---
title: "Crypto-Quant: A Quantitative Trading Framework"
url: https://stacklist.com/card/6a1b26ff-a4a1-4441-be2a-f2a86cda03c4
source_url: "https://github.com/tubakhxn/Crypto-Quant?fbclid=PAVERTVgSDWjhleHRuA2FlbQIxMABzcnRjBmFwcF9pZA81NjcwNjczNDMzNTI0MjcAAacJloKbWRwPtwFQzhmGbPXmONhH0XblxVUrSvc3W2juLaLceHDu-spraQn70g_aem_I4xq3aR0jeXFrK7Cs_JQiA"
stack: https://stacklist.com/stack/1f56149d-77f6-4695-a2b2-3d5fd727bd69
summary: "Crypto Quant is a comprehensive Python-based repository containing experimental quantitative finance and cryptocurrency market research projects. It includes tools for market making, funding rate arbitrage, order flow analysis, volatility modeling, and machine learning-based market regime detection."
tags: "cryptocurrency, quantitative-finance, market-microstructure, algorithmic-trading, machine-learning, python"
key_entities: "tubakhxn (person), Python (technology), Machine Learning (technology), NumPy (technology), Pandas (technology), Plotly (technology), Streamlit (technology), Scikit-learn (technology), CCXT (technology), Market Microstructure (concept), Order Flow Analytics (concept), Volatility Modeling (concept), Algorithmic Trading (concept), Financial Engineering (concept), Market Making (concept), Arbitrage (concept)"
classification: "framework"
content_hash: "sha256:24bb56561ba76ac5cbc20066ae7a2c7d6bb2de2b75ccbd5c1c53e45a4f1de6e1"
acp_version: "0.2"
token_counts_approximate: 847
visibility: public
agent_accessible: true
status: "final"
---

# Crypto-Quant: A Quantitative Trading Framework

Crypto + Quant Dev Crypto + Quant Development Systems built using Python, Machine Learning, Market Microstructure, and Financial Engineering concepts. This repository contains experimental quantitative finance and crypto market research projects focused on: Market microstructure Order flow analytics Volatility modeling Algorithmic trading systems Liquidity simulations Quantitative research Financial visualization Dev/Creator tubakhxn GitHub: https://github.com/tubakhxn Projects Included 1. Crypto Market Making Engine A research-focused crypto market-making simulator that models bid/ask spread dynamics, liquidity provisioning, inventory risk, and execution behavior. Core Features Simulated limit order book Dynamic spread generation Inventory management Liquidity heatmaps Real-time execution simulation PnL dashboard Volatility-adjusted market making Technologies Python, NumPy, Pandas, Plotly, Streamlit Relevant Wikipedia Links Market making Limit order book Bid–ask spread High-frequency trading 2. Funding Rate Arbitrage Dashboard A quantitative dashboard for tracking funding rate inefficiencies across perpetual futures exchanges. Core Features Funding rate tracking Exchange comparison engine Arbitrage spread scanner Historical analytics Opportunity ranking Real-time dashboard Technologies Python, CCXT, Pandas, Plotly, Streamlit Relevant Wikipedia Links Arbitrage Cryptocurrency exchange Perpetual futures 3. Order Flow Imbalance Predictor An experimental order flow analytics system for predicting short-term market direction using liquidity pressure and order book imbalance. Core Features Order book simulation Bid/ask imbalance analytics OFI calculations ML-based predictions Liquidity pressure visualization Heatmaps and confidence scoring Technologies Python, NumPy, Scikit-learn, Plotly, Streamlit Relevant Wikipedia Links Order flow Market microstructure Machine learning 4. Volatility Surface Modeling System A crypto options analytics platform for visualizing implied volatility surfaces, smiles, and skews. Core Features Synthetic options chain generation Implied volatility calculations 3D volatility surface rendering Volatility smile/skew visualization Greeks analytics Interactive charts Technologies Python, NumPy, SciPy, Plotly, Pandas Relevant Wikipedia Links Implied volatility Black–Scholes model Options pricing 5. Market Regime Detection Engine A machine learning-based market regime analytics system for identifying bull, bear, accumulation, and panic conditions. Core Features Market regime classification Volatility regime analysis KMeans/HMM clustering Momentum analytics Regime transition analysis Interactive dashboards Technologies Python, Scikit-learn, Pandas, Plotly, yfinance Relevant Wikipedia Links K-means clustering Hidden Markov model Quantitative finance Installation Install dependencies: pip install -r requirements.txt Run a project: python main.py or streamlit run app.py Repository Goals This repository is focused on: Quantitative finance research Crypto market analytics Experimental AI trading systems Financial engineering concepts Educational quant development Disclaimer This repository is intended strictly for educational and research purposes only. Nothing in this repository should be considered financial advice, trading advice, investment guidance, or production-ready trading infrastructure. License MIT License