test-trading-strategies

What this skill does

# Test Trading Strategies

## Quick Start

This skill validates strategy performance on historical data before risking real capital. Testing is fast (20-60s), cheap ($0.001), and essential for safe trading.

**Load the tools first**:
```
Use MCPSearch to select: mcp__workbench__run_backtest
Use MCPSearch to select: mcp__workbench__get_latest_backtest_results
```

**Basic backtest**:
```
run_backtest(
    strategy_name="MyStrategy",
    start_date="2024-01-01",
    end_date="2024-12-31",
    symbol="BTC-USDT",
    timeframe="1h"
)
```

Returns performance metrics in 20-40 seconds:
- Sharpe ratio: 1.4 (good risk-adjusted return)
- Max drawdown: 12% (moderate risk)
- Win rate: 52% (realistic)
- Profit factor: 1.8 (profitable)

**When to use this skill**:
- After building new strategy (validate it works)
- After improving strategy (confirm improvement)
- Before deploying to live trading (ALWAYS)
- Comparing multiple strategy versions
- Testing parameter variations

**Critical rule**: NEVER deploy without backtesting 6+ months of data

## Available Tools (3)

### run_backtest

**Purpose**: Test crypto trading strategy performance on historical data

**Parameters**:
- `strategy_name` (required): Strategy to test
- `start_date` (required): Start date (YYYY-MM-DD)
- `end_date` (required): End date (YYYY-MM-DD)
- `symbol` (required): Trading pair (e.g., "BTC-USDT")
- `timeframe` (required): Timeframe (1m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d)
- `config` (optional): Backtest configuration object:
  - `fee`: Trading fee per side (default: 0.0005 = 0.05%)
  - `slippage`: Slippage per trade (default: 0.0005 = 0.05%)
  - `leverage`: Position multiplier (default: 1, max: 5)

**Returns**: Performance metrics:
- **Net profit**: Total profit/loss in USDC
- **Total return**: Percentage return
- **Annual return**: Annualized return percentage
- **Sharpe ratio**: Risk-adjusted return (industry standard metric)
- **Max drawdown**: Largest peak-to-trough decline
- **Win rate**: Percentage of profitable trades
- **Profit factor**: Gross profit / gross loss
- **Trade statistics**: Total trades, average trade duration, consecutive losses
- **Equity curve**: Balance over time (for visualization)

**Pricing**: $0.001 (essentially free)

**Execution Time**: ~20-40 seconds

**Use when**: Testing crypto perpetual strategies on Hyperliquid

### run_prediction_market_backtest

**Purpose**: Test Polymarket prediction market strategy on historical data

**Parameters**:
- `strategy_name` (required): PolymarketStrategy to test
- `start_date` (required): Start date (YYYY-MM-DD)
- `end_date` (required): End date (YYYY-MM-DD)
- `condition_id` (for single market): Specific Polymarket condition ID
- `asset` (for rolling markets): Asset symbol ("BTC", "ETH")
- `interval` (for rolling markets): Market interval ("15m", "1h")
- `initial_balance` (optional): Starting USDC (default: 10000)
- `timeframe` (optional): Execution timeframe (default: 1m)

**Returns**: Backtest metrics:
- Profit/loss
- Win rate
- Position history for YES/NO tokens
- Market resolution outcomes

**Pricing**: $0.001

**Execution Time**: ~20-60 seconds

**Use when**: Testing Polymarket prediction market strategies

### get_latest_backtest_results

**Purpose**: View recent backtest results without re-running

**Parameters**:
- `strategy_name` (optional): Filter by strategy name
- `limit` (optional, 1-100): Number of results (default: 10)
- `include_equity_curve` (optional): Include equity curve data
- `equity_curve_max_points` (optional, 50-1000): Curve resolution

**Returns**: List of recent backtest records with metrics

**Pricing**: Free

**Use when**: Checking if backtest already exists, comparing strategies, avoiding redundant backtests

## Core Concepts

### Performance Metrics Interpretation

**Sharpe Ratio** (risk-adjusted return):
```
Formula: (Mean Return - Risk-Free Rate) / Standard Deviation of Returns

Interpretation:
>2.0  → Excellent (very rare for algo strategies)
1.0-2.0 → Good (achievable with solid strategy)
0.5-1.0 → Acceptable (worth testing further)
<0.5  → Poor (likely not profitable after costs)

Why it matters:
- Accounts for volatility (high return with high volatility = lower Sharpe)
- Industry standard for comparing strategies
- More useful than total return alone
```

**Max Drawdown** (largest peak-to-trough decline):
```
Example: Strategy grows from $10k → $15k → $12k
Drawdown: ($15k - $12k) / $15k = 20%

Interpretation:
<10%  → Conservative (lower returns, safer)
10-20% → Moderate (balanced risk/reward)
20-40% → Aggressive (higher returns, higher risk)
>40%  → Very risky (difficult to recover from)

Why it matters:
- Measures worst-case scenario
- Predicts emotional difficulty of holding strategy
- 50% drawdown requires 100% return to recover
```

**Win Rate** (percentage of profitable trades):
```
Formula: (Winning Trades / Total Trades) × 100%

Interpretation:
45-65% → Realistic for most strategies
>70%  → Suspicious (possible overfitting or unrealistic fills)
<40%  → Needs improvement (unless very high profit factor)

Why it matters:
- High win rate doesn't guarantee profitability
- Can have 40% win rate but profitable (if winners > losers)
- Very high win rate (>75%) often indicates overfitting

Common misconception: Higher is always better
Reality: 40% win rate with 3:1 reward:risk is better than 60% win rate with 1:1
```

**Profit Factor** (gross profit / gross loss):
```
Formula: Sum of All Winning Trades / Sum of All Losing Trades

Interpretation:
>2.0  → Excellent
1.5-2.0 → Good
1.2-1.5 → Acceptable
<1.2  → Marginal (risky to deploy)
<1.0  → Unprofitable (losses exceed profits)

Why it matters:
- Simple profitability measure
- <1.5 means small edge, vulnerable to slippage/fees
- Combines win rate and win size into single metric

Example:
10 trades: 6 winners ($100 each), 4 losers ($50 each)
Gross profit: $600, Gross loss: $200
Profit factor: $600 / $200 = 3.0 (excellent)
```

**Total Return vs Annual Return**:
```
Total Return: 50% over 6 months
Annual Return: ~100% (extrapolated to 12 months)

Why both matter:
- Total return: Actual profit over test period
- Annual return: Standardized for comparison across time periods
- Longer test periods more reliable (6-12 months minimum)
```

### Testing Methodology

**Minimum data requirements**:
```
Quick test: 1-3 months
- Limited validation
- Use for initial screening only
- High risk of luck/overfitting

Standard test: 6-12 months (RECOMMENDED MINIMUM)
- Captures multiple market regimes
- Sufficient trades for statistical significance
- Industry standard for strategy validation

Robust test: 12-24 months
- Ideal for high-confidence validation
- Includes bull, bear, and ranging markets
- Best for strategies before live deployment
```

**Multi-period testing** (essential for robustness):
```
1. Train period: 2024-01-01 to 2024-08-31
   run_backtest(..., start_date="2024-01-01", end_date="2024-08-31")
   → Sharpe: 1.5

2. Validation period: 2024-09-01 to 2024-12-31
   run_backtest(..., start_date="2024-09-01", end_date="2024-12-31")
   → Sharpe: 1.3

3. Compare:
   Performance similar → Robust strategy ✓
   Performance degraded significantly → Overfit to train period ✗
```

**Market regime testing**:
```
Test strategy across different market conditions:

1. Trending up (bull market): 2023-10 to 2024-03
   → Sharpe: 1.8

2. Trending down (bear market): 2024-04 to 2024-07
   → Sharpe: 0.9

3. Ranging (sideways): 2024-08 to 2024-12
   → Sharpe: 1.1

Analysis:
- Works well in all regimes ✓
- Or works in specific regime (trend-following good in trends)
- Fails in all regimes → Fundamentally broken ✗
```

### Red Flags (Overfitting Indicators)

**Warning signs that backtest results may not persist**:

**1. Unrealistically high win rate (>70%)**:
```
Win rate: 82%
Problem: Markets are noisy; >70% suggests strategy memorized past data
Solution: Test on out-of-sample data; expect performance degradation
```

**2. Very few trades (<20 in 6 months