If you're asking about my recommended production workflow for alternative data (rather than a specific firm's proprietary pipeline), a robust process usually looks like this:
| Stage | News & Sentiment | Satellite/Geospatial |
|---|
| Ingestion | Multiple news APIs, filings, social feeds | Raw imagery, weather, AIS, SAR, multispectral |
| Cleaning | Deduplication, language detection, timestamp normalization | Cloud masking, georeferencing, atmospheric correction |
| Entity Alignment | Company ticker mapping, entity resolution | Facility/location matching to assets |
| Feature Engineering | Sentiment scores, event tags, topic embeddings | Vehicle counts, inventory estimates, crop indices |
| Signal Construction | Rolling aggregates, surprise measures | Trend changes, anomalies, growth rates |
| Validation | Information coefficient, turnover, decay analysis | Compare with reported fundamentals |
| Portfolio Integration | Risk-adjusted weighting, transaction cost model | Combine with traditional alpha factors |
1. Data cleaning
For news:
- Remove duplicate stories syndicated across outlets.
- Normalize timestamps into a single timezone.
- Filter spam and machine-generated articles.
- Detect language and translate if necessary.
- Resolve company names ("Apple" → AAPL vs. fruit).
- Remove articles outside the investment universe.
For sentiment data:
- Standardize scores from different vendors.
- Winsorize extreme observations.
- Smooth noisy intraday spikes.
- Flag missing values instead of imputing blindly.
For satellite data:
- Cloud removal.
- Atmospheric correction.
- Orthorectification.
- Sensor calibration.
- Quality filtering based on viewing angle and weather.
- Remove corrupted image tiles.
2. Entity alignment
This is often the hardest part.
Typical mapping pipeline:
Article
↓
Named Entity Recognition
↓
Company Resolution
↓
Ticker Mapping
↓
Security Master
Example:
"Alphabet announced..."
↓
Entity:
Alphabet Inc.
↓
GOOGL
GOOG
For geospatial data:
Warehouse
↓
GPS Coordinates
↓
Corporate Asset Database
↓
Ticker
Every observation must map cleanly to a tradable security.
3. Feature engineering
Rather than using raw sentiment, transform it into predictive features.
Examples:
News
- sentiment z-score
- sentiment acceleration
- article count
- unique publisher count
- executive mention frequency
- earnings surprise language
- supply-chain mentions
Instead of
Sentiment = 0.74
use
5-day average sentiment
20-day deviation
90-day percentile
Those tend to generalize better.
4. Satellite-derived signals
Raw pixels are rarely useful.
Convert imagery into business metrics.
Examples include:
- Retail parking lot occupancy
- Oil storage tank fill levels
- Shipping container counts
- Factory activity
- Crop health (NDVI)
- Mine production
- Solar farm utilization
Then aggregate to company level.
Store A
Store B
Store C
↓
Average parking utilization
↓
Quarter-over-quarter change
↓
Retail sales estimate
5. Time alignment
One of the biggest sources of backtest error is look-ahead bias.
Every feature should be timestamped by when it became available, not when the underlying event occurred.
Example:
Satellite image:
Captured:
May 1
Processed:
May 2
Available:
May 3
The model should not use it before May 3.
The same applies to news revisions and delayed data feeds.
6. Normalization
Alternative datasets often have different scales.
Common approaches:
- z-score normalization
- percentile ranks
- sector-neutral ranking
- market-cap normalization
- volatility scaling
Example:
Sentiment:
+0.8
Vehicle count:
12,000
Twitter mentions:
450
↓
Standardized
↓
Combined feature vector
7. Signal generation
Rather than acting on individual features, combine multiple sources.
Example:
Positive sentiment
+
Increasing parking traffic
+
Improving shipping volumes
+
Positive earnings revisions
↓
Composite alpha score
Ensemble signals are generally more stable than relying on a single alternative dataset.
8. Validation
Before deploying any signal, evaluate:
- Information coefficient (IC)
- IC stability over time
- Hit rate
- Factor decay
- Turnover
- Capacity
- Sector exposure
- Market beta
- Correlation with existing factors
If a signal disappears after transaction costs or only works during one market regime, it's unlikely to be robust.
9. Ongoing monitoring
Alternative data pipelines need continuous health checks because vendors and data generation processes change.
Monitor for:
- Missing observations
- Vendor methodology changes
- Distribution shifts
- Feature drift
- Entity mapping errors
- Latency increases
- Sudden changes in coverage
Automated alerts can detect when a feature's distribution deviates materially from its historical behavior.
A reference architecture
A common end-to-end pipeline is:
Alternative data
│
▼
Data quality checks
│
▼
Cleaning & normalization
│
▼
Entity resolution
│
▼
Time alignment
│
▼
Feature engineering
│
▼
Signal generation
│
▼
Backtesting & validation
│
▼
Portfolio construction
│
▼
Production monitoring
This workflow emphasizes reproducibility, point-in-time correctness, and rigorous validation so that alternative data contributes genuine predictive value rather than introducing noise or inadvertent look-ahead bias.
