Recent Announcements (Regulatory)
Top 5 recent regulatory filings from company disclosure platforms.Format: Array of announcement objectsDefault: Location:
[] (empty array if no announcements)Source: Company filings from NSE/BSE disclosure platformsMax Items: 5 (most recent)Data Sources:company_filings/*_filings.json- Historical regulatory filingsall_company_announcements.json- Live announcements feed
add_corporate_events.py:121-212, 251-252Announcement Object Schema
Date and time of the regulatory announcement.Format:
YYYY-MM-DD HH:MM:SS or YYYY-MM-DDExample: "2024-03-15 18:30:00" or "2024-03-15"Source: news_date field from filing or announcement APITitle or description of the regulatory filing.Example:
"Outcome of Board Meeting - Approval of Quarterly Results"Source Priority:caption(preferred)descriptor(fallback)news_body(last resort)
" ".join(headline.split())Default: "N/A" if all fields emptyDirect link to the PDF/document of the regulatory filing.Example:
"https://nsearchives.nseindia.com/corporate/RELIANCE_15032024_result.pdf"Source: file_url field from filing dataDefault: "N/A" if URL not available (common for live announcements)Note: Some announcements from all_company_announcements.json may not have PDF linksNews Feed (Media)
Top 5 recent media news articles with AI-powered sentiment analysis.Format: Array of news objectsDefault: Location:
[] (empty array if no news)Source: Aggregated market news from media sourcesMax Items: 5 (most recent)Source Directory: market_news/*_news.jsonExtraction:add_corporate_events.py:215-240, 254-255News Object Schema
Headline of the news article.Example:
"Reliance Industries to invest $10B in green energy expansion"Source: Title field from news APIAI-analyzed sentiment classification of the news article.Possible Values:
"positive"- Bullish news (growth, expansion, positive earnings, deals)"neutral"- Factual reporting without directional bias"negative"- Bearish news (losses, layoffs, investigations, downgrades)
"positive"Source: Sentiment field from news API (likely NLP/LLM-powered)Use Cases:- Sentiment aggregation (count positive vs negative news)
- News-based momentum signals
- Risk assessment (cluster of negative news = caution)
Publication timestamp of the news article.Format: ISO 8601 timestamp or date stringExample:
"2024-03-15T14:30:00Z" or "2024-03-15"Source: PublishDate field from news APINote: Raw timestamp preserved (not normalized)Data Sources Comparison
| Feature | Recent Announcements | News Feed |
|---|---|---|
| Nature | Regulatory filings (mandatory disclosures) | Media articles (journalistic coverage) |
| Source | NSE/BSE/Company secretary | News aggregators, financial media |
| Timeliness | Official, may have filing delays | Real-time to near real-time |
| Accuracy | Authoritative (from company) | Subject to media interpretation |
| PDF Links | Usually available | Not applicable |
| Sentiment | Not analyzed | AI-classified |
| Use Case | Compliance tracking, event verification | Market sentiment, momentum signals |
Combined Analysis Strategies
1. Event Confirmation
Cross-reference News Feed sentiment with Recent Announcements:- Positive news + Results announcement = Potential earnings beat
- Negative sentiment + No announcement = Market rumor (investigate)
2. Sentiment Aggregation
Count sentiment distribution in last 5 news items:3. Announcement Type Detection
Parse Recent Announcements headlines for key events:Pipeline Integration
Both news systems are populated byadd_corporate_events.py, which runs after the main fundamental analysis pipeline:
- Loads
all_stocks_fundamental_analysis.json - Scans multiple data sources (filings, announcements, news)
- Populates
Event Markers,Recent Announcements, andNews Feed - Writes updated JSON back to disk
Missing Data Handling
- No filings found:
Recent Announcements=[] - No news coverage:
News Feed=[] - Symbol not in news sources: Empty arrays (not
"N/A"string)
Source Code Reference
- Announcement processing:
add_corporate_events.py:120-212 - News feed processing:
add_corporate_events.py:215-240 - Field updates:
add_corporate_events.py:251-255 - Output schema:
all_stocks_fundamental_analysis.json