Strategy Framework

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  1.0 | > 1.0 | > 1.5 |
| Profit Factor | > 1.5 | > 1.5 | > 1.3 |
| Max Drawdown |  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

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [agiprolabs](https://github.com/agiprolabs)
- **Source:** [agiprolabs/claude-trading-skills](https://github.com/agiprolabs/claude-trading-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.