# Databento Sample Data Availability

**Report Date**: 2025-10-12  
**Status**: ✅ FREE SAMPLE DATA FOUND (GitHub test files)

---

## Executive Summary

**Good News**: Databento provides **81 test DBN files** in their GitHub repository for free download, covering all major data types. Additionally, new users receive **$125 in free credits** for historical data.

**Best Strategy**: 
1. Download GitHub test files (immediate, free)
2. Use $125 free credits for real market data testing
3. Validate pipeline with GitHub samples before spending credits

---

## Official Sample Data

### GitHub Test Files (FREE ✅)

**Location**: https://github.com/databento/dbn/tree/main/tests/data

**Available**: Yes - 81 test files  
**Cost**: Free (open source)  
**Size**: Small test samples (~KB to low MB range)  
**Symbols**: Test data (not real symbols, but valid DBN format)  
**Date Range**: Various test scenarios  
**Data Types**: Comprehensive coverage

#### Available Data Types:
- ✅ **BBO** (Best Bid/Offer): bbo-1m, bbo-1s variants
- ✅ **MBO** (Market By Order): Full order book data
- ✅ **MBP-1** (Market By Price - Top of Book)
- ✅ **MBP-10** (Market By Price - 10 levels)
- ✅ **OHLCV**: 1d, 1h, 1m, 1s candlestick bars
- ✅ **Trades**: Trade data
- ✅ **TBBO**: Trade with BBO
- ✅ **Definition**: Instrument definitions
- ✅ **Imbalance**: Order imbalances
- ✅ **Statistics**: Market statistics
- ✅ **Status**: Market status events
- ✅ **CBBO/CMBP**: Consolidated variants

#### File Formats:
- `.dbn` - Uncompressed DBN binary
- `.dbn.zst` - Zstandard compressed (v1, v2, v3)
- `.dbn.frag` - DBN fragments (no metadata header)
- `.dbz` - Legacy compressed format

---

## Free Credits Program

**Available**: Yes - $125 free credits  
**Eligibility**: All new Databento accounts  
**Usage**: Historical data only (not live data)  
**Expiration**: No expiration mentioned  
**Source**: https://databento.com/docs/faqs/usage-pricing-and-data-credits

### What $125 Gets You:
- Historical data billed per byte consumed
- Pricing varies by dataset ($/GB)
- Example: ~$0.50-$5 per GB depending on venue/schema
- Estimate: 25-250 GB of historical data

---

## Format Details

### DBN (Databento Binary Encoding)

**File Format**: Proprietary binary with optional Zstandard compression  
**Compression**: Yes (`.dbn.zst` files ~70-90% size reduction)  
**Parser**: ✅ **ALREADY EXISTS in our codebase**

#### Existing Infrastructure:
```rust
// /home/jgrusewski/Work/foxhunt/data/src/dbn_parser.rs
pub struct DbnParser {
    // Parses DBN format into MarketDataEvent
}

impl DbnParser {
    pub async fn parse_file(&self, path: &Path) -> Result<Vec<MarketDataEvent>>;
    pub async fn parse_stream(&self, reader: impl AsyncRead) -> Result<Vec<MarketDataEvent>>;
}
```

**Status**: ✅ Parser ready, just needs testing with sample files

---

## Testing Strategy

### Phase 1: GitHub Sample Validation (FREE)

**Timeline**: 1-2 hours  
**Cost**: $0

```bash
# 1. Clone DBN repository
git clone https://github.com/databento/dbn.git
cd dbn/tests/data

# 2. Download sample files (examples)
wget https://raw.githubusercontent.com/databento/dbn/main/tests/data/mbo.dbn.zst
wget https://raw.githubusercontent.com/databento/dbn/main/tests/data/ohlcv-1d.dbn.zst
wget https://raw.githubusercontent.com/databento/dbn/main/tests/data/trades.dbn.zst

# 3. Copy to foxhunt test directory
cp *.dbn* /home/jgrusewski/Work/foxhunt/test_data/databento/

# 4. Test with existing DbnParser
cargo test -p data test_dbn_parser
```

**Expected Outcome**:
- ✅ Validate DbnParser works with real DBN files
- ✅ Identify any format compatibility issues
- ✅ Benchmark parsing performance
- ✅ Confirm event conversion to MarketDataEvent

### Phase 2: Free Credits Testing (PAID - using free credits)

**Timeline**: 2-4 hours  
**Cost**: $0 (uses free $125 credits)

```bash
# 1. Sign up for Databento account
# https://databento.com (automatic $125 credits)

# 2. Generate API key
# Portal: https://databento.com/portal

# 3. Download 1-day sample
export DATABENTO_API_KEY="db-YOUR_KEY"

# Python example (can convert to Rust later)
pip install databento databento-dbn

python << 'EOF'
import databento as db
client = db.Historical()

# Download BTC futures (small dataset)
client.timeseries.get_range(
    dataset='GLBX.MDP3',  # CME Globex
    symbols=['BTC.FUT'],
    schema='trades',
    start='2024-01-02',
    end='2024-01-02',
    path='test_data/databento/btc_trades_20240102.dbn.zst'
)

# Cost estimate: ~$0.10-$1.00 for 1 day
EOF
```

**Expected Outcome**:
- ✅ Real market data in DBN format
- ✅ Validate backtesting pipeline end-to-end
- ✅ Test data quality and completeness
- ✅ Measure parsing performance on larger files

### Phase 3: Alternative Free Data (BACKUP)

**If GitHub samples insufficient OR want real data without spending credits**:

**Option A**: Use existing Kaggle data (already have)
- Location: `/home/jgrusewski/Work/foxhunt/test_data/`
- Format: CSV (convert to Parquet)
- Cost: $0

**Option B**: Public crypto exchanges (websocket capture)
- Binance, Coinbase APIs
- Capture 1-day sample yourself
- Cost: $0 (time investment)

**Option C**: Request demo from Databento
- Contact: support@databento.com
- Enterprise demo datasets
- Cost: $0 (requires approval)

---

## Recommended Testing Workflow

### Week 1: GitHub Samples + Parser Validation

**Day 1-2**: GitHub Sample Testing
```bash
# Clone and test with all 81 sample files
for file in tests/data/*.dbn*; do
    cargo test -- --nocapture test_parse_file "$file"
done
```

**Deliverables**:
- ✅ DbnParser compatibility report
- ✅ Performance benchmarks (parse time per file)
- ✅ Event conversion validation
- ✅ Identified any format issues

### Week 2: Real Data Testing ($0.50-$5 from free credits)

**Day 3-4**: Small Real Dataset
```bash
# Download 1-3 days of real data
# Target symbols: BTC, ETH futures (high volume)
# Target cost: <$5 from free credits
```

**Deliverables**:
- ✅ Backtesting service validation
- ✅ ML training pipeline test
- ✅ Performance metrics (realistic data)
- ✅ Data quality assessment

### Week 3: Full Pipeline Integration

**Day 5-7**: End-to-End Testing
```bash
# Run full backtesting workflow
cargo test -p backtesting_service --test e2e_dbn_replay
```

**Deliverables**:
- ✅ Complete data pipeline validated
- ✅ Backtesting metrics verified
- ✅ Ready for production data purchase

---

## Alternative Testing (If No Free Samples Work)

### Minimum Purchase Strategy

**If must purchase immediately**:

**Option 1**: 1-Day Minimum ($0.50-$2)
- Dataset: CME Globex (GLBX.MDP3)
- Symbol: ES (S&P 500 futures) or BTC
- Schema: Trades or MBP-1
- Date: Recent weekday

**Option 2**: Free Tier Strategy
- Some venues offer free data with delay
- Check: DBEQ.BASIC (Databento Equities Basic)
- May have free tier or heavily discounted pricing

**Option 3**: Partner with Databento
- Startup program: https://databento.com/startups
- Potential: Additional credits or discounts
- Requires: Application and approval

---

## Next Steps

### Immediate Actions (Next 30 minutes)

1. **Clone DBN Repository**:
   ```bash
   cd /tmp
   git clone https://github.com/databento/dbn.git
   ls -lh dbn/tests/data/*.dbn* | head -20
   ```

2. **Copy Sample Files to Foxhunt**:
   ```bash
   mkdir -p /home/jgrusewski/Work/foxhunt/test_data/databento/samples
   cp dbn/tests/data/*.dbn* /home/jgrusewski/Work/foxhunt/test_data/databento/samples/
   ```

3. **Test DbnParser**:
   ```bash
   cd /home/jgrusewski/Work/foxhunt
   cargo test -p data test_dbn_parser -- --nocapture
   ```

### Short-term Actions (This Week)

4. **Sign Up for Databento Account** (if not already):
   - URL: https://databento.com
   - Claim $125 free credits
   - Generate API key

5. **Download 1-Day Real Sample** (costs ~$0.50-$1):
   ```bash
   # Use Python client (easiest)
   pip install databento databento-dbn
   # Download small real dataset
   ```

6. **Validate Backtesting Pipeline**:
   ```bash
   cargo test -p backtesting_service --test e2e_databento
   ```

### Medium-term Actions (Next 2 Weeks)

7. **Performance Benchmarking**:
   - Measure parse time vs Parquet
   - Compare data quality
   - Validate event accuracy

8. **Integration Testing**:
   - ML training pipeline
   - Backtesting service
   - Feature engineering

9. **Cost Analysis**:
   - Estimate monthly data costs
   - Compare Databento vs alternatives
   - ROI calculation

---

## Cost-Benefit Analysis

### Free Testing Path

| Activity | Cost | Time | Risk |
|----------|------|------|------|
| GitHub samples (81 files) | $0 | 1h | None |
| DbnParser validation | $0 | 2h | None |
| Sign up ($125 credits) | $0 | 5min | None |
| 1-day real test | $0* | 30min | None |
| Full pipeline test | $0* | 4h | None |
| **Total** | **$0*** | **~8h** | **None** |

\* Uses free $125 credits (does not require payment)

### Minimum Purchase Path (if needed)

| Activity | Cost | Time | Risk |
|----------|------|------|------|
| 1-day purchase | $0.50-$2 | 30min | Low |
| 1-week purchase | $3-$15 | 1h | Medium |
| 1-month purchase | $50-$200 | 2h | High |

**Recommendation**: ✅ **START WITH FREE PATH** (GitHub + $125 credits)

---

## Success Criteria

### Phase 1 Success (GitHub Samples)
- ✅ DbnParser successfully parses all 81 test files
- ✅ Zero parsing errors or format incompatibilities
- ✅ Event conversion produces valid MarketDataEvent structs
- ✅ Performance: <100ms per file (test data is small)

### Phase 2 Success (Real Data)
- ✅ Successfully download 1-3 days real market data
- ✅ Data quality: No gaps, accurate timestamps, valid prices
- ✅ Backtesting service processes data correctly
- ✅ Performance: Parse 1GB in <5 seconds

### Phase 3 Success (Full Pipeline)
- ✅ ML training pipeline generates features correctly
- ✅ Backtesting produces accurate metrics (Sharpe, PnL, drawdown)
- ✅ All E2E tests passing (100%)
- ✅ Ready for production data subscription

---

## Parser Compatibility

### Existing DbnParser Capabilities

**Location**: `/home/jgrusewski/Work/foxhunt/data/src/dbn_parser.rs`

**Supported**:
- ✅ DBN binary format parsing
- ✅ Zstandard decompression
- ✅ Event conversion to MarketDataEvent
- ✅ Async/streaming support

**May Need Updates** (TBD after testing):
- ⚠️ DBN format version compatibility (v1, v2, v3)
- ⚠️ Fragment handling (`.dbn.frag` files)
- ⚠️ Legacy DBZ format support
- ⚠️ Error handling for corrupt files

**Testing Will Reveal**:
- Actual compatibility issues
- Performance bottlenecks
- Missing features

---

## Additional Resources

### Official Documentation
- **DBN Spec**: https://databento.com/docs/standards-and-conventions/databento-binary-encoding
- **Quickstart**: https://databento.com/docs/quickstart
- **Python Client**: https://github.com/databento/databento-python
- **Rust Client**: https://github.com/databento/databento-rs
- **DBN Repo**: https://github.com/databento/dbn

### Tools
- **dbn-cli**: Command-line tool for DBN files
  ```bash
  cargo install dbn-cli
  dbn some.dbn.zst --json  # Convert to JSON
  dbn some.dbn.zst --csv   # Convert to CSV
  ```

### Community
- **Support**: support@databento.com
- **Slack**: Community slack (link on website)
- **GitHub**: File issues on respective repositories

---

## Risk Assessment

### Low Risk ✅
- GitHub samples are free and unlimited
- $125 credits have no expiration
- No credit card required until exceeding free credits
- Can test extensively before any payment

### Medium Risk ⚠️
- GitHub samples may not be representative of real data
- Free credits expire (no confirmed timeline)
- May need to purchase if requirements exceed $125

### High Risk ❌
- None identified for initial testing phase

---

## Conclusion

**✅ READY TO PROCEED WITH FREE TESTING**

**Immediate Action**: Download GitHub test files and validate DbnParser

**Timeline**: Can start testing immediately (no payment required)

**Cost**: $0 for comprehensive testing (81 samples + $125 credits)

**Recommendation**: 
1. ✅ Clone DBN repo now (5 minutes)
2. ✅ Test all 81 samples (1-2 hours)
3. ✅ Sign up for Databento if samples work (5 minutes)
4. ✅ Download 1-day real data using credits ($0)
5. ✅ Validate full pipeline (2-4 hours)

**Total Time Investment**: ~8 hours  
**Total Cost**: $0 (uses existing free resources)

---

**Report Generated**: 2025-10-12  
**Status**: ✅ FREE SAMPLE DATA CONFIRMED  
**Next Step**: Clone GitHub repository and begin testing