The go-stock's AI module adopts the BERT+GNN hybrid model architecture, which is mainly applied to the following investment decision support scenarios:
Typical Application Scenarios
- intelligent diagnosis::
- Automatically recognize 58 classic K-line patterns such as top propeller and dawn.
- Combining the turnover ratio with the MACD indicator to generate buy and sell point confidence scores
- Public Opinion Monitoring::
- Real-time crawling of retail investor sentiment indexes from Snowball, Oriental Wealth and other platforms
- Predicting Short-term Sentiment Inflection Points via LSTM Modeling
- Strategy Backtesting::
- After entering customized trading rules, the validity is automatically verified by going back 5 years of historical data
- Generate evaluation reports containing 12 core metrics such as win rate, maximum retracement, etc.
Technical realization features
The model uses quantized compression, which is available on the RTX 3060 graphics card:
- Multi-factor analysis of individual stocks within 200ms
- Support simultaneous monitoring of 50 stocks for dynamic alerts
- Local knowledge base updated quarterly with benchmark data on industry PEs, capital flows, etc.
(Note: AI analysis results are for reference only, the actual investment should be combined with personal risk appetite)
This answer comes from the articlego-stock: AI-enabled stock analysis tool, real-time monitoring of self-picked stock quotes and in-depth analysis based on AIThe
May not be reproduced without permission:AI productivity tools " What are the specific application scenarios included in go-stock's AI analytics feature?
