Strategy Framework Skill for Claude

Strategy Framework

A standardized system for defining, documenting, testing, and managing trading strategies. This skill provides templates and tools that enforce discipline, enable reproducibility, and make strategies testable.

Why a Strategy Framework Matters

Trading without a written strategy framework leads to:

• Inconsistency: ad-hoc decisions driven by emotion rather than rules
• Untestability: vague ideas that cannot be backtested or evaluated
• Scope creep: strategies that drift without version-controlled definitions
• Unmanaged risk: missing stop losses, position limits, or drawdown halts

A strategy framework forces you to:

• State a falsifiable hypothesis about a market inefficiency
• Define precise, machine-testable entry and exit rules
• Specify position sizing and risk parameters before trading
• Set minimum performance criteria for continuation or retirement
• Track changes through versioned strategy documents

Strategy Definition Template

Every strategy must be documented using the standard template. The full copy-paste template is in references/strategy_template.md.

Core Sections

Identity

Name: SOL-EMA-Cross v1.0
Asset class: Solana tokens (top 50 by 24h volume)
Timeframe: Primary 1H, confirmation 4H
Style: Trend following

Edge Hypothesis: State what market inefficiency you are exploiting and why it exists.

Hypothesis: Solana mid-cap tokens exhibit momentum persistence
on the 1H timeframe due to retail herding behavior and low
institutional participation. EMA crossovers capture the
initiation of these trends.

Entry Rules: Specific, testable conditions combined with AND/OR logic.

def entry_signal(data: pd.DataFrame) -> bool:
    """All conditions must be True (AND logic)."""
    ema_cross = data["ema_12"] > data["ema_26"]  # EMA 12 crossed above 26
    ema_rising = data["ema_26"].diff(3) > 0       # 26 EMA trending up
    volume_ok = data["volume"] > data["vol_sma_20"] * 1.5  # Volume confirmation
    regime_ok = data["adx"] > 20                  # Trending regime
    return ema_cross & ema_rising & volume_ok & regime_ok

Exit Rules: Every strategy needs multiple exit mechanisms.

| Exit Type | Method | Parameters |

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

| Stop Loss | ATR-based | 2.0 × ATR(14) below entry |

| Take Profit | Risk multiple | 3.0 × risk (3:1 R:R) |

| Trailing Stop | Chandelier | 3.0 × ATR(14) from highest high |

| Time Stop | Bar count | Close if flat after 20 bars |

| Signal Exit | EMA reversal | EMA 12 crosses below EMA 26 |

Position Sizing: Method and parameters. See the position-sizing skill for details.

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
• Indicators: Price feeds from multiple venues, funding rates
• Win rate: > 80% when executed correctly
• Best regime: High volatility, fragmented liquidity

Market Making

• Edge: Capturing bid-ask spread while managing inventory risk
• Indicators: Order book depth, volatility, inventory position
• Win rate: > 60%, relies on volume and spread capture
• Best regime: Stable markets with consistent volume

Common Strategy Mistakes

• No written rules: Trading on intuition, unable to backtest or reproduce
• Curve fitting: Optimizing parameters until backtest looks perfect, fails live
• Missing stops: "I'll exit when it feels right" leads to catastrophic losses
• Ignoring regime: Using a trend strategy in a ranging market (or vice versa)
• Survivorship bias: Only backtesting tokens that still exist
• Lookahead bias: Using future information in backtest signals
• Ignoring costs: Not accounting for slippage, fees, and market impact
• Over-trading: Entering on marginal signals to "stay active"
• Strategy hopping: Abandoning strategies after normal losing streaks

10. No retirement plan: Continuing to trade a broken strategy out of attachment

Integration with Other Skills

| Skill | Integration |

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

| vectorbt | Backtest strategy definitions programmatically |

| pandas-ta | Compute technical indicators for entry/exit signals |

| regime-detection | Market regime filters for strategy activation |

| exit-strategies | Detailed exit rule implementation |

| position-sizing | Position size calculation methods |

| risk-management | Portfolio-level risk parameter enforcement |

| slippage-modeling | Realistic execution cost estimation |

| feature-engineering | ML feature computation from strategy signals |

Files

References

• references/strategy_template.md — Complete copy-paste strategy definition template
• references/strategy_types.md — Detailed guide to each strategy type with parameters and examples

Scripts

• scripts/define_strategy.py — Interactive strategy definition tool with --demo mode
• scripts/strategy_scorecard.py — Strategy evaluation scorecard with GO/REVIEW/NO-GO recommendations