Open Source Data Management Systems

Python scripts for ETL (extract, transform and load) jobs for Ethereum blocks, transactions, ERC20 / ERC721 tokens, transfers, receipts, logs, contracts, internal transactions. Data is available in Google BigQuery. Ethereum ETL lets you convert blockchain data into convenient formats like CSVs and relational databases.

Build a standard data schema, and then implement various connectors to import systems you are familiar with for analysis. fooltrader is a quantitative analysis trading system designed using big data technology, including data capture, cleaning, structuring, calculation, display, backtesting and trading. Its goal is to provide a unified framework for the whole market (stock, futures, bonds, foreign exchange, digital currency, macroeconomics, etc.) for research, backtesting, forecasting, and trading. Its applicable objects include quantitative traders, teachers, and students majoring in finance, people interested in economic data, programmers, and people who like freedom and the spirit of exploration. You could write the Strategy using an event-driven or time walkway and view and analyze the performance in a uniform way.

JD Cloud Blockchain Data Service (BDS) is a real-time data aggregating, analyzing, and visualization service for chain-like unstructured data from all kinds of 3rd party Blockchains. Splitter is the key module of Blockchain Data Service (BDS) and provides data analysis capability. Splitter is responsible for consuming blockchain data from message queue (kafka) and inserting data into persistent data storage services (relational database, data warehouse, etc.) for further processing. Before compiling and running BDS, you must install go's compilation environment locally.

The Neuro NRO cryptocurrency is designed to support solutions of machine learning tasks, big data and neural networks. Neuro is a scientific-technical project uniting scientists, engineers and programmers inspired by the idea to build something big, kind and bright. From the first stages of work, we will be engaged in the development of new architectures and algorithms of neural networks. Someday we will undoubtedly enter the annual ImageNet Challenge contest to compete with such giants as GoogLeNet Inception and Microsoft ResNet. At further stages of the work, we adapt the neural networks to calculate molecular interactions in protein environments. Our system will help to look for new types of drugs for cancer, Alzheimer's and other serious problems of modern medicine. We plan to make a serious contribution to the increase of human life expectancy.

Abu Quantitative Integrated AI Big Data System, K-Line Pattern System, Classic Indicator System, Trend Analysis System, Time Series Dimension System, Statistical Probability System, and Traditional Moving Average System conduct in-depth quantitative analysis of investment varieties, completely crossing the user's complex code quantification stage, more suitable for ordinary people to use, towards the era of vectorization 2.0. The above system combines hundreds of seed quantitative models, such as financial time series loss model, deep pattern quality assessment model, long and short pattern combination evaluation model, long pattern stop-loss strategy model, short pattern covering strategy model, big data K-line pattern Historical portfolio fitting model, trading position mentality model, dopamine quantification model, inertial residual resistance support model, long-short swap revenge probability model, strong and weak confrontation model, trend angle change rate model, etc.