canigothere777

canigothere777's Stars

• joshday/OnlineStats.jl

  ⚡ Single-pass algorithms for statistics

  Language:Julia86064

• PacktPublishing/Time-Series-Analysis-with-Python-Cookbook

  Time Series Analysis with Python Cookbook, published by Packt

  Language:Jupyter Notebook272174

• alexbotsula/XTX_Challenge

  Models and programs developed as part of XTX Forecastin Challenge 2019

  Language:Jupyter Notebook2614

• 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

  Language:Jupyter Notebook5.5k682

• gowen100/Jane-Street-Solutions

  Solutions to the Jane St monthly puzzles

  Language:Jupyter Notebook26442

• Aniruddha-Deb/quant-prep

  Quant prep resources/logs

  Language:Python31050

• ixinzhi/zhihu-quant-qa

  :books: 知乎 Quant 问答集

  78

• bradleyboyuyang/Statistical-Arbitrage

  High-frequency statistical arbitrage

  Language:Jupyter Notebook17540

• JerBouma/AlgorithmicTrading

  This repository contains three ways to obtain arbitrage which are Dual Listing, Options and Statistical Arbitrage. These are projects in collaboration with Optiver and have been peer-reviewed by staff members of Optiver.

  Language:Jupyter Notebook928189

• tibkiss/huba-v1

  Pairs Trading using Statistical Arbitrage

  Language:Python16117

• varun-seth/puzzles

  puzzles website

  Language:JavaScript41

• mrsamsami/Convex-Optimization

  Implementation and analysis of convex optimization algorithms

  Language:Jupyter Notebook112

• labuladong/fucking-algorithm

  刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.

  Language:Markdown127k23.3k

• yangshun/tech-interview-handbook

  💯 Curated coding interview preparation materials for busy software engineers

  Language:TypeScript125k15.2k

• halfrost/LeetCode-Go

  ✅ Solutions to LeetCode by Go, 100% test coverage, runtime beats 100% / LeetCode 题解

  Language:Go33.4k5.8k

• ydataai/ydata-synthetic

  Synthetic data generators for tabular and time-series data

  Language:Jupyter Notebook1.5k249

• fjxmlzn/DoppelGANger

  [IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

  Language:Python30574

• BrookingsInstitution/Treasury-Debt-Model

  Language:Python258

• stakahashy/fingan

  The implementation of "modeling financial time-series with generative adversarial networks"

  Language:Python6214

• eai2x/Time-Series-Analysis-Tutorial

  time series analysis tutorial

  Language:Jupyter Notebook896212

• ranaroussi/yfinance

  Download market data from Yahoo! Finance's API

  Language:Python16.4k2.6k

• qingsongedu/time-series-transformers-review

  A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

  2.7k255

• Melelery/c-binance-futures-quant

  low-cost, high-efficiency, easy-to-implement

  Language:Python674359

• MLWave/Kaggle-Ensemble-Guide

  Code for the Kaggle Ensembling Guide Article on MLWave

  Language:Python1.6k555

• nyanp/nyaggle

  Code for Kaggle and Offline Competitions

  Language:Python29229

• alteryx/featuretools

  An open source python library for automated feature engineering

  Language:Python7.4k891

• pycaret/pycaret

  An open-source, low-code machine learning library in Python

  Language:Jupyter Notebook9.2k1.8k

• microsoft/nni

  An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

  Language:Python14.2k1.8k

• tgcsaba/KSig

  A scikit-learn compatible Python package for GPU-accelerated computation of the signature kernel using CuPy.

  Language:Jupyter Notebook366

• ilchen/US_Economic_Data_Analysis

  Jupyter notebooks for analysis of US federal debt levels, tax revenues, budget deficit, evolution of yields on treasury borrowings, treasury yield curves and inflation expectations, unemployment and participation rates, quantitative easing, industrial production, personal consumption and savings. All analysis is based on data provided by FRED.

  Language:Jupyter Notebook5222