strategy-framework Checked

risk_per_trade = 0.02        # 2% of portfolio
stop_distance_pct = 0.05     # 5% from entry (ATR-derived)
position_size = (portfolio * risk_per_trade) / stop_distance_pct

**Risk Parameters**: Portfolio-level guardrails. See the risk-management skill.

Max concurrent positions: 5
Risk per trade: 2% of portfolio
Daily loss limit: 5% of portfolio
Max drawdown halt: 15% — stop trading, review strategy
Correlated exposure limit: 10% (e.g., meme tokens combined)

**Filters**: Conditions that prevent entry even if signals fire.

def filters_pass(token: dict, market: dict) -> bool:
    """All filters must pass before entry is allowed."""
    volume_ok = token["volume_24h"] > 500_000      # Min $500K volume
    liquidity_ok = token["liquidity"] > 100_000    # Min $100K liquidity
    age_ok = token["age_days"] > 7                 # Not brand new
    holders_ok = token["holder_count"] > 500       # Sufficient distribution
    regime_ok = market["regime"] != "crisis"       # No crisis regime
    return all([volume_ok, liquidity_ok, age_ok, holders_ok, regime_ok])

**Performance Criteria**: When to continue, review, or retire.

Continue: Sharpe > 1.0, PF > 1.5, Win Rate > 40%, MDD < 20%
Review:   Any metric degrades 25% from baseline
Retire:   Rolling 30-day Sharpe < 0, or 3 consecutive losing months

Strategy Lifecycle

### 1. Hypothesis

Identify a market inefficiency and explain why it exists and why it might persist.

**Good hypothesis**: "New PumpFun tokens that reach 80+ SOL in bonding curve within 10 minutes have a 65% probability of graduating to Raydium, creating a predictable price spike at graduation."

**Bad hypothesis**: "SOL will go up." (Not specific, not testable, no edge identified.)

### 2. Definition

Write the full strategy document using the template in references/strategy_template.md. Every field must be filled. If you cannot fill a field, the strategy is not ready.

### 3. Backtest

Test on historical data using vectorbt or equivalent. Requirements:

• Minimum 100 trades in the test period
• Use walk-forward validation (train on 70%, test on 30%)
• Account for slippage and fees (see slippage-modeling skill)
• Report both in-sample and out-of-sample metrics

### 4. Paper Trade

Run the strategy in simulation for at least 2 weeks (or 30 trades, whichever is longer).

• Compare paper results to backtest expectations
• If results differ by more than 25%, investigate before proceeding

### 5. Small Live

Trade with minimum viable size (enough to cover fees, small enough to be inconsequential).

• Run for at least 30 trades
• Compare to paper trade results

### 6. Scale

If small-live metrics match expectations (within 25% of backtest):

• Increase position size gradually (25% increments per week)
• Monitor metrics continuously

### 7. Monitor

Ongoing performance tracking:

• Daily: P&L, trade count, win rate
• Weekly: Sharpe ratio, profit factor, drawdown
• Monthly: Full strategy review against performance criteria

### 8. Retire

Stop using a strategy when:

• Rolling 30-day Sharpe drops below 0
• Three consecutive losing months
• Market regime permanently shifts (e.g., regulatory change)
• A better strategy replaces it for the same edge

Strategy Evaluation Criteria

Minimum thresholds before a strategy should be traded live:

| Metric | Trend Following | Mean Reversion | Scalping |

|--------|----------------|----------------|----------|

| Min Trades | 100 | 100 | 500 |

| Sharpe (OOS) | > 1.0 | > 1.0 | > 1.5 |

| Profit Factor | > 1.5 | > 1.5 | > 1.3 |

| Max Drawdown | < 20% | < 15% | < 10% |

| Win Rate | > 35% | > 55% | > 55% |

| Avg Win/Avg Loss | > 2.0 | > 1.0 | > 1.0 |

Strategy Types for Crypto

Detailed descriptions of each strategy type are in references/strategy_types.md.

### Momentum / Trend Following

• **Edge**: Price trends persist due to behavioral biases and information asymmetry
• **Indicators**: EMA crossovers, SuperTrend, ADX, MACD
• **Win rate**: 35-45%, relies on large winners
• **Best regime**: Trending markets with moderate volatility

### Mean Reversion

• **Edge**: Price oscillates around equilibrium due to overreaction
• **Indicators**: RSI, Bollinger Bands, z-score, VWAP deviation
• **Win rate**: 55-65%, relies on high win rate with smaller gains
• **Best regime**: Ranging markets with low-moderate volatility

### Breakout

• **Edge**: Compressed volatility leads to directional expansion
• **Indicators**: Bollinger Band squeeze, Donchian channels, volume breakout
• **Win rate**: 30-40%, relies on catching large moves
• **Best regime**: Transitioning from low to high volatility

### Copy Trading / Wallet Following

• **Edge**: Skilled wallets have informational or analytical advantages
• **Indicators**: Wallet PnL history, trade frequency, token selection
• **Win rate**: Depends on followed wallet quality
• **Best regime**: Any (depends on followed wallet's strategy)

### PumpFun Sniping

• **Edge**: Predictable price dynamics around token creation and graduation
• **Strategies**: Creation snipe, volume confirmation, graduation play
• **Win rate**: Highly variable (20-60% depending on approach)
• **Best regime**: High retail activity periods

### Arbitrage

• **Edge**: Price discrepancies across DEXs or between spot and perpetuals
• **Indi