nhu2000's repositories
jane_street
kaggle competition
stocksight
Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis
FinRL-Library
A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
quant-trading
Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD
stocktrading-rl
Exploring actor critic deep reinforcement learning methods for maximizing profits by learning stock trading strategies
asx_gym
Open AI Gym Env for Australia Stock Exchange (ASX)
Deep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020
Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. ICAIF 2020.
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
RLcycle
A library for ready-made reinforcement learning agents and reusable components for neat prototyping
stock_predict_with_LSTM
Predict stock with LSTM supporting pytorch, keras and tensorflow
Stock_prediction_hybrid_model
A hybrid of ANN, RNN and Regressor models to predict stock prices
ta-lib
Python wrapper for TA-Lib (http://ta-lib.org/).
StockSharp
Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins and options).
Deep-Learning-Machine-Learning-Stock
Stock for Deep Learning and Machine Learning
HandwrittenDigitRecognition
Hand written digit recognition
runner
Job runner for tbase experiments
Deep-RL-Keras
Keras Implementation of popular Deep RL Algorithms (A3C, DDQN, DDPG, Dueling DDQN)
tbase
Baselines of reinforcement learning trading agents for China stock market
tensortrade
An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.
gym-anytrading
The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)
rl
Deep Reinforcement Learning For Trading
chainerrl
ChainerRL is a deep reinforcement learning library built on top of Chainer.
Benjamin-Graham-and-Warren-Buffett-Model-Stock-Exchange-
There are about 4000 stocks which are actively traded on the stock exchanges at BSE and NSE. Can we extract public financial data from sites like moneycontrol.com to find which are the fundamentally strong stocks. On what stocks would the father of value investing, Benjamin Graham and Warren Buffett the most successful investors in the world make their investments on. Benjamin Graham and Warren Buffett Model Step 1: Filter out all companies with sales less than Rs 250 cr. Companies with sales lower than this are very small companies and might not have the business stability and access to finance that is required for a safe investment. This eliminates the basic business risk. Step 2: Filter out all companies with debt to equity greater than 30%. Companies with low leverage are safer. Step 3: Filter out all companies with interest coverage ratio of less than 4. Companies with high interest coverage ratio have a highly reduced bankruptcy risk. Step 4: Filter out all companies with ROE less than 15% since they are earning less than their cost of capital. High ROE companies have a robust business model, which generates increased earnings for the company typically. Step 5: Filter out all companies with PE ratio greater than 25 since they are too expensive even for a high-quality company. This enables us to pick companies which are relatively cheaper as against their actual value. He points out that applying these filters enables us to reduce and even eliminate a lot of fundamental risks while ensuring a robust business model, strong earning potential and a good buying price.
COVID-19
Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE
trading-bot
Stock Trading Bot using Deep Q-Learning
AutomatedStockTrading-DeepQ-Learning
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
Deep-Reinforcement-Stock-Trading
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.