ML Predictive Trading Strategy
Builds scikit-learn models that predict future price direction and turn them into trading signals
- Repository
- HKUDS/Vibe-Trading
- Stars
- 37,433
- Licence
- MIT
- Files
- 1 file
What it does
When this skill is installed, the agent can design a machine-learning trading strategy from price and volume history (OHLCV data). It computes technical indicators such as momentum, RSI and moving-average ratios, trains classifiers from the scikit-learn library in a walk-forward way that only uses past data, and converts predictions into buy/sell-style signals between -1 and 1. The file includes a sample Python pipeline the agent can adapt.
Great for
- Quantitative researchers prototyping ML-based trading ideas on historical price data
- Developers who want a template for walk-forward backtesting with scikit-learn
- Students learning how feature engineering and time-series validation work in finance
Skip it if
- Live trading with real money: the file describes backtest-style signal generation only, and predictions from historical patterns are unreliable and can lose money
- Users without Python and pandas/scikit-learn set up, since the code will not run otherwise
Try asking
- “Build a random forest strategy that predicts 5-day return direction for AAPL from my OHLCV CSV”
- “Create a walk-forward gradient boosting signal generator with a sliding 2-year training window”
- “Add RSI, Bollinger band position and volume ratio features to my existing price dataset and output signals”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 1 original files, the MIT licence and an attribution note.
ml-strategy.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip ml-strategy.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip ml-strategy.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Provide clean daily OHLCV data with a DatetimeIndex and enough history (the template expects at least 300 rows) for meaningful walk-forward results.
- Review the generated code before running it, since the agent may execute the Python scripts it writes, and check that no future data leaks into features.
- Treat outputs as research signals to be validated out of sample, including transaction costs and slippage, before any real trading decision.
What's inside
- SKILL.md
Redistributed unmodified from HKUDS/Vibe-Trading under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.