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How to Trade the News: Turning Noisy Sentiment Data Into a Real Signal (Python)
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ticker lesson
Breakdown
0:00lesson
Filtering news with metadata
- News filtering requires metadata not keywords
- LSEG data anchors sentiment analysis
- News file and scores file tabs structure data
- Each row represents a news item with timestamp, headline, language, source, and subjects
1:49lesson
Sentiment scoring for companies
- News items stamp probabilities for companies
- Three-class sentiment: -1/0/1 scores
- Micro sentiments capture term-specific signals
- Commodity production volume sentiment is critical
3:40lesson
Price overlay with sentiment
- Sentiment data is high-frequency and noisy
- Kalman filter smoothes volatile signals
- Alibaba example shows daily sentiment summary
- Raw sentiment fluctuates but patterns emerge
5:29lesson
State space models for filtering
- Kalman filter grounds high-frequency data
- State space models reduce noise systematically
- Sentiment becomes a model-informed signal
- Filtering adds predictive value to raw data
7:24lesson
Multi-dimensional sentiment correlations
- Price correlates with volatility and supply-demand
- Trust sentiment and price forecasts align with price
- Correlation plots reveal factor relationships
- Multi-dimensional analysis provides richer signals
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