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
alphagen
Generating sets of formulaic alpha (predictive) stock factors via reinforcement learning.
AlphaTrade
Research in Limit Order Book
analysis-sharing
Sharing quantitative analyses on Crypto Lake data
autoAlpha
The source code for the paper
bbgo
The modern cryptocurrency trading bot framework written in Go.
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
Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
hft-market-making
Implementation of HFT backtesting simulator and Stoikov strategy
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.
hyperliquid-yolo
Crypto trading strategy for Hyperliquid DEX
macrosynergy
Macrosynergy Quant Research
quant-club
notebooks used in quant club episodes
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.
QuantsPlaybook
量化研究-券商金工研报复现
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
🧮 A deeper look into the Kelly Criterion
toraniko
A multi-factor equity risk model for quantitative trading.
torchqtm
TorchQuantum is a backtesting framework that integrates the structure of PyTorch and WorldQuant's Operator for efficient quantitative financial analysis.