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Twelve Data Market Data Basics - Time Series, Quotes, and Prices

The foundation of any trading or investment strategy lies in reliable market data. The Twelve Data Market Data Connector provides three core operations that form the basis of market analysis: time_series for historical data, quote for comprehensive snapshots, and price for simple real-time prices. This guide walks you through each operation with step-by-step interface instructions and practical examples.

Understanding the Core Operations​

Time Series - Historical OHLCV Data​

The time_series operation provides historical and intraday price data in OHLCV (Open, High, Low, Close, Volume) format. This is essential for technical analysis, backtesting strategies, and understanding price movements over time.

Key Features:

  • Support for intraday intervals (1 minute to 4 hours)
  • Daily, weekly, and monthly data
  • Custom date ranges and output sizes
  • Multi-symbol batch requests
  • Automatic timezone handling

Quote - Real-time Market Snapshots​

The quote operation delivers comprehensive real-time market information for a single symbol, including current prices, trading volumes, and market statistics.

Key Features:

  • Real-time price and volume data
  • Bid/ask spreads and market depth
  • Trading session information
  • 52-week high/low data
  • Market capitalization and averages

Price - Simple Real-time Prices​

The price operation provides the most basic real-time price information - just the current price for quick checks or simple monitoring.

Key Features:

  • Minimal response size
  • Fastest API response
  • Current price only
  • Perfect for price monitoring dashboards

Step-by-Step Usage Guide​

Getting Historical Time Series Data​

Step 1: Select the Worker

  • In your workflow interface, add a new worker
  • Choose worker type: twelve_data
  • Select operation: time_series

Step 2: Configure Basic Parameters

  • api_key: (Optional) Your personal Twelve Data API key
  • symbol: Enter the stock ticker, forex pair, or crypto symbol (e.g., AAPL, EUR/USD, BTC/USD)
  • interval: Choose your desired time interval:
    • Intraday: 1min, 5min, 15min, 30min, 45min, 1h, 2h, 4h
    • Daily: 1day
    • Weekly: 1week
    • Monthly: 1month

Step 3: Set Data Parameters

  • outputsize: Number of data points (1-5000, default 100)
  • start_date: Beginning date for historical data (YYYY-MM-DD format)
  • end_date: End date for historical data (YYYY-MM-DD format)
  • timezone: Target timezone (e.g., America/New_York, UTC)

Step 4: Advanced Options

  • order: Data order (ASC for chronological, DESC for reverse)

Example Configuration for Daily AAPL Data:

{
  "operation": "time_series",
  "symbol": "AAPL",
  "interval": "1day",
  "outputsize": 250,
  "start_date": "2024-01-01",
  "end_date": "2024-10-31",
  "timezone": "America/New_York"
}

Output Structure:

{
  "operation": "time_series",
  "result": {
    "symbol": "AAPL",
    "interval": "1day",
    "values": [
      {
        "datetime": "2024-10-31",
        "open": 225.59,
        "high": 229.83,
        "low": 225.17,
        "close": 225.91,
        "volume": 64370100
      }
    ]
  }
}

Getting Real-time Quote Data​

Step 1: Select the Worker

  • Add worker type: twelve_data
  • Select operation: quote

Step 2: Configure Parameters

  • api_key: (Optional) Your personal API key
  • symbol: The symbol to quote (e.g., MSFT, GBP/USD)

Example Configuration:

operation: quote
symbol: MSFT

Output Structure:

{
  "operation": "quote",
  "quote": {
    "symbol": "MSFT",
    "name": "Microsoft Corporation",
    "price": 415.26,
    "change": 5.26,
    "change_percent": 1.28,
    "previous_close": 410.00,
    "open": 412.50,
    "day_high": 418.50,
    "day_low": 410.00,
    "volume": 24567890,
    "average_volume": 23456789,
    "market_cap": 3090000000000,
    "pe_ratio": 32.45,
    "week_52_high": 468.35,
    "week_52_low": 362.90,
    "ytd_change": 15.67,
    "last_trade_time": "2024-10-31 16:00:00"
  }
}

Getting Simple Price Data​

Step 1: Select the Worker

  • Add worker type: twelve_data
  • Select operation: price

Step 2: Configure Parameters

  • api_key: (Optional) Your personal API key
  • symbol: The symbol to check (e.g., GOOGL, ETH/USD)

Example Configuration:

operation: price
symbol: GOOGL

Output Structure:

{
  "operation": "price",
  "symbol": "GOOGL",
  "price": 175.23
}

Practical Applications and Examples​

Building a Price Monitoring Dashboard​

Workflow Setup:

  1. Worker 1: Time Series for historical context

    • operation: time_series
    • symbol: SPY
    • interval: 1day
    • outputsize: 30

  2. Worker 2: Real-time Quote for current data

    • operation: quote
    • symbol: SPY

  3. Worker 3: Price checks for related assets

    • operation: price
    • symbol: QQQ,VXX

Use Case: Monitor S&P 500 ETF with context and related volatility products.

Intraday Trading Analysis​

Configuration for Scalping:

{
  "operation": "time_series",
  "symbol": "EUR/USD",
  "interval": "1min",
  "outputsize": 100,
  "timezone": "UTC"
}

Configuration for Swing Trading:

{
  "operation": "time_series",
  "symbol": "BTC/USD",
  "interval": "4h",
  "outputsize": 200,
  "timezone": "UTC"
}

Multi-Asset Portfolio Tracking​

Batch Symbol Request:

{
  "operation": "time_series",
  "symbol": "AAPL,MSFT,GOOGL,AMZN",
  "interval": "1day",
  "outputsize": 5
}

Real-time Portfolio Check:

  • Use multiple price workers for instant portfolio valuation
  • Combine with quote for detailed analysis of specific holdings

Best Practices and Tips​

Optimizing Time Series Requests​

  • Interval Selection: Match your strategy timeframe

    • Scalping: 1-5 minute intervals
    • Day trading: 15-60 minute intervals
    • Swing trading: 4-hour to daily
    • Position trading: Daily to weekly

  • Output Size Management: Balance data needs with API limits

    • Use smaller outputsize for frequent updates
    • Request larger datasets for backtesting
    • Consider pagination for very large datasets

  • Date Range Optimization: Specify precise ranges to reduce data transfer

    • Use start_date/end_date for specific periods
    • Leave blank for most recent data

Real-time Data Considerations​

  • Quote vs Price: Use quote for comprehensive data, price for speed
  • Update Frequency: Respect API rate limits (5 calls/minute on shared keys)
  • Caching Strategy: Cache frequently accessed data to reduce API calls

Error Handling​

  • Invalid Symbols: Verify symbol format before requests
  • Rate Limits: Monitor usage and implement retry logic
  • Network Issues: Handle timeouts gracefully with fallback data

Common Use Cases​

Technical Analysis Platform​

Combine time series data with technical indicators:

  1. Fetch historical data with time_series
  2. Calculate indicators using the indicator operation
  3. Generate signals based on indicator crossovers

Algorithmic Trading Bot​

Build automated strategies:

  1. Monitor prices with price for fast updates
  2. Get detailed quotes with quote for entry signals
  3. Use historical data for strategy validation

Financial Dashboard​

Create comprehensive market views:

  1. Multi-symbol time series for charts
  2. Real-time quotes for watchlists
  3. Price feeds for portfolio tracking

Backtesting Engine​

Develop and test strategies:

  1. Request extensive historical data
  2. Apply technical analysis
  3. Simulate trades with realistic data

Integration Examples​

With Technical Analysis​

Sequential Workflow:

  • Step 1: Fetch price data
    • Worker: twelve_data (time_series)
  • Step 2: Calculate indicators
    • Worker: technical_analysis_worker
    • Input: Step 1 result
  • Step 3: Generate signals
    • Worker: signal_generator
    • Input: Step 2 indicators

With Portfolio Management​

Parallel Processing:

  • Price Monitor: Continuous price updates for all holdings
  • Quote Analyzer: Periodic quote calls for detailed analysis
  • Historical Tracker: Weekly time_series for performance review

Troubleshooting Common Issues​

No Data Returned​

  • Check Symbol: Verify symbol format and exchange
  • Validate Dates: Ensure date range contains trading data
  • Confirm API Key: Test with a simple symbol first

Rate Limit Errors​

  • Use Personal Key: Higher limits with personal API keys
  • Implement Delays: Add delays between frequent requests
  • Batch Requests: Combine multiple symbols in single calls

Timezone Issues​

  • Specify Timezone: Always set explicit timezone for consistency
  • Convert Locally: Handle timezone conversion in your application
  • Document Assumptions: Note timezone behavior in your workflows

Performance Optimization​

API Efficiency​

  • Batch Symbols: Use comma-separated lists for multiple assets
  • Minimal Data: Request only needed fields and periods
  • Cache Results: Store frequently accessed data locally

Workflow Design​

  • Parallel Processing: Run independent requests simultaneously
  • Conditional Logic: Only fetch detailed data when needed
  • Error Recovery: Implement retry mechanisms for reliability

Conclusion​

Mastering the core market data operations - time_series, quote, and price - provides the foundation for any financial analysis or trading system. By understanding how to configure these operations through the interface and applying best practices for data management, you can build robust and efficient financial workflows.

The key to success lies in selecting the right operation for your specific needs, optimizing your requests for performance, and handling data appropriately for your analysis requirements. With these core operations mastered, you're ready to explore more advanced features like technical indicators and fundamental data.

For more advanced operations, check out our other guides covering technical indicators, fundamental analysis, and specialized market data.