Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity

Portfolio Optimization and Quantitative Strategic Asset Allocation in Python

A program for financial portfolio management, analysis and optimisation.

An Open Source Portfolio Backtesting Engine for Everyone | 面向所有人的开源投资组合回测引擎

Modular Python library that provides an advanced event driven backtester and a set of high quality tools for quantitative finance. Integrated with various data vendors and brokers, supports Crypto, Stocks and Futures.

Python package designed for general financial and security returns analysis.

Оценка эффективности инвестиций с учетом комиссий, налогов (удержанных и ожидающихся), дивидендов и купонов.

Entropy Pooling views and stress-testing combined with Conditional Value-at-Risk (CVaR) portfolio optimization in Python.

MorningStar.com scraper that consolidates tens of thousands of financial records into a SQLite relational database. Class 'dataframes' easily converts the SQLite data into pandas DataFrames (see Jupyter notebook for examples)

Quantitative Investment Strategies (QIS) package implements Python analytics for visualisation of financial data, performance reporting, analysis of quantitative strategies.

Displays corporate earnings and fundamentals in the easy to analyze format

Simple Stock Investment Recommendation System based on Machine-Learning algorithms for prediction and Twitter Sentiment Analysis.

A sentiment analyzer package for financial assets and securities utilizing GPT models.

R Shiny app to compare the relative performance of cryptos and equities.

SilverFir: Investment Report 🌲                 Инвестиционный отчёт в гугл доках, куда можно добавить любой актив и скачивать цену автоматически

Code and data for my blogs

Noba not only backtrader as a quantitative investment platform, but also visualized using bokeh, which can get richer plot effects. Additionally, Noba also provide 'Ioc Container', 'Event System', 'Database Abstraction Layer', 'Pipeline System' and more.

🤖 Predict the stock market with AI 用AI预测股票市场

Scrape, analyze & visualize stock market data for the S&P500 using Python. Build a basic trading strategy using machine learning to assess company performance and determine buy, sell, hold. Read me & instructions available in Spanish. This is a working repo, with plans to expand the project from technical analysis to fundamental analysis.

Data repository of JSON files that are filed by US Senators on efdsearch.senate.gov where they must report their stock trades. This is the same data as on senatestockwatcher.com