tforce_btc_trader

Integrates Proximate Policy Optimization (PPO) from TensorForce for automated decision-making in trading, moving beyond simple prediction to strategy learning.

Uses Bayesian Optimization and gradient boosting to systematically tune neural network configurations, which the README emphasizes as critical for RL success in trading.

Supports both LSTM and CNN models, allowing experimentation with different approaches for time-series data, as discussed in the LSTM v CNN section.

Includes dry-run test mode and live trading mode with explicit risk warnings, enabling real-world deployment after validation.

Provides a React/Flask dashboard for analyzing hyperparameter runs and agent performance signals, offering more customization than TensorBoard.