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π Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.
The main features of ceruleanacg/personae are: Deep Learning Trading, Machine Learning Models, Quantitative Trading Strategies.
Projects with overlapping indexed features include: borisbanushev/stockpredictionai β This project is a collection of predictive models and quantitative tools for stock price forecasting. It implements aβ¦ cbailes/awesome-deep-trading β List of awesome resources for machine learning-based algorithmic trading. ai4finance-llc/finrl-library β FinRL-Library is a reinforcement learning trading framework and algorithmic trading library used to develop andβ¦ achillesrasquinha/bulbea β :boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling. ai4finance-llc/deep-reinforcement-learning-for-automated-stock-trading-ensemble-strategy-icaif-2020 β FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading. hackthemarket/gym-trading β Environment for reinforcement-learning algorithmic trading models.
This project is a collection of predictive models and quantitative tools for stock price forecasting. It implements a variety of machine learning architectures, including generative adversarial networks, long short-term memory networks, and language models for financial analysis. The system distinguishes itself by combining time-series forecasting with natural language processing to convert financial news into numerical sentiment scores. It also incorporates synthetic market data generation and automated hyperparameter optimization using Bayesian and reinforcement learning methods to reduce p
FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
FinRL-Library is a reinforcement learning trading framework and algorithmic trading library used to develop and backtest automated financial trading strategies. It functions as a quantitative trading pipeline and financial market simulator, allowing users to build decision policies that optimize asset trading across various financial markets. The framework features a modular integration system for swapping reinforcement learning algorithms through a consistent API. It utilizes a standardized environment wrapper to encapsulate market dynamics into a state-action-reward interface, facilitating
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling