OPEN-SOURCE SCRIPT
AetherEdge - Adaptive Risk Engine
🖊️ Overview
Most sizing is a static "1% every time." AE-RISK sizes to a learned edge and volatility, and pulls risk back automatically when you're cold. It learns the trigger's win-probability online, sizes via fractional Kelly, and lays out the full per-trade risk package — ATR stop/target, $ risk and reward, and position size in units.
🔶 Key Features
Edge-linked sizing — fractional Kelly from the learned win-rate (size up only when the odds justify it)
Loss-streak throttle — scales risk down/up with realized losing/winning runs (drawdown-aware)
Adaptive stop/target — ATR stop distance, R target
Per-regime optimal R — a UCB learns the best-expectancy R per volatility regime (information, decoupled from sizing)
Full risk package — units / % equity / $ risk-reward / R:R / win-rate / Kelly f*
Level lines + trigger triangles; bar-close training — no repaint
🧠 Technical Architecture
Trigger: EMA cross (default) or Donchian breakout; used for both learning and display.
ML (online logistic): at each trigger, six features (trend alignment, momentum, RSI, vol regime, volume, breakout position) predict P(win) = P(reach the R-target before the stop within N bars) via a triple barrier over an array<Setup>, resolved conservatively with SL priority (max-favorable-excursion counted only up to the stop).
Sizing (fractional Kelly): f* = max((p(b+1)−1)/b, 0) with b = target R; risk % = Kelly fraction × f* (capped); units = equity × risk % ÷ stop distance. A vol-target size is shown for reference.
RL (UCB / per-regime optimal R): per volatility regime, a UCB learns the best-expectancy R from the realized MFE distribution, shown as "opt R" (decoupled — information, not an override).
Throttle: a realized win/loss streak scales the multiplier (down through losing runs).
Honest scope: a linear classifier + Kelly + a bandit. Not deep learning, and not financial advice.
⚙️ Recommended Settings & Tuning Guide (crypto 15m–4H)
Key parameters: equity, base/max risk %, Kelly fraction, ATR stop, target R, trigger, barrier horizon N, throttle factor.
Kelly fraction ≈ 0.2–0.3 (full Kelly overbets; stay conservative)
Always set max risk % (caps large Kelly outputs)
Design payoff with stop (ATR) / target R; consult the per-regime "opt R"
A higher throttle factor cuts risk harder on losing runs
Start from defaults for crypto 15m–1H (slMult 1.5, R 2.0, N 20); enter your real equity
💡 How to Use in Practice
Use the trigger's risk package (size / SL / TP / $ risk) as your order template
Stand aside when P(win) and Kelly f* are low (thin edge)
A throttle multiple below 1 signals poor fit with the market — go smaller
If opt R diverges from your target R, reconsider the target
Combine with AE-ACE or AE-MRB signals, using this tool to manage size and stops
⚠️ Important Notes
Needs a learning period (warmup); learning resets on parameter/timeframe change
Intrabar order is unknown, so MFE/SL resolution is a conservative approximation (it can differ from real fills)
Kelly is sensitive to the estimated win-rate — keep the fraction small and always cap risk
This is not financial advice. The displayed size is only a starting point; final decisions and orders are your own
🚨 Disclaimer
This indicator is for educational and informational purposes only and is not financial advice, a trading recommendation, or money-management advice. Displayed position sizes and risk amounts are mechanical example calculations; no method guarantees future profits. Past performance does not indicate future results, and trading carries the risk of loss. All decisions and capital allocation are your own — use proper validation and disciplined risk management.
Most sizing is a static "1% every time." AE-RISK sizes to a learned edge and volatility, and pulls risk back automatically when you're cold. It learns the trigger's win-probability online, sizes via fractional Kelly, and lays out the full per-trade risk package — ATR stop/target, $ risk and reward, and position size in units.
🔶 Key Features
Edge-linked sizing — fractional Kelly from the learned win-rate (size up only when the odds justify it)
Loss-streak throttle — scales risk down/up with realized losing/winning runs (drawdown-aware)
Adaptive stop/target — ATR stop distance, R target
Per-regime optimal R — a UCB learns the best-expectancy R per volatility regime (information, decoupled from sizing)
Full risk package — units / % equity / $ risk-reward / R:R / win-rate / Kelly f*
Level lines + trigger triangles; bar-close training — no repaint
🧠 Technical Architecture
Trigger: EMA cross (default) or Donchian breakout; used for both learning and display.
ML (online logistic): at each trigger, six features (trend alignment, momentum, RSI, vol regime, volume, breakout position) predict P(win) = P(reach the R-target before the stop within N bars) via a triple barrier over an array<Setup>, resolved conservatively with SL priority (max-favorable-excursion counted only up to the stop).
Sizing (fractional Kelly): f* = max((p(b+1)−1)/b, 0) with b = target R; risk % = Kelly fraction × f* (capped); units = equity × risk % ÷ stop distance. A vol-target size is shown for reference.
RL (UCB / per-regime optimal R): per volatility regime, a UCB learns the best-expectancy R from the realized MFE distribution, shown as "opt R" (decoupled — information, not an override).
Throttle: a realized win/loss streak scales the multiplier (down through losing runs).
Honest scope: a linear classifier + Kelly + a bandit. Not deep learning, and not financial advice.
⚙️ Recommended Settings & Tuning Guide (crypto 15m–4H)
Key parameters: equity, base/max risk %, Kelly fraction, ATR stop, target R, trigger, barrier horizon N, throttle factor.
Kelly fraction ≈ 0.2–0.3 (full Kelly overbets; stay conservative)
Always set max risk % (caps large Kelly outputs)
Design payoff with stop (ATR) / target R; consult the per-regime "opt R"
A higher throttle factor cuts risk harder on losing runs
Start from defaults for crypto 15m–1H (slMult 1.5, R 2.0, N 20); enter your real equity
💡 How to Use in Practice
Use the trigger's risk package (size / SL / TP / $ risk) as your order template
Stand aside when P(win) and Kelly f* are low (thin edge)
A throttle multiple below 1 signals poor fit with the market — go smaller
If opt R diverges from your target R, reconsider the target
Combine with AE-ACE or AE-MRB signals, using this tool to manage size and stops
⚠️ Important Notes
Needs a learning period (warmup); learning resets on parameter/timeframe change
Intrabar order is unknown, so MFE/SL resolution is a conservative approximation (it can differ from real fills)
Kelly is sensitive to the estimated win-rate — keep the fraction small and always cap risk
This is not financial advice. The displayed size is only a starting point; final decisions and orders are your own
🚨 Disclaimer
This indicator is for educational and informational purposes only and is not financial advice, a trading recommendation, or money-management advice. Displayed position sizes and risk amounts are mechanical example calculations; no method guarantees future profits. Past performance does not indicate future results, and trading carries the risk of loss. All decisions and capital allocation are your own — use proper validation and disciplined risk management.
สคริปต์โอเพนซอร์ซ
ด้วยเจตนารมณ์หลักของ TradingView ผู้สร้างสคริปต์นี้ได้ทำให้เป็นโอเพนซอร์ส เพื่อให้เทรดเดอร์สามารถตรวจสอบและยืนยันฟังก์ชันการทำงานของมันได้ ขอชื่นชมผู้เขียน! แม้ว่าคุณจะใช้งานได้ฟรี แต่โปรดจำไว้ว่าการเผยแพร่โค้ดซ้ำจะต้องเป็นไปตาม กฎระเบียบการใช้งาน ของเรา
คำจำกัดสิทธิ์ความรับผิดชอบ
ข้อมูลและบทความไม่ได้มีวัตถุประสงค์เพื่อก่อให้เกิดกิจกรรมทางการเงิน, การลงทุน, การซื้อขาย, ข้อเสนอแนะ หรือคำแนะนำประเภทอื่น ๆ ที่ให้หรือรับรองโดย TradingView อ่านเพิ่มเติมใน ข้อกำหนดการใช้งาน
สคริปต์โอเพนซอร์ซ
ด้วยเจตนารมณ์หลักของ TradingView ผู้สร้างสคริปต์นี้ได้ทำให้เป็นโอเพนซอร์ส เพื่อให้เทรดเดอร์สามารถตรวจสอบและยืนยันฟังก์ชันการทำงานของมันได้ ขอชื่นชมผู้เขียน! แม้ว่าคุณจะใช้งานได้ฟรี แต่โปรดจำไว้ว่าการเผยแพร่โค้ดซ้ำจะต้องเป็นไปตาม กฎระเบียบการใช้งาน ของเรา
คำจำกัดสิทธิ์ความรับผิดชอบ
ข้อมูลและบทความไม่ได้มีวัตถุประสงค์เพื่อก่อให้เกิดกิจกรรมทางการเงิน, การลงทุน, การซื้อขาย, ข้อเสนอแนะ หรือคำแนะนำประเภทอื่น ๆ ที่ให้หรือรับรองโดย TradingView อ่านเพิ่มเติมใน ข้อกำหนดการใช้งาน