DRW - Crypto Market Prediction

Built a high-performancemodel to forecast crypto price movements using:
1. Feature Engineering:

• Created rolling statistics (mean,std, min/max over 5-30 periods)
• Added technical indicators (RSI, momentum) and interactions (ratios, differences)
• Optimized memory with downcasting (np.int8, np.float32)

2. Modeling:

• Trained an esemble (VotingRegressor) of Ridge Regression (L2 regularization) and XGBoost (tree_method="hist")
• Validation via TimeSeriesSplit to pervent leakage, cross-valiadted correlation for scoring

3. Pipeline Optimization:

• Processed lagged features in chunks (chunk_size=10) for memory efficiency
• Rconstructed test-set timestamps using precomputed offsets to align temporal dependecies

4. Analysis:

• Results interpretation with feature importance plots (matplotlib) and prediction distributions
• Deployed batch prediction (batch_size=50k) to handle large-scale inference