AMF PG Strategy v2.3AMF PG Strategy v2.3
1. Core Philosophy: Filtered and Volatility-Aware Trend Following
"AMF PG Strategy" is an advanced trend-following system designed to adapt to the dynamic nature of modern markets. The strategy's core philosophy is not just to follow the trend but also to wait for the right conditions to enter the market.
This is not a "black box." It is a rules-based framework that gives the user full control over various market filters. By requiring multiple conditions to be met simultaneously, the strategy aims to filter out low-quality signals and focus only on high-probability trend opportunities.
2. Core Engine: AMF PG Trend Following
At the heart of the strategy is a proprietary, volatility-aware trend-following mechanism called AMF PG (Praetorian Guard). This engine operates as follows:
Dynamic Bands: Creates a dynamic upper and lower band around the price that is constantly recalculated. The width of these bands is not fixed; It dynamically adjusts based on recent market volatility, volume flow, and price expansion. This adaptive structure allows the strategy to adapt to both calm and high-volatility markets.
Entry Signals: A buy signal is triggered when the price rises above the upper band. A sell signal is triggered when the price falls below the lower band. However, these signals are executed only when all the active filters described below give the green light.
Trailing Stop-Loss: When a position is entered, the opposite band automatically acts as a trailing stop-loss level. For example, when a buy position is opened, the lower band follows the price as a stop-loss. This allows for profit retention and trend continuation.
3. Multi-Layered Filter System: Understanding the Market
The power of this strategy comes from its modular filter system, which allows the user to filter market conditions based on their own analysis. Each filter can be enabled or disabled individually in the settings:
Filter 1: Trend Strength (ADX Filter): This filter confirms whether there is a strong trend in the market. It uses the ADX (Average Directional Index) indicator and only allows trades if the ADX value is above a certain threshold. This helps avoid trading in weak or directionless markets. It also confirms the direction of the trend by checking the position of the DMI (+DI and -DI) lines.
Filter 2: Sideways Market (Chop Index Filter): This filter determines whether the market is excessively choppy or directionless. Using the Chop Index, this filter aims to protect against fakeouts by blocking trades when the market is highly indecisive.
Filter 3: Market Structure (Hurst Exponent Filter): This is one of the strategy's most advanced filters. It analyzes the current market behavior using the Hurst Exponent. This mathematical tool attempts to determine whether a market tends to trend (permanent), tends to revert to the mean (anti-permanent), or moves randomly. This filter ensures that signals are generated only when market structure supports trending trades.
4. Risk Management: Maximum Drawdown Protection
This strategy includes a built-in capital protection mechanism. Users can specify the percentage of their capital they will tolerate to decline from its peak. If the strategy's capital reaches this set drawdown limit, the protection feature is activated, closing all open positions and preventing new trades from being opened. This acts as an emergency brake to protect capital against unexpected market conditions.
5. Automation Ready: Customizable Webhook Alerts
The strategy is designed for traders who want to automate their signals. From the Settings menu, you can configure custom alert messages in JSON format, compatible with third-party automation services (via Webhooks).
6. Strategy Backtest Information
Please note that past performance is not indicative of future results. The published chart and performance report were generated on the 4-hour timeframe of the BTCUSD pair with the following settings:
Test Period: January 1, 2016 - October 31, 2025
Default Position Size: 15% of Capital
Pyramiding: Closed
Commission: 0.0008
Slippage: 2 ticks (Please enter the slippage you used in your own tests)
Testing Approach: The published test includes 423 trades and is statistically significant. It is strongly recommended that you test on different assets and timeframes for your own analysis. The default settings are a template and should be adjusted by the user for their own analysis.
インジケーターとストラテジー
Candle Breakout StrategyShort description (one-liner)
Candle Breakout Strategy — identifies a user-specified candle (UTC time), draws its high/low range, then enters on breakouts with configurable stop-loss, take-profit (via Risk:Reward) and optional alerts.
Full description (ready-to-paste)
Candle Breakout Strategy
Version 1.0 — Strategy script (Pine v5)
Overview
The Candle Breakout Strategy automatically captures a single "range candle" at a user-specified UTC time, draws its high/low as a visible box and dashed level lines, and waits for a breakout. When price closes above the range high it enters a Long; when price closes below the range low it enters a Short. Stop-loss is placed at the opposite range boundary and take-profit is calculated with a user-configurable Risk:Reward multiplier. Alerts for entries can be enabled.
This strategy is intended for breakout style trading where a clearly defined intraday range is established at a fixed time. It is simple, transparent and easy to adapt to multiple symbols and timeframes.
How it works (step-by-step)
On every bar the script checks the current UTC time.
When the first bar that matches the configured Target Hour:Target Minute (UTC) appears, the script records that candle’s high and low. This defines the breakout range.
A box and dashed lines are drawn on the chart to display the range and extended to the right while the range is active.
The script then waits for price to close outside the box:
Close > Range High → Long entry
Close < Range Low → Short entry
When an entry triggers:
Stop-loss = opposite range boundary (range low for longs, range high for shorts).
Take-profit = entry ± (risk × Risk:Reward). Risk is computed as the distance between entry price and stop-loss.
After entry the range becomes inactive (waitingForBreakout = false) until the next configured target time.
Inputs / Parameters
Target Hour (UTC) — the hour (0–23) in UTC when the range candle is detected.
Target Minute — minute (0–59) of the target candle.
Risk:Reward Ratio — multiplier for computing take profit from risk (0.5–10). Example: 2 means TP = entry + 2×risk.
Enable Alerts — turn on/off entry alerts (string message sent once per bar when an entry occurs).
Show Last Box Only (internal behavior) — when enabled the previous box is deleted at the next range creation so only the most recent range is visible (default behavior in the script).
Visuals & On-chart Info
A semi-transparent blue box shows the recorded range and extends to the right while active.
Dashed horizontal lines mark the range high and low.
On-chart shapes: green triangle below bar for Long signals, red triangle above bar for Short signals.
An information table (top-right) displays:
Target Time (UTC)
Active Range (Yes / No)
Range High
Range Low
Risk:Reward
Alerts
If Enable Alerts is on, the script sends an alert with the following formats when an entry occurs:
Long alert:
🟢 LONG SIGNAL
Entry Price:
Stop Loss:
Take Profit:
Short alert:
🔴 SHORT SIGNAL
Entry Price:
Stop Loss:
Take Profit:
Use TradingView's alert dialog to create alerts based on the script — select the script’s alert condition or use the alert() messages.
Recommended usage & tips
Timeframe: This strategy works on any timeframe but the definition of "candle at target time" depends on the chart timeframe. For intraday breakout styles, use 1m — 60m charts depending on the session you want to capture.
Target Time: Choose a time that is meaningful for the instrument (e.g., market open, economic release, session overlap). All times are handled in UTC.
Position Sizing: The script’s example uses strategy.percent_of_equity with 100% default — change default_qty_value or strategy settings to suit your risk management.
Filtering: Consider combining this breakout with trend filters (EMA, ADX, etc.) to reduce false breakouts.
Backtesting: Always backtest over a sufficiently large and recent sample. Pay attention to slippage and commission settings in TradingView’s strategy tester.
Known behavior & limitations
The script registers the breakout on close outside the recorded range. If you prefer intrabar breakout rules (e.g., high/low breach without close), you must adjust the condition accordingly.
The recorded range is taken from a single candle at the exact configured UTC time. If there are missing bars or the chart timeframe doesn't align, the intended candle may differ — choose the target time and chart timeframe consistently.
Only a single active position is allowed at a time (the script checks strategy.position_size == 0 before entries).
Example setups
EURUSD (Forex): Target Time 07:00 UTC — captures London open range.
Nifty / Index: Target Time 09:15 UTC — captures local session open range.
Crypto: Target Time 00:00 UTC — captures daily reset candle for breakout.
Risk disclaimer
This script is educational and provided as-is. Past performance is not indicative of future results. Use proper risk management, test on historical data, and consider slippage and commissions. Do not trade real capital without sufficient testing.
Change log
v1.0 — Initial release: range capture, box and level drawing, long/short entry by close breakout, SL at opposite boundary, TP via Risk:Reward, alerts, info table.
If you want, I can also:
Provide a short README version (2–3 lines) for the TradingView “Short description” field.
Add a couple of suggested alert templates for the TradingView alert dialog (if you want alerts that include variable placeholders).
Convert the disclaimer into multiple language versions.
AMF PG Consensus Engine v3.5AMF PG Consensus Engine v3.5
1. Core Philosophy: A Multi-Stage Confirmation System for High-Probability Signals
In the world of automated trading, the real challenge isn't generating signals, but filtering out the noise. The AMF PG Consensus Engine is designed to address this challenge. It operates on a simple yet powerful philosophy: a buy or sell signal is valid only if it receives confirmation from multiple, independent analysis modules.
This strategy isn't a "black box." It's a transparent, rules-based framework that transforms market momentum and momentum into a final consensus and then directs a core trend-following engine. The goal is to avoid trading in adverse market conditions and only act when the different analysis layers agree.
2. How the Consensus Engine Works: Two Confirmation Layers
Before the core engine is allowed to seek a trade, the market must go through a two-stage "confirmation" process. Both filters can be enabled or disabled from the settings, allowing users to customize the strategy's stringency level.
Confirmation Module 1: Renko Regime Filter
This module's purpose is to answer a critical question: "Is the market currently in a stable, directional trend, or is it volatile and unstable?" Instead of standard indicators, it creates a timeless Renko chart in the background. A trend is confirmed only if a minimum number of consecutive Renko bricks form in the same direction. This method is extremely effective at filtering out noisy, sideways price movements, which are often unsuccessful for trend-following systems. The brick size can be set to a fixed value or automatically calculated based on the Average True Range (ATR) for better fit.
Confirmation Module 2: Candle Scoring Engine
This module analyzes the raw strength of price action by scoring each candle individually. It evaluates the candle's direction, body size relative to the previous candle, and the change in closing price. These factors are converted into a score for each bar. A cumulative score is then calculated over a user-defined period. A buy trade is only confirmed if this cumulative momentum score exceeds a positive threshold, indicating sustained buying pressure. Conversely, a sell trade requires the score to fall below a negative threshold, indicating sustained selling pressure.
3. Core Engine: AMF PG Trend Follower
When both confirmation modules give the "green light" for a specific direction (e.g., buy), the core AMF PG (Praetorian Guard) engine is activated. This is a proprietary, volatility-sensitive trend-following mechanism.
It calculates a dynamic upper and lower band around the price. These bands are not static; their distance from the price is constantly adjusted based on recent market volatility and price expansion. A trade is initiated when the price breaks out of these bands in the direction confirmed by the consensus engine. The opposing band then serves as the initial trailing stop-loss, adjusted as the trend progresses.
4. Embedded Filters for Additional Security
To further enhance signal quality, the core engine has several embedded filters that are always active and cannot be disabled by the user:
Trend Strength Filter: To confirm that a trend has sufficient strength, a trade will not be initiated unless the ADX (Average Directional Index) is above a certain threshold.
Sideways Market Filter: The Chop Index is used to prevent trading in extremely sideways and directionless markets.
5. Risk Management: Maximum Drawdown Protection
A key feature of this strategy is its built-in capital protection mechanism. Users can set a maximum capital drawdown limit of a percentage. If the strategy's capital falls by this percentage from its peak, the "DD Protect" feature is activated, closing all open positions and preventing new trades from being opened. This acts as a final emergency brake to protect capital during unpredictable market conditions or underperformance of the strategy.
6. Automation-Ready: Customizable Webhook Alerts
This strategy was developed for modern investors looking to automate their trading. Instead of generic alert messages, you can define your own custom alert text directly from the script's settings.
This feature is particularly powerful for connecting to third-party automation services via Webhooks. You can configure the alert message in the JSON format required by your service (such as {"action": "buy", "symbol": "{{ticker}}"}). This allows you to seamlessly connect your strategy signals directly to your trading account.
7. Strategy Backtest Information
Please remember that past performance is not indicative of future results. The published chart and performance report were generated on the 4-hour timeframe of the BTC/USD pair with the following settings:
Test Period: January 1, 2016 - October 31, 2025
Default Position Size: 15% of Capital
Pyramiding: Closed
Commission: 0.0008
Slippage: 2 ticks (Please enter the slippage you used in your own tests)
Testing Approach: The published test includes 799 trades and is statistically significant. It is strongly recommended that you test on different assets and timeframes for your own analysis. The default settings are a template and should be adjusted by the user for their own analysis.
Gold 15m: Trend + S/R + Liquidity Sweep (RR 1:2)This strategy is designed for short-term trading on XAUUSD (Gold) using the 15-minute timeframe. It combines trend direction, support/resistance pivots, liquidity sweep detection, and momentum confirmation to identify high-probability reversal setups in line with the dominant market trend.
⚙️ Core Logic:
Trend Filter (EMA 200):
The strategy only takes long positions when price is above the 200 EMA and short positions when price is below it.
Support/Resistance via Pivots:
Dynamic swing highs and lows are identified using pivot points. These act as local supply and demand levels where liquidity is likely to accumulate.
Liquidity Sweep Detection:
A bullish liquidity sweep occurs when price briefly breaks below the last pivot low (grabbing liquidity) and then closes back above it.
A bearish sweep occurs when price breaks above the last pivot high and then closes back below.
Momentum & Candle Strength:
The strategy filters signals based on candle range and body size to ensure entries occur during strong price reactions, not weak retracements.
Risk Management (1:2 RR):
Stop-loss is placed slightly beyond the last pivot level using ATR-based buffers, and take-profit is set at 2× the risk distance, maintaining a reward-to-risk ratio of 1:2.
💼 Trade Logic Summary:
Long Entry:
After a bullish liquidity sweep & reclaim, momentum confirmation, and trend alignment (above EMA 200).
Short Entry:
After a bearish sweep & reclaim, momentum confirmation, and trend alignment (below EMA 200).
Exit:
Automated via ATR-based Stop Loss and Take Profit targets.
📊 Customization Options:
Adjustable EMA length, pivot settings, ATR multipliers, and RR ratio.
Option to enable/disable trend filter.
Toggle display of S/R zones on chart.
🧠 Best Use:
Works best during London and New York sessions when Gold shows strong momentum.
Can be adapted for forex pairs and indices by tuning ATR and pivot parameters.
INDIAN INTRADAY BEASTThe Indian Intraday Beast is a precision-built intraday strategy optimized for the 15-minute timeframe.
It captures high-probability momentum shifts and trend reversals using adaptive price-action logic and proprietary confirmation filters.
Designed for traders who demand clarity, speed, and consistency in India’s fast-paced markets.
Maxtra Reversal Range Breakout StrategyReversal Range Breakout Strategy
This strategy uses the first candle as a directional filter. If the first candle is green, it anticipates a potential reversal and takes sell trades only. If the first candle is red, it looks for buy opportunities. The logic is to trade against the initial move, expecting a reversal after the early breakout or momentum spike.
Maxtra Range Breakout StrategyRange Breakout Strategy
This strategy identifies periods of price consolidation (range) and enters trades when the price breaks above or below the defined range. A breakout above the range signals a potential uptrend (buy), while a breakout below indicates a potential downtrend (sell). It helps capture strong directional moves following low-volatility phases.
Option Buying Strategy By Raj PandyaThis strategy is designed for intraday trading on BankNifty using a powerful confluence of trend, structure and momentum. It combines the 9-period Exponential Moving Average (EMA) with Daily Traditional Pivot Points to identify high-probability breakout trades.
A Long (CALL) signal is generated when price crosses and closes above both the 9 EMA and the Daily Pivot Point (PP), confirming upward trend strength. A Short (PUT) signal triggers when price crosses and closes below the 9 EMA and PP, signaling downside momentum. To reduce false signals, the strategy uses RSI with a moving average filter to ensure momentum aligns with price action.
Risk management is built-in with previous candle high/low stop-loss, a fixed 50-point target, and an automatic trailing stop system to protect profits on trending days. This helps capitalize on strong momentum while managing risk effectively.
This strategy works best on the 5-minute timeframe and is optimized for BankNifty futures/options. It aims to capture clean directional moves around key intraday value levels used by institutional traders.
BUY LOW, BUY MORE, SELL HIGH - MARKET FLOW STRATEGY-JTM────────────────────────────────────────────────────────
BUY LOW, BUY MORE, SELL HIGH – MARKET FLOW STRATEGY (v594) – JTM
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Category: Quantitative Momentum & Liquidity Flow Strategy
Author: JTM
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An Adaptive Contrarian live trading strategy that scales into deep pullbacks,
rides liquidity waves, and locks profit automatically, using your TradersPost.io webhook.
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DESCRIPTION
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The "Buy Low, Buy More, Sell High" strategy combines value-based accumulation
with adaptive profit protection. It adds exposure on weakness (new lower lows
and confirmed liquidity support) and closes trades dynamically once a profitable
run-up retraces by a set percentage (default 13%).
It reconstructs multi-timeframe market structure
(Intraday → Daily → Weekly → Monthly → Yearly)
using synthetic OHLCs to avoid repainting, and integrates VWAP anchoring,
adaptive KAMA smoothing, RSI validation, and rolling lower-low tracking.
Contrarian strategies are difficult for the human to be easily comfortable with...
because but the results can be worth the wait. Believe that you truly want to Sell when everyone is buying and buy when everyone is almost done selling. Not vice-versa!
Forget about trying to time the market at the true top or the true bottom - just ride
the rolling waves and the profits will come ashore.
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CORE FEATURES
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• Non-repainting by design – only confirmed bars update persistent state
• Synthetic HTF OHLC construction avoids lookahead bias
• Adaptive trailing TakeProfit logic aims for (80–87% of peak profit retained)
• AI-like market flow dashboard with Bull/Bear liquidity dominance
• Dynamic rolling "Lowest Low" (LLL) detection and visualization
• Modular time detection and session alignment (New York market default)
• Optional Liquidity Dashboard with intraday/swing dual-mode analytics
1. Non-Repainting Architecture - Long side strategy. Not coded for short selling.
2. Works best on a 1hr/60m intraday chart with a 3hr higher Timeframe (strategy input)
3. Immediately Uncheck Show Trade Signals in the Strategy's Style Settings to eliminate
onscreen clutter
4. Market Liquidity Dashboard can be displayed by setting in the strategy's inputs.
5. You set a price range of stocks you wish to allow to trade when using an alert list.
6. You can set a preview period where gray TV trades alerts to Traderspost are withheld.
7. If you check "Do not LIVE sell any assets today" and update the alert, no Traderspost
8. As well as the Market Flow Dashboard, you can display a table of HLs, and LLs.
Trending stocks will display HLs and Vice Versa.
9. You can switch away from Big Rolling Waves Mode (default) to Intraday Short Moves mode
10. Default Big Wave Rolling Cycle is 120 bars (Recommended) 30,60,90,120 are typical.
(this affects the strategy profitability and choice of entry and exits).
11. Reference to FIFO is about the array size for the stored non-repainting HLs and LLs
12. Option to execute trades on unconfirmed bar is BETA. The strategy Only trades on
confirmed bars always, but I am wanting/hoping to eventually Take Profit on a
BIG ASS institutional candle, morning session bar when they occur.
(a new bar is unconfirmed, building, and tricky to code without
breaking the strategy's reliability). Use at your own risk.
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Technical Highlights
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• AI-inspired “Flow Confidence” through liquidity imbalance between Bull and Bear pressure.
• Adaptive KAMA smoothing for non-lag confirmation.
• Dynamic trailing take-profit computed as 80–87% of the highest profitable run-up.
• Non-repainting multi-timeframe framework (daily–weekly–monthly aggregation).
• LLL Table module for Lowest-Low progression and trend exhaustion.
• Market Flow Dashboard visualizing rolling liquidity dominance and delta pressure.
• Session-aware logic for intraday vs. swing operation.
• All logic has been tuned to peak performance that satisfies the TradingView Profiler.
• Strategy Buys again at a lower price instead of selling and taking a loss. Drawdown Peaks!
but this requires you configure TradingView Strategy Pyramiding orders to 2 or more
and % of equity.
(definitely is more $$ risky, potentially more profitable and might beat Buy and Hold profits)
See the chart above.
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WHY IT DOES NOT REPAINT (even thought the TradingView alert message says it might.)
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1. All trade, table, and OHLC updates occur only on `barstate.isconfirmed`
2. Synthetic HTFs are self-built without `lookahead` or future data
3. All arrays use `var` persistence to freeze past state
4. Trailing TakeProfit peak updates only on confirmed highs
5. No forward-referencing of real-time partial bars
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LIMITATIONS
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1. Heavy computation on long intraday histories (optimize `max_bars_back`)
2. Requires intraday chart resolution (1m–4h)
3. U.S. session defaults (09:30–16:00 ET); adjust for other exchanges
4. Arrays and tables consume memory – disable extra visuals if unneeded
5. Not for use with Crypto, Forex, or Futures.
6. Only to be used with Stocks/Tickers having volume data.
7. Live trading is coded for a TradersPost.io Webhook and a brokerage account. (IBKR)
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WARNINGS
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1. This is an Adaptive and Contrarian Trading strategy that scales into DEEP pullbacks,
rides long liquidity waves, and locks profit automatically.
2. Trades can have a long duration but are minimal. This is not a strategy that will
generate thousands of trades. It will save you on commissions to.
3. It does its best to limit losses and but I cannot guarantee it will work for
all assets, all the time. Market conditions vary. Tickers vary.
4. Use it with assets that you trust not to race to zero dollars.
5. Use this strategy with healthy tickers that have medium to high volatility.
6. To eliminate onscreen clutter, uncheck strategy settings/style/trade signals.
7. I use this strategy exclusively. TradingView alerts run for me 24/7 and ROBO trade.
but you should vet the system with manual trades signals it generates for yourself.
8. It does not prevent you from losing profits in after hours trading and Market News.
9. The BIG ASS new bar of opening candle is Not calculated into the stats until it is confirmed. Especially bothersome for me when its a big 10% down candle. You still need to watch the first 9:30 candle with your eyes. I recommend you watch liquidity at Open using a 1m timeframe.
10. This is a pro-grade coded strategy but is NOT a “Wall Street Quant grade.” strategy.
Institutions rely on:
• slippage models
• depth-of-book impact modeling
• latency simulation
• volume-weighted fills
• partial fills & queue priority
• order slicing (TWAP, VWAP, POV)
Pine Script cannot simulate the above.
11. This Pine Script strategy is market-internal (chart & volume) and for personal-use only. The code remains private to me.
12. To the full-time traders — my respect.
I live in the world of code, not charts, scalps, candles and screens,
so I engineered this 2500-line strategy to trade for me with un-emotional discipline, as you do.
If this strategy resonates with you, I provide it for you to use in the TradingView spirit of community. Backtested results are hypothetical and not indicative of future performance.
Nothing here constitutes investment advice.
Trading involves risk, and users must assume full responsibility for their trading decisions.
Remember, its not about making 'all' the money a stock theoretically could return as if you did a buy and hold many years ago - its about making good money as you navigate the waves of the stormy seas, and not sinking. It about making better trade decisions compared to the persons you are trading against. Have fun all the while making your money - work for you.
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“Be fearful when others are greedy, and be greedy when others are fearful.”
— Warren E. Buffett
“The stock market is a device for transferring money from the impatient to the patient.”
— Warren E. Buffett
True wealth grows through time, not reaction. A person sits in the shade today because
of a tree that somebody planted years ago.
— Warren E. Buffett
Happy Trading!
VWAP & Band Cross Strategy v6 - AdvancedThese are a few updates made to the original script. The daily take profit and stop loss functions correctly for 1 contract but because of the pyramiding input even if not used you'll need to multiply the values by the number of contracts to keep consistent results. I have been unable to correct that function. Let me know if you test the script and have any recommendations for improvement. If trading an actual account I do recommend setting hard daily limits with your provider because there is still slippage from the original exit alerts even with the daily stop loss in place.
1. Real-Time Execution & Hard PnL Limits (The Focus)
The most critical changes were implemented to ensure the daily profit and loss limits act as hard, real-time barriers instead of waiting for the candle to close.
• Intrabar Tick Execution: The parameter calc_on_every_tick=true was added to the strategy() declaration. This forces the entire script to re-evaluate its logic on every single price update (tick), enabling immediate action.
• Real-Time PnL Tracking: The PnL calculation was updated to track the total_daily_pnl by summing the realized profit/loss (from closed trades) and the unrealized profit/loss (strategy.openprofit) on every tick.
• Immediate Closure: The script now checks the total_daily_pnl against the user-defined limits (daily_take_profit_value, daily_stop_loss_value) and immediately executes strategy.close_all() the moment the threshold is breached, preventing further trading.
• Combined Risk Enforcement: The user-defined "Max Intraday Risk ($)" and the "Daily Stop Loss (Value)" are compared, and the script enforces the tighter of the two limits.
2. Visibility and External Alerting
To address the unavoidable issue of slippage (which causes price overshoot in fast markets even with tick execution), dedicated alert mechanisms were added.
• Dedicated Alert Condition: An alertcondition named DAILY PNL LIMIT REACHED was added. This allows you to set up a TradingView alert that triggers the instant the daily_limit_reached variable turns true, giving you the fastest possible notification.
• Visual Marker: A large red triangle (\u25b2) is plotted on the chart using plotchar at the exact moment the daily limit condition is met, providing a clear visual confirmation of the trigger bar.
3. Strategy Features and Input Flexibility
Several user-requested features were integrated to make the strategy more robust and customizable.
• Trailing Stop / Breakeven (TSL/BE): A new exit option, Fixed Ticks + TSL, was added, allowing you to set a fixed profit target while also deploying a trailing stop or breakeven level based on points/ticks gained.
• Multiple Exit Types: The exit strategy was expanded to include logic for several types: Fixed Ticks, ATR-based, Capped ATR-based, VWAP Cross, and Price/Band Crosses.
• Pyramiding Control: An input Max Pyramiding Entries was introduced to control how many positions the strategy can have open at the same time.
• Confirmation Logic Toggle: Added an input to choose how multiple confirmation indicators (RSI, SMMA, MACD) are combined: "AND" (all must be true) or "OR" (at least one must be true).
• Indicator Confirmations: Logic for three external indicators—RSI, SMMA (EMA), and MACD—was fully integrated to act as optional filters for entry.
• VWAP Reset Anchors: Logic was corrected to properly reset the VWAP calculation based on the selected period ("Daily", "Weekly", or "Session") by using Pine Script v6's required anchor series.
Trading Day Filters: Inputs were added to select which specific days of the week the strategy is allowed to trade.
Vandan V2Vandan V2 is an automated trend-following strategy for NASDAQ E-mini Futures (NQ1!).
It uses multi-timeframe momentum and volatility filters to identify high-probability entries.
Includes dynamic risk management and trailing logic optimized for intraday trading.
ORBSMMAATRVOLREENTRY2Contracts📈 Opening Range Fibonacci Breakout (TradingView Strategy)
Overview:
The Opening Range Fibonacci Breakout strategy is designed to capture high-probability intraday moves by combining the power of the 15-minute opening range, trend confirmation via SMMA, and volume-based momentum filtering.
At the start of each trading session, the script automatically plots the Opening Range Box based on the first 15 minutes of price action — highlighting key intraday support and resistance levels.
How It Works:
Opening Range Setup
The first 15 minutes of the session define the range high and low.
A visual box marks this zone on the chart for easy reference.
Signal Generation
A Smoothed Moving Average (SMMA) with a user-defined period determines overall trend bias.
Candle volume is analyzed to confirm momentum strength.
Long Signal: Price breaks above the opening range high, SMMA trending up, and volume supports the move.
Short Signal: Price breaks below the opening range low, SMMA trending down, and volume supports the move.
Take Profit & Targets
Fibonacci extension levels are automatically plotted from the opening range.
These dynamic levels serve as structured Take Profit (TP) zones for partial or full exits.
Features:
✅ 15-Minute Opening Range Box
✅ Adjustable SMMA period
✅ Volume-based confirmation filter
✅ Automatic Fibonacci profit targets
✅ Visual Long/Short alerts & signals
Ideal For:
Scalpers and intraday traders who rely on early-session momentum, breakout confirmation, and precision exit targets.
Backtested for MNQ/NQ futures trading
BH BTC LS Atopetrader Bitcoin 15M Advanced Trading Strategy
This strategy is designed to trade Bitcoin on the 15-minute timeframe for long and short positions. It uses an advanced system adapted to price action, combined with automated risk management through stop loss and take profit. It is optimized to adapt to the high volatility and speculative nature of BTC, seeking out trend-driven momentum opportunities and avoiding low-probability periods detected through historical analysis.
Timeframe Compatibility
While the strategy is specifically adapted and optimized for the 15-minute timeframe (15M), it has been engineered to perform across multiple timeframes ranging from 5-minute to 4-hour intervals. This multi-timeframe versatility allows traders to adjust the strategy parameters according to their preferred trading style and market conditions.
This adaptability across different timeframes significantly enhances the strategy's robustness, making it more resilient to varying market regimes and reducing over-optimization to a single timeframe. By testing and validating across 5-minute to 4-hour intervals, the strategy demonstrates consistent edge across diverse trading environments, which strengthens confidence in its performance across broader market conditions.
Cross-Asset Testing
Beyond Bitcoin, this strategy could be tested and adapted for trading other cryptocurrencies, making it a flexible framework for exploring momentum-based opportunities across different digital assets with varying volatility profiles.
Performance Summary
This strategy has significantly outperformed a simple buy-and-hold approach over the 6-year backtest period. Here are the standout metrics:
Total P&L: +$41,277.80 USDT (+2,063.89%)
Net Profit: +$41,277.80 USDT with only 18.35% max drawdown
Total Trades: 2,169 with 44.63% win rate
Profit Factor: 2.17x (strong edge)
Key Advantage Over Buy & Hold
The Buy & Hold return was +$16,576.63 USDT (+828.83%), meaning this strategy more than doubled Buy & Hold returns over the same period. The active trading approach consistently captured momentum while the 2.17x profit factor demonstrates edge-based entries.
Commission Structure: A 0.1% commission per trade has been factored into the backtesting analysis, which is more than sufficient to cover typical exchange trading fees on major platforms. This conservative fee structure ensures the reported results account for real-world trading costs while still demonstrating substantial profitability.
Important Disclaimer
This strategy does not guarantee future profits and should be used after testing and analyzing in a simulated environment. A disciplined approach and appropriate risk management are recommended for the cryptocurrency market. Past performance is not indicative of future results, and actual trading may differ from backtested scenarios due to market slippage, liquidity conditions, and changing market dynamics.
Gold H1 Breakout Failure (V11.0)This strategy is designed for trading XAU/USD (Gold) on the 1-hour timeframe. It identifies and trades fake breakouts of the Asian session range.
The logic is simple yet effective:
The script first marks the Asian session high and low.
Once price breaks out of this range and closes outside, it waits for confirmation by watching for price to close back inside the range.
When this re-entry occurs, the strategy takes a position in the opposite direction of the initial breakout, anticipating a false breakout or liquidity trap setup.
By focusing on these fakeouts, the strategy aims to capture reversal momentum after liquidity sweeps, making it especially effective during sessions when volatility transitions from Asia to London or New York.
XAUUSD 9-Grid Scalper (9-levels, 3pt TP)📈 Overview
The XAUUSD 9-Grid Scalper is a precision-based intraday strategy designed for gold scalping around key 9-based price zones. Gold (XAUUSD) often reacts strongly to levels that are multiples of 9, and this script builds a dynamic grid of 18 levels around the current price to capture short-term momentum moves.
This strategy uses 9-point take profits (TP) and configurable stop-loss levels, allowing for fast in-and-out scalps within volatile gold sessions. It’s optimized for short-term traders who focus on 1M–5M charts.
⚙️ Core Logic
Dynamic 9-Multiples Grid: Automatically plots 18 nearby levels spaced by multiples of 9.
Entry Signals:
Long when price breaks above a 9-level.
Short when price breaks below a 9-level.
Take Profit: Fixed at 9 points (configurable).
Stop Loss: Adjustable for flexible risk management.
Backtest-Ready: Uses strategy() for full performance analytics (win rate, profit factor, drawdown).
💡 Best Use Cases
Ideal for gold scalpers during London and New York sessions.
Works best on 1M–5M timeframes with high volatility.
Combine with volume or trend filters (e.g., RSI, MA slope) for improved accuracy.
🧠 Customization Options
Number of grid levels (default: 18)
Take profit & stop loss distance (default: 9pt TP)
Display toggle for 9-grid visualization
Optional filters for session time or volatility
⚠️ Disclaimer
This strategy is for educational and research purposes only.
Past performance does not guarantee future results. Always test on demo before trading live.
PriceAction & Economic StrategyThis indicator combines price-action logic with macroeconomic data to generate trading signals.
Features:
- Price-action signals: A bullish signal occurs when a candle closes above its open; a bearish signal occurs when a candle closes below its open.
- Signal gap: The indicator includes an input called "Signal Gap (bars)" that defines the minimum number of bars between signals. By default the gap is set to 3, but you can adjust this between 1 and 10 to control signal frequency.
- Alerts: The script defines alert conditions for long and short signals, allowing you to create TradingView alerts that notify you when a new signal occurs.
- Economic data: The script uses TradingView's built-in `request.economic()` function to request U.S. GDP data. The GDP series is plotted in the Data Window for additional macroeconomic context.
How to use:
1. Add the indicator to a chart.
2. Open the indicator's settings and adjust the "Signal Gap (bars)" input to set the minimum bar gap between signals.
3. Look for green triangles plotted below the bars (bullish signals) and red triangles plotted above the bars (bearish signals). These appear only when the gap criterion is met.
4. If you want alerts, click the Alert button in TradingView, select this indicator, and choose either the Long or Short alert conditions.
5. To view the GDP data, open the Data Window; the GDP value will be shown alongside other series for each bar.
6. Use these signals in combination with your own analysis; this indicator is for educational purposes and does not constitute financial advice.
【MasterHSC】CCI Mean Derivative Smart Strategy🧾 Strategy Description (English)
CCI Mean Slope Smart Strategy
This strategy is built on the derivative slope behavior of the Commodity Channel Index (CCI) mean line.
It identifies key turning points or trend continuations based on how the smoothed CCI (mean value) changes direction after reaching overbought or oversold zones.
Core Idea:
When the CCI mean reverses slope after exceeding ±100, it signals a potential mean reversion (range-trading opportunity).
When the CCI mean remains above +100 or below −100 with a consistent slope, it indicates a strong trending phase (momentum continuation).
The strategy dynamically adapts between these two behaviors depending on market conditions.
Modes:
🌀 Range Reversal Mode — Focuses on slope reversals after overbought/oversold conditions.
🚀 Trend Following Mode — Captures strong momentum when the CCI mean stays extended.
🧠 Auto Mode — Automatically switches between Range and Trend logic based on CCI mean volatility.
Key Features:
Dual-direction toggle: Enable or disable long/short entries independently.
Adjustable tolerance: Choose fixed or dynamic thresholds for flexibility.
Automatic mode label and visual buy/sell markers on the chart.
Pure CCI-based system — no external filters or indicators required.
Purpose:
This system is designed to reduce false signals in sideways markets while preventing missed opportunities during strong directional trends, offering a clean balance between precision and adaptability.
RSI potente 2.0rsi mas refinado e indicadores correctos a corto ,mediano y largo plazo .. el mejor indicador
NLR-ADX Divergence Strategy Triple-ConfirmedHow it works
Builds a cleaner DMI/ADX
Recomputes classic +DI, −DI, ADX over a user-set length.
Then “non-linear regresses” each series toward a mean (your choice: dynamic EMA of the series or a fixed Static Mid like 50).
The further a value is from the mean, the stronger the pull (controlled by alphaMin/alphaMax and the γ exponent), giving smoother, more stable DI/ADX lines with less whipsaw.
Optional EMA smoothing on top of that.
Lock in values at confirmed pivots
Uses price pivots (left/right bars) to confirm swing lows and highs.
When a pivot confirms, the script captures (“freezes”) the current +DI, −DI, and ADX values at that bar and stores them. This avoids later drift from smoothing/EMAs.
Check for triple divergence
For a bullish setup (potential long):
Price makes a Lower Low vs. a prior pivot low,
+DI is higher than before (bulls quietly stronger),
−DI is lower (bears weakening),
ADX is lower (trend fatigue).
For a bearish setup (potential short)
Price makes a Higher High,
+DI is lower, −DI is higher,
ADX is lower.
Adds a “no-intersection” sanity check: between the two pivots, the live series shouldn’t snake across the straight line connecting endpoints. This filters messy, low-quality structures.
Trade logic
On a valid triple-confirm, places a strategy.entry (Long for bullish, Short for bearish) and optionally labels the bar (BUY or SELL with +DI/−DI/ADX arrows).
Simple flip behavior: if you’re long and a new short signal prints (or vice versa), it closes the open side and flips.
Key inputs you can tweak
Custom DMI Settings
DMI Length — base length for DI/ADX.
Non-Linear Regression Model
Mean Reference — EMA(series) (dynamic) or Static mid (e.g., 50).
Dynamic Mean Length & Deviation Scale Length — govern the mean and scale used for regression.
Min/Max Regression & Non-Linearity Exponent (γ) — how strongly values are pulled toward the mean (stronger when far away).
Divergence Engine
Pivot Left/Right Bars — how strict the swing confirmation is (larger = more confirmation, more delay).
Min Bars Between Pivots — avoids comparing “near-duplicate” swings.
Max Historical Pivots to Store — memory cap.
TQQQ Strategy based on QQQ Signals (with Alerts)Trading view script for TQQQ and SQQ, Entry condition for TQQ and exit SQQQ - Close above 200 MA, close below 20 day MA, 5 days RSI below 45 for QQQ Exit condition for TQQQ and entry condition for SQQQ - 5 days RSI ends above 65
SigmaKernel - AdaptiveSigmaKernel - Adaptive Self-Optimizing Multi-Factor Trading System
SigmaKernel - Adaptive is a self-learning algorithmic trading strategy that combines four distinct analytical dimensions—momentum, market structure, volume flow, and reversal patterns—within a machine-learning-inspired framework that continuously adjusts its own parameters based on realized trading performance. Unlike traditional fixed-parameter strategies that maintain static weightings regardless of market conditions or results, this system implements a feedback loop that tracks which signal types, directional biases, and market conditions produce profitable outcomes, then mathematically adjusts component weightings, minimum score thresholds, position sizing multipliers, and trade spacing requirements to optimize future performance.
The strategy is designed for futures traders operating on prop firm accounts or live capital, incorporating realistic execution mechanics including configurable entry modes (stop breakout orders, limit pullback entries, or market-on-open), commission structures calibrated to retail futures contracts ($0.62 per contract default), one-tick slippage modeling, and professional risk controls including trailing drawdown guards, daily loss limits, and weekly profit targets. The system features universal futures compatibility—it automatically detects and adapts to any futures contract by reading the instrument's tick size and point value directly from the chart, eliminating the need for manual configuration across different markets.
What Makes This Approach Different
Adaptive Weight Optimization System
The core differentiation is the adaptive learning architecture. The strategy maintains four independent scoring components: momentum analysis (using RSI multi-timeframe, MACD histogram, and DMI/ADX), market structure detection (breakout identification via pivot-based support/resistance and moving average positioning), volume flow analysis (Volume Price Trend indicator with standard deviation confirmation), and reversal pattern recognition (oversold/overbought conditions combined with structural levels).
Each component generates a directional score that is multiplied by its current weight. After every closed trade, the system performs a retrospective analysis on the last N trades (configurable Learning Period, default 15 trades) to calculate win rates for each signal type independently. For example, if momentum-driven trades won 65% of the time while reversal trades won only 35%, the adaptive algorithm increases the momentum weight and decreases the reversal weight proportionally. The adjustment formula is:
New_Weight = Current_Weight + (Component_Win_Rate - Average_Win_Rate) × Adaptation_Speed
This creates a self-correcting mechanism where successful signal generators receive more influence in future composite scores, while underperforming components are de-emphasized. The system separately tracks long versus short win rates and applies directional bias corrections—if shorts consistently outperform longs, the strategy applies a 10% reduction to bullish signals to prevent fighting the prevailing market character.
Dynamic Parameter Adjustment
Beyond component weightings, three critical strategy parameters self-adjust based on performance:
Minimum Signal Score: The threshold required to trigger a trade. If overall win rate falls below 45%, the system increments this threshold by 0.10 per adjustment cycle, making the strategy more selective. If win rate exceeds 60%, the threshold decreases to allow more opportunities. This prevents the strategy from overtrading during unfavorable conditions and capitalizes on high-probability environments.
Risk Multiplier: Controls position sizing aggression. When drawdown exceeds 5%, risk per trade reduces by 10% per cycle. When drawdown falls below 2%, risk increases by 5% per cycle. This implements the professional risk management principle of "bet small when losing, bet bigger when winning" algorithmically.
Bars Between Trades: Spacing filter to prevent overtrading. Base value (default 9 bars) multiplies by drawdown factor and losing streak factor. During drawdown or consecutive losses, spacing expands up to 2x to allow market conditions to change before re-entering.
All adaptation operates during live forward-testing or real trading—there is no in-sample optimization applied to historical data. The system learns solely from its own realized trades.
Universal Futures Compatibility
The strategy implements universal futures instrument detection that automatically adapts to any futures contract without requiring manual configuration. Instead of hardcoding specific contract specifications, the system reads three critical values directly from TradingView's symbol information:
Tick Size Detection: Uses `syminfo.mintick` to obtain the minimum price increment for the current instrument. This value varies widely across markets—ES trades in 0.25 ticks, crude oil (CL) in 0.01 ticks, gold (GC) in 0.10 ticks, and treasury futures (ZB) in increments of 1/32nds. The strategy adapts all entry buffer calculations and stop placement logic to the detected tick size.
Point Value Detection: Uses `syminfo.pointvalue` to determine the dollar value per full point of price movement. For ES, one point equals $50; for crude oil, one point equals $1,000; for gold, one point equals $100. This automatic detection ensures accurate P&L calculations and risk-per-contract measurements across all instruments.
Tick Value Calculation: Combines tick size and point value to compute dollar value per tick: Tick_Value = Tick_Size × Point_Value. This derived value drives all position sizing calculations, ensuring the risk management system correctly accounts for each instrument's economic characteristics.
This universal approach means the strategy functions identically on emini indices (ES, MES, NQ, MNQ), micro indices, energy contracts (CL, NG, RB), metals (GC, SI, HG), agricultural futures (ZC, ZS, ZW), treasury futures (ZB, ZN, ZF), currency futures (6E, 6J, 6B), and any other futures contract available on TradingView. No parameter adjustments or instrument-specific branches exist in the code—the adaptation happens automatically through symbol information queries.
Stop-Out Rate Monitoring System
The strategy includes an intelligent stop-out rate tracking system that monitors the percentage of your last 20 trades (or available trades if fewer than 20) that were stopped out. This metric appears in the dashboard's Performance section with color-coded guidance:
Green (<30% stop-out rate): Very few trades are being stopped out. This suggests either your stops are too loose (giving back profits on reversals) or you're in an exceptional trending market. Consider tightening your Stop Loss ATR multiplier to lock in profits more efficiently.
Orange (30-65% stop-out rate): Healthy range. Your stop placement is appropriately sized for current market conditions and the strategy's risk-reward profile. No adjustment needed.
Red (>65% stop-out rate): Too many trades are being stopped out prematurely. Your stops are likely too tight for the current volatility regime. Consider widening your Stop Loss ATR multiplier to give trades more room to develop.
Critical Design Philosophy: Unlike some systems that automatically adjust stops based on performance statistics, this strategy intentionally keeps stop-loss control in the user's hands. Automatic stop adjustment creates dangerous feedback loops—widening stops increases risk per contract, which forces position size reduction, which distorts performance metrics, leading to incorrect adaptations. Instead, the dashboard provides visibility into stop performance, empowering you to make informed manual adjustments when warranted. This preserves the integrity of the adaptive system while giving you the critical data needed for stop optimization.
Execution Kernel Architecture
The entry system offers three distinct execution modes to match trader preference and market character:
StopBreakout Mode: Places buy-stop orders above the prior bar's high (for longs) or sell-stop orders below the prior bar's low (for shorts), plus a 2-tick buffer. This ensures entries only occur when price confirms directional momentum by breaking recent structure. Ideal for trending and momentum-driven markets.
LimitPullback Mode: Places limit orders at a pullback price calculated as: Entry_Price = Close - (ATR × Pullback_Multiplier) for longs, or Close + (ATR × Pullback_Multiplier) for shorts. Default multiplier is 0.5 ATR. This waits for mean-reversion before entering in the signal direction, capturing better prices in volatile or oscillating markets.
MarketNextOpen Mode: Executes at market on the bar immediately following signal generation. This provides fastest execution but sacrifices the filtering effect of requiring price confirmation.
All pending entry orders include a configurable Time-To-Live (TTL, default 6 bars). If an order is not filled within the TTL period, it cancels automatically to prevent stale signals from executing in changed market conditions.
Professional Exit Management
The exit system implements a three-stage progression: initial stop loss, breakeven adjustment, and dynamic trailing stop.
Initial Stop Loss: Calculated as entry price ± (ATR × User_Stop_Multiplier × Volatility_Adjustment). Users have direct control via the Stop Loss ATR multiplier (default 1.25). The system then applies volatility regime adjustments: ×1.2 in high-volatility environments (stops automatically widen), ×0.8 in low volatility (stops tighten), ×1.0 in normal conditions. This ensures stops adapt to market character while maintaining user control over baseline risk tolerance.
Breakeven Trigger: When profit reaches a configurable multiple of initial risk (default 1.0R), the stop loss automatically moves to breakeven (entry price). This locks in zero-loss status once the trade demonstrates favorable movement.
Trailing Stop Activation: When profit reaches the Trail_Trigger_R multiple (default 1.2R), the system cancels the fixed stop and activates a dynamic trailing stop. The trail uses Step and Offset parameters defined in R-multiples. For example, with Trail_Offset_R = 1.0 and Trail_Step_R = 1.5, the stop trails 1.0R behind price and moves in 1.5R increments. This captures extended moves while protecting accumulated profit.
Additional failsafes include maximum time-in-trade (exits after N bars if specified) and end-of-session flatten (automatically closes all positions X minutes before session end to avoid overnight exposure).
Core Calculation Methodology
Signal Component Scoring
Momentum Component:
- Calculates 14-period DMI (Directional Movement Index) with ADX strength filter (trending when ADX > 25)
- Computes three RSI timeframes: fast (7-period), medium (14-period), slow (21-period)
- Analyzes MACD (12/26/9) histogram for directional acceleration
- Bullish momentum: uptrend (DI+ > DI- with ADX > 25) + MACD histogram rising above zero + RSI fast between 50-80 = +1.6 score
- Bearish momentum: downtrend (DI- > DI+ with ADX > 25) + MACD histogram falling below zero + RSI fast between 20-50 = -1.6 score
- Score multiplies by volatility adjustment factor: ×0.8 in high volatility (momentum less reliable), ×1.2 in low volatility (momentum more persistent)
Structure Component:
- Identifies swing highs and lows using 10-bar pivot lookback on both sides
- Maintains most recent swing high as dynamic resistance, most recent swing low as dynamic support
- Detects breakouts: bullish when close crosses above resistance with prior bar below; bearish when close crosses below support with prior bar above
- Breakout score: ±1.0 for confirmed break
- Moving average alignment: +0.5 when price > SMA20 > SMA50 (bullish structure); -0.5 when price < SMA20 < SMA50 (bearish structure)
- Total structure range: -1.5 to +1.5
Volume Component:
- Calculates Volume Price Trend: VPT = Σ [(Close - Close ) / Close × Volume]
- Compares VPT to its 10-period EMA as signal line (similar to MACD logic)
- Computes 20-period volume moving average and standard deviation
- High volume event: current volume > (volume_average + 1× std_dev)
- Bullish volume: VPT > VPT_signal AND high_volume = +1.0
- Bearish volume: VPT < VPT_signal AND high_volume = -1.0
- No score if volume is not elevated (filters out low-conviction moves)
Reversal Component:
- Identifies extreme RSI conditions: RSI slow < 30 (oversold) or > 70 (overbought)
- Requires structural confluence: price at or below support level for bullish reversal; at or above resistance for bearish reversal
- Requires momentum shift: RSI fast must be rising (for bull) or falling (for bear) to confirm reversal in progress
- Bullish reversal: RSI < 30 AND price ≤ support AND RSI rising = +1.0
- Bearish reversal: RSI > 70 AND price ≥ resistance AND RSI falling = -1.0
Composite Score Calculation
Final_Score = (Momentum × Weight_M) + (Structure × Weight_S) + (Volume × Weight_V) + (Reversal × Weight_R)
Initial weights: Momentum = 1.0, Structure = 1.2, Volume = 0.8, Reversal = 0.6
These weights adapt after each trade based on component-specific performance as described above.
The system also applies directional bias adjustment: if recent long trades have significantly lower win rate than shorts, bullish scores multiply by 0.9 to reduce aggressive long entries. Vice versa for underperforming shorts.
Position Sizing Algorithm
The position sizing calculation incorporates multiple confidence factors and automatically scales to any futures contract:
1. Base risk amount = Account_Size × Base_Risk_Percent × Adaptive_Risk_Multiplier
2. Stop distance in price units = ATR × User_Stop_Multiplier × Volatility_Regime_Multiplier × Entry_Buffer
3. Risk per contract = Stop_Distance × Dollar_Per_Point (automatically detected from instrument)
4. Raw position size = Risk_Amount / Risk_Per_Contract
Then applies confidence scaling:
- Signal confidence = min(|Weighted_Score| / Min_Score_Threshold, 2.0) — higher scores receive larger size, capped at 2×
- Direction confidence = Long_Win_Rate (for bulls) or Short_Win_Rate (for bears)
- Type confidence = Win_Rate of dominant signal type (momentum/structure/volume/reversal)
- Total confidence = (Signal_Confidence + Direction_Confidence + Type_Confidence) / 3
Adjusted size = Raw_Size × Total_Confidence × Losing_Streak_Reduction
Losing streak reduction = 0.5 if losing_streak ≥ 5, otherwise 1.0
Universal Maximum Position Calculation: Instead of hardcoded limits per instrument, the system calculates maximum position size as: Max_Contracts = Account_Size / 25000, clamped between 1 and 10 contracts. This means a $50,000 account allows up to 2 contracts, a $100,000 account allows up to 4 contracts, regardless of which futures contract is being traded. This universal approach maintains consistent risk exposure across different instruments while preventing overleveraging.
Final size is rounded to integer and bounded by the calculated maximum.
Session and Risk Management System
Timezone-Aware Session Control
The strategy implements timezone-correct session filtering. Users specify session start hour, end hour, and timezone from 12 supported zones (New York, Chicago, Los Angeles, London, Frankfurt, Moscow, Tokyo, Hong Kong, Shanghai, Singapore, Sydney, UTC). The system converts bar timestamps to the selected timezone before applying session logic.
For split sessions (e.g., Asian session 18:00-02:00), the logic correctly handles time wraparound. Weekend trading can be optionally disabled (default: disabled) to avoid low-liquidity weekend price action.
Multi-Layer Risk Controls
Daily Loss Limit: Strategy ceases all new entries when daily P&L reaches negative threshold (default $2,000). This prevents catastrophic drawdown days. Resets at timezone-corrected day boundary.
Weekly Profit Target: Strategy ceases trading when weekly profit reaches target (default $10,000). This implements the professional principle of "take the win and stop pushing luck." Resets on timezone-corrected Monday.
Maximum Daily Trades: Hard cap on entries per day (default 20) to prevent overtrading during volatile conditions when many signals may generate.
Trailing Drawdown Guard: Optional prop-firm-style trailing stop on account equity. When enabled, if equity drops below (Peak_Equity - Trailing_DD_Amount), all trading halts. This simulates the common prop firm rule where exceeding trailing drawdown results in account termination.
All limits display status in the real-time dashboard, showing "MAX LOSS HIT", "WEEKLY TARGET MET", or "ACTIVE" depending on current state.
How To Use This Strategy
Initial Setup
1. Apply the strategy to your desired futures chart (tested on 5-minute through daily timeframes)
2. The strategy will automatically detect your instrument's specifications—no manual configuration needed for different contracts
3. Configure your account size and risk parameters in the Core Settings section
4. Set your trading session hours and timezone to match your availability
5. Adjust the Stop Loss ATR multiplier based on your risk tolerance (0.8-1.2 for tighter stops, 1.5-2.5 for wider stops)
6. Select your preferred entry execution mode (recommend StopBreakout for beginners)
7. Enable adaptation (recommended) or disable for fixed-parameter operation
8. Review the strategy's Properties in the Strategy Tester settings and verify commission/slippage match your broker's actual costs
The universal futures detection means you can switch between ES, NQ, CL, GC, ZB, or any other futures contract without changing any strategy parameters—the system will automatically adapt its calculations to each instrument's unique specifications.
Dashboard Interpretation
The strategy displays a comprehensive real-time dashboard in the top-right corner showing:
Market State Section:
- Trend: Shows UPTREND/DOWNTREND/CONSOLIDATING/NEUTRAL based on ADX and DMI analysis
- ADX Value: Current trend strength (>25 = strong trend, <20 = consolidating)
- Momentum: BULL/BEAR/NEUTRAL classification with current momentum score
- Volatility: HIGH/LOW/NORMAL regime with ATR percentage of price
Volume Profile Section (Large dashboard only):
- VPT Flow: Directional bias from volume analysis
- Volume Status: HIGH/LOW/NORMAL with relative volume multiplier
Performance Section:
- Daily P&L: Current day's profit/loss with color coding
- Daily Trades: Number of completed trades today
- Weekly P&L: Current week's profit/loss
- Target %: Progress toward weekly profit target
- Stop-Out Rate: Percentage of last 20 trades (or available trades if <20) that were stopped out. Includes all stop types: initial stops, breakeven stops, trailing stops, timeout exits, and EOD flattens. Color coded with actionable guidance:
- Green (<30%): Shows "TIGHTEN" guidance. Very few stop-outs suggests stops may be too loose or exceptional market conditions. Consider reducing Stop Loss ATR multiplier.
- Orange (30-65%): Shows "OK" guidance. Healthy stop-out rate indicating appropriate stop placement for current conditions.
- Red (>65%): Shows "WIDEN" guidance. Too many premature stop-outs. Consider increasing Stop Loss ATR multiplier to give trades more room.
- Status: Overall trading status (ACTIVE/MAX LOSS HIT/WEEKLY TARGET MET/FILTERS ACTIVE)
Adaptive Engine Section:
- Min Score: Current minimum threshold for trade entry (higher = more selective)
- Risk Mult: Current position sizing multiplier (adjusts with performance)
- Bars BTW: Current minimum bars required between trades
- Drawdown: Current drawdown percentage from equity peak
- Weights: M/S/V/R showing current component weightings
Win Rates Section:
- Type: Win rates for Momentum, Structure, Volume, Reversal signal types
- Direction: Win rates for Long vs Short trades
Color coding shows green for >50% win rate, red for <50%
Session Info Section:
- Session Hours: Active trading window with timezone
- Weekend Trading: ENABLED/DISABLED status
- Session Status: ACTIVE/INACTIVE based on current time
Signal Generation and Entry
The strategy generates entries when the weighted composite score exceeds the adaptive minimum threshold (initial value configurable, typically 1.5 to 2.5). Entries display as layered triangle markers on the chart:
- Long Signal: Three green upward triangles below the entry bar
- Short Signal: Three red downward triangles above the entry bar
Triangle tooltip shows the signal score and dominant signal type (MOMENTUM/STRUCTURE/VOLUME/REVERSAL).
Position Management and Stop Optimization
Once entered, the strategy automatically manages the position through its three-stage exit system. Monitor the Stop-Out Rate metric in the dashboard to optimize your stop placement:
If Stop-Out Rate is Green (<30%): You're rarely being stopped out. This could mean:
- Your stops are too loose, allowing trades to give back too much profit on reversals
- You're in an exceptional trending market where tight stops would work better
- Action: Consider reducing your Stop Loss ATR multiplier by 0.1-0.2 to tighten stops and lock in profits more efficiently
If Stop-Out Rate is Orange (30-65%): Optimal range. Your stops are appropriately sized for the strategy's risk-reward profile and current market volatility. No adjustment needed.
If Stop-Out Rate is Red (>65%): You're being stopped out too frequently. This means:
- Your stops are too tight for current market volatility
- Trades need more room to develop before reaching profit targets
- Action: Increase your Stop Loss ATR multiplier by 0.1-0.3 to give trades more breathing room
Remember: The stop-out rate calculation includes all exit types (initial stops, breakeven stops, trailing stops, timeouts, EOD flattens). A trade that reaches breakeven and gets stopped out at entry price counts as a stop-out, even though it didn't lose money. This is intentional—it indicates the stop placement didn't allow the trade to develop into profit.
Optimization Workflow
For traders wanting to customize the strategy for their specific instrument and timeframe:
Week 1-2: Run with defaults, adaptation enabled
Allow the system to execute at least 30-50 trades (the Learning Period plus additional buffer). Monitor which session periods, signal types, and market conditions produce the best results. Observe your stop-out rate—if it's consistently red or green, plan to adjust Stop Loss ATR multiplier after the learning period. Do not adjust parameters yet—let the adaptive system establish baseline performance data.
Week 3-4: Analyze adaptation behavior and optimize stops
Review the dashboard's adaptive weights and win rates. If certain signal types consistently show <40% win rate, consider slightly reducing their base weight. If a particular entry mode produces better fill quality and win rate, switch to that mode. If you notice the minimum score threshold has climbed very high (>3.0), market conditions may not suit the strategy's logic—consider switching instruments or timeframes.
Based on your Stop-Out Rate observations:
- Consistently <30%: Reduce Stop Loss ATR multiplier by 0.2-0.3
- Consistently >65%: Increase Stop Loss ATR multiplier by 0.2-0.4
- Oscillating between zones: Leave stops at default and let volatility regime adjustments handle it
Ongoing: Fine-tune risk and execution
Adjust the following based on your risk tolerance and account type:
- Base Risk Per Trade: 0.5% for conservative, 0.75% for moderate, 1.0% for aggressive
- Stop Loss ATR Multiplier: 0.8-1.2 for tight stops (scalping), 1.5-2.5 for wide stops (swing trading)
- Bars Between Trades: Lower (5-7) for more opportunities, higher (12-20) for more selective
- Entry Mode: Experiment between modes to find best fit for current market character
- Session Hours: Narrow to specific high-performance session windows if certain hours consistently underperform
Never adjust: Do not manually modify the adaptive weights, minimum score, or risk multiplier after the system has begun learning. These parameters are self-optimizing and manual interference defeats the adaptive mechanism.
Parameter Descriptions and Optimization Guidelines
Adaptive Intelligence Group
Enable Self-Optimization (default: true): Master switch for the adaptive learning system. When enabled, component weights, minimum score, risk multiplier, and trade spacing adjust based on realized performance. Disable to run the strategy with fixed parameters (useful for comparing adaptive vs non-adaptive performance).
Learning Period (default: 15 trades): Number of most recent trades to analyze for performance calculations. Shorter values (10-12) adapt more quickly to recent conditions but may overreact to variance. Longer values (20-30) produce more stable adaptations but respond slower to regime changes. For volatile markets, use shorter periods. For stable trends, use longer periods.
Adaptation Speed (default: 0.25): Controls the magnitude of parameter adjustments per learning cycle. Lower values (0.05-0.15) make gradual, conservative changes. Higher values (0.35-0.50) make aggressive adjustments. Faster adaptation helps in rapidly changing markets but increases parameter instability. Start with default and increase only if you observe the system failing to adapt quickly enough to obvious performance patterns.
Performance Memory (default: 100 trades): Maximum number of historical trades stored for analysis. This array size does not affect learning (which uses only Learning Period trades) but provides data for future analytics features including stop-out rate tracking. Higher values consume more memory but provide richer historical dataset. Typical users should not need to modify this.
Core Settings Group
Account Size (default: $50,000): Starting capital for position sizing calculations. This should match your actual account size for accurate risk per trade. The strategy uses this value to calculate dollar risk amounts and determine maximum position size (1 contract per $25,000).
Weekly Profit Target (default: $10,000): When weekly P&L reaches this value, the strategy stops taking new trades for the remainder of the week. This implements a "quit while ahead" rule common in professional trading. Set to a realistic weekly goal—20% of account size per week ($10K on $50K) is very aggressive; 5-10% is more sustainable.
Max Daily Loss (default: $2,000): When daily P&L reaches this negative threshold, strategy stops all new entries for the day. This is your maximum acceptable daily loss. Professional traders typically set this at 2-4% of account size. A $2,000 loss on a $50,000 account = 4%.
Base Risk Per Trade % (default: 0.5%): Initial percentage of account to risk on each trade before adaptive multiplier and confidence scaling. 0.5% is conservative, 0.75% is moderate, 1.0-1.5% is aggressive. Remember that actual risk per trade = Base Risk × Adaptive Risk Multiplier × Confidence Factors, so the realized risk will vary.
Trade Filters Group
Base Minimum Signal Score (default: 1.5): Initial threshold that composite weighted score must exceed to generate a signal. Lower values (1.0-1.5) produce more trades with lower average quality. Higher values (2.0-3.0) produce fewer, higher-quality setups. This value adapts automatically when adaptive mode is enabled, but the base sets the starting point. For trending markets, lower values work well. For choppy markets, use higher values.
Base Bars Between Trades (default: 9): Minimum bars that must elapse after an entry before another signal can trigger. This prevents overtrading and allows previous trades time to develop. Lower values (3-6) suit scalping on lower timeframes. Higher values (15-30) suit swing trading on higher timeframes. This value also adapts based on drawdown and losing streaks.
Max Daily Trades (default: 20): Hard limit on total trades per day regardless of signal quality. This prevents runaway trading during extremely volatile days when many signals may generate. For 5-minute charts, 20 trades/day is reasonable. For 1-hour charts, 5-10 trades/day is more typical.
Session Group
Session Start Hour (default: 5): Hour (0-23 format) when trading is allowed to begin, in the timezone specified. For US futures trading in Chicago time, session typically starts at 5:00 or 6:00 PM (17:00 or 18:00) Sunday evening.
Session End Hour (default: 17): Hour when trading stops and no new entries are allowed. For US equity index futures, regular session ends at 4:00 PM (16:00) Central Time.
Allow Weekend Trading (default: false): Whether strategy can trade on Saturday/Sunday. Most futures have low volume on weekends; keeping this disabled is recommended unless you specifically trade Sunday evening open.
Session Timezone (default: America/Chicago): Timezone for session hour interpretation. Select your local timezone or the timezone of your instrument's primary exchange. This ensures session logic aligns with your intended trading hours.
Prop Guards Group
Trailing Drawdown Guard (default: false): Enables prop-firm-style trailing maximum drawdown. When enabled, if equity drops below (Peak Equity - Trailing DD Amount), all trading halts for the remainder of the backtest/live session. This simulates rules used by funded trader programs where exceeding trailing drawdown terminates the account.
Trailing DD Amount (default: $2,500): Dollar amount of drawdown allowed from equity peak. If your equity reaches $55,000, the trailing stop sets at $52,500. If equity then drops to $52,499, the guard triggers and trading ceases.
Execution Kernel Group
Entry Mode (default: StopBreakout):
- StopBreakout: Places stop orders above/below signal bar requiring price confirmation
- LimitPullback: Places limit orders at pullback prices seeking better fills
- MarketNextOpen: Executes immediately at market on next bar
Limit Offset (default: 0.5x ATR): For LimitPullback mode, how far below/above current price to place the limit order. Smaller values (0.3-0.5) seek minor pullbacks. Larger values (0.8-1.2) wait for deeper retracements but may miss trades.
Entry TTL (default: 6 bars, 0=off): Bars an entry order remains pending before cancelling. Shorter values (3-4) keep signals fresh. Longer values (8-12) allow more time for fills but risk executing stale signals. Set to 0 to disable TTL (orders remain active indefinitely until filled or opposite signal).
Exits Group
Stop Loss (default: 1.25x ATR): Base stop distance as a multiple of the 14-period ATR. This is your primary risk control parameter and directly impacts your stop-out rate. Lower values (0.8-1.0) create tighter stops that reduce risk per trade but may get stopped out prematurely in volatile conditions—expect stop-out rates above 65% (red zone). Higher values (1.5-2.5) give trades more room to breathe but increase risk per contract—expect stop-out rates below 30% (green zone). The system applies additional volatility regime adjustments on top of this base: ×1.2 in high volatility environments (stops widen automatically), ×0.8 in low volatility (stops tighten), ×1.0 in normal conditions. For scalping on lower timeframes, use 0.8-1.2. For swing trading on higher timeframes, use 1.5-2.5. Monitor the Stop-Out Rate metric in the dashboard and adjust this parameter to keep it in the healthy 30-65% orange zone.
Move to Breakeven at (default: 1.0R): When profit reaches this multiple of initial risk, stop moves to breakeven. 1.0R means after price moves in your favor by the distance you risked, you're protected at entry price. Lower values (0.5-0.8R) lock in breakeven faster. Higher values (1.5-2.0R) allow more room before protection.
Start Trailing at (default: 1.2R): When profit reaches this multiple, the fixed stop transitions to a dynamic trailing stop. This should be greater than the BE trigger. Values typically range 1.0-2.0R depending on how much profit you want secured before trailing activates.
Trail Offset (default: 1.0R): How far behind price the trailing stop follows. Tighter offsets (0.5-0.8R) protect profit more aggressively but may exit prematurely. Wider offsets (1.5-2.5R) allow more room for profit to run but risk giving back more on reversals.
Trail Step (default: 1.5R): How far price must move in profitable direction before the stop advances. Smaller steps (0.5-1.0R) move the stop more frequently, tightening protection continuously. Larger steps (2.0-3.0R) move the stop less often, giving trades more breathing room.
Max Bars In Trade (default: 0=off): Maximum bars allowed in a position before forced exit. This prevents trades from "going stale" during periods of no meaningful price action. For 5-minute charts, 50-100 bars (4-8 hours) is reasonable. For daily charts, 5-10 bars (1-2 weeks) is typical. Set to 0 to disable.
Flatten near Session End (default: true): Whether to automatically close all positions as session end approaches. Recommended to avoid carrying positions into off-hours with low liquidity.
Minutes before end (default: 5): How many minutes before session end to flatten. 5-15 minutes provides buffer for order execution before the session boundary.
Visual Effects Configuration Group
Dashboard Size (default: Normal): Controls information density in the dashboard. Small shows only critical metrics (excludes stop-out rate). Normal shows comprehensive data including stop-out rate. Large shows all available metrics including weights, session info, and volume analysis. Larger sizes consume more screen space but provide complete visibility.
Show Quantum Field (default: true): Displays animated grid pattern on the chart indicating market state. Disable if you prefer cleaner charts or experience performance issues on lower-end hardware.
Show Wick Pressure Lines (default: true): Draws dynamic lines from bars with extreme wicks, indicating potential support/resistance or liquidity absorption zones. Disable for simpler visualization.
Show Morphism Energy Beams (default: true): Displays directional beams showing momentum energy flow. Beams intensify during strong trends. Disable if you find this visually distracting.
Show Order Flow Clouds (default: true): Draws translucent boxes representing volume flow bullish/bearish bias. Disable for cleaner price action visibility.
Show Fractal Grid (default: true): Displays multi-timeframe support/resistance levels based on fractal price structure at 10/20/30/40/50 bar periods. Disable if you only want to see primary pivot levels.
Glow Intensity (default: 4): Controls the brightness and thickness of visual effects. Lower values (1-2) for subtle visualization. Higher values (7-10) for maximum visibility but potentially cluttered charts.
Color Theme (default: Cyber): Visual color scheme. Cyber uses cyan/magenta futuristic colors. Quantum uses aqua/purple. Matrix uses green/red terminal style. Aurora uses pastel pink/purple gradient. Choose based on personal preference and monitor calibration.
Show Watermark (default: true): Displays animated watermark at bottom of chart with creator credit and current P&L. Disable if you want completely clean charts or need screen space.
Performance Characteristics and Best Use Cases
Optimal Conditions
This strategy performs best in markets exhibiting:
Trending phases with periodic pullbacks: The combination of momentum and structure components excels when price establishes directional bias but provides retracement opportunities for entries. Markets with 60-70% trending bars and 30-40% consolidation produce the highest win rates.
Medium to high volatility: The ATR-based stop sizing and dynamic risk adjustment require sufficient price movement to generate meaningful profit relative to risk. Instruments with 2-4% daily ATR relative to price work well. Extremely low volatility (<1% daily ATR) generates too many scratch trades.
Clear volume patterns: The VPT volume component adds significant edge when volume expansions align with directional moves. Instruments and timeframes where volume data reflects actual transaction flow (versus tick volume proxies) perform better.
Regular session structure: Futures markets with defined opening and closing hours, consistent liquidity throughout the session, and clear overnight/day session separation allow the session controls and time-based failsafes to function optimally.
Sufficient liquidity for stop execution: The stop breakout entry mode requires that stop orders can fill without significant slippage. Highly liquid contracts work better than illiquid instruments where stop orders may face adverse fills.
Suboptimal Conditions
The strategy may struggle with:
Extreme chop with no directional persistence: When ADX remains below 15 for extended periods and price oscillates rapidly without establishing trends, the momentum component generates conflicting signals. Win rate typically drops below 40% in these conditions, triggering the adaptive system to increase minimum score thresholds until conditions improve. Stop-out rates may also spike into the red zone.
Gap-heavy instruments: Markets with frequent overnight gaps disrupt the continuous price assumptions underlying ATR stops and EMA-based structure analysis. Gaps can also cause stop orders to fill at prices far from intended levels, distorting stop-out rate metrics.
Very low timeframes with excessive noise: On 1-minute or tick charts, the signal components react to micro-structure noise rather than meaningful price swings. The strategy works best on 5-minute through daily timeframes where price movements reflect actual order flow shifts.
Extended low-volatility compression: During historically low volatility periods, profit targets become difficult to reach before mean-reversion occurs. The trail offset, even when set to minimum, may be too wide for the compressed price environment. Stop-out rates may drop to green zone indicating stops should be tightened.
Parabolic moves or climactic exhaustion: Vertical price advances or selloffs where price moves multiple ATRs in single bars can trigger momentum signals at exhaustion points. The structure and reversal components attempt to filter these, but extreme moves may override normal logic.
The adaptive learning system naturally reduces signal frequency and position sizing during unfavorable conditions. If you observe multiple consecutive days with zero trades and "FILTERS ACTIVE" status, this indicates the strategy has self-adjusted to avoid poor conditions rather than forcing trades.
Instrument Recommendations
Emini Index Futures (ES, MES, NQ, MNQ, YM, RTY): Excellent fit. High liquidity, clear volatility patterns, strong volume signals, defined session structure. These instruments have been extensively tested and the universal detection handles all contract specifications automatically.
Micro Index Futures (MES, MNQ, M2K, MYM): Excellent fit for smaller accounts. Same market characteristics as the standard eminis but with reduced contract sizes allowing proper risk management on accounts below $50,000.
Energy Futures (CL, NG, RB, HO): Good to mixed fit. Crude oil (CL) works well due to strong trends and reasonable volatility. Natural gas (NG) can be extremely volatile—consider reducing Base Risk to 0.3-0.4% and increasing Stop Loss ATR multiplier to 1.8-2.2 for NG. The strategy automatically detects the $10/tick value for CL and adjusts position sizing accordingly.
Metal Futures (GC, SI, HG, PL): Good fit. Gold (GC) and silver (SI) exhibit clear trending behavior and work well with the momentum/structure components. The strategy automatically handles the different point values ($100/point for gold, $5,000/point for silver).
Agricultural Futures (ZC, ZS, ZW, ZL): Good fit. Grain futures often trend strongly during seasonal periods. The strategy handles the unique tick sizes (1/4 cent increments) and point values ($50/point for corn/wheat, $60/point for soybeans) automatically.
Treasury Futures (ZB, ZN, ZF, ZT): Good fit for trending rates environments. The strategy automatically handles the fractional tick sizing (32nds for ZB/ZN, halves of 32nds for ZF/ZT) through the universal detection system.
Currency Futures (6E, 6J, 6B, 6A, 6C): Good fit. Major currency pairs exhibit smooth trending behavior. The strategy automatically detects point values which vary significantly ($12.50/tick for 6E, $12.50/tick for 6J, $6.25/tick for 6B).
Cryptocurrency Futures (BTC, ETH, MBT, MET): Mixed fit. These markets have extreme volatility requiring parameter adjustment. Increase Base Risk to 0.8-1.2% and Stop Loss ATR multiplier to 2.0-3.0 to account for wider stop distances. Enable 24-hour trading and weekend trading as these markets have no traditional sessions.
The universal futures compatibility means you can apply this strategy to any of these markets without code modification—simply open the chart of your desired contract and the strategy will automatically configure itself to that instrument's specifications.
Important Disclaimers and Realistic Expectations
This is a sophisticated trading strategy that combines multiple analytical methods within an adaptive framework designed for active traders who will monitor performance and market conditions. It is not a "set and forget" fully automated system, nor should it be treated as a guaranteed profit generator.
Backtesting Realism and Limitations
The strategy includes realistic trading costs and execution assumptions:
- Commission: $0.62 per contract per side (accurate for many retail futures brokers)
- Slippage: 1 tick per entry and exit (conservative estimate for liquid futures)
- Position sizing: Realistic risk percentages and maximum contract limits based on account size
- No repainting: All calculations use confirmed bar data only—signals do not change retroactively
However, backtesting cannot fully capture live trading reality:
- Order fill delays: In live trading, stop and limit orders may not fill instantly at the exact tick shown in backtest
- Volatile periods: During high volatility or low liquidity (news events, rollover days, pre-holidays), slippage may exceed the 1-tick assumption significantly
- Gap risk: The backtest assumes stops fill at stop price, but gaps can cause fills far beyond intended exit levels
- Psychological factors: Seeing actual capital at risk creates emotional pressures not present in backtesting, potentially leading to premature manual intervention
The strategy's backtest results should be viewed as best-case scenarios. Real trading will typically produce 10-30% lower returns than backtest due to the above factors.
Risk Warnings
All trading involves substantial risk of loss. The adaptive learning system can improve parameter selection over time, but it cannot predict future price movements or guarantee profitable performance. Past wins do not ensure future wins.
Losing streaks are inevitable. Even with a 60% win rate, you will encounter sequences of 5, 6, or more consecutive losses due to normal probability distributions. The strategy includes losing streak detection and automatic risk reduction, but you must have sufficient capital to survive these drawdowns.
Market regime changes can invalidate learned patterns. If the strategy learns from 50 trades during a trending regime, then the market shifts to a ranging regime, the adapted parameters may initially be misaligned with the new environment. The system will re-adapt, but this transition period may produce suboptimal results.
Prop firm traders: understand your specific rules. Every prop firm has different rules regarding maximum drawdown, daily loss limits, consistency requirements, and prohibited trading behaviors. While this strategy includes common prop guardrails, you must verify it complies with your specific firm's rules and adjust parameters accordingly.
Never risk capital you cannot afford to lose. This strategy can produce substantial drawdowns, especially during learning periods or market regime shifts. Only trade with speculative capital that, if lost, would not impact your financial stability.
Recommended Usage
Paper trade first: Run the strategy on a simulated account for at least 50 trades or 1 month before committing real capital. Observe how the adaptive system behaves, identify any patterns in losing trades, monitor your stop-out rate trends, and verify your understanding of the entry/exit mechanics.
Start with minimum position sizing: When transitioning to live trading, reduce the Base Risk parameter to 0.3-0.4% initially (vs 0.5-1.0% in testing) to reduce early impact while the system learns your live broker's execution characteristics.
Monitor daily, but do not micromanage: Check the dashboard daily to ensure the strategy is operating normally and risk controls have not triggered unexpectedly. Pay special attention to the Stop-Out Rate metric—if it remains in the red or green zones for multiple days, adjust your Stop Loss ATR multiplier accordingly. However, resist the urge to manually adjust adaptive weights or disable trades based on short-term performance. Allow the adaptive system at least 30 trades to establish patterns before making manual changes.
Combine with other analysis: While this strategy can operate standalone, professional traders typically use systematic strategies as one component of a broader approach. Consider using the strategy for trade execution while applying your own higher-timeframe analysis or fundamental view for trade filtering or sizing adjustments.
Keep a trading journal: Document each week's results, note market conditions (trending vs ranging, high vs low volatility), record stop-out rates and any Stop Loss ATR adjustments you made, and document any manual interventions. Over time, this journal will help you identify conditions where the strategy excels versus struggles, allowing you to selectively enable or disable trading during certain environments.
Technical Implementation Notes
All calculations execute on closed bars only (`calc_on_every_tick=false`) ensuring that signals and values do not repaint. Once a bar closes and a signal generates, that signal is permanent in the history.
The strategy uses fixed-quantity position sizing (`default_qty_type=strategy.fixed, default_qty_value=1`) with the actual contract quantity determined by the position sizing function and passed to the entry commands. This approach provides maximum control over risk allocation.
Order management uses Pine Script's native `strategy.entry()` and `strategy.exit()` functions with appropriate parameters for stops, limits, and trailing stops. All orders include explicit from_entry references to ensure they apply to the correct position.
The adaptive learning arrays (trade_returns, trade_directions, trade_types, trade_hours, trade_was_stopped) are maintained as circular buffers capped at PERFORMANCE_MEMORY size (default 100 trades). When a new trade closes, its data is added to the beginning of the array using `array.unshift()`, and the oldest trade is removed using `array.pop()` if capacity is exceeded. The stop-out tracking system analyzes the trade_was_stopped array to calculate the rolling percentage displayed in the dashboard.
Dashboard rendering occurs only on the confirmed bar (`barstate.isconfirmed`) to minimize computational overhead. The table is pre-created with sufficient rows for the selected dashboard size and cells are populated with current values each update.
Visual effects (fractal grid, wick pressure, morphism beams, order flow clouds, quantum field) recalculate on each bar for real-time chart updates. These are computationally intensive—if you experience chart lag, disable these visual components. The core strategy logic continues to function identically regardless of visual settings.
Timezone conversions use Pine Script's built-in timezone parameter on the `hour()`, `minute()`, and `dayofweek()` functions. This ensures session logic and daily/weekly resets occur at correct boundaries regardless of the chart's default timezone or the server's timezone.
The universal futures detection queries `syminfo.mintick` and `syminfo.pointvalue` on each strategy initialization to obtain the current instrument's specifications. These values remain constant throughout the strategy's execution on a given chart but automatically update when the strategy is applied to a different instrument.
The strategy has been tested on TradingView across timeframes from 5-minute through daily and across multiple futures instrument types including equity indices, energy, metals, agriculture, treasuries, and currencies. It functions identically on all instruments due to the percentage-based risk model and ATR-relative calculations which adapt automatically to price scale and volatility, combined with the universal futures detection system that handles contract-specific specifications.
