{"repo":"ebrahimpichka/DeepRL-trade","free":true,"listed":false,"github":"https://github.com/ebrahimpichka/DeepRL-trade","clone":"git clone https://github.com/ebrahimpichka/DeepRL-trade.git","description":"Algorithmic Trading Using Deep Reinforcement Learning algorithms (PPO and DQN)","language":"Jupyter Notebook","stars":21,"topics":["algorithmic-trading","deep-learning","deep-reinforcement-learning","dqn","ppo","quantitative-finance","quantitative-trading","reinforcement-learning","reinforcement-learning-algorithms","rl"],"license":null,"category":"trading","readme_excerpt":"DeepRL-trade Algorithmic Trading Using Deep Reinforcement Learning (PPO & DQN) --- Introduction In quantitative finance, stock trading is essentially a dynamic decision problem — deciding where, at what price, and how much to trade in a stochastic, dynamic, and complex market. Deep reinforcement learning (DRL) enables modelling and solving these sequential decision problems with a human-like approach. This project trains two DRL agents — Proximal Policy Optimization (PPO) and Deep Q-Learning (DQN) — to autonomously make trading decisions on GOOG stock and compares their performance against a Buy & Hold benchmark using risk-adjusted metrics. --- Project Structure --- Quick Start 1. Install 2. Configure API keys (optional) 3. Train 4. Evaluate 5. Visualise --- Configuration All settings are in YAML files under configs/ . The system works in layers: 1. configs/default.yaml — all defaults 2. Experiment YAML (e.g. ppo goog.yaml ) — overrides specific keys 3. CLI --override — overrides anything at runtime Key sections: data , env , agent (with ppo / dqn sub-sections), evaluation , tracking , paths . Experiment Tracking Set tracking.use wandb: true in your config and ensure WANDB API KEY is in .env (or run wandb login ). Metrics, models, and configs are auto-logged to your Weights & Biases project. --- How It Works Component Description ----------- ------------- Environment Gymnasium-compatible discrete-action env. Actions: Long (+1), Cash (0), Short (-1). Reward = % change in total","default_branch":null,"files":null,"tree":[],"storefront":"/r/ebrahimpichka","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ebrahimpichka/DeepRL-trade/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}