend-following logic using a Triangular Hull Moving Average with dynamic volatility overlays, providing actionable trade signals with responsive visual feedback. It's designed for traders who want a non-lagging trend filter while also monitoring market volatility in real time.
How the Strategy Works
1. Triangular Hull Moving Average (THMA) Core
At its core, the strategy uses a Triangular Hull Moving Average (THMA) — a variation of the traditional Hull Moving Average with triple-smoothing logic:
It combines multiple weighted moving averages (WMAs) to create a faster and smoother trend line.
This reduces lag without compromising trend accuracy.
The THMA reacts more responsively to price movements than classic MAs.
THMA Formula:
thma(_src, _length) =>
ta.wma(ta.wma(_src,_length / 3) * 3 - ta.wma(_src, _length / 2) - ta.wma(_src, _length), _length)
This logic filters out short-term noise while still being sensitive to genuine trend shifts.
2. Volatility-Enhanced Candle Plotting
An optional volatility mode overlays the chart with custom candles that incorporate volatility bands:
Wicks expand and contract dynamically based on market volatility.
The volatility value is computed using a HMA of high-low range over a user-defined length.
The candle bodies reflect THMA values, while the wicks reflect the current volatility spread.
This feature allows traders to visually gauge the strength of price moves and anticipate possible breakouts or slowdowns.
3. Trend Reversal Signal Detection
The strategy identifies trend reversals when the THMA line crosses over/under its own past value:
A bullish signal is triggered when THMA crosses above its value from two bars ago.
A bearish signal is triggered when THMA crosses below its value from two bars ago.
These shifts are marked on the chart with triangle-shaped signals for clear visibility.
This logic helps detect momentum shifts early and enables reactive trade entries.
Trade Entry & Exit Logic
Trade Modes Supported
Users can choose between:
Only Long – Enters long trades only.
Only Short – Enters short trades only.
Long & Short – Enables both directions.
Entry Conditions
Long Entry:
Triggered when a bullish crossover is detected.
Active only if the strategy mode allows long trades.
Short Entry:
Triggered when a bearish crossover is detected.
Active only if the strategy mode allows short trades.
Exit Conditions
In Only Long mode, the strategy closes long positions when a bearish signal appears.
In Only Short mode, the strategy closes short positions when a bullish signal appears.
In Long & Short mode, the strategy does not auto-close positions — instead, it opens new positions on each confirmed signal.
Dashboard Visualization
In the bottom-right corner of the chart, a live dashboard displays:
The current trend direction (🢁 for bullish, 🢃 for bearish).
The current volatility level as a percentage.
This helps traders quickly assess market status and adjust their decisions accordingly.
Customization Options
THMA Length: Adjust how smooth or reactive the trend detection should be.
Volatility Toggle & Length: Enable or disable volatility visualization and set sensitivity.
Color Settings: Choose colors for up/down trend visualization.
Trade Direction Mode: Limit the strategy to long, short, or both types of trades.
Use Cases & Strategy Strengths
1. Trend Following
Use the THMA-based candles and triangle signals to enter with momentum. The indicator adapts quickly, reducing lag and improving trade timing.
2. Volatility Monitoring
Visualize the strength of the trend with volatility wicks. Use expanding bands to confirm breakouts and contracting ones to detect weakening moves.
3. Signal Confirmation
Combine this tool with other indicators or use the trend shift triangles as confirmations for manual entries.
Conclusion
The THMA + Volatility Strategy is a non-repainting trend-following system that integrates:
Triangular Hull MA for advanced trend detection.
Real-time volatility visualization.
Clear entry signals based on trend reversals.
Configurable trade direction settings.
It is ideal for traders who:
Prefer smoothed price analysis.
Want to follow trends with precision.
Value visual volatility feedback for breakout detection.
Full credit for the original concept and indicator goes to BigBeluga.
Strategy Stats [presentTrading]Hello! it's another weekend. This tool is a strategy performance analysis tool. Looking at the TradingView community, it seems few creators focus on this aspect. I've intentionally created a shared version. Welcome to share your idea or question on this.
█ Introduction and How it is Different
Strategy Stats is a comprehensive performance analytics framework designed specifically for trading strategies. Unlike standard strategy backtesting tools that simply show cumulative profits, this analytics suite provides real-time, multi-timeframe statistical analysis of your trading performance.
Multi-timeframe analysis: Automatically tracks performance metrics across the most recent time periods (last 7 days, 30 days, 90 days, 1 year, and 4 years)
Advanced statistical measures: Goes beyond basic metrics to include Information Coefficient (IC) and Sortino Ratio
Real-time feedback: Updates performance statistics with each new trade
Visual analytics: Color-coded performance table provides instant visual feedback on strategy health
Integrated risk management: Implements sophisticated take profit mechanisms with 3-step ATR and percentage-based exits
BTCUSD Performance
The table in the upper right corner is a comprehensive performance dashboard showing trading strategy statistics.
Note: While this presentation uses Vegas SuperTrend as the underlying strategy, this is merely an example. The Stats framework can be applied to any trading strategy. The Vegas SuperTrend implementation is included solely to demonstrate how the analytics module integrates with a trading strategy.
⚠️ Timeframe Limitations
Important: TradingView's backtesting engine has a maximum storage limit of 10,000 bars. When using this strategy stats framework on smaller timeframes such as 1-hour or 2-hour charts, you may encounter errors if your backtesting period is too long.
Recommended Timeframe Usage:
Ideal for: 4H, 6H, 8H, Daily charts and above
May cause errors on: 1H, 2H charts spanning multiple years
Not recommended for: Timeframes below 1H with long history
█ Strategy, How it Works: Detailed Explanation
The Strategy Stats framework consists of three primary components: statistical data collection, performance analysis, and visualization.
🔶 Statistical Data Collection
The system maintains several critical data arrays:
equityHistory: Tracks equity curve over time
tradeHistory: Records profit/loss of each trade
predictionSignals: Stores trade direction signals (1 for long, -1 for short)
actualReturns: Records corresponding actual returns from each trade
For each closed trade, the system captures:
float tradePnL = strategy.closedtrades.profit(tradeIndex)
float tradeReturn = strategy.closedtrades.profit_percent(tradeIndex)
int tradeType = entryPrice < exitPrice ? 1 : -1 // Direction
🔶 Performance Metrics Calculation
The framework calculates several key performance metrics:
Information Coefficient (IC):
The correlation between prediction signals and actual returns, measuring forecast skill.
IC = Correlation(predictionSignals, actualReturns)
Where Correlation is the Pearson correlation coefficient:
Correlation(X,Y) = (nΣXY - ΣXY) / √
Sortino Ratio:
Measures risk-adjusted return focusing only on downside risk:
Sortino = (Avg_Return - Risk_Free_Rate) / Downside_Deviation
Where Downside Deviation is:
Downside_Deviation = √
R_i represents individual returns, T is the target return (typically the risk-free rate), and n is the number of observations.
Maximum Drawdown:
Tracks the largest percentage drop from peak to trough:
DD = (Peak_Equity - Trough_Equity) / Peak_Equity * 100
🔶 Time Period Calculation
The system automatically determines the appropriate number of bars to analyze for each timeframe based on the current chart timeframe:
bars_7d = math.max(1, math.round(7 * barsPerDay))
bars_30d = math.max(1, math.round(30 * barsPerDay))
bars_90d = math.max(1, math.round(90 * barsPerDay))
bars_365d = math.max(1, math.round(365 * barsPerDay))
bars_4y = math.max(1, math.round(365 * 4 * barsPerDay))
Where barsPerDay is calculated based on the chart timeframe:
barsPerDay = timeframe.isintraday ?
24 * 60 / math.max(1, (timeframe.in_seconds() / 60)) :
timeframe.isdaily ? 1 :
timeframe.isweekly ? 1/7 :
timeframe.ismonthly ? 1/30 : 0.01
🔶 Visual Representation
The system presents performance data in a color-coded table with intuitive visual indicators:
Green: Excellent performance
Lime: Good performance
Gray: Neutral performance
Orange: Mediocre performance
Red: Poor performance
█ Trade Direction
The Strategy Stats framework supports three trading directions:
Long Only: Only takes long positions when entry conditions are met
Short Only: Only takes short positions when entry conditions are met
Both: Takes both long and short positions depending on market conditions
█ Usage
To effectively use the Strategy Stats framework:
Apply to existing strategies: Add the performance tracking code to any strategy to gain advanced analytics
Monitor multiple timeframes: Use the multi-timeframe analysis to identify performance trends
Evaluate strategy health: Review IC and Sortino ratios to assess predictive power and risk-adjusted returns
Optimize parameters: Use performance data to refine strategy parameters
Compare strategies: Apply the framework to multiple strategies to identify the most effective approach
For best results, allow the strategy to generate sufficient trade history for meaningful statistical analysis (at least 20-30 trades).
█ Default Settings
The default settings have been carefully calibrated for cryptocurrency markets:
Performance Tracking:
Time periods: 7D, 30D, 90D, 1Y, 4Y
Statistical measures: Return, Win%, MaxDD, IC, Sortino Ratio
IC color thresholds: >0.3 (green), >0.1 (lime), <-0.1 (orange), <-0.3 (red)
Sortino color thresholds: >1.0 (green), >0.5 (lime), <0 (red)
Multi-Step Take Profit:
ATR multipliers: 2.618, 5.0, 10.0
Percentage levels: 3%, 8%, 17%
Short multiplier: 1.5x (makes short take profits more aggressive)
Stop loss: 20%
Scalping Strategy Signal v2 by [INFINITYTRADER]Overview
This Pine Script (v6) implements a scalping strategy that uses higher timeframe data (default: 4H) to generate entry and exit signals, originally designed for the 15-minute timeframe with an option for 30-minute charts. The "Scalping Strategy Signal v2 by " integrates moving averages, RSI, volume, ATR, and candlestick patterns to identify trading opportunities. It features adjustable risk management with ATR-based stop-loss, take-profit, and trailing stops, plus dynamic position sizing based on user-set capital. Trades trigger only on the higher timeframe candle close (e.g., 4H) to limit activity within the same period. This closed-source script offers a structured scalping approach, blending multiple entry methods and risk controls for adaptability across market conditions.
What Makes It Unique
Unlike typical scalping scripts relying on single-indicator triggers (e.g., RSI alone or basic MA crossovers), this strategy combines four distinct entry methods—standard MA crossovers, RSI-based momentum shifts, trend-following shorts, and candlestick pattern logic—evaluated on a 4H timeframe for confirmation. This multi-layered design, paired with re-entry logic after losses and a mix of manual, ATR-based, and trailing exits, aims to balance trade frequency and reliability. The higher timeframe filter adds precision not commonly found in simpler scalping tools, while the 30-minute option enhances consistency by reducing noise.
How It Works
Timeframe Logic
Runs on a base timeframe (designed for 15-minute charts, with a 30-minute option) while pulling data from a user-chosen higher timeframe (default: 4H) for signal accuracy.
Limits entries to the close of each 4H candle, ensuring one trade per period to avoid over-trading in volatile conditions.
Indicators and Data
Moving Averages : Employs 21-period and 50-period simple moving averages on the higher timeframe to detect trends and signal entries/exits.
Volume : Requires volume to exceed 70% of its 20-period average on the higher timeframe for momentum confirmation.
RSI : Uses a 14-period RSI for overbought/oversold filtering and a 6-period RSI for precise entry timing.
ATR : Applies a 14-period Average True Range on the higher timeframe to set adaptive stop-loss and take-profit levels.
Candlestick Patterns : Analyzes consecutive green or red 4H bars for trend continuation signals.
Why These Indicators
The blend of moving averages, RSI, volume, ATR, and candlestick patterns forms a robust scalping framework. Moving averages establish trend context, RSI filters momentum and avoids extremes, volume confirms market activity, ATR adjusts risk to volatility, and candlestick patterns enhance entry timing with price action insights. Together, they target small, frequent moves in flat or trending markets, with the 4H filter reducing false signals common in lower-timeframe scalping.
Entry Conditions
Four entry methods are evaluated at the 4H candle close:
Standard Long Entry: Price crosses above the 21-period moving average, volume exceeds 70% of its 20-period average, and the 1H 14-period RSI is below 70—confirms uptrend momentum.
Special Long Entry: The 6-period RSI crosses above 23, price is more than 1.5 times the ATR from the 21-period moving average, and price exceeds its prior close—targets oversold bounces with a stop-loss at the 4H candle’s low.
Short Entries:
- RSI-Based: The 6-period RSI crosses below 68 with volume support—catches overbought pullbacks.
- Trend-Based: Price crosses below the 21-period moving average, volume is above 70% of its average, and the 1H 14-period RSI is above 30—confirms downtrends.
Red/Green Bar Logic: Two consecutive green 4H bars for longs or red 4H bars for shorts—uses candlestick patterns for continuation, with a tight stop-loss from the base timeframe candle.
Re-Entry Logic
Long : After a losing special long, triggers when the 6-period RSI crosses 27 and price crosses the 21-period moving average.
Short : After a losing short, triggers when the 6-period RSI crosses 50 and price crosses below the 21-period moving average.
Purpose: Offers recovery opportunities with stricter conditions.
Exit Conditions
Manual Exits: Longs close if the 21-period MA crosses below the 50-period MA or the 1H 14-period RSI exceeds 68; shorts close if the 21-period MA crosses above the 50-period MA or RSI drops below 25.
ATR-Based TP/SL: Stop-loss is entry price ± ATR × 1.5 (default); take-profit is ± ATR × 4 (default), checked at 4H close.
Trailing Stop: Adjusts ±6x ATR from peak/trough, closing if price retraces within 1x ATR.
Special/Tight SL: Special longs exit if price opens below the 4H candle’s low; 4th method entries use the base timeframe candle’s low/high, checked every bar.
Position Sizing
Bases trade value on user-set capital (default: 100 USDT), dividing by the higher timeframe close price for dynamic sizing.
Visualization
Displays a table at the bottom-right with current/previous signals, TP/SL levels, equity, trading pair, and trade size—color-coded for clarity (green for buy, red for sell).
Inputs
Initial Capital (USDT): Sets trade value (default: 100, min: 1).
ATR Stop-Loss Multiplier: Adjusts SL distance (default: 1.5, min: 1).
ATR Take-Profit Multiplier: Adjusts TP distance (default: 4, min: 1).
Higher Timeframe: Selects analysis timeframe (options: 1m, 5m, 15m, 30m, 1H, 4H, D, W; default: 4H).
Usage Notes
Intended Timeframe: Designed for 15-minute charts with 4H confirmation for precision and frequency; 30-minute charts improve consistency by reducing noise.
Backtesting: Adjust ATR multipliers and capital to match your asset’s volatility and risk tolerance.
Risk Management: Combines manual, ATR, and trailing exits—monitor to avoid overexposure.
Limitations: 4H candle-close dependency may delay entries in fast markets; RSI/volume filters can reduce trades in low-momentum periods.
Backtest Observations
Tested on BTC/USDT (4H higher timeframe, default settings: Initial Capital: 100 USDT, ATR SL: 1.5x, ATR TP: 4x) across market conditions, comparing 15-minute and 30-minute charts:
Bull Market (Jul 2023 - Dec 2023):
15-Minute: 277 long, 219 short; Win Rate: 42.74%; P&L: 108%; Drawdown: 1.99%; Profit Factor: 3.074.
30-Minute: 257 long, 215 short; Win Rate: 49.58%; P&L: 116.85%; Drawdown: 2.34%; Profit Factor: 3.14.
Notes: Moving average crossovers and green bar patterns suited this bullish phase; 30-minute improved win rate and P&L by filtering weaker signals.
Bear Market (Jan 2022 - Jun 2022):
15-Minute: 262 long, 211 short; Win Rate: 44.4%; P&L: 239.80%; Drawdown: 3.74%; Profit Factor: 3.419.
30-Minute: 250 long, 200 short; Win Rate: 52.22%; P&L: 258.77%; Drawdown: 5.34%; Profit Factor: 3.461.
Notes: Red bar patterns and RSI shorts thrived in the downtrend; 30-minute cut choppy reversals for better consistency.
Flat Market (Jan 2021 - Jun 2021):
15-Minute: 280 long, 208 short; Win Rate: 51.84%; P&L: 340.33%; Drawdown: 9.59%; Profit Factor: 2.924.
30-Minute: 270 long, 209 short; Win Rate: 55.11%; P&L: 315.42%; Drawdown: 7.21%; Profit Factor: 2.598.
Notes: High trade frequency and P&L showed strength in ranges; 30-minute lowered drawdown for better risk control.
Results reflect historical performance on BTC/USDT with default settings—users should test on their assets and timeframes. Past performance does not guarantee future results and is shared only to illustrate the strategy’s behavior.
Why It Works Well in Flat Markets
A "flat market" lacks strong directional trends, with price oscillating around moving averages, as in Jan 2021 - Jun 2021 for BTC/USDT. This strategy excels here because its crossover-based entries trigger frequently in tight ranges. In trending markets, an exit might not be followed by a new entry without a pullback, but flat markets produce multiple crossovers, enabling more trades. ATR-based TP/SL and trailing stops capture these small swings, while RSI and volume filters ensure momentum, driving high P&L and win rates.
Technical Details
Built in Pine Script v6 for TradingView compatibility.
Prevents overlapping trades with long/short checks.
Handles edge cases like zero division and auto-detects the trading pair’s base currency (e.g., BTC from BTCUSDT).
This strategy suits scalpers seeking structured entries and risk management. Test on 15-minute or 30-minute charts to match your style and market conditions.
QuantJazz Turbine Trader BETA v1.17QuantJazz Turbine Trader BETA v1.17 - Strategy Introduction and User Guide
Strategy Introduction
Welcome to the QuantJazz Turbine Trader BETA v1.17, a comprehensive trading strategy designed for TradingView. This strategy is built upon oscillator principles, drawing inspiration from the Turbo Oscillator by RedRox, and incorporates multiple technical analysis concepts including RSI, MFI, Stochastic oscillators, divergence detection, and an optional FRAMA (Fractal Adaptive Moving Average) filter.
The Turbine Trader aims to provide traders with a flexible toolkit for identifying potential entry and exit points in the market. It presents information through a main signal line oscillator, a histogram, and various visual cues like dots, triangles, and divergence lines directly on the indicator panel. The strategy component allows users to define specific conditions based on these visual signals to trigger automated long or short trades within the TradingView environment.
This guide provides an overview of the strategy's components, settings, and usage. Please remember that this is a BETA version (v1.17). While developed with care, it may contain bugs or behave unexpectedly.
LEGAL DISCLAIMER: QuantJazz makes no claims about the fitness or profitability of this tool. Trading involves significant risk, and you may lose all of your invested capital. All trading decisions made using this strategy are solely at the user's discretion and responsibility. Past performance is not indicative of future results. Always conduct thorough backtesting and risk assessment before deploying any trading strategy with real capital.
This work is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International.
Core Concepts and Visual Elements
The Turbine Trader indicator displays several components in its own panel below the main price chart:
1. Signal Line (Avg & Avg2): This is the primary oscillator. It's a composite indicator derived from RSI, MFI (Money Flow Index), and Stochastic calculations, smoothed using an EMA (Exponential Moving Average).
Avg: The faster smoothed signal line.
Avg2: The slower smoothed signal line.
Color Coding: The space between Avg and Avg2 is filled. The color (Neon Blue/gColor or Neon Purple/rColor) indicates the trend based on the relationship between Avg and Avg2. Blue suggests bullish momentum (Avg > Avg2), while Purple suggests bearish momentum (Avg2 > Avg).
Zero Line Crosses: Crossovers of the Avg line with the zero level can indicate shifts in momentum.
2. Histogram (resMfi): This histogram is based on smoothed and transformed MFI calculations (Fast MFI and Slow MFI).
Color Coding: Bars are colored Neon Blue (histColorUp) when above zero, suggesting bullish pressure, and Neon Purple (histColorDn) when below zero, suggesting bearish pressure. Transparency is applied.
Zero Line Crosses: Crossovers of the histogram with the zero level can signal potential shifts in money flow.
3. Reversal Points (Dots): Dots appear on the Signal Line (specifically on Avg2) when the color changes (i.e., Avg crosses Avg2).
Small Dots: Appear when a reversal occurs while the oscillator is in an "extreme" zone (below -60 for bullish reversals, above +60 for bearish reversals).
Large Dots: Appear when a reversal occurs outside of these extreme zones.
Colors: Blue (gRdColor) for bullish reversals (Avg crossing above Avg2), Purple (rRdColor) for bearish reversals (Avg crossing below Avg2).
4. Take Profit (TP) Signals (Triangles): Small triangles appear above (+120) or below (-120) the zero line.
Bearish Triangle (Down, Purple rTpColor): Suggests a potential exit point for long positions or an entry point for short positions, based on the oscillator losing upward momentum above the 50 level.
Bullish Triangle (Up, Blue gTpColor): Suggests a potential exit point for short positions or an entry point for long positions, based on the oscillator losing downward momentum below the -50 level.
5. Divergence Lines: The strategy automatically detects and draws potential regular and hidden divergences between the price action (highs/lows) and the Signal Line (Avg).
Regular Bullish Divergence (White bullDivColor line, ⊚︎ label): Price makes a lower low, but the oscillator makes a higher low. Suggests potential bottoming.
Regular Bearish Divergence (White bearDivColor line, ⊚︎ label): Price makes a higher high, but the oscillator makes a lower high. Suggests potential topping.
Hidden Bullish Divergence (bullHidDivColor line, ⊚︎ label): Price makes a higher low, but the oscillator makes a lower low. Suggests potential continuation of an uptrend.
Hidden Bearish Divergence (bearHidDivColor line, ⊚︎ label): Price makes a lower high, but the oscillator makes a higher high. Suggests potential continuation of a downtrend.
Delete Broken Divergence Lines: If enabled, newer divergence lines originating from a similar point will replace older ones.
6. Status Line: A visual bar at the top (95 to 105) and bottom (-95 to -105) of the indicator panel. Its color intensity reflects the confluence of signals:
Score Calculation: +1 if Avg > Avg2, +1 if Avg > 0, +1 if Histogram > 0.
Top Bar (Bullish): Bright Blue (gStatColor) if score is 3, Faded Blue if score is 2, Black otherwise.
Bottom Bar (Bearish): Bright Purple (rStatColor) if score is 0, Faded Purple if score is 1, Black otherwise.
Strategy Settings Explained
The strategy's behavior is controlled via the settings panel (gear icon).
1. Date Range:
Start Date, End Date: Define the period for backtesting. Trades will only occur within this range.
2. Optional Webhook Configuration: (For Automation)
3C Email Token, 3C Bot ID: Enter your 3Commas API credentials if you plan to automate trading using webhooks. The strategy generates JSON alert messages compatible with 3Commas. You can go ahead and just leave the text field as defaulted, "TOKEN HERE" / "BOT ID HERE" if not using any bot automations at this time. You can always come back later and automate it. More info can be made available from QuantJazz should you need automation assistance with custom indicators and trading strategies.
3. 🚀 Signal Line:
Turn On/Off: Show or hide the main signal lines (Avg, Avg2).
gColor, rColor: Set the colors for bullish and bearish signal line states.
Length (RSI): The lookback period for the internal RSI calculation. Default is 2.
Smooth (EMA): The smoothing period for the EMAs applied to the composite signal. Default is 9.
RSI Source: The price source used for RSI calculation (default: close).
4. 📊 Histogram:
Turn On/Off: Show or hide the histogram.
histColorUp, histColorDn: Set the colors for positive and negative histogram bars.
Length (MFI): The base lookback period for MFI calculations. Default is 5. Fast and Slow MFI lengths are derived from this.
Smooth: Smoothing period for the final histogram output. Default is 1 (minimal smoothing).
5.💡 Other:
Show Divergence Line: Toggle visibility of regular divergence lines.
bullDivColor, bearDivColor: Colors for regular divergence lines.
Show Hidden Divergence: Toggle visibility of hidden divergence lines.
bullHidDivColor, bearHidDivColor: Colors for hidden divergence lines.
Show Status Line: Toggle visibility of the top/bottom status bars.
gStatColor, rStatColor: Colors for the status line bars.
Show TP Signal: Toggle visibility of the TP triangles.
gTpColor, rTpColor: Colors for the TP triangles.
Show Reversal points: Toggle visibility of the small/large dots on the signal line.
gRdColor, rRdColor: Colors for the reversal dots.
Delete Broken Divergence Lines: Enable/disable automatic cleanup of older divergence lines.
6. ⚙️ Strategy Inputs: (CRITICAL for Trade Logic)
This section defines which visual signals trigger trades. Each signal (Small/Large Dots, TP Triangles, Bright Bars, Signal/Histogram Crosses, Signal/Histogram Max/Min, Divergences) has a dropdown menu:
Long: This signal can trigger a long entry.
Short: This signal can trigger a short entry.
Disabled: This signal will not trigger any entry.
Must Be True Checkbox: If checked for a specific signal, that signal's condition must be met for any trade (long or short, depending on the dropdown selection for that signal) to be considered. Multiple "Must Be True" conditions act as AND logic – all must be true simultaneously.
How it Works:
The strategy first checks if all conditions marked as "Must Be True" (for the relevant trade direction - long or short) are met.
If all "Must Be True" conditions are met, it then checks if at least one of the conditions not marked as "Must Be True" (and set to "Long" or "Short" respectively) is also met.
If both steps pass, and other filters (like Date Range, FRAMA) allow, an entry order is placed.
Example: If "Large Bullish Dot" is set to "Long" and "Must Be True" is checked, AND "Bullish Divergence" is set to "Long" but "Must Be True" is not checked: A long entry requires BOTH the Large Bullish Dot AND the Bullish Divergence to occur simultaneously. If "Large Bullish Dot" was "Long" but not "Must Be True", then EITHER a Large Bullish Dot OR a Bullish Divergence could trigger a long entry (assuming no other "Must Be True" conditions are active).
Note: By default, the strategy is configured for long-only trades (strategy.risk.allow_entry_in(strategy.direction.long)). To enable short trades, you would need to comment out or remove this line in the Pine Script code and configure the "Strategy Inputs" accordingly.
7. 💰 Take Profit Settings:
Take Profit 1/2/3 (%): The percentage above the entry price (for longs) or below (for shorts) where each TP level is set. (e.g., 1.0 means 1% profit).
TP1/2/3 Percentage: The percentage of the currently open position to close when the corresponding TP level is hit. The percentages should ideally sum to 100% if you intend to close the entire position across the TPs.
Trailing Stop (%): The percentage below the highest high (for longs) or above the lowest low (for shorts) reached after the activation threshold, where the stop loss will trail.
Trailing Stop Activation (%): The minimum profit percentage the trade must reach before the trailing stop becomes active.
Re-entry Delay (Bars): The minimum number of bars to wait after a TP is hit before considering a re-entry. Default is 1 (allows immediate re-entry on the next bar if conditions met).
Re-entry Price Offset (%): The percentage the price must move beyond the previous TP level before a re-entry is allowed. This prevents immediate re-entry if the price hovers around the TP level.
8. 📈 FRAMA Filter: (Optional Trend Filter)
Use FRAMA Filter: Enable or disable the filter.
FRAMA Source, FRAMA Period, FRAMA Fast MA, FRAMA Slow MA: Parameters for the FRAMA calculation. Defaults provided are common starting points.
FRAMA Filter Type:
FRAMA > previous bars: Allows trades only if FRAMA is significantly above its recent average (defined by FRAMA Percentage and FRAMA Lookback). Typically used to confirm strong upward trends for longs.
FRAMA < price: Allows trades only if FRAMA is below the current price (framaSource). Can act as a baseline trend filter.
FRAMA Percentage (X), FRAMA Lookback (Y): Used only for the FRAMA > previous bars filter type.
How it Affects Trades: If Use FRAMA Filter is enabled:
Long entries require the FRAMA filter condition to be true.
Short entries require the FRAMA filter condition to be false (as currently coded, this acts as an inverse filter for shorts if enabled).
How to Use the Strategy
1. Apply to Chart: Open your desired chart on TradingView. Click "Indicators", find "QuantJazz Turbine Trader BETA v1.17" (you might need to add it via Invite-only scripts or if published publicly), and add it to your chart. The oscillator appears below the price chart, and the strategy tester panel opens at the bottom.
2. Configure Strategy Properties: In the Pine Script code itself (or potentially via the UI if supported), adjust the strategy() function parameters like initial_capital, default_qty_value, commission_value, slippage, etc., to match your account, broker fees, and risk settings. The user preferences provided (e.g., 1000 initial capital, 0.1% commission) are examples. Remember use_bar_magnifier is false by default in v1.17.
3. Configure Inputs (Settings Panel):
Set the Date Range for backtesting.
Crucially, configure the ⚙️ Strategy Inputs. Decide which signals should trigger entries and whether they are mandatory ("Must Be True"). Start simply, perhaps enabling only one or two signals initially, and test thoroughly. Remember the default long-only setting unless you modify the code.
Set up your 💰 Take Profit Settings, including TP levels, position size percentages for each TP, and the trailing stop parameters. Decide if you want to use the re-entry feature.
Decide whether to use the 📈 FRAMA Filter and configure its parameters if enabled.
Adjust visual elements (🚀 Signal Line, 📊 Histogram, 💡 Other) as needed for clarity.
4. Backtest: Use the Strategy Tester panel in TradingView. Analyze the performance metrics (Net Profit, Max Drawdown, Profit Factor, Win Rate, Trade List) across different assets, timeframes, and setting configurations. Pay close attention to how different "Strategy Inputs" combinations perform.
5. Refine: Based on backtesting results, adjust the input settings, TP/SL strategy, and signal combinations to optimize performance for your chosen market and timeframe, while being mindful of overfitting.
6. Automation (Optional): If using 3Commas or a similar platform:
Enter your 3C Email Token and 3C Bot ID in the settings.
Create alerts in TradingView (right-click on the chart or use the Alert panel).
Set the Condition to "QuantJazz Turbine Trader BETA v1.17".
In the "Message" box, paste the corresponding placeholder, which will pass the message in JSON from our custom code to TradingView to pass through your webhook: {{strategy.order.alert_message}}.
In the next tab, configure the Webhook URL provided by your automation platform. Put a Whale sound, while you're at it! 🐳
When an alert triggers, TradingView will send the pre-formatted JSON message from the strategy code to your webhook URL.
Final Notes
The QuantJazz Turbine Trader BETA v1.17 offers a wide range of customizable signals and strategy logic. Its effectiveness heavily depends on proper configuration and thorough backtesting specific to the traded asset and timeframe. Start with the default settings, understand each component, and methodically test different combinations of signals and parameters. Remember the inherent risks of trading and never invest capital you cannot afford to lose.
Dow Theory Trend StrategyDow Theory Trend Strategy (Pine Script)
Overview
This Pine Script implements a trading strategy based on the core principles of Dow Theory. It visually identifies trends (uptrend, downtrend) by analyzing pivot highs and lows and executes trades when the trend direction changes. This script is an improved version that features refined trend determination logic and strategy implementation.
Core Concept: Dow Theory
The script uses a fundamental Dow Theory concept for trend identification:
Uptrend: Characterized by a series of Higher Highs (HH) and Higher Lows (HL).
Downtrend: Characterized by a series of Lower Highs (LH) and Lower Lows (LL).
How it Works
Pivot Point Detection:
It uses the built-in ta.pivothigh() and ta.pivotlow() functions to identify significant swing points (potential highs and lows) in the price action.
The pivotLookback input determines the number of bars to the left and right required to confirm a pivot. Note that this introduces a natural lag (equal to pivotLookback bars) before a pivot is confirmed.
Improved Trend Determination:
The script stores the last two confirmed pivot highs and the last two confirmed pivot lows.
An Uptrend (trendDirection = 1) is confirmed only when the latest pivot high is higher than the previous one (HH) AND the latest pivot low is higher than the previous one (HL).
A Downtrend (trendDirection = -1) is confirmed only when the latest pivot high is lower than the previous one (LH) AND the latest pivot low is lower than the previous one (LL).
Key Improvement: If neither a clear uptrend nor a clear downtrend is confirmed based on the latest pivots, the script maintains the previous trend state (trendDirection := trendDirection ). This differs from simpler implementations that might switch to a neutral/range state (e.g., trendDirection = 0) more frequently. This approach aims for smoother trend following, acknowledging that trends often persist through periods without immediate new HH/HL or LH/LL confirmations.
Trend Change Detection:
The script monitors changes in the trendDirection variable.
changedToUp becomes true when the trend shifts to an Uptrend (from Downtrend or initial state).
changedToDown becomes true when the trend shifts to a Downtrend (from Uptrend or initial state).
Visualizations
Background Color: The chart background is colored to reflect the currently identified trend:
Blue: Uptrend (trendDirection == 1)
Red: Downtrend (trendDirection == -1)
Gray: Initial state or undetermined (trendDirection == 0)
Pivot Points (Optional): Small triangles (shape.triangledown/shape.triangleup) can be displayed above pivot highs and below pivot lows if showPivotPoints is enabled.
Trend Change Signals (Optional): Labels ("▲ UP" / "▼ DOWN") can be displayed when a trend change is confirmed (changedToUp / changedToDown) if showTrendChange is enabled. These visually mark the potential entry points for the strategy.
Strategy Logic
Entry Conditions:
Enters a long position (strategy.long) using strategy.entry("L", ...) when changedToUp becomes true.
Enters a short position (strategy.short) using strategy.entry("S", ...) when changedToDown becomes true.
Position Management: The script uses strategy.entry(), which automatically handles position reversal. If the strategy is long and a short signal occurs, strategy.entry() will close the long position and open a new short one (and vice-versa).
Inputs
pivotLookback: The number of bars on each side to confirm a pivot high/low. Higher values mean pivots are confirmed later but may be more significant.
showPivotPoints: Toggle visibility of pivot point markers.
showTrendChange: Toggle visibility of the trend change labels ("▲ UP" / "▼ DOWN").
Key Improvements from Original
Smoother Trend Logic: The trend state persists unless a confirmed reversal pattern (opposite HH/HL or LH/LL) occurs, reducing potential whipsaws in choppy markets compared to logic that frequently resets to neutral.
Strategy Implementation: Converted from a pure indicator to a strategy capable of executing backtests and potentially live trades based on the Dow Theory trend changes.
Disclaimer
Dow Theory signals are inherently lagging due to the nature of pivot confirmation.
The effectiveness of the strategy depends heavily on the market conditions and the chosen pivotLookback setting.
This script serves as a basic template. Always perform thorough backtesting and implement proper risk management (e.g., stop-loss, take-profit, position sizing) before considering any live trading.
Trendline Breaks with Multi Fibonacci Supertrend StrategyTMFS Strategy: Advanced Trendline Breakouts with Multi-Fibonacci Supertrend
Elevate your algorithmic trading with institutional-grade signal confluence
Strategy Genesis & Evolution
This advanced trading system represents the culmination of a personal research journey, evolving from my custom " Multi Fibonacci Supertrend with Signals " indicator into a comprehensive trading strategy. Built upon the exceptional trendline detection methodology pioneered by LuxAlgo in their " Trendlines with Breaks " indicator, I've engineered a systematic framework that integrates multiple technical factors into a cohesive trading system.
Core Fibonacci Principles
At the heart of this strategy lies the Fibonacci sequence application to volatility measurement:
// Fibonacci-based factors for multiple Supertrend calculations
factor1 = input.float(0.618, 'Factor 1 (Weak/Fibonacci)', minval = 0.01, step = 0.01)
factor2 = input.float(1.618, 'Factor 2 (Medium/Golden Ratio)', minval = 0.01, step = 0.01)
factor3 = input.float(2.618, 'Factor 3 (Strong/Extended Fib)', minval = 0.01, step = 0.01)
These precise Fibonacci ratios create a dynamic volatility envelope that adapts to changing market conditions while maintaining mathematical harmony with natural price movements.
Dynamic Trendline Detection
The strategy incorporates LuxAlgo's pioneering approach to trendline detection:
// Pivotal swing detection (inspired by LuxAlgo)
pivot_high = ta.pivothigh(swing_length, swing_length)
pivot_low = ta.pivotlow(swing_length, swing_length)
// Dynamic slope calculation using ATR
slope = atr_value / swing_length * atr_multiplier
// Update trendlines based on pivot detection
if bool(pivot_high)
upper_slope := slope
upper_trendline := pivot_high
else
upper_trendline := nz(upper_trendline) - nz(upper_slope)
This adaptive trendline approach automatically identifies key structural market boundaries, adjusting in real-time to evolving chart patterns.
Breakout State Management
The strategy implements sophisticated state tracking for breakout detection:
// Track breakouts with state variables
var int upper_breakout_state = 0
var int lower_breakout_state = 0
// Update breakout state when price crosses trendlines
upper_breakout_state := bool(pivot_high) ? 0 : close > upper_trendline ? 1 : upper_breakout_state
lower_breakout_state := bool(pivot_low) ? 0 : close < lower_trendline ? 1 : lower_breakout_state
// Detect new breakouts (state transitions)
bool new_upper_breakout = upper_breakout_state > upper_breakout_state
bool new_lower_breakout = lower_breakout_state > lower_breakout_state
This state-based approach enables precise identification of the exact moment when price breaks through a significant trendline.
Multi-Factor Signal Confluence
Entry signals require confirmation from multiple technical factors:
// Define entry conditions with multi-factor confluence
long_entry_condition = enable_long_positions and
upper_breakout_state > upper_breakout_state and // New trendline breakout
di_plus > di_minus and // Bullish DMI confirmation
close > smoothed_trend // Price above Supertrend envelope
// Execute trades only with full confirmation
if long_entry_condition
strategy.entry('L', strategy.long, comment = "LONG")
This strict requirement for confluence significantly reduces false signals and improves the quality of trade entries.
Advanced Risk Management
The strategy includes sophisticated risk controls with multiple methodologies:
// Calculate stop loss based on selected method
get_long_stop_loss_price(base_price) =>
switch stop_loss_method
'PERC' => base_price * (1 - long_stop_loss_percent)
'ATR' => base_price - long_stop_loss_atr_multiplier * entry_atr
'RR' => base_price - (get_long_take_profit_price() - base_price) / long_risk_reward_ratio
=> na
// Implement trailing functionality
strategy.exit(
id = 'Long Take Profit / Stop Loss',
from_entry = 'L',
qty_percent = take_profit_quantity_percent,
limit = trailing_take_profit_enabled ? na : long_take_profit_price,
stop = long_stop_loss_price,
trail_price = trailing_take_profit_enabled ? long_take_profit_price : na,
trail_offset = trailing_take_profit_enabled ? long_trailing_tp_step_ticks : na,
comment = "TP/SL Triggered"
)
This flexible approach adapts to varying market conditions while providing comprehensive downside protection.
Performance Characteristics
Rigorous backtesting demonstrates exceptional capital appreciation potential with impressive risk-adjusted metrics:
Remarkable total return profile (1,517%+)
Strong Sortino ratio (3.691) indicating superior downside risk control
Profit factor of 1.924 across all trades (2.153 for long positions)
Win rate exceeding 35% with balanced distribution across varied market conditions
Institutional Considerations
The strategy architecture addresses execution complexities faced by institutional participants with temporal filtering and date-range capabilities:
// Time Filter settings with flexible timezone support
import jason5480/time_filters/5 as time_filter
src_timezone = input.string(defval = 'Exchange', title = 'Source Timezone')
dst_timezone = input.string(defval = 'Exchange', title = 'Destination Timezone')
// Date range filtering for precise execution windows
use_from_date = input.bool(defval = true, title = 'Enable Start Date')
from_date = input.time(defval = timestamp('01 Jan 2022 00:00'), title = 'Start Date')
// Validate trading permission based on temporal constraints
date_filter_approved = time_filter.is_in_date_range(
use_from_date, from_date, use_to_date, to_date, src_timezone, dst_timezone
)
These capabilities enable precise execution timing and market session optimization critical for larger market participants.
Acknowledgments
Special thanks to LuxAlgo for the pioneering work on trendline detection and breakout identification that inspired elements of this strategy. Their innovative approach to technical analysis provided a valuable foundation upon which I could build my Fibonacci-based methodology.
This strategy is shared under the same Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license as LuxAlgo's original work.
Past performance is not indicative of future results. Conduct thorough analysis before implementing any algorithmic strategy.
Best MA Pair Finder (Crossover Strategy)This indicator automatically identifies the optimal pair of moving averages (MAs) for a crossover strategy using all available historical data. It offers several MA options—including SMA, EMA, and TEMA—allowing users to select the desired type in the settings. The indicator supports two strategy modes: “Long Only” and “Buy & Sell”, which can be chosen via the options.
For each MA pair combination, the indicator performs a backtest and calculates the profit factor, considering only those pairs where the total number of trades meets or exceeds the user-defined "Minimum Trades" threshold. This parameter ensures that the selected optimal pair is based on a statistically meaningful sample rather than on a limited number of trades.
The results provided by this indicator are based on historical data and backtests, which may not guarantee future performance. Users should conduct their own analysis and use proper risk management before making trading decisions.
Rally Base Drop SND Pivots Strategy [LuxAlgo X PineIndicators]This strategy is based on the Rally Base Drop (RBD) SND Pivots indicator developed by LuxAlgo. Full credit for the concept and original indicator goes to LuxAlgo.
The Rally Base Drop SND Pivots Strategy is a non-repainting supply and demand trading system that detects pivot points based on Rally, Base, and Drop (RBD) candles. This strategy automatically identifies key market structure levels, allowing traders to:
Identify pivot-based supply and demand (SND) zones.
Use fixed criteria for trend continuation or reversals.
Filter out market noise by requiring structured price formations.
Enter trades based on breakouts of key SND pivot levels.
How the Rally Base Drop SND Pivots Strategy Works
1. Pivot Point Detection Using RBD Candles
The strategy follows a rigid market structure methodology, where pivots are detected only when:
A Rally (R) consists of multiple consecutive bullish candles.
A Drop (D) consists of multiple consecutive bearish candles.
A Base (B) is identified as a transition between Rallies and Drops, acting as a pivot point.
The pivot level is confirmed when the formation is complete.
Unlike traditional fractal-based pivots, RBD Pivots enforce stricter structural rules, ensuring that each pivot:
Has a well-defined bullish or bearish price movement.
Reduces false signals caused by single-bar fluctuations.
Provides clear supply and demand levels based on structured price movements.
These pivot levels are drawn on the chart using color-coded boxes:
Green zones represent bullish pivot levels (Rally Base formations).
Red zones represent bearish pivot levels (Drop Base formations).
Once a pivot is confirmed, the high or low of the base candle is used as the reference level for future trades.
2. Trade Entry Conditions
The strategy allows traders to select from three trading modes:
Long Only – Only takes long trades when bullish pivot breakouts occur.
Short Only – Only takes short trades when bearish pivot breakouts occur.
Long & Short – Trades in both directions based on pivot breakouts.
Trade entry signals are triggered when price breaks through a confirmed pivot level:
Long Entry:
A bullish pivot level is formed.
Price breaks above the bullish pivot level.
The strategy enters a long position.
Short Entry:
A bearish pivot level is formed.
Price breaks below the bearish pivot level.
The strategy enters a short position.
The strategy includes an optional mode to reverse long and short conditions, allowing traders to experiment with contrarian entries.
3. Exit Conditions Using ATR-Based Risk Management
This strategy uses the Average True Range (ATR) to calculate dynamic stop-loss and take-profit levels:
Stop-Loss (SL): Placed 1 ATR below entry for long trades and 1 ATR above entry for short trades.
Take-Profit (TP): Set using a Risk-Reward Ratio (RR) multiplier (default = 6x ATR).
When a trade is opened:
The entry price is recorded.
ATR is calculated at the time of entry to determine stop-loss and take-profit levels.
Trades exit automatically when either SL or TP is reached.
If reverse conditions mode is enabled, stop-loss and take-profit placements are flipped.
Visualization & Dynamic Support/Resistance Levels
1. Pivot Boxes for Market Structure
Each pivot is marked with a colored box:
Green boxes indicate bullish demand zones.
Red boxes indicate bearish supply zones.
These boxes remain on the chart to act as dynamic support and resistance levels, helping traders identify key price reaction zones.
2. Horizontal Entry, Stop-Loss, and Take-Profit Lines
When a trade is active, the strategy plots:
White line → Entry price.
Red line → Stop-loss level.
Green line → Take-profit level.
Labels display the exact entry, SL, and TP values, updating dynamically as price moves.
Customization Options
This strategy offers multiple adjustable settings to optimize performance for different market conditions:
Trade Mode Selection → Choose between Long Only, Short Only, or Long & Short.
Pivot Length → Defines the number of required Rally & Drop candles for a pivot.
ATR Exit Multiplier → Adjusts stop-loss distance based on ATR.
Risk-Reward Ratio (RR) → Modifies take-profit level relative to risk.
Historical Lookback → Limits how far back pivot zones are displayed.
Color Settings → Customize pivot box colors for bullish and bearish setups.
Considerations & Limitations
Pivot Breakouts Do Not Guarantee Reversals. Some pivot breaks may lead to continuation moves instead of trend reversals.
Not Optimized for Low Volatility Conditions. This strategy works best in trending markets with strong momentum.
ATR-Based Stop-Loss & Take-Profit May Require Optimization. Different assets may require different ATR multipliers and RR settings.
Market Noise May Still Influence Pivots. While this method filters some noise, fake breakouts can still occur.
Conclusion
The Rally Base Drop SND Pivots Strategy is a non-repainting supply and demand system that combines:
Pivot-based market structure analysis (using Rally, Base, and Drop candles).
Breakout-based trade entries at confirmed SND levels.
ATR-based dynamic risk management for stop-loss and take-profit calculation.
This strategy helps traders:
Identify high-probability supply and demand levels.
Trade based on structured market pivots.
Use a systematic approach to price action analysis.
Automatically manage risk with ATR-based exits.
The strict pivot detection rules and built-in breakout validation make this strategy ideal for traders looking to:
Trade based on market structure.
Use defined support & resistance levels.
Reduce noise compared to traditional fractals.
Implement a structured supply & demand trading model.
This strategy is fully customizable, allowing traders to adjust parameters to fit their market and trading style.
Full credit for the original concept and indicator goes to LuxAlgo.
Liquidity Sweep Filter Strategy [AlgoAlpha X PineIndicators]This strategy is based on the Liquidity Sweep Filter developed by AlgoAlpha. Full credit for the concept and original indicator goes to AlgoAlpha.
The Liquidity Sweep Filter Strategy is a non-repainting trading system designed to identify liquidity sweeps, trend shifts, and high-impact price levels. It incorporates volume-based liquidation analysis, trend confirmation, and dynamic support/resistance detection to optimize trade entries and exits.
This strategy helps traders:
Detect liquidity sweeps where major market participants trigger stop losses and liquidations.
Identify trend shifts using a volatility-based moving average system.
Analyze volume distribution with a built-in volume profile visualization.
Filter noise by differentiating between major and minor liquidity sweeps.
How the Liquidity Sweep Filter Strategy Works
1. Trend Detection Using Volatility-Based Filtering
The strategy applies a volatility-adjusted moving average system to determine trend direction:
A central trend line is calculated using an EMA smoothed over a user-defined length.
Upper and lower deviation bands are created based on the average price deviation over multiple periods.
If price closes above the upper band, the strategy signals an uptrend.
If price closes below the lower band, the strategy signals a downtrend.
This approach ensures that trend shifts are confirmed only when price significantly moves beyond normal market fluctuations.
2. Liquidity Sweep Detection
Liquidity sweeps occur when price temporarily breaks key levels, triggering stop-loss liquidations or margin call events. The strategy tracks swing highs and lows, marking potential liquidity grabs:
Bearish Liquidity Sweeps – Price breaks a recent high, then reverses downward.
Bullish Liquidity Sweeps – Price breaks a recent low, then reverses upward.
Volume Integration – The strategy analyzes trading volume at each sweep to differentiate between major and minor sweeps.
Key levels where liquidity sweeps occur are plotted as color-coded horizontal lines:
Red lines indicate bearish liquidity sweeps.
Green lines indicate bullish liquidity sweeps.
Labels are displayed at each sweep, showing the volume of liquidated positions at that level.
3. Volume Profile Analysis
The strategy includes an optional volume profile visualization, displaying how trading volume is distributed across different price levels.
Features of the volume profile:
Point of Control (POC) – The price level with the highest traded volume is marked as a key area of interest.
Bounding Box – The profile is enclosed within a transparent box, helping traders visualize the price range of high trading activity.
Customizable Resolution & Scale – Traders can adjust the granularity of the profile to match their preferred time frame.
The volume profile helps identify zones of strong support and resistance, making it easier to anticipate price reactions at key levels.
Trade Entry & Exit Conditions
The strategy allows traders to configure trade direction:
Long Only – Only takes long trades.
Short Only – Only takes short trades.
Long & Short – Trades in both directions.
Entry Conditions
Long Entry:
A bullish trend shift is confirmed.
A bullish liquidity sweep occurs (price sweeps below a key level and reverses).
The trade direction setting allows long trades.
Short Entry:
A bearish trend shift is confirmed.
A bearish liquidity sweep occurs (price sweeps above a key level and reverses).
The trade direction setting allows short trades.
Exit Conditions
Closing a Long Position:
A bearish trend shift occurs.
The position is liquidated at a predefined liquidity sweep level.
Closing a Short Position:
A bullish trend shift occurs.
The position is liquidated at a predefined liquidity sweep level.
Customization Options
The strategy offers multiple adjustable settings:
Trade Mode: Choose between Long Only, Short Only, or Long & Short.
Trend Calculation Length & Multiplier: Adjust how trend signals are calculated.
Liquidity Sweep Sensitivity: Customize how aggressively the strategy identifies sweeps.
Volume Profile Display: Enable or disable the volume profile visualization.
Bounding Box & Scaling: Control the size and position of the volume profile.
Color Customization: Adjust colors for bullish and bearish signals.
Considerations & Limitations
Liquidity sweeps do not always result in reversals. Some price sweeps may continue in the same direction.
Works best in volatile markets. In low-volatility environments, liquidity sweeps may be less reliable.
Trend confirmation adds a slight delay. The strategy ensures valid signals, but this may result in slightly later entries.
Large volume imbalances may distort the volume profile. Adjusting the scale settings can help improve visualization.
Conclusion
The Liquidity Sweep Filter Strategy is a volume-integrated trading system that combines liquidity sweeps, trend analysis, and volume profile data to optimize trade execution.
By identifying key price levels where liquidations occur, this strategy provides valuable insight into market behavior, helping traders make better-informed trading decisions.
Key use cases for this strategy:
Liquidity-Based Trading – Capturing moves triggered by stop hunts and liquidations.
Volume Analysis – Using volume profile data to confirm high-activity price zones.
Trend Following – Entering trades based on confirmed trend shifts.
Support & Resistance Trading – Using liquidity sweep levels as dynamic price zones.
This strategy is fully customizable, allowing traders to adapt it to different market conditions, timeframes, and risk preferences.
Full credit for the original concept and indicator goes to AlgoAlpha.
Market Trend Levels Non-Repainting [BigBeluga X PineIndicators]This strategy is based on the Market Trend Levels Detector developed by BigBeluga. Full credit for the concept and original indicator goes to BigBeluga.
The Market Trend Levels Detector Strategy is a non-repainting trend-following strategy that identifies market trend shifts using two Exponential Moving Averages (EMA). It also detects key price levels and allows traders to apply multiple filters to refine trade entries and exits.
This strategy is designed for trend trading and enables traders to:
Identify trend direction based on EMA crossovers.
Detect significant market levels using labeled trend lines.
Use multiple filter conditions to improve trade accuracy.
Avoid false signals through non-repainting calculations.
How the Market Trend Levels Detector Strategy Works
1. Core Trend Detection Using EMA Crossovers
The strategy detects trend shifts using two EMAs:
Fast EMA (default: 12 periods) – Reacts quickly to price movements.
Slow EMA (default: 25 periods) – Provides a smoother trend confirmation.
A bullish crossover (Fast EMA crosses above Slow EMA) signals an uptrend , while a bearish crossover (Fast EMA crosses below Slow EMA) signals a downtrend .
2. Market Level Detection & Visualization
Each time an EMA crossover occurs, a trend level line is drawn:
Bullish crossover → A green line is drawn at the low of the crossover candle.
Bearish crossover → A purple line is drawn at the high of the crossover candle.
Lines can be extended to act as support and resistance zones for future price action.
Additionally, a small label (●) appears at each crossover to mark the event on the chart.
3. Trade Entry & Exit Conditions
The strategy allows users to choose between three trading modes:
Long Only – Only enters long trades.
Short Only – Only enters short trades.
Long & Short – Trades in both directions.
Entry Conditions
Long Entry:
A bullish EMA crossover occurs.
The trade direction setting allows long trades.
Filter conditions (if enabled) confirm a valid long signal.
Short Entry:
A bearish EMA crossover occurs.
The trade direction setting allows short trades.
Filter conditions (if enabled) confirm a valid short signal.
Exit Conditions
Long Exit:
A bearish EMA crossover occurs.
Exit filters (if enabled) indicate an invalid long position.
Short Exit:
A bullish EMA crossover occurs.
Exit filters (if enabled) indicate an invalid short position.
Additional Trade Filters
To improve trade accuracy, the strategy allows traders to apply up to 7 additional filters:
RSI Filter: Only trades when RSI confirms a valid trend.
MACD Filter: Ensures MACD histogram supports the trade direction.
Stochastic Filter: Requires %K line to be above/below threshold values.
Bollinger Bands Filter: Confirms price position relative to the middle BB line.
ADX Filter: Ensures the trend strength is above a set threshold.
CCI Filter: Requires CCI to indicate momentum in the right direction.
Williams %R Filter: Ensures price momentum supports the trade.
Filters can be enabled or disabled individually based on trader preference.
Dynamic Level Extension Feature
The strategy provides an optional feature to extend trend lines until price interacts with them again:
Bullish support lines extend until price revisits them.
Bearish resistance lines extend until price revisits them.
If price breaks a line, the line turns into a dotted style , indicating it has been breached.
This helps traders identify key levels where trend shifts previously occurred, providing useful support and resistance insights.
Customization Options
The strategy includes several adjustable settings :
Trade Direction: Choose between Long Only, Short Only, or Long & Short.
Trend Lengths: Adjust the Fast & Slow EMA lengths.
Market Level Extension: Decide whether to extend support/resistance lines.
Filters for Trade Confirmation: Enable/disable individual filters.
Color Settings: Customize line colors for bullish and bearish trend shifts.
Maximum Displayed Lines: Limit the number of drawn support/resistance lines.
Considerations & Limitations
Trend Lag: As with any EMA-based strategy, signals may be slightly delayed compared to price action.
Sideways Markets: This strategy works best in trending conditions; frequent crossovers in sideways markets can produce false signals.
Filter Usage: Enabling multiple filters may reduce trade frequency, but can also improve trade quality.
Line Overlap: If many crossovers occur in a short period, the chart may become cluttered with multiple trend levels. Adjusting the "Display Last" setting can help.
Conclusion
The Market Trend Levels Detector Strategy is a non-repainting trend-following system that combines EMA crossovers, market level detection, and customizable filters to improve trade accuracy.
By identifying trend shifts and key price levels, this strategy can be used for:
Trend Confirmation – Using EMA crossovers and filters to confirm trend direction.
Support & Resistance Trading – Identifying dynamic levels where price reacts.
Momentum-Based Trading – Combining EMA crossovers with additional momentum filters.
This strategy is fully customizable and can be adapted to different trading styles, timeframes, and market conditions.
Full credit for the original concept and indicator goes to BigBeluga.
Democratic MultiAsset Strategy [BerlinCode42]Happy Trade,
Intro
Included Trade Concept
Included Indicators and Compare-Functions
Usage and Example
Settings Menu
Declaration for Tradingview House Rules on Script Publishing
Disclaimer
Conclusion
1. Intro
This is the first multi-asset strategy available on TradingView—a market breadth multi-asset trading strategy with integrated webhooks, backtesting capabilities, and essential strategy components like Take Profit, Stop Loss, Trailing, Hedging, Time & Session Filters, and Alerts.
How It Trades? At the start of each new bar, one asset from a set of eight is selected to go long or short. As long there is available cash and the selected asset meets the minimum criteria.
The selection process works through a voting system, similar to a democracy. Each asset is evaluated using up to five indicators that the user can choose. The asset with the highest overall voting score is picked for the trade. If no asset meets all criteria, no trade is executed, and the cash reserve remains untouched for future opportunities.
How to Set Up This Market Breadth Strategy:
Choose eight assets from the same market (e.g., cryptos or big tech stocks).
Select one to five indicators for the voting system.
Refine the strategy by adjusting Take Profit, Stop Loss, Hedging, Trailing, and Filters.
2. Voting as the included Trade Concept
The world of financial trading is filled with both risks and opportunities, and the key challenge is to identify the right opportunities, manage risks, and do both right on time.
There are countless indicators designed to spot opportunities and filter out risks, but no indicator is perfect—they only work statistically, hitting the right signals more often than the wrong ones.
The goal of this strategy is to increase the accuracy of these Indicators by:
Supervising a larger number of assets
Filtering out less promising opportunities
This is achieved through a voting system that compares indicator values across eight different assets. It doesn't just compare long trades—it also evaluates long vs. short positions to identify the most promising trade.
Why focus on one asset class? While you can randomly select assets from different asset classes, doing so prevents the algorithm from identifying the strongest asset within a single class. Think about, within one asset class there is often a major trend whereby different asset classes has not really such behavior.
And, you don’t necessarily need trading in multiple classes—this algorithm is designed to generate profits in both bullish and bearish markets. So when ever an asset class rise or fall the voting system ensure to jump on the strongest asset. So this focusing on one asset class is an integral part of this strategy. This all leads to more stable and robust trading results compared to handling each asset separately.
3. Included Indicators and Compare-Functions
You can choose from 17 different indicators, each offering different types of signals:
Some provide a directional signal
Some offer a simple on/off signal
Some provide both
Available Indicators: RSI, Stochastic RSI, MFI, Price, Volume, Volume Oscillator, Pressure, Bilson Gann Trend, Confluence, TDI, SMA, EMA, WMA, HMA, VWAP, ZLMA, T3MA
However, these indicators alone do not generate trade signals. To do so, they must be compared with thresholds or other indicators using specific comparison functions.
Example – RSI as a Trade Signal. The RSI provides a value between 0 and 100. A common interpretation is:
RSI over 80 → Signal to go short or exit a long trade
RSI under 20 → Signal to go long or exit a short trade
Here, two comparison functions and two thresholds are used to determine trade signals.
Below is the full set of available comparison functions, where: I represents the indicator’s value and A represents the comparator’s value.
I < A if I smaller A then trade signal
I > A if I bigger A then trade signal
I = A if I equal to A then trade signal
I != A if I not equal to A then trade signal
A <> B if I bigger A and I smaller B then trade signal
A >< B if I smaller A then long trade signal or if I bigger B then short trade signal
Image 1
In Image 1, you can see one of five input sections, where you define an indicator along with its function, comparator, and constants. For our RSI example, we select:
Indicator: RSI
Function: >< (greater/less than)
Comparator: Constant
Constants: A = 20, B = 80
With these settings a go short signal is triggered when RSI crosses above 80. And a go long signal is triggered when RSI crosses below 20.
Relative Strength Indicator: The RSI from the public TradingView library provides a directional trade signal. You can adjust the price source and period length in the indicator settings.
Stochastic Relative Strength Indicator: As above the Stoch RSI offers a trade signal with direction. It is calculated out of the RSI, the stochastic derivation and the SMA from the Tradingview library. You can set the in-going price source and the period length for the RSI, for the Stochastic Derivation and for the SMA as blurring in the Indicator settings section.
Money Flow Indicator: As above the MFI from the public Tradingview library offers a trade signal with direction. You can set the in-going price source and the period length in the Indicator settings section.
Price: The Price as Indicator is as simple as it can be. You can chose Open, High, Low or Close or combinations of them like HLC3 or even you can import an external Indicator. The absolute price or value can later be used to generate a trade signals when certain constant thresholds or other indicators signals are crossed.
Volume: Similar as above the Volume as Indicator offers the average volume as absolute value. You can set the period length for the smoothing and you can chose where it is presented in the base currency $ or is the other. For example the trade pair BTCUSD you can chose to present the value in $ or in BTC.
Volume Oscillator: The Volume Oscillator Indicator offers a value in the range of . Whereby a value close to 0 means that the volume is very low. A value around 1 means the volume is same high as before and Values higher as 1 means the volume is bigger then before. You can set the period length for the smoothing and you can chose where it is presented in the base currency $ or is the other. For example the trade pair BTCUSD you can chose to present the value in $ or in BTC.
Pressure Indicator: The Pressure is an adapted version of LazyBear's script (Squeeze Momentum Indicator) Pressure is a Filter that highlight bars before a bigger price move in any direction. The result are integer numbers between 0 and 4 whereby 0 means no bigger price move excepted, while 4 means huge price move expected. You can set the in-going price source and the period length in the Indicator settings section.
Bilson Gann Trend: The Bilson Gann Trend Indicator is a specific re-implementation of the widely known Bilson Gann Count Algorithm to detect Highs and Lows. On base of the last four Highs and Lows a trend direction can be calculated. It is based on 2 rules to confirm a local pivot candidate. When a local pivot candidate is confirmed, let it be a High then it looks for Lows to confirm. The result range is whereby -1 means down trend, 1 means uptrend and 0 sideways.
Confluence: The Confluence Indicator is a simplified version of Dale Legan's "Confluence" indicator written by Gary Fritz. It uses five SMAs with different periods lengths. Whereby the faster SMA get compared with the (slower) SMA with the next higher period lengths. Is the faster SMA smaller then the slower SMA then -1, otherwise +1. This is done with all SMAs and the final sum range between . Whereby values around 0 means price is going side way, Crossing under 0 means trend change from bull to bear. Is the value>2 means a strong bull trend and <-2 a strong bear trend.
Trades Dynamic Index: The TDI is an adapted version from the "Traders Dynamic Index" of LazyBear. The range of the result is whereby 2 means Top goShort, -2 means Bottom goLong, 0 is neutral, 1 is up trend, -1 is down trend.
Simple Moving Average: The SMA is the one from the Tradingview library. You can compare it with the last close price or any other moving average indicator to indicate up and down trends. You can set the in-going price source and the period length in the Indicator settings section.
Exponential Moving Average: The EMA as above is the one from the Tradingview library. You can compare it with the last close price or any other moving average indicator to indicate up and down trends. You can set the in-going price source and the period length in the Indicator settings section.
Weighted Moving Average: The WMA as above is the one from the Tradingview library. You can compare it with the last close price or any other moving average indicator to indicate up and down trends. You can set the in-going price source and the period length in the Indicator settings section.
Hull Moving Average: HMA as above is the one from the Tradingview library. You can compare it with the last close price or any other moving average indicator to indicate up and down trends. You can set the in-going price source and the period length in the Indicator settings section.
Volume Weighted Average Price: The VWAP as above is the one from the Tradingview library. You can compare it with the last close price or any other moving average indicator to indicate up and down trends. You can set the in-going price source in the Indicator settings section.
Zero Lag Moving Average: The ZLMA by John Ehlers and Ric Way describe in their paper: www.mesasoftware.com
As the other moving averages you can compare it with the last close price or any other moving average indicator to indicate up and down trends. You can set the in-going price source and the period length in the Indicator settings section.
T3 Moving Average: The T3MA is the one from the Tradingview library. You can compare it with the last close price or any other moving average indicator to indicate up and down trends. You can set the in-going price source, the period length and a factor in the Indicator settings section. Keep this factor at 1 and the T3MA swing in the same range as the input. Bigger 1 and it swings over. Factors close to 0 and the T3MA becomes a center line.
All MA's following the price. The function to compare any MA Indicators would be < or > to generate a trade direction. An example follows in the next section.
4. Example and Usage
In this section, you see how to set up the strategy using a simple example. This example was intentionally chosen at random and has not undergone any iterations to refine the trade results.
We use the RSI as the trade signal indicator and apply a filter using a combination of two moving averages (MAs). The faster MA is an EMA, while the slower MA is an SMA. By comparing these two MAs, we determine a trend direction. If the faster MA is above the slower MA the trend is upwards etc. This trend direction can then be used for filtering trades.
The strategy follows these rules:
If the RSI is below 20, a buy signal is generated.
If the RSI is above 80, a sell signal is generated.
However, this RSI trade signal is filtered so that a trade is only given the maximum voting weight if the RSI trade direction aligns with the trend direction determined by the MA filter.
So first, you need to add your chosen assets or simply keep the default ones. In Image 2, you can see one of the eight asset input sections.
Image 2
This strategy offers some general trade settings that apply equally to all assets and some asset-specific settings. This distinction is necessary because some assets have higher volatility than others, requiring asset-specific Take Profit and Stop Loss levels.
Once you have made your selections, proceed to the Indicators and Compare Functions for the voting. Image 3 shows an example of this setup.
Image 3
Later on go to the Indicator specific settings shown in Image 4 to refine the trade results.
Image 4
For refine the trade results take also a look on the result summary table, development of capital plot, on the list of closed and open trades and screener table shown in Image 5.
Image 5
To locate any trade for any asset in the chronological and scroll-able trade list, each trade is marked with a label:
An opening label displaying the trade direction, ticker ID, trade number, invested amount, and remaining cash reserves.
A closing label showing the closing reason, ticker ID, trade number, trade profit (%), trade revenue ($), and updated cash reserves.
Additionally: a green line marks each Take Profit level. An orange line indicates the (trailing) Stop Loss.
The summary table in the bottom-left corner provides insights into how effective the trade strategy is. By analyzing the trade list, you can identify trades that should be avoided.
To find those bad trades on the chart, use the trade number or timestamp. With replay mode, you can go back in time to review a specific trade in detail.
Image 6
In Image 6, you can see an example where replay mode and the start time filter are used to display specific trades within a narrow time range. By identifying a large number of bad trades, you may recognize patterns and formulate conditions to avoid them in the future.
This is the backtesting tool that allows you to develop and refine your trading strategy continuously. With each iteration—from general adjustments to detailed optimizations—you can use these tools to improve your strategy. You can:
Add other indicators with trade signals and direction
Add more indicators signals as filter
Adjust the settings of your indicators to optimize results
Configure key strategy settings, such as Time and Session Filters, Stop Loss, Take Profit, and more
By doing so, you can identify a profitable strategy and its optimal settings.
5. Settings Menu
In the settings menu you will find the following high-lighted sections. Most of the settings have a i mark on their right side. Move over it with the cursor to read specific explanation.
Backtest Results: Here you can decide about visibility of the trade list, of the Screener Table and of the Results Summary. And the colors for bullish, side ways, bearish and no signal. Go above and see Image 5.
Time Filter: You can set a Start time or deactivate it by leave it unhooked. The same with End Time and Duration Days . Duration Days can also count from End time in case you deactivate Start time.
Session Filter: Here, you can chose to activate trading on a weekly basis, specifying which days of the week trading is allowed and which are excluded. Additionally, you can configure trading on a daily basis, setting the start and end times for when trades are permitted. If activated, no new trades will be initiated outside the defined times and sessions.
Trade Logic: Here you can set an extra time frame for all indicators. You can enable Longs or Shorts or both trades.
The min Criteria percentage setting defines the minimum number of voices an asset has to get to be traded. So if you set this to 50% or less also weak winners of the voting get traded while 100% means that the winner of the voting has to get all possible voices.
Additionally, you have the option to delay entry signals. This feature is particularly useful when trade signals exhibit noise and require smoothing.
Enable Trailing Stop and force the strategy to trade only at bar closing. Other-ways the strategy trade intrabar, so when ever a voting present an asset to trade, it will send the alert and the webhooks.
The Hedging is basic as shown in the following Image 7 and serves as a catch if price moves fast in the wrong direction. You can activate a hedging mechanism, which opens a trade in the opposite direction if the price moves x% against the entry price. If both the Stop Loss and Hedging are triggered within the same bar, the hedging action will always take precedence.
Image 6
Indicators to use for Trade Signal Generating: Here you chose the Indicators and their Compare Function for the Voting . Any activated asset will get their indicator valuation which get compared over all assets. The asset with the highest valuation is elected for the trade as long free cash is present and as long the minimum criteria are met.
The Screener Table will show all indicators results of the last bar of all assets. Those indicator values which met the threshold get a background color to high light it. Green for bullish, red for bearish and orange for trade signals without direction. If you chose an Indicator here but without any compare function it will show also their results but with just gray background.
Indicator Settings: here you can setup the indicator specific settings. for deeper insights see 3. Included Indicators and Compare-Functions .
Assets, TP & SL Settings: Asset specific settings. Chose here the TickerID of all Assets you wanna trade. Take Profit 1&2 set the target prices of any trade in relation to the entry price. The Take Profit 1 exit a part of the position defined by the quantity value. Stop Loss set the price to step out when a trade goes the wrong direction.
Invest Settings: Here, you can set the initial amount of cash to start with. The Quantity Percentage determines how much of the available cash is allocated to each trade, while the Fee percentage specifies the trading fee applied to both opening and closing positions.
Webhooks: Here, you configure the License ID and the Comment . This is particularly useful if you plan to use multiple instances of the script, ensuring the webhooks target the correct positions. The Take Profit and Stop Loss values are displayed as prices.
6. Declaration for Tradingview House Rules on Script Publishing
The unique feature of this Democratic Multi-Asset Strategy is its ability to trade multiple assets simultaneously. Equipped with a set of different standard Indicators, it's new democratic Voting System does more robust trading decisions compared to single-asset. Interchangeable Indicators and customizable strategy settings allowing for a wide range of trading strategies.
This script is closed-source and invite-only to support and compensate for over a year of development work. Unlike other single asset strategies, this one cannot use TradingView's strategy functions. Instead, it is designed as an indicator.
7. Disclaimer
Trading is risky, and traders do lose money, eventually all. This script is for informational and educational purposes only. All content should be considered hypothetical, selected post-factum and is not to be construed as financial advice. Decisions to buy, sell, hold, or trade in securities, commodities, and other investments involve risk and are best made based on the advice of qualified financial professionals. Past performance does not guarantee future results. Using this script on your own risk. This script may have bugs and I declare don't be responsible for any losses.
8. Conclusion
Now it’s your turn! Chose an asset class and pick 8 of them and chose some indicators to see the trading results of this democratic voting system. Refine your multi-asset strategy to favorable settings. Once you find a promising configuration, you can set up alerts to send webhooks directly. Configure all parameters, test and validate them in paper trading, and if results align with your expectations, you even can deploy this script as your trading bit.
Cheers
