duggar's repositories
algotrading-example
algorithmic trading backtest and optimization examples using order book imbalances. (bitcoin, cryptocurrency, bitmex, binance futures, market making)
udacity-ai-for-trading
Rep tho share codes and projects from the Artificial Intelligence for Trading Algorithms course @Udacity.
AI-for-Trading2
Udacity nanodegree: AI for Trading
AlphaTrade
Research in Limit Order Book
artificial-intelligence-for-trading
Content for Udacity's AI in Trading NanoDegree.
blogScripts
Repository for code used in my blog posts
CQF
This repository stores several Jupyter Notebooks that were developed while studying for the Certificate in Quantitative Finance.
cryptocurrency_price_prediction
This work proposal is based on extracting meaningful patterns and attributes from historical cryptocurrency data to predict future prices using machine learning for time series (AUTO TS). However, it's important to emphasize that buying and selling trends depend on many factors and the model obtained is only capable of working with historical data.
equity-risk-model
Attribution and optimisation using a multi-factor equity risk model.
factorlab
FactorLab is a python library that enables the discovery and analysis of alpha and risk factors used in the investment algorithm development process
ffn
ffn - a financial function library for Python
Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
hftbacktest
A high-frequency trading and market-making backtesting tool accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books.
macrosynergy
Macrosynergy Quant Research
notebooks
Analysis on systematic trading strategies (e.g., trend-following, carry and mean-reversion). The result is regularly updated.
quant-club
notebooks used in quant club episodes
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
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.
Statistical-Arbitrage-in-Cryptocurrencies
The goal of this project is to develop a statistical arbitrage strategy for cryptocurrencies using Python
Statistical-Arbitrage2
High-frequency statistical arbitrage
stock-vcpscreener
A python stock screener that calculates market breadth and selects US stocks on a daily basis
Stockformer
StockFormer: A Swing Trading Strategy Based on STL Decomposition and Self-Attention Networks
The-Kelly-Criterion
š§® A deeper look into the Kelly Criterion
torchqtm
TorchQuantum is a backtesting framework that integrates the structure of PyTorch and WorldQuant's Operator for efficient quantitative financial analysis.
vcp_screener.github.io
A program screens stocks following Mark Minervini's strategy.