Machine Learning Finance / 2025
CNN Volume Based Trading Strategy
A machine-learning trading research project using technical indicators, CNN modeling, confidence thresholds, and backtesting.
- Role
- Model experimentation, backtesting, visualization
- Status
- Research project
Primary project visualization from the trading strategy research.
Premise
A full ML research loop, not only a model file
The project covers data ingestion, feature engineering, model training, thresholded signal generation, backtesting, and visual evaluation.
System Design
From market data to signal confidence
Daily SPY data is transformed into lookback windows with price, volume, and indicator features. The CNN predicts trade classes, and confidence thresholds decide when the model is allowed to act.
Engineering Challenge
Prevent leakage and overfitting from dominating the story
Financial ML work can look convincing while being fragile. The important engineering constraints are clean train/test separation, robust preprocessing, transaction assumptions, and honest comparison against buy-and-hold.
Next Iteration
Risk management and richer validation
Future work should include stop-loss logic, dynamic thresholds, broader assets, walk-forward validation, and sharper treatment of transaction costs.
Media