Data Fetcher Integration

Overview

This skill provides step-by-step guidance for adding new data indicators to the Cycle Navigator's background worker system. It ensures that new data integrations respect rate limits, caching logic, database constraints, and Celery task patterns.

When to Use This Skill

•Adding a new FRED (Federal Reserve Economic Data) series to macro indicators
•Integrating a new CoinGecko cryptocurrency metric
•Expanding data coverage without breaking existing rate limit budgets
•Ensuring two-tier data architecture (PostgreSQL + Redis cache) consistency

Instructions

Setup

1. Verify Rate Limit Budget

Before adding a new data source, check your current usage:

FRED Rate Limits:

•Safe threshold: 800 requests/day
•Check existing tasks in backend/tasks/fred_tasks.py to estimate cumulative daily calls
•Calculate: (number_of_series × calls_per_series × 365 days) ÷ 800

CoinGecko Rate Limits:

•Demo tier: 30 calls/minute (soft limit)
•Production tier: 50 calls/minute
•Check backend/tasks/crypto_tasks.py for existing tasks

2. Understand the Data Flow Architecture

New data must follow this two-tier pattern:

code

External API (FRED/CoinGecko)
    ↓
PostgreSQL (source of truth)
    ↓
Redis Cache (24-hour TTL)
    ↓
API Response to Frontend

Implementation

Step 1: Create the Celery Task

Add your task to the appropriate file:

•FRED data: backend/tasks/fred_tasks.py
•Crypto data: backend/tasks/crypto_tasks.py

Example FRED Task:

python

from celery import shared_task
from backend.services.macro import fetch_and_store_fred_series
from backend.utils import get_redis_client
import logging

logger = logging.getLogger(__name__)

@shared_task(bind=True, max_retries=3)
def fetch_unemployment_rate(self):
    """Fetch monthly unemployment rate from FRED."""
    series_id = "UNRATE"
    redis_client = get_redis_client()
    lock_key = f"fred:fetch:{series_id}"
    
    try:
        # Acquire distributed lock to prevent concurrent API calls
        lock_acquired = redis_client.set(
            lock_key, 
            "locked", 
            nx=True, 
            ex=300  # 5-minute lock timeout
        )
        
        if not lock_acquired:
            logger.info(f"Lock held for {series_id}, skipping this run")
            return
        
        # Fetch from FRED API and store in PostgreSQL
        data = fetch_and_store_fred_series(series_id)
        
        # Update Redis cache (24-hour TTL)
        cache_key = f"macro:fred:{series_id}"
        redis_client.setex(cache_key, 86400, json.dumps(data))
        
        logger.info(f"Successfully updated {series_id}")
        
    except Exception as exc:
        # Exponential backoff: 2s, 4s, 8s, 16s, 32s
        retry_delay = 2 ** self.request.retries
        self.retry(exc=exc, countdown=retry_delay)
    finally:
        # Always release the lock
        redis_client.delete(lock_key)

Example Crypto Task:

python

@shared_task(bind=True, max_retries=3)
def fetch_bitcoin_market_cap(self):
    """Fetch Bitcoin market cap from CoinGecko."""
    crypto_id = "bitcoin"
    redis_client = get_redis_client()
    lock_key = f"crypto:fetch:{crypto_id}"
    
    try:
        lock_acquired = redis_client.set(lock_key, "locked", nx=True, ex=60)
        
        if not lock_acquired:
            logger.info(f"Lock held for {crypto_id}, skipping")
            return
        
        # Fetch from CoinGecko
        data = fetch_and_store_crypto_series(crypto_id)
        
        # Cache with 24-hour TTL
        cache_key = f"crypto:coingecko:{crypto_id}"
        redis_client.setex(cache_key, 86400, json.dumps(data))
        
        logger.info(f"Updated {crypto_id}")
        
    except Exception as exc:
        retry_delay = 2 ** self.request.retries
        self.retry(exc=exc, countdown=retry_delay)
    finally:
        redis_client.delete(lock_key)

Step 2: Register the Task in Celery Beat

Edit backend/celery_app.py to schedule the new task:

python

from celery.schedules import crontab

# In app.conf.beat_schedule dictionary:
beat_schedule = {
    # ... existing tasks ...
    'fetch-unemployment-rate': {
        'task': 'backend.tasks.fred_tasks.fetch_unemployment_rate',
        'schedule': crontab(hour=0, minute=0),  # Daily at midnight UTC
    },
    'fetch-bitcoin-market-cap': {
        'task': 'backend.tasks.crypto_tasks.fetch_bitcoin_market_cap',
        'schedule': crontab(minute='*/30'),  # Every 30 minutes
    },
}

Step 3: Update Database Schema (if needed)

If adding a new metric requires a new PostgreSQL table or column:

•Check backend/models.py for existing schemas
•Add the new model if it doesn't exist
•Create a migration: python scripts/run_timescale_migrations.py
•⚠️ WARNING: TimescaleDB hypertables are irreversible — test migrations in development first

Step 4: Update the Service Layer

Add a function to backend/services/macro.py or backend/services/crypto.py:

python

def fetch_and_store_fred_series(series_id: str) -> dict:
    """
    Fetch from FRED API and store in PostgreSQL.
    Returns: dict with latest data point
    """
    # Call FRED API
    fred = fredapi.FRED(api_key=settings.FRED_API_KEY)
    data = fred.get(series_id)
    
    # Store in PostgreSQL
    # (Implementation depends on your ORM)
    store_to_db(series_id, data)
    
    return {
        "series_id": series_id,
        "latest_value": data.iloc[-1],
        "timestamp": data.index[-1]
    }

Step 5: Add API Endpoint (if exposing new data)

Add a route to backend/routers/macro.py or backend/routers/crypto.py:

python

@router.get("/unemployment-rate")
async def get_unemployment_rate():
    """Fetch cached unemployment rate data."""
    redis_client = get_redis_client()
    cached = redis_client.get("macro:fred:UNRATE")
    
    if cached:
        return json.loads(cached)
    
    # Fallback to database if cache miss
    return db_query_latest("UNRATE")

Verification

Test Rate Limits

bash

# Run a test fetch locally
podman-compose exec backend python -c "
from backend.tasks.fred_tasks import fetch_unemployment_rate
result = fetch_unemployment_rate()
print(result)
"

Verify PostgreSQL Storage

bash

# Check data was written to database
podman-compose exec db psql -U postgres -d cycle_navigator -c "
SELECT * FROM macro_indicators WHERE series_id = 'UNRATE' LIMIT 5;
"

Verify Redis Cache

bash

# Check cache entry
podman-compose exec redis redis-cli GET "macro:fred:UNRATE"

Monitor Celery Task Execution

bash

# Watch Celery worker logs
podman-compose logs -f celery-worker | grep fetch_unemployment_rate

Examples

Example 1: Adding FRED Employment Level

You want to track monthly employment level (PAYEMS):

•Add task to backend/tasks/fred_tasks.py
•Set schedule in backend/celery_app.py: daily at 01:00 UTC (after FRED updates)
•Verify in PostgreSQL: rate limit budget is 801/day (still safe at 800)
•Test locally, then deploy with podman-compose up -d

Example 2: Adding CoinGecko Ethereum Metrics

You want to track Ethereum daily volume:

•Create task in backend/tasks/crypto_tasks.py
•Set schedule: every 4 hours (6 calls/day = safe within 30/min limit)
•Add to /crypto/ethereum-volume endpoint
•Verify cache hits with redis-cli MONITOR

Safety/Verification

⚠️ Critical Warnings

Database Migrations:

•TimescaleDB hypertables are irreversible
•Always test schema changes in development first
•Run migrations during maintenance windows only
•Backup PostgreSQL before schema modifications

Rate Limit Overages:

•Exceeding rate limits will cause API bans (FRED: 24-hour cooldown, CoinGecko: 1-hour)
•Calculate total daily API calls across all tasks before deploying
•Monitor actual usage in production with logs

Redis Lock Failures:

•If Redis is down, locks fail silently and multiple workers will call the API
•Ensure Redis is monitored and configured with persistence
•Use redis:fetch:{series_id} naming convention consistently

Concurrent Updates:

•Without distributed locks, multiple Celery workers cause duplicate API calls
•Always acquire lock before external API call
•Release lock in finally block to prevent stuck locks

Pre-Deployment Checklist

Troubleshooting

Issue: "Task not found" error

Cause: Task not registered in Celery app Solution: Verify task is in backend/celery_app.py beat schedule and imported correctly

bash

podman-compose exec backend celery -A backend.celery_app inspect active

Issue: Rate limit exceeded (429 error)

Cause: Total daily API calls exceed service limits Solution: Reduce task frequency or remove lower-priority data

bash

# Calculate current usage
grep "fetch_" backend/celery_app.py | wc -l

Issue: Redis cache not updating

Cause: Lock held for too long or cache key mismatch Solution: Check lock TTL and cache key naming

bash

redis-cli KEYS "fred:fetch:*"  # Find stuck locks
redis-cli TTL "fred:fetch:UNRATE"  # Check lock timeout

Issue: PostgreSQL constraint violation

Cause: Duplicate data insertion or missing foreign keys Solution: Review schema and check for UNIQUE or FOREIGN KEY constraints

bash

podman-compose exec db psql -U postgres -d cycle_navigator -c "
SELECT constraint_name FROM information_schema.table_constraints 
WHERE table_name = 'macro_indicators';
"

Issue: Exponential backoff causing late updates

Cause: Task failing repeatedly and retrying with long delays Solution: Check API credentials, network connectivity, rate limits

bash

# Review error logs
podman-compose logs celery-worker | grep "Retry"

Related Skills

•skill-creator: For creating new skills following project standards
•celery-task-builder (future): Detailed Celery task development patterns
•postgres-migration-guide (future): Safe database schema changes

References

•TECHNICAL_ARCHITECTURE.md
•DEVELOPER_SETUP.md
•FRED API Documentation: https://fred.stlouisfed.org/docs/api
•CoinGecko API Documentation: https://www.coingecko.com/en/api