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A high frequency trading and market making backtesting and trading bot in Python and Rust, which accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and o…
Performance of Market States Based Trading Strategies (EPFL - Financial Big Data)
Econometrics cheat sheets with a concise review of the subject, going from the basics of an econometric model to the solution of the most popular problems.
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
PortfolioLab is a python library that enables traders to take advantage of the latest portfolio optimisation algorithms used by professionals in the industry.
A curated list of practical financial machine learning tools and applications.
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 Advers…
🔎 📈 🐍 💰 Backtest trading strategies in Python.
Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.
Algorithmic Trading in Python with Machine Learning
Python sync/async framework for Interactive Brokers API (replaces ib_insync)
A dockerized Jupyter quant research environment.
Python for Algorithmic Trading Cookbook, published by Packt
Python library for portfolio optimization built on top of scikit-learn
Framework for developing, backtesting, and deploying automated trading algorithms and trading bots.
A sentiment analyzer package for financial assets and securities utilizing GPT models.
Quantitative Investment Strategies (QIS) package implements Python analytics for visualisation of financial data, performance reporting, analysis of quantitative strategies.
A convenient class for scraping all the existing FOMC meeting statements
Empirical asset pricing via Machine Learning in the Korean market
Machine learning methods for identifing investment factors
Replication of https://ssrn.com/abstract=3984925
The project involves performing clustering analysis (K-Means, Hierarchical clustering, visualization post PCA) to segregate stocks based on similar characteristics or with minimum correlation. Havi…
The aim of this challenge is to predict intra- and end of the day returns using already given historical stock performance and a host of masked features. The different approaches adopted in this pr…
In this recruiting competition, Winton challenges you to take on the very difficult task of predicting the future (stock returns).
Predicting the stock returns over 3 days period based on data presented in The Winton Stock Market Challenge.
For the Winton Stock Market Challenge on Kaggle