rule based strategy specification builder
by nowrich
Converts qualitative trading ideas into precise, institutional-grade systematic rule sets and specifications.
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Works with the AI tools you already use
See it in action
You say
I want to build a strategy that buys the 20-day breakout in tech stocks, but only if volatility is low, and exits after a few weeks or if the trend reverses.
Your agent does
ENTRY (LONG): IF close > highest(high, 20) AND ATR(14) < SMA(ATR(14), 50) * 0.8 THEN BUY next open.
EXIT:
- Initial Stop: 2 * ATR below entry.
- Time Exit: Close after 15 bars.
- Trailing: If price > 3 * ATR gain, move stop to breakeven.
RISK:
- 1% equity risk per trade.
What you get
About this skill
The problem
Vague trading ideas lead to inconsistent execution and impossible backtesting. Developers often waste weeks coding strategies based on ambiguous logic that fails in production due to undefined edge cases or slippage assumptions.
What it does
- Converts qualitative market hypotheses into precise, machine-readable logic using IF-THEN syntax.
- Generates comprehensive risk management frameworks including position sizing, drawdown limits, and correlation constraints.
- Formalizes testing protocols with specific walk-forward methods, transaction cost models, and slippage assumptions.
- Defines operational implementation rules for execution platforms, including kill switches and error handling.
- Identifies and documents critical edge cases like liquidity gaps, contract rollovers, and data staleness.
Why this beats prompting it yourself
A simple prompt often misses the "boring" parts of professional trading like trade-level t-statistics or kill switch conditions. This skill enforces a rigorous institutional-grade template that ensures no logic gaps remain for a developer to guess at during implementation.
Use cases
- Architecting a systematic trend-following strategy for a crypto trading bot.
- Standardizing internal documentation for a proprietary trading desk or fund.
- Converting a manual price-action setup into a verifiable backtesting specification.
- Preparing a strategy for automated execution on platforms like Interactive Brokers or Binance.
Known limitations
Does not provide financial advice or guarantee profitability. Requires the user to provide the initial core hypothesis or edge.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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