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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.

qlib_predict_daily-stock_prices

This is test qlib from microsoft

qtpylib

QTPyLib, Pythonic Algorithmic Trading

qtrader

Reinforcement Learning for Portfolio Management

qtrader-1

A Light Event-Driven Algorithmic Trading Engine

quant-research

A collection of projects published by Bloomberg's Quantitative Finance Research team.

quant_finance

Repository for teachings on Quant Finance

quantitative-finance-papers-using-deep-learning

quantstats

Portfolio analytics for quants, written in Python

reinforcement-learning-in-portfolio-management-

In this paper, we implement three state-of-art continuous reinforcement learning algorithms, Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO) and Policy Gradient (PG)in portfolio management.

reopt-api-analysis

rl

Deep Reinforcement Learning For Trading

rlquant

Applying Reinforcement Learning in Quantitative Trading

scipy_con_2019

Tutorial Sessions for SciPy Con 2019

series_to_supervised

Simple function to turn a time series into an ML ready dataset

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.

short-term-electricity-price-forecasting-at-the-polish-power-exchange-day-ahead-market

Applied time series electricity price forecasting using Deep Learning

skforecast

Time series forecasting with scikit-learn models

sktime

A unified framework for machine learning with time series

solar-forecasting

Software Record SWR-18-36 "A Physics-based Smart Persistent Model for Intra-hour Solar Forecasting"

solargisapiserver

Returns averages for ghi, windspeed and temperature

solariot

Leverage your IoT enabled Solar PV Inverter to stream your solar energy usage data to a real time dashboard.

sonnet

Winning data science solution for Energy Hack NL 2018. Sonnet: forecasting station load caused by solar panels.

st-metanet

The codes and data of paper "Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning"

stable-baselines

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

stable-baselines3

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

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).

statistics_from_stock_data

Machine Learning

stock-market-prediction-web-app-using-machine-learning-and-sentiment-analysis

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

stock-movement-prediction

Predicting Stock Movement by the Application of Machine Learning and Deep Learning Algorithms