Twelve Data API Integration

Comprehensive financial data API providing stocks, forex, crypto, ETFs, indices, and 100+ technical indicators with excellent Python SDK support.

Quick Start

Authentication

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

# Or in .env file
TWELVEDATA_API_KEY=your_api_key

Basic Usage (Python)

import requests
import os

API_KEY = os.getenv("TWELVEDATA_API_KEY")
BASE_URL = "https://api.twelvedata.com"

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

# Example
quote = get_quote("AAPL")
print(f"AAPL: ${quote['close']} ({quote['percent_change']}%)")

Using Official SDK

from twelvedata import TDClient

td = TDClient(apikey="your_api_key")

# Get time series
ts = td.time_series(
    symbol="AAPL",
    interval="1day",
    outputsize=30
).as_pandas()

# Get quote
quote = td.quote(symbol="AAPL").as_json()

# Get with technical indicators
ts_with_indicators = td.time_series(
    symbol="AAPL",
    interval="1day",
    outputsize=50
).with_sma(time_period=20).with_rsi().as_pandas()

API Endpoints Reference

Core Data

Reference Data

Fundamental Data

Technical Indicators (100+)

All indicators cost 1 credit and are included with time_series:

Trend: SMA, EMA, WMA, DEMA, TEMA, KAMA, MAMA, T3, TRIMA, VWMA Momentum: RSI, MACD, Stochastic, Williams %R, ADX, CCI, MFI, ROC, AROON Volatility: Bollinger Bands, ATR, Keltner Channels, Donchian Volume: OBV, AD, ADOSC, VWAP

Rate Limits

Credit System:

• Standard endpoints: 1 credit per symbol
• Fundamental data: 100 credits per symbol
• Batch requests: Same cost as individual

Common Tasks

Task: Get Time Series with Pandas

from twelvedata import TDClient
import pandas as pd

td = TDClient(apikey=API_KEY)

def get_historical_data(symbol: str, days: int = 100) -> pd.DataFrame:
    """Get historical OHLCV data as DataFrame."""
    ts = td.time_series(
        symbol=symbol,
        interval="1day",
        outputsize=days
    )
    return ts.as_pandas()

# Example
df = get_historical_data("AAPL", 100)
print(df.head())

Task: Technical Analysis with Multiple Indicators

def get_technical_analysis(symbol: str) -> pd.DataFrame:
    """Get price data with technical indicators."""
    ts = td.time_series(
        symbol=symbol,
        interval="1day",
        outputsize=100
    )

    # Chain indicators
    df = (ts
        .with_sma(time_period=20)
        .with_sma(time_period=50)
        .with_rsi(time_period=14)
        .with_macd()
        .with_bbands()
        .as_pandas()
    )

    return df

# Example
analysis = get_technical_analysis("AAPL")

Task: Get Multiple Symbols (Batch)

def get_batch_quotes(symbols: list) -> dict:
    """Get quotes for multiple symbols efficiently."""
    symbol_str = ",".join(symbols)

    response = requests.get(
        f"{BASE_URL}/quote",
        params={
            "symbol": symbol_str,
            "apikey": API_KEY
        }
    )
    return response.json()

# Example: Up to 120 symbols per request
quotes = get_batch_quotes(["AAPL", "MSFT", "GOOGL", "AMZN"])

Task: Get Fundamental Data

def get_fundamentals(symbol: str) -> dict:
    """Get comprehensive fundamental data."""
    # Note: Each call costs 100 credits

    profile = requests.get(
        f"{BASE_URL}/profile",
        params={"symbol": symbol, "apikey": API_KEY}
    ).json()

    stats = requests.get(
        f"{BASE_URL}/statistics",
        params={"symbol": symbol, "apikey": API_KEY}
    ).json()

    return {
        "name": profile.get("name"),
        "sector": profile.get("sector"),
        "industry": profile.get("industry"),
        "market_cap": stats.get("statistics", {}).get("valuations_metrics", {}).get("market_capitalization"),
        "pe_ratio": stats.get("statistics", {}).get("valuations_metrics", {}).get("trailing_pe"),
        "dividend_yield": stats.get("statistics", {}).get("dividends_and_splits", {}).get("dividend_yield")
    }

Task: Get Forex Rates

def get_forex_rate(from_currency: str, to_currency: str) -> dict:
    """Get currency exchange rate."""
    response = requests.get(
        f"{BASE_URL}/exchange_rate",
        params={
            "symbol": f"{from_currency}/{to_currency}",
            "apikey": API_KEY
        }
    )
    return response.json()

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

Task: Get Crypto Data

def get_crypto_price(symbol: str, exchange: str = "Binance") -> dict:
    """Get cryptocurrency price."""
    response = requests.get(
        f"{BASE_URL}/quote",
        params={
            "symbol": f"{symbol}/USD",
            "exchange": exchange,
            "apikey": API_KEY
        }
    )
    return response.json()

# Example
btc = get_crypto_price("BTC")
print(f"BTC: ${btc['close']}")

Task: Search for Symbols

def search_symbols(query: str, show_plan: bool = False) -> list:
    """Search for stock/crypto symbols."""
    params = {
        "symbol": query,
        "apikey": API_KEY
    }
    if show_plan:
        params["show_plan"] = "true"

    response = requests.get(f"{BASE_URL}/symbol_search", params=params)
    return response.json().get("data", [])

# Example
results = search_symbols("Apple")
for r in results[:5]:
    print(f"{r['symbol']}: {r['instrument_name']}")

WebSocket Real-Time Streaming

from twelvedata import TDClient

td = TDClient(apikey=API_KEY)

def on_event(event):
    """Handle real-time price updates."""
    print(f"{event['symbol']}: ${event['price']}")

# Create WebSocket (requires Pro plan)
ws = td.websocket(
    symbols=["AAPL", "MSFT", "GOOGL"],
    on_event=on_event
)

ws.connect()
ws.keep_alive()

Error Handling

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

    try:
        response = requests.get(f"{BASE_URL}/{endpoint}", params=params)
        data = response.json()

        # Check for API errors
        if "status" in data and data["status"] == "error":
            print(f"API Error: {data.get('message', 'Unknown error')}")
            return {}

        # Check remaining credits
        credits_used = response.headers.get("api-credits-used")
        credits_left = response.headers.get("api-credits-left")
        if credits_left:
            print(f"Credits remaining: {credits_left}")

        return data

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

Free vs Premium Features

Free Tier Includes

• 8 API calls/minute, 800/day
• US stocks, forex, crypto
• Time series data (end of day)
• All technical indicators
• Basic reference data
• 1-2 years intraday history
• 30+ years daily history

Premium Required

• Higher rate limits
• International stocks (Grow+)
• WebSocket streaming (Pro+)
• Pre/post market data (Pro+)
• Extended hours trading
• Mutual funds & ETF breakdown (Enterprise)
• No daily limits

Best Practices

1. Use batch requests - Up to 120 symbols per call
2. Cache reference data - Exchanges, instruments rarely change
3. Use SDK pandas output - Easier data manipulation
4. Chain indicators - Include with time_series (1 credit total)
5. Monitor credits - Check response headers
6. Use appropriate intervals - 1day for analysis, 1min for trading

Installation

# Python SDK with all features
pip install twelvedata[matplotlib,plotly]

# Basic installation
pip install twelvedata

# For WebSocket
pip install websocket-client

Related Skills

• finnhub-api - Real-time news focus
• alphavantage-api - Alternative indicator source
• fmp-api - Fundamental analysis focus

References

• Official Documentation
• Python SDK
• Technical Indicators
• Pricing