DUCKQUANT / THE QUANT & AI DIRECTORY
1,177 resources, ranked by in-directory PageRank
Jane Street has 411 repositories available. Follow their code on GitHub.
C++ - Real-time L2 order books from 21 exchanges. Free WebSocket API, historical replay, sub-ms latency
REST API, WebSocket, MCP - Polymarket historical order book snapshots (crypto, sports, weather, economics, equities, social) since March 2026, with trades…
Cutting-edge investment research, market analysis, and academic insights, developed in partnership with leading universities to advance the future of investing.
Jane Street is a quantitative trading firm and liquidity provider with a unique focus on technology and collaborative problem solving.
Learn nonlinear predictive relationships responsibly. Representative subjects: Regularized regression, trees/boosting, random forests, Gaussian processes, sequence models, transformers.
barchart: Financial markets data library implemented in go.
High Frequency Trading using Vivado HLS
The High-Frequency Trading FPGA System is an ultra-low latency platform for electronic trading on FPGAs. It features a TCP/IP stack, order matching engine…
Golang implementation of a Limit Order Book (LOB) for high frequency trading in crypto exchanges
Example Order Book Imbalance Algorithm
OpenGym with Deep Q-learning and Policy Gradient
Python modules and jupyter notebook examples for the paper Detect and Repair Arbitrage in Price Data of Traded Options.
This repository hosts my reading notes for academic papers.
This course in applied data science covers the theoretical foundations of advanced quantitative approaches in machine learning, econometrics, risk and…
Cloud-native Financial Reinforcement Learning Python · 505
Chinese interview and study guide covering quantitative finance, factor and alpha strategies and machine learning.
Chinese-language learning roadmap covering data acquisition, strategy development, backtesting and live deployment for quants.
Comparative Analysis of Techniques for Forecasting Time Series in Financial Markets Python · 37
Deep q learning on determining buy/sell signal and placing orders Python · 0
Using tabular and deep reinforcement learning methods to infer optimal market making strategies Jupyter Notebook · 256
Learning Golang one day HTML · 0
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality. Jupyter Notebook · 3,440
bits of sklearn ported to Go #golang Go · 0
The repository for freeCodeCamp's YouTube course, Algorithmic Trading in Python Jupyter Notebook · 2,845
DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration Go · 0
Tutorials of econometrics featuring Python programming. This is a crash course for reviewing the most important concepts and techniques of basic econometrics…
robcarver17/systematictradingexamples — Examples of code related to book www.systematictrading.org and blog qoppac.blogspot.com Python · ⭐ 483 · forks 0 ·…
Hands-On Machine Learning for Algorithmic Trading, published by Packt
Researches for Natural Language Processing for Financial Domain.
Retrieve limit order book level data from coinbase pro and bitfinex -> record in timeseries database then implemented trend following strategies (market…
Code repo for the paper BiT Robustly Binarized Multi-distilled Transformer
Publication: PTQ4SAM: Post-Training Quantization for Segment Anything. Published 2024.
Publication: Post-Training Quantization on Diffusion Models. Published 2023.
Publication: Enhancing Post-training Quantization Calibration through Contrastive Learning. Published 2024.
Publication: Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices. Published 2024.
