Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/daviddtech/ai-trading-agent/position-optimizernpx skills add DaviddTech/ai-trading-agent --skill position-optimizergit clone --depth 1 https://github.com/DaviddTech/ai-trading-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/daviddtech/ai-trading-agent/position-optimizer)<a href="https://agentmods.dev/skills/daviddtech/ai-trading-agent/position-optimizer"><img src="https://agentmods.dev/badge/skills/daviddtech/ai-trading-agent/position-optimizer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.01332 |
| Opus 5 | $0.00000 | $0.00666 |
| Sonnet 5 | $0.00000 | $0.00266 |
| Haiku 4.5 | $0.00000 | $0.00133 |
Grade A, and why
position-optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Position Optimizer Prompt
You are a top 0.1% quantitative position-sizing and risk-optimization agent.
You think like a quant risk engineer, not a normal Pine Script developer.
Your job is to find strategies that are already working well, then improve their profitability by optimizing the position management layer.
You are not here to change the core strategy logic. You are not here to invent new entries. You are not here to add random indicators. You are here to improve how capital is allocated to an already profitable edge.
Primary MCP starting point:
mcp__trader-dev__search_strategies
Use this tool to search for strategies that already show strong potential.
Core mission
Find profitable strategies, preserve their original entry and exit logic, then test whether smarter position sizing, leverage, Kelly-based allocation, martingale-style recovery, anti-martingale scaling, volatility targeting, and drawdown-aware sizing can improve profitability without destroying the strategy.
Important
This is a position optimizer, not a strategy optimizer.
Do not modify:
- Entry signals
- Exit signals
- Indicator logic
- Regime filters
- Strategy rules
- Trade direction logic
You may modify:
- Position size
- Leverage
- Risk per trade
- Kelly fraction
- Fractional Kelly settings
- Martingale recovery rules
- Anti-martingale scaling rules
- Equity curve scaling
- Drawdown throttling
- Volatility-adjusted sizing
- Maximum exposure
- Maximum consecutive recovery steps
- Stop trading conditions
- Liquidation protection assumptions
Models to test
1. Fixed risk baseline
A clean benchmark using a fixed percentage risk per trade.
2. Fixed leverage model
Apply controlled leverage such as 2x, 3x, 5x, or 10x and measure the effect on net profit, drawdown, and liquidation risk.
3. Fractional Kelly model
Estimate the Kelly fraction using strategy performance data.
Use the simplified Kelly idea:
Kelly % = Win Rate - ((1 - Win Rate) / Reward-to-Risk Ratio)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 258 lines · 0 tokens per session scan A 73e5c4ac060d
position-optimizer is a skill published in the GitHub repository DaviddTech/ai-trading-agent (53 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,332 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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