Quantitative Systems Integration Implementation Program
The integration of FinGPT into existing quantitative trading systems requires a phased implementation:
- API Layer Design: Encapsulate the prediction module through Flask/FastAPI to provide a RESTful interface for the trading system to call.
- Data Docking: Configure a real-time data pipeline (Kafka/Pulsar recommended) to ensure that market data is entered in sync with the news streams
- signal fusion
- risk adaptation: Set a prediction confidence threshold (e.g. 80%) below which the manual review process is automatically triggered
- Performance Monitoring: Deploy Prometheus + Grafana to monitor predicted latency and resource utilization to ensure transaction timeliness
: Establishment of a weighting mechanism at the strategy level to combine FinGPT forecasts with traditional technical indicators.
Note: It is recommended to test in a demo trading environment for 1-2 months to verify the stability of the strategy before committing to live trading."
This answer comes from the articleFinGPT: Open Source Financial Big Language Modeling Platform for Financial Analytics and PredictionThe
