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nao

Neural Architecture Optimization

neural_prophet

NeuralProphet: A simple forecasting package

neuroevolution-btc-trader

Building a population of models that trade crypto and mutate iteratively

nevergrad

A Python toolbox for performing gradient-free optimization

nupic-stock-prediction

Uses Hierarchical Temporal Memory to predict the price of RLG based on historical data

omnianomaly

KDD 2019: Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network

open_spiel

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

pettingzoo

Gym for multi-agent reinforcement learning

portfolio-investment-system

an intelligent portfolio investment system for Chinese stock based on machine learning and fuzzy time series.

predictstock-svm

ML model for stock trend prediction using Python

propopol

PROPhet based Otimized stock portPOLio

py-ml-rl-trade

DQN - Deep Q network for stock trading

pycapsnet

A pure NumPy / CuPy implementation of Capsule Networks

pyfolio

pysyft

A library for answering questions using data you cannot see

pytorch-gan-timeseries

GANs for time series generation in pytorch

pytorchtrainer

A utility to train pytorch models

qlib

Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.

qtpylib

QTPyLib, Pythonic Algorithmic Trading

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

reinforcement_learning_oe

The work aims to explore Value based, Deep Reinforcment Learning (Deep Q-Learning and Double Deep Q-Learning) for the problem of Optimal Trade Execution. The problem of Optimal Trade Execution aims to find the the optimal "path" of executing a stock order, or in other words the number of shares to be executed at different steps given a time constraint, such that the price impact from the market is minimised and consequently revenue from executing a stock order maximised.

research_paper_novel-approach-to-short-term-stock-price-movement-prediction-using-transfer-learning

Research paper attempt to improve stock market forecasting

risk-based_rl_for_optimal_trading_execution

rl

Deep Reinforcement Learning For Trading

rl-deepqlearning-trading

An RL model that uses double deep Q learning to generate an optimal policy of stock market trades

scikit-opt

Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

sentiment-analysis-in-event-driven-stock-price-movement-prediction

Use NLP to predict stock price movement associated with news

sentiment-analysis-of-financial-news-data

Sentiment Analysis of news on stock prices

sgx-full-orderbook-tick-data-trading-strategy

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

spatiallob-learning-spatial-properties-of-limit-order-book

SpatialLOB is designed for stock price movement prediction by exploiting spatial and temporal properties of the Limit Order books. SpatialLOB consists of a deep network combining CNN and Stacked GRU.