2480 Fmp API Guide 26950e10

FMP Earnings Calendar API Guide

This reference provides guidance on using the Financial Modeling Prep (FMP) Earnings Calendar API to retrieve upcoming earnings announcements for US stocks.

FMP API Overview

Financial Modeling Prep (FMP) provides a comprehensive financial data API with earnings calendar endpoints that return structured JSON data including announcement dates, EPS estimates, revenue estimates, and actual results for publicly traded companies.

Official Documentation: https://site.financialmodelingprep.com/developer/docs/earnings-calendar-api

API Endpoint

Earnings Calendar Endpoint:

https://financialmodelingprep.com/api/v3/earning_calendar

Authentication

FMP API requires an API key for authentication:

https://financialmodelingprep.com/api/v3/earning_calendar?apikey=YOUR_API_KEY&from=2025-11-03&to=2025-11-09

Getting an API Key:

• Free tier: https://site.financialmodelingprep.com/developer/docs
• Sign up for free account
• Receive API key immediately
• Free tier: 250 API calls/day

Request Parameters

Required Parameters

Constraints

• Maximum Records: 4000 records per request
• Maximum Date Range: 90 days
• Rate Limiting: Free tier = 250 calls/day, Premium = 750-2500 calls/day
• Date Format: YYYY-MM-DD (ISO 8601)

Example Request

curl "https://financialmodelingprep.com/api/v3/earning_calendar?apikey=YOUR_KEY&from=2025-11-03&to=2025-11-09"

Response Format

JSON Structure

[
    {
        "symbol": "AAPL",
        "date": "2025-11-04",
        "eps": null,
        "epsEstimated": 1.54,
        "time": "amc",
        "revenue": null,
        "revenueEstimated": 123400000000,
        "fiscalDateEnding": "2025-09-30",
        "updatedFromDate": "2025-11-01"
    },
    {
        "symbol": "MSFT",
        "date": "2025-11-05",
        "eps": null,
        "epsEstimated": 2.75,
        "time": "amc",
        "revenue": null,
        "revenueEstimated": 56200000000,
        "fiscalDateEnding": "2025-09-30",
        "updatedFromDate": "2025-11-02"
    }
]

Field Descriptions

Timing Conventions

BMO (Before Market Open)

• API value: "bmo" or "pre-market"
• Announced before US market opens at 9:30 AM ET
• Typically around 6:00-8:00 AM ET
• Impact: Provides time for market to digest before trading begins

AMC (After Market Close)

• API value: "amc" or "after-market"
• Announced after US market closes at 4:00 PM ET
• Typically around 4:00-5:00 PM ET
• Impact: Overnight reaction, gap up/down at next day's open

TBA (To Be Announced)

• API value: "tba" or null
• Specific time not yet announced
• Could be BMO or AMC
• Monitor company investor relations for updates

Filtering by Market Cap

FMP API doesn't provide market cap in the earnings calendar endpoint. To filter by market cap:

Option 1: Use Company Profile API (Recommended)

Step 1: Get earnings calendar data

GET /api/v3/earning_calendar?apikey=KEY&from=2025-11-03&to=2025-11-09

Step 2: For each symbol, fetch market cap from profile endpoint

GET /api/v3/profile/{symbol}?apikey=KEY

Response includes:

{
    "symbol": "AAPL",
    "companyName": "Apple Inc.",
    "mktCap": 3000000000000,
    "sector": "Technology",
    "industry": "Consumer Electronics"
}

Step 3: Filter companies with mktCap > 2000000000 ($2B+)

Option 2: Use Batch Profile API (More Efficient)

Request profiles for multiple symbols in one call:

GET /api/v3/profile/AAPL,MSFT,GOOGL,AMZN?apikey=KEY

This reduces API calls significantly.

Python Implementation Strategy

Basic Request

import requests
from datetime import datetime, timedelta

def fetch_earnings_calendar(api_key, start_date, end_date):
    """
    Fetch earnings calendar from FMP API

    Args:
        api_key: FMP API key
        start_date: Start date (YYYY-MM-DD)
        end_date: End date (YYYY-MM-DD)

    Returns:
        List of earnings announcements
    """
    url = "https://financialmodelingprep.com/api/v3/earning_calendar"
    params = {
        "apikey": api_key,
        "from": start_date,
        "to": end_date
    }

    response = requests.get(url, params=params)
    response.raise_for_status()

    return response.json()

# Usage
api_key = "YOUR_API_KEY"
earnings = fetch_earnings_calendar(api_key, "2025-11-03", "2025-11-09")

With Market Cap Filtering

def fetch_company_profiles(api_key, symbols):
    """
    Fetch company profiles for multiple symbols (batch)

    Args:
        api_key: FMP API key
        symbols: List of ticker symbols

    Returns:
        Dictionary mapping symbol to profile data
    """
    # Batch symbols (max 100 per request)
    batch_size = 100
    profiles = {}

    for i in range(0, len(symbols), batch_size):
        batch = symbols[i:i+batch_size]
        symbols_str = ",".join(batch)

        url = f"https://financialmodelingprep.com/api/v3/profile/{symbols_str}"
        params = {"apikey": api_key}

        response = requests.get(url, params=params)
        response.raise_for_status()

        for profile in response.json():
            profiles[profile["symbol"]] = profile

    return profiles

def filter_by_market_cap(earnings, profiles, min_market_cap=2000000000):
    """
    Filter earnings by minimum market cap ($2B default)

    Args:
        earnings: List of earnings announcements
        profiles: Dictionary of company profiles
        min_market_cap: Minimum market cap in dollars

    Returns:
        Filtered list of earnings for mid-cap+ companies
    """
    filtered = []

    for earning in earnings:
        symbol = earning["symbol"]
        profile = profiles.get(symbol)

        if profile and profile.get("mktCap", 0) >= min_market_cap:
            # Merge earnings data with profile data
            earning["marketCap"] = profile["mktCap"]
            earning["companyName"] = profile.get("companyName", symbol)
            earning["sector"] = profile.get("sector", "N/A")
            earning["industry"] = profile.get("industry", "N/A")
            filtered.append(earning)

    return filtered

# Complete workflow
api_key = "YOUR_API_KEY"

# Step 1: Get earnings calendar
earnings = fetch_earnings_calendar(api_key, "2025-11-03", "2025-11-09")

# Step 2: Get company profiles
symbols = [e["symbol"] for e in earnings]
profiles = fetch_company_profiles(api_key, symbols)

# Step 3: Filter by market cap (>$2B)
filtered_earnings = filter_by_market_cap(earnings, profiles)

API Key Management - Multi-Environment Support

Environment 1: Claude Code (CLI)

Set environment variable:

export FMP_API_KEY="your-api-key-here"

Access in Python:

import os
api_key = os.environ.get('FMP_API_KEY')

Environment 2: Claude Desktop

Configure MCP server settings or use environment variable in system.

Environment 3: Claude Web

API key cannot be stored persistently. Request from user during execution:

Workflow:

1. Check for environment variable
2. If not found, prompt user: "Please provide your FMP API key"
3. Store in session variable for current conversation
4. Use for all API calls in this session

Security Note:

• Key stored only in conversation context
• Forgotten when session ends
• User should use free tier or limited-scope keys

Error Handling

Common Errors

401 Unauthorized:

{
    "Error Message": "Invalid API KEY. Please retry or visit our documentation to create one FREE https://site.financialmodelingprep.com/developer/docs"
}

Solution: Verify API key is correct

429 Rate Limit Exceeded:

{
    "Error Message": "Limit Reach. Please upgrade your plan or visit our documentation for more details at https://site.financialmodelingprep.com/developer/docs"
}

Solution: Reduce API calls or upgrade plan

400 Bad Request:

• Invalid date format
• Date range exceeds 90 days
• Missing required parameters

Error Handling Code

def fetch_earnings_with_error_handling(api_key, start_date, end_date):
    """Fetch earnings with proper error handling"""
    try:
        url = "https://financialmodelingprep.com/api/v3/earning_calendar"
        params = {
            "apikey": api_key,
            "from": start_date,
            "to": end_date
        }

        response = requests.get(url, params=params, timeout=30)

        # Check for API errors
        if response.status_code == 401:
            print("ERROR: Invalid API key")
            print("Get free API key: https://site.financialmodelingprep.com/developer/docs")
            return None

        if response.status_code == 429:
            print("ERROR: Rate limit exceeded")
            print("Free tier: 250 calls/day. Consider upgrading.")
            return None

        response.raise_for_status()
        data = response.json()

        # Check if response is error message
        if isinstance(data, dict) and "Error Message" in data:
            print(f"API Error: {data['Error Message']}")
            return None

        return data

    except requests.exceptions.Timeout:
        print("ERROR: Request timeout. Please try again.")
        return None

    except requests.exceptions.ConnectionError:
        print("ERROR: Connection error. Check your internet connection.")
        return None

    except Exception as e:
        print(f"ERROR: Unexpected error: {str(e)}")
        return None

Data Quality Considerations

Accuracy

• FMP data is generally reliable for earnings dates
• EPS and revenue estimates updated regularly from analyst consensus
• Companies can change dates last-minute; verify critical dates

Completeness

• Covers most US publicly traded companies
• Some smaller companies may have limited data
• Pre-IPO companies won't appear until trading begins

Timeliness

• API updated regularly throughout the day
• Earnings dates typically known 2-4 weeks in advance
• Some companies announce dates very close to earnings (1-2 days prior)

Data Freshness

• Check updatedFromDate field for last update timestamp
• Estimates may change as earnings date approaches
• Time field (bmo/amc/tba) may update closer to date

Best Practices

1. Calculate Date Range Accurately

Always get current date first:

from datetime import datetime, timedelta

today = datetime.now()
start_date = (today + timedelta(days=1)).strftime("%Y-%m-%d")
end_date = (today + timedelta(days=7)).strftime("%Y-%m-%d")

2. Batch API Calls

Use batch endpoints to reduce API calls:

• Earnings calendar: One call for entire week
• Company profiles: Batch up to 100 symbols per call

3. Cache Results

For repeated analyses within same day:

import json
from pathlib import Path

def cache_earnings_data(data, cache_file="earnings_cache.json"):
    """Save earnings data to cache file"""
    cache_path = Path(cache_file)
    cache_path.write_text(json.dumps(data, indent=2))

def load_cached_data(cache_file="earnings_cache.json", max_age_hours=6):
    """Load cached data if recent enough"""
    cache_path = Path(cache_file)
    if not cache_path.exists():
        return None

    # Check cache age
    cache_age = datetime.now() - datetime.fromtimestamp(cache_path.stat().st_mtime)
    if cache_age.total_seconds() > max_age_hours * 3600:
        return None

    return json.loads(cache_path.read_text())

4. Handle Missing Data Gracefully

def safe_get(data, key, default="N/A"):
    """Safely get value from dict with default"""
    value = data.get(key)
    return value if value is not None else default

5. Sort and Prioritize by Market Cap

def sort_by_market_cap(earnings):
    """Sort earnings by market cap descending"""
    return sorted(
        earnings,
        key=lambda x: x.get("marketCap", 0),
        reverse=True
    )

API Call Optimization

Minimize API Calls

For a typical weekly earnings calendar:

1. Earnings Calendar API: 1 call (all week)
2. Company Profiles API: N/100 calls (where N = number of symbols)

Example:

• 200 companies reporting
• Profile API calls: 200/100 = 2 calls
• Total: 3 API calls (well within 250/day free limit)

Rate Limit Management

import time

def rate_limited_request(url, params, delay=0.1):
    """Make request with rate limiting"""
    time.sleep(delay)
    return requests.get(url, params=params)

For free tier (250 calls/day):

• Per hour limit: ~10 calls/hour safe
• Add 0.5-1 second delay between calls if making many requests

Alternative Endpoints

For Real-Time Market Cap Data

Market Capitalization Endpoint:

GET /api/v3/market-capitalization/{symbol}?apikey=KEY

Returns current market cap only (faster, but requires per-symbol calls).

For Historical Earnings Results

Historical Earnings Endpoint:

GET /api/v3/historical/earning_calendar/{symbol}?apikey=KEY

Returns past earnings results with actual vs. estimated comparisons.

Comparison: FMP vs Other Data Sources

Recommendation: FMP API is the most reliable and structured option for programmatic access.

Troubleshooting

Problem: API returns empty array

Solutions:

• Verify date range is valid (future dates)
• Check date format is YYYY-MM-DD
• Verify API key is active
• Try wider date range (e.g., +14 days instead of +7)

Problem: Some major companies missing

Solutions:

• Company may not have announced earnings date yet
• Some companies announce dates very late (1-2 days before)
• Cross-reference with company investor relations website
• Check if company is on earnings date hold

Problem: Market cap data missing

Solutions:

• Company profile may not be available
• Use alternative market cap endpoint
• Fall back to manual market cap lookup
• Some thinly traded stocks may lack profile data

Problem: Rate limit hit unexpectedly

Solutions:

• Check other scripts/tools using same API key
• Implement caching to reduce repeated calls
• Add delays between requests
• Consider upgrading to paid tier

Example Output Structure

After fetching and processing FMP data:

{
    "symbol": "AAPL",
    "companyName": "Apple Inc.",
    "date": "2025-11-04",
    "time": "amc",
    "marketCap": 3000000000000,
    "sector": "Technology",
    "industry": "Consumer Electronics",
    "epsEstimated": 1.54,
    "revenueEstimated": 123400000000
}

This enriched data can then be organized by date, timing, and market cap for the final earnings calendar report.

Resources

• FMP Documentation: https://site.financialmodelingprep.com/developer/docs
• API Key Signup: https://site.financialmodelingprep.com/developer/docs
• Earnings Calendar API: https://site.financialmodelingprep.com/developer/docs/earnings-calendar-api
• Company Profile API: https://site.financialmodelingprep.com/developer/docs/companies-key-metrics-api
• Rate Limits: https://site.financialmodelingprep.com/developer/docs/pricing

This guide is optimized for the earnings-calendar skill and FMP API integration.