hmn21's Stars
valyala/fasthttp
Fast HTTP package for Go. Tuned for high performance. Zero memory allocations in hot paths. Up to 10x faster than net/http
KindXiaoming/pykan
Kolmogorov Arnold Networks
panjf2000/ants
🐜🐜🐜 ants is a high-performance and low-cost goroutine pool in Go.
optuna/optuna
A hyperparameter optimization framework
panjf2000/gnet
🚀 gnet is a high-performance, lightweight, non-blocking, event-driven networking framework written in pure Go.
unit8co/darts
A python library for user-friendly forecasting and anomaly detection on time series.
firmai/financial-machine-learning
A curated list of practical financial machine learning tools and applications.
interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
timeseriesAI/tsai
Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
awslabs/gluonts
Probabilistic time series modeling in Python
robertmartin8/PyPortfolioOpt
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
jdb78/pytorch-forecasting
Time series forecasting with PyTorch
Nixtla/statsforecast
Lightning ⚡️ fast forecasting with statistical and econometric models.
pytorch/torchtune
A Native-PyTorch Library for LLM Fine-tuning
google-research/timesfm
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
tslearn-team/tslearn
The machine learning toolkit for time series analysis in Python
Nixtla/neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms.
benedekrozemberczki/pytorch_geometric_temporal
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
robcarver17/pysystemtrade
Systematic Trading in python
SeldonIO/alibi
Algorithms for explaining machine learning models
elastic/otel-profiling-agent
The production-scale datacenter profiler (C/C++, Go, Rust, Python, Java, NodeJS, .NET, PHP, Ruby, Perl, ...)
Trusted-AI/AIX360
Interpretability and explainability of data and machine learning models
lxzan/gws
simple, fast, reliable websocket server & client, supports running over tcp/kcp/unix domain socket. keywords: ws, proxy, chat, go, golang...
KimMeen/Time-LLM
[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"
cvxgrp/cvxportfolio
Portfolio optimization and back-testing.
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.
phuslu/log
Fastest structured logging
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 ]
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.