القائمة
Trading strategy optimization methodology, an iterative process from diagnosis to hitting the target
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | strategy-optimization |
| description | Trading strategy optimization methodology, an iterative process from diagnosis to hitting the target |
| schema | 1.0 |
| version | 1.0.0 |
| triggers | {"keywords":{"primary":["strategy optimization","optimize strategy","improve win rate","improve returns"],"secondary":["win rate","backtest","parameter tuning","performance improvement"],"context_boost":["trading","quant","return","profit"]},"context_penalty":["design","marketing","frontend"],"priority":"high"} |
| keywords | ["finance","trading","optimization","strategy","backtest"] |
| dependencies | {"domain-skills":["quant-trading"]} |
| author | claude-domain-skills |
| metadata | {"mcpmarket-version":"1.0.0"} |
A systematic strategy optimization process, from diagnosing the problem to reaching the goal
┌─────────────────────────────────────────────────────────────────┐
│ │
│ Strategy Optimization Iteration Loop │
│ │
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ ANALYZE │ → │ DIAGNOSE│ → │ RESEARCH│ → │IMPLEMENT│ │
│ │ │ │ │ │ │ │ │ │
│ └────┬────┘ └────┬────┘ └────┬────┘ └────┬────┘ │
│ │ │ │ │ │
│ ▼ ▼ ▼ ▼ │
│ gather metrics find root cause search solutions make changes │
│ │
│ ┌─────────────────────────────────────────┐ │
│ │ │ │
│ ▼ │ │
│ ┌─────────┐ ┌─────────┐ │ │
│ │VALIDATE │ → │ ITERATE │ ────────────────────┘ │
│ │ │ │ │ not met → back to ANALYZE │
│ └────┬────┘ └─────────┘ │
│ │ │
│ ▼ │
│ target met → document & deploy │
│ │
└─────────────────────────────────────────────────────────────────┘
Gather current performance metrics:
| Metric | Description | Healthy Standard |
|---|---|---|
| Win rate | Proportion of winning trades | > 40% |
| Return | Total profit/loss | Positive |
| Drawdown | Maximum loss magnitude | < 20% |
| Trade count | Statistical significance | > 100 trades |
| Expectancy | Expected profit per trade | Positive |
Expectancy = (WinRate × AvgWin) - ((1-WinRate) × AvgLoss)
Example:
Win rate: 54.5%, Avg win: $7.42, Avg loss: $8.26
Expectancy: (0.545 × 7.42) - (0.455 × 8.26) = +$0.29/trade
Positive expectancy × high trade count = steady profit
Key: find the root cause, not the symptom
| Symptom | Possible Root Cause | Diagnostic Method |
|---|---|---|
| Low win rate | Strategy doesn't fit the market state | Check ADX / market regime |
| Low returns | Poor SL/TP ratio | Analyze profit/loss distribution |
| Large drawdown | Leverage too high | Compute risk exposure |
| Few trades | Conditions too strict | Loosen entry conditions |
┌─────────────────────────────────────────────────────────────────┐
│ Market State vs Strategy Fit │
│ │
│ ADX < 25 (ranging) → Grid Trading, Mean Reversion │
│ ADX > 25 (trending) → Trend Following, Momentum │
│ High volatility → Scalping, reduce position size │
│ Low volatility → Grid Trading, prepare for breakout │
│ │
│ ⚠️ 90% of the time the market is ranging; trend strategies │
│ will keep losing! │
└─────────────────────────────────────────────────────────────────┘
Symptom: win rate only 25%
↓
Analysis: using an RSI+MACD trend strategy
↓
Check: average ADX < 25 (ranging 90% of the time)
↓
Root cause: trend strategy used in a ranging market
↓
Direction: need a ranging-market strategy (Grid Trading)
| Market State | Recommended Strategy | Reason |
|---|---|---|
| Ranging (ADX < 25) | Grid Trading | Exploit price oscillation |
| Mean Reversion | Revert to the mean | |
| Scalping | Quick in and out | |
| Trending (ADX > 25) | Trend Following | Go with the trend |
| Momentum | Ride the momentum | |
| Breakout | Follow the breakout | |
| High volatility | Scalping | Fast profit |
| Reduce position | Control risk |
┌─────────────────────────────────────────────────────────────────┐
│ ⚠️ SE-1: Research data must be validated with real testing │
│ │
│ Severity: critical │
│ │
│ Case: │
│ - Research claimed chart patterns had 80-84% success rate │
│ - Actual backtest: made the strategy perform worse │
│ - Reason: stock market data doesn't apply to crypto │
│ │
│ Lesson: │
│ ❌ Implementing as soon as you see research data │
│ ✅ First validate by backtesting with real data │
└─────────────────────────────────────────────────────────────────┘
| Parameter | Tuning Direction | Impact |
|---|---|---|
| SL/TP ratio | Symmetric (1:1) vs asymmetric | Win rate vs risk/reward |
| Leverage | 1x → 10x | Return vs risk |
| Entry threshold | Loose vs strict | Trade count vs quality |
┌─────────────────────────────────────────────────────────────────┐
│ Optimization Findings │
│ │
│ 1. Symmetric SL/TP is better │
│ - 1.8%/1.8% yields ~55% win rate │
│ - Outperforms 1%/3% │
│ │
│ 2. Grid Trading in ranging markets │
│ - Win rate rose from 25% to 55% │
│ - Key: enable when ADX < 25 │
│ │
│ 3. More trades + positive expectancy │
│ - Small profits accumulate into a large return │
│ - 191 trades × $0.29/trade = +$55 │
└─────────────────────────────────────────────────────────────────┘
| Item | Minimum | Recommended |
|---|---|---|
| Backtest period | 60 days | 180 days |
| Trade count | 50 trades | 100+ trades |
| Out-of-sample test | Yes | Required |
| Trading costs | Included | Include slippage |
□ Did the win rate improve?
□ Did returns hit the target?
□ Is the drawdown acceptable?
□ Is expectancy positive?
□ How does it perform out of sample?
Target not met → back to ANALYZE
│
├── Analyze the new results
├── Adjust parameters or strategy
├── Re-validate
└── Repeat until target met
| Mistake | Consequence | Fix |
|---|---|---|
| ❌ Only tuning parameters, never changing strategy | Can't solve the underlying problem | Diagnose the root cause first |
| ❌ Backtest period too short | Overfitting | 180+ days |
| ❌ Ignoring market state | Strategy-market mismatch | Use regime detection |
| ❌ Chasing high win rate | Sacrifices risk/reward | Focus on expectancy |
Before optimization:
- Return: 0.46%
- Win rate: 23.5%
- Strategy: RSI+MACD (trend strategy used in a ranging market)
Diagnosis:
- ADX < 25 90% of the time (ranging)
- Trend strategy is a poor fit
After optimization:
- Return: +19.93%
- Win rate: 54.5%
- Strategy: Grid Trading + Regime Detection
Key changes:
1. Added a Grid Trading strategy
2. Used regime detection to switch dynamically
3. Symmetric SL/TP (1.8%/1.8%)
4. 10x leverage + 191 trades
⚠️ High returns come with high risk
- The case above had a maximum drawdown of 48.84%
- Consider lowering leverage (5x) for lower risk
- Continuously monitor changes in market state
- Set up a stop-loss mechanism
# Validated configuration
config = {
"leverage": 10.0, # or 5.0 to reduce risk
"stop_loss_pct": 0.018, # 1.8%
"take_profit_pct": 0.018, # 1.8%
"top_n_signals": 3,
"min_confidence": 0.5,
"use_regime_selector": True,
}
finance/quant-trading - Quantitative trading fundamentalsfinance/investment-analysis - Investment analysismethodology/knowledge-acquisition-4c - Learning a new domain