AI Reconfiguration of Specialized Investment Processes
ContestTrade accurately restores the decision-making chain of institutional investors through a two-stage mechanism, which is a key technical feature that distinguishes it from ordinary quantitative systems.
Process Architecture Analysis
- Data-processing phase: 7 types of data cleaners to process raw data and generate 30+ feature factors
- Research decision-making phase: Configure GPT-4 level model for deep inference and output investment recommendations with weights
Collaborative decision-making advantage
In practice, the system adopts a 'partition-aggregation' model: each intelligent body focuses on a specific factor analysis, and ultimately integrates its views through a competitive mechanism. For example, when dealing with performance forecasts, some intelligences focus on growth sustainability, while others focus on market expectation spread.
Effectiveness verification data
Backtesting shows that the two-phase design improves decision accuracy by 401 TP3T: the data processing phase filters 901 TP3T of noisy data, and the research phase keeps the misclassification rate below 151 TP3T through multi-perspective analysis.
This answer comes from the articleContestTrade: an AI multi-intelligence trading framework for event-driven investingThe
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