Alpha Vantage API Integration

Financial data API providing stocks, forex, crypto, technical indicators, fundamental data, economic indicators, and AI-powered news sentiment analysis.

Quick Start

Authentication

# Environment variable (recommended)
export ALPHAVANTAGE_API_KEY="your_api_key"

# Or in .env file
ALPHAVANTAGE_API_KEY=your_api_key

Basic Usage (Python)

import requests
import os

API_KEY = os.getenv("ALPHAVANTAGE_API_KEY")
BASE_URL = "https://www.alphavantage.co/query"

def get_quote(symbol: str) -> dict:
    """Get real-time quote for a symbol."""
    response = requests.get(BASE_URL, params={
        "function": "GLOBAL_QUOTE",
        "symbol": symbol,
        "apikey": API_KEY
    })
    return response.json().get("Global Quote", {})

# Example
quote = get_quote("AAPL")
print(f"AAPL: ${quote['05. price']} ({quote['10. change percent']})")

Using Python Package

from alpha_vantage.timeseries import TimeSeries
from alpha_vantage.techindicators import TechIndicators

# Time series data
ts = TimeSeries(key=API_KEY, output_format='pandas')
data, meta = ts.get_daily(symbol='AAPL', outputsize='compact')

# Technical indicators
ti = TechIndicators(key=API_KEY, output_format='pandas')
rsi, meta = ti.get_rsi(symbol='AAPL', interval='daily', time_period=14)

API Functions Reference

Stock Time Series

TIME_SERIES_INTRADAY

TIME_SERIES_DAILY

TIME_SERIES_DAILY_ADJUSTED

TIME_SERIES_WEEKLY

TIME_SERIES_MONTHLY

GLOBAL_QUOTE

SYMBOL_SEARCH

Fundamental Data

OVERVIEW

INCOME_STATEMENT

BALANCE_SHEET

CASH_FLOW

EARNINGS

EARNINGS_CALENDAR

IPO_CALENDAR

Forex

CURRENCY_EXCHANGE_RATE

FX_INTRADAY

FX_DAILY

FX_WEEKLY

FX_MONTHLY

Cryptocurrency

CURRENCY_EXCHANGE_RATE

DIGITAL_CURRENCY_DAILY

DIGITAL_CURRENCY_WEEKLY

DIGITAL_CURRENCY_MONTHLY

Technical Indicators (50+)

Economic Indicators

REAL_GDP

CPI

INFLATION

UNEMPLOYMENT

FEDERAL_FUNDS_RATE

TREASURY_YIELD

Alpha Intelligence

NEWS_SENTIMENT

TOP_GAINERS_LOSERS

INSIDER_TRANSACTIONS

ANALYTICS_FIXED_WINDOW

Rate Limits

Important: Rate limits are IP-based, not key-based.

Common Tasks

Task: Get Daily Stock Data

def get_daily_data(symbol: str, full: bool = False) -> dict:
    """Get daily OHLCV data."""
    response = requests.get(BASE_URL, params={
        "function": "TIME_SERIES_DAILY",
        "symbol": symbol,
        "outputsize": "full" if full else "compact",
        "apikey": API_KEY
    })
    return response.json().get("Time Series (Daily)", {})

# Example
data = get_daily_data("AAPL")
latest = list(data.items())[0]
print(f"{latest[0]}: Close ${latest[1]['4. close']}")

Task: Get Technical Indicator

def get_rsi(symbol: str, period: int = 14) -> dict:
    """Get RSI indicator values."""
    response = requests.get(BASE_URL, params={
        "function": "RSI",
        "symbol": symbol,
        "interval": "daily",
        "time_period": period,
        "series_type": "close",
        "apikey": API_KEY
    })
    return response.json().get("Technical Analysis: RSI", {})

# Example
rsi = get_rsi("AAPL")
latest_rsi = list(rsi.values())[0]["RSI"]
print(f"AAPL RSI(14): {latest_rsi}")

Task: Get Company Overview

def get_company_overview(symbol: str) -> dict:
    """Get comprehensive company information."""
    response = requests.get(BASE_URL, params={
        "function": "OVERVIEW",
        "symbol": symbol,
        "apikey": API_KEY
    })
    data = response.json()

    return {
        "name": data.get("Name"),
        "description": data.get("Description"),
        "sector": data.get("Sector"),
        "industry": data.get("Industry"),
        "market_cap": data.get("MarketCapitalization"),
        "pe_ratio": data.get("PERatio"),
        "dividend_yield": data.get("DividendYield"),
        "eps": data.get("EPS"),
        "52_week_high": data.get("52WeekHigh"),
        "52_week_low": data.get("52WeekLow"),
        "beta": data.get("Beta")
    }

Task: Get Forex Rate

def get_forex_rate(from_currency: str, to_currency: str) -> dict:
    """Get currency exchange rate."""
    response = requests.get(BASE_URL, params={
        "function": "CURRENCY_EXCHANGE_RATE",
        "from_currency": from_currency,
        "to_currency": to_currency,
        "apikey": API_KEY
    })
    return response.json().get("Realtime Currency Exchange Rate", {})

# Example
rate = get_forex_rate("USD", "EUR")
print(f"USD/EUR: {rate['5. Exchange Rate']}")

Task: Get Crypto Price

def get_crypto_price(symbol: str, market: str = "USD") -> dict:
    """Get cryptocurrency price."""
    response = requests.get(BASE_URL, params={
        "function": "CURRENCY_EXCHANGE_RATE",
        "from_currency": symbol,
        "to_currency": market,
        "apikey": API_KEY
    })
    data = response.json().get("Realtime Currency Exchange Rate", {})
    return {
        "symbol": symbol,
        "price": data.get("5. Exchange Rate"),
        "last_updated": data.get("6. Last Refreshed")
    }

# Example
btc = get_crypto_price("BTC")
print(f"BTC: ${float(btc['price']):,.2f}")

Task: Get News Sentiment

def get_news_sentiment(tickers: str = None, topics: str = None) -> list:
    """Get AI-powered news sentiment analysis."""
    params = {
        "function": "NEWS_SENTIMENT",
        "apikey": API_KEY
    }
    if tickers:
        params["tickers"] = tickers
    if topics:
        params["topics"] = topics

    response = requests.get(BASE_URL, params=params)
    return response.json().get("feed", [])

# Example
news = get_news_sentiment(tickers="AAPL")
for article in news[:3]:
    sentiment = article.get("overall_sentiment_label", "N/A")
    print(f"{article['title'][:50]}... [{sentiment}]")

Task: Get Economic Indicators

def get_economic_indicator(indicator: str) -> dict:
    """Get US economic indicator data."""
    response = requests.get(BASE_URL, params={
        "function": indicator,
        "apikey": API_KEY
    })
    return response.json()

# Examples
gdp = get_economic_indicator("REAL_GDP")
cpi = get_economic_indicator("CPI")
unemployment = get_economic_indicator("UNEMPLOYMENT")
fed_rate = get_economic_indicator("FEDERAL_FUNDS_RATE")

Task: Get Earnings Calendar

def get_earnings_calendar(horizon: str = "3month") -> list:
    """Get upcoming earnings releases."""
    import csv
    from io import StringIO

    response = requests.get(BASE_URL, params={
        "function": "EARNINGS_CALENDAR",
        "horizon": horizon,  # 3month, 6month, 12month
        "apikey": API_KEY
    })

    # Returns CSV format
    reader = csv.DictReader(StringIO(response.text))
    return list(reader)

# Example
earnings = get_earnings_calendar()
for e in earnings[:5]:
    print(f"{e['symbol']}: {e['reportDate']}")

Error Handling

def safe_api_call(params: dict) -> dict:
    """Make API call with error handling."""
    params["apikey"] = API_KEY

    try:
        response = requests.get(BASE_URL, params=params)
        data = response.json()

        # Check for rate limit
        if "Note" in data:
            print(f"Rate limit: {data['Note']}")
            return {}

        # Check for error message
        if "Error Message" in data:
            print(f"API Error: {data['Error Message']}")
            return {}

        # Check for information message (often rate limit)
        if "Information" in data:
            print(f"Info: {data['Information']}")
            return {}

        return data

    except Exception as e:
        print(f"Request error: {e}")
        return {}

Free vs Premium Features

Free Tier Includes

• 25 requests per day
• 5 requests per minute
• Historical time series (20+ years)
• 50+ technical indicators
• Fundamental data
• Forex and crypto
• Economic indicators
• News sentiment

Premium Required

• Unlimited daily requests
• Adjusted time series
• Realtime US market data
• 15-minute delayed data
• Insider transactions
• Advanced analytics
• Priority support

Best Practices

1. Cache responses - Data doesn't change frequently
2. Use compact outputsize - Unless you need full history
3. Batch requests wisely - 25/day limit is strict
4. Handle rate limits - Check for "Note" key in response
5. Use pandas output - With alpha_vantage package
6. Store historical data - Avoid re-fetching same data

Installation

# Official Python wrapper
pip install alpha_vantage pandas

# For async support
pip install aiohttp

Usage with Pandas

from alpha_vantage.timeseries import TimeSeries
from alpha_vantage.techindicators import TechIndicators
import pandas as pd

# Initialize with pandas output
ts = TimeSeries(key=API_KEY, output_format='pandas')
ti = TechIndicators(key=API_KEY, output_format='pandas')

# Get daily data
data, meta = ts.get_daily(symbol='AAPL', outputsize='compact')

# Get indicators
sma, _ = ti.get_sma(symbol='AAPL', interval='daily', time_period=20)
rsi, _ = ti.get_rsi(symbol='AAPL', interval='daily', time_period=14)

# Combine
analysis = data.join([sma, rsi])

Related Skills

• finnhub-api

  - Real-time quotes and news

• twelvedata-api

  - More indicators, better rate limits

• fmp-api

  - Fundamental analysis focus

References

• Official Documentation
• Python Package
• API Support