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Position Sizer

CommunityPopular
tradermonty
position-sizer

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, fractional-share sizing, or portfolio risk allocation. Supports stop-loss distance calculation, volatility scaling, and sector concentration checks.

Overview

Publishertradermonty
Repositoryclaude-trading-skills
Skill nameposition-sizer
Stars
2.8K
Forks
647
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tradermonty on GitHub. Read the source before you install it.

Installation

Install the Position Sizer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git /tmp/claude-trading-skills
mkdir -p .claude/skills
cp -r /tmp/claude-trading-skills/skills/position-sizer .claude/skills/position-sizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Position Sizer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Position Sizer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Position Sizer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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. The default output is whole shares. Use --fractional only when the user's broker supports fractional shares for the security and order type.

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 has a small account where whole-share rounding would under-deploy a defined risk budget
  • 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
  • Optional share mode: Whole shares by default, or fractional shares with --fractional --share-precision N when supported by the broker

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:

bash
# 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/

# Fractional shares for small accounts or high-priced stocks
python3 skills/position-sizer/scripts/position_sizer.py \
  --account-size 1000 \
  --entry 155 \
  --stop 148.50 \
  --risk-pct 1.0 \
  --fractional \
  --share-precision 4 \
  --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:

bash
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)
  • Small-account reminders: fractional shares do not remove broker minimums, spread/slippage, commissions/fees, margin limits, borrow availability, or day-trading controls

Output Format

JSON Report

json
{
  "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. Default to whole shares: Existing workflows remain integer-share by default
  4. Floor, never round up: Whole-share mode floors to an integer; fractional mode floors to the requested precision so risk and concentration budgets are not exceeded
  5. Strictest constraint wins: When multiple limits apply, the tightest one determines final size
  6. Half Kelly: Never use full Kelly in practice; half Kelly captures 75% of growth with far less risk
  7. Portfolio heat: Total open risk should not exceed 6-8% of account equity
  8. Intraday rules are broker-specific: FINRA replaced the old pattern-day-trader day-count and $25,000 minimum-equity requirements with intraday margin standards effective 2026-06-04, with broker phase-in allowed through 2027-10-20. Check the broker's current rules before repeated same-day trading in a margin account.
  9. Asymmetry of losses: A 50% loss requires a 100% gain to recover; size accordingly

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Position Sizer AI skill do?

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, fractional-share sizing, or portfolio risk allocation. Supports stop-loss distance calculation, volatility scaling, and sector concentration checks.

Why use Position Sizer on TypingMind?

Because you install it once and use it with any model. Position Sizer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Position Sizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tradermonty/claude-trading-skills/tree/main/skills/position-sizer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Position Sizer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Position Sizer?

As many as you like. As long as a model supports skills, you can use Position Sizer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Position Sizer AI skill free?

Yes. It is published on GitHub by tradermonty under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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