asset-allocation

Portfolio Optimization and Quantitative Strategic Asset Allocation in Python

Quantitative analysis, strategies and backtests

A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.

Python library for portfolio optimization built on top of scikit-learn

Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy

Helps you with managing your investments

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

Investing library and command-line interface inspired by the Bogleheads philosophy

Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics

Portfolio Construction and Risk Management book's Python code.

Asset Allocation application

Python financial widgets with okama and Dash (plotly)

Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics

Entropy Pooling in Python with a BSD 3-Clause license.

Value or Momentum? Comparing Random Forests, Support Vector Machines, and Multi-layer Perceptrons for Financial Time Series Prediction & Tactical Asset Allocation

R package AssetAllocation

Implements different approaches to tactical and strategic asset allocation

A flask web app that analyzes your stock portfolio performance, optimizes your asset allocation, and provides performance enhancement alerts.

portfolio-backtest is a python library for backtest portfolio asset allocation on Python 3.7 and above.

This repository consists several bots encoding various algorithmic trading strategies. The aim here is for absolute beginners in stock trading to get familiar with the various aspects of the market. All you need is basics of statistics and python to understand the underlying metrics and conditions utilized to make decisions. Contributions welcome.