Multi-Model Fusion Stock Market Prediction System: Collaborative Application of LSTM, Random Forest, and XGBoost

This project is the financial-prediction-system on GitHub (author: kaanozzeybek00-crypto). Its core idea is to combine deep learning LSTM with traditional machine learning Random Forest and XGBoost to address the non-linearity and randomness of the financial market. It captures data patterns from multiple dimensions such as time-series features, feature importance, and gradient optimization to enhance the comprehensiveness of stock price prediction.

Financial market prediction is influenced by multiple factors, showing high non-linearity and randomness, which is difficult for a single model to handle. LSTM excels at capturing long-term dependencies in time series, Random Forest performs well in feature interaction and overfitting avoidance, and XGBoost is suitable for high-dimensional noisy data processing. The fusion of the three can complement their advantages and cover more data patterns.

Data Layer: Obtain basic stock data (opening/closing prices, etc.) and supplementary features; preprocessing includes missing value/outlier handling, standardization, etc. Feature Engineering: LSTM uses sliding time windows to construct sequences; tree models build statistical features (moving average, volatility, etc.) Model Training: Tune hyperparameters for each model (number of hidden units for LSTM, number of trees for Random Forest, learning rate for XGBoost, etc.) Prediction Fusion: Integrate results using simple average, weighted average, stacking method, etc.

Evaluation needs to combine Sharpe ratio (risk-adjusted return), maximum drawdown (magnitude of capital loss), win rate, and profit-loss ratio; risks include unpredictable factors such as the efficient market hypothesis (historical data is difficult to predict continuously), black swan events, and regulatory policy adjustments.

Application scenarios: quantitative trading strategy development, risk management, portfolio optimization, market sentiment monitoring; limitations: past performance does not guarantee future returns, overfitting risk, model updates required for market structure changes, quality issues with free data sources.

The project demonstrates the application value of multi-model fusion in financial prediction, covering the complete process; live trading applications need to supplement work such as risk management and transaction cost modeling; in the future, LLM and multi-modal data (news, social media, etc.) can be integrated to enhance prediction robustness.