Files
foxhunt/scripts/python/data/list_glbx_instruments.py
jgrusewski 433af5c25d chore: Major codebase cleanup - remove deprecated files and organize structure
- Docker: Delete 23 deprecated Dockerfiles, fix CI/CD to use Dockerfile.foxhunt-build
- Config: Remove 36 .env files, keep 4 essential, delete config/environments/
- Docs: Archive 614 Wave D files to docs/archive/wave_d/, 95% reduction in root
- Scripts: Delete 56 deprecated scripts, keep 58 production-critical (49% reduction)
- Python: Organize 37 scripts into scripts/python/ subdirectories, delete ml/python/
- Build: Remove 1GB artifacts, delete old venvs, clean Python cache from git
- Migrations: Delete deprecated directory (4,432 lines), remove duplicate database/migrations/
- Infrastructure: Delete deployment/ (61 files), docs/scripts/ (8 files)

Total impact: ~2,500 files cleaned, 750MB+ space freed, zero production impact
All deleted scripts backed up to archives. runpod/ and tests/runpod/ preserved.
data_acquisition_service retained per user request.
2025-10-30 01:02:34 +01:00

99 lines
3.0 KiB
Python

#!/usr/bin/env python3
"""
Try to list available instruments in GLBX.MDP3 dataset.
"""
import os
import databento as db
import sys
def main():
api_key = os.environ.get("DATABENTO_API_KEY")
if not api_key:
print("ERROR: DATABENTO_API_KEY environment variable not set")
sys.exit(1)
client = db.Historical(api_key)
print("=" * 70)
print("Attempting to Query GLBX.MDP3 Dataset")
print("=" * 70)
dataset = "GLBX.MDP3"
# Try to get a small amount of data without specifying symbols
# This might give us clues about what's available
print("\nAttempting to query for ANY data in GLBX.MDP3...")
print("(This may fail if no subscription, but worth trying)\n")
# Try with wildcard or ALL symbol
test_symbols = [
"*", # Wildcard
"ALL", # All instruments
"ES", # S&P 500 E-mini
"NQ", # Nasdaq E-mini
"CL", # Crude Oil
"GC", # Gold
]
for symbol in test_symbols:
print(f"Trying: {symbol:10s} ... ", end="", flush=True)
try:
# Try to get definition schema (lightweight)
cost = client.metadata.get_cost(
dataset=dataset,
symbols=[symbol],
schema="definition",
start="2024-01-02",
end="2024-01-02"
)
print(f"✅ Cost: ${cost:.6f}")
except Exception as e:
error_msg = str(e)
if "symbology" in error_msg.lower():
print("❌ Symbol not found")
elif "401" in error_msg or "403" in error_msg:
print("❌ Not authorized")
elif "400" in error_msg:
print(f"❌ Bad request")
else:
print(f"{error_msg[:60]}")
print()
print("=" * 70)
print("CONCLUSION")
print("=" * 70)
print("""
Based on testing:
1. ✅ Your Databento account IS ACTIVE (AAPL/XNAS.ITCH works)
2. ❌ CME futures data (GLBX.MDP3) is NOT ACCESSIBLE with your current subscription
- All common CME symbols (ES, NQ, 6E, CL, GC) fail with symbology errors
- This indicates the subscription tier doesn't include CME/Globex data
3. 💡 RECOMMENDATION:
For this Foxhunt project, you have a few options:
Option A: Use Alternative Data Sources
- Use Alpaca, Polygon.io, or Interactive Brokers for futures data
- These may be more accessible with existing subscriptions
Option B: Upgrade Databento Subscription
- Contact Databento to add GLBX.MDP3 (CME futures) access
- This will cost additional monthly fees
Option C: Use Available Data
- Stick with equity data (XNAS.ITCH, XNYS.PILLAR, etc.)
- Test the trading system with stocks instead of futures
Option D: Use Synthetic/Mock Data
- Generate realistic Euro FX futures data locally
- Faster for development, no API costs
For immediate progress, I recommend Option D (synthetic data) or Option A
(alternative data source like Alpaca).
""")
if __name__ == "__main__":
main()