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Ingest real-time financial data using WebSocket
Set up a data pipeline to get data from different financial APIs
cloud
mst
self_hosted
finance
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data pipeline

import CreateHypertableStocks from "versionContent/_partials/_create-hypertable-twelvedata-stocks.mdx"; import GraphOhlcv from "versionContent/_partials/_graphing-ohlcv-data.mdx";

Ingest real-time financial data using WebSocket

This tutorial shows you how to ingest real-time time-series data into TimescaleDB using a websocket connection. The tutorial sets up a data pipeline to ingest real-time data from our data partner, Twelve Data. Twelve Data provides a number of different financial APIs, including stock, cryptocurrencies, foreign exchanges, and ETFs. It also supports websocket connections in case you want to update your database frequently. With websockets, you need to connect to the server, subscribe to symbols, and you can start receiving data in real-time during market hours.

When you complete this tutorial, you'll have a data pipeline set up that ingests real-time financial data into your Timescale.

This tutorial uses Python and the API wrapper library provided by Twelve Data.

Prerequisites

Before you begin, make sure you have:

  • Signed up for a free Timescale account.
  • Downloaded the file that contains your Timescale service credentials such as <HOST>, <PORT>, and <PASSWORD>. Alternatively, you can find these details in the Connection Info section for your service.
  • Installed Python 3
  • Signed up for Twelve Data. The free tier is perfect for this tutorial.
  • Made a note of your Twelve Data API key.

When you connect to the Twelve Data API through a websocket, you create a persistent connection between your computer and the websocket server. You set up a Python environment, and pass two arguments to create a websocket object and establish the connection.

Set up a new Python environment

Create a new Python virtual environment for this project and activate it. All the packages you need to complete for this tutorial are installed in this environment.

Setting up a new Python environment

  1. Create and activate a Python virtual environment:

    virtualenv env
source env/bin/activate

  2. Install the Twelve Data Python wrapper library with websocket support. This library allows you to make requests to the API and maintain a stable websocket connection.

    pip install twelvedata websocket-client

  3. Install Psycopg2 so that you can connect the TimescaleDB from your Python script:

    pip install psycopg2-binary

Create the websocket connection

A persistent connection between your computer and the websocket server is used to receive data for as long as the connection is maintained. You need to pass two arguments to create a websocket object and establish connection.

Websocket arguments

  • on_event

    This argument needs to be a function that is invoked whenever there's a new data record is received from the websocket:

    def on_event(event):
    print(event) # prints out the data record (dictionary)

    This is where you want to implement the ingestion logic so whenever there's new data available you insert it into the database.

  • symbols

    This argument needs to be a list of stock ticker symbols (for example, MSFT) or crypto trading pairs (for example, BTC/USD). When using a websocket connection you always need to subscribe to the events you want to receive. You can do this by using the symbols argument or if your connection is already created you can also use the subscribe() function to get data for additional symbols.

Connecting to the websocket server

  1. Create a new Python file called websocket_test.py and connect to the Twelve Data servers using the <YOUR_API_KEY>:

       import time
   from twelvedata import TDClient

    messages_history = []

    def on_event(event):
     print(event) # prints out the data record (dictionary)
     messages_history.append(event)

   td = TDClient(apikey="<YOUR_API_KEY>")
   ws = td.websocket(symbols=["BTC/USD", "ETH/USD"], on_event=on_event)
   ws.subscribe(['ETH/BTC', 'AAPL'])
   ws.connect()
   while True:
   print('messages received: ', len(messages_history))
   ws.heartbeat()
   time.sleep(10)

  2. Run the Python script:

    python websocket_test.py

  3. When you run the script, you receive a response from the server about the status of your connection:

    {'event': 'subscribe-status',
 'status': 'ok',
 'success': [
        {'symbol': 'BTC/USD', 'exchange': 'Coinbase Pro', 'mic_code': 'Coinbase Pro', 'country': '', 'type': 'Digital Currency'},
        {'symbol': 'ETH/USD', 'exchange': 'Huobi', 'mic_code': 'Huobi', 'country': '', 'type': 'Digital Currency'}
    ],
 'fails': None
}

    When you have established a connection to the websocket server, wait a few seconds, and you can see data records, like this:

    {'event': 'price', 'symbol': 'BTC/USD', 'currency_base': 'Bitcoin', 'currency_quote': 'US Dollar', 'exchange': 'Coinbase Pro', 'type': 'Digital Currency', 'timestamp': 1652438893, 'price': 30361.2, 'bid': 30361.2, 'ask': 30361.2, 'day_volume': 49153}
{'event': 'price', 'symbol': 'BTC/USD', 'currency_base': 'Bitcoin', 'currency_quote': 'US Dollar', 'exchange': 'Coinbase Pro', 'type': 'Digital Currency', 'timestamp': 1652438896, 'price': 30380.6, 'bid': 30380.6, 'ask': 30380.6, 'day_volume': 49157}
{'event': 'heartbeat', 'status': 'ok'}
{'event': 'price', 'symbol': 'ETH/USD', 'currency_base': 'Ethereum', 'currency_quote': 'US Dollar', 'exchange': 'Huobi', 'type': 'Digital Currency', 'timestamp': 1652438899, 'price': 2089.07, 'bid': 2089.02, 'ask': 2089.03, 'day_volume': 193818}
{'event': 'price', 'symbol': 'BTC/USD', 'currency_base': 'Bitcoin', 'currency_quote': 'US Dollar', 'exchange': 'Coinbase Pro', 'type': 'Digital Currency', 'timestamp': 1652438900, 'price': 30346.0, 'bid': 30346.0, 'ask': 30346.0, 'day_volume': 49167}

    Each price event gives you multiple data points about the given trading pair such as the name of the exchange, and the current price. You can also occasionally see heartbeat events in the response; these events signal the health of the connection over time. At this point the websocket connection is working successfully to pass data.

To ingest the data into your Timescale service, you need to implement the on_event function.

After the websocket connection is set up, you can use the on_event function to ingest data into the database. This is a data pipeline that ingests real-time financial data into your Timescale service.

Stock trades are ingested in real-time Monday through Friday, typically during normal trading hours of the New York Stock Exchange (9:30 AM to 4:00 PM EST).

When you ingest data into a transactional database like Timescale, it is more efficient to insert data in batches rather than inserting data row-by-row. Using one transaction to insert multiple rows can significantly increase the overall ingest capacity and speed of your Timescale database.

Batching in memory

A common practice to implement batching is to store new records in memory first, then after the batch reaches a certain size, insert all the records from memory into the database in one transaction. The perfect batch size isn't universal, but you can experiment with different batch sizes (for example, 100, 1000, 10000, and so on) and see which one fits your use case better. Using batching is a fairly common pattern when ingesting data into TimescaleDB from Kafka, Kinesis, or websocket connections.

You can implement a batching solution in Python with Psycopg2. You can implement the ingestion logic within the on_event function that you can then pass over to the websocket object.

This function needs to:

  1. Check if the item is a data item, and not websocket metadata.
  2. Adjust the data so that it fits the database schema, including the data types, and order of columns.
  3. Add it to the in-memory batch, which is a list in Python.
  4. If the batch reaches a certain size, insert the data, and reset or empty the list.

Ingesting data in real-time

  1. Update the Python script that prints out the current batch size, so you can follow when data gets ingested from memory into your database. Use the <HOST>, <PASSWORD>, and <PORT> details for the Timescale service where you want to ingest the data and your API key from Twelve Data:

    import time
import psycopg2

from twelvedata import TDClient
from psycopg2.extras import execute_values
from datetime import datetime

class WebsocketPipeline():
    # name of the hypertable
    DB_TABLE = "stocks_real_time"

    # columns in the hypertable in the correct order
    DB_COLUMNS=["time", "symbol", "price", "day_volume"]

    # batch size used to insert data in batches
    MAX_BATCH_SIZE=100

    def __init__(self, conn):
        """Connect to the Twelve Data web socket server and stream
        data into the database.

        Args:
            conn: psycopg2 connection object
        """
        self.conn = conn
        self.current_batch = []
        self.insert_counter = 0

    def _insert_values(self, data):
        if self.conn is not None:
            cursor = self.conn.cursor()
            sql = f"""
            INSERT INTO {self.DB_TABLE} ({','.join(self.DB_COLUMNS)}) 
            VALUES %s;"""
            execute_values(cursor, sql, data)
            self.conn.commit()

    def _on_event(self, event):
        """This function gets called whenever there's a new data record coming
        back from the server.

        Args:
            event (dict): data record
        """
        if event["event"] == "price":
            # data record
            timestamp = datetime.utcfromtimestamp(event["timestamp"])
            data = (timestamp, event["symbol"], event["price"], event.get("day_volume"))

            # add new data record to batch
            self.current_batch.append(data)
            print(f"Current batch size: {len(self.current_batch)}")

            # ingest data if max batch size is reached then reset the batch
            if len(self.current_batch) == self.MAX_BATCH_SIZE:
                self._insert_values(self.current_batch)
                self.insert_counter += 1
                print(f"Batch insert #{self.insert_counter}")
                self.current_batch = []
        def start(self, symbols):
            """Connect to the web socket server and start streaming real-time data 
            into the database.

            Args:
                symbols (list of symbols): List of stock/crypto symbols
            """
            td = TDClient(apikey="<YOUR_API_KEY")
            ws = td.websocket(on_event=self._on_event)
            ws.subscribe(symbols)
            ws.connect()
            while True:
               ws.heartbeat()
               time.sleep(10)
    onn = psycopg2.connect(database="tsdb", 
                        host="<HOST>", 
                        user="tsdbadmin", 
                        password="<PASSWORD>",
                        port="<PORT>")

    symbols = ["BTC/USD", "ETH/USD", "MSFT", "AAPL"]
    websocket = WebsocketPipeline(conn)
    websocket.start(symbols=symbols)
    ```

  2. Run the script:

    python websocket_test.py

You can even create separate Python scripts to start multiple websocket connections for different types of symbols, for example, one for stock, and another one for cryptocurrency prices.

Troubleshooting

If you see an error message similar to this:

2022-05-13 18:51:41,976 - ws-twelvedata - ERROR - TDWebSocket ERROR: Handshake status 200 OK

Then check that you use a proper API key received from Twelve Data.

To look at OHLCV values, the most effective way is to create a continuous aggregate. You can create a continuous aggregate to aggregate data for each hour, then set the aggregate to refresh every hour, and aggregate the last two hours' worth of data.

Creating a continuous aggregate

  1. Connect to the Timescale database tsdb that contains the Twelve Data stocks dataset.

  2. At the psql prompt, create the continuous aggregate to aggregate data every minute:

    CREATE MATERIALIZED VIEW one_hour_candle
WITH (timescaledb.continuous) AS
    SELECT
        time_bucket('1 hour', time) AS bucket,
        symbol,
        FIRST(price, time) AS "open",
        MAX(price) AS high,
        MIN(price) AS low,
        LAST(price, time) AS "close",
        LAST(day_volume, time) AS day_volume
    FROM stocks_real_time
    GROUP BY bucket, symbol;

    When you create the continuous aggregate, it refreshes by default.

  3. Set a refresh policy to update the continuous aggregate every hour, if there is new data available in the hypertable for the last two hours:

    SELECT add_continuous_aggregate_policy('one_hour_candle',
    start_offset => INTERVAL '3 hours',
    end_offset => INTERVAL '1 hour',
    schedule_interval => INTERVAL '1 hour');

Query the continuous aggregate

When you have your continuous aggregate set up, you can query it to get the OHLCV values.

Querying the continuous aggregate

  1. Connect to the Timescale database that contains the Twelve Data stocks dataset.

  2. At the psql prompt, use this query to select all AAPL OHLCV data for the past 5 hours, by time bucket:

    SELECT * FROM one_hour_candle
WHERE symbol = 'AAPL' AND bucket >= NOW() - INTERVAL '5 hours'
ORDER BY bucket;

    The result of the query looks like this:

             bucket         | symbol  |  open   |  high   |   low   |  close  | day_volume
------------------------+---------+---------+---------+---------+---------+------------
 2023-05-30 08:00:00+00 | AAPL   | 176.31 | 176.31 |    176 | 176.01 |           
 2023-05-30 08:01:00+00 | AAPL   | 176.27 | 176.27 | 176.02 |  176.2 |           
 2023-05-30 08:06:00+00 | AAPL   | 176.03 | 176.04 | 175.95 |    176 |           
 2023-05-30 08:07:00+00 | AAPL   | 175.95 |    176 | 175.82 | 175.91 |           
 2023-05-30 08:08:00+00 | AAPL   | 175.92 | 176.02 |  175.8 | 176.02 |           
 2023-05-30 08:09:00+00 | AAPL   | 176.02 | 176.02 |  175.9 | 175.98 |           
 2023-05-30 08:10:00+00 | AAPL   | 175.98 | 175.98 | 175.94 | 175.94 |           
 2023-05-30 08:11:00+00 | AAPL   | 175.94 | 175.94 | 175.91 | 175.91 |           
 2023-05-30 08:12:00+00 | AAPL   |  175.9 | 175.94 |  175.9 | 175.94 |

You can visualize the OHLCV data that you created using the queries in Grafana.