| 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:
- 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_rising = data["ema_26"].diff(3) > 0
volume_ok = data["volume"] > data["vol_sma_20"] * 1.5
regime_ok = data["adx"] > 20
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
stop_distance_pct = 0.05
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
liquidity_ok = token["liquidity"] > 100_000
age_ok = token["age_days"] > 7
holders_ok = token["holder_count"] > 500
regime_ok = market["regime"] != "crisis"
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
- 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 templatereferences/strategy_types.md — Detailed guide to each strategy type with parameters and examples
Scripts
scripts/define_strategy.py — Interactive strategy definition tool with --demo modescripts/strategy_scorecard.py — Strategy evaluation scorecard with GO/REVIEW/NO-GO recommendations
