Real-time financial data streaming using WebSockets

midasSSS — Sun, 20 Sep 2020 14:28:11 +0000

This short article was written to cover the lack of any tutorial of how to simply begin receiving stocks, forex, and crypto quotes in under 5 minutes. We will mainly focus on Python implementation, however, the same logic can be easily extended to other languages.

WebSockets might be a crucial part of the apps which rely on accurate real-time data, such as chart application, portfolio management, and algorithmic trading systems.

Preparation

We will be using the service Twelve Data. Make sure to have your API Key upgraded to be eligible for the usage of WebSockets.

For python users, install the library pip install twelvedata - GitHub.

Python

First things first, define the function which will be used when new data is received.

def on_event(e):
    print(e)

This function should perform any consecutive actions according to your logic: update the price, update a record, display the changes, etc.

Remember that all data received is in json format or to be more precise a dict object.

The main block should initialize the TDClient object and perform the consecutive connection to the server.

from twelvedata import TDClient

td = TDClient('your_api_key')

ws = td.websocket(symbols=['BTC/USD', 'AAPL', 'EUR/USD'], on_event=on_event)
ws.connect()
ws.keep_alive()

WebSocket supports the following methods:
subscribe - new symbols to be included.
unsubscribe - remove old symbols from the subscription.
reset - unsubscribe from all symbols.
connect - open a new connection with the server.
disconnect - close existing connection with the server.
keep_alive - run an infinite loop to receive all the data.

Response

In response, you will be getting an object with basic meta-information, tick price, and daily volume. E.g:

{'event': 'price', 'symbol': 'BTC/USD', 'currency_base': 'Bitcoin', 'currency_quote': 'US Dollar', 'exchange': 'Binance', 'type': 'Digital Currency', 'timestamp': 1600595462, 'price': 10964.8, 'day_volume': 38279}

Do not forget about market open hours, for instance over the weekends only crypto markets are opened.

The average latency is about ~150ms, which makes the data reliable and applicable to any usage. Moreover, the single format allows flexibility in streaming data from across different types of assets.
Now you can easily use it for your application, e.g. charting.

Free Market Data for Python using Twelve Data API

midasSSS — Sun, 06 Sep 2020 14:48:14 +0000

What is Twelve Data API?

Twelve Data is a company that provides high-quality financial market data. The main features are the variety of data (stocks, forex, crypto, indices, etfs), a large number of historical entries, and support of over a hundred technical indicators.

Nevertheless, Twelve Data allows us to begin using data at no cost without time constraints.

Getting Started

The Twelve Data API provides the following features:

OHLCV values in real-time and historical retrospective.
Company data, such as earnings.
Stream data into your app through WebSocket

To begin install the Python library

pip install twelvedata

You will also need an API key that you can obtain in seconds here.

Historical and Real-Time quotes

To gather any kind of data you have to call the TDClient module. Below is a simple example to retrieve GOOG stock OHLCV values at the weekly interval:

from twelvedata import TDClient

td = TDClient(my_api_key)
ts = td.time_series(symbol="GOOG", interval="1week").as_pandas()

This will output something like this:

By the way, there are a lot of parameters that might be passed to time_series, which makes it possible to retrieve any kind of data at any intervals and dates.

Plot Charts

There are two types of charting available. The first one is plotting the static charts using matplotlib module. However, my fav is the dynamic chart based on plotly library that allows the build complex charts even with technical indicators.

ts = td.time_series(symbol="GOOG", interval="1week")
ts.with_aroon().with_stoch().with_ema(time_period=24).as_plotly_figure().show()

Export data

Apart from pandas.Dataframe you may also export data in json and csv formats.

ts = td.time_series(symbol="GOOG", interval="1week")
ts.as_json()
ts.as_csv()

Fundamentals

Similar logic applies when you want to request company fundamentals.

from twelvedata import TDClient

td = TDClient(my_api_key)
earnings = td.get_earnings(symbol="GOOG", outputsize=30)
earnings.as_pandas()

#             eps_estimate  eps_actual  difference  surprise_prc
# date                                                          
# 2020-07-30          8.34       10.13        1.79         21.46
# 2020-04-28         10.38        9.87       -0.51         -4.91
# 2020-02-03         12.59       15.35        2.76         21.92
# 2019-10-28         12.46       10.12       -2.34        -18.78
# 2019-07-25         11.33       14.21        2.88         25.42
# 2019-04-29         10.63        9.50       -1.13        -10.63
# 2019-02-04         10.82       12.77        1.95         18.02
# 2018-10-25         10.40       13.06        2.66         25.58
# 2018-07-23          9.54        4.54       -5.00        -52.41
# 2018-04-23          9.32       13.33        4.01         43.03
# 2018-02-01          9.96        9.70       -0.26         -2.61
# 2017-10-26          8.33        9.57        1.24         14.89
# 2017-07-24          4.47        5.01        0.54         12.08
# 2017-04-27          7.39        7.73        0.34          4.60

Wrapping up

At this post, we made a brief overview of some features that the Twelve Data Python library provides. In the next article, we will cover the topic of streaming market data with WebSockets.
Meanwhile, check out the GitHub repo.

Stay safe!

DEV Community: midasSSS