Reinforcement Learning

8 concepts in this category.

• Reinforcement Learning

  RL is how an AI learns to make decisions by trying things and getting rewards or penalties. Think of training a dog — good moves get treats, bad moves get nothing. The AI keeps adjusting until it finds the best strategy.

• PPO Algorithm

  PPO is one of the best algorithms for training AI agents. It learns slowly and steadily instead of making wild policy changes that could destabilize training. Think of it as a careful student who improves step by step.

• Observation Space

  The observation space is everything the AI trading agent can see before making a decision. It might include price data, technical indicators, sentiment scores, and portfolio state. More useful data = better decisions (up to a point).

• Reward Function

  The reward function is the AI's score card. If we reward just raw returns, the agent might take huge risks. Our agent is rewarded for Sharpe ratio — returns relative to risk — which incentivizes consistent, risk-adjusted gains.

• Action Space

  The action space defines what choices the AI can make. A simple agent has 3 actions: buy, sell, hold. A more sophisticated one can trade any fraction of its portfolio. More actions = more flexibility but harder to train.

• RL Model Training

  Training the RL model means running the AI through years of historical market data thousands of times, letting it learn from its mistakes. Like a chess AI playing millions of games — it gradually figures out what works.

• Sharpe Reward

  Instead of rewarding the AI just for making money, we reward it for making money efficiently relative to risk. An AI rewarded with Sharpe ratio learns to avoid unnecessary risks and build consistent returns.

• Walk-Forward Validation

  Walk-forward validation trains the AI on old data, then tests it on the next period — then repeats. This mimics real-world conditions and prevents the AI from 'memorizing' the training data instead of learning real patterns.