/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 los