machine-learning-strategy
CommunityPredict returns with walk-forward ML signals.
Data & Analytics#machine learning#feature engineering#OHLCV#trading signals#scikit-learn#walk-forward validation#return prediction
Authorloanntc
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill turns OHLCV market data into future-direction trading signals by training machine-learning models without look-ahead leakage.
Core Features & Use Cases
- Walk-forward training: Uses expanding or sliding windows so each prediction is made using only historical data.
- OHLCV feature engineering: Builds robust factors (momentum, volatility, RSI, moving-average ratios, volume ratios, Bollinger position, and intraday ratios) with sanitization and zero-division guards.
- Signal generation contract: Produces clean outputs by mapping model probabilities to a continuous signal in [-1.0, 1.0], with NaN-safe fallbacks.
- Use Case: When you have daily OHLCV for multiple symbols, you can generate per-symbol long/short confidence signals suited to a prediction horizon like N-day returns.
Quick Start
Use the machine-learning-strategy Skill to generate per-symbol signals from your OHLCV DataFrames by ensuring the columns open, high, low, close, and volume are present.
Dependency Matrix
Required Modules
scikit-learnpandasnumpy
Components
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: machine-learning-strategy Download link: https://github.com/loanntc/Paave/archive/main.zip#machine-learning-strategy Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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