Enhance trading models with Deep Q-Network (DQN) reinforcement learning, train agents through market interactions, and integrate RL insights with fine-tuned language models for optimal performance.
a
in a given state s
. A separate target network ensures training stability by providing consistent learning targets, and experience replay helps the model learn efficiently from varied historical market scenarios.
We’ve adapted DQN specifically for trading by customizing the state representation to include market data and current portfolio positions. The actions map directly to trading decisions—buy, sell, or hold. The reward function evaluates portfolio returns and uses risk penalties to guide the trading behavior. Each training “episode” corresponds to a clearly defined trading session, complete with precise start and end conditions.
off-chain/rl_trading/trading.py
) implements a Gymnasium-compatible interface.
State representation:
rl_lora_config.yaml
: