duggar Goto Github PK

py-mlfactor

Rewriting the code in "Machine Learning for Factor Investing" in Python

pyktrader

python trading and backtest platform

pylimitorderbook

Limit Order Book Implemented in Python

pystratassetalloc

pytrader

cryptocurrency trading robot

pytrendfollow

PyTrendFollow - systematic futures trading using trend following

pytrendseries

Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum drawdown, time underwater.

qtc2019

This program focused on the core concepts and practice of quantitative investment (multi-factor combination analysis, technical analysis CTA strategy, real-time stock selection and timing strategy, etc.).

quant

This is a quantum finance project containing from data collection, platform introduction and factor digging & strategies sharing

quant-club

notebooks used in quant club episodes

quant-finance

Some notebooks with powerful trading strategies.

quant-notes

Quantitative Interview Preparation Guide, updated version here ==>

quant-trading

Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD

quant_finance

Repository for teachings on Quant Finance

quantative-trading-models

These are trading results and arbitrage models from Southern China Center for Statistical Science (SC2S2), Sun Yat-sen University

quantequitymanagement

Research on Factor Models, Alpha Generation etc

quantitative-investments

Work related to a Quantitative Investing/Econometrics/Trading with R & C++.

quantitative-notebooks

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

quantitative-trading-strategy-based-on-machine-learning

Firstly, multiple effective factors are discovered through IC value, IR value, and correlation analysis and back-testing. Then, XGBoost classification model is adopted to predict whether the stock is profitable in the next month, and the positions are adjusted monthly. The idea of mean-variance analysis is adopted for risk control, and the volatility of the statistical benchmark index (HS300 Index) is used as a threshold for risk control. Back-testing results: the annual return rate is 11.54%, and the maximum drawdown is 17.91%.

quantitativeinvesting

quantportfolio

My quant portfolio leverages quantitative finance and data-driven insights to optimize investment strategies. Using advanced models, statistical analysis, and machine learning, I develop systematic trading strategies to capitalize on market inefficiencies and generate alpha.

random_thoughts

real-time-stock-market-prediction

In this repository, I have developed the entire server-side principal architecture for real-time stock market prediction with Machine Learning. I have used Tensorflow.js for constructing ml model architecture, and Kafka for real-time data streaming and pipelining.

scalping_robintrack

short-term_momentum_strategy

Short-term momentum trading strategy implemented for the lecture "Systematic risk premia strategies traded at hedge funds" at University of Zurich, FS 2018.

slow-momentum-fast-reversion

This code accompanies the the paper Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection (https://arxiv.org/pdf/2105.13727.pdf).

spy_analysis

statistical-arbitrage

statistical-arbitrage-1

statistical-arbitrage-avellaneda-