Standardized template for defining trading strategies with entry rules, exit rules, position sizing, risk parameters, and performance criteria

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: 1. State a falsifiable hypothesis about a market inefficiency 2. Define precise, machine testable entry and exit rules 3. Specify position sizing and risk parameters before trading 4. Set minimum performance criteria for continuation or retirement 5. 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 Edge Hypothesis : State what market inefficiency you are exploiting and why it exists. Entry Rules : Specific, testable conditions combined with AND/OR logic. 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 Parameters : Portfolio level guardrails. See the risk management skill. Filters : Conditions that prevent entry even if signals fire. Performance Criteria : When to continue, review, or retire. 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 1. No written rules : Trading on intuition, unable to backtest or reproduce 2. Curve fitting : Optimizing parameters until backtest looks perfect, fails live 3. Missing stops : "I'll exit when it feels right" leads to catastrophic losses 4. Ignoring regime : Using a trend strategy in a ranging market (or vice versa) 5. Survivorship bias : Only backtesting tokens that still exist 6. Lookahead bias : Using future information in backtest signals 7. Ignoring costs : Not accounting for slippage, fees, and market impact 8. Over trading : Entering on marginal signals to "stay active" 9. 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

strategy-framework