truedata-ws 3.0.2

Truedata's Official Python Package

Official Python repository for TrueData (Market Data APIs)

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This Python library attempts to make it easy for you to connect to TrueData Market Data Apis, thereby allowing you to concentrate on startegy development, while this library works to get you all the data you need from the TrueData backend both for Real Time & Historical.

Please make sure you follow us on our Telegram channel where we push a lot of information with regards to API updates, implementation ideas, tips & tricks to use this library & raw feed, etc...

• TrueData Market Data API - Telegram Channel
• TrueData Market Data APIs - Website

We have also built a sandbox environmemt for testing the raw feed. Please feel free to use this environment to check/test/compare your results with the raw data feed (real time & historical).

• TrueData Market Data API - Sandbox Environment

We are trying to improve this library continuously and feedback for the same is welcome.

What have we covered so far ?

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WebSocket APIs

• Live data (Streaming Ticks) - Enabled by Default
• Live Data (Streaming 1 min bars) - Needs to be enabled from our backend
• Live Data (Streaming 5 min bars) - Needs to be enabled from our backend
• Historical APIs (Deprecated) - Removed from this library

REST APIs

• Historical Data

Getting Started

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For beginners

• Installing the truedata-ws library from PyPi

python3 -m pip install truedata_ws

or

pip3 install truedata_ws

Minimum Requirements

The minimum required versions are:-

- Python >= 3.7

Dependancies:-

All these dependancies should get installed automatically when you install the truedata_ws library. In case of an issue, make sure you meet the requirements as mentioned below.

- websocket-client>=0.57.0
- colorama>=0.4.3
- python-dateutil>=2.8.1
- pandas>=1.0.3
- setuptools>=50.3.2
- requests>=2.25.0

Connecting / Logging in

• Connecting / Logging in (Both Real time & Historical data feed subscriptions)

from truedata_ws.websocket.TD import TD
td_obj = TD('<enter_your_login_id>', '<enter_your_password>')
# This connects you to the default real time port which is 8082 & the REST History feed. 
# If you have been authourised on another live port please enter another parameter
# Example
# td_obj = TD('<enter_your_login_id>', '<enter_your_password>', live_port=8084)

• Connecting / Logging in (For Historical Data Subscription Only)

from truedata_ws.websocket.TD import TD
td_obj = TD('<enter_your_login_id>', '<enter_your_password>', live_port=None)

• Connecting / Logging in (For Real time Data Subscription Only)

from truedata_ws.websocket.TD import TD
td_obj = TD('<enter_your_login_id>', '<enter_your_password>', historical_port=None)
# There is no historical_port as such. We continue to use this argument for the time being.
# This needs to be None if you are not subscribed to the Historical data feed.

Automatic Reconnection of Real Time Streaming Feed

In case of a Websocket disconnection, the library will check for the internet connection and once the connetion is steady will try to re-connect the Websocket, automatically.

Once the websocket connection is reestablished, the library will automatically re-subscribe the symbols & restart the live data seamlessly.

Real Time Data Streaming (Live)

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Please note that you can use the same code as below for both Tick (Defauly) & Bar streaming, depending upon what is activted from the backend.

• Starting Live Data For a Single Symbol

req_ids = td_obj.start_live_data(['<enter_symbol>'])
# Example:
# req_id = start_live_data(['CRUDEOIL-I'])
# This returns a single element list that can be used later to reference the data

• Starting Live Data For Multiple symbols

req_ids = td_obj.start_live_data(['<symbol_1>', '<symbol_2>', '<symbol_3>', ...])
# Example:
# req_ids = td_obj.start_live_data(['CRUDEOIL-I', 'BANKNIFTY-I', 'RELIANCE', 'ITC'])
# This returns a list that can be used to reference data later

• Accessing Touchline Data

The touchline data is useful post market hours as this provides the last updates / settlement updates / snap quotes.

Please note that it is not recommended to use this during market hours as all the fields of the touchline data already form a part of the real time market data feed.

import time
time.sleep(1)
# You need to wait until for 1 sec for all of the touchline data to populate
for req_id in req_ids:
    print(td_obj.touchline_data[req_id])

• Sample code for testing Real Time Live Market Data

The Real Time Market data stream contains all the fields from the raw feed and does calculations to provide additional fields like "change", "change %"", "oi change" & "oi change %".

from copy import deepcopy
import time

symbols = ['CRUDEOIL-I', 'BANKNIFTY-I', 'RELIANCE', 'ITC']
req_ids = td_obj.start_live_data(symbols)
live_data_objs = {}
time.sleep(1)
for req_id in req_ids:    
    live_data_objs[req_id] = deepcopy(td_obj.live_data[req_id])

while True:
    for req_id in req_ids:
        if live_data_objs[req_id] != td_obj.live_data[req_id]:
            print(td_obj.live_data[req_id])
            live_data_objs[req_id] = deepcopy(td_obj.live_data[req_id])

Tips for Beginners - Sample code to get any field in Real Time

Everything is an object You can get any data field you need from this object... Say, you only need the LTP in Real time, then just use '.ltp', like so...

td_app.live_data[req_id].ltp

Here is a sample code to get the real time data in the following format:-

Symbol > LTP > Change

Try this code to understand how to use it better in your code.

req_ids = td_app.start_live_data(symbols)
live_data_objs = {}

time.sleep(1)

for req_id in req_ids:
    live_data_objs[req_id] = deepcopy(td_app.live_data[req_id])

while True:
    for req_id in req_ids:
        if not td_app.live_data[req_id] == live_data_objs[req_id]:
            print(td_app.live_data[req_id].symbol, '>',
                  td_app.live_data[req_id].ltp,'>',
                  f'{td_app.live_data[req_id].change:.2f}')
            live_data_objs[req_id] = deepcopy(td_app.live_data[req_id])

• Stopping live data

td_obj.stop_live_data(['<symbol_1>', '<symbol_2>', '<symbol_3>', ...])

• Disconnect from the WebSocket service

td_obj.disconnect()

Getting Historical data

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Historical Data is provided over REST (from the backend) using Start time, End time or Duration

• Using no parameters

hist_data_1 = td_obj.get_historic_data('BANKNIFTY-I')
# This returns 1 minute bars from the start of the present day until current time

• Using a given duration (For available duration options, please read the limitations section)

hist_data_2 = td_obj.get_historic_data('BANKNIFTY-I', duration='3 D')

• Using a specified bar_size (For available bar_size options, please read the limitations section)

hist_data_3 = td_obj.get_historic_data('BANKNIFTY-I', bar_size='30 mins')

• Using start time INSTEAD of duration

from dateutil.relativedelta import relativedelta
hist_data_4 = td_obj.get_historic_data('BANKNIFTY-I', start_time=datetime.now()-relativedelta(days=3))

• Using a specific ending time

from datetime import datetime
hist_data_5 = td_obj.get_historic_data('BANKNIFTY-I', end_time=datetime(2021, 3, 5, 12, 30))
# Any time can be given here

• Enabling / Disabling Bid Ask data with Tick Data History

If you have subscribed for Historical Bid Ask (Not activated by Default), you can control the visibility of this data depending upon your needs by using bidask = True.

Default is bidask=False > BidAsk data will not come in this case.

hist_data_6 = td_app.get_historic_data(symbol, duration='1 D', bar_size='tick', bidask=True)

IMPORTANT NOTE:

Now that we have covered the basic parameters, you can mix and match the parameters as you please... If a parameter is not specified, the defaults are as follows

end_time = datetime.now()
duration = "1 D"
bar_size = "1 min"

• Example of mix and match

hist_data_7 = td_obj.get_historic_data('BANKNIFTY-I', duration='3 D', bar_size='15 mins')

On a side note: You can convert historical data to Pandas DataFrames with a single line

import pandas as pd
df = pd.DataFrame(hist_data_1)

Get Bhavcopy

This function enables you to get the NSE & MCX bhavcopies for the day / date.

hist_data_8 = td_app.get_bhavcopy('EQ')
# hist_data_9 = td_app.get_bhavcopy('FO')
# hist_data_10 = td_app.get_bhavcopy('MCX')

When the request is sent the function checkes if the latest completed bhavcopy has arrived for that segment and if arrived automatically provides the data.

In case it has not arrived it provides the date and time of the last bhavcopy available which can also be pulled by provideing the bhavcopy date

*No complete bhavcopy found for requested date. Last available for 2021-03-19 16:46:00.*

In this case, if you need the bhavcopy of the date provided, you can get it by giving the date for which the bhavcopy is required as shown below..

hist_data_11 = td_app.get_bhavcopy('EQ', date=datetime.datetime(2021, 3, 19))

Limitations and caveats for historical data

1. If you provide both duration and start time, duration will be used and start time will be ignored.

2. If you provide neither duration nor start time, duration = "1 D" will be used

3. If you donot provide the bar size bar_size = "1 min" will be used

4. The following BAR_SIZES are available:

   • tick
   • 1 min
   • 2 mins
   • 3 mins
   • 5 mins
   • 10 mins
   • 15 mins
   • 30 mins
   • 60 mins
   • EOD

5. The following annotation can be used for DURATION:-

   • D = Days
   • W = Weeks
   • M = Months
   • Y = Years

Sample Code & Tests

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Running tests to ensure everything is in order

python3.7 -m truedata_ws run_all_tests <enter_your_login_id> <enter_your_password>

Sample Code - Historical & Real time data

1. Please copy this code and paste > run it in your python environment.
2. You need to enter your login id & password to make this code works
3. This code first downloads Historical data in 9 different ways and also shows you the commands used to get that data
4. The Historical data is then put into a pandas Dataframe also.
5. Please note that if the market is closed on the test day, you will get 'No Data Exists for ' returns for the current day's data tests.
6. Real time Live Data tests come towards the end and include printing of the touchline data followed by the live real time data stream.

from truedata_ws.websocket.TD import TD
from copy import deepcopy
from time import sleep
import pandas as pd
from datetime import datetime
from dateutil.relativedelta import relativedelta
from colorama import Style, Fore

username = '<enter_your_username>'
password = '<enter_your_password>'

symbols = ['NIFTY-I', 'RELIANCE', 'BANKNIFTY-I']

# ------- In this sample, we use the first of the above given symbols for historical data
symbol = symbols[0]
test_time = datetime.today()

#  Creating our object
td_obj = TD(username, password, live_port=8082)

print('Please wait.. Beginning Historical Data Test !')

# ------- Beginning historical data test...
hist_data_1 = td_obj.get_historic_data(f'{symbol}')
hist_data_2 = td_obj.get_historic_data(f'{symbol}', duration='3 D')
hist_data_3 = td_obj.get_historic_data(f'{symbol}', duration='3 D', bar_size='15 mins')
hist_data_4 = td_obj.get_historic_data(f'{symbol}', bar_size='30 mins')
hist_data_5 = td_obj.get_historic_data(f'{symbol}', bar_size='30 mins', start_time=test_time - relativedelta(days=3))
hist_data_6 = td_obj.get_historic_data(f'{symbol}', bar_size='EOD', duration='1 M')

# Testing tick historical data
tick_hist_data_1 = td_obj.get_historic_data(f'{symbol}', bar_size='tick')
tick_hist_data_2 = td_obj.get_historic_data(f'{symbol}', bar_size='tick', duration='3 D')
tick_hist_data_3 = td_obj.get_historic_data(f'{symbol}', bar_size='tick', start_time=test_time - relativedelta(days=3))

# Printing out the results
print(f'{Style.BRIGHT}{Fore.BLUE}------------- HIST BAR DATA TEST RESULTS -------------{Style.RESET_ALL}')
print()
print(f"{Style.BRIGHT}{Fore.BLUE}HISTDATA 1...\n"
      f"\tCommand used -> hist_data_1 = td_app.get_historic_data('{symbol}')\n"
      f"\tLENGTH OF RESULT = {len(hist_data_1)}{Style.RESET_ALL}")

for hist_point in hist_data_1[-20:]:
    print(hist_point)
print()
print(f"{Style.BRIGHT}{Fore.BLUE}HISTDATA 2...\n"
      f"\tCommand used -> hist_data_2 = td_app.get_historic_data('{symbol}', duration='3 D')\n"
      f"\tLENGTH OF RESULT = {len(hist_data_2)}{Style.RESET_ALL}")

for hist_point in hist_data_2[-20:]:
    print(hist_point)
print()
print(f"{Style.BRIGHT}{Fore.BLUE}HISTDATA 3...\n"
      f"\tCommand used -> hist_data_3 = td_app.get_historic_data('{symbol}', duration='3 D', bar_size='15 mins')\n"
      f"\tLENGTH OF RESULT = {len(hist_data_3)}{Style.RESET_ALL}")

for hist_point in hist_data_3[-20:]:
    print(hist_point)
print()
print(f"{Style.BRIGHT}{Fore.BLUE}HISTDATA 4...\n"
      f"\tCommand used -> hist_data_4 = td_app.get_historic_data('{symbol}', bar_size='30 mins')\n"
      f"\tLENGTH OF RESULT = {len(hist_data_4)}{Style.RESET_ALL}")

for hist_point in hist_data_4[-20:]:
    print(hist_point)
print()
print(f"{Style.BRIGHT}{Fore.BLUE}HISTDATA 5...\n"
      f"\tCommand used -> hist_data_5 = td_app.get_historic_data('{symbol}', bar_size='30 mins', start_time=datetime({(test_time - relativedelta(days=3)).strftime('%Y, %m, %d, %H, %M, %S').replace(' 0', ' ')}))\n"
      f"\tLENGTH OF RESULT = {len(hist_data_5)}{Style.RESET_ALL}")

for hist_point in hist_data_5[-20:]:
    print(hist_point)
print()
print(f"{Style.BRIGHT}{Fore.BLUE}HISTDATA 6...\n"
      f"\tCommand used -> hist_data_6 = td_obj.get_historic_data(f'{symbol}', bar_size='EOD', duration='1 M'))\n"
      f"\tLENGTH OF RESULT = {len(hist_data_6)}{Style.RESET_ALL}")

for hist_point in hist_data_6[-20:]:
    print(hist_point)
print()
print()
print(f'{Style.BRIGHT}{Fore.BLUE}------------- HIST TICK DATA TEST RESULTS -------------{Style.RESET_ALL}')
print()
print(f"{Style.BRIGHT}{Fore.BLUE}TICKDATA 1...\n"
      f"\tCommand used -> tick_data_1 = td_app.get_historic_data('{symbol}', bar_size='tick')\n"
      f"\tLENGTH OF RESULT = {len(tick_hist_data_1)}{Style.RESET_ALL}")

for hist_point in tick_hist_data_1[-20:]:
    print(hist_point)
print()
print(f"{Style.BRIGHT}{Fore.BLUE}TICKDATA 2...\n"
      f"\tCommand used -> tick_data_2 = td_app.get_historic_data('{symbol}', bar_size='tick', duration='3 D')\n"
      f"\tLENGTH OF RESULT = {len(tick_hist_data_2)}{Style.RESET_ALL}")

for hist_point in tick_hist_data_2[-20:]:
    print(hist_point)
print()
print(f"{Style.BRIGHT}{Fore.BLUE}TICKDATA 3...\n"
      f"\tCommand used -> tick_data_3 = td_app.get_historic_data('{symbol}', bar_size='tick', start_time=datetime({(test_time - relativedelta(days=3)).strftime('%Y, %m, %d, %H, %M, %S').replace(' 0', ' ')}))\n"
      f"\tLENGTH OF RESULT = {len(tick_hist_data_3)}{Style.RESET_ALL}")

for hist_point in tick_hist_data_3[-20:]:
    print(hist_point)

# Testing conversion to pandas dataframe
print(f'{Style.BRIGHT}{Fore.BLUE}Converting HISTDATA 1 to a Pandas DataFrame{Style.RESET_ALL}')
print(f'Command used -> df = pd.DataFrame(hist_data_1)')
df = pd.DataFrame(hist_data_1)
print(df)

print(f'{Style.BRIGHT}{Fore.BLUE}Converting TICKDATA 1 to a Pandas DataFrame{Style.RESET_ALL}')
print(f'Command used -> df = pd.DataFrame(tick_hist_data_1)')
df = pd.DataFrame(tick_hist_data_1)
print(df)

# ------- Beginning live data test...
req_ids = td_obj.start_live_data(symbols)
sleep(3)  # This is here just to give the touchline data some time to populate... (Generally takes less than 1 sec)
print(f"{Style.BRIGHT}{Fore.BLUE}Here's the touchline data...{Style.RESET_ALL}")

for req_id in req_ids:
    print(td_obj.touchline_data[req_id])
print()
print(f"{Style.BRIGHT}{Fore.BLUE}Here's the LIVE stream... Stop code execution to exit...{Style.RESET_ALL}")
sleep(2)  # This is here just to give the user time to read the instructions...
live_data_objs = {}

for req_id in req_ids:    
    live_data_objs[req_id] = deepcopy(td_obj.live_data[req_id])
while True:
    try:
        for req_id in req_ids:
            if live_data_objs[req_id] != td_obj.live_data[req_id]:
                print(td_obj.live_data[req_id])
                live_data_objs[req_id] = deepcopy(td_obj.live_data[req_id])
    except KeyboardInterrupt:
        td_obj.stop_live_data(symbols)
        td_obj.disconnect()
        exit()

Release Notes

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Version 3.0.2

• Automatic streaming websocket reconnect enabled.
• Automatic subscription of symbols & restart of live data after reconnect
• Now Avoiding server calls for repeated / duplicate streaming symbols...

Version 3.0.1

• Historical feed via Websocket - Deprecated
• Historical feed via REST - Added
• More time frames have been added
• get_bhavcopy added
• Code cleaned up to improve dependancy handling (eg. for websocket-client)

Version 0.3.11

• Refactored query_time to end_time in td_obj.get_historic_data() function.
• Refactored truedata_id to symbol_id in td_obj.touchline_data objects.
• Filled missing values in td_obj.live_data objects upon initialization.
• Added better debugging information for error reporting.
• Cleaned up the code base.

Stay Updated with the latest on this API

Please make sure you follow us on our Telegram channel where we push a lot of information with regards to API updates, implementation ideas, tips & tricks to use this library & raw feed, etc...

• TrueData Market Data API - Telegram Channel
• TrueData Market Data APIs - Website

Sandbox Environment

We have also built a sandbox environmemt for testing the raw feed. Please feel free to use this environment to check/test/compare your results with the raw data feed (real time & historical).

• TrueData Market Data API - Sandbox Environment

We are trying to improve this library continuously and feedback for the same is welcome.

:D