RESSET Quantitative Research and Training Platform is an all-round intelligent, Python based, Quantitative Strategy Research Platform specially developed for colleges and universities. The platform supports comprehensive stock, options, funds, bonds, futures data and factor API, shared function of quantitative strategy and Reiss factor library function, quantitative strategy retest, simulated real data (day and Tick data), quantitative classroom, quantitative community and teaching management.

This platform raises questions based on the practical application scenarios in business, then reviews the quantitative theory and methods, and then solves the problems. Finally, through homework to consolidate and strengthen knowledge and skills, students can experience how to think and solve problems in real business practice. Examples such as option pricing, stock quantification strategy, asset allocation and fund selection show the teaching idea of integration of industry, education and research in detail. The platform showing management and experiment module makes the market close to teaching, helps teachers and students to better use the platform, and strengthens students'ability to implement investment strategy procedures and engineering concepts.

It is suitable for Windows, Mac and Linux operating systems, supports Python 2.7 and python 3.0, breaks free from the python version shackles, farewell to platform constraints, and enables local research.

It supports stock, options, funds, bonds, futures market data, and Tick data. Professional data team maintains/compares multiple data sources, cleans/tests transaction validation strategy back, and the data is precise and suitable for quantification. Convenient and abundant data extraction access API meets the research needs and transaction needs of quantitative investment strategy in all respects. API categories include: historical transaction data and complete quantitative factor library.

Quantitative investment research terminal adapts to the needs of teaching experiment, it can seamlessly dock with Python and other platforms, use comprehensive financial functions and powerful toolbox to support efficient strategy development; the system uses advanced file caching technology to speed up strategy operation.

The platform provides classical quantitative strategy and complete transaction API. The strategy can acquire account rights and interests, the status of unsolved proxy orders, positions, available funds and other data to control transaction risk through API.

To meet the needs of financial derivatives market research, the platform supports cross-variety, cross-cycle and cross-market strategies. The investment varieties include stocks, stock index futures, commodity futures, individual stock options, ETF, LOF funds.

The platform collects 7 kinds of data quantification factor database and complete risk factor database, including macro factor, value factor, growth factor, scale factor, behavior factor, quality factor and technology factor. It saves a lot of manpower and material resources cost for factor development, and greatly shortens the process.

The platform execut the initializesd strategy, it splices the historical data and the market data automatically according to the length of the data subscribed by the policy and cleans and aligns the data. It does not need the policy researcher to process the real-time market data in the strategy logic. According to the transaction frequency of the strategy, it provides two driving modes: time-driven and tick event-driven and provides the execution transaction calculation. The method splits the transaction and reduces the impact cost.

The platform provides default teaching experiments (strategies + questions) according to the curriculum, and teachers can customize teaching experiments according to their needs, through the diversity of the platform.