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defined_outcome_investment

Defined Outcome ETF model

eiten

Statistical and Algorithmic Investing Strategies for Everyone

financephd

Development space for PhD in Finance

financial-models-numerical-methods

Collection of notebooks about quantitative finance, with interactive python code.

hands-on-machine-learning-for-algorithmic-trading

Hands-On Machine Learning for Algorithmic Trading, published by Packt

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

liability_driven_investing-1

Shown how to hedge liabilities using long and short coupon bearing bonds; and how this process can help with an interest rate immunization strategy

machine-learning-for-finance

Machine Learning for Finance, published by Packt

machine-learning-for-trading

Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.

pgportfolio

PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).

python-for-coding-test

code test reference

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.

spring-2023-orie-4741

stock_analysis_for_quant

Different Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau

yahoofinancials

A powerful financial data module used for pulling data from Yahoo Finance. This module can pull fundamental and technical data for stocks, indexes, currencies, cryptos, ETFs, Mutual Funds, U.S. Treasuries, and commodity futures.