strategy-generate
strategy-generate lets you design trading strategies by defining entry/exit logic, then automatically backtests them across stocks, crypto, and other instruments. Write your signal engine in Python, configure your parameters, and the skill handles data loading and performance analysis—no boilerplate required.
strategy-generate creates and backtests quantitative trading strategies by writing signal logic and evaluating performance metrics.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-07-27
strategy-generate creates and backtests quantitative trading strategies by writing signal logic and evaluating performance metrics. strategy-generate lets you design trading strategies by defining entry/exit logic, then automatically backtests them across stocks, crypto, and other instruments. Write your signal engine in Python, configure your parameters, and the skill handles data loading and performance analysis—no boilerplate required.
Use it when
- Yes.
- strategy-generate generates trading signals based on the custom signal logic you define in Python.
Verify before relying
Read SKILL.md below before installing (2 files). Open directory: indexed for reading, not audited.
Install
HKUDS/Vibe-Trading/strategy-generate · repository language: Python
Open directory. Skills are indexed for reading, not audited. Review a skill's body before installing it.
Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I create a quantitative trading strategy with strategy-generate?
strategy-generate lets you design trading strategies by defining entry/exit logic in Python, then automatically backtests them across stocks, crypto, and other instruments. Write your signal engine with custom conditions, configure your parameters, and the skill handles data loading and performance analysis—no boilerplate required.
Can strategy-generate backtest a trading idea on historical data?
Yes. strategy-generate backtests your strategy across historical price data, evaluating how your entry/exit conditions would have performed. The skill generates performance metrics and trading signals automatically, letting you validate your idea before deploying it live.
What does strategy-generate use to generate trading signals?
strategy-generate generates trading signals based on the custom signal logic you define in Python. You specify your indicators, thresholds, and conditions; the skill applies them to historical and real-time data to produce entry and exit signals for your strategy.
How does strategy-generate optimize position sizing and risk management?
strategy-generate lets you design position sizing and risk management rules within your strategy definition. Configure your position sizing logic, stop-loss levels, and portfolio constraints; the skill applies them during backtesting and evaluation to show how your risk parameters affect overall performance.
Can I optimize trading strategy parameters with strategy-generate?
Yes. strategy-generate supports parameter optimization and validation across multiple markets and instruments. Test different configurations of your entry/exit logic, position sizing, and risk rules to find the settings that maximize your strategy's performance metrics.
What markets and assets does strategy-generate support?
strategy-generate works with stocks, cryptocurrencies, and other instruments. Build and backtest algorithmic trading strategies across any market where you have historical price data, then validate performance before deploying your strategy live.
SKILL.md
Rendered from the published skill. Quoted content, verbatim.
Workflow
- Requirements parsing: parse user intent, extract instrument codes, time range, and strategy logic, then write
config.json - Strategy design: think through the 5 questions of data / signal / position sizing / backtest / validation
- Strategy coding: write
code/signal_engine.py(following theSignalEnginecontract) - Syntax check:
bash("python -c \"import ast; ast.parse(open('code/signal_engine.py').read()); print('OK')\"") - Run backtest: call the
backtesttool (built into the engine; no need to writerun_backtest.py) - Evaluate results: read
artifacts/metrics.csvand judge by the review criteria - Iterative fixing: if results are poor, modify with
edit_file→ runbacktest→ re-evaluate
You only need to write signal_engine.py and config.json. The backtest tool automatically handles data loading and backtest execution.
Requirements Parsing
Extract the following from the user's description: - Instrument codes: process them according to the
(truncated - see the full file via the links below)
File tree — 2 files
agent/src/skills/strategy-generate/SKILL.md
agent/src/skills/strategy-generate/examples.md
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