hmn21's Stars
logdyhq/logdy-core
Web based real-time log viewer. Stream ANY content to a web UI with autogenerated filters. Parse any format with TypeScript.
goldmansachs/gs-quant
Python toolkit for quantitative finance
ZONG0004/MacroHFT
avhz/RustQuant
Rust library for quantitative finance.
rolling-panda-san/notebooks
Analysis on systematic trading strategies (e.g., trend-following, carry and mean-reversion). The result is regularly updated.
google/highway
Performance-portable, length-agnostic SIMD with runtime dispatch
RL-MLDM/alphagen
Generating sets of formulaic alpha (predictive) stock factors via reinforcement learning.
Trusted-AI/AIX360
Interpretability and explainability of data and machine learning models
SeldonIO/alibi
Algorithms for explaining machine learning models
interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
UBS-IB/bayesian_tree
monty-se/PINstimation
A comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.
robertmartin8/PyPortfolioOpt
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
google-research/timesfm
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
panjf2000/gnet
🚀 gnet is a high-performance, lightweight, non-blocking, event-driven networking framework written in pure Go.
rburkholder/trade-frame
C++ 17 based library (with sample applications) for testing equities, futures, etfs & options based automated trading ideas using DTN IQFeed real time data feed and Interactive Brokers (IB TWS API) for trade execution. Some support for Alpaca & Phemex. Notifications via Telegram [irc: Libra #tradeframe ]
panjf2000/ants
🐜🐜🐜 ants is the most powerful and reliable pooling solution for Go.
valyala/fasthttp
Fast HTTP package for Go. Tuned for high performance. Zero memory allocations in hot paths. Up to 10x faster than net/http
lxzan/gws
simple, fast, reliable websocket server & client, supports running over tcp/kcp/unix domain socket. keywords: ws, proxy, chat, go, golang...
phuslu/log
Fastest structured logging
KindXiaoming/pykan
Kolmogorov Arnold Networks
pytorch/torchtune
A Native-PyTorch Library for LLM Fine-tuning
open-telemetry/opentelemetry-ebpf-profiler
The production-scale datacenter profiler (C/C++, Go, Rust, Python, Java, NodeJS, .NET, PHP, Ruby, Perl, ...)
firmai/financial-machine-learning
A curated list of practical financial machine learning tools and applications.
cvxgrp/cvxportfolio
Portfolio optimization and back-testing.
tslearn-team/tslearn
The machine learning toolkit for time series analysis in Python
robcarver17/pysystemtrade
Systematic Trading in python
Nixtla/statsforecast
Lightning ⚡️ fast forecasting with statistical and econometric models.
qingsongedu/Awesome-TimeSeries-SpatioTemporal-LM-LLM
A professional list on Large (Language) Models and Foundation Models (LLM, LM, FM) for Time Series, Spatiotemporal, and Event Data.
KimMeen/Time-LLM
[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"