This is the code for "Reinforcement Learning for Stock Prediction" By Siraj Raval on Youtube

Reinforcement Learning for Stock Market Prediction

Applying Reinforcement learning models for stock price predictions

Actor-Critic Reinforcement Learning POC for Stock Market Prediction

A tool for developing reinforcement learning algorithms focused in stock prediction

This repository implements a stock trading agent using Deep Reinforcement Learning with Diffusion Policy, an effective technique for enhancing decision-making. It utilizes technical and fundamental data to refine market predictions and optimize risk management, focusing on maximizing profitability and minimizing risk.

We are developing a time series forecasting model using reinforcement learning, based on OneNet, for stock market data prediction.

Predicts opening stock prices of 469 S&P companies. Uses five different datasets merged into one using the date column. Three regression techniques are used and also reinforcement learning is used among these algorithms to determine weights among these regressor predictions. Accuracy for each company on an average is 97%.

Repository for using reinforcement learning on real stock market data(historic)

It's implementation of Q-learning applied to (short-term) stock trading. The model uses n-day windows of closing prices to determine if the best action to take at a given time is to buy, sell or sit.

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This repo contains our project for CPE 800 which focuses on reinforcement learning for stock prediction

Reinforcement Learning for Stock Prediction

강화학습을 이용한 주가 예측 AI

Simple Reinforcement Learning, Deep Reinforcement Learning, Federated, Multi Agent stock prediction code for this Google stocks dataset: https://www.kaggle.com/thevirusx3/google-stock-market-data

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This project addresses the problem of predicting direction of movement of stock and stock price index for Indian stock markets. The study compares four prediction models, Gradient Boost Regression, Support Vector Machine (SVM), random forest regression and Reinforcement learning (Bagging Model) with two approaches for input to these models. The first approach for input data involves computation of ten technical parameters using stock trading data (open, high, low & close prices) while the second approach focuses on representing these technical parameters as trend deterministic data. Accuracy of each of the prediction models for each of the two input approaches is evaluated. Evaluation is carried out on specific years when stock prices rise or fall. Accuracy of each of the 4 machine learning algorithms is calculated and printed with the correlation between stock opening price and oil price.

Reinforcement Learning for Stock price prediction

Stock Prediction using Reinforcement Learning (for my youtube channel: Data Science with Ruben)

This project implements a Q-learning based stock prediction system that utilizes reinforcement learning techniques to make buy, sell, or hold decisions for a given stock symbol. It incorporates real-time data fetching, technical indicator calculation, sentiment analysis on news articles, and machine learning models for prediction.

Combining Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Deep Reinforcement Learning (DRL) for stock market prediction can leverage the strengths of each approach

A comprehensive educational guide to machine learning for stock market prediction. Covers statistical methods (ARIMA), classical ML (Random Forest, XGBoost), deep learning (LSTM), and reinforcement learning. Includes working code, financial terminology explanations, and tutorials for developers new to financial ML.

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I put into practice an RL algorithm to predict values of Tesla shares.

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This is the code for "Reinforcement Learning for Stock Prediction" with real-time fetching of stocks.Inspiration from Siraj Raval on Youtube

Reinforcement Machine Learning Model for Stock Price Prediction using Python

Reinforcement Learning for Stock Market Prediction