w1nn1ethepooh's starred repositories
deep-reinforcement-learning
Repo for the Deep Reinforcement Learning Nanodegree program
awesome-quant
**的Quant相关资源索引
efficient-kan
An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).
cvxpylayers
Differentiable convex optimization layers
robustlearn
Robust machine learning for responsible AI
DQN_pytorch
Vanilla DQN, Double DQN, and Dueling DQN implemented in PyTorch
Crossformer
Official implementation of our ICLR 2023 paper "Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting"
StockFormer
PyTorch implementation for Paper "StockFormer: Learning Hybrid Trading Machines with Predictive Coding".
unstable_baselines
Re-implementations of SOTA RL algorithms.
time-series-momentum
🚂💨 Deep Momentum Networks for Time Series Strategies
Stockformer
StockFormer: A Swing Trading Strategy Based on STL Decomposition and Self-Attention Networks
SharkStock
Automate swing trading using deep reinforcement learning. The deep deterministic policy gradient-based neural network model trains to choose an action to sell, buy, or hold the stocks to maximize the gain in asset value. The paper also acknowledges the need for a system that predicts the trend in stock value to work along with the reinforcement learning algorithm. We implement a sentiment analysis model using a recurrent convolutional neural network to predict the stock trend from the financial news. The objective of this paper is not to build a better trading bot, but to prove that reinforcement learning is capable of learning the tricks of stock trading.
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.
aevoTrading
AEVO刷交易程序
Stockformer2022
Initially a fork of the GitHub repository for the paper "Informer" accepted by AAAI 2021. Heavily modified since then.
TradeMaster
TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning :fire: :zap: :rainbow: