requirements.txt

# ============================================================================
# AI-Driven Multi-Factor Trading Strategy - Dependencies
# IIQF CPAIT Batch 2025 Capstone Project
# ============================================================================

# Data Acquisition & Processing
yfinance>=0.2.0        # Yahoo Finance market data
pandas>=1.5.0          # Data manipulation
numpy>=1.23.0          # Numerical computing

# Technical Analysis
ta>=0.10.0             # Technical indicators library

# Machine Learning - Core
scikit-learn>=1.2.0    # ML models, preprocessing, metrics

# Machine Learning - Gradient Boosting (V1, V2, V3)
xgboost>=1.7.0         # XGBoost (all versions)
lightgbm>=4.0.0        # LightGBM (V3 stacking ensemble)
catboost>=1.2.0        # CatBoost (V3 stacking ensemble - optional)

# Deep Learning (V1 only - LSTM)
tensorflow>=2.12.0     # Keras LSTM model

# Hyperparameter Optimization (V2, V3)
optuna>=3.0.0          # Bayesian optimization framework

# Model Explainability
shap>=0.42.0           # SHAP analysis for all versions

# Visualization
matplotlib>=3.6.0      # Plotting library
seaborn>=0.12.0        # Statistical visualizations
plotly>=5.10.0         # Interactive plots

# Utilities
joblib>=1.2.0          # Model serialization
tqdm>=4.64.0           # Progress bars

# ============================================================================
# Installation:
#   pip install -r requirements.txt
#
# Quick install (without optional CatBoost):
#   pip install yfinance pandas numpy ta scikit-learn xgboost lightgbm \
#               tensorflow optuna shap matplotlib seaborn plotly joblib tqdm
# ============================================================================