Imported from wind-information-co-ltd/wind-skills (skills/position-sizer/SKILL.md). Install upstream with npx skills add wind-information-co-ltd/wind-skills --skill position-sizer. Copyright stays with the author.

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

Survival first: Position sizing is about surviving losing streaks, not maximizing winners
The 1% rule: Default to 1% risk per trade; never exceed 2% without exceptional reason
Round down: Always round shares down to whole numbers (never round up)
Strictest constraint wins: When multiple limits apply, the tightest one determines final size
Half Kelly: Never use full Kelly in practice; half Kelly captures 75% of growth with far less risk
Portfolio heat: Total open risk should not exceed 6-8% of account equity
Asymmetry of losses: A 50% loss requires a 100% gain to recover; size accordingly

position-sizer

About