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io2t's Projects
abu
阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构
acm-icpc-preparation
ACM-ICPC Preparation Guide
algorithm-essentials
算法精粹--举一反三，抛弃题海战术
algs4
Algorithms, 4th edition textbook code and libraries
aoapc-bac2nd
Source codes for book <<<BeginningAlgorithmContests>> Second edition
awesome-algorithms
A curated list of awesome places to learn and/or practice algorithms.
awesome-competitive-programming
:gem: A curated list of awesome Competitive Programming, Algorithm and Data Structure resources
awesome-deep-learning
A curated list of awesome Deep Learning tutorials, projects and communities.
awesome-deep-learning-papers
The most cited deep learning papers
awesome-quant
**的Quant相关资源索引
awesome-quant-1
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
awesome-quant-machine-learning-trading
Quant/Algorithm trading resources with an emphasis on Machine Learning
competitive-programming-docs
🌺算法竞赛论文，课件，文档，笔记，平台等资料，不断更新中
contests_for_noip
training history, code, ideas
deeplearningtutorials
Deep Learning Tutorial notes and code. See the wiki for more info.
devilyuan
DevilYuan可视化股票量化系统，支持选股，历史数据自动下载，策略回测及参数优化，实盘交易和常用的统计功能
easyquant
股票量化框架，支持行情获取以及交易
easyquotation
实时获取新浪 / 腾讯 的免费股票行情 / 集思路的分级基金行情
easytrader
提供同花顺客户端/国金/华泰客户端/雪球的基金、股票自动程序化交易以及自动打新，支持跟踪 joinquant /ricequant 模拟交易 和 实盘雪球组合, 量化交易组件
example-scripts
Example Machine Learning Scripts for Numerai's Tournament
fewshotpapers
This repository contains few-shot learning (FSL) papers mentioned in our FSL survey.
google-mirrors
Google谷歌、Wikipedia维基百科、谷歌学术镜像2021年1月最新 新增各种镜像站
hustoj
Open Source Online Judge based on PHP/C++/MySQL/Linux for ACM/ICPC and NOIP training, with easy installation. 开源OJ系统
io2t.github.io
readme
kubernetes
Production-Grade Container Scheduling and Management
lazzyquant
期货/期权量化交易系统
lottery
年会抽奖程序
models
Models and examples built with TensorFlow
opennero
Game platform for Artificial Intelligence research and education
pandas-technical-indicators
Technical Indicators implemented in Python using Pandas
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