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Financial Engineering Articles

Collection of notebooks and scripts related to financial engineering, quant-research and algo-trading.

Table of Contents

  • Portfolio Machine Learning Trials Without P-Hacking with Honest PSR, DSR, and SPA Tests Github or Kaggle.
  • Machine Learning and Stochastic Models for Predicting FOMC Meetings Impact Github or Kaggle.
  • Building an Equity Universe with Fama-French 5 Factors Github or Kaggle.
  • Bond Valuations Explained Github or Kaggle.
  • Basket of Indices Github or Kaggle.
  • Pairs Trading via Unsupervised Learning Github or Kaggle.
  • Temporal Convolution Neural Network with Conditioning for Broad Market Signals: Github or Kaggle.
  • Momentum and Reversion Trading Signals Analysis: Github or Kaggle
  • Pairs-Trading Strategy Analysis: Github or Kaggle
  • Oscillators as Technical Trading Signals Analysis: Github or Kaggle
  • Solving for the Efficient Frontier in Stock Portfolios: Github or Kaggle

Dependencies and Datasets

requirements.txt was created using: jupyter nbconvert --to script *.ipynb & pipreqs --force

All financial timeseries datasets are free and available on Yahoo Finance.

Usage and Licenses

Code and Media

All media used (in the form of code or images) are either solely owned by me, acquired through licensing, or part of the Public Domain and granted use through Creative Commons License.

CC Licensing and Use

This Articles Collection by Adam Darmanin is licensed under Attribution-NonCommercial-ShareAlike 4.0 International