Pinned Repositories
adaptive-forex-forecast
An adaptive model for prediction of one day ahead foreign currency exchange rates using machine learning algorithms
Advanced-Deep-Trading
Mostly experiments based on "Advances in financial machine learning" book
AFML
All the answers for exercises from Advances in Financial Machine Learning by Dr Marco Lopez de Parodo.
ai-alpha
AI based alpha research for trading
AI-for-Trading
📈This repo contains detailed notes and multiple projects implemented in Python related to AI and Finance. Follow the blog here: https://purvasingh.medium.com
AI-for-Trading2
Udacity nanodegree: AI for Trading
algotrading-example
algorithmic trading backtest and optimization examples using order book imbalances. (bitcoin, cryptocurrency, bitmex, binance futures, market making)
backtesting-crypto-trading-strategies
PCA
Construction of PCA class from scratch and 3 implementations of PCA.
udacity-ai-for-trading
Rep tho share codes and projects from the Artificial Intelligence for Trading Algorithms course @Udacity.
duggar's Repositories
duggar/Advanced-Deep-Trading
Mostly experiments based on "Advances in financial machine learning" book
duggar/AFML
All the answers for exercises from Advances in Financial Machine Learning by Dr Marco Lopez de Parodo.
duggar/AI-for-Trading
📈This repo contains detailed notes and multiple projects implemented in Python related to AI and Finance. Follow the blog here: https://purvasingh.medium.com
duggar/algorithmic_intraday_trading
duggar/AlgorithmicTrading
This repository contains three ways to obtain arbitrage which are Dual Listing, Options and Statistical Arbitrage. These are projects in collaboration with Optiver and have been peer-reviewed by staff members of Optiver.
duggar/bittrends
A decade of trend following returns in crypto-asset markets
duggar/bubble
duggar/crypto_algo_trading
This quant framework applies algorithm trading in Crypto market. The trading pairs focus on spots, perpetuals, futures, and options in Deribit and BitMex.
duggar/csvtotable
Simple command-line utility to convert CSV files to searchable and sortable HTML table.
duggar/deep-orderbook
Deep learning modelling of orderbooks
duggar/flowrisk
A Python Implementation of Measures for Order Flow Risk, e.g. VPIN
duggar/gamma-ray
High frequency trading bot for crypto currencies
duggar/Genetic-Alpha
A genetic programming algorithm used for generating alpha factors in the multi-factor investment strategy
duggar/MarkovRegimeSwitchingStrategy
Our strategy is designed by selecting value and momentum factors based on turbulence regimes. According to some research, in high-variance regimes, value stocks tend to perform better, and in low-variance regimes, the momentum stocks give better returns. Hence, we use two 2-state Markov Regime-Switching models to construct two similar strategies. Here are the steps: ● Calculating a turbulence measure based on prior 36-month index returns as follows d y ) y ) t = ( t − μ * Σ * ( t − μ ′ ● Applying the turbulence measure to two Markov Regime-Switching models 1. Using the regular Markov Regression 2. Using an order-2 Markov Auto-regression which assumes an AR(2) process of the turbulence time series ● Identifying regimes and determining the factor to be used 1. Using the Value factor in high turbulence regime 2. Using the Momentum factor in low turbulence regime
duggar/microprice
duggar/mlfinlab
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
duggar/Portfolio-Optimization
Optimize portfolio allocation under transaction costs
duggar/py-mlfactor
Rewriting the code in "Machine Learning for Factor Investing" in Python
duggar/Quant-Finance
Some notebooks with powerful trading strategies.
duggar/QuantEquityManagement
Research on Factor Models, Alpha Generation etc
duggar/Quantitative
Alpha Generation using Data Science and Quantitative Analysis with integrated Risk Model
duggar/Quantitative-Notebooks
Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy
duggar/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%.
duggar/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.
duggar/scalping_robintrack
duggar/StockGram-Intelligent-Portfolio-Manager
📈This repo describes a framework that leverages sentiment stability of a financial 10-K report as the trading signal (alpha factor)
duggar/TDA_finance
Predicting stock market crashes
duggar/TestingForRationalBubbles
An implementation of the test statistics from Homm, Breitung (2012) using the statistical software package R.
duggar/Time-series-for-price-action-prediction
Using time series analysis, this research is to evaluate how different ML models perform in predicting stock market.
duggar/trading
Utility framework for back-testing technical trading strategies in Python.