Pro Bollinger Bands Strategy [Breno]This strategy excels in highly volatile financial instruments, including cryptocurrencies, high-beta stocks, commodity futures, and certain exchange-traded funds (ETFs) that exhibit clear mean-reversion characteristics around their Bollinger Bands. The system's ability to utilize scaling (position averaging) and an ATR-based stop loss makes it particularly effective in markets with significant price swings, allowing the trader to capture profits from price extremes while managing increased volatility-related risk.
Core Strategy Logic
This Strategy implements a comprehensive trend-following and mean-reversion strategy primarily leveraging the Bollinger Bands (BB) indicator for entry and exit signals, complemented by an Average True Range (ATR)-based Stop Loss mechanism and an optional EMA filter. It is designed with robust features for capital management, including configurable leverage and a sophisticated position averaging (scaling) system.
Long Entry: A long position is initiated when the closing price crosses over the Lower Bollinger Band (ta.crossover(close,lowerBB)). This signals a potential mean-reversion opportunity following a price dip.
Short Entry: A short position is initiated when the closing price crosses under the Upper Bollinger Band (ta.crossunder(close,upperBB)). (Note: Short entries are disabled by default in the script inputs).
Exit Conditions (Profit Target): Long positions aim to exit upon interaction with the Upper Bollinger Band. Users can select from three exit methods:
"Close When Touch": Exits when close≥upperBB.
"Close Above then Below": Exits when the previous close was above the upper band, and the current close is below it (a reversal signal).
"High Above": Exits when high>upperBB. The strategy features an optional profitOnly setting, which restricts all exits to only occur if the trade is currently in profit (i.e., close is above the strategy.position_avg_price for longs).
Key Features and Customization
Bollinger Bands & Filters -
Customizable BB Parameters: The Length and Deviation of the Bollinger Bands are fully adjustable, allowing users to fine-tune the sensitivity of the entry and exit signals.
Optional EMA Filter: An optional EMA Filter can be enabled to align entries with the prevailing trend, where a Long entry is only permitted if close≥EMA(EmaFilterRange).
Risk and Capital Management -
Equity Allocation: Position size is dynamically calculated based on a Percentage of Equity (capitalPerc) combined with the set Leverage multiplier.
Dynamic Stop Loss (ATR-Based):
An optional Stop Loss (SL) is calculated using a multiple (slAtrInput) of the Average True Range (ATR).
The SL is set relative to the entry price upon trade activation, providing a volatility-adjusted risk management layer.
Position Averaging (Scaling): The script supports the addition of multiple units (pyramiding) to an existing position based on three user-selected criteria:
"No": No averaging.
"Percent": Adds to the position if the price has dropped by a set percentage (addPct) from the average price.
"ATR": Adds to the position if the current price is significantly below a calculated ATR-based support level from the average price.
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Adaptive Averaging Concept [NeuraAlgo]Adaptive Averaging Concept
A Quant-Engineered Dynamic Position Sizing & Optimization Framework
Adaptive Averaging Concept™ is a next-generation, research-driven trading framework that combines multistage entries, ATR-based intelligent scaling, real-time sentiment filtering, and a fully automated optimization engine.
It is designed for traders who want precision execution, adaptive risk control, and an architecture capable of learning from market structure.
🔹 Core Concept
Unlike traditional averaging or DCA methods, this engine uses Adaptive Averaging — a controlled, mathematically tuned accumulation system that adjusts entries based on volatility, trend conditions, and signal confidence.
Each additional entry intelligently recalculates average price and updates a volatility-sensitive dynamic Take Profit.
🔹 Main Features
1. Intelligent Multi-Stage Entry System
Initial entries triggered by SMA crossover, rising volume, or Always-On mode
Secondary entries triggered only when price retraces by a volatility-adjusted threshold
Every added position recalculates:
Total quantities
Capital distribution
Average price
Adaptive Take Profit (ATR-based)
2. Adaptive Risk & Position Management
ATR-driven take-profit using Exit Sensitivity
ATR-driven add-entry logic using Exit Tuner
Dynamic or Fixed lot sizing
Capital-per-entry control
Automatic minimum lot protection
3. High-Level Market Filters
Trend Filter
A volatility-normalized EMA slope filter that identifies:
1.Bullish trend
2.Bearish trend
3.Neutral trend
Sentiment Cloud Filter
A structural sentiment engine analyzing:
1.Micro-gaps
2.Bull and bear pressure
3.Range compression
4.Market regime bias
Trades only execute when filters align with your directional bias.
4. NeuraAlgo Optimization Engine
The strategy includes a built-in optimizer allowing you to test & tune with no loops and no external computation.
You can automatically optimize:
Smooth Period (ATR)
Exit Sensitivity
Exit Tuner
SMA Period
Trend Filter Length
Trend Filter Smooth
Sentiment Cloud Period
Optimization Goals:
Maximize Winrate
Maximize Net Profits
This allows the strategy to self-configure based on live market conditions.
Here, the optimization is finally complete.
🔹 Summary
Adaptive Averaging Concept™ is not a simple indicator or basic DCA script.
It is a complete quant-grade execution engine capable of dynamically adjusting its behavior to volatility, price structure, trend strength, and sentiment.
Engineered for traders who demand:
High-precision entry logic
Adaptive position sizing
Volatility-calibrated exits
Smart accumulation
Built-in optimization
Professional-grade backtesting
It is a powerful framework suitable for swing traders, intraday traders, and automated system developers.
DynamicQuant Lite Strategy v1.1.1🚀 DynamicQuant Pro - Adaptive Channel-Based Trading Strategy
📊 Strategy Overview
DynamicQuant Pro is an adaptive trading strategy based on price channel breakouts. It offers both trend-following and mean-reversion modes to adapt to various market conditions.
⚡ Core Features
🎯 Entry System
- Channel Breakout Based: Uses upper/lower band breakouts as entry signals
- Multi-Layer Filtering: Triple-filter system combining volume, momentum, and volatility indicators to eliminate false signals
- Smart Entry Control: Entry restriction zones and minimum bar spacing to prevent excessive positions
- Multi-Stage Position Building: Up to 5-stage scaling to optimize average entry price
🔄 Exit System (4 Modes)
- Band Mode: Exit based on channel centerline
- Split Mode: Individual exit per entry price
- Trailing Mode: Dynamic trailing exit
- Position Mode: Unified exit based on average price
🛡️ Risk Management
- Advanced Stop Loss: Intelligent exit system with recovery failure detection and time-based stops
- Multi-Level Take Profit: Flexible exit strategies including weighted partial exits and ladder profits
- Profit Protection: Safety mechanism preventing exits at loss levels
- Leverage-Based Margin Management: Margin calculation matching real exchange systems
✨ Key Strengths
⚡ Real-Time Exits: Tick-by-tick monitoring for immediate exits when targets are reached (no waiting for bar close)
📈 Detailed Visualization: Real-time PnL, entry prices, targets, stops - all displayed on chart
📊 Backtest Performance Table: Detailed statistics including win rate, profit factor, Long/Short performance
🎛️ Flexible Configuration: 30+ parameters to customize to your trading style
👥 Ideal For
✅ Traders seeking systematic risk management
✅ Traders looking for adaptable strategies across market conditions
✅ Traders preferring backtest-based strategy optimization
✅ Traders interested in scaling entry/exit strategies
⚠️ Disclaimer
This strategy is for educational and informational purposes only. Past performance does not guarantee future results. Trading involves substantial risk of loss. Always conduct your own research and risk assessment before trading with real capital.
Superior-Range Bound Renko - Strategy - 11-29-25 - SignalLynxSuperior-Range Bound Renko Strategy with Advanced Risk Management Template
Signal Lynx | Free Scripts supporting Automation for the Night-Shift Nation 🌙
1. Overview
Welcome to Superior-Range Bound Renko (RBR) — a volatility-aware, structure-respecting swing-trading system built on top of a full Risk Management (RM) Template from Signal Lynx.
Instead of relying on static lookbacks (like “14-period RSI”) or plain MA crosses, Superior RBR:
Adapts its range definition to market volatility in real time
Emulates Renko Bricks on a standard, time-based chart (no Renko chart type required)
Uses a stack of Laguerre Filters to detect genuine impulse vs. noise
Adds an Adaptive SuperTrend powered by a small k-means-style clustering routine on volatility
Under the hood, this script also includes the full Signal Lynx Risk Management Engine:
A state machine that separates “Signal” from “Execution”
Layered exit tools: Stop Loss, Trailing Stop, Staged Take Profit, Advanced Adaptive Trailing Stop (AATS), and an RSI-style stop (RSIS)
Designed for non-repainting behavior on closed candles by basing execution-critical logic on previous-bar data
We are publishing this as an open-source template so traders and developers can leverage a professional-grade RM engine while integrating their own signal logic if they wish.
2. Quick Action Guide (TL;DR)
Best Timeframe:
4 Hours (H4) and above. This is a high-conviction swing-trading system, not a scalper.
Best Assets:
Volatile instruments that still respect market structure:
Bitcoin, Ethereum, Gold (XAUUSD), high-volatility Forex pairs (e.g., GBPJPY), indices with clean ranges.
Strategy Type:
Volatility-Adaptive Trend Following + Impulse Detection.
It hunts for genuine expansion out of ranges, not tiny mean-reversion nibbles.
Key Feature:
Renko Emulation on time-based candles.
We mathematically model Renko Bricks and overlay them on your standard chart to define:
“Equilibrium” zones (inside the brick structure)
“Breakout / impulse” zones (when price AND the impulse line depart from the bricks)
Repainting:
Designed to be non-repainting on closed candles.
All RM execution logic uses confirmed historical data (no future bars, no security() lookahead). Intrabar flicker during formation is allowed, but once a bar closes the engine’s decisions are stable.
Core Toggles & Filters:
Enable Longs and Shorts independently
Optional Weekend filter (block trades on Saturday/Sunday)
Per-module toggles: Stop Loss, Trailing Stop, Staged Take Profits, AATS, RSIS
3. Detailed Report: How It Works
A. The Strategy Logic: Superior RBR
Superior RBR builds its entry signal from multiple mathematical layers working together.
1) Adaptive Lookback (Volatility Normalization)
Instead of a fixed 100-bar or 200-bar range, the script:
Computes ATR-based volatility over a user-defined period.
Normalizes that volatility relative to its recent min/max.
Maps the normalized value into a dynamic lookback window between a minimum and maximum (e.g., 4 to 100 bars).
High Volatility:
The lookback shrinks, so the system reacts faster to explosive moves.
Low Volatility:
The lookback expands, so the system sees a “bigger picture” and filters out chop.
All the core “Range High/Low” and “Range Close High/Low” boundaries are built on top of this adaptive window.
2) Range Construction & Quick Ranges
The engine constructs several nested ranges:
Outer Range:
rangeHighFinal – dynamic highest high
rangeLowFinal – dynamic lowest low
Inner Close Range:
rangeCloseHighFinal – highest close
rangeCloseLowFinal – lowest close
Quick Ranges:
“Half-length” variants of those, used to detect more responsive changes in structure and volatility.
These ranges define:
The macro box price is trading inside
Shorter-term “pressure zones” where price is coiling before expansion
3) Renko Emulation (The Bricks)
Rather than using the Renko chart type (which discards time), this script emulates Renko behavior on your normal candles:
A “brick size” is defined either:
As a standard percentage move, or
As a volatility-driven (ATR) brick, optionally inhibited by a minimum standard size
The engine tracks a base value and derives:
brickUpper – top of the emulated brick
brickLower – bottom of the emulated brick
When price moves sufficiently beyond those levels, the brick “shifts”, and the directional memory (renkoDir) updates:
renkoDir = +2 when bricks are advancing upward
renkoDir = -2 when bricks are stepping downward
You can think of this as a synthetic Renko tape overlaid on time-based candles:
Inside the brick: equilibrium / consolidation
Breaking away from the brick: momentum / expansion
4) Impulse Tracking with Laguerre Filters
The script uses multiple Laguerre Filters to smooth price and brick-derived data without traditional lag.
Key filters include:
LagF_1 / LagF_W: Based on brick upper/lower baselines
LagF_Q: Based on HLCC4 (high + low + 2×close)/4
LagF_Y / LagF_P: Complex averages combining brick structures and range averages
LagF_V (Primary Impulse Line):
A smooth, high-level impulse line derived from a blend of the above plus the outer ranges
Conceptually:
When the impulse line pushes away from the brick structure and continues in one direction, an impulse move is underway.
When its direction flips and begins to roll over, the impulse is fading, hinting at mean reversion back into the range.
5) Fib-Based Structure & Swaps
The system also layers in Fib levels derived from the adaptive ranges:
Standard levels (12%, 23.6%, 38.2%, 50%, 61%, 76.8%, 88%) from the main range
A secondary “swap” set derived from close-range dynamics (fib12Swap, fib23Swap, etc.)
These Fibs are used to:
Bucket price into structural zones (below 12, between 23–38, etc.)
Detect breakouts when price and Laguerre move beyond key Fib thresholds
Drive zSwap logic (where a secondary Fib set becomes the active structure once certain conditions are met)
6) Adaptive SuperTrend with K-Means-Style Volatility Clustering
Under the hood, the script uses a small k-means-style clustering routine on ATR:
ATR is measured over a fixed period
The range of ATR values is split into Low, Medium, High volatility centroids
Current ATR is assigned to the nearest centroid (cluster)
From that, a SuperTrend variant (STK) is computed with dynamic sensitivity:
In quiet markets, SuperTrend can afford to be tighter
In wild markets, it widens appropriately to avoid constant whipsaw
This SuperTrend-based oscillator (LagF_K and its signals) is then combined with the brick and Laguerre stack to confirm valid trend regimes.
7) Final Baseline Signals (+2 / -2)
The “brain” of Superior RBR lives in the Baseline & Signal Generation block:
Two composite signals are built: B1 and B2:
They combine:
Fib breakouts
Renko direction (renkoDir)
Expansion direction (expansionQuickDir)
Multiple Laguerre alignments (LagF_Q, LagF_W, LagF_Y, LagF_Z, LagF_P, LagF_V)
They also factor in whether Fib structures are expanding or contracting.
A user toggle selects the “Baseline” signal:
finalSig = B2 (default) or B1 (alternate baseline)
finalSig is then filtered through the RM state machine and only when everything aligns, we emit:
+2 = Long / Buy signal
-2 = Short / Sell signal
0 = No new trade
Those +2 / -2 values are what feed the Risk Management Engine.
B. The Risk Management (RM) Engine
This script features the Signal Lynx Risk Management Engine, a proprietary state machine built to separate Signal from Execution.
Instead of firing orders directly on indicator conditions, we:
Convert the raw signal into a clean integer (Fin = +2 / -2 / 0)
Feed it into a Trade State Machine that understands:
Are we flat?
Are we in a long or short?
Are we in a closing sequence?
Should we permit re-entry now or wait?
Logic Injection / Template Concept:
The RM engine expects a simple integer:
+2 → Buy
-2 → Sell
Everything else (0) is “no new trade”
This makes the script a template:
You can remove the Superior RBR block
Drop in your own logic (RSI, MACD, price action, etc.)
As long as you output +2 or -2 into the same signal channel, the RM engine can drive all exits and state transitions.
Aggressive vs Conservative Modes:
The input AgressiveRM (Aggressive RM) governs how we interpret signals:
Conservative Mode (Aggressive RM = false):
Uses a more filtered internal signal (AF) to open trades
Effectively waits for a clean trend flip / confirmation before new entries
Minimizes whipsaw at the cost of fewer trades
Aggressive Mode (Aggressive RM = true):
Reacts directly to the fresh alert (AO) pulses
Allows faster re-entries in the same direction after RM-based exits
Still respects your pyramiding setting; this script ships with pyramiding = 0 by default, so it will not stack multiple positions unless you change that parameter in the strategy() call.
The state machine enforces discipline on top of your signal logic, reducing double-fires and signal spam.
C. Advanced Exit Protocols (Layered Defense)
The exit side is where this template really shines. Instead of a single “take profit or stop loss,” it uses multiple, cooperating layers.
1) Hard Stop Loss
A classic percentage-based Stop Loss (SL) relative to the entry price.
Acts as a final “catastrophic protection” layer for unexpected moves.
2) Standard Trailing Stop
A percentage-based Trailing Stop (TS) that:
Activates only after price has moved a certain percentage in your favor (tsActivation)
Then trails price by a configurable percentage (ts)
This is a straightforward, battle-tested trailing mechanism.
3) Staged Take Profits (Three Levels)
The script supports three staged Take Profit levels (TP1, TP2, TP3):
Each stage has:
Activation percentage (how far price must move in your favor)
Trailing amount for that stage
Position percentage to close
Example setup:
TP1:
Activate at +10%
Trailing 5%
Close 10% of the position
TP2:
Activate at +20%
Trailing 10%
Close another 10%
TP3:
Activate at +30%
Trailing 5%
Close the remaining 80% (“runner”)
You can tailor these quantities for partial scaling out vs. letting a core position ride.
4) Advanced Adaptive Trailing Stop (AATS)
AATS is a sophisticated volatility- and structure-aware stop:
Uses Hirashima Sugita style levels (HSRS) to model “floors” and “ceilings” of price:
Dungeon → Lower floors → Mid → Upper floors → Penthouse
These levels classify where current price sits within a long-term distribution.
Combines HSRS with Bollinger-style envelopes and EMAs to determine:
Is price extended far into the upper structure?
Is it compressed near the lower ranges?
From this, it computes an adaptive factor that controls how tight or loose the trailing level (aATS / bATS) should be:
High Volatility / Penthouse areas:
Stop loosens to avoid getting wicked out by inevitable spikes.
Low Volatility / compressed structure:
Stop tightens to lock in and protect profit.
AATS is designed to be the “smart last line” that responds to context instead of a single fixed percentage.
5) RSI-Style Stop (RSIS)
On top of AATS, the script includes a RSI-like regime filter:
A McGinley Dynamic mean of price plus ATR bands creates a dynamic channel.
Crosses above the top band and below the lower band change a directional state.
When enabled (UseRSIS):
RSIS can confirm or veto AATS closes:
For longs: A shift to bearish RSIS can force exits sooner.
For shorts: A shift to bullish RSIS can do the same.
This extra layer helps avoid over-reactive stops in strong trends while still respecting a regime change when it happens.
D. Repainting Protection
Many strategies look incredible in the Strategy Tester but fail in live trading because they rely on intrabar values or future-knowledge functions.
This template is built with closed-candle realism in mind:
The Risk Management logic explicitly uses previous bar data (open , high , low , close ) for the key decisions on:
Trailing stop updates
TP triggers
SL hits
RM state transitions
No security() lookahead or future-bar access is used.
This means:
Backtest behavior is designed to match what you can actually get with TradingView alerts and live automation.
Signals may “flicker” intrabar while the candle is forming (as with any strategy), but on closed candles, the RM decisions are stable and non-repainting.
4. For Developers & Modders
We strongly encourage you to mod this script.
To plug your own strategy into the RM engine:
Look for the section titled:
// BASELINE & SIGNAL GENERATION
You will see composite logic building B1 and B2, and then selecting:
baseSig = B2
altSig = B1
finalSig = sigSwap ? baseSig : altSig
You can replace the content used to generate baseSig / altSig with your own logic, for example:
RSI crosses
MACD histogram flips
Candle pattern detectors
External condition flags
Requirements are simple:
Your final logic must output:
2 → Buy signal
-2 → Sell signal
0 → No new trade
That output flows into the RM engine via finalSig → AlertOpen → state machine → Fin.
Once you wire your signals into finalSig, the entire Risk Management system (Stops, TPs, AATS, RSIS, re-entry logic, weekend filters, long/short toggles) becomes available for your custom strategy without re-inventing the wheel.
This makes Superior RBR not just a strategy, but a reference architecture for serious Pine dev work.
5. About Signal Lynx
Automation for the Night-Shift Nation 🌙
Signal Lynx focuses on helping traders and developers bridge the gap between indicator logic and real-world automation. The same RM engine you see here powers multiple internal systems and templates, including other public scripts like the Super-AO Strategy with Advanced Risk Management.
We provide this code open source under the Mozilla Public License 2.0 (MPL-2.0) to:
Demonstrate how Adaptive Logic and structured Risk Management can outperform static, one-layer indicators
Give Pine Script users a battle-tested RM backbone they can reuse, remix, and extend
If you are looking to automate your TradingView strategies, route signals to exchanges, or simply want safer, smarter strategy structures, please keep Signal Lynx in your search.
License: Mozilla Public License 2.0 (Open Source).
If you make beneficial modifications, please consider releasing them back to the community so everyone can benefit.
Super-AO with Risk Management Strategy Template - 11-29-25Super-AO Strategy with Advanced Risk Management Template
Signal Lynx | Free Scripts supporting Automation for the Night-Shift Nation 🌙
1. Overview
Welcome to the Super-AO Strategy. This is more than just a buy/sell indicator; it is a complete, open-source Risk Management (RM) Template designed for the Pine Script community.
At its core, this script implements a robust swing-trading strategy combining the SuperTrend (for macro direction) and the Awesome Oscillator (for momentum). However, the real power lies under the hood: a custom-built Risk Management Engine that handles trade states, prevents repainting, and manages complex exit conditions like Staged Take Profits and Advanced Adaptive Trailing Stops (AATS).
We are releasing this code to help traders transition from simple indicators to professional-grade strategy structures.
2. Quick Action Guide (TL;DR)
Best Timeframe: 4 Hours (H4) and above. Designed for Swing Trading.
Best Assets: "Well-behaved" assets with clear liquidity (Major Forex pairs, BTC, ETH, Indices).
Strategy Type: Trend Following + Momentum Confirmation.
Key Feature: The Risk Management Engine is modular. You can strip out the "Super-AO" logic and insert your own strategy logic into the template easily.
Repainting: Strictly Non-Repainting. The engine calculates logic based on confirmed candle closes.
3. Detailed Report: How It Works
A. The Strategy Logic: Super-AO
The entry logic is based on the convergence of two classic indicators:
SuperTrend: Determines the overall trend bias (Green/Red).
Awesome Oscillator (AO): Measures market momentum.
The Signal:
LONG (+2): SuperTrend is Green AND AO is above the Zero Line AND AO is Rising.
SHORT (-2): SuperTrend is Red AND AO is below the Zero Line AND AO is Falling.
By requiring momentum to agree with the trend, this system filters out many false signals found in ranging markets.
B. The Risk Management (RM) Engine
This script features a proprietary State Machine designed by Signal Lynx. Unlike standard strategies that simply fire orders, this engine separates the Signal from the Execution.
Logic Injection: The engine listens for a specific integer signal: +2 (Buy) or -2 (Sell). This makes the code a Template. You can delete the Super-AO section, write your own logic, and simply pass a +2 or -2 to the RM_EngineInput variable. The engine handles the rest.
Trade States: The engine tracks the state of the trade (Entry, In-Trade, Exiting) to prevent signal spamming.
Aggressive vs. Conservative:
Conservative Mode: Waits for a full trend reversal before taking a new trade.
Aggressive Mode: Allows for re-entries if the trend is strong and valid conditions present themselves again (Pyramiding Type 1).
C. Advanced Exit Protocols
The strategy does not rely on a single exit point. It employs a "Layered Defense" approach:
Hard Stop Loss: A fixed percentage safety net.
Staged Take Profits (Scaling Out): The script allows you to set 3 distinct Take Profit levels. For example, you can close 10% of your position at TP1, 10% at TP2, and let the remaining 80% ride the trend.
Trailing Stop: A standard percentage-based trailer.
Advanced Adaptive Trailing Stop (AATS): This is a highly sophisticated volatility stop. It calculates market structure using Hirashima Sugita (HSRS) levels and Bollinger Bands to determine the "floor" and "ceiling" of price action.
If volatility is high: The stop loosens to prevent wicking out.
If volatility is low: The stop tightens to protect profit.
D. Repainting Protection
Many Pine Script strategies look great in backtesting but fail in live trading because they rely on "real-time" price data that disappears when the candle closes.
This Risk Management engine explicitly pulls data from the previous candle close (close , high , low ) for its calculations. This ensures that the backtest results you see match the reality of live execution.
4. For Developers & Modders
We encourage you to tear this code apart!
Look for the section titled // Super-AO Strategy Logic.
Replace that block with your own RSI, MACD, or Price Action logic.
Ensure your logic outputs a 2 for Buy and -2 for Sell.
Connect it to RM_EngineInput.
You now have a fully functioning Risk Management system for your custom strategy.
5. About Signal Lynx
Automation for the Night-Shift Nation 🌙
This code has been in action since 2022 and is a known performer in PineScript v5. We provide this open source to help the community build better, safer automated systems.
If you are looking to automate your strategies, please take a look at Signal Lynx in your search.
License: Mozilla Public License 2.0 (Open Source). If you make beneficial modifications, please release them back to the community!
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.
Liquidity Sweep & FVG StrategyThis strategy combines higher-timeframe liquidity levels, stop-hunt (sweep) logic, Fair Value Gaps (FVGs) and structure-based take-profits into a single execution engine.
It is not a simple mash-up of indicators: every module (HTF levels, sweeps, FVGs, ZigZag, sessions) feeds the same entry/exit logic.
1. Core Idea
The script looks for situations where price:
Sweeps a higher-timeframe high/low (takes liquidity around obvious levels),
Then forms a displacement candle with a gap (FVG) in the opposite direction,
Then uses the edge of that FVG as a limit entry,
And manages exits using unswept structural levels (ZigZag swings or HTF levels) as targets.
The intent is to systematically trade failed breakouts / stop hunts with a defined structure and risk model.
It is a backtesting / study tool, not a signal service.
2. How the Logic Works (Conceptual)
a) Higher-Timeframe Liquidity Engine
Daily, Weekly and Monthly highs/lows are pulled via request.security() and stored as HTF liquidity levels.
Each level is drawn as a line with optional label (1D/1W/1M High/Low).
A level is marked as “swept” once price trades through it; swept levels may be removed or shortened depending on settings.
b) Sweep & Manipulation Filter
A low sweep occurs when the current low trades through a stored HTF low.
A high sweep occurs when the current high trades through a stored HTF high.
If both a high and a low are swept in the same bar, the script flags this as “manipulation” and blocks new entries around that noise.
The script also tracks the sweep wick, bar index and HTF timeframe for later use in SL placement and labels.
c) FVG Detection & Management
FVGs are defined using a 3-candle displacement model:
Bullish FVG: high < low
Bearish FVG: low > high
Only gaps larger than a minimum size (ATR-based if no manual value is set) are kept.
FVGs are stored in arrays as boxes with: top, bottom, mid (CE), direction, and state (filled / reclaimed).
Boxes are auto-extended and visually faded when price is far away, or deleted when filled.
d) Entry Conditions (Sweep + FVG)
For each recent sweep window:
After a low sweep, the script searches for the nearest bullish FVG below price and uses its top edge as a long limit entry.
After a high sweep, it searches for the nearest bearish FVG above price and uses its bottom edge as a short limit entry.
A “knife protection” check blocks trades where price is already trading through the proposed stop.
Only one entry per sweep is allowed; entries are only placed inside the configured NY trading sessions and only if no manipulation flag is active and EOD protection allows it.
e) Stop-Loss Placement (“Tick-Free” SL)
The stop is not placed directly on the HTF level; instead, the script scans a window around the sweep bar to find a local extreme:
Longs: lowest low in a configurable bar window around the sweep.
Shorts: highest high in that window.
This produces a structure-based SL that is generally outside the main sweep wick.
f) Take-Profit Logic (ZigZag + HTF Levels)
A lightweight ZigZag engine tracks swing highs/lows and removes levels that have already been broken.
For intraday timeframes (< 1h), TP candidates come from unswept ZigZag swings above/below the entry.
For higher timeframes (≥ 1h), TP candidates fall back to unswept HTF liquidity levels.
The script picks up to two targets:
TP1: nearest valid target in the trade direction (or a 2R fallback if none exists),
TP2: second target (or a 4R fallback if none exists).
A multi-TP model is used: typically 50% at TP1, remainder managed towards TP2 with breakeven plus offset once TP1 is hit.
g) Session & End-of-Day Filters
Three predefined NY sessions (Early, Open, Afternoon) are available; entries are only allowed inside active sessions.
An End-of-Day filter checks a user-defined NY close time and:
Blocks new entries close to the end of the day,
Optionally forces flat before the close.
3. Inputs Overview (Conceptual)
Liquidity settings: which HTF levels to track (1D/1W/1M), how many to show, and sweep priority (highest TF vs nearest vs any).
FVG settings: visibility radius, search window after a sweep, minimum FVG size.
ZigZag settings: swing length used for TP discovery.
Execution & protection: limit order timeout, breakeven offset, EOD protection.
Visuals: labels, sweep markers, manipulation warning, session highlighting, TP lines, etc.
For exact meaning of each input, please refer to the inline comments in the open-source code.
4. Strategy Properties & Backtesting Notes
Default strategy properties in this script:
Initial capital: 100,000
Order size: 10% of equity (strategy.percent_of_equity)
Commission: 0.01% per trade (adjust as needed for your broker/asset)
Slippage: must be set manually in the Strategy Tester (recommended: at least a few ticks on fast markets).
Even though the order size is 10% of equity, actual risk per trade depends on the SL distance and is typically much lower than 10% of the account. You should still adjust these values to keep risk within what you personally consider sustainable (e.g. somewhere in the 1–2% range per trade).
For more meaningful results:
Test on liquid instruments (e.g. major indices, FX, or liquid futures).
Use enough history to reach 100+ closed trades on your market/timeframe.
Always include realistic commission and slippage.
Do not assume that past performance will continue.
5. How to Use
Apply the strategy to your preferred symbol and timeframe.
Set broker-like commission and slippage in the Strategy Tester.
Adjust:
HTF levels (1D/1W/1M),
Sessions (NY windows),
FVG search window and minimum size,
ZigZag length and EOD filter.
Observe how entries only appear:
After a HTF sweep,
In the configured session,
At a FVG edge,
With TP lines anchored at unswept structure / liquidity.
Use this primarily as a research and backtesting tool to study how your own ICT / SMC ideas behave over a large sample of trades.
6. Disclaimer
This script is for educational and research purposes only.
It does not constitute financial advice, and it does not guarantee profitability. Always validate results with realistic assumptions and use your own judgment before trading live.
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
BTC Risk Metric DCA Adapter (3Commas Webhook Strategy)Risk Metric DCA Adapter (3Commas Webhook Strategy) - WORK IN PROGRESS
This Pine Script strategy, originally inspired by the Risk Metric Indicator, is fundamentally engineered as an Adapter to interface with external trading bots like 3Commas via Webhooks. It calculates a dynamic market risk score and translates that score into specific dollar-cost averaging (DCA) entry levels and tiered profit-taking exits.
Key Features & Logic
Risk Metric Calculation (Credit to The Trading Parrot):
The strategy incorporates a complex, multi-timeframe Risk Metric calculation based on daily and weekly moving averages (SMA) and standard deviation (StDev). This metric aims to quantify the current market overextension or compression relative to long-term historical data. The resulting score dictates the level of conviction for a new trade.
Tiered DCA Entry Sizing:
The strategy defines three distinct Buy Levels (L1, L2, L3) corresponding to increasingly favorable (lower) Risk Metric scores.
L1 (Base): Risk is moderate, initiating the minimum defined trade amount.
L2 (Scaled): Risk is low, initiating L1 amount + L2 amount.
L3 (Aggressive): Risk is very low, initiating L1 + L2 + L3 amounts.
Tiered Profit-Taking Exits:
The strategy implements a staggered, partial profit-taking approach based on the Risk Metric rising:
Sell L1 & L2: Closes a percentage of the current position when the Risk Metric reaches defined high thresholds, locking in partial profits.
Sell L3 (Full Exit): Closes the remaining position when the Risk Metric reaches the highest defined threshold.
The Adapter Function (Webhook Integration)
This script is unique because it uses the Pine Script strategy() function to trigger Order Fills, which are necessary to access powerful placeholders in the TradingView alert system.
Trigger Type: The alert must be set to trigger on Any order fill.
Dynamic Webhook Data: Instead of using fixed alert() commands, the strategy generates dynamic labels (e.g., BUY_ENTRY_L3_USD_1000 or SELL_L1_PCT_25) using the strategy.entry and strategy.close commands.
Data Transfer: The alert message then uses the placeholder {{strategy.order.comment}} to pass these dynamic labels to the 3Commas bot, allowing the bot to execute the precise action (e.g., start_deal_with_volume_in_quote_currency or close_deal_at_market_percentage).
Full Strategy Webhook payload
{
"secret": "YOUR_3COMMAS_SECRET_KEY",
"max_lag": "300",
"timestamp": "{{timenow}}",
"trigger_price": "{{close}}",
"tv_exchange": "{{exchange}}",
"tv_instrument": "{{ticker}}",
"action": "{{strategy.order.action}}",
"bot_uuid": "YOUR_BOT_UUID",
"strategy_info": {
"market_position": "{{strategy.market_position}}",
"market_position_size": "{{strategy.market_position_size}}",
"prev_market_position": "{{strategy.prev_market_position}}",
"prev_market_position_size": "{{strategy.prev_market_position_size}}"
},
"order": {
"amount": "{{strategy.order.contracts}}",
"currency_type": "base",
"comment": "{{strategy.order.comment}}"
}
}
Disclaimer: This script is an adapter tool and does not guarantee profit. Trading requires manual configuration of risk settings, bot parameters, and adherence to platform-specific setup instructions.
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.
RastaRasta — Educational Strategy (Pine v5)
Momentum · Smoothing · Trend Study
Overview
The Rasta Strategy is a visual and educational framework designed to help traders study momentum transitions using the interaction between a fast-reacting EMA line and a slower smoothed reference line.
It is not a signal generator or profit system; it’s a learning tool for understanding how smoothing, crossovers, and filters interact under different market conditions.
The script displays:
A primary EMA line (the fast reactive wave).
A Smoothed line (using your chosen smoothing method).
Optional fog zones between them for quick visual context.
Optional DNA rungs connecting both lines to illustrate volatility compression and expansion.
Optional EMA 8 / EMA 21 trend filter to observe higher-time-frame alignment.
Core Idea
The Rasta model focuses on wave interaction. When the fast EMA crosses above the smoothed line, it reflects a shift in short-term momentum relative to background trend pressure. Cross-unders suggest weakening or reversal.
Rather than treating this as a trading “signal,” use it to observe structure, study trend alignment, and test how smoothing type affects reaction speed.
Smoothing Types Explained
The script lets you experiment with multiple smoothing techniques:
Type Description Use Case
SMA (Simple Moving Average) Arithmetic mean of the last n values. Smooth and steady, but slower. Trend-following studies; filters noise on higher time frames.
EMA (Exponential Moving Average) Weights recent data more. Responds faster to new price action. Momentum or reactive strategies; quick shifts and reversals.
RMA (Relative Moving Average) Used internally by RSI; smooths exponentially but slower than EMA. Momentum confirmation; balanced response.
WMA (Weighted Moving Average) Linear weights emphasizing the most recent data strongly. Intraday scalping; crisp but potentially noisy.
None Disables smoothing; uses the EMA line alone. Raw comparison baseline.
Each smoothing method changes how early or late the strategy reacts:
Faster smoothing (EMA/WMA) = more responsive, good for scalping.
Slower smoothing (SMA/RMA) = more stable, good for trend following.
Modes of Study
🔹 Scalper Mode
Use short EMA lengths (e.g., 3–5) and fast smoothing (EMA or WMA).
Focus on 1 min – 15 min charts.
Watch how quick crossovers appear near local tops/bottoms.
Fog and rung compression reveal volatility contraction before bursts.
Goal: study short-term rhythm and liquidity pulses.
🔹 Momentum Mode
Use moderate EMA (5–9) and RMA smoothing.
Ideal for 1 H–4 H charts.
Observe how the fog color aligns with trend shifts.
EMA 8 / 21 filter can act as macro bias; “Enter” labels will appear only in its direction when enabled.
Goal: study sustained motion between pullbacks and acceleration waves.
🔹 Trend-Follower Mode
Use longer EMA (13–21) with SMA smoothing.
Great for daily/weekly charts.
Focus on periods where fog stays unbroken for long stretches — these illustrate clear trend dominance.
Watch rung spacing: tight clusters often precede consolidations; wide rungs signal expanding volatility.
Goal: visualize slow-motion trend transitions and filter whipsaw conditions.
Components
EMA Line (Red): Fast-reacting short-term direction.
Smoothed Line (Yellow): Reference trend baseline.
Fog Zone: Green when EMA > Smoothed (up-momentum), red when below.
DNA Rungs: Thin connectors showing volatility structure.
EMA 8 / 21 Filter (optional):
When enabled, the strategy will only allow Enter events if EMA 8 > EMA 21.
Use this to study higher-trend gating effects.
Educational Applications
Momentum Visualization: Observe how the fast EMA “breathes” around the smoothed baseline.
Trend Transitions: Compare different smoothing types to see how early or late reversals are detected.
Noise Filtering: Experiment with fog opacity and smoothing lengths to understand trade-off between responsiveness and stability.
Risk Concept Simulation: Includes a simple fixed stop-loss parameter (default 13%) for educational demonstrations of position management in the Strategy Tester.
How to Use
Add to Chart → “Strategy.”
Works on any timeframe and instrument.
Adjust Parameters:
Length: base EMA speed.
Smoothing Type: choose SMA, EMA, RMA, or WMA.
Smoothing Length: controls delay and smoothness.
EMA 8 / 21 Filter: toggles trend gating.
Fog & Rungs: visual study options only.
Study Behavior:
Use Strategy Tester → List of Trades for entry/exit context.
Observe how different smoothing types affect early vs. late “Enter” points.
Compare trend periods vs. ranging periods to evaluate efficiency.
Combine with External Tools:
Overlay RSI, MACD, or Volume for deeper correlation analysis.
Use replay mode to visualize crossovers in live sequence.
Interpreting the Labels
Enter: Marks where fast EMA crosses above the smoothed line (or when filter flips positive).
Exit: Marks where fast EMA crosses back below.
These are purely analytical markers — they do not represent trade advice.
Educational Value
The Rasta framework helps learners explore:
Reaction time differences between moving-average algorithms.
Impact of smoothing on signal clarity.
Interaction of local and global trends.
Visualization of volatility contraction (tight DNA rungs) and expansion (wide fog zones).
It’s a sandbox for studying price structure, not a promise of profit.
Disclaimer
This script is provided for educational and research purposes only.
It does not constitute financial advice, trading signals, or performance guarantees. Past market behavior does not predict future outcomes.
Users are encouraged to experiment responsibly, record observations, and develop their own understanding of price behavior.
Author: Michael Culpepper (mikeyc747)
License: Educational / Open for study and modification with credit.
Philosophy:
“Learning the rhythm of the market is more valuable than chasing its profits.” — Rasta
NEXT GEN INSPIRED BY OLIVER VELEZDYOR NFA
1. Initial Setup & Application
Load the Strategy to your desired chart (e.g., EURUSD M5, as suggested by the script's backtest).
Overlay: Ensure the script is set to overlay=true (which it is) so the signals and Moving Averages plot directly on the price chart.
Equity Management: Review the initial strategy settings for capital and position sizing:
Initial Capital: Defaults to 10,000.
Default Qty Type: Set to strategy.percent_of_equity (22%), meaning 22% of your available equity is used per trade. Adjust this percentage based on your personal risk tolerance.
2. Reviewing Key Indicator Inputs
The script uses default values that are optimized, but you can adjust them in the settings panel:
Fast EMA: Defaults to 9 (e.g., a 9-period Exponential Moving Average).
Slow EMA: Defaults to 21 (e.g., a 21-period Exponential Moving Average). These EMAs define the short-term trend.
ATR: Defaults to 14 (Average True Range). Used to dynamically calculate volatility for SL/TP distances.
Final R:R: Defaults to 4.5 (minimum R:R required for a signal). This is the core of the strategy's high reward goal.
3. Interpreting Entry Signals
A trade signal is generated only when all conditions—EMA trend, "Elephant Logic" momentum, and non-ranging market—are met.
Long Signal: Appears as a green triangle (▲) below the bar, labeled "COMBO".
Short Signal: Appears as a red triangle (▼) above the bar, labeled "COMBO".
Live Plan: Upon signal, a detailed label is immediately plotted on the chart showing the FULL BATTLE PLAN:
SL: Calculated Stop Loss price.
TP: Calculated Take Profit price (based on the Final R:R).
Risk/Reward Pips: The calculated pips for the trade's risk and reward.
R:R = 1:4.5: The exact Risk-to-Reward ratio.
4. Understanding Market Conditions & Visuals
The script provides visuals to help you understand the current market state:
Trend EMAs: The 9 EMA (green) and 21 EMA (purple/magenta) are plotted to show the underlying trend.
Long trades only fire when Price > 9 EMA > 21 EMA.
Short trades only fire when Price < 9 EMA < 21 EMA.
Ranging Market (Rejection): Bars turn a light gray/silver when the proprietary "Reject Ranging" logic is active, indicating a low-volatility period. No new trades will be taken during these bars.
Momentum Bar: Bars turn a gold/yellow color when the "Elephant Logic" (high-momentum, large-body candles over 2-3 periods) is detected, highlighting powerful price movement.
5. Execution and Exit Logic
The strategy handles entry, scaling, and exit automatically:
Entry: A market order is placed (strategy.entry) immediately upon the bar where the longSetup or shortSetup condition is met.
Scaling Out (+1R): If the trade moves favorably by an amount equal to the initial risk (1R), the script closes a portion of the position (strategy.close with comment "+1R"). This partial exit locks in profit equivalent to the initial risk.
Re-entry (Pyramiding): After the +1R exit, the strategy attempts a re-entry (LONG RE/SHORT RE diamond plot) if the price meets certain criteria near the 9 EMA, trying to capitalize on further trend continuation.
Final Exits:
Take Profit: A limit order is set at the calculated TP level (stopDist * minRR).
Stop Loss: A stop order is set at the calculated SL level (stopDist * 1.3), slightly wider than the initial SL distance, likely to account for spread/slippage, ensuring the maximum loss is defined.
Trailing Stop: A trailing stop is applied to the re-entry positions (LONG RE/SHORT RE) to protect profits as the market moves further in the direction of the trade.
W%R Pullback+EMA Trend [TS_Indie]🔰 Core Concept of the Strategy
The main idea is “Trend-Following with Momentum Pullback.”
This means trading in the direction of the main trend (defined by EMA) while using Williams %R to identify pullback entries (buying the dip or selling the rally) where momentum returns to the trend direction.
📊 Indicators Used
1. EMA Fast – Defines the short-term trend.
2. EMA Slow – Defines the long-term trend (used as a trend filter).
3. Williams %R
• Overbought zone: above -20
• Oversold zone: below -80
⚙️ Entry Rules
🔹 Buy Setup
1. EMA Fast > EMA Slow → Uptrend condition.
2. Williams %R on the previous candle dropped below -80, and on the current candle, it crosses back above -80 → indicates momentum returning to the upside.
3. Current close is above EMA Fast.
4. Entry Buy at the close of the candle where %R crosses above -80.
🎯 Entry, Stop Loss, and Take Profit
1. Entry : At the candle close where the signal occurs.
2. Stop Loss : At the lowest low between the current and previous candles.
3. Take Profit : Calculated based on entry price and stop loss distance multiplied by the Risk/Reward Ratio.
🔹 Sell Setup
1. EMA Fast < EMA Slow → Downtrend condition.
2. Williams %R on the previous candle went above -20, and on the current candle, it crosses back below -20 → indicates renewed selling momentum.
3. Current price is below EMA Fast.
4. Entry Sell at the close of the candle where %R crosses below -20.
🎯 Entry, Stop Loss, and Take Profit
1. Entry : At the candle close where the signal occurs.
2. Stop Loss : At the highest high between the current and previous candles.
3. Take Profit : Calculated based on entry price and stop loss distance multiplied by the Risk/Reward Ratio.
⚙️ Optional Parameters
• Custom Risk/Reward Ratio for Take Profit.
• Option to add ATR buffer to Stop Loss.
• Adjustable EMA Fast period.
• Adjustable EMA Slow period.
• Adjustable Williams %R period.
• Option to enable Long only / Short only positions.
• Customizable Backtest start and end date.
• Customizable trading session time.
⏰ Alert Function
Alerts display:
• Entry price
• Stop Loss price
• Take Profit price
Guys, try adjusting the parameters yourselves!
I’ve been tweaking the settings for several days and managed to get great results on XAU/USD in the 5-minute timeframe.
I think this strategy is quite interesting and could potentially deliver good results on other instruments as well.
⚠️ Disclaimer
This indicator is designed for educational and research purposes only.
It does not guarantee profits and should not be considered financial advice.
Trading in financial markets involves significant risk, including the potential loss of capital.
Quantura - Quantitative AlgorythmIntroduction
“Quantura – Quantitative Algorithm” is an invite-only Pine Script strategy designed for multi-timeframe analysis, combining technical filters with user-adjustable fundamental sentiment. It was primarily developed for cryptocurrency markets but can also be applied across other assets such as Forex, stocks, and indices. The goal is to generate structured trade signals through a confluence of techniques rather than relying on a single indicator.
Originality & Value
Quantura is not a simple mashup of indicators. Its originality comes from how multiple layers of analysis are integrated into a single decision framework . Instead of showing indicators separately, the strategy only issues trades when several conditions align simultaneously:
RSI entry triggers confirm overbought/oversold reversals.
Market structure on a higher timeframe confirms trend direction.
Order block detection highlights zones of concentrated supply and demand.
Premium/Discount zones identify potential over- and undervaluation.
HTF EMA provides trend confirmation.
Optional candlestick patterns strengthen reversal or continuation signals.
An optional correlation filter compares the main asset to a reference instrument.
This design forces agreement between different methodologies (momentum, structure, value, volume, sentiment), which reduces noise compared to using them in isolation.
Functionality & Indicators
Entry trigger: RSI exits from extreme zones.
Filters: Only valid when all selected filters (HTF structure, EMA, order blocks, premium/discount, candlesticks, correlation, volume) confirm the direction.
Fundamental bias: User-defined sentiment and analysis settings (bullish, bearish, neutral) influence whether long or short trades are permitted.
Exits: ATR-based take profit and stop loss, with optional breakeven, opposite-signal exit, and session-end exit.
Visualization: Buy/Sell markers, trend-colored candles, and an optional dashboard summarizing indicator status.
Parameters & Customization
Timeframes: Independent HTF and LTF selection.
Trading direction: Long / Short / Both.
Session and weekday filters.
RSI length and thresholds.
Filters: HTF structure, order blocks, premium/discount, EMA, candlestick, ATR volatility, volume zones, correlation.
Exit rules: ATR multipliers for TP/SL, breakeven logic, session-end exit, opposite-signal exit.
Visuals: Toggle signals, candles, dashboard, custom colors.
Default Properties (Strategy Settings)
Initial Capital: 100,000 USD
Position Size: 15% of equity per trade
Commission: 0.25%
Slippage: enabled
Pyramiding: 0 (one position at a time)
Note: The position sizing of 15% equity per trade is intentionally set for backtesting demonstration. In real trading, risking this much is considered aggressive. Most traders prefer to risk 1-5% of equity, and rarely above 10%.
Backtesting & Performance
Backtests on BTCUSD (2 years) with the above defaults showed:
112 trades
Win rate: 40%
Profit factor: 1.4
Maximum drawdown: 34%
These results illustrate how the confluence model behaves, but they are not predictive of future performance . The trade sample size (72 trades) is below the 100+ usually recommended for statistical robustness. Users should re-test with their own preferred symbols, settings, and timeframes.
Risk Management
ATR-based stops and targets scale with volatility.
Commission and slippage are included by default for realistic modeling.
Opposite-signal exit helps capture trend reversals.
Session-end exit can close intraday positions before illiquid hours.
Breakeven option protects profits when available.
Although the default allocation uses 15% per trade for demonstration, this is not a recommendation. Users are encouraged to adjust risk sizing downwards to sustainable levels (commonly 1-5%).
Limitations & Market Conditions
Performs best in volatile, liquid markets (e.g., crypto).
May struggle in prolonged sideways markets with low volatility.
News events and fundamentals outside user inputs can override signals.
Backtests below 100 trades should be considered exploratory, not statistically conclusive.
Usage Guide
Add “Quantura – Quantitative Algorithm” to your chart in strategy mode.
Select HTF and LTF timeframes, trading direction, and session filters.
Configure confluence filters (structure, EMA, order blocks, premium/discount, candlestick, correlation, volume).
Set sentiment and analysis bias in fundamental settings.
Adjust ATR multipliers and exits.
Review buy/sell signals and analyze performance in the Strategy Tester.
Author & Access
Developed 100% by Quantura . Distributed as an Invite-Only script . Details are provided in the Author’s Instructions field.
Important: This description complies with TradingView’s Script Publishing Rules and House Rules. It does not guarantee profitability, avoids unrealistic claims, and explains how the strategy integrates multiple methods into a coherent decision framework.
SMC Adaptive Breakout v1XSMC Adaptive Breakout v1X — Adaptive Smart Money Breakout Strategy
SMC Adaptive Breakout v1X is a Smart-Money–inspired breakout strategy that adapts to changing volatility and market structure in real time. It identifies recent pivot structure, verifies volatility expansion, uses ATR-scaled stops, and manages exits with fixed profit targets plus price-based trailing.
Why this strategy is unique / original
This strategy combines three concept layers into a single, cohesive system: (1) structure detection using adaptive pivots, (2) a normalized volatility filter (range percentile over a long lookback) to permit only expansion-phase breakouts, and (3) context-aware trade management using ATR-scaled stops and percentage-based profit/ trailing rules. The combination reduces false breakouts during low-volatility periods while preserving entries when institutional-style expansion occurs.
Core logic (high level)
1. Structure detection: recent pivot highs and lows (configurable lookback) form the active Support and Resistance reference levels used to define breakouts.
2. Volatility confirmation: raw bar range is normalized into a percentile within a long volatility lookback window; breakouts are only considered when normalized volatility exceeds the user filter threshold.
3. Order-block / gap detection: the script detects large price gaps relative to ATR(200) and flags them as bullish/bearish gaps (order-block style footprints) to add confluence to entries.
4. Entry criteria: a long entry is signalled when price closes above the most recent resistance and the volatility filter is satisfied (or a bullish gap condition is met). Shorts mirror this logic below support. Debug/force flags allow manual/backtest forcing of trades.
5. Risk & exits: stops are ATR-based (ATR length configurable, multiplier configurable) giving context-aware stop distances. Each entry sets a profit target as a percent of entry and attaches a trailing exit (points and offset defined as percent of price) to protect profits. Exits are placed with one strategy.exit per entry so they are executed by the strategy engine.
6. Non-premature confirmation: entries are determined using closed-bar conditions (no intrabar triggers), consistent with strategy backtesting expectations.
Key inputs (and what they control)
1. Levels Period (length) — pivot lookback used to compute support/resistance structure; larger values = larger, fewer zones.
2. Volatility Filter (filter 0–100) — normalized volatility threshold (percentile) required to allow breakout signals. Increase to reduce signals during quiet markets.
3. Volatility lookback (volatility_len) — window length used to normalize the raw range into a percentile.
4. ATR length (atr_len) & ATR Stop Multiplier (atr_multiplier) — ATR parameters used for stop distance; ATR gives volatility-adaptive stop sizing.
5. Profit target (%) — target as percent of entry price.
6. Trailing points (%) & offset (%) — trailing stop size and activation offset, expressed as percent of price (converted internally to price points).
7. Visual & debug toggles — show/hide levels, entry markers, and enable debug/force entry flags for manual/backtest validation.
Practical Usage & Recommended Settings
Timeframes – Works efficiently across multiple time horizons.
• 5–15 minutes → Scalping setups.
• 15 minutes–1 hour → Intraday opportunities.
• 4 hours–1 day → Swing trading confirmation.
Adjust length and Volatility Filter parameters to match your timeframe and instrument behavior.
Default Sensitivity –
The default length = 20 offers balanced structure detection.
• Lower values → faster, more frequent signals.
• Higher values → smoother structure and fewer breakouts.
Volatility Tuning –
Modify the Volatility Filter (0–100) according to market conditions.
• Increase the filter during low-volume or choppy sessions to reduce false signals.
• Decrease it during trending or high-volatility markets for greater responsiveness.
Stop / Target Sizing –
ATR-based stop-losses automatically adapt to market volatility.
• Recommended starting point: ATR Multiplier = 1.5 and Profit Target = 1.5%.
• Fine-tune both based on each asset’s typical volatility profile.
Backtesting –
Use TradingView’s built-in Strategy Tester to analyze results over different symbols and timeframes.
The strategy executes only on bar close, ensuring accurate, non-repainting backtest results.
What the strategy plots / visual cues
•Forward-extended pivot lines for support/resistance (configurable color/transparency).
•Order-block / gap markers when large ATR-scaled gaps are detected.
•Entry labels (“LONG” / “SHORT”) at position changes if enabled.
•Strategy entries/exits are placed through strategy.entry and strategy.exit so performance reports are available in the Tester.
Risk management & notes
•This script is a discretionary tool — it automates entries and exits for backtesting and strategy simulation, but users should still confirm trades with broader market context and higher-timeframe bias.
•Always run thorough backtests (multi-symbol, multi-timeframe) and forward test on a paper account before any live deployment.
•Adjust position sizing externally; the strategy code sets orders and exits but does not enforce a specific money-management sizing rule. Use the strategy tester’s default position size controls or integrate a sizing method in your own workflow.
Technical details & behavior
•Pine Script v6 strategy.
•Uses closed-bar confirmation for signals (no repainting on close).
•Order-block / gap detection uses ATR(200) as a volatility reference to identify large structural gaps.
•Trail calculations convert percent-based inputs to absolute price units each bar to maintain consistent behavior across price levels.
Limitations & disclaimers
•Past performance is not indicative of future results. This strategy does not guarantee profits and will produce losing trades.
•Results depend on parameter choices, instrument volatility, market regime, and execution slippage. Always test on the exact symbol and timeframe you intend to trade.
Invite-only / Access note (for Publish window)
This strategy is invite-only. Please use the TradingView Request Access button on this page to request access.
Strategy Builder v1.0.0 [BigBeluga]🔵 OVERVIEW
The Strategy Builder combines advanced price-action logic, smart-money concepts, and volatility-adaptive momentum signals to automate high-quality entries and exits across any market. It blends trend recognition, market structure shifts, order block reactions, imbalance (FVG) signals, liquidity sweeps, candlestick confirmations, and oscillator-powered divergences into one cohesive engine.
Whether used as a full automation workflow or as a structured confirmation framework, this strategy provides a disciplined, rules-driven method to trade with logic — not emotion.
🔵 BACKTEST WINDOW CONTROL
This module allows you to restrict strategy execution to a specific historical period.
Ideal for performance isolation, regime testing, and forward-walk validation.
Limit Backtest Window
Enabling this option activates custom date filters for the backtest engine.
Start — Define the starting date & time for backtesting
End — Define the ending date & time for backtesting
Only trades and signals inside this window are executed
Reduces computation load on large datasets
Useful for testing specific market environments (e.g., bull cycles, crash periods, sideways regimes)
🔵 SIGNAL GLOSSARY (Advanced Technical Explanation)
Traders can build long and short setups using up to 6 configurable entry conditions for each direction.
Every condition can be set as Bullish or Bearish and mapped to any signal source — allowing deep customization
Below is the full internal logic overview of every signal available in the Strategy Builder.
Signals are based on trend models, volatility structures, liquidity logic, oscillator behavior, and market structure mapping.
Trend Signals (Low-Lag Trend Engine)
Uses a proprietary low-lag baseline + momentum gradient model to detect directional bias.
Trend Signal — Momentum breaks above/below adaptive trend baseline.
Trend Signal+ — Stronger trend confirmation using volatility-weighted momentum.
Trend Signal Any — Triggers when any bullish/bearish trend signal appears.
SmartBand & Retests (Adaptive Volatility Bands)
Dynamic envelope that contracts/expands with volatility & trend strength.
SmartBand Retest — Price retests dynamic band and rejects, confirming continuation.
ActionWave Signals (Impulse-Pullback Engine)
Tracks wave behavior, acceleration and deceleration in price.
ActionWave — Detects directional impulse strength vs pullback weakness.
ActionWave Cross — Momentum acceleration threshold crossed → trend ignition.
Magnet Signals (Liquidity Gravity + Mean Reversion Bias)
Detects zones where price is being drawn due to liquidity voids or imbalance.
Magnet — Trend and liquidity pressure align, creating directional “pull.”
MagnetBar Low Momentum — Low-volatility compression → pre-breakout condition.
Flow Trend (Directional Flow State + ATR Envelope)
Higher-timeframe bias confirmation + dynamic volatility filter.
FlowTrend — Confirms major directional bias (uptrend or downtrend).
FlowTrend Retest — Price tests HTF flow band and rejects → trend resume.
Voltix (Volatility Expansion Pulse)
Detects regime shift from quiet accumulation → trending expansion.
Voltix — Breakout volatility signature, trend acceleration trigger.
Candlestick Pattern (Algorithmic Price Action Recognition)
Auto-recognizes meaningful reversal or continuation candle formations.
Candlestick Pattern — Confirms momentum reversal/continuation via candle logic.
OrderBlock Logic (Institutional Footprint System)
Institutional demand/supply zone tracking with mitigation logic.
Order Block Touch — Price taps institutional zone → reaction filter.
Order Block Break — OB invalidation → institutional flow shift.
Market Structure Engine (Swing Logic + Volume Confirmation)
Tracks major swing breaks and structural reversals.
BoS — Break of Structure in trend direction (continuation bias).
ChoCh — Change of Character — early reversal marker.
Fair Value Gaps (Imbalance & Volume Displacement)
Identifies inefficiencies caused by rapid displacement moves.
FVG Created — Price leaves inefficiency behind.
FVG Retest — Price returns to rebalance inefficiency → reaction zone.
Liquidity Events (Stop-Run & Reversal Logic)
Detects stop-hunt events and liquidity sweeps.
SFP — Swing failure & wick sweep → reversal confirmation.
Liquidity Created — New equal highs/lows form liquidity pool.
Liquidity Grab — Sweep through liquidity line followed by rejection.
Support / Resistance Break Logic
Adaptive zone recognition + momentum confirmation.
Support/Resistance Cross — Zone decisively broken → structural shift.
Pattern Breakouts (Market Geometry Engine)
Tracks breakout from compression & expansion formations.
Channel Break — Channel breakout → trend acceleration.
Wedge Break — Break from contraction wedge → burst of momentum.
Session Logic (Opening Range Behavior)
Session-based volatility trigger.
Session Break — Break above/below session opening range.
Momentum / Reversal Oscillator Suite
Oscillator-driven exhaustion & reversal signals.
Nautilus Signals — Momentum reversal signature (oscillator shift).
Nautilus Peak — Momentum peak → exhaustion risk.
OverSold/Overbought ❖ — Extreme exhaustion zones → reversal setup.
DipX Signals ✦ — Dip buy / Dip sell timing, micro-reversal engine.
Advanced Divergence Engine
Momentum/price disagreement layer with multi-trigger confirmation.
Normal Divergence — Classic divergence reversal.
Hidden Divergence — Trend continuation divergence.
Multiple Divergence — Multiple divergence confirmations stacked → high confidence.
🔧 Adjustable Signal Logic
Some signals in this system can be additionally refined through the strategy settings panel.
This allows traders to tune internal behavior for different market regimes, assets, and volatility conditions.
🔵 LONG / SHORT EXIT CONDITIONS
This section allows you to automate exits using the same advanced market conditions available for entries.
Each exit rule consists of:
Toggle — Enable/disable individual exit rule.
Direction Filter — Trigger exit only if selected market bias appears (Bullish/Bearish).
Signal Type — Choose which market event triggers the exit (same list as entry conditions).
When the active conditions are met, the strategy automatically closes the current position — ensuring emotion-free risk management and systematic trade control.
🔵 TAKE PROFIT & STOP LOSS SYSTEM
This strategy builder provides a fully dynamic risk-management engine designed for both systematic traders and discretionary confirmation users.
Take Profit Logic
Scale out of trades progressively or exit fully using algorithmic TP levels.
Up to 3 Take-Profit targets available
Choose TP calculation method:
• ATR-based distance (volatility-adaptive targets)
• %-based distance (fixed percentage from entry)
Define Size — ATR multiplier or % value
Custom Exit Size per TP (e.g., 25% / 25% / 50%)
Visual TP plotting on chart for clarity
Stop Loss Logic
Automated protection logic for every trade.
Two SL Modes:
• Fixed Stop Loss — static SL from entry
• Trailing Stop Loss — SL follows price as trade progresses
Distance options:
• ATR multiplier (adapts to volatility)
• %-based from entry (fixed distance)
SL dynamically draws on chart for transparency
Trailing SL behavior:
Follows price only in profitable direction
Never moves against the trade
Locks profits as trend develops
🔵 Strategy Dashboard
A compact on-chart performance dashboard is included to help monitor live trade status and backtest results in real time.
It displays key metrics:
Start Capital — Initial account balance used in simulation.
Position Size — % of capital allocated per trade based on user settings (It changes if the trade hits take profits, when more than one take profit is selected).
Current Trade — Shows active trade direction (Long / Short) and real-time % return from entry.
Closed Trades — Counter of completed positions, useful for reading sample size during testing.
🔵 CONCLUSION
The Strategy Builder brings together a powerful suite of smart-money and momentum-driven signals, allowing traders to automate robust trade logic built on modern market structure concepts. With access to trend filters, order blocks, liquidity events, divergence signals, volatility cues, and session-based triggers, it provides a deeply adaptive trade engine capable of fitting many market environments.
v2.0—Tristan's Multi-Indicator Reversal Strategy🎯 Multi-Indicator Reversal Strategy - Optimized for High Win Rates
A powerful confluence-based strategy that combines RSI, MACD, Williams %R, Bollinger Bands, and Volume analysis to identify high-probability reversal points . Designed to let winners run with no stop loss or take profit - positions close only when opposite signals occur.
Also, the 3 hour timeframe works VERY well—just a lot less trades.
📈 Proven Performance
This strategy has been backtested and optimized on multiple blue-chip stocks with 80-90%+ win rates on 1-hour timeframes from Aug 2025 through Oct 2025:
✅ V (Visa) - Payment processor
✅ MSFT (Microsoft) - Large-cap tech
✅ WMT (Walmart) - Retail leader
✅ IWM (Russell 2000 ETF) - Small-cap index
✅ NOW (ServiceNow) - Enterprise software
✅ WM (Waste Management) - Industrial services
These stocks tend to mean-revert at extremes, making them ideal candidates for this reversal-based approach. I only list these as a way to show you the performance of the script. These values and stock choices may change over time as the market shifts. Keep testing!
🔑 How to Use This Strategy Successfully
Step 1: Apply to Chart
Open your desired stock (V, MSFT, WMT, IWM, NOW, WM recommended)
Set timeframe to 1 Hour
Apply this strategy
Check that the Williams %R is set to -20 and -80, and "Flip All Signals" is OFF (can flip this for some stocks to perform better.)
Step 2: Understand the Signals
🟢 Green Triangle (BUY) Below Candle:
Multiple indicators (RSI, Williams %R, MACD, Bollinger Bands) show oversold conditions
Enter LONG position
Strategy will pyramid up to 10 entries if more buy signals occur
Hold until red triangle appears
🔴 Red Triangle (SELL) Above Candle:
Multiple indicators show overbought conditions
Enter SHORT position (or close existing long)
Strategy will pyramid up to 10 entries if more sell signals occur
Hold until green triangle appears
🟣 Purple Labels (EXIT):
Shows when positions close
Displays count if multiple entries were pyramided (e.g., "Exit Long x5")
Step 3: Let the Strategy Work
Key Success Principles:
✅ Be Patient - Signals don't occur every day, wait for quality setups
✅ Trust the Process - Don't manually close positions, let opposite signals exit
✅ Watch Pyramiding - The strategy can add up to 10 positions in the same direction
✅ No Stop Loss - Positions ride through drawdowns until reversal confirmed
✅ Session Filter - Only trades during NY session (9:30 AM - 4:00 PM ET)
⚙️ Winning Settings (Already Set as Defaults)
INDICATOR SETTINGS:
- RSI Length: 14
- RSI Overbought: 70
- RSI Oversold: 30
- MACD: 12, 26, 9 (standard)
- Williams %R Length: 14
- Williams %R Overbought: -20 ⭐ (check this! And adjust to your liking)
- Williams %R Oversold: -80 ⭐ (check this! And adjust to your liking)
- Bollinger Bands: 20, 2.0
- Volume MA: 20 periods
- Volume Multiplier: 1.5x
SIGNAL REQUIREMENTS:
- Min Indicators Aligned: 2
- Require Divergence: OFF
- Require Volume Spike: OFF
- Require Reversal Candle: OFF
- Flip All Signals: OFF ⭐
RISK MANAGEMENT:
- Use Stop Loss: OFF ⭐⭐⭐
- Use Take Profit: OFF ⭐⭐⭐
- Allow Pyramiding: ON ⭐⭐⭐
- Max Pyramid Entries: 10 ⭐⭐⭐
SESSION FILTER:
- Trade Only NY Session: ON
- NY Session: 9:30 AM - 4:00 PM ET
**⭐ = Critical settings for success**
## 🎓 Strategy Logic Explained
### **How It Works:**
1. **Multi-Indicator Confluence**: Waits for at least 2 out of 4 technical indicators to align before generating signals
2. **Oversold = Buy**: When RSI < 30, Williams %R < -80, price below lower Bollinger Band, and/or MACD turning bullish → BUY signal
3. **Overbought = Sell**: When RSI > 70, Williams %R > -20, price above upper Bollinger Band, and/or MACD turning bearish → SELL signal
4. **Pyramiding Power**: As trend continues and more signals fire in the same direction, adds up to 10 positions to maximize gains
5. **Exit Only on Reversal**: No arbitrary stops or targets - only exits when opposite signal confirms trend change
6. **Session Filter**: Only trades during liquid NY session hours to avoid overnight gaps and low-volume periods
### **Why No Stop Loss Works:**
Traditional reversal strategies fail because they:
- Get stopped out too early during normal volatility
- Miss the actual reversal that happens later
- Cut winners short with tight take profits
This strategy succeeds because it:
- ✅ Rides through temporary noise
- ✅ Captures full reversal moves
- ✅ Uses multiple indicators for confirmation
- ✅ Pyramids into winning positions
- ✅ Only exits when technical picture completely reverses
---
## 📊 Understanding the Display
**Live Indicator Counter (Top Corner / end of current candles):**
Bull: 2/4
Bear: 0/4
(STANDARD)
Shows how many indicators currently align bullish/bearish
"STANDARD" = normal reversal mode (buy oversold, sell overbought)
"FLIPPED" = momentum mode if you toggle that setting
Visual Indicators:
🔵 Blue background = NY session active (trading window)
🟡 Yellow candle tint = Volume spike detected
💎 Aqua diamond = Bullish divergence (price vs RSI)
💎 Fuchsia diamond = Bearish divergence
⚡ Advanced Tips
Optimizing for Different Stocks:
If Win Rate is Low (<50%):
Try toggling "Flip All Signals" to ON (switches to momentum mode)
Increase "Min Indicators Aligned" to 3 or 4
Turn ON "Require Divergence"
Test on different timeframe (4-hour or daily)
If Too Few Signals:
Decrease "Min Indicators Aligned" to 2
Turn OFF all requirement filters
Widen Williams %R bands to -15 and -85
If Too Many False Signals:
Increase "Min Indicators Aligned" to 3 or 4
Turn ON "Require Divergence"
Turn ON "Require Volume Spike"
Reduce Max Pyramid Entries to 5
Stock Selection Guidelines:
Best Suited For:
Large-cap stable stocks (V, MSFT, WMT)
ETFs (IWM, SPY, QQQ)
Stocks with clear support/resistance
Mean-reverting instruments
Avoid:
Ultra low-volume penny stocks
Extremely volatile crypto (try traditional settings first)
Stocks in strong one-directional trends lasting months
🔄 The "Flip All Signals" Feature
If backtesting shows poor results on a particular stock, try toggling "Flip All Signals" to ON:
STANDARD Mode (OFF):
Buy when oversold (reversal strategy)
Sell when overbought
May work best for: V, MSFT, WMT, IWM, NOW, WM
FLIPPED Mode (ON):
Buy when overbought (momentum strategy)
Sell when oversold
May work best for: Strong trending stocks, momentum plays, crypto
Test both modes on your stock to see which performs better!
📱 Alert Setup
Create alerts to notify you of signals:
📊 Performance Expectations
With optimized settings on recommended stocks:
Typical results we are looking for:
Win Rate: 70-90%
Average Winner: 3-5%
Average Loser: 1-3%
Signals Per Week: 1-3 on 1-hour timeframe
Hold Time: Several hours to days
Remember: Past performance doesn't guarantee future results. Always use proper risk management.
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.
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.
Zero Lag Trend Signals (MTF) [Quant Trading] V7Overview
The Zero Lag Trend Signals (MTF) V7 is a comprehensive trend-following strategy that combines Zero Lag Exponential Moving Average (ZLEMA) with volatility-based bands to identify high-probability trade entries and exits. This strategy is designed to reduce lag inherent in traditional moving averages while incorporating dynamic risk management through ATR-based stops and multiple exit mechanisms.
This is a longer term horizon strategy that takes limited trades. It is not a high frequency trading and therefore will also have limited data and not > 100 trades.
How It Works
Core Signal Generation:
The strategy uses a Zero Lag EMA (ZLEMA) calculated by applying an EMA to price data that has been adjusted for lag:
Calculate lag period: floor((length - 1) / 2)
Apply lag correction: src + (src - src )
Calculate ZLEMA: EMA of lag-corrected price
Volatility bands are created using the highest ATR over a lookback period multiplied by a band multiplier. These bands are added to and subtracted from the ZLEMA line to create upper and lower boundaries.
Trend Detection:
The strategy maintains a trend variable that switches between bullish (1) and bearish (-1):
Long Signal: Triggers when price crosses above ZLEMA + volatility band
Short Signal: Triggers when price crosses below ZLEMA - volatility band
Optional ZLEMA Trend Confirmation:
When enabled, this filter requires ZLEMA to show directional momentum before entry:
Bullish Confirmation: ZLEMA must increase for 4 consecutive bars
Bearish Confirmation: ZLEMA must decrease for 4 consecutive bars
This additional filter helps avoid false signals in choppy or ranging markets.
Risk Management Features:
The strategy includes multiple stop-loss and take-profit mechanisms:
Volatility-Based Stops: Default stop-loss is placed at ZLEMA ± volatility band
ATR-Based Stops: Dynamic stop-loss calculated as entry price ± (ATR × multiplier)
ATR Trailing Stop: Ratcheting stop-loss that follows price but never moves against position
Risk-Reward Profit Target: Take-profit level set as a multiple of stop distance
Break-Even Stop: Moves stop to entry price after reaching specified R:R ratio
Trend-Based Exit: Closes position when price crosses EMA in opposite direction
Performance Tracking:
The strategy includes optional features for monitoring and analyzing trades:
Floating Statistics Table: Displays key metrics including win rate, GOA (Gain on Account), net P&L, and max drawdown
Trade Log Labels: Shows entry/exit prices, P&L, bars held, and exit reason for each closed trade
CSV Export Fields: Outputs trade data for external analysis
Default Strategy Settings
Commission & Slippage:
Commission: 0.1% per trade
Slippage: 3 ticks
Initial Capital: $1,000
Position Size: 100% of equity per trade
Main Calculation Parameters:
Length: 70 (range: 70-7000) - Controls ZLEMA calculation period
Band Multiplier: 1.2 - Adjusts width of volatility bands
Entry Conditions (All Disabled by Default):
Use ZLEMA Trend Confirmation: OFF - Requires ZLEMA directional momentum
Re-Enter on Long Trend: OFF - Allows multiple entries during sustained trends
Short Trades:
Allow Short Trades: OFF - Strategy is long-only by default
Performance Settings (All Disabled by Default):
Use Profit Target: OFF
Profit Target Risk-Reward Ratio: 2.0 (when enabled)
Dynamic TP/SL (All Disabled by Default):
Use ATR-Based Stop-Loss & Take-Profit: OFF
ATR Length: 14
Stop-Loss ATR Multiplier: 1.5
Profit Target ATR Multiplier: 2.5
Use ATR Trailing Stop: OFF
Trailing Stop ATR Multiplier: 1.5
Use Break-Even Stop-Loss: OFF
Move SL to Break-Even After RR: 1.5
Use Trend-Based Take Profit: OFF
EMA Exit Length: 9
Trade Data Display (All Disabled by Default):
Show Floating Stats Table: OFF
Show Trade Log Labels: OFF
Enable CSV Export: OFF
Trade Label Vertical Offset: 0.5
Backtesting Date Range:
Start Date: January 1, 2018
End Date: December 31, 2069
Important Usage Notes
Default Configuration: The strategy operates in its most basic form with default settings - using only ZLEMA crossovers with volatility bands and volatility-based stop-losses. All advanced features must be manually enabled.
Stop-Loss Priority: If multiple stop-loss methods are enabled simultaneously, the strategy will use whichever condition is hit first. ATR-based stops override volatility-based stops when enabled.
Long-Only by Default: Short trading is disabled by default. Enable "Allow Short Trades" to trade both directions.
Performance Monitoring: Enable the floating stats table and trade log labels to visualize strategy performance during backtesting.
Exit Mechanisms: The strategy can exit trades through multiple methods: stop-loss hit, take-profit reached, trend reversal, or trailing stop activation. The trade log identifies which exit method was used.
Re-Entry Logic: When "Re-Enter on Long Trend" is enabled with ZLEMA trend confirmation, the strategy can take multiple long positions during extended uptrends as long as all entry conditions remain valid.
Capital Efficiency: Default setting uses 100% of equity per trade. Adjust "default_qty_value" to manage position sizing based on risk tolerance.
Realistic Backtesting: Strategy includes commission (0.1%) and slippage (3 ticks) to provide realistic performance expectations. These values should be adjusted based on your broker and market conditions.
Recommended Use Cases
Trending Markets: Best suited for markets with clear directional moves where trend-following strategies excel
Medium to Long-Term Trading: The default length of 70 makes this strategy more appropriate for swing trading rather than scalping
Risk-Conscious Traders: Multiple stop-loss options allow traders to customize risk management to their comfort level
Backtesting & Optimization: Comprehensive performance tracking features make this strategy ideal for testing different parameter combinations
Limitations & Considerations
Like all trend-following strategies, performance may suffer in choppy or ranging markets
Default 100% position sizing means full capital exposure per trade - consider reducing for conservative risk management
Higher length values (70+) reduce signal frequency but may improve signal quality
Multiple simultaneous risk management features may create conflicting exit signals
Past performance shown in backtests does not guarantee future results
Customization Tips
For more aggressive trading:
Reduce length parameter (minimum 70)
Decrease band multiplier for tighter bands
Enable short trades
Use lower profit target R:R ratios
For more conservative trading:
Increase length parameter
Enable ZLEMA trend confirmation
Use wider ATR stop-loss multipliers
Enable break-even stop-loss
Reduce position size from 100% default
For optimal choppy market performance:
Enable ZLEMA trend confirmation
Increase band multiplier
Use tighter profit targets
Avoid re-entry on trend continuation
Visual Elements
The strategy plots several elements on the chart:
ZLEMA line (color-coded by trend direction)
Upper and lower volatility bands
Long entry markers (green triangles)
Short entry markers (red triangles, when enabled)
Stop-loss levels (when positions are open)
Take-profit levels (when enabled and positions are open)
Trailing stop lines (when enabled and positions are open)
Optional ZLEMA trend markers (triangles at highs/lows)
Optional trade log labels showing complete trade information
Exit Reason Codes (for CSV Export)
When CSV export is enabled, exit reasons are coded as:
0 = Manual/Other
1 = Trailing Stop-Loss
2 = Profit Target
3 = ATR Stop-Loss
4 = Trend Change
Conclusion
Zero Lag Trend Signals V7 provides a robust framework for trend-following with extensive customization options. The strategy balances simplicity in its core logic with sophisticated risk management features, making it suitable for both beginner and advanced traders. By reducing moving average lag while incorporating volatility-based signals, it aims to capture trends earlier while managing risk through multiple configurable exit mechanisms.
The modular design allows traders to start with basic trend-following and progressively add complexity through ZLEMA confirmation, multiple stop-loss methods, and advanced exit strategies. Comprehensive performance tracking and export capabilities make this strategy an excellent tool for systematic testing and optimization.
Note: This strategy is provided for educational and backtesting purposes. All trading involves risk. Past performance does not guarantee future results. Always test thoroughly with paper trading before risking real capital, and adjust position sizing and risk parameters according to your risk tolerance and account size.
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TAGS:
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trend following, ZLEMA, zero lag, volatility bands, ATR stops, risk management, swing trading, momentum, trend confirmation, backtesting
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CATEGORY:
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Strategies
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CHART SETUP RECOMMENDATIONS:
================================================================================
For optimal visualization when publishing:
Use a clean chart with no other indicators overlaid
Select a timeframe that shows multiple trade signals (4H or Daily recommended)
Choose a trending asset (crypto, forex major pairs, or trending stocks work well)
Show at least 6-12 months of data to demonstrate strategy across different market conditions
Enable the floating stats table to display key performance metrics
Ensure all indicator lines (ZLEMA, bands, stops) are clearly visible
Use the default chart type (candlesticks) - avoid Heikin Ashi, Renko, etc.
Make sure symbol information and timeframe are clearly visible
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COMPLIANCE NOTES:
================================================================================
✅ Open-source publication with complete code visibility
✅ English-only title and description
✅ Detailed explanation of methodology and calculations
✅ Realistic commission (0.1%) and slippage (3 ticks) included
✅ All default parameters clearly documented
✅ Performance limitations and risks disclosed
✅ No unrealistic claims about performance
✅ No guaranteed results promised
✅ Appropriate for public library (original trend-following implementation with ZLEMA)
✅ Educational disclaimers included
✅ All features explained in detail
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D Money – EMA/TEMA Touch Strategy (Distance) What it’s trying to capture
You want mean-reversion “tags” back to a moving average after price has stretched away and momentum flips:
Bearish setup (short): price has been above EMA(9) for a few bars, then MACD turns bearish, and price is far enough above the EMA (by an adaptive threshold). Exit when price tags the EMA.
Bullish setup (long): price has been below your chosen TEMA rail (actually an EMA of 50/100/200 you pick) for a few bars, then MACD turns bullish, and price is far enough below that TEMA. Exit when price tags that TEMA.
The moving averages it uses
EMA(9) — your fast “tag” for short take-profits.
“TEMA line” input = one of EMA(50) / EMA(100) / EMA(200). (Labelled “Chosen TEMA” in the plot; it’s an EMA rail you pick.)
When it will enter trades
It requires four things per side:
Short (EMA-Touch Short)
MACD bearish cross on the signal bar
If “Require NO MA touch on cross bar” = true, the bar’s low must be above EMA(9), so it didn’t touch EMA on the cross bar (fake-out guard).
Extension/Context: you’ve had at least barsAbove consecutive closes above EMA(9) (default 3), so it’s truly stretched.
Distance test: absolute % distance from price to EMA(9) must be ≥ minDistEMA_eff (an adaptive threshold; details below).
Bounce filter: there was no bullish bounce off the EMA in the last bounceLookback bars (excluding the current one).
If all pass and you’re inside the backtest window → strategy.entry short.
Long (TEMA-Touch Long)
MACD bullish cross on the signal bar
With the same fake-out guard: the bar’s high must be below the chosen TEMA if the guard is on.
Extension/Context: at least barsAbove consecutive closes below the chosen TEMA.
Distance test: absolute % distance from price to TEMA must be ≥ minDistTEMA_eff (adaptive).
Bounce filter: there was no bearish bounce off the TEMA in the last bounceLookback bars.
If all pass and you’re in the window → strategy.entry long.
MACD timing option:
If Pure MACD Timing = ON, it only checks for the cross.
If OFF (default), it also enforces “no touch on the cross bar” if that checkbox is true. That’s your “fake-out” filter.
The adaptive distance threshold (the “secret sauce”)
You can choose how “far enough away” is determined—per side:
Fixed %
Short uses Fixed: Min distance ABOVE EMA (%)
Long uses Fixed: Min distance BELOW TEMA (%)
Auto (ATR%) (default)
Short threshold = max(floorEMA, kAtrShort × ATR%)
Long threshold = max(floorTEMA, kAtrLong × ATR%)
This scales distance by recent volatility, with a floor.
Auto (AvgDist%)
Short threshold = max(floorEMA, kAvgShort × average(|Dist to EMA|) over avgLen)
Long threshold = max(floorTEMA, kAvgLong × average(|Dist to TEMA|) over avgLen)
This adapts to the instrument’s typical stretch away from the rails.
These become minDistEMA_eff and minDistTEMA_eff and are re-computed each bar.
Fake-out / bounce logic (the “don’t get tricked” part)
A touch means the bar’s high/low overlapped the MA ± a small buffer % (touchBufPct).
A bounce is a touch plus a close on the “wrong” side (e.g., touch EMA and close above it on shorts = bullish bounce).
The script blocks entries if a bounce happened within bounceLookback bars (excluding the current signal bar).
Exits & risk
Take profit: when price touches the target MA:
Short TP = touch EMA(9)
Long TP = touch chosen TEMA
Stop loss: either
ATR stop: entry ± (atrMultStop × ATR) (default ON), or
Percent stop: entry × (1±stopPct%)
Time stop: if timeExitBars > 0, close after that many bars if still open.
Quality-of-life features
Backtest window (btFrom, btTo) so you can limit evaluation.
Labels on signal bars that show:
MACD bucket (Small/Moderate/HUGE/Violent — based on % separation on the bar),
the current absolute distance to the target MA,
and the effective minimum the engine used (plus which engine mode).
Data Window fields so you can audit:
abs distance to EMA/TEMA,
the effective min distance used on each side,
ATR%,
average absolute distances (for the AvgDist mode).
Alerts fire when a short/long signal is confirmed.
Optional debug panel to see the exact booleans & thresholds the bar had.
Quick mental model
Are we properly stretched away from the rail (by an adaptive threshold) and held on that side for a few bars?
Did MACD flip the way we want without price already tagging the rail that bar?
Have we avoided recent bounces off that rail (no fake-out)?
→ If yes, enter and aim for a tag back to the rail, with ATR/% stop and optional time stop.
If you want, I can add a simple on-chart “rating” (0–100) similar to your Python scorer (distance beyond min, MACD bucket, extension streak) so you can visually rank signals in TradingView too.
KCandle Strategy 1.0# KCandle Strategy 1.0 - Trading Strategy Description
## Overview
The **KCandle Strategy** is an advanced Pine Script trading system based on bullish and bearish engulfing candlestick patterns, enhanced with sophisticated risk management and position optimization features.
## Core Logic
### Entry Signal Generation
- **Pattern Recognition**: Detects bullish and bearish engulfing candlestick formations
- **EMA Filter**: Uses a customizable EMA (default 25) to filter trades in the direction of the trend
- **Entry Levels**:
- **Long entries** at 25% of the candlestick range from the low
- **Short entries** at 75% of the candlestick range from the low
- **Signal Validation**: Orange candlesticks indicate valid setup conditions
### Risk Management System
#### 1. **Stop Loss & Take Profit**
- Configurable stop loss in pips
- Risk-reward ratio setting (default 2:1)
- Visual representation with colored lines and labels
#### 2. **Break-Even Management**
- Automatically moves stop loss to break-even when specified R:R is reached
- Customizable break-even offset for added protection
- Prevents losing trades after reaching profitability
#### 3. **Trailing Stop System**
- **Activation Trigger**: Activates when position reaches specified R:R level
- **Distance Control**: Maintains trailing stop at defined distance from entry
- **Step Management**: Moves stop loss forward in incremental R steps
- **Dynamic Protection**: Locks in profits while allowing for continued upside
### Advanced Features
#### Position Management
- **Pyramiding Support**: Optional multiple position entries with size reduction
- **Order Expiration**: Pending orders automatically cancel after specified bars
- **Position Sizing**: Percentage-based allocation with pyramid level adjustments
#### Visual Interface
- **Real-time Monitoring**: Comprehensive information panel with all strategy metrics
- **Historical Tracking**: Visual representation of past trades and levels
- **Color-coded Indicators**: Different colors for break-even, trailing, and standard stops
- **Debug Options**: Optional labels for troubleshooting and optimization
## Key Parameters
### Basic Settings
- **EMA Length**: Trend filter period
- **Stop Loss**: Risk per trade in pips
- **Risk/Reward**: Target profit ratio
- **Order Validity**: Duration of pending orders
### Risk Management
- **Break-Even R:R**: Profit level to trigger break-even
- **Trailing Activation**: R:R level to start trailing
- **Trailing Distance**: Stop distance from entry when trailing
- **Trailing Step**: Increment for stop loss advancement
## Strategy Benefits
1. **Objective Entry Signals**: Based on proven candlestick patterns
2. **Trend Alignment**: EMA filter ensures trades align with market direction
3. **Robust Risk Control**: Multiple layers of protection (SL, BE, Trailing)
4. **Profit Optimization**: Trailing stops maximize winning trade potential
5. **Flexibility**: Extensive customization options for different market conditions
6. **Visual Clarity**: Complete visual feedback for trade management
## Ideal Use Cases
- **Swing Trading**: Medium-term positions with trend-following approach
- **Breakout Trading**: Capturing momentum from engulfing patterns
- **Risk-Conscious Trading**: Suitable for traders prioritizing capital preservation
- **Multi-Timeframe**: Adaptable to various timeframes and instruments
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*The KCandle Strategy combines traditional technical analysis with modern risk management techniques, providing traders with a comprehensive tool for systematic market participation.*
My Backtest Module### 📊 Universal Backtest Module - Pro Structure
**A Fully Customizable Strategy Framework for Advanced Backtesting & Signal Analysis**
This powerful Pine Script strategy is designed as a **universal testing module** for traders and developers who want to evaluate custom trading logic across multiple conditions, timeframes, and risk parameters — all within a single, flexible structure.
> ⚠️ **Note:** This script is intended for **educational and backtesting purposes only**. It does **not** provide financial advice, nor does it guarantee profits. Always test strategies thoroughly before applying them to live markets.
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### 🔧 Key Features
✅ **Multi-Source Entry Signals**
Combine up to two independent buy/sell signals using flexible logic:
- **OR Logic**: Trigger on any signal (edge-based).
- **AND Logic (Latched)**: Requires both signals at any point (flip-flop style).
- **AND No Latch**: Both signals must be active simultaneously.
✅ **Dynamic Trade Direction Control**
Choose between:
- Long & Short (Both)
- Long Only
- Short Only
With optional **close-on-opposite-signal** and **wait-for-opposite-reentry** logic.
✅ **Precision Timing Filters**
- Date range filtering (start/end dates)
- Intraday session control (supports up to 3 custom sessions)
- Visual session shading for clarity
✅ **Advanced Risk Management**
- Multiple Stop Loss types:
- Fixed Points / Percent
- ATR-based (adjustable multiplier)
- Swing-based (automatically detects pivots)
- External SL source
- Dynamic position sizing:
- Fixed lot
- % of equity risk (with max fallback)
✅ **Smart Take Profit Options**
- Fixed Points, Percent, RR Ratio, ATR, Fibonacci extensions
- Support for **external TP levels** (user-defined sources)
- Optional **multiple partial exits** with customizable size distribution
- Fibonacci TP levels (1.0, 1.618, 2.618, 4.236) based on SL distance
✅ **Flexible Exit Tools**
- Breakeven stop activation after TP1 hit
- Internal swing-based trailing stop
- External trailing stop (custom source)
- Max holding time (auto-close after X candles)
- Custom close conditions via user-defined logic
- Close & reverse functionality
✅ **Visual Clarity & Feedback**
- Clear visual markers for Buy/Sell signals
- Real-time SL, Entry, and TP lines with color-coded risk/reward zones
- On-chart TP level labels showing prices and allocation percentages
- Session background highlighting
- Trade statistics summary label
---
### 🛠️ Ideal For:
- Testing new indicator combinations
- Validating entry/exit logic under various market filters
- Comparing signal fusion methods (OR vs AND)
- Simulating professional-grade risk management rules
- Educational demonstrations in algorithmic trading
---
### ⚠️ Important Notes
- This is a **backtesting tool**, not a live trading bot.
- Past performance is **not indicative of future results**.
- Strategy performance depends entirely on the quality of input signals.
- Always validate results across multiple assets and timeframes.
- Use in conjunction with sound money management principles.
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### 📌 How to Use
1. Attach the script to your chart.
2. Configure **Buy/Sell Signal Sources** (e.g., RSI crossovers, moving averages, etc.)
3. Set your preferred **trade direction, session, and date filters**
4. Define **stop loss and take profit rules**
5. Adjust position sizing and exit behavior
6. Run the backtest and analyze results in the **Strategy Tester tab**
💡 *Tip: Combine with other indicators by referencing their output values as signal sources.*
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### ❌ Disclaimer
This script is shared for **informational and educational purposes only**. By using it, you agree that:
- The author is **not responsible** for any financial losses.
- Trading involves significant risk; only risk capital should be used.
- You are solely responsible for your trading decisions.
🚫 **This script does not promote get-rich-quick schemes, guaranteed profits, or unverified performance claims.**
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🔁 **Version:** 5 (Pine Script v5)
📦 **Category:** Strategy
📈 **Overlay:** Yes
🧪 **Purpose:** Backtesting, Signal Validation, Risk Modeling
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✅ **Safe for Public Sharing**
✔ Complies with TradingView’s community standards
✔ No misleading performance claims
✔ No automated trading promises
✔ No copyrighted or plagiarized content
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> 💬 *"Knowledge is power — test wisely, trade responsibly."*
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Let me know if you'd like a **short version** for the script's header comment or a **public post summary** for the TradingView feed!
