strategy-framework

name	strategy-framework
description	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

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

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