ORB Strategy: Extensions & Custom SL (EOD & Live Lines)That's a great request. Since you've now built a complex Pine Script Strategy with several user-configurable risk management, targeting, and exit options, the description should focus on the systematic rules used for entering, managing, and exiting trades.
Here is a clear, written description of the trading strategy you have built:
Trading Strategy Description: ORB Extension Breakout with Custom Stop, Live Tracking, and EOD Exit
This strategy is a systematic, momentum-based system designed for intraday trading. It operates on the principle of an Opening Range Breakout (ORB), utilizing the initial market consolidation to project high-probability targets, while offering multiple methods for managing risk and enforcing a mandatory end-of-day closure.
1. Market Identification (The Opening Range)
The strategy first defines the market's initial boundaries and volatility:
Session Window: The strategy calculates the Opening Range (OR) over a user-defined time period (default: 9:30 AM to 10:30 AM New York Time).
ORB Levels: Two key price levels are established and locked once the time window closes:
Wick High/Low: The absolute highest and lowest prices of the session. These serve as the entry trigger lines.
Body High/Low (Shaded Range): The highest and lowest open/close prices of the session. The height of this range is used as the basis for calculating all targets and stops.
2. Entry Rule (The Breakout)
The strategy waits passively for a breakout that confirms direction and ensures the move has not yet reached its immediate target.
Trigger Condition: A trade is signaled when a candle closes either:
Above the Wick High (for a Long entry).
Below the Wick Low (for a Short entry).
Constraint (Fresh Breakout): The entry is invalidated if the breakout candle's price action (High for Long, Low for Short) has already touched or surpassed the projected Take Profit (0.5 Extension) level before the candle closes.
Execution: The entry is a Market Order executed on the candle that meets the trigger conditions, subject to a user-defined Entry Delay (default 0 bars).
Direction Control: The user can select to trade Long Only, Short Only, or Both.
3. Exit and Risk Management
All trades are placed with simultaneous Take Profit and Stop Loss orders (a bracket order) upon entry.
A. Take Profit (TP)
The Take Profit is set at the 0.5 Extension of the Shaded Range (Body Range).
Calculation: The distance from the Body High/Low to the TP level is exactly 50% of the total height of the Shaded Range.
B. Stop Loss (SL)
The Stop Loss is dynamically calculated based on a user-selected method for risk control:
Range 0.5 (Body Range): The SL is placed an equal distance (0.5 times the Body Range height) outside the opposite side of the Body Range.
ATR Multiple: The Stop Loss distance is calculated as a user-defined Multiplier times the Average True Range (ATR).
Recent Swing Low/High: The Stop Loss is placed based on a structural low (for Long) or high (for Short) within a user-defined lookback period.
C. End-of-Day (EOD) Exit
Any open position is forced closed at the market price if it is still open when the user-defined closing time (default: 16:00 HHMM) is reached. This prevents carrying intraday risk overnight.
4. Visualization
The strategy includes comprehensive visual cues for analysis:
ORB Drawing: Displays the Wick High/Low and the shaded Body Range.
Breakout Signals: Highlights the specific bar where the validated entry signal occurs.
Closed Trades: Draws persistent lines for the Entry and Exit prices of the last few closed trades.
Live Open Trades: Draws persistent lines for the current Entry Price, active Take Profit Level, and active Stop Loss Level for any open position.
Pivot noktaları ve seviyeleri
DR/IDR Break .5 TPDR/IDR Extension Breakout with Custom Stop
This strategy is a systematic, counter-trend, and momentum-based system designed for intraday trading. It operates on the principle of an Opening Range Breakout (ORB), utilizing the initial market consolidation to project high-probability targets, while offering multiple methods for managing risk.
1. Market Identification (The Opening Range)
The strategy begins by defining the market's initial boundaries and volatility:
Session Window: The strategy calculates the Opening Range (OR) over a user-defined time period (default: 9:30 AM to 10:30 AM New York Time).
ORB Levels: Two key price levels are established and locked once the time window closes:
Wick High/Low: The absolute highest and lowest prices of the session. These serve as the entry trigger lines.
Body High/Low (Shaded Range): The highest and lowest open/close prices of the session. The height of this range is used to calculate the Take Profit and Stop Loss levels.
2. Entry Rule (The Breakout)
The strategy is passive until the range is violated, looking for a strong move out of the consolidation area.
Trigger Condition: A trade is signaled when a candle closes either:
Above the Wick High (for a Long entry).
Below the Wick Low (for a Short entry).
Execution: The entry is a Market Order executed on the candle that meets the trigger condition, subject to a user-defined Entry Delay (default 0 bars, meaning the entry is taken immediately upon the breakout candle's close).
Direction Control: The user can select to trade Long Only, Short Only, or Both.
3. Exit and Risk Management
All trades are placed with simultaneous Take Profit and Stop Loss orders (a bracket order) once the entry is filled.
A. Take Profit (TP)
The Take Profit is set at the 0.5 Extension of the Shaded Range (Body Range).
Calculation: The distance from the Body High/Low to the TP level is exactly 50% of the total height of the Shaded Range.
B. Stop Loss (SL)
The Stop Loss is dynamically calculated based on a user-selected method for risk control:
Range 0.5 (Body Range): The Stop Loss is placed an equal distance (0.5 times the Body Range height) outside the opposite side of the Body Range.
Example (Long): If entry is above the Wick High, the SL is set 0.5 times the Body Range height below the Body Low.
ATR Multiple: The Stop Loss distance is determined by the asset's recent volatility.
Calculation: The distance is calculated as a user-defined Multiplier (default 2.0) times the Average True Range (ATR).
Recent Swing Low/High: The Stop Loss is placed based on a structural level defined by recent price action.
Long Entry: SL is placed at the Lowest Swing Low within a user-defined lookback period.
Short Entry: SL is placed at the Highest Swing High within a user-defined lookback period.
Summary of Workflow
The market sets the Wick and Body boundaries (e.g., 9:30–10:30 AM).
Price breaks and closes beyond a Wick boundary, triggering a signal.
The trade enters after the specified delay.
A bracket order is placed: TP is fixed at the 0.5 Extension, and SL is set based on the user's chosen risk method.
The trade is closed upon reaching either the TP or the SL level.
ATR ZigZag BreakoutATR ZigZag Breakout
This strategy uses my ATR ZigZag indicator (powered by the ZigZagCore library) to scalp breakouts at volatility-filtered highs and lows.
Everyone knows stops cluster around clear swing highs and lows. Breakout traders often pile in there, too. These levels are predictable areas where aggressive orders hit the tape. The idea here is simple:
→ Let ATR ZigZag define clean, volatility-filtered pivots
→ Arm a stop market order at those pivots
→ Join the breakout when the crowd hits the level
The key to greater success in this simple strategy lies in the ZigZag. Because the pivots are filtered by ATR instead of fixed bar counts or fractals, the levels tend to be more meaningful and less noisy.
This approach is especially suited for intraday trading on volatile instruments (e.g., NQ, GC, liquid crypto pairs).
How It Works
1. Pivot detection
The ATR ZigZag uses an ATR-based threshold to confirm swing highs and lows. Only when price has moved far enough in the opposite direction does a pivot become “official.”
2. Candidate breakout level
When a new swing direction is detected and the most recent high/low has not yet been broken in the current leg, the strategy arms a stop market order at that pivot.
• Long candidate → most recent swing high
• Short candidate → most recent swing low
These “candidate trades” are shown as dotted lines.
3. Entry, SL, and TP
If price breaks through the level, the stop order is filled and a bracket is placed:
• Stop loss = ATR × SL multiplier
• Take profit = SL distance × RR multiplier
Once a level has traded, it is not reused in the same swing leg.
4. Cancel & rotate
If the market reverses and forms a new swing in the opposite direction before the level is hit, the pending order is cancelled and a new candidate is considered in the new direction.
Additional Features
• Optional session filter for backtesting specific trading hours
A13: Micro MAP Scalping StrategyA13: Micro MAP Scalping Strategy — Institutional Breakout Scalper (Pine Script v6 – Protected Source)
A completely original, professional scalping strategy developed from scratch over several months of research and live-market testing. The system is built around institutional breakout zones with a unique multi-stage validation process, strict confirmation requirements, and sophisticated risk management — all designed specifically for 1–15 minute timeframes.
Why this implementation is original and the source code is protected
The entire logic — from breakout detection to entry confirmation, multi-filter stop-loss engines, and dynamic position sizing — was built independently without relying on any existing public libraries, built-ins, or open-source code beyond standard Pine functions. The proprietary validation rules, ATR-scaled gap filtering, and layered confirmation system required extensive original development to achieve consistent performance in real-market conditions. Protecting the source code is necessary to preserve the unique edge that distinguishes this system from standard or publicly available implementations.
Core concepts and methodology (fully transparent — no code revealed)
1. Institutional Breakout Zone Detection
• Real-time identification of high-probability zones using a custom ATR-based minimum gap filter
• Zones are only considered valid when accompanied by clear price displacement and volume confirmation
• No reliance on standard Fair Value Gap or order block libraries — completely custom validation
2. Strict Dual Confirmation Entry Logic
• Entry requires one of two precise conditions:
— Confirmed pullback retest of the validated breakout zone, or
— Clean inside-bar formation fully contained within the zone
• Both conditions must align with the directional bias of the breakout
3. Five Independent Stop-Loss Engines
• ATR-based (default and recommended)
• Swing Low/High levels
• Pivot Point structure
• Trailing Stop with ATR offset
• Fixed percentage
• Every engine includes minimum and maximum stop-loss filters to prevent unrealistic risk during extreme volatility
4. Professional Risk & Position Sizing Engine
• Fixed percentage risk per trade (default 1%)
• Optional compounding mode for growing accounts
• Real-time calculation based on exact stop distance and current equity
• Full integration with leverage settings
5. Multi-Layer Filtering System
• Multi-timeframe EMA filter (default 60-period, fully customizable timeframe)
• Complete trading session control with UTC offset support
• Date range filtering for strategy deployment control
• Consecutive loss protection (optional multi-stop filter)
• Minimum/maximum stop-loss filters to eliminate low-probability setups
6. Real-Time Performance Dashboard
• Live display of win rate, net profit, maximum drawdown, total trades
• Consecutive win/loss streak tracking
• Current position size and average entry price
• All statistics visible directly on chart
Backtesting settings used in the published chart
• Symbol: BTC/USD
• Timeframe: 15-minute
• Initial capital: $10,000
• Risk per trade: 1%
• Commission: 0.04% (realistic for major brokers)
• Slippage: enabled
• Sample size: 200+ trades
These are the exact default Properties settings of the strategy.
The strategy is completely free to add and use on your charts.
#Scalping #Breakout #Intraday #Institutional #RiskManagement #ProfessionalStrategy
Pivot Trendline Breakout StrategyHow it works:
Long entry: triggered immediately when price closes above the green upper pivot trendline.
Exit (go flat): triggered immediately when price closes below the red lower pivot trendline.
Uses 100% of equity per trade (you can change default_qty_value if you prefer fixed size or risk %).
Works on any timeframe.
Double MOST with Pivot and EMAMOST Long Strategy with Multi-Filter Confirmation (Pivot + VAR Trend Filter)
This strategy combines a custom MOST stop-line structure with a moving average trend filter (EMA / VAR / ZLMA), daily pivot levels, and a 9-period VAR filter to generate clean long-only entries.
It aims to capture early trend continuations while avoiding reversals and false breakouts.
✔ Buy Conditions
A long position is opened only if all of the conditions below occur simultaneously:
MA (ort) > MOST Line (s2)
– Confirms that momentum is on the long side.
Price > Daily Pivot (pvt_gun)
– Ensures the market is trading above the day’s fair-value level.
Price > VAR(9)
– Short-term VAR filter to confirm trend strength and reduce noise.
Only the first bar where all conditions turn true generates a position.
✔ Sell Condition
A long position is closed when:
MOST Line (s2) crosses above MA (ort)
– Indicates a momentum shift against the long position.
✔ Execution Style (MetaStock-like)
Strategy operates long-only
Orders are filled on the next bar open, not on the signal bar
Commission: 0.03% (3 bps / on-binde 3)
Position size: 100% of equity per trade
This makes the behavior comparable to classical MetaStock backtesting logic.
✔ Chart Markers
Only actual trade entries and exits are drawn
No repeated signals or overlapping markers
Clean visual trade history
Purpose
This strategy is ideal for traders who want:
A structured long-only trend model
A multi-layer confirmation filter
Clean execution without repaint
High-quality entries above market structure levels
Auto Div ADX STO RSI (Flip+P) v2This strategy combines multi-indicator divergence detection, momentum confirmation and adaptive position management into a unified automated trading framework.
It identifies regular bullish and bearish divergences using RSI and Stochastic (K), with configurable confirmation logic (RSI+STO, RSI only, or STO only). Divergences are validated only when price forms a lower low / higher high while the oscillator forms a higher low / lower high within a user-defined lookback window.
To filter low-quality setups, the strategy applies an ADX trend strength requirement, ensuring signals are taken only when market conditions reflect sufficient directional energy. Optional stochastic filters (oversold/overbought K levels) can further refine long and short entries.
Once a valid signal appears, the system supports Automatic Flip Logic:
If a bullish divergence forms during a short position, the strategy closes the short and flips long.
If a bearish divergence forms during a long position, it closes the long and flips short.
Position sizing uses adaptive pyramiding: the initial flip takes size proportional to the opposite side’s accumulated units, and new signals in the same direction can add incremental units (scale-in) if enabled. This models progressive conviction as new divergence signals occur.
All entries can optionally be required to confirm on bar close.
Alerts are included for both Long and Short entries.
Key Features
• Automatic detection of RSI and Stochastic divergences
• User-selectable confirmation rules (RSI, STO, or both)
• ADX-based strength filter
• Optional Stochastic K oversold/overbought filters
• Full flip logic between Long and Short
• Dynamic pyramiding and configurable scale-ins
• Bar-close confirmation option
• Alerts for Long/Short entries
• Status-line visualization of ADX, RSI, Stochastic, and unit cycles
This strategy is designed for traders who want a structured, divergence-based model enhanced with trend strength filtering and flexible position management logic, suitable for systematic discretionary trading or fully automated execution.
inyerneck Diaper Sniper v16 — LOW VOL V CATCHERDiaper Sniper v16 — Low-Vol Reversal Hunter
Catches dead-cat bounces and V-shaped reversals on the day’s biggest losers.
Designed for pennies and trash stocks that drop 6 %+ from recent high and snap back on any volume + green candle.
Features:
• Tiny green “D” = reversal signal
• Works on 1m → daily
• Fully adjustable filters
Best on low-float runners that bleed hard and bounce harder.
Use tiny size — it fires a lot.
Public version — code visible. No invite-only on Essential plan.
do not alter settings with out first recording defaults.. defaults are quite effective
2025 build. Test at your own risk.
Retracement Strategy [OmegaTools]Retracement Strategy is a systematic trend–retracement framework designed to identify directional opportunities after a confirmed momentum shift, and to manage exits using either trend reversals or overextension conditions. It is built around a smoothed RSI regime filter and a simple, price-based retracement trigger, making it applicable across a wide range of markets and timeframes while remaining transparent and easy to interpret.
The strategy begins by defining the underlying trend through a two-stage RSI signal. A standard RSI is computed over the user-defined Length input, then smoothed with a short moving average to reduce noise. Two symmetric thresholds are derived from the Threshold parameter: an upper band at 100 minus the threshold and a lower band at the threshold itself. When the smoothed RSI crosses above the upper band, the environment is classified as bullish and the internal trend state is set to uptrend. When the smoothed RSI crosses below the lower band, the environment is classified as bearish and the trend state becomes downtrend. When RSI moves back into the central zone between the two bands, the trend is considered neutral. In addition to the current trend, the strategy tracks the last non-neutral trend direction, which is used to detect genuine trend changes rather than transient oscillations.
Once a trend is established, the strategy looks for retracement entries in the direction of that trend. For long setups in an uptrend, it computes the lowest low over the previous Length minus one bars, excluding the current bar. A long signal is generated when price dips below this recent low while the trend state remains bullish. Symmetrically, for short setups in a downtrend, it computes the highest high over the previous Length minus one bars and enters short when price spikes above this recent high while the trend state remains bearish. This logic is designed to capture pullbacks against the prevailing RSI-defined trend, entering when the market tests or slightly violates recent extremes, rather than chasing breakouts. The candles are visually coloured to reflect the detected trend, highlighting bullish and bearish environments while keeping neutral phases distinguishable on the chart. An ATR-based measure is used solely to position the “UP” and “DN” labels on the chart for clearer visualisation of entry points; it does not directly influence position sizing or stop calculation in this implementation.
Take profit and stop loss behaviour are fully parameterized through the “Take Profit” and “Stop Loss” inputs, each offering three modes: None, Trend Change and Extension. When “Trend Change” is selected for the take profit, the strategy will only exit profitable positions when a confirmed trend reversal occurs. For a long position, this means that the strategy will close the trade when the trend state flips from uptrend to downtrend, and the last recorded trend direction validates that this is a genuine reversal rather than a neutral fluctuation; the same logic applies symmetrically for short positions. When “Extension” is selected as the take profit mode, the strategy closes profitable long trades when the smoothed RSI reaches or exceeds the upper threshold, interpreted as an overbought extension within the bullish regime, and closes profitable short trades when the smoothed RSI falls to or below the lower threshold, interpreted as an oversold extension within the bearish regime. When “None” is chosen, the strategy does not apply any explicit take profit logic, leaving trades to be managed by the stop loss settings or by user discretion in backtesting.
The stop loss parameter works in a parallel way. With “Trend Change” selected as stop loss, any open long position is closed when the trend flips from uptrend to downtrend, regardless of whether the trade is currently in profit or loss, and any open short is closed when the trend flips from downtrend to uptrend. This turns the RSI trend regime into a hard invalidation rule: once the underlying momentum structure reverses, the position is exited. With “Extension” selected for stop loss, long positions are closed when RSI falls back below the upper band and moves towards the opposite side of the range, while short positions are closed when RSI rises above the lower band and moves towards the upper side. In practice, this acts as a dynamic exit based on the oscillator moving out of a favourable context for the existing trade. Selecting “None” for stop loss disables these automatic exits, leaving only the take profit logic, if any, to manage the position. Because take profit and stop loss configuration are independent, the user can construct different profiles, such as pure trend-change exits on both sides, pure overextension exits, or a mix (for example, take profit on overextension and stop loss on trend reversal).
This strategy is designed as an analytical and backtesting framework rather than a finished plug-and-play trading system. It does not include position sizing, risk-per-trade controls, multi-timeframe confirmation, volatility filters or instrument-specific fine-tuning. Its primary purpose is to provide a clear, rule-based structure for testing retracement logic within RSI-defined trends, and to allow users to explore how different exit regimes (trend-change based versus extension based) affect performance on their instruments and timeframes of interest.
Nothing in this script or its description should be interpreted as financial advice, investment recommendation or solicitation to buy or sell any financial instrument. Past performance on backtests does not guarantee future results. The behaviour of this strategy can vary significantly across symbols, timeframes and market conditions, and correlations, volatility and liquidity can change without warning. Before considering any live application, users should thoroughly backtest and forward test the strategy on their own data, adjust parameters to their risk profile and instrument characteristics, and integrate proper money management and trade management rules. Use of this script is entirely at the user’s own risk.
15m ORB Breakout NAS100 (5m Mgmt) v6 - OptimizedOpening Range Breakout Strategy
Buy and sell signals are given upon break of market session opening range. Best utilized for 30 minute NY opening range, managed on 5 min timeframe on NAS100. Tweak the settings for higher win rate on backtesting dashboard before implementing strategy.
smart honey 2.0The smart honey 2.0 is a long-only trading strategy based on averaging entries.
At "Entry" you can set to enter a trade at a specified averaging level. The best backtest result at "only 4th averaging".
"Tp" is take profit.
"Sensitivity" controls the frequency of trades - lower sensitivity means fewer, but higher-quality trades.
Settings recommendations
For 1m-5m timeframes, use low sensitivity and take profit values. For higher timeframes, increase the take profit value.
For example, a profitable setting for many coins on a 5-minute timeframe is
Tp = 1.5%
Sensitivity = 2.7
Entry = only 4th averaging
The strategy features a "Blue line" showing liquidity clusters influenced by Sensitivity. Price often bounces off this line.
You can also set alerts for lists of coins, receiving notifications at each new candle about active positions
15m ORB + FVG Strategy (ChadAnt)Core Logic
The indicator's logic revolves around three main phases:
1. Defining the 15-Minute Opening Range (ORB)
The script calculates the highest high (rangeHigh) and lowest low (rangeLow) that occurred during the first 15 minutes of the trading day.
This time window is defined by the sessionStr input, which defaults to 0930-0945 (exchange time).
The high and low of this range are plotted as small gray dots once the session ends (rangeSet = true).
2. Identifying a Fair Value Gap (FVG) Setup
After the 15-minute range is set, the indicator waits for a breakout of either the range high or range low.
A "Strict FVG breakout" requires two conditions on the first candle that closes beyond the range:
The candle before the breakout candle ( bars ago) must have been inside the range.
The breakout candle ( bar ago) must have closed outside the range.
A Fair Value Gap (FVG) must form on the most recent three candles (the current bar and the two previous bars).
Bullish FVG (Long Setup): The low of the current bar (low) is greater than the high of the bar two periods prior (high ). This FVG represents a price inefficiency that the trade expects to fill.
Bearish FVG (Short Setup): The high of the current bar (high) is less than the low of the bar two periods prior (low ).
If a valid FVG setup occurs, the indicator marks a pending setup and draws a colored box to highlight the FVG area (Green for Bullish FVG, Red for Bearish FVG).
3. Trade Entry and Management
If a pending setup is identified, the trade is structured as a re-entry trade into the FVG zone:
Entry Price: Set at the outer boundary of the FVG, which is the low of the current bar for a Long setup, or the high of the current bar for a Short setup.
Stop Loss (SL): Set at the opposite boundary of the FVG, which is the low for a Long setup, or the high for a Short setup.
The trade is triggered (tradeActive = true) once the price retraces to the pendingEntry level.
Risk/Reward (RR) Targets: Three Take Profit (TP) levels are calculated based on the distance between the Entry and Stop Loss:
$$\text{Risk} = | \text{Entry} - \text{SL} |$$
$$\text{TP}n = \text{Entry} \pm (\text{Risk} \times \text{RR}n)$$
where $n$ is 1, 2, or 3, corresponding to the input $\text{RR}1$, $\text{RR}2$, and $\text{RR}3$ values (defaults: 1.0, 1.5, and 2.0).
Trade Lines: Upon triggering, lines for the Entry, Stop Loss, and three Take Profit levels are drawn on the chart for a specified length (lineLength).
A crucial feature is the directional lock (highBroken / lowBroken):
If the price breaks a range level (e.g., simpleBrokeHigh) but without a valid FVG setup, the corresponding directional flag (e.g., highBroken) is set to true permanently for the day.
This prevents the indicator from looking for any subsequent trade setups in that direction for the rest of the day, suggesting that the initial move, without an FVG, exhausted the opportunity.
Open-source script
In true TradingView spirit, the creator of this script has made it open-source, so that traders can review and verify its functionality. Kudos to the author! While you can use it for free, remember that republishing the code is subject to our House Rules.
ChadAnt
Disclaimer
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied
TradeX Labs Pivot MasterLucrorStrategies — Automated Price Action Execution Framework
This indicator-strategy automation is built for traders who want a simple, consistent, and rules-based trading system—no multi-timeframe chaos or overcomplicated confirmation layers. It trades purely from prior-day price action, keeping volatility, structure, and logic constant across all sessions.
Every entry, stop, and target comes directly from the same volatility-adjusted model. If the trade can’t fit your defined dollar risk, it simply won’t execute or plot.
⸻
IMPORTANT NOTE
***Since TradingView utilizes close of bar for plots, this is best utilized for real time entry/exit signals on 1 second charts or lower. If you do not have 1 second charts we can not recommend you to upgrade your subscription but we HIGHLY recommend utilizing this script on a 1 second chart. If utilizing on any higher time frame any signals or trade logic will be delayed and inaccurate or signals can be entirely skipped altogether and populate incorrect entries***
⸻
Purpose & Core Design
The framework is anchored to prior-day settlement data and mathematically transforms it into real-time, session-specific trading levels. This creates a daily map of opportunity that evolves with volatility while maintaining a consistent structure.
This approach eliminates guesswork and ensures the same conditions that produced historical edge apply to every live session.
⸻
Key Inputs & Control
1. Dollar Risk
Set your maximum dollar risk per trade. The system automatically sizes positions to stay at or below that risk limit based on stop distance.
• If the trade qualifies: a red-to-green gradient fill and entry label appear.
• If not: no fill, no entry, no false visual signals.
2. Timer Exit (Default: 30 Minutes)
The strategy is designed for momentum capture in the first 30 minutes after market open. If a trade remains active beyond that time, it is closed automatically.
All back tests and live reports reference this same window to maintain integrity. (Adjustable if you wish.)
3. Days to Keep Lines
Controls how many sessions of plotted levels and fills stay visible (up to 10).
To explore further back, use TradingView’s replay mode. The indicator will continue plotting as far as platform data allows.
4. Font & Label Size
• Price Label Size: Adjusts the numerical price levels beside pivots for manual pre-market entries.
• Level Label Size: Controls the on-chart text size for active trade signals. Both fully customizable.
⸻
Level Structure & Trade Mechanics
All plotted levels originate from a proprietary prior-day volatility formula. You will see:
• Middle Green Horizontal Lines — Support Levels
These mark historically reactive zones where price has a higher probability of holding or bouncing.
• Middle Blue Horizontal Lines — Resistance Levels
These represent opposing zones where price tends to reject or stall.
(Solid and dotted variants handle different roles in execution logic.)
• Red Horizontal Lines — Points of Control (POC Zones)
These are high-impact levels where price historically either rejects violently or breaks with strength.
⸻
Trade Logic
Long Trades
• Trigger: The solid blue line above the current structure acts as the long trigger.
• Stop: The solid blue line below is the stop-loss.
• Target: The next solid blue line above serves as the target.
Long trades are executed when price hits the solid blue trigger above the current level, using solid levels exclusively for entry, stop, and target.
Short Trades
• Trigger: The dotted blue line below the current structure is the short trigger.
• Stop: The dotted blue line above is the stop-loss.
• Target: The next dotted blue line below becomes the target.
Short trades use only dotted levels to define all key mechanics — entry, stop, and target — keeping short setups visually distinct and structurally independent from longs.
This dual structure allows for clean, symmetrical trade logic across both sides of the market, with consistent volatility mapping from prior-day data.
⸻
High-Priority Red Levels (Points of Control)
Red horizontal levels represent areas of major interest — typically where institutional activity concentrated previously. Price often reacts sharply here: either reversing instantly or breaking through with momentum.
These are optional reference points but often signal where the strongest reactions occur.
⸻
Visualization & Behavior
• Executed trades show the red-to-green gradient fill.
• Trades that exceed risk parameters simply do not appear.
• Levels remain clean and persistent day to day for back testing, journaling, or educational
use.
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Disclaimer
This is a closed, proprietary LucrorStrategies tool. It is provided for analytical and educational use only. It does not predict price or guarantee profit. All trade execution, configuration, and outcomes remain the responsibility of the user.
Reversal Point Dynamics - Machine Learning⇋ Reversal Point Dynamics - Machine Learning
RPD Machine Learning: Self-Adaptive Multi-Armed Bandit Trading System
RPD Machine Learning is an advanced algorithmic trading system that implements genuine machine learning through contextual multi-armed bandits, reinforcement learning, and online adaptation. Unlike traditional indicators that use fixed rules, RPD learns from every trade outcome , automatically discovers which strategies work in current market conditions, and continuously adapts without manual intervention .
Core Innovation: The system deploys six distinct trading policies (ranging from aggressive trend-following to conservative range-bound strategies) and uses LinUCB contextual bandit algorithms with Random Fourier Features to learn which policy performs best in each market regime. After the initial learning phase (50-100 trades), the system achieves autonomous adaptation , automatically shifting between policies as market conditions evolve.
Target Users: Quantitative traders, algorithmic trading developers, systematic traders, and data-driven investors who want a system that adapts over time . Suitable for stocks, futures, forex, and cryptocurrency on any liquid instrument with >100k daily volume.
The Problem This System Solves
Traditional Technical Analysis Limitations
Most trading systems suffer from three fundamental challenges :
Fixed Parameters: Static settings (like "buy when RSI < 30") work well in backtests but may struggle when markets change character. What worked in low-volatility environments may not work in high-volatility regimes.
Strategy Degradation: Manual optimization (curve-fitting) produces systems that perform well on historical data but may underperform in live trading. The system never adapts to new market conditions.
Cognitive Overload: Running multiple strategies simultaneously forces traders to manually decide which one to trust. This leads to hesitation, late entries, and inconsistent execution.
How RPD Machine Learning Addresses These Challenges
Automated Strategy Selection: Instead of requiring you to choose between trend-following and mean-reversion strategies, RPD runs all six policies simultaneously and uses machine learning to automatically select the best one for current conditions. The decision happens algorithmically, removing human hesitation.
Continuous Learning: After every trade, the system updates its understanding of which policies are working. If the market shifts from trending to ranging, RPD automatically detects this through changing performance patterns and adjusts selection accordingly.
Context-Aware Decisions: Unlike simple voting systems that treat all conditions equally, RPD analyzes market context (ADX regime, entropy levels, volatility state, volume patterns, time of day, historical performance) and learns which combinations of context features correlate with policy success.
Machine Learning Architecture: What Makes This "Real" ML
Component 1: Contextual Multi-Armed Bandits (LinUCB)
What Is a Multi-Armed Bandit Problem?
Imagine facing six slot machines, each with unknown payout rates. The exploration-exploitation dilemma asks: Should you keep pulling the machine that's worked well (exploitation) or try others that might be better (exploration)? RPD solves this for trading policies.
Academic Foundation:
RPD implements Linear Upper Confidence Bound (LinUCB) from the research paper "A Contextual-Bandit Approach to Personalized News Article Recommendation" (Li et al., 2010, WWW Conference). This algorithm is used in content recommendation and ad placement systems.
How It Works:
Each policy (AggressiveTrend, ConservativeRange, VolatilityBreakout, etc.) is treated as an "arm." The system maintains:
Reward History: Tracks wins/losses for each policy
Contextual Features: Current market state (8-10 features including ADX, entropy, volatility, volume)
Uncertainty Estimates: Confidence in each policy's performance
UCB Formula: predicted_reward + α × uncertainty
The system selects the policy with highest UCB score , balancing proven performance (predicted_reward) with potential for discovery (uncertainty bonus). Initially, all policies have high uncertainty, so the system explores broadly. After 50-100 trades, uncertainty decreases, and the system focuses on known-performing policies.
Why This Matters:
Traditional systems pick strategies based on historical backtests or user preference. RPD learns from actual outcomes in your specific market, on your timeframe, with your execution characteristics.
Component 2: Random Fourier Features (RFF)
The Non-Linearity Challenge:
Market relationships are often non-linear. High ADX may indicate favorable conditions when volatility is normal, but unfavorable when volatility spikes. Simple linear models struggle to capture these interactions.
Academic Foundation:
RPD implements Random Fourier Features from "Random Features for Large-Scale Kernel Machines" (Rahimi & Recht, 2007, NIPS). This technique approximates kernel methods (like Support Vector Machines) while maintaining computational efficiency for real-time trading.
How It Works:
The system transforms base features (ADX, entropy, volatility, etc.) into a higher-dimensional space using random projections and cosine transformations:
Input: 8 base features
Projection: Through random Gaussian weights
Transformation: cos(W×features + b)
Output: 16 RFF dimensions
This allows the bandit to learn non-linear relationships between market context and policy success. For example: "AggressiveTrend performs well when ADX >25 AND entropy <0.6 AND hour >9" becomes naturally encoded in the RFF space.
Why This Matters:
Without RFF, the system could only learn "this policy has X% historical performance." With RFF, it learns "this policy performs differently in these specific contexts" - enabling more nuanced selection.
Component 3: Reinforcement Learning Stack
Beyond bandits, RPD implements a complete RL framework :
Q-Learning: Value-based RL that learns state-action values. Maps 54 discrete market states (trend×volatility×RSI×volume combinations) to 5 actions (4 policies + no-trade). Updates via Bellman equation after each trade. Converges toward optimal policy after 100-200 trades.
TD(λ) with Eligibility Traces: Extension of Q-Learning that propagates credit backwards through time . When a trade produces an outcome, TD(λ) updates not just the final state-action but all states visited during the trade, weighted by eligibility decay (λ=0.90). This accelerates learning from multi-bar trades.
Policy Gradient (REINFORCE): Learns a stochastic policy directly from 12 continuous market features without discretization. Uses gradient ascent to increase probability of actions that led to positive outcomes. Includes baseline (average reward) for variance reduction.
Meta-Learning: The system learns how to learn by adapting its own learning rates based on feature stability and correlation with outcomes. If a feature (like volume ratio) consistently correlates with success, its learning rate increases. If unstable, rate decreases.
Why This Matters:
Q-Learning provides fast discrete decisions. Policy Gradient handles continuous features. TD(λ) accelerates learning. Meta-learning optimizes the optimization. Together, they create a robust, multi-approach learning system that adapts more quickly than any single algorithm.
Component 4: Policy Momentum Tracking (v2 Feature)
The Recency Challenge:
Standard bandits treat all historical data equally. If a policy performed well historically but struggles in current conditions due to regime shift, the system may be slow to adapt because historical success outweighs recent underperformance.
RPD's Solution:
Each policy maintains a ring buffer of the last 10 outcomes. The system calculates:
Momentum: recent_win_rate - global_win_rate (range: -1 to +1)
Confidence: consistency of recent results (1 - variance)
Policies with positive momentum (recent outperformance) get an exploration bonus. Policies with negative momentum and high confidence (consistent recent underperformance) receive a selection penalty.
Effect: When markets shift, the system detects the shift more quickly through momentum tracking, enabling faster adaptation than standard bandits.
Signal Generation: The Core Algorithm
Multi-Timeframe Fractal Detection
RPD identifies reversal points using three complementary methods :
1. Quantum State Analysis:
Divides price range into discrete states (default: 6 levels)
Peak signals require price in top states (≥ state 5)
Valley signals require price in bottom states (≤ state 1)
Prevents mid-range signals that may struggle in strong trends
2. Fractal Geometry:
Identifies swing highs/lows using configurable fractal strength
Confirms local extremum with neighboring bars
Validates reversal only if price crosses prior extreme
3. Multi-Timeframe Confirmation:
Analyzes higher timeframe (4× default) for alignment
MTF confirmation adds probability bonus
Designed to reduce false signals while preserving valid setups
Probability Scoring System
Each signal receives a dynamic probability score (40-99%) based on:
Base Components:
Trend Strength: EMA(velocity) / ATR × 30 points
Entropy Quality: (1 - entropy) × 10 points
Starting baseline: 40 points
Enhancement Bonuses:
Divergence Detection: +20 points (price/momentum divergence)
RSI Extremes: +8 points (RSI >65 for peaks, <40 for valleys)
Volume Confirmation: +5 points (volume >1.2× average)
Adaptive Momentum: +10 points (strong directional velocity)
MTF Alignment: +12 points (higher timeframe confirms)
Range Factor: (high-low)/ATR × 3 - 1.5 points (volatility adjustment)
Regime Bonus: +8 points (trending ADX >25 with directional agreement)
Penalties:
High Entropy: -5 points (entropy >0.85, chaotic price action)
Consolidation Regime: -10 points (ADX <20, no directional conviction)
Final Score: Clamped to 40-99% range, classified as ELITE (>85%), STRONG (75-85%), GOOD (65-75%), or FAIR (<65%)
Entropy-Based Quality Filter
What Is Entropy?
Entropy measures randomness in price changes . Low entropy indicates orderly, directional moves. High entropy indicates chaotic, unpredictable conditions.
Calculation:
Count up/down price changes over adaptive period
Calculate probability: p = ups / total_changes
Shannon entropy: -p×log(p) - (1-p)×log(1-p)
Normalized to 0-1 range
Application:
Entropy <0.5: Highly ordered (ELITE signals possible)
Entropy 0.5-0.75: Mixed (GOOD signals)
Entropy >0.85: Chaotic (signals blocked or heavily penalized)
Why This Matters:
Prevents trading during choppy, news-driven conditions where technical patterns may be less reliable. Automatically raises quality bar when market is unpredictable.
Regime Detection & Market Microstructure - ADX-Based Regime Classification
RPD uses Wilder's Average Directional Index to classify markets:
Bull Trend: ADX >25, +DI > -DI (directional conviction bullish)
Bear Trend: ADX >25, +DI < -DI (directional conviction bearish)
Consolidation: ADX <20 (no directional conviction)
Transitional: ADX 20-25 (forming direction, ambiguous)
Filter Logic:
Blocks all signals during Transitional regime (avoids trading during uncertain conditions)
Blocks Consolidation signals unless ADX ≥ Min Trend Strength
Adds probability bonus during strong trends (ADX >30)
Effect: Designed to reduce signal frequency while focusing on higher-quality setups.
Divergence Detection
Bearish Divergence:
Price makes higher high
Velocity (price momentum) makes lower high
Indicates weakening upward pressure → SHORT signal quality boost
Bullish Divergence:
Price makes lower low
Velocity makes higher low
Indicates weakening downward pressure → LONG signal quality boost
Bonus: Adds probability points and additional acceleration factor. Divergence signals have historically shown higher success rates in testing.
Hierarchical Policy System - The Six Trading Policies
1. AggressiveTrend (Policy 0):
Probability Threshold: 60% (trades more frequently)
Entropy Threshold: 0.70 (tolerates moderate chaos)
Stop Multiplier: 2.5× ATR (wider stops for trends)
Target Multiplier: 5.0R (larger targets)
Entry Mode: Pyramid (scales into winners)
Best For: Strong trending markets, breakouts, momentum continuation
2. ConservativeRange (Policy 1):
Probability Threshold: 75% (more selective)
Entropy Threshold: 0.60 (requires order)
Stop Multiplier: 1.8× ATR (tighter stops)
Target Multiplier: 3.0R (modest targets)
Entry Mode: Single (one-shot entries)
Best For: Range-bound markets, low volatility, mean reversion
3. VolatilityBreakout (Policy 2):
Probability Threshold: 65% (moderate)
Entropy Threshold: 0.80 (accepts high entropy)
Stop Multiplier: 3.0× ATR (wider stops)
Target Multiplier: 6.0R (larger targets)
Entry Mode: Tiered (splits entry)
Best For: Compression breakouts, post-consolidation moves, gap opens
4. EntropyScalp (Policy 3):
Probability Threshold: 80% (very selective)
Entropy Threshold: 0.40 (requires extreme order)
Stop Multiplier: 1.5× ATR (tightest stops)
Target Multiplier: 2.5R (quick targets)
Entry Mode: Single
Best For: Low-volatility grinding moves, tight ranges, highly predictable patterns
5. DivergenceHunter (Policy 4):
Probability Threshold: 70% (quality-focused)
Entropy Threshold: 0.65 (balanced)
Stop Multiplier: 2.2× ATR (moderate stops)
Target Multiplier: 4.5R (balanced targets)
Entry Mode: Tiered
Best For: Divergence-confirmed reversals, exhaustion moves, trend climax
6. AdaptiveBlend (Policy 5):
Probability Threshold: 68% (balanced)
Entropy Threshold: 0.75 (balanced)
Stop Multiplier: 2.0× ATR (standard)
Target Multiplier: 4.0R (standard)
Entry Mode: Single
Best For: Mixed conditions, general trading, fallback when no clear regime
Policy Clustering (Advanced/Extreme Modes)
Policies are grouped into three clusters based on regime affinity:
Cluster 1 (Trending): AggressiveTrend, DivergenceHunter
High regime affinity (0.8): Performs well when ADX >25
Moderate vol affinity (0.6): Works in various volatility
Cluster 2 (Ranging): ConservativeRange, AdaptiveBlend
Low regime affinity (0.3): Better suited for ADX <20
Low vol affinity (0.4): Optimized for calm markets
Cluster 3 (Breakout): VolatilityBreakout
Moderate regime affinity (0.6): Works in multiple regimes
High vol affinity (0.9): Requires high volatility for optimal characteristics
Hierarchical Selection Process:
Calculate cluster scores based on current regime and volatility
Select best-matching cluster
Run UCB selection within chosen cluster
Apply momentum boost/penalty
This two-stage process reduces learning time - instead of choosing among 6 policies from scratch, system first narrows to 1-2 policies per cluster, then optimizes within cluster.
Risk Management & Position Sizing
Dynamic Kelly Criterion Sizing (Optional)
Traditional Fixed Sizing Challenge:
Using the same position size for all signal probabilities may be suboptimal. Higher-probability signals could justify larger positions, lower-probability signals smaller positions.
Kelly Formula:
f = (p × b - q) / b
Where:
p = win probability (from signal score)
q = loss probability (1 - p)
b = win/loss ratio (average_win / average_loss)
f = fraction of capital to risk
RPD Implementation:
Uses Fractional Kelly (1/4 Kelly default) for safety. Full Kelly is theoretically optimal but can recommend large position sizes. Fractional Kelly reduces volatility while maintaining adaptive sizing benefits.
Enhancements:
Probability Bonus: Normalize(prob, 65, 95) × 0.5 multiplier
Divergence Bonus: Additional sizing on divergence signals
Regime Bonus: Additional sizing during strong trends (ADX >30)
Momentum Adjustment: Hot policies receive sizing boost, cold policies receive reduction
Safety Rails:
Minimum: 1 contract (floor)
Maximum: User-defined cap (default 10 contracts)
Portfolio Heat: Max total risk across all positions (default 4% equity)
Multi-Mode Stop Loss System
ATR Mode (Default):
Stop = entry ± (ATR × base_mult × policy_mult)
Consistent risk sizing
Ignores market structure
Best for: Futures, forex, algorithmic trading
Structural Mode:
Finds swing low (long) or high (short) over last 20 bars
Identifies fractal pivots within lookback
Places stop below/above structure + buffer (0.1× ATR)
Best for: Stocks, instruments that respect structure
Hybrid Mode (Intelligent):
Attempts structural stop first
Falls back to ATR if:
Structural level is invalid (beyond entry)
Structural stop >2× ATR away (too wide)
Best for: Mixed instruments, adaptability
Dynamic Adjustments:
Breakeven: Move stop to entry + 1 tick after 1.0R profit
Trailing: Trail stop 0.8R behind price after 1.5R profit
Timeout: Force close after 30 bars (optional)
Tiered Entry System
Challenge: Equal sizing on all signals may not optimize capital allocation relative to signal quality.
Solution:
Tier 1 (40% of size): Enters immediately on all signals
Tier 2 (60% of size): Enters only if probability ≥ Tier 2 trigger (default 75%)
Example:
Calculated optimal size: 10 contracts
Signal probability: 72%
Tier 2 trigger: 75%
Result: Enter 4 contracts only (Tier 1)
Same signal at 80% probability
Result: Enter 10 contracts (4 Tier 1 + 6 Tier 2)
Effect: Automatically scales size to signal quality, optimizing capital allocation.
Performance Optimization & Learning Curve
Warmup Phase (First 50 Trades)
Purpose: Ensure all policies get tested before system focuses on preferred strategies.
Modifications During Warmup:
Probability thresholds reduced 20% (65% becomes 52%)
Entropy thresholds increased 20% (more permissive)
Exploration rate stays high (30%)
Confidence width (α) doubled (more exploration)
Why This Matters:
Without warmup, system might commit to early-performing policy without testing alternatives. Warmup forces thorough exploration before focusing on best-performing strategies.
Curriculum Learning
Phase 1 (Trades 1-50): Exploration
Warmup active
All policies tested
High exploration (30%)
Learning fundamental patterns
Phase 2 (Trades 50-100): Refinement
Warmup ended, thresholds normalize
Exploration decaying (30% → 15%)
Policy preferences emerging
Meta-learning optimizing
Phase 3 (Trades 100-200): Specialization
Exploration low (15% → 8%)
Clear policy preferences established
Momentum tracking fully active
System focusing on learned patterns
Phase 4 (Trades 200+): Maturity
Exploration minimal (8% → 5%)
Regime-policy relationships learned
Auto-adaptation to market shifts
Stable performance expected
Convergence Indicators
System is learning well when:
Policy switch rate decreasing over time (initially ~50%, should drop to <20%)
Exploration rate decaying smoothly (30% → 5%)
One or two policies emerge with >50% selection frequency
Performance metrics stabilizing over time
Consistent behavior in similar market conditions
System may need adjustment when:
Policy switch rate >40% after 100 trades (excessive exploration)
Exploration rate not decaying (parameter issue)
All policies showing similar selection (not differentiating)
Performance declining despite relaxed thresholds (underlying signal issue)
Highly erratic behavior after learning phase
Advanced Features
Attention Mechanism (Extreme Mode)
Challenge: Not all features are equally important. Trading hour might matter more than price-volume correlation, but standard approaches treat them equally.
Solution:
Each RFF dimension has an importance weight . After each trade:
Calculate correlation: sign(feature - 0.5) × sign(reward)
Update importance: importance += correlation × 0.01
Clamp to range
Effect: Important features get amplified in RFF transformation, less important features get suppressed. System learns which features correlate with successful outcomes.
Temporal Context (Extreme Mode)
Challenge: Current market state alone may be incomplete. Historical context (was volatility rising or falling?) provides additional information.
Solution:
Includes 3-period historical context with exponential decay (0.85):
Current features (weight 1.0)
1 bar ago (weight 0.85)
2 bars ago (weight 0.72)
Effect: Captures momentum and acceleration of market features. System learns patterns like "rising volatility with falling entropy" that may precede significant moves.
Transfer Learning via Episodic Memory
Short-Term Memory (STM):
Last 20 trades
Fast adaptation to immediate regime
High learning rate
Long-Term Memory (LTM):
Condensed historical patterns
Preserved knowledge from past regimes
Low learning rate
Transfer Mechanism:
When STM fills (20 trades), patterns consolidated into LTM . When similar regime recurs later, LTM provides faster adaptation than starting from scratch.
Practical Implementation Guide - Recommended Settings by Instrument
Futures (ES, NQ, CL):
Adaptive Period: 20-25
ML Mode: Advanced
RFF Dimensions: 16
Policies: 6
Base Risk: 1.5%
Stop Mode: ATR or Hybrid
Timeframe: 5-15 min
Forex Majors (EURUSD, GBPUSD):
Adaptive Period: 25-30
ML Mode: Advanced
RFF Dimensions: 16
Policies: 6
Base Risk: 1.0-1.5%
Stop Mode: ATR
Timeframe: 5-30 min
Cryptocurrency (BTC, ETH):
Adaptive Period: 20-25
ML Mode: Extreme (handles non-stationarity)
RFF Dimensions: 32 (captures complexity)
Policies: 6
Base Risk: 1.0% (volatility consideration)
Stop Mode: Hybrid
Timeframe: 15 min - 4 hr
Stocks (Large Cap):
Adaptive Period: 25-30
ML Mode: Advanced
RFF Dimensions: 16
Policies: 5-6
Base Risk: 1.5-2.0%
Stop Mode: Structural or Hybrid
Timeframe: 15 min - Daily
Scaling Strategy
Phase 1 (Testing - First 50 Trades):
Max Contracts: 1-2
Goal: Validate system on your instrument
Monitor: Performance stabilization, learning progress
Phase 2 (Validation - Trades 50-100):
Max Contracts: 2-3
Goal: Confirm learning convergence
Monitor: Policy stability, exploration decay
Phase 3 (Scaling - Trades 100-200):
Max Contracts: 3-5
Enable: Kelly sizing (1/4 Kelly)
Goal: Optimize capital efficiency
Monitor: Risk-adjusted returns
Phase 4 (Full Deployment - Trades 200+):
Max Contracts: 5-10
Enable: Full momentum tracking
Goal: Sustained consistent performance
Monitor: Ongoing adaptation quality
Limitations & Disclaimers
Statistical Limitations
Learning Sample Size: System requires minimum 50-100 trades for basic convergence, 200+ trades for robust learning. Early performance (first 50 trades) may not reflect mature system behavior.
Non-Stationarity Risk: Markets change over time. A system trained on one market regime may need time to adapt when conditions shift (typically 30-50 trades for adjustment).
Overfitting Possibility: With 16-32 RFF dimensions and 6 policies, system has substantial parameter space. Small sample sizes (<200 trades) increase overfitting risk. Mitigated by regularization (λ) and fractional Kelly sizing.
Technical Limitations
Computational Complexity: Extreme mode with 32 RFF dimensions, 6 policies, and full RL stack requires significant computation. May perform slowly on lower-end systems or with many other indicators loaded.
Pine Script Constraints:
No true matrix inversion (uses diagonal approximation for LinUCB)
No cryptographic RNG (uses market data as entropy)
No proper random number generation for RFF (uses deterministic pseudo-random)
These approximations reduce mathematical precision compared to academic implementations but remain functional for trading applications.
Data Requirements: Needs clean OHLCV data. Missing bars, gaps, or low liquidity (<100k daily volume) can degrade signal quality.
Forward-Looking Bias Disclaimer
Reward Calculation Uses Future Data: The RL system evaluates trades using an 8-bar forward-looking window. This means when a position enters at bar 100, the reward calculation considers price movement through bar 108.
Why This is Disclosed:
Entry signals do NOT look ahead - decisions use only data up to entry bar
Forward data used for learning only, not signal generation
In live trading, system learns identically as bars unfold in real-time
Simulates natural learning process (outcomes are only known after trades complete)
Implication: Backtested metrics reflect this 8-bar evaluation window. Live performance may vary if:
- Positions held longer than 8 bars
- Slippage/commissions differ from backtest settings
- Market microstructure changes (wider spreads, different execution quality)
Risk Warnings
No Guarantee of Profit: All trading involves substantial risk of loss. Machine learning systems can fail if market structure fundamentally changes or during unprecedented events.
Maximum Drawdown: With 1.5% base risk and 4% max total risk, expect potential drawdowns. Historical drawdowns do not predict future drawdowns. Extreme market conditions can exceed expectations.
Black Swan Events: System has not been tested under: flash crashes, trading halts, circuit breakers, major geopolitical shocks, or other extreme events. Such events can exceed stop losses and cause significant losses.
Leverage Risk: Futures and forex involve leverage. Adverse moves combined with leverage can result in losses exceeding initial investment. Use appropriate position sizing for your risk tolerance.
System Failures: Code bugs, broker API failures, internet outages, or exchange issues can prevent proper execution. Always monitor automated systems and maintain appropriate safeguards.
Appropriate Use
This System Is:
✅ A machine learning framework for adaptive strategy selection
✅ A signal generation system with probabilistic scoring
✅ A risk management system with dynamic sizing
✅ A learning system designed to adapt over time
This System Is NOT:
❌ A price prediction system (does not forecast exact prices)
❌ A guarantee of profits (can and will experience losses)
❌ A replacement for due diligence (requires monitoring and understanding)
❌ Suitable for complete beginners (requires understanding of ML concepts, risk management, and trading fundamentals)
Recommended Use:
Paper trade for 100 signals before risking capital
Start with minimal position sizing (1-2 contracts) regardless of calculated size
Monitor learning progress via dashboard
Scale gradually over several months only after consistent results
Combine with fundamental analysis and broader market context
Set account-level risk limits (e.g., maximum drawdown threshold)
Never risk more than you can afford to lose
What Makes This System Different
RPD implements academically-derived machine learning algorithms rather than simple mathematical calculations or optimization:
✅ LinUCB Contextual Bandits - Algorithm from WWW 2010 conference (Li et al.)
✅ Random Fourier Features - Kernel approximation from NIPS 2007 (Rahimi & Recht)
✅ Q-Learning, TD(λ), REINFORCE - Standard RL algorithms from Sutton & Barto textbook
✅ Meta-Learning - Learning rate adaptation based on feature correlation
✅ Online Learning - Real-time updates from streaming data
✅ Hierarchical Policies - Two-stage selection with clustering
✅ Momentum Tracking - Recent performance analysis for faster adaptation
✅ Attention Mechanism - Feature importance weighting
✅ Transfer Learning - Episodic memory consolidation
Key Differentiators:
Actually learns from trade outcomes (not just parameter optimization)
Updates model parameters in real-time (true online learning)
Adapts to changing market regimes (not static rules)
Improves over time through reinforcement learning
Implements published ML algorithms with proper citations
Conclusion
RPD Machine Learning represents a different approach from traditional technical analysis to adaptive, self-learning systems . Instead of manually optimizing parameters (which can overfit to historical data), RPD learns behavior patterns from actual trading outcomes in your specific market.
The combination of contextual bandits, reinforcement learning, random fourier features, hierarchical policy selection, and momentum tracking creates a multi-algorithm learning system designed to handle non-stationary markets better than static approaches.
After the initial learning phase (50-100 trades), the system achieves autonomous adaptation - automatically discovering which strategies work in current conditions and shifting allocation without human intervention. This represents an approach where systems adapt over time rather than remaining static.
Use responsibly. Paper trade extensively. Scale gradually. Understand that past performance does not guarantee future results and all trading involves risk of loss.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
XAU/USD Weekly Volatility Strategy by WeTradeAIWeTradeAI - XAU/USD Weekly Volatility Strategy
This strategy is designed for Gold (XAU/USD) trading, leveraging a weekly market structure and volatility projection model. It dynamically identifies high-probability zones based on the prior week’s price action and adapts to intraday movement.
🔍 Core Logic:
Weekly High, Low & Midpoint: Defines structural balance for directional bias.
Projected Volatility Zones:
Green Zone: Upward projection from last week’s low.
Red Zone: Downward projection from last week’s high.
Half-Volatility Lines: Act as breakout or reversal triggers.
Monday Open: Serves as a temporary directional reference until midweek.
Daily High, Low, and Mid: Used for intraday stop-loss placement and validation.
🎯 Trade Entries:
Breakout Entries: Triggered when price breaks and holds above/below the Half-Vol levels.
Reversal Entries: Triggered by strong rejections near outer zones, reverting back toward equilibrium.
🛡️ Risk Management:
Dynamic Stop-Loss: Based on the previous day’s midpoint.
⏱️ Multi-Timeframe Usage:
4H – Weekly structure & context
1H – Trend alignment
15M – Precision entries
Futures Fighter MO: Multi-Confluence Day Trading System ADX/SMI👋 Strategy Overview: The Multi-Confluence Mashup
The Futures Fighter MO is a comprehensive, multi-layered day trading strategy designed for experienced traders focusing on high-liquidity futures contracts (e.g., NQ, ES, R2K).
This strategy is a sophisticated mashup that uses the 1-minute chart for surgical entries while enforcing strict environmental filtering through higher-timeframe data. We aim to capture high-conviction moves only when multiple, uncorrelated signals align.
🧠 How the Logic Works (Concepts & Confluence)
Our logic is built on four pillars, which must align for a trade to be executed:
Primary Trend Filter
Indicators :
ADX/DMI (15-Minute Lookback)
Role :
Price action is filtered to ensure the ADX (17/14) is above 25, confirming a strong, prevailing market trend (Bullish or Bearish). Trades are strictly rejected during "Flat" (sideways) market regimes.
Entry Signal Types
The system uses multiple entry types:
- 🟢 Trend Long/Short: A breakout/rejection near the 200-Period EMA is confirmed by the primary ADX trend.
- 🔴 Engulfing Rejection: A strong signal when a Bullish/Bearish Engulfing or Doji prints near the long-term 500-Period EMA (emaGOD) while the Stochastic Momentum Index (SMI on 30M) is in an extreme overbought/oversold state (below $-40$ or above $40$).
Volatility & Volume Confirmation
Indicators: Average True Range (ATR) and 20-Period SMA of Volume
Role: Every entry requires a volume spike (Current Volume $> 1.5 \times$ SMA Volume) to confirm that the move is supported by significant liquidity. Volatility is tracked via ATR to define bar range and stop boundaries.
Structural Guardrails
Indicators: Daily Pivot Points (PP, S1-S3, R1-R3)
Role: Trades are disabled if the current bar's price range intersects with a Daily Pivot Point. This is a critical filter to avoid high-chop consolidation zones near key structural levels.
📊 Strategy Results & Required Disclosures
I strive to publish backtesting results that are transparent and realistic for the retail futures trader.
- Initial Capital: $50,000 - A realistic base for Mini/Micro futures contracts.
- Order Size: 1 Contract (Pyramiding up to 3) - Conservative risk relative to the account size.
- Commission: $0.11 USD per order - Represents realistic costs for low-cost brokers.
- Slippage: 2 Ticks - Accounts for expected market friction.
⚠️ Risk Management & Deviations
Stop-Loss: The strategy uses a dynamic stop-loss system where positions are closed upon a reversal (e.g., breaking the 50-Period EMA or failure to hold a Pivot Point), rather than a fixed tick-based stop. This is suited for experienced traders using a low relative risk (single Micro-contract entry) on a larger account. Users must confirm that the first entry's maximum potential loss remains below $10\%$ of their capital for compliance.
Trade Sample Size: Due to data limitations of the TradingView Essential plan (showing $\approx 50$ trades over 2 weeks), the sample size is under the ideal $100+$ target. Justification: This system is designed to generate signals across a portfolio of correlated futures markets (NQ, ES, R2K, Gold, Crude), meaning the real sample size for a user tracking the portfolio is significantly higher.
Drawdown Control: This strategy is designed for manual management. It requires the user to turn the script/alerts OFF after a significant drawdown and only reactivate it once a recovery trend is established externally.
The strategy uses a combination of dynamic trailing stops, structural support/resistance zones, and a fixed profit target to manage open positions.
🛑 Strategy Exit Logic
1. General Stop-Loss (Dynamic Trailing Stop)
These conditions act as the primary dynamic stop, closing the position if the market reverses past a key Moving Average (MA):
- Long Positions Closed When: The current bar's close crosses under the 50-Period EMA (emaLong).
- Short Positions Closed When: The current bar's close crosses above the 50-Period EMA (emaLong).
2. Profit Target (Fixed Percentage)
The script includes a general exit based on a user-defined profit percentage:
Take Profit Trigger: The position is closed when the currentProfitPercent meets or exceeds the input Profit Target (%) (default is 1.0% of the entry price).
3. Structural Exits (Daily Pivot Points)
These exits are high-priority, "close all" orders that trigger when the price fails to hold or reclaims a recent Daily Pivot Point, suggesting a failure of the current move.
- VR Close All - Long ($\sym{size} > 0$) - Price crosses under a Daily Resistance Level (R1, R2, or R3) minus 1 ATR within the last 10 bars. This indicates the current momentum failed to hold Resistance as support.
- VS Close All - Short ($\sym{size} < 0$) - Price crosses above a Daily Support Level (S1, S2, or S3) plus 1 ATR within the last 10 bars. This indicates the current momentum failed to hold Support as resistance.
4. Trend Failure Exit (Trend-Following Signals Only)
This exit protects against holding a position when the primary high-timeframe trend used for the entry has failed:
- Long Positions Closed When: The primary trend is no longer "bullish" for more than 2 consecutive bars (i.e., it turned "bearish" or "flat").
- Short Positions Closed When: The primary trend is no longer "bearish" for more than 2 consecutive bars (i.e., it turned "bullish" or "flat").
5. End of Day (EOD) Session Control
The final hard exits based on time:
- End of Session (EoS): At 11:30 AM, new trades are disabled (TradingDay := false). Open positions are kept.
- End of Day (EoD): At 1:30 PM, all remaining open positions are closed (strategy.close_all).
🤝 Development & Disclaimer
This script and description were created with assistance from Gemini and GitHub Copilot. My focus is on helping fellow real estate investors and day traders develop mechanically sound systems.
Disclaimer: This is for educational purposes only and does not constitute financial advice. Always abide by the Realtor Code and manage your own risk.
Adaptive Cortex Strategy (ACS)Strategy Title: Adaptive Cortex Strategy (ACS)
This script is invite-only.
Part 1: Philosophy and the Fundamental Problem It Solves
Adaptive Cortex Strategy (ACS) is an advanced decision support system designed to dynamically adapt to the ever-changing characteristics of the market. A major weakness of traditional approaches is that while successful in a specific market condition (e.g., a strong trend), they become ineffective when the market changes course (e.g., enters a sideways range). ACS solves this problem by continuously analyzing the market's current "regime" and instantly adapting its decision-making logic accordingly.
Its primary goal is to enable the strategy itself to "think" and evolve with the market, without requiring the trader to change their strategy.
Part 2: Original Methodology and Proprietary Logic
A Note on the Original Methodology and Intellectual Property
This algorithm is not based on or copied from any open-source strategy code. The system utilizes the mathematical principles of widely accepted indicators such as ADX, RSI, and Ichimoku as data sources for its analyses.
However, the intellectual property and unique value of the algorithm lies in its unique and closed-source architecture that processes, prioritizes, and synthesizes data from these standard tools. The methods used in core components, particularly the adaptive 'Cortex' memory system and statistical 'Forecast' engine, represent a unique set of logic developed from scratch for this script. The parameters, order of operations, and conditional logic are entirely custom-designed. Therefore, the system's performance is a result of its unique design, not a repetition of publicly available code.
ACS's power lies not in the individual indicators it uses, but in the unique and proprietary logic layers that process the information from these indicators.
1. Multi-Factor Scoring and Adaptive Weighting:
The heart of the methodology is a scoring system that analyzes the market in four main categories: Trend, Support/Resistance, Momentum, and Volume. However, what makes ACS unique is that it dynamically changes the importance it assigns to these categories based on the market regime.
Unique Application: Using ADX, DMI, and ATR indicators, the system detects whether the market is in different regimes, such as "Strong Trend" or "High Volatility Squeeze." When it detects a strong trend, it automatically increases the weight of the Trend scores from the Ichimoku and proprietary AMF Trend Engine. When it detects sideways or tightness, it shifts its focus to Support/Resistance zones determined by Dynamic Channels and the author's "Cortex" Memory System. A different approach was added here, inspired by the classic Fibonacci estimation. This "adaptive weighting" ensures that the strategy always focuses its attention on the most appropriate area.
2. Statistical Forecast Engine:
ACS goes beyond standard indicators and includes a proprietary forecasting algorithm that measures the probability of a potential price movement's success.
Unique Implementation: The system stores the results of past tests (successful bounces/breakouts) at key price levels in a "brain" (memory). At the time of a new test, it compares the current RSI momentum, volume anomalies, and market regime with similar past situations. Based on this comparison, it calculates the probability of the current test being successful as a statistical percentage and adds this percentage to the final score as a "bonus" or "penalty."
3. Walk-Forward Architecture:
Markets constantly evolve. ACS continues to learn from the latest market dynamics by resetting its memory at regular intervals (e.g., monthly) through its "Re-Learn Mode," rather than being trapped by old data. This is an advanced approach aimed at ensuring the strategy remains current and effective over the long term.
Part 3: Practical Features and User Benefits
HOW DOES IT HELP INVESTORS?
Customizable Trading Profiles: ACS does not come with a single set of settings. Users can instantly adapt all the algorithm's key periods and decision thresholds to their trading style by selecting one of the pre-configured trading profiles, such as "SCALPING," "INTRADAY TREND," or "SWING TRADE." Additionally, they can further fine-tune the selected profile with "Speed Adjustment."
Full Automation Compatibility (JSON): The strategy is equipped with fully configurable JSON-formatted alert messages for buy, sell, and position closing transactions. This makes it possible to establish a fully automated trading system by connecting ACS signals to automation platforms such as 3Commas and PineConnector. Dynamic values such as position size ({{strategy.order.contracts}}) are automatically added to alerts.
Advanced and Adaptive Risk Management: Protecting capital is as important as making a profit. ACS offers a multi-layered risk management framework for this purpose:
Flexible Position Size: Allows you to set the risk for each trade as a percentage of capital or a fixed dollar amount.
Adaptive ATR Stop: The stop-loss level is dynamically expanded or contracted based on current market volatility (the ratio of short-term ATR to long-term ATR).
Contingency Mechanisms: Includes safety nets such as "Maximum Drawdown Protection" and the "Praetorian Guard" engine, which detects sudden market shocks.
Clear and Comprehensible Dashboard: Transforms dozens of complex data points into an intuitive dashboard that provides critical information such as market trends, major trends, support/resistance zones, and final signals at a glance.
Section 4: Disclaimers and Rules
Transparency Note: This algorithm uses the mathematical foundations of publicly available indicators such as ADX, ATR, RSI, and Ichimoku. However, ACS's intellectual property and unique value lies in its unique architecture, which combines data from these standard tools, prioritizes it by market trend, and synthesizes it with its proprietary "Cortex" and "Statistical Forecast" engines.
Educational Use:
IMPORTANT WARNING: The Adaptive Cortex Strategy is a professional decision support and analysis tool. It is NOT a system that promises "guaranteed profits." All trading activities involve the risk of capital loss. Past performance is no guarantee of future results. All signals and analysis generated by this script are for educational purposes only and should not be construed as investment advice. Users are solely responsible for applying their own risk management rules and making their final trading decisions.
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 - November 2, 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 123 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.
Trend Pullback System```{"variant":"standard","id":"36492","title":"Trend Pullback System Description"}
Trend Pullback System is a price-action trend continuation model that looks to enter on pullbacks, not breakouts. It’s designed to find high-quality long/short entries inside an already established trend, place the stop at meaningful structure, trail that stop as structure evolves, and warn you when the trade thesis is no longer valid.
Developed by: Mohammed Bedaiwi
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HOW IT WORKS
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1. Trend Detection
• The strategy defines overall bias using moving averages.
• Bullish environment (“uptrend”): price above the slower MA, fast MA above slow MA, and the slow MA is sloping up.
• Bearish environment (“downtrend”): price below the slower MA, fast MA below slow MA, and the slow MA is sloping down.
This prevents trading against chop and focuses on continuation moves in the dominant direction.
2. Pullback + Re-entry Logic
• The script waits for price to pull back into structure (support in an uptrend, resistance in a downtrend), and then push back in the direction of the main trend.
• That “push back” is the setup trigger. We don’t chase the first breakout candle — we buy/sell the retest + resume.
3. Structural Levels (“Diamonds”)
• Green diamond (below bar): bullish pivot low formed while the trend is bullish. This marks defended support.
- Use it as a re-entry zone for longs.
- Use it to trail a stop higher when you’re already long.
- Shorts can take profit here because buyers stepped in.
• Red diamond (above bar): bearish pivot high formed while the trend is bearish. This marks defended resistance.
- Use it as a re-entry zone for shorts.
- Use it to trail a stop lower when you’re already short.
- Longs can take profit here because sellers stepped in.
4. Entry Signals
• BUY arrow (green triangle up under the candle, text like “BUY” / “BUY Zone”):
- LongSetup is true.
- Trend is bullish or turning bullish.
- Price just bounced off recent defended support (green diamond) and reclaimed short-term momentum.
Meaning: enter long here or cover/exit shorts.
• SELL arrow (red triangle down above the candle):
- ShortSetup is true.
- Trend is bearish or turning bearish.
- Price just rolled down from defended resistance (red diamond) and lost short-term momentum.
Meaning: enter short here or take profit on longs.
These are the primary trade entries. They are meant to be actionable.
5. Weak Setups (“W” in yellow)
• Yellow triangle with “W”:
- A possible long/short idea is trying to form, BUT the higher-timeframe confirmation is not fully there yet.
- Think of it as early pressure / early caution, not a full signal.
• You usually watch these areas rather than jumping in immediately.
6. Exit Warning (orange “EXIT” label above a bar)
• The strategy will raise an EXIT marker when you’re in a trade and the *opposite* side just produced a confirmed setup.
- You’re short and a valid longSetup appears → EXIT.
- You’re long and a valid shortSetup appears → EXIT.
• This is basically: “Close or reduce — the other side just took control.”
• It’s not just a trailing stop hit; it’s a regime flip warning.
7. Stop, Target, and Trailing
• On every new setup, the script records:
- Initial stop: recent swing beyond the defended level (below support for longs, above resistance for shorts).
- Initial target: recent opposing swing.
• While you’re in position, if new confirming diamonds print in your favor, the stop can trail toward the new defended level.
• This creates structure-based risk management (not just fixed % or ATR).
8. Reference Levels
• The strategy also plots prior higher-timeframe closes (last week’s close, last month’s close, last year’s close). These can behave as magnets or stall points.
• They’re helpful for take-profit timing and for reading “are we trading above or below last month’s close?”
9. Momentum Panel (hidden by default)
• Internally, the script calculates an SMI-style momentum oscillator with overbought/oversold zones.
• This is optional visual confirmation and does not drive the core entry/exit logic.
---------------------------------
WHAT A TRADE LOOKS LIKE IN REAL PRICE ACTION
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Early warning
• Yellow W + red diamonds + red down arrows = “This is getting weak. Short setups are here.”
• You may also see something like “My Short Entry Id.” That’s where the short side actually engages.
Bearish follow-through, then exhaustion
• Price bleeds down.
• Then the orange EXIT appears.
→ Translation: “If you’re still short, close it. Buyers are stepping in hard. Risk of reversal is now high.”
Regime flip
• Right after EXIT, multiple green BUY arrows fire together (“BUY”, “BUYZone”).
• That’s the true long trigger.
→ This is where you either enter long or flip from short to long.
Expansion leg
• After that flip, price rips up for multiple candles / days / weeks.
• While it runs:
- Green diamonds appear under pullbacks → “dip buy zones / trail stop up here.”
- More BUY arrows show on minor pullbacks → continuation long / scale adds.
Distribution / topping
• Later, you start seeing new yellow W triangles again near local highs. That’s your “careful, this might be topping” warning.
• You finally get a hard red candle, and green diamonds stop stacking.
→ That’s where you tighten risk, scale out, or assume the move is mature.
In plain terms, the model is doing the following for you:
• It puts you short during weakness.
• It tells you when to get OUT of the short.
• It flips you long right as control changes.
• It gives you a structure-based trail the whole way up.
• It warns you again when momentum at the top starts cracking.
That is exactly how the logic was designed.
---------------------------------
QUICK INTERPRETATION CHEAT SHEET
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🔻 Red triangle + “Short Entry” near a red diamond
→ Short entry zone (or take profit on a long).
🟥 Red diamond above bar
→ Sellers defended here. Treat it as resistance. Good place to trail short stops just above that level. Avoid chasing longs straight into it.
🟨 Yellow W
→ Attention only. Early pressure / possible turn. Not fully confirmed.
🟧 EXIT (orange label)
→ The opposite side just printed a real setup. Close the old idea (cover shorts if you’re short, exit longs if you’re long). Thesis invalid.
🟩 Burst of green BUY triangles after EXIT
→ Long entry. Also a “cover shorts now” alert. This is the core money entry in bullish reversals.
💎 Green diamond below bar
→ Bulls defended that level. Good for trailing your long stop up, and good “buy the dip in trend” locations.
📈 Blue / teal MAs stacked and rising
→ Confirmed bullish structure. You’re in trend continuation mode, so dips are opportunities, not automatic exits.
---------------------------------
COLOR / SHAPE KEY
---------------------------------
• Green triangle up (“BUY”, “BUY Zone”):
Long entry / cover shorts / continuation long trigger.
• Red triangle down:
Short entry / take profit on longs / continuation short trigger.
• Orange “EXIT” label:
Opposite side just fired a real setup. The previous trade thesis is now invalid.
• Green diamond below price:
Bullish defended support in an uptrend. Use for dip buys, trailing stops on longs, and objective cover zones for shorts.
• Red diamond above price:
Bearish defended resistance in a downtrend. Use for re-entry shorts, trailing stops on shorts, and objective scale-out zones for longs.
• Yellow “W”:
Weak / early potential setup. Watch it, don’t blindly trust it.
• Moving average bands (fast MA, slow MA, Hull MA):
When stacked and rising, bullish control. When stacked and falling, bearish control.
---------------------------------
INTENT
---------------------------------
This system is built to:
• Trade with momentum, not against it.
• Enter on pullbacks into proven structure, not chase stretched breakouts.
• Automate stop/target logic around actual defended swing levels.
• Warn you when the other side takes over so you don’t give back gains.
Typical usage:
1. In an uptrend, wait for price to pull back, print a green diamond (support proved), then take the first BUY arrow that fires.
2. In a downtrend, wait for a bounce into resistance, print a red diamond (sellers proved), then take the first SELL arrow that fires.
3. Respect EXIT when it appears — that’s the model saying “this trade is done.”
---------------------------------
DISCLAIMER
---------------------------------
This script is for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security, cryptoasset, or derivative. Markets carry risk. Past performance does not guarantee future results. You are fully responsible for your own decisions, position sizing, risk management, and compliance with all applicable laws and regulations.
Algoritmictrader2025 ALGO System profitability works with a minimum profit margin of 75% and the maximum profit margin per share is around 95%. The software costs $150 per month.
FUTURA ORB.o3 Stategy (Gap + Dynamic Risk)ORB Strategy
Includes Mini & Micro Futures
Dynamic Risk based position sizing
Adjustable RR Levels
Gap Detection
Default settings are for NQ & MNQ.
Adjust as needed for different futures.
Pitchfork-Trading Friendsuses the pitchfork to give entry and exit zones, and gives a net overall summary for a beginner trader to enter into.
