/Quant-Finance

A main CTA backtesting system and several research of utilizing machine learning on asset pricing

Primary LanguagePython

Quant-Finance

Qunatitative Trading Space

Note: This repo is also for all my mentees in the department of Algorithm Trading, TMBA.

Author:

  • Yu-Chen (Abner) Den
  • Tzu-Hao (Howard) Liu

Folders

CTA (Ongoing)

Single asset CTA trading strategy and backtesting system, including various data collection APIs.

Full documentation can be found in the CTA/docs folder

Usage:

  • Create environment

    • venv

      python3 -m venv your_venv
source your_venv/bin/activate

pip3 install -r requirements.txt

    • Docker (On-going)

      docker build -t nlcta .
docker run -it nlcta

  • Change the config/combine_test.yaml file to your own settings (including API keys).

  • Run the main.py file.

python3 -m src.main

BetterRSP / ETF_entry (Ongoing)

A better RSP strategy for ETFs that utilize machine learning models to predict the confidence score of next-month entry point

HWs

Homeworks for my mentees @ TMBA.

Learning to Rank on Portfolio Construction (LR_rank)

Instead of classifying portfolio return into 10 independent classes, we rank those returns because we want the relationships between them.

Options

Untidy folder full of options payoff diagrams and strategies. (I don't want to clean it up.)

Start

First create a virtual environment and activate it.

python3 -m venv your_venv
source your_venv/bin/activate

Then install the required packages.

pip3 install -r requirements.txt