OPEN-SOURCE SCRIPT
Trend Signals with TP/SL + Liquidation Filter
Trend Signals with TP/SL + Adaptive Liquidation Filter
Research & Educational Framework (Synced v3)
This script is based on the original Trend Signals with TP/SL concept by UAlgo.
The original implementation provides an ATR-based trend detection and risk management structure.
This version preserves the core trend logic while extending it with an adaptive volatility-based liquidation-style filter.
This publication is intended strictly for research and educational purposes.
It should not be considered a finished trading system or financial advice.
🔹 Research Objective
This framework explores the following structural question:
Can trend reversal signals be improved by requiring statistically meaningful price displacement before allowing entries?
The script combines:
A state-driven ATR trend engine
A volatility-adaptive deviation filter
The purpose of this combination is to study whether requiring both directional confirmation and displacement confirmation can reduce entries during low-momentum ranging environments.
The filter is not added for indicator aggregation, but to test interaction between trend state changes and volatility-adjusted extremes.
🔹 Structural Components
1️⃣ Trend Engine (Original Concept)
• Hull-based momentum calculation
• ATR-derived dynamic trailing bands
• State-based trend reversal logic
• Bar-close confirmation (no repainting)
Signals occur only when a full trend flip is confirmed.
2️⃣ Adaptive Deviation Filter (Extension Layer)
• Hybrid SMA + EMA midline
• Volatility-adjusted smoothing lengths via ATR ratio
• Deviation-based extreme detection
• Optional tolerance filtering
• Optional clustering requirement
• Bar-close confirmation
The filter acts as a confirmation layer, not as a standalone signal generator.
3️⃣ Risk & Execution Model
• ATR-based Take Profit and Stop Loss (default)
• Optional percent-based exits
• Fixed position sizing
• No pyramiding
Orders are executed through TradingView’s native strategy engine.
🔹 Reference Backtest Context
To provide structural context, the following test configuration was used:
• Market: XRPUSDT
• Timeframe: 15 minutes
• Initial Capital: 10,000 USD
• Position Size: Fixed 100 USD per trade
• Commission: 0.075%
• Slippage: 0
• Pyramiding: Disabled
One-year sample produced:
• 47 trades
• Profit Factor ≈ 3.6
• Win rate ≈ 68%
• Max drawdown ≈ 0.13%
• Net profit ≈ 143 USD
🔹 Interpretation Notes
This framework is low-frequency by design.
The relatively small trade count reflects:
• Selective filtering
• Conservative position sizing
• State-based trend participation
The low drawdown observed in this configuration is primarily the result of small fixed position sizing relative to total capital.
Results will vary significantly if position sizing, leverage, market, or timeframe are modified.
Users are encouraged to test across:
• Multiple assets
• Extended historical periods
• Alternative volatility conditions
No performance assumptions should be made based solely on the provided sample.
🔹 Intended Use
This script is intended for:
• Developers researching trend-filter interaction
• Traders studying volatility-adaptive filtering
• Educational experimentation with structured risk models
It is not optimized for high-frequency scalping or aggressive leveraged trading.
🔹 Open-Source Purpose
This script is shared to encourage:
• Experimentation
• Methodological improvements
• Regime detection extensions
• Alternative risk models
Constructive modifications and research iterations are welcome.
Research & Educational Framework (Synced v3)
This script is based on the original Trend Signals with TP/SL concept by UAlgo.
The original implementation provides an ATR-based trend detection and risk management structure.
This version preserves the core trend logic while extending it with an adaptive volatility-based liquidation-style filter.
This publication is intended strictly for research and educational purposes.
It should not be considered a finished trading system or financial advice.
🔹 Research Objective
This framework explores the following structural question:
Can trend reversal signals be improved by requiring statistically meaningful price displacement before allowing entries?
The script combines:
A state-driven ATR trend engine
A volatility-adaptive deviation filter
The purpose of this combination is to study whether requiring both directional confirmation and displacement confirmation can reduce entries during low-momentum ranging environments.
The filter is not added for indicator aggregation, but to test interaction between trend state changes and volatility-adjusted extremes.
🔹 Structural Components
1️⃣ Trend Engine (Original Concept)
• Hull-based momentum calculation
• ATR-derived dynamic trailing bands
• State-based trend reversal logic
• Bar-close confirmation (no repainting)
Signals occur only when a full trend flip is confirmed.
2️⃣ Adaptive Deviation Filter (Extension Layer)
• Hybrid SMA + EMA midline
• Volatility-adjusted smoothing lengths via ATR ratio
• Deviation-based extreme detection
• Optional tolerance filtering
• Optional clustering requirement
• Bar-close confirmation
The filter acts as a confirmation layer, not as a standalone signal generator.
3️⃣ Risk & Execution Model
• ATR-based Take Profit and Stop Loss (default)
• Optional percent-based exits
• Fixed position sizing
• No pyramiding
Orders are executed through TradingView’s native strategy engine.
🔹 Reference Backtest Context
To provide structural context, the following test configuration was used:
• Market: XRPUSDT
• Timeframe: 15 minutes
• Initial Capital: 10,000 USD
• Position Size: Fixed 100 USD per trade
• Commission: 0.075%
• Slippage: 0
• Pyramiding: Disabled
One-year sample produced:
• 47 trades
• Profit Factor ≈ 3.6
• Win rate ≈ 68%
• Max drawdown ≈ 0.13%
• Net profit ≈ 143 USD
🔹 Interpretation Notes
This framework is low-frequency by design.
The relatively small trade count reflects:
• Selective filtering
• Conservative position sizing
• State-based trend participation
The low drawdown observed in this configuration is primarily the result of small fixed position sizing relative to total capital.
Results will vary significantly if position sizing, leverage, market, or timeframe are modified.
Users are encouraged to test across:
• Multiple assets
• Extended historical periods
• Alternative volatility conditions
No performance assumptions should be made based solely on the provided sample.
🔹 Intended Use
This script is intended for:
• Developers researching trend-filter interaction
• Traders studying volatility-adaptive filtering
• Educational experimentation with structured risk models
It is not optimized for high-frequency scalping or aggressive leveraged trading.
🔹 Open-Source Purpose
This script is shared to encourage:
• Experimentation
• Methodological improvements
• Regime detection extensions
• Alternative risk models
Constructive modifications and research iterations are welcome.
Скрипт с открытым кодом
В истинном духе TradingView, создатель этого скрипта сделал его открытым исходным кодом, чтобы трейдеры могли проверить и убедиться в его функциональности. Браво автору! Вы можете использовать его бесплатно, но помните, что перепубликация кода подчиняется нашим Правилам поведения.
Отказ от ответственности
Информация и публикации не предназначены для предоставления и не являются финансовыми, инвестиционными, торговыми или другими видами советов или рекомендаций, предоставленных или одобренных TradingView. Подробнее читайте в Условиях использования.
Скрипт с открытым кодом
В истинном духе TradingView, создатель этого скрипта сделал его открытым исходным кодом, чтобы трейдеры могли проверить и убедиться в его функциональности. Браво автору! Вы можете использовать его бесплатно, но помните, что перепубликация кода подчиняется нашим Правилам поведения.
Отказ от ответственности
Информация и публикации не предназначены для предоставления и не являются финансовыми, инвестиционными, торговыми или другими видами советов или рекомендаций, предоставленных или одобренных TradingView. Подробнее читайте в Условиях использования.