Delta Exchange API in Python

Delta Exchange India Trade API and Ticker in Python:

Trade Futures & Options on Bitcoin and Ether Elevate your F&O trading with 24/7 open markets, efficient margining and INR settlement

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GET Customized Price | Trade Alert in Telegram Using AWS Lambda:

For Instructions Follow the Video then go For the Code Input:

import requests 
import pandas as pd  
from datetime import datetime, timedelta 
import json
config=open('token_chat_id.txt','r').read()
config=json.loads(config)
token=config['token']
chat_id=config['chat_id']
tele_url=f'https://api.telegram.org/bot{token}'  

base_url="https://api.india.delta.exchange"
url=f"{base_url}/v2/history/candles"
end=int(datetime.now().timestamp())
start=int((datetime.now()-timedelta(days=1)).timestamp())
params={
    'symbol':'BTCUSD',
    'resolution':'1m',
    'start':start,
    'end':end
}
r=requests.get(url,params=params)
data=r.json().get('result',[])
df=pd.DataFrame(data,columns=['time','open','high','low','close','volume'])
if df['close'].iloc[-1] < df['open'].iloc[-1]:
    Candle='RED'
    msg=f"'BTCUSD',{Candle},{df['close'].iloc[-1]},{df['open'].iloc[-1]}"
send_alert=requests.get(f'{tele_url}/sendMessage?chat_id={chat_id}&text={msg}').json()['result']['text']
print(send_alert)

Generate API Key:

For Instructions Follow the Video then go For the Code Input:

CMD:pip install delta-rest-client

import json
from delta_rest_client import DeltaRestClient
config=open('delta.txt','r').read()
config=json.loads(config)
api_key=config['api_key']
api_secret=config['api_secret']


delta_client = DeltaRestClient(
  base_url='https://api.india.delta.exchange',
  api_key=api_key,
  api_secret=api_secret
)
response = delta_client.get_ticker('C-BTC-94400-280425')
print(response['mark_price'])

Get Product ID for Crypto Trading Symbol Using Delta Exchange API in Python:

For Instructions Follow the Video then go For the Code Input:

import json
from delta_rest_client import DeltaRestClient
config=open('delta.txt','r').read()
config=json.loads(config)
api_key=config['api_key']
api_secret=config['api_secret']


delta_client = DeltaRestClient(
  base_url='https://api.india.delta.exchange',
  api_key=api_key,
  api_secret=api_secret
)
response = delta_client.get_ticker('C-BTC-103800-190525')
print(response['product_id'])

How to Place Orders in Delta Exchange using Python API:

For Instructions Follow the Video then go For the Code Input:

import json
from delta_rest_client import DeltaRestClient
from delta_rest_client import OrderType
config=open('delta.txt','r').read()
config=json.loads(config)
api_key=config['api_key']
api_secret=config['api_secret']


delta_client = DeltaRestClient(
  base_url='https://api.india.delta.exchange',
  api_key=api_key,
  api_secret=api_secret
)
response = delta_client.get_ticker('BTCUSD')
product_id=response['product_id']
mark_price=round(float(response['mark_price'])-10,1)
order_response=delta_client.place_order(product_id=product_id,
  side='buy',
  size=1,
  limit_price=mark_price,
  order_type=OrderType.LIMIT)
print(order_response)

GET Historical OHLC Candles Utilizing Delta Exchange API in Python:

For Instructions Follow the Video then go For the Code Input:

import requests 
import pandas as pd  
from datetime import datetime, timedelta 

base_url="https://api.india.delta.exchange"
url=f"{base_url}/v2/history/candles"
end=int(datetime.now().timestamp())
start=int((datetime.now()-timedelta(days=1)).timestamp())
params={
    'symbol':'BTCUSD',
    'resolution':'1m',
    'start':start,
    'end':end
}
r=requests.get(url,params=params)
data=r.json().get('result',[])
df=pd.DataFrame(data,columns=['time','open','high','low','close','volume'])
print(df)

Customized Interactive Historical OHLC Candlestick Chart Using Delta Exchange API:

For Instructions Follow the Video then go For the Code Input:

import requests 
import pandas as pd  
from datetime import datetime, timedelta 
import mplfinance as mpf
base_url="https://api.india.delta.exchange"
url=f"{base_url}/v2/history/candles"
end=int(datetime.now().timestamp())
start=int((datetime.now()-timedelta(days=1)).timestamp())
params={
    'symbol':'BTCUSD',
    'resolution':'15m',
    'start':start,
    'end':end
}
r=requests.get(url,params=params)
data=r.json().get('result',[])
df=pd.DataFrame(data,columns=['time','open','high','low','close','volume']).sort_values('time')
df['time']=pd.to_datetime(df['time'],unit='s')
df['time']=df['time'].dt.tz_localize('UTC').dt.tz_convert('Asia/Kolkata')
df.set_index('time',inplace=True)
mpf.plot(df,type='candle',volume=False,style='charles',title='BTCUSD 15-Minute Candlestick Chart')

Customized Interactive Historical OHLC Candlestick Chart Using API Part2:

For Instructions of Connecting Chart GUI Follow the Video then go For the Code Input:

import requests 
import pandas as pd  
from datetime import datetime, timedelta 
import mplfinance as mpf
import toga  
from toga.style import Pack 
base_url="https://api.india.delta.exchange"
url=f"{base_url}/v2/history/candles"
end=int(datetime.now().timestamp())
start=int((datetime.now()-timedelta(days=1)).timestamp())
params={
    'symbol':'BTCUSD',
    'resolution':'5m',
    'start':start,
    'end':end
}
r=requests.get(url,params=params)
data=r.json().get('result',[])
df=pd.DataFrame(data,columns=['time','open','high','low','close','volume']).sort_values('time')
df['time']=pd.to_datetime(df['time'],unit='s')
df['time']=df['time'].dt.tz_localize('UTC').dt.tz_convert('Asia/Kolkata')
df.set_index('time',inplace=True)
mpf.plot(df,type='candle',volume=False,style='charles',title='BTCUSD 5-Minute Candlestick Chart',savefig=dict(fname='btc_chart.png',dpi=150,pad_inches=0.25,bbox_inches='tight'))
class ChartApp(toga.App):
    def startup(self):
        self.main_window=toga.MainWindow(title=self.formal_name)
        image=toga.Image('btc_chart.png')

        image_view=toga.ImageView(image=image,style=Pack(padding=10))

        self.main_window.content=image_view  
        self.main_window.show()

def main():
    return ChartApp('BTCUSD','369')
main().main_loop()

Customized Interactive Historical OHLC Candlestick Chart Using API Part3:

For Instructions of Charts Cache Follow the Video then go For the Code Input:

import requests 
import pandas as pd  
from datetime import datetime, timedelta 
import mplfinance as mpf
import toga  
from toga.style import Pack 
import os
import matplotlib 
matplotlib.use("Agg")



class ChartApp(toga.App):
    def fetch_candlestic_data(self,symbol):
        base_url="https://api.india.delta.exchange"
        url=f"{base_url}/v2/history/candles"
        end=int(datetime.now().timestamp())
        start=int((datetime.now()-timedelta(days=1)).timestamp())
        params={
            'symbol':symbol,
            'resolution':self.resolution,
            'start':start,
            'end':end
        }
        r=requests.get(url,params=params)
        data=r.json().get('result',[])
        df=pd.DataFrame(data,columns=['time','open','high','low','close','volume']).sort_values('time')
        df['time']=pd.to_datetime(df['time'],unit='s')
        df['time']=df['time'].dt.tz_localize('UTC').dt.tz_convert('Asia/Kolkata')
        df.set_index('time',inplace=True)
        return df
    def startup(self):
        symbol='BTCUSD'
        self.resolution='5m'
        df=self.fetch_candlestic_data(symbol)
        os.makedirs(self.paths.cache,exist_ok=True)
        chart_path=os.path.join(self.paths.cache,f'{symbol}_{self.resolution}_chart.png')
        mpf.plot(df,type='candle',volume=False,style='charles',title=f'{symbol} {self.resolution} Candlestick Chart',savefig=dict(fname=chart_path,dpi=150))
        self.main_window=toga.MainWindow(title=self.formal_name)
        image=toga.Image(chart_path)

        image_view=toga.ImageView(image=image,style=Pack(padding=10))

        self.main_window.content=image_view  
        self.main_window.show()

def main():
    return ChartApp('BTCUSD','369')
main().main_loop()

Zerodha KiteConnect Utilize Free of Cost API to Automate Trades in Python

Zerodha KiteConnect Utilize Free of Cost API to Automate Trades:

Kite Connect is a set of REST-like HTTP APIs that expose many capabilities required to build a complete stock market investment and trading platform. It lets you execute orders in real time (equities, commodities, mutual funds), manage user portfolios, stream live market data over WebSockets, and more.

This module provides an easy to use abstraction over the HTTP APIs. The HTTP calls have been converted to methods and their JSON responses are returned as native Python structures, for example, dicts, lists, bools etc.

The login flow starts by navigating to the public Kite login endpoint.

https://kite.zerodha.com/connect/login?v=3&api_key=xxx

Follow the Above Video for Instructions then go for the Code Input:

from kiteconnect import KiteConnect

api_key=open('api_key.txt','r').read().strip()
api_secret=open('api_secret.txt','r').read().strip()

kite = KiteConnect(api_key=api_key)

access_token=kite.generate_session("your_request_token_here", api_secret=api_secret)

print(access_token['access_token'])