Reinforcement Learning Trading Agent Development
Reinforcement Learning (RL) is fundamentally different approach to algorithmic trading. Instead of price prediction and rule building, agent learns itself by interacting with environment (market) and receiving rewards/penalties for actions. RL agent can open positions, close them, adjust size — and learns to do this optimally through trial and error.
Problem as Markov Decision Process (MDP):
State: what agent sees each moment: OHLCV last N candles, technical indicators, current position, unrealized PnL, account balance.
Action: discrete (0=hold, 1=buy, 2=sell) or continuous [-1, 1] where -1=full short, 0=no position, 1=full long.
Reward: critical part. Wrong reward breaks training. Basic portfolio return as reward leads to agents taking huge risk for big reward. Improvements: Sharpe Ratio reward, drawdown penalties, max position duration penalties.
Algorithms:
- PPO (Proximal Policy Optimization): most popular for finance. Stable, works with continuous and discrete actions.
- SAC (Soft Actor-Critic): best for continuous action space. Maximizes reward + policy entropy.
- DQN (Deep Q-Network): only discrete actions. Simpler. Double DQN, Dueling DQN improvements.
Curriculum Learning: start on "easy" periods (low volatility, clear trend), gradually add complex (high volatility, sideways).
Backtesting RL agent: simulate trading on test data. Calculate total return, Sharpe, max drawdown, win rate.
Develop RL trading agent with PPO/SAC, custom trading environment, reward shaping (Sharpe-based), walk-forward validation on multiple test periods and production deployment.