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machine-learning-with-python

Python code for common Machine Learning Algorithms

ml-stock-prediction

A toolset for stock prediction using reinforced learning, including a custom OpenAI environment.

mql-zmq

ZMQ binding for the MQL language (both 32bit MT4 and 64bit MT5)

onlyone

All development related with the ONLYONE token.

prediction-on-forex-using-lstm

This is my final year project. Prediction on Forex using LSTM

predictive-analytics-with-tensorflow

Predictive Analytics with TensorFlow, published by Packt

python_for_microscopists

https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

pytorch-lstm-stock-price-predict

LSTM 实现的股票最高价预测

q-learning-for-trading

q-trader

Deep Q-learning driven stock trader bot

r_project

reinforcement-learning

Minimal and Clean Reinforcement Learning Examples

reinforcement-learning-stocks

Applying Reinforcement learning models for stock price predictions

reinforcementlearning-atarigame

Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games

rl-forex-trader-lstm

Deep LSTM Duel DQN Reinforcement Learning Forex EUR/USD Trader

stanford-project-predicting-stock-prices-using-a-lstm-network

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

stock-market-forecasting

Forecasting directional movements of stock prices for intraday trading using LSTM and random forest

stock-prediction-models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

stock-prediction-pytorch

Neural Networks to predict stock price

stock-prediction-with-rnn-in-pytorch

Basic Stock Prediction using a RNN in Pytorch

stock-price-prediction-lstm

OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

stock-price-prediction-lstm-and-trading-strategy-reinforcement-learning

Stock Price Prediction with LSTM and Trading Strategy with Reinforcement Learning

stock-price-prediction-on-bitcoin-trading-data-using-lstm-with-pytorch

stock-price-prediction-using-lstm

using LSTM neural network, in order to perform stock price forecasting

stock-price-prediction-using-lstm-neural-networks

stock-price-predictor

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.

stock-trading-environment

A custom OpenAI gym environment for simulating stock trades on historical price data.

stockprediction

Stock Prediction by Reinforcement Learning and RNN

stockpriceprediction

LSTM and Sentiment Analysis to predict Stock Prices

stocktradingrl

Stock Trading Model using Q Learning