Ideal experimental platform for AI financial research
As an open source framework, ContestTrade provides three key types of value to quantitative financial research.
Research Application Direction
- Collaborative multi-intelligence validation: Adjustable number of intelligences (2-20) to test group intelligence effects
- Simulation of market mechanisms: Examining the price discovery process under different market structures by modifying competition rules
- An experiment in behavioral finance: Injecting Cognitive Bias Parameters to Observe the Impact of Irrational Factors on AI Decision Making
Typical research results
Using the framework, a university team found that when configuring seven intelligences with complementary properties, the combined SHARP ratio can reach 1.8, which is significantly better than a single model. The related paper has been included in the KDD conference.
Educational Value Extension
The clear module division of the system (data layer/strategy layer/decision layer) makes it a living teaching material for Fintech education, and 15 universities have already incorporated it into the practical part of quantitative trading courses.
This answer comes from the articleContestTrade: an AI multi-intelligence trading framework for event-driven investingThe
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