007-koeffel Goto Github PK
Type: User
Type: User
:exclamation: This is a read-only mirror of the CRAN R package repository. AssetAllocation — Backtesting Simple Asset Allocation Strategies. Homepage: https://github.com/rubetron/AssetAllocation Report bugs for this package: https://github.com/rubetron/AssetAllocation/issues
Cryptocurrency Historical Market Data R Package
Cryptocurrency Market Data
Machine Learning for Finance, published by Packt
Is used for executing the MasterLab code.
Here I store my master machine learning code
Success in any financial market requires one to identify solid investments. When a stock or derivative is undervalued, it makes sense to buy. If it's overvalued, perhaps it's time to sell. While these finance decisions were historically made manually by professionals, technology has ushered in new opportunities for retail investors. Data scientists, specifically, may be interested to explore quantitative trading, where decisions are executed programmatically based on predictions from trained models. There are plenty of existing quantitative trading efforts used to analyze financial markets and formulate investment strategies. To create and execute such a strategy requires both historical and real-time data, which is difficult to obtain especially for retail investors. This competition will provide financial data for the Japanese market, allowing retail investors to analyze the market to the fullest extent. Japan Exchange Group, Inc. (JPX) is a holding company operating one of the largest stock exchanges in the world, Tokyo Stock Exchange (TSE), and derivatives exchanges Osaka Exchange (OSE) and Tokyo Commodity Exchange (TOCOM). JPX is hosting this competition and is supported by AI technology company AlpacaJapan Co.,Ltd. This competition will compare your models against real future returns after the training phase is complete. The competition will involve building portfolios from the stocks eligible for predictions (around 2,000 stocks). Specifically, each participant ranks the stocks from highest to lowest expected returns and is evaluated on the difference in returns between the top and bottom 200 stocks. You'll have access to financial data from the Japanese market, such as stock information and historical stock prices to train and test your model. All winning models will be made public so that other participants can learn from the outstanding models. Excellent models also may increase the interest in the market among retail investors, including those who want to practice quantitative trading. At the same time, you'll gain your own insights into programmatic investment methods and portfolio analysis―and you may even discover you have an affinity for the Japanese market.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.