{"repo":"shivamakhauri04/TradingBot","free":true,"listed":false,"github":"https://github.com/shivamakhauri04/TradingBot","clone":"git clone https://github.com/shivamakhauri04/TradingBot.git","description":"Stock price prediction and automated trading using Deep Reinforcement Learning and Machine Learning","language":"Jupyter Notebook","stars":48,"topics":["ai","deep-learning","finance","machine-learning","python","reinforcement-learning","stock-market","trading-bot","algorithmic-trading","deep-reinforcement-learning"],"license":"MIT","category":"machine-learning","readme_excerpt":"TradingBot Stock price prediction and automated trading using Deep Q-Network (DQN) reinforcement learning. Trains an agent to make buy/sell/hold decisions on historical stock data. Architecture This project implements a Deep Q-Network (DQN) agent for automated stock trading. The key components are: Trading Environment A custom gym-compatible environment that simulates stock trading. At each time step, the agent observes the current portfolio value and recent price change history, then selects one of three actions: - Hold (action 0): Do nothing - Buy (action 1): Purchase stock at the current price - Sell (action 2): Sell all held positions Rewards are clipped to +1 (profitable trade), -1 (unprofitable trade or selling with no positions), or 0 (hold/buy). Q-Network A fully connected neural network that maps observations to Q-values for each action. The network consists of three linear layers with ReLU activations: Training Strategy - Experience Replay : Stores past transitions in a replay buffer and samples random mini-batches for training, breaking temporal correlations - Target Network : A separate target Q-network is periodically updated with the weights of the online network, stabilizing training - Epsilon-Greedy Exploration : Starts with random actions (epsilon=1.0) and gradually shifts to greedy policy (epsilon=0.1) as the agent learns Results The DQN agent was trained on Google (GOOG) stock data from 2014-2016 and tested on 2016-2017 data. The agent achieved test profits","default_branch":null,"files":null,"tree":[],"storefront":"/r/shivamakhauri04","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/shivamakhauri04/TradingBot/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."}