Without-girlfriend

Without-girlfriend's Stars

• Bin-Huang/chatbox

  User-friendly Desktop Client App for AI Models/LLMs (GPT, Claude, Gemini, Ollama...)

  Language:TypeScript24.7k1541.6k2.5k

• dennybritz/reinforcement-learning

  Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

  Language:Jupyter Notebook20.8k8661556.1k

• microsoft/qlib

  Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

  Language:Python16k3039252.7k

• ranaroussi/yfinance

  Download market data from Yahoo! Finance's API

  Language:Python15.4k2461.4k2.5k

• akfamily/akshare

  AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库

  Language:Python10k2121.6k2k

• DLR-RM/stable-baselines3

  PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

  Language:Python9.5k661.5k1.7k

• MorvanZhou/Reinforcement-learning-with-tensorflow

  Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

  Language:Python9k2901935k

• vwxyzjn/cleanrl

  High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

  Language:Python6k37186683

• quantopian/pyfolio

  Portfolio and risk analytics in Python

  Language:Jupyter Notebook5.8k3054181.8k

• p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

  PyTorch implementations of deep reinforcement learning algorithms and environments

  Language:Python5.7k106711.2k

• SUSYUSTC/MathTranslate

  translate scientific papers in latex, especially arxiv papers

  Language:Python1.2k85581

• uvipen/Super-mario-bros-PPO-pytorch

  Proximal Policy Optimization (PPO) algorithm for Super Mario Bros

  Language:Python1.1k2824206

• RL-MLDM/alphagen

  Generating sets of formulaic alpha (predictive) stock factors via reinforcement learning.

  Language:Python568952180

• golsun/deep-RL-trading

  playing idealized trading games with deep reinforcement learning

  Language:Python357284123

• jackchen0120/EC-Platform-Monitor

  新版Echarts电商平台数据可视化大屏监控系统进阶实战项目

  Language:Vue1305030

• mayingfa/steamed-bread-roll-mall

  花卷商城是一款基于 Springboot、Vue 的电商项目，前后端分离技术；

  Language:Vue101308

• shuaigege1766/BlogV2.0

  前后端分离博客springboot+vue+mybatisplus+springsecurity

  Language:Java992528

• leerumor/rl-atari-tennis

  Play atari Tennis game by dqn

  Language:Python711414

• HFTHaidra/Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Strategy

  Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose a deep ensemble reinforcement learning scheme that automatically learns a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using the three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market conditions. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand approach for processing very large data. We test our algorithms on the 30 Dow Jones stocks which have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble scheme is shown to outperform the three individual algorithms and the two baselines in terms of the risk-adjusted return measured by the Sharpe ratio.

  Language:Jupyter Notebook361112

• andreaslillevangbech/PortfolioManager-pytorch

  Language:Jupyter Notebook13114

• alireza-jafari/GCNET-Code

  GCNET: graph-based prediction of stock price movement using graph convolutional network

  9112

• watermeloncq/drl-pm-shorting-and-arbitraging

  Language:Jupyter Notebook61

• satopi/vue-vite-shop

  一个基于vue3+vite2+ts+element plus的电商平台后台管理系统，用于学习交流

  Language:Vue52

• wangxianglong/zhkmall_admin

  跨境电商后台管理系统

  Language:Vue5105

• yunfeiluo/Autonomous-Stock-Trading

  This is the official repository for the workshop paper "Agent Performing Autonomous Stock Trading under Good and Bad Situations" by authors Yunfei Luo and Zhangqi Duan in AI4ABM at ICLR 2023.

  Language:Jupyter Notebook5134

• kongminseok/EAASTUDRL

  Language:Jupyter Notebook4110

• JEEVAN-REJI/RL-PROJECT

  A Q-learning Agent for Automated Trading in Equity Stock Markets

  21

• bearbro/Financial_data_from_tushare

  通过tushare获取金融数据

  Language:Python10

• hetumessi/reinforcement-learning-stock

  强化学习练习项目

  Language:Jupyter Notebook1

• ut-kdd/GCNET-Code

  GCNET: graph-based prediction of stock price movement using graph convolutional network

  11