/position-sizer

name: position-sizer
description: Calculate risk-based position sizes for long stock trades. Use when user asks about position sizing, how many shares to buy, risk per trade, Kelly criterion, ATR-based sizing, or portfolio risk allocation. Supports stop-loss distance calculation, volatility scaling, and sector concentration checks.

Position Sizer

Overview

Calculate the optimal number of shares to buy for a long stock trade based on risk management principles. Supports three sizing methods:

• **Fixed Fractional**: Risk a fixed percentage of account equity per trade (default: 1%)
• **ATR-Based**: Use Average True Range to set volatility-adjusted stop distances
• **Kelly Criterion**: Calculate mathematically optimal risk allocation from historical win/loss statistics

All methods apply portfolio constraints (max position %, max sector %) and output a final recommended share count with full risk breakdown.

When to Use

• User asks "how many shares should I buy?"
• User wants to calculate position size for a specific trade setup
• User mentions risk per trade, stop-loss sizing, or portfolio allocation
• User asks about Kelly Criterion or ATR-based position sizing
• User wants to check if a position fits within portfolio concentration limits

Prerequisites

• No API keys required
• Python 3.9+ with standard library only

Workflow

Step 1: Gather Trade Parameters

Collect from the user:

• **Required**: Account size (total equity)
• **Mode A (Fixed Fractional)**: Entry price, stop price, risk percentage (default 1%)
• **Mode B (ATR-Based)**: Entry price, ATR value, ATR multiplier (default 2.0x), risk percentage
• **Mode C (Kelly Criterion)**: Win rate, average win, average loss; optionally entry and stop for share calculation
• **Optional constraints**: Max position % of account, max sector %, current sector exposure

If the user provides a stock ticker but not specific prices, use available tools to look up the current price and suggest entry/stop levels based on technical analysis.

Step 2: Execute Position Sizer Script

Run the position sizing calculation:

# Fixed Fractional (most common)
python3 skills/position-sizer/scripts/position_sizer.py \
  --account-size 100000 \
  --entry 155 \
  --stop 148.50 \
  --risk-pct 1.0 \
  --output-dir reports/

# ATR-Based
python3 skills/position-sizer/scripts/position_sizer.py \
  --account-size 100000 \
  --entry 155 \
  --atr 3.20 \
  --atr-multiplier 2.0 \
  --risk-pct 1.0 \
  --output-dir reports/

# Kelly Criterion (budget mode - no entry)
python3 skills/position-sizer/scripts/position_sizer.py \
  --account-size 100000 \
  --win-rate 0.55 \
  --avg-win 2.5 \
  --avg-loss 1.0 \
  --output-dir reports/

# Kelly Criterion (shares mode - with entry/stop)
python3 skills/position-sizer/scripts/position_sizer.py \
  --account-size 100000 \
  --entry 155 \
  --stop 148.50 \
  --win-rate 0.55 \
  --avg-win 2.5 \
  --avg-loss 1.0 \
  --output-dir reports/

Step 3: Load Methodology Reference

Read `references/sizing_methodologies.md` to provide context on the chosen method, risk guidelines, and portfolio constraint best practices.

Step 4: Calculate Multiple Scenarios

If the user has not specified a single method, run multiple scenarios for comparison:

• Fixed Fractional at 0.5%, 1.0%, and 1.5% risk
• ATR-based at 1.5x, 2.0x, and 3.0x multipliers
• Present a comparison table showing shares, position value, and dollar risk for each

Step 5: Apply Portfolio Constraints and Determine Final Size

Add constraints if the user has portfolio context:

python3 skills/position-sizer/scripts/position_sizer.py \
  --account-size 100000 \
  --entry 155 \
  --stop 148.50 \
  --risk-pct 1.0 \
  --max-position-pct 10 \
  --max-sector-pct 30 \
  --current-sector-exposure 22 \
  --output-dir reports/

Explain which constraint is binding and why it limits the position.

Step 6: Generate Position Report

Present the final recommendation including:

• Method used and rationale
• Exact share count and position value
• Dollar risk and percentage of account
• Stop-loss price
• Any binding constraints
• Risk management reminders (portfolio heat, loss-cutting discipline)

Output Format

JSON Report

{
  "schema_version": "1.0",
  "mode": "shares",
  "parameters": {
    "entry_price": 155.0,
    "account_size": 100000,
    "stop_price": 148.50,
    "risk_pct": 1.0
  },
  "calculations": {
    "fixed_fractional": {
      "method": "fixed_fractional",
      "shares": 153,
      "risk_per_share": 6.50,
      "dollar_risk": 1000.0,
      "stop_price": 148.50
    },
    "atr_based": null,
    "kelly": null
  },
  "constraints_applied": [],
  "final_recommended_shares": 153,
  "final_position_value": 23715.0,
  "final_risk_dollars": 994.50,
  "final_risk_pct": 0.99,
  "binding_constraint": null
}

Markdown Report

Generated automatically alongside the JSON report. Contains:

• Parameters summary
• Calculation details for the active method
• Constraints analysis (if any)
• Final recommendation with shares, value, and risk

Reports are saved to `reports/` with filenames `position_sizer_YYYY-MM-DD_HHMMSS.json` and `.md`.

Resources

• `references/sizing_methodologies.md`: Comprehensive guide to Fixed Fractional, ATR-based, and Kelly Criterion methods with examples, comparison table, and risk management principles
• `scripts/position_sizer.py`: Main calculation script (CLI interface)

Key Principles

1. **Survival first**: Position sizing is about surviving losing streaks, not maximizing winners 2. **The 1% rule**: Default to 1% risk per trade; never exceed 2% without exceptional reason 3. **Round down**: Always round shares down to whole numbers (never round up) 4. **Strictest constraint wins**: When multiple limits apply, the tightest one determines final size 5. **Half Kelly**: Never use full Kelly in practice; half Kelly captures 75% of growth with far less risk 6. **Portfolio heat**: Total