Top Repositories
https://www.kaggle.com/c/jane-street-market-prediction/overview “Buy low, sell high.” It sounds so easy…. In reality, trading for profit has always been a difficult problem to solve, even more so in today’s fast-moving and complex financial markets. Electronic trading allows for thousands of transactions to occur within a fraction of a second, resulting in nearly unlimited opportunities to potentially find and take advantage of price differences in real time. In a perfectly efficient market, buyers and sellers would have all the agency and information needed to make rational trading decisions. As a result, products would always remain at their “fair values” and never be undervalued or overpriced. However, financial markets are not perfectly efficient in the real world. Developing trading strategies to identify and take advantage of inefficiencies is challenging. Even if a strategy is profitable now, it may not be in the future, and market volatility makes it impossible to predict the profitability of any given trade with certainty. As a result, it can be hard to distinguish good luck from having made a good trading decision. In the first three months of this challenge, you will build your own quantitative trading model to maximize returns using market data from a major global stock exchange. Next, you’ll test the predictiveness of your models against future market returns and receive feedback on the leaderboard. Your challenge will be to use the historical data, mathematical tools, and technological tools at your disposal to create a model that gets as close to certainty as possible. You will be presented with a number of potential trading opportunities, which your model must choose whether to accept or reject. In general, if one is able to generate a highly predictive model which selects the right trades to execute, they would also be playing an important role in sending the market signals that push prices closer to “fair” values. That is, a better model will mean the market will be more efficient going forward. However, developing good models will be challenging for many reasons, including a very low signal-to-noise ratio, potential redundancy, strong feature correlation, and difficulty of coming up with a proper mathematical formulation.
Towards Interpretability over Smart Contract Vulnerability Detection based on Deep Neural Networks
A generative AI extension for JupyterLab
WeCross Router 跨链解决方案
本项目是基于dify开源项目实现的dsl工作流脚本合集
Supercharge your workflow automation with this curated collection of n8n templates! Instantly connect your favorite apps-like Gmail, Telegram, Google Drive, Slack, and more-with ready-to-use, AI-powered automations. Save time, boost productivity, and unlock the true potential of n8n in just a few clicks.
Repositories
98https://www.kaggle.com/c/jane-street-market-prediction/overview “Buy low, sell high.” It sounds so easy…. In reality, trading for profit has always been a difficult problem to solve, even more so in today’s fast-moving and complex financial markets. Electronic trading allows for thousands of transactions to occur within a fraction of a second, resulting in nearly unlimited opportunities to potentially find and take advantage of price differences in real time. In a perfectly efficient market, buyers and sellers would have all the agency and information needed to make rational trading decisions. As a result, products would always remain at their “fair values” and never be undervalued or overpriced. However, financial markets are not perfectly efficient in the real world. Developing trading strategies to identify and take advantage of inefficiencies is challenging. Even if a strategy is profitable now, it may not be in the future, and market volatility makes it impossible to predict the profitability of any given trade with certainty. As a result, it can be hard to distinguish good luck from having made a good trading decision. In the first three months of this challenge, you will build your own quantitative trading model to maximize returns using market data from a major global stock exchange. Next, you’ll test the predictiveness of your models against future market returns and receive feedback on the leaderboard. Your challenge will be to use the historical data, mathematical tools, and technological tools at your disposal to create a model that gets as close to certainty as possible. You will be presented with a number of potential trading opportunities, which your model must choose whether to accept or reject. In general, if one is able to generate a highly predictive model which selects the right trades to execute, they would also be playing an important role in sending the market signals that push prices closer to “fair” values. That is, a better model will mean the market will be more efficient going forward. However, developing good models will be challenging for many reasons, including a very low signal-to-noise ratio, potential redundancy, strong feature correlation, and difficulty of coming up with a proper mathematical formulation.
本项目是基于dify开源项目实现的dsl工作流脚本合集
Supercharge your workflow automation with this curated collection of n8n templates! Instantly connect your favorite apps-like Gmail, Telegram, Google Drive, Slack, and more-with ready-to-use, AI-powered automations. Save time, boost productivity, and unlock the true potential of n8n in just a few clicks.
Helm Chart & Documentation for deploying JupyterHub on Kubernetes
A blockchain benchmark framework to measure performance of multiple blockchain solutions
基于K8S平台的区块链即服务BaaS（Blockchain as a Service），借鉴于hyperledger/cello，支持Hyperledger Fabric，但更加轻量级的架构实现
一键自动安装配置GO最新版脚本
金山云sdk python版本
A generative AI extension for JupyterLab
Towards Interpretability over Smart Contract Vulnerability Detection based on Deep Neural Networks
No description provided.
Starter Application and Deployment Scripts for Hyperledger Fabric 1.0
No description provided.
An kubernetes opterator to manager the yarn nodemanager, which can be used to deploy yarn on kubernetes.
设计模式 Golang实现－《研磨设计模式》读书笔记
No description provided.
HDP-hadoop
Substra Network initializes an Hyperledger Fabric network with Certificate Authorities
This lab enables Secure Chaincode Execution using Intel SGX for Hyperledger Fabric.
基于go micro + gin + kafka + etcd的分布式消息即时通信微服务系统
SDK for ksyun, Go version
No description provided.
No description provided.
kubeadm-ha 使用 kubeadm 进行高可用 kubernetes 集群搭建，利用 ansible-playbook 实现自动化安装，既提供一键安装脚本，也可以根据 playbook 分步执行安装各个组件。
Official Python client library for kubernetes
✅ Solutions to LeetCode by Go, 100% test coverage, runtime beats 100% / LeetCode 题解
LeeCode题目1008道，go语言版全解。
WeCross Router 跨链解决方案
A user-friendly distributed ledger platform.
WeCross-Fabric2-Stub