NOTE

This is a fork of the original TvDatafeed project by StreamAlpha. This fork has live data retrieving feature implemented. More information about this will be found in the TvDatafeedLive section down below in the README.

TvDatafeed

A simple TradingView historical Data Downloader. Tvdatafeed allows downloading upto 5000 bars on any of the supported timeframe.

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Installation

This module can be installed from github repo

pip install --upgrade --no-cache-dir git+https://github.com/rongardF/tvdatafeed.git

For usage instructions, watch these videos-

v1.2 tutorial with installation and backtrader usage

Full tutorial

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About release 2.0.0

Version 2.0.0 is a major release and is not backward compatible. make sure you update your code accordingly. Thanks to stefanomorni for contributing and removing selenium dependancy.

Usage

Import the packages and initialize with your tradingview username and password.

from tvDatafeed import TvDatafeed, Interval

username = 'YourTradingViewUsername'
password = 'YourTradingViewPassword'

tv = TvDatafeed(username, password)

You may use without logging in, but in some cases tradingview may limit the symbols and some symbols might not be available.

To use it without logging in

tv = TvDatafeed()

when using without login, following warning will be shown you are using nologin method, data you access may be limited

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Getting Data

To download the data use tv.get_hist method.

It accepts following arguments and returns pandas dataframe

(symbol: str, exchange: str = 'NSE', interval: Interval = Interval.in_daily, n_bars: int = 10, fut_contract: int | None = None, extended_session: bool = False) -> DataFrame)

for example-

# index
nifty_index_data = tv.get_hist(symbol='NIFTY',exchange='NSE',interval=Interval.in_1_hour,n_bars=1000)

# futures continuous contract
nifty_futures_data = tv.get_hist(symbol='NIFTY',exchange='NSE',interval=Interval.in_1_hour,n_bars=1000,fut_contract=1)

# crudeoil
crudeoil_data = tv.get_hist(symbol='CRUDEOIL',exchange='MCX',interval=Interval.in_1_hour,n_bars=5000,fut_contract=1)

# downloading data for extended market hours
extended_price_data = tv.get_hist(symbol="EICHERMOT",exchange="NSE",interval=Interval.in_1_hour,n_bars=500, extended_session=False)

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Search Symbol

To find the exact symbols for an instrument you can use tv.search_symbol method.

You need to provide search text and optional exchange. This will return a list of macthing instruments and their symbol.

tv.search_symbol('CRUDE','MCX')

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Calculating Indicators

Indicators data is not downloaded from tradingview. For that you can use TA-Lib. Check out this video for installation and usage instructions-

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Live feed (TvDatafeedLive)

Description

TvDatafeedLive is a sub-class of TvDatafeed to extend the functionality and provide live data feed feature. The live data feed feature means that the user can specify symbol, exchange and interval set (also called as seis) for which they want the new data bar to be retrieved from TradingView whenever it is produced. The user can then provide any number of callback functions for that seis which will be called with the newly retrieved data bar. Callback functions and retrieving data from TradingView is implemented in threads so live data is as close to realtime as possible, but it does not provide realtime data samples!

Usage

Import the packages and initialize with your tradingview username and password. As TvDatafeedLive is an extension of TvDatafeed class then all the rules about initialization are the same.

from tvDatafeed import TvDatafeedLive, Interval

username = 'YourTradingViewUsername'
password = 'YourTradingViewPassword'

tvl = TvDatafeedLive(username, password)

Creating new seis

TvDatafeedLive works with Seis and Consumer objects. Seis is short for symbol-exchange-interval-set. It is a class to contain a unique combination of symbol, exchange and interval values together with methods to make managing and using various symbols easier for the user.

User can create a new Seis by calling tvl.new_seis method.

seis = tvl.new_seis('ETHUSDT', 'BINANCE', Interval.in_1_hour)
seis2 = tvl.new_seis('ETHUSDT', 'BINANCE', Interval.in_2_hour)

The interface for this method is similar to the get_hist method as it accepts the same three arguments - symbol, exchange and interval. Once the seis is created it will automatically be added into live feed of tvl. This means that a thread will be created which will continously wait until new data bar is produced for this symbol in TradingView and will retrieve it. If no consumer instances are added to seis then nothing will be done with the retrieved data sample and it will be discarded.

All TvDatafeedLive method calls have an optional timeout parameter. TvDatafeedLive uses threading so method calls are blocking if the resources are in use. The user can specify maximum amount to wait before aborting the call and returning. This parameter defaults to -1 which means no timeout.

seis = tvl.new_seis('ETHUSDT', 'BINANCE', Interval.in_1_hour, timeout=10)
seis2 = tvl.new_seis('ETHUSDT', 'BINANCE', Interval.in_2_hour, timeout=10)

Removing seis

The user can remove the seis from tvl using the tvl.del_seis(seis) or seis.del_seis method. In the former case the method must have the seis to be deleted provided as an argument to reference a specific seis instance.

tvl.del_seis(seis)
seis2.del_seis()

Creating new consumer

The user can consume/use retrieved data by registering callback functions to seis. The tvl.new_consumer method accepts seis and a function as an argument and returns a consumer object. The seis.new_consumer method simply needs the function as an argument. The function provided must follow the prototype function shown below:

def consumer_func1(seis, data):
	print("Open price for "+seis.symbol+" on "+seis.exchange+" exchange with "+seis.interval.name+" interval was "+str(data.open[0]))

def consumer_func2(seis, data):
	print("Volume of "+seis.symbol+" on "+seis.exchange+" exchange with "+seis.interval.name+" interval was "+str(data.volume[0]))

def consumer_func3(seis, data):
	print("Close price for "+seis.symbol+" on "+seis.exchange+" exchange with "+seis.interval.name+" interval was "+str(data.close[0]))

consumer1=tvl.new_consumer(seis, consumer_func1)
consumer2=seis.new_consumer(consumer_func2)
consumer3=seis.new_consumer(consumer_func3)

When there is new data produced and retrieved from TradingView for this seis then the provided function will be called with seis and pandas DataFrame as arguments. The user can add one or many callback functions to seis - each of them will create a new consumer.

Removing consumer

The user can remove a consumer from seis by using the tvl.del_consumer, seis.del_consumer or consumer.del_consumer methods.

tvl.del_consumer(consumer1)
seis.del_consumer(consumer2)
consumer3.del_consumer()

Getting Data

TvDatafeedLive supports retrieving historic data in addition to retrieving live data. The user can use the tvl.get_hist or seis.get_hist method. The former method has the same API as the TvDatafeed get_hist method, except it accepts one additional optional argument - timeout. This parameter defaults to -1 which means no timeout. The seis.get_hist method only accepts two arguments - n_bars and timeout. Both of these parameters are optional and default to 10 bars and no timeout.

data=tvl.get_hist(symbol, exchange, interval=tvDatafeed.Interval.in_daily, n_bars=10, fut_contract=None, extended_session=False, timeout=-1)
data=seis.get_hist(n_bars=10, timeout=-1)

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Supported Time Intervals

Following timeframes intervals are supported-

Interval.in_1_minute

Interval.in_3_minute

Interval.in_5_minute

Interval.in_15_minute

Interval.in_30_minute

Interval.in_45_minute

Interval.in_1_hour

Interval.in_2_hour

Interval.in_3_hour

Interval.in_4_hour

Interval.in_daily

Interval.in_weekly

Interval.in_monthly

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Read this before creating an issue

Before creating an issue in this library, please follow the following steps.

1. Search the problem you are facing is already asked by someone else. There might be some issues already there, either solved/unsolved related to your problem. Go to issues page, use is:issue as filter and search your problem.

2. If you feel your problem is not asked by anyone or no issues are related to your problem, then create a new issue.

3. Describe your problem in detail while creating the issue. If you don't have time to detail/describe the problem you are facing, assume that I also won't be having time to respond to your problem.

4. Post a sample code of the problem you are facing. If I copy paste the code directly from issue, I should be able to reproduce the problem you are facing.

5. Before posting the sample code, test your sample code yourself once. Only sample code should be tested, no other addition should be there while you are testing.

6. Have some print() function calls to display the values of some variables related to your problem.

7. Post the results of print() functions also in the issue.

8. Use the insert code feature of github to inset code and print outputs, so that the code is displyed neat. !

9. If you have multiple lines of code, use tripple grave accent ( ``` ) to insert multiple lines of code.

   Example: