"For GUIDANCE On Programmatic Trading WhatsApp Profit Adda Web @ +91-7795780804"

Delta Exchange API in Python

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

Open Online Delta Exchange India Trading Account Fill the Form with Details as Required.

Visit Delta Exchange India

Place Bracket Order as an Automated Take Profit and Stop Loss Order in Delta:


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




from cred import *  
import time, hashlib, hmac, requests, json

def generate_signature(secret, msg:str): 
    return hmac.new(secret.encode(), msg.encode(), hashlib.sha256).hexdigest()

method="POST" 
timestamp=str(int(time.time()))
base_url="https://api.india.delta.exchange"
path="/v2/orders/bracket"
url=base_url + path 


body_string={
  "product_id": 86667,
  "product_symbol": "P-BTC-112000-260925",
  "stop_loss_order": {
    "order_type": "market_order",
    "stop_price": "1600"
  },
  "take_profit_order": {
    "order_type": "market_order",
    "stop_price": "100"
  },
  "bracket_stop_trigger_method": "last_traded_price"
}

body=json.dumps(body_string)


signature_data=method + timestamp + path + body
signature=generate_signature(api_secret, signature_data)

headers={
    "api-key":api_key,
    "timestamp":timestamp,
    "signature":signature,
    "Content-Type":"application/json"
}
boid=requests.post(url,headers=headers,data=body)
print(boid.json())

Get Wallet Balances of Delta Exchange India using API in Python:


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



from cred import *
from delta_rest_client import DeltaRestClient


delta_client = DeltaRestClient(
  base_url='https://api.india.delta.exchange',
  api_key=api_key,
  api_secret=api_secret
)

assets=delta_client.get_assets()

for asset in assets:
  balance=delta_client.get_balances(asset['id'])
  if balance:
    print(balance['asset_id'])

Add Customized | Additional | Subplots such as MACD Indicator to Candlestick Plots:


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)
fastperiod=df['close'].ewm(span=12,adjust=False).mean()
slowperiod=df['close'].ewm(span=26,adjust=False).mean()
df['MACD']=fastperiod - slowperiod
df['Signal']=df['MACD'].ewm(span=9,adjust=False).mean()
df['Histogram']=df['MACD'] - df['Signal']
add_plots=[
mpf.make_addplot(df['MACD'],panel=2,color='blue',ylabel='MACD'),
mpf.make_addplot(df['Signal'],panel=2,color='orange'),
mpf.make_addplot(df['Histogram'],type='bar',panel=2,color='gray')
]
mpf.plot(df,type='candle',style='charles',volume=True,addplot=add_plots,title='BTCUSD 15-Minute Candlestick Chart')

Authenticate to Place Orders or Fetch Account related information using Delta API:


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



from cred import *  
import time, hashlib, hmac, requests 

def generate_signature(secret, msg:str): 
    return hmac.new(secret.encode(), msg.encode(), hashlib.sha256).hexdigest()

method="GET" 
timestamp=str(int(time.time()))
base_url="https://api.india.delta.exchange"
path="/v2/positions"
url=base_url + path 

query_string="?product_id=27"

payload=""

signature_data=method + timestamp + path + query_string + payload 
signature=generate_signature(api_secret, signature_data)

headers={
    "api-key":api_key,
    "timestamp":timestamp,
    "signature":signature,
    "Content-Type":"application/json"
}
positions=requests.get(url,headers=headers,params={"product_id":27})
print(positions.json())

Option Chain Bitcoin - ATM,OTM,ITM Fetched for BTC | ETH Option Trading:


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



from cred import *
from delta_rest_client import DeltaRestClient
import pandas as pd 

delta_client = DeltaRestClient(
  base_url='https://api.india.delta.exchange',
  api_key=api_key,
  api_secret=api_secret
)
spot_symbol='BTCUSD'
strike_level=500
pe_level=-4000
spot_ticker = delta_client.get_ticker(spot_symbol)
spot_price=round(float(spot_ticker['mark_price']))
strike_price=strike_level * round(spot_price/strike_level) + pe_level
pe_symbol=f'P-BTC-{strike_price}-010825'
pe_ticker = delta_client.get_ticker(pe_symbol)
print(pd.json_normalize(pe_ticker).T)

Fetch Order History of Delta Exchange India using Python API:


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



from cred import *
from delta_rest_client import DeltaRestClient
import pandas as pd 

delta_client = DeltaRestClient(
  base_url='https://api.india.delta.exchange',
  api_key=api_key,
  api_secret=api_secret
)

response = delta_client.get_ticker('P-BTC-114500-010825')
product_id=response['product_id']
query={'product_id':product_id,'product_id':27}
order_history=delta_client.order_history(query,page_size=1)
print(order_history)

How to Cancel Orders in Delta Exchange India Using Python API:


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



from cred import *
from delta_rest_client import DeltaRestClient
from delta_rest_client import OrderType
import pandas as pd 

delta_client = DeltaRestClient(
  base_url='https://api.india.delta.exchange',
  api_key=api_key,
  api_secret=api_secret
)

response = delta_client.get_ticker('P-BTC-114500-010825')
product_id=response['product_id']
mark_price=round(float(response['mark_price']),1)
print(product_id,mark_price)
position=delta_client.get_margined_position(product_id)
print(position)
entry_price=round(float(position['entry_price']),1)
lqp=round(float(position['liquidation_price']),1)
size=position['size']
print(entry_price,lqp,size)
order_response=delta_client.place_stop_order(product_id=product_id,
  side='buy',
  size=abs(size),
  limit_price=str(lqp - 100),
  order_type=OrderType.LIMIT,
  stop_price=str(lqp - 50))
order_id=order_response['id']
cancel_order=delta_client.cancel_order(product_id,order_id)
print(cancel_order)

Get Margined Position of Delta Exchange India in Python:


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


Importing credentials and libraries: The code starts by importing credentials (api_key, api_secret) from a cred module and essential components from the delta_rest_client library. Setting up the API client: It initializes a DeltaRestClient with your API keys and base URL to interact with Delta Exchange's REST API. Fetching ticker information: The code requests the latest ticker data for a specific product: 'P-BTC-117500-010825', which is likely a BTC options contract expiring on 01 Aug 2025 with a strike of 117500. Extracting product ID and mark price: From the ticker response, it extracts the product_id and rounds the mark_price to 1 decimal place. Printing basic market data: It prints the product ID and the rounded mark price. Getting open position: It then fetches the user's current margined position for this product using get_margined_position. Extracting position details: It extracts the entry_price, liquidation_price, and size (number of contracts in the position) from the position response. Printing position data: The entry price, liquidation price, and position size are printed for reference. Calculating stop-loss level: A stop-loss order is prepared with a stop_price set 200 points below the liquidation price and a limit_price 150 points below the same. Placing stop-limit order: The code places a buy stop-limit order with: Size = absolute of current position size (to reverse/exit), Limit Price = liquidation_price - 150, Stop Price = liquidation_price - 200, Order Type = LIMIT. Order side logic: The side='buy' indicates this stop-limit order is probably to close a short (sell) position if the price rises toward liquidation. Final confirmation: Prints the order_response from the API, which includes order ID and confirmation details. Summary: This script automates risk management for a short position on a BTC options contract by dynamically placing a buy stop-limit order near the liquidation zone to reduce losses if the market moves against the trade. 


from cred import *
from delta_rest_client import DeltaRestClient
from delta_rest_client import OrderType
import pandas as pd 

delta_client = DeltaRestClient(
  base_url='https://api.india.delta.exchange',
  api_key=api_key,
  api_secret=api_secret
)

response = delta_client.get_ticker('P-BTC-117500-010825')
product_id=response['product_id']
mark_price=round(float(response['mark_price']),1)
print(product_id,mark_price)
position=delta_client.get_margined_position(product_id)
print(position)
entry_price=round(float(position['entry_price']),1)
lqp=round(float(position['liquidation_price']),1)
size=position['size']
print(entry_price,lqp,size)
order_response=delta_client.place_stop_order(product_id=product_id,
  side='buy',
  size=abs(size),
  limit_price=str(lqp - 150),
  order_type=OrderType.LIMIT,
  stop_price=str(lqp - 200))
print(order_response)

How to Place Stop Loss Orders in Delta Exchange India Using Python API:


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



from cred import *
from delta_rest_client import DeltaRestClient
from delta_rest_client import OrderType
import pandas as pd 

delta_client = DeltaRestClient(
  base_url='https://api.india.delta.exchange',
  api_key=api_key,
  api_secret=api_secret
)

response = delta_client.get_ticker('P-BTC-117500-010825')
product_id=response['product_id']
mark_price=round(float(response['mark_price']),1)
print(product_id,mark_price)
position=delta_client.get_position(product_id)
entry_price=round(float(position['entry_price']),1)
size=position['size']
print(entry_price,size)
order_response=delta_client.place_stop_order(product_id=product_id,
  side='buy',
  size=abs(size),
  limit_price=str(entry_price - 200),
  order_type=OrderType.LIMIT,
  stop_price=str(entry_price - 250))
print(order_response)

Get Real-Time Positions Data of Derivatives using Python API:


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



from cred import *
from delta_rest_client import DeltaRestClient
import pandas as pd 

delta_client = DeltaRestClient(
  base_url='https://api.india.delta.exchange',
  api_key=api_key,
  api_secret=api_secret
)

response = delta_client.get_ticker('P-BTC-114000-250725')
product_id=response['product_id']
mark_price=round(float(response['mark_price']),1)
print(product_id,mark_price)
position=delta_client.get_position(product_id)
entry_price=round(float(position['entry_price']),1)
size=position['size']
print(entry_price,size)

GET Futures | Options OrderBook Depth + Price + Size via Python API:


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



from cred import *
from delta_rest_client import DeltaRestClient
import pandas as pd 

delta_client = DeltaRestClient(
  base_url='https://api.india.delta.exchange',
  api_key=api_key,
  api_secret=api_secret
)


response = delta_client.get_ticker('P-BTC-116000-250725')
product_id=response['product_id']
mark_price=round(float(response['mark_price']),1)
print(product_id,mark_price)
order_book=delta_client.get_l2_orderbook(product_id)
print(pd.DataFrame(order_book['sell'])['price'])

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)

Delta Exchange India Trade API and Ticker in Python:

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 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'])