strategy-optimization

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"}

Trading Strategy Optimization

A systematic strategy optimization process, from diagnosing the problem to reaching the goal

When to Use

• Win rate too low (< 40%)
• Returns below target
  • Strategy performs poorly in specific market states
  • Need to hit a specific return target (e.g. 15%+)

Core Process

┌─────────────────────────────────────────────────────────────────┐
  │                                                                 │
  │                   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                               │
  │                                                                 │
  └─────────────────────────────────────────────────────────────────┘

Phase 1: ANALYZE

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 Formula

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

Phase 2: DIAGNOSE

Key: find the root cause, not the symptom

Common Root Causes

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 Analysis

┌─────────────────────────────────────────────────────────────────┐
  │  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!                                           │
  └─────────────────────────────────────────────────────────────────┘

Diagnostic Example

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)

Phase 3: RESEARCH

Strategy-to-Market-State Mapping

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

Sharp Edge: Validate Research Data

┌─────────────────────────────────────────────────────────────────┐
  │  ⚠️ 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                │
  └─────────────────────────────────────────────────────────────────┘

Phase 4: IMPLEMENT

Parameter Tuning Strategy

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

Key Findings

┌─────────────────────────────────────────────────────────────────┐
  │  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                          │
  └─────────────────────────────────────────────────────────────────┘

Phase 5: VALIDATE

Backtest Requirements

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

Validation Checklist

  □ Did the win rate improve?
□ Did returns hit the target?
□ Is the drawdown acceptable?
□ Is expectancy positive?
□ How does it perform out of sample?

Phase 6: ITERATE

Target not met → back to ANALYZE
  │
    ├── Analyze the new results
    ├── Adjust parameters or strategy
    ├── Re-validate
    └── Repeat until target met

Best Practices

1. Diagnose before acting - Don't blindly tune parameters
2. Change one variable at a time - So you know what worked
3. Validate research conclusions - Theoretical data can't be trusted
4. Enough trades - For statistical significance
5. Document successful experience - Write it up as an ADR

Common Mistakes

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

Success Case

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

Risk Warning

⚠️ 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

Reference Configuration

# 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,
}

Related Skills

• finance/quant-trading - Quantitative trading fundamentals
• finance/investment-analysis - Investment analysis
• methodology/knowledge-acquisition-4c - Learning a new domain