Crowdsourced Practice And Analysis Based On Quantitative Trading System And Data Service

As the continuous development of Internet, More and more traditional industries embrace the Internet, including financial and stock industry. Quantitative trading is the area which stock trading can be done by computer based on internet and computing. Traditional stock trading makes traders focus on stock quotes all the time, which makes both the traders’pressure and low effectiveness, especially when the stock quotes volates heavily, resulting in the incomplete of trading. In the meantime, fetching stock data is diffculty in traditional stock quotes, which needs large amount of data manually. Operating manually also means higher error rate.To solve above problems, We develop a quantitative trading system based on backtest library and data service based on this system. This system supply a platform of quantitative trading environment, traders can research and develop quantitative strategies based on this platform. Strategy is tradiing algorithm implementing definitive interfaces, which will be used in backtest. After completing and submitting strategy to system, backtest can be started. Visualization will then be displayed after completing backtest based on data provided by the platform. Besides, Open data api is provided based on this platform, providing day-level trading data in A share since 2006, users can get any data by simple requests, including prices, volume of each stock. Users can process further stock analysis, market analysis based on the data.Based on quantitative system and open data service, some crowdsourced practice are introduced. Crowdsourcing is a kind of solution that solve problems distributly, which receives solutions after publishing problems and then choose the best solution. In this article, we mainly introduce the crawdsourcing practice based on quantitative system and data service. Crowdsourced strategy focus on quantitative strategy, aims to getting more strategies from users, choosing best strategy by backtesting, in this progress, we analysis on models and code of crowdsourced strategies, ensuring the quality of strategies. Crowdsourced analysis mainly focus on data visualization, stock quotes analysis and stock prediection based on open data api. Transforming original data into high-level charts and conclusions by using all kind of methods. In this progress, we anaylsis all states of crowdsourced analysis, including performance anaylsis of data service. ensuring the normal performance of data service.