Hello @Tradehull_Imran Sir,
atm_strike, option_chain = tsl.get_option_chain(Underlying="NIFTY", exchange="INDEX", expiry=1,num_strikes=50)
but latest Dhan_tradehull library does not support num_strikes=50, and it also does not get the atm_strike, just gets the option_chain.
where can we get the new function get_option_chain?
@Tradehull_Imran
Are there any options to call OHLC data of more then one stock at a time?
like LTP: “ltp_data = tsl.get_ltp_data(names = [“HDFC”, “BSE”, “NHPC”, “ACC”, “CIPLA”])”
Hi @Tradehull_Imran ,
I’ve been trying to get the historical last one year data points at 5 min and 15 min time frame and despite multiple attempts only getting for last few days. is this the limitation at API side or I’m missing something in code. due to lack of historical data all other calculations are going for toss . please advise.
import pdb
import time
import datetime
import traceback
from Dhan_Tradehull_V2 import Tradehull
import pandas as pd
from pprint import pprint
import talib
import plotly.graph_objects as go
from datetime import datetime, timedelta
import numpy as np
from plotly.subplots import make_subplots
import pytz
def plot_candlestick_chart(df, title, timeframe="5min"):
"""
Create an interactive candlestick chart with volume and remove gaps
"""
fig = go.Figure()
# Add candlestick
fig.add_trace(go.Candlestick(
x=df.index,
open=df['open'],
high=df['high'],
low=df['low'],
close=df['close'],
name='Price'
))
# Add volume bars
fig.add_trace(go.Bar(
x=df.index,
y=df['volume'],
name='Volume',
yaxis='y2',
opacity=0.3
))
# Update layout with rangebreaks to remove non-trading hours and weekends
fig.update_layout(
title=title,
yaxis_title='Price',
yaxis2=dict(
title='Volume',
overlaying='y',
side='right'
),
xaxis_title='Date',
template='plotly_dark'
)
# Add range breaks to remove non-trading periods
fig.update_xaxes(
rangebreaks=[
dict(bounds=["sat", "mon"]), # Remove weekends
dict(bounds=[16, 9], pattern="hour"), # Remove non-trading hours
],
rangeslider_visible=True,
)
fig.show()
def fetch_historical_data(tsl, symbol, exchange='INDEX', timeframe="15", lookback_days=365):
"""
Fetch historical data with proper timezone handling and chunked requests
"""
try:
print(f"Fetching {timeframe}-minute data for {symbol}...")
# Calculate date ranges for chunked requests
end_date = datetime.now()
start_date = end_date - timedelta(days=lookback_days)
# Initialize empty DataFrame for storing all data
all_data = pd.DataFrame()
# Fetch data in 30-day chunks to handle API limitations
current_start = start_date
while current_start < end_date:
current_end = min(current_start + timedelta(days=30), end_date)
print(f"Fetching chunk from {current_start.date()} to {current_end.date()}")
# Get historical data for current chunk
chunk_data = tsl.get_historical_data(
tradingsymbol=symbol,
exchange=exchange,
timeframe=timeframe
)
if chunk_data is not None and not chunk_data.empty:
# Convert to dataframe
df_chunk = chunk_data.copy()
# Handle timestamp conversion
df_chunk['timestamp'] = pd.to_datetime(df_chunk['timestamp'])
# Filter for the current chunk's date range
df_chunk = df_chunk[
(df_chunk['timestamp'].dt.date >= current_start.date()) &
(df_chunk['timestamp'].dt.date <= current_end.date())
]
# Append to main DataFrame
all_data = pd.concat([all_data, df_chunk], axis=0)
# Move to next chunk
current_start = current_end
time.sleep(1) # Add delay between requests to avoid rate limiting
if not all_data.empty:
# Process the combined data
all_data = all_data.drop_duplicates()
all_data = all_data.sort_values('timestamp')
# Set timestamp as index
all_data = all_data.set_index('timestamp')
# Convert timezone if needed
if all_data.index.tz is None:
ist = pytz.timezone('Asia/Kolkata')
all_data.index = all_data.index.tz_localize('UTC').tz_convert(ist)
elif all_data.index.tz.zone != 'Asia/Kolkata':
all_data.index = all_data.index.tz_convert('Asia/Kolkata')
# Filter for trading hours (9:15 AM to 3:30 PM IST)
all_data = all_data.between_time('09:15', '15:30')
# Remove weekends
all_data = all_data[all_data.index.dayofweek < 5]
print(f"\nData Statistics:")
print(f"Total Days: {len(all_data.index.date.unique())}")
print(f"Total Candles: {len(all_data)}")
print(f"Date Range: {all_data.index.min().date()} to {all_data.index.max().date()}")
return all_data
else:
print("No data received")
return None
except Exception as e:
print(f"Error fetching data: {str(e)}")
traceback.print_exc() # Print full traceback for debugging
return None
def calculate_cvd(df):
"""
Calculate Cumulative Volume Delta
"""
# Calculate buy/sell volume based on close position relative to high/low
df['buy_volume'] = df['volume'] * (df['close'] - df['low']) / (df['high'] - df['low'])
df['sell_volume'] = df['volume'] - df['buy_volume']
# Calculate CVD
df['cvd'] = (df['buy_volume'] - df['sell_volume']).cumsum()
return df
def plot_data(df, symbol):
"""
Create a plot similar to TradingView with candlesticks, EMAs, and CVD
"""
# Calculate EMAs using pandas
df['ema9'] = df['close'].ewm(span=9, adjust=False).mean()
df['ema12'] = df['close'].ewm(span=12, adjust=False).mean()
# Create the figure with secondary y-axis
fig = make_subplots(rows=2, cols=1,
shared_xaxes=True,
vertical_spacing=0.03,
row_heights=[0.7, 0.3])
# Add candlestick
fig.add_trace(go.Candlestick(
x=df.index,
open=df['open'],
high=df['high'],
low=df['low'],
close=df['close'],
name='Price'
), row=1, col=1)
# Add EMAs
fig.add_trace(go.Scatter(
x=df.index,
y=df['ema9'],
name='EMA 9',
line=dict(color='blue', width=1)
), row=1, col=1)
fig.add_trace(go.Scatter(
x=df.index,
y=df['ema12'],
name='EMA 12',
line=dict(color='red', width=1)
), row=1, col=1)
# Add CVD
fig.add_trace(go.Scatter(
x=df.index,
y=df['cvd'],
name='CVD',
line=dict(color='purple', width=1.5)
), row=2, col=1)
# Add volume bars to CVD subplot
colors = ['red' if close < open else 'green'
for open, close in zip(df['open'], df['close'])]
fig.add_trace(go.Bar(
x=df.index,
y=df['volume'],
name='Volume',
marker_color=colors,
opacity=0.3
), row=2, col=1)
# Update layout
fig.update_layout(
title=f'{symbol} Price with CVD',
yaxis_title='Price',
yaxis2_title='CVD',
xaxis_title='Date',
template='plotly_dark',
xaxis_rangeslider_visible=False,
height=800 # Make the chart taller
)
# Remove non-trading periods
fig.update_xaxes(
rangebreaks=[
dict(bounds=["sat", "mon"]), # Remove weekends
dict(bounds=[15.5, 9.25], pattern="hour"), # Remove non-trading hours
]
)
fig.show()
def main():
# Initialize client
client_code = ""
token_id = ""
tsl = Tradehull(client_code, token_id)
# Fetch data for NIFTY
symbol = 'NIFTY'
df = fetch_historical_data(tsl, symbol)
if df is not None:
# Calculate CVD
df = calculate_cvd(df)
# Plot the data
plot_data(df, symbol)
# Print some statistics
print("\nData Statistics:")
print(f"Total Days: {len(df.index.date.unique())}")
print(f"Total Candles: {len(df)}")
print(f"Date Range: {df.index.min().date()} to {df.index.max().date()}")
if __name__ == "__main__":
main()
# Initialize Dhan client
client_code = "1"
token_id = "ey"
tsl = Tradehull(client_code, token_id)
# List of instruments to track
instruments = [
'NIFTY',
'BANKNIFTY',
'FINNIFTY',
'RELIANCE',
'TCS',
'HDFC',
'INFY',
'ICICIBANK'
]
# Get LTP for all instruments
print("\nFetching Last Traded Prices...")
print("-" * 50)
all_ltp_data = tsl.get_ltp_data(names=instruments)
for instrument in instruments:
if instrument in all_ltp_data:
print(f"{instrument:<15}: ₹{all_ltp_data[instrument]:,.2f}")
# Get and plot intraday data for indices
indices = ['NIFTY', 'BANKNIFTY', 'FINNIFTY']
print("\nFetching and plotting 5-minute intraday data...")
for index in indices:
print(f"\nGetting data for {index}...")
# Get 5-minute intraday data
hist_data = tsl.get_historical_data(
tradingsymbol=index,
exchange='INDEX',
timeframe="5" # Changed to 5-minute timeframe
)
if hist_data is not None and not hist_data.empty:
# Process the data
hist_data = hist_data.set_index(pd.to_datetime(hist_data['timestamp']))
hist_data = hist_data.sort_index()
# Calculate some basic statistics
current_price = hist_data['close'].iloc[-1]
high_price = hist_data['high'].max()
low_price = hist_data['low'].min()
total_return = ((hist_data['close'].iloc[-1] - hist_data['close'].iloc[0]) / hist_data['close'].iloc[0]) * 100
print(f"\n{index} Statistics:")
print(f"Current Price: ₹{current_price:,.2f}")
print(f"Period High: ₹{high_price:,.2f}")
print(f"Period Low: ₹{low_price:,.2f}")
print(f"Period Return: {total_return:.2f}%")
# Plot the candlestick chart
plot_candlestick_chart(hist_data, f"{index} 5-Minute Intraday Chart")
else:
print(f"No data available for {index}")
# Get NIFTY Options Data
print("\nFetching NIFTY Options Data...")
print("-" * 50)
# Get ATM Strikes
print("\nATM Strike Selection:")
ce_name, pe_name, strike = tsl.ATM_Strike_Selection(Underlying='NIFTY', Expiry=0)
print(f"Call Option: {ce_name}")
print(f"Put Option: {pe_name}")
print(f"Strike Price: {strike}")
# Get OTM Strikes
print("\nOTM Strike Selection (3 strikes):")
ce_name, pe_name, ce_strike, pe_strike = tsl.OTM_Strike_Selection(Underlying='NIFTY', Expiry=0, OTM_count=3)
print(f"Call Options: {ce_name}")
print(f"Put Options: {pe_name}")
print(f"Call Strikes: {ce_strike}")
print(f"Put Strikes: {pe_strike}")
# Get ITM Strikes
print("\nITM Strike Selection (5 strikes):")
ce_name, pe_name, ce_strike, pe_strike = tsl.ITM_Strike_Selection(Underlying='NIFTY', Expiry=0, ITM_count=5)
print(f"Call Options: {ce_name}")
print(f"Put Options: {pe_name}")
print(f"Call Strikes: {ce_strike}")
print(f"Put Strikes: {pe_strike}")
# Get current positions and holdings
print("\nCurrent Positions:")
positions = tsl.get_positions()
pprint(positions)
print("\nCurrent Holdings:")
holdings = tsl.get_holdings()
pprint(holdings)
Error code-
venvINR-HQ-2609:PythonProjects radharaman.jha$ /Users/radharaman.jha/Documents/PythonProjects/venv/bin/python "/Users/radharaman.jha/Documents/PythonProjects/How to use updated codebase.py"
Codebase Version 2.8 : Solved - Strike Selection Issue
-----Logged into Dhan-----
This BOT Is Picking New File From Dhan
Got the instrument file
Fetching 15-minute data for NIFTY...
Fetching chunk from 2024-02-24 to 2024-03-25
Fetching chunk from 2024-03-25 to 2024-04-24
Fetching chunk from 2024-04-24 to 2024-05-24
Fetching chunk from 2024-05-24 to 2024-06-23
Fetching chunk from 2024-06-23 to 2024-07-23
Fetching chunk from 2024-07-23 to 2024-08-22
Fetching chunk from 2024-08-22 to 2024-09-21
Fetching chunk from 2024-09-21 to 2024-10-21
Fetching chunk from 2024-10-21 to 2024-11-20
Fetching chunk from 2024-11-20 to 2024-12-20
Fetching chunk from 2024-12-20 to 2025-01-19
Fetching chunk from 2025-01-19 to 2025-02-18
Fetching chunk from 2025-02-18 to 2025-02-23
Error fetching data: 'datetime.timezone' object has no attribute 'zone'
Traceback (most recent call last):
File "/Users/radharaman.jha/Documents/PythonProjects/How to use updated codebase.py", line 124, in fetch_historical_data
elif all_data.index.tz.zone != 'Asia/Kolkata':
^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'datetime.timezone' object has no attribute 'zone'
-----Logged into Dhan-----
This BOT Is Picking New File From Dhan
Got the instrument file
Fetching Last Traded Prices...
--------------------------------------------------
Exception for instrument name HDFC as Check the Tradingsymbol
NIFTY : ₹22,795.90
BANKNIFTY : ₹48,981.20
FINNIFTY : ₹23,174.35
RELIANCE : ₹1,228.15
TCS : ₹3,786.00
INFY : ₹1,815.00
ICICIBANK : ₹1,232.95
Fetching and plotting 5-minute intraday data...
Getting data for NIFTY...
NIFTY Statistics:
Current Price: ₹22,764.35
Period High: ₹22,923.85
Period Low: ₹22,720.30
Period Return: -0.41%
Getting data for BANKNIFTY...
BANKNIFTY Statistics:
Current Price: ₹48,908.50
Period High: ₹49,455.80
Period Low: ₹48,764.00
Period Return: -0.93%
Getting data for FINNIFTY...
FINNIFTY Statistics:
Current Price: ₹23,135.20
Period High: ₹23,396.05
Period Low: ₹23,030.90
Period Return: -0.95%
Fetching NIFTY Options Data...
--------------------------------------------------
ATM Strike Selection:
Call Option: NIFTY 27 FEB 22800 CALL
Put Option: NIFTY 27 FEB 22800 PUT
Strike Price: 22800
OTM Strike Selection (3 strikes):
Call Options: FINNIFTY 27 FEB 22950 CALL
Put Options: FINNIFTY 27 FEB 22650 PUT
Call Strikes: 22950
Put Strikes: 22650
ITM Strike Selection (5 strikes):
Call Options: FINNIFTY 27 FEB 22550 CALL
Put Options: FINNIFTY 27 FEB 23050 PUT
Call Strikes: 22550
Put Strikes: 23050
Current Positions:
Empty DataFrame
Columns: []
Index: []
Current Holdings:
exchange tradingSymbol securityId isin totalQty dpQty t1Qty availableQty collateralQty avgCostPrice lastTradedPrice
0 ALL TATASTEEL 3499 INE081A01020 3 3 0 3 3 112.10 140.76
1 NSE AXISNIFTY 21252 INF846K01W98 3 3 0 3 3 199.89 247.98type or paste code here
ce_name, pe_name, ce_strike, pe_strike = tsl.OTM_Strike_Selection(Underlying='NIFTY', Expiry=0, OTM_count=3)
ce_name, pe_name, strike = tsl.ATM_Strike_Selection(Underlying='NIFTY', Expiry=0)
I am getting “FINNIFTY” options when i am using ce_name, pe_name, ce_strike, pe_strike = tsl.OTM_Strike_Selection(Underlying=‘NIFTY’, Expiry=0, OTM_count=3).
Kindly check it !!!
@Dhan TradingView chart is not working. please check.
chart = tsl.get_historical_data('INDIA VIX', 'INDEX', '1')
Dear Sir,
How to get India VIX data, above code not working, error: Exception in Getting OHLC data as ‘INDEX’.
Kindly suggest solution.
My algo is for NIFTY…
and I am getting finnifty atm options. this happened on friday too…
Also TV chart not working
Hi @Tradehull_Imran ,
How to create a spread symbol,
Like,
12*HDFCBANK+8*ICICIBANK+7*RELIANCE
and get the option chain of that spread symbol…?
I can create these on TradingView, not sure how to code for this…
Problem solved…? at my end i am still getting finnifty calls for nifty…
Hi @jacgohil ,
It seems there is no issues in ATM selection, Ltp fetching and getting the live ltp, Refer the below:
Code:
CE_symbol_name, PE_symbol_name, atm_strike = tsl.ATM_Strike_Selection(Underlying='FINNIFTY', Expiry=0)
ltp_data = tsl.get_ltp_data([CE_symbol_name, PE_symbol_name])
ce_ltp = ltp_data[CE_symbol_name]
pe_ltp = ltp_data[PE_symbol_name]
tsl.get_live_pnl()
Let me know if you face any issue.
Hi @Akshay_Bawane ,
Dhan provides last 5 days historical data.
To get the last 5 days data from dhan api, make the changes in Dhan_Tradehull_V2 file get_start_date() function.
start_date = df.iloc[-2]['timestamp'] # change -2 to -5
to export the data use
chart.to_csv('NIfty_data.csv')
Hi @ankur1019 :
Codebase link : Dhan-Tradehull · PyPI
Hi @anandc ,
Refer the below pseudocode for solution:
# if the Target-1 level gets Hit in only one candle, I want to move my SL to target-1 level and new target level will be TARGET-2…
taregt_hit = (ltp > t1) # if the Target-1 level gets Hit
taregt_hit_in_first_candle = (current_time < entry time + dattetime.timedelta(mins=5)) # gets Hit in only one candle
if taregt_hit and taregt_hit_in_first_candle:
orderbook[name]['sl'] = orderbook[name]['tg'] # I want to move my SL to target-1 level
orderbook[name]['tg'] = orderbook[name]['tg2'] # and new target level will be TARGET-2…
I have added this solution in tested framework, do check
import pdb
import time
import datetime
import traceback
from Dhan_Tradehull import Tradehull
import pandas as pd
from pprint import pprint
import talib
import pandas_ta as ta
import xlwings as xw
import winsound
client_code = "1102790337"
token_id = "bhgvsaoieqhjvhg"
tsl = Tradehull(client_code,token_id)
# Uncomment below code to do pre market scanning
# pre_market_watchlist = ['ASIANPAINT', 'BAJAJ-AUTO', 'BERGEPAINT', 'BEL', 'BOSCHLTD', 'BRITANNIA', 'COALINDIA', 'COLPAL', 'DABUR', 'DIVISLAB', 'EICHERMOT', 'GODREJCP', 'HCLTECH', 'HDFCBANK', 'HAVELLS', 'HEROMOTOCO', 'HAL', 'HINDUNILVR', 'ITC', 'IRCTC', 'INFY', 'LTIM', 'MARICO', 'MARUTI', 'NESTLEIND', 'PIDILITIND', 'TCS', 'TECHM', 'WIPRO']
# watchlist = []
# for name in pre_market_watchlist:
# print("Pre market scanning ", name)
# day_chart = tsl.get_historical_data(tradingsymbol = name,exchange = 'NSE',timeframe="DAY")
# day_chart['upperband'], day_chart['middleband'], day_chart['lowerband'] = talib.BBANDS(day_chart['close'], timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
# last_day_candle = day_chart.iloc[-1]
# upper_breakout = last_day_candle['high'] > last_day_candle['upperband']
# lower_breakout = last_day_candle['low'] < last_day_candle['lowerband']
# if upper_breakout or lower_breakout:
# watchlist.append(name)
# print(f"\t selected {name} for trading")
# pdb.set_trace()
# print(watchlist)
# # pdb.set_trace()
watchlist = ['BEL', 'BOSCHLTD', 'COLPAL', 'HCLTECH', 'HDFCBANK', 'HAVELLS', 'HAL', 'ITC', 'IRCTC', 'INFY', 'LTIM', 'MARICO', 'MARUTI', 'NESTLEIND', 'PIDILITIND', 'TCS', 'TECHM', 'WIPRO']
single_order = {'name':None, 'date':None , 'entry_time': None, 'entry_price': None, 'buy_sell': None, 'qty': None, 'sl': None, 'exit_time': None, 'exit_price': None, 'pnl': None, 'remark': None, 'traded':None}
orderbook = {}
wb = xw.Book('Live Trade Data.xlsx')
live_Trading = wb.sheets['Live_Trading']
completed_orders_sheet = wb.sheets['completed_orders']
reentry = "yes" #"yes/no"
completed_orders = []
bot_token = "8059847390:AAECSnQK-yOaGJ-clJchb1cx8CDhx2VQq-M"
receiver_chat_id = "1918451082"
live_Trading.range("A2:Z100").value = None
completed_orders_sheet.range("A2:Z100").value = None
for name in watchlist:
orderbook[name] = single_order.copy()
while True:
print("starting while Loop \n\n")
current_time = datetime.datetime.now().time()
if current_time < datetime.time(13, 55):
print(f"Wait for market to start", current_time)
time.sleep(1)
continue
if current_time > datetime.time(15, 15):
order_details = tsl.cancel_all_orders()
print(f"Market over Closing all trades !! Bye Bye See you Tomorrow", current_time)
pdb.set_trace()
break
all_ltp = tsl.get_ltp_data(names = watchlist)
for name in watchlist:
orderbook_df = pd.DataFrame(orderbook).T
live_Trading.range('A1').value = orderbook_df
completed_orders_df = pd.DataFrame(completed_orders)
completed_orders_sheet.range('A1').value = completed_orders_df
current_time = datetime.datetime.now()
print(f"Scanning {name} {current_time}")
try:
chart = tsl.get_historical_data(tradingsymbol = name,exchange = 'NSE',timeframe="5")
chart['rsi'] = talib.RSI(chart['close'], timeperiod=14)
cc = chart.iloc[-2]
# buy entry conditions
bc1 = cc['rsi'] > 45
bc2 = orderbook[name]['traded'] is None
except Exception as e:
print(e)
continue
if bc1 and bc2:
print("buy ", name, "\t")
margin_avialable = tsl.get_balance()
margin_required = cc['close']/4.5
if margin_avialable < margin_required:
print(f"Less margin, not taking order : margin_avialable is {margin_avialable} and margin_required is {margin_required} for {name}")
continue
orderbook[name]['name'] = name
orderbook[name]['date'] = str(current_time.date())
orderbook[name]['entry_time'] = str(current_time.time())[:8]
orderbook[name]['buy_sell'] = "BUY"
orderbook[name]['qty'] = 1
try:
entry_orderid = tsl.order_placement(tradingsymbol=name ,exchange='NSE', quantity=orderbook[name]['qty'], price=0, trigger_price=0, order_type='MARKET', transaction_type='BUY', trade_type='MIS')
orderbook[name]['entry_orderid'] = entry_orderid
orderbook[name]['entry_price'] = tsl.get_executed_price(orderid=orderbook[name]['entry_orderid'])
orderbook[name]['tg'] = round(orderbook[name]['entry_price']*1.002, 1) # 1.01 # Defining Target 2
orderbook[name]['tg2'] = round(orderbook[name]['entry_price']*1.004, 1) # 1.01 # Defining Target 2
orderbook[name]['sl'] = round(orderbook[name]['entry_price']*0.998, 1) # 99
sl_orderid = tsl.order_placement(tradingsymbol=name ,exchange='NSE', quantity=orderbook[name]['qty'], price=0, trigger_price=orderbook[name]['sl'], order_type='STOPMARKET', transaction_type ='SELL', trade_type='MIS')
orderbook[name]['sl_orderid'] = sl_orderid
orderbook[name]['traded'] = "yes"
message = "\n".join(f"'{key}': {repr(value)}" for key, value in orderbook[name].items())
message = f"Entry_done {name} \n\n {message}"
tsl.send_telegram_alert(message=message,receiver_chat_id=receiver_chat_id,bot_token=bot_token)
except Exception as e:
print(e)
pdb.set_trace(header= "error in entry order")
if orderbook[name]['traded'] == "yes":
bought = orderbook[name]['buy_sell'] == "BUY"
if bought:
try:
ltp = all_ltp[name]
sl_hit = tsl.get_order_status(orderid=orderbook[name]['sl_orderid']) == "TRADED"
tg_hit = ltp > orderbook[name]['tg']
except Exception as e:
print(e)
pdb.set_trace(header = "error in sl order cheking")
# if the Target-1 level gets Hit in only one candle, I want to move my SL to target-1 level and new target level will be TARGET-2…
taregt_hit = (ltp > t1) # if the Target-1 level gets Hit
taregt_hit_in_first_candle = (current_time < entry time + dattetime.timedelta(mins=5)) # gets Hit in only one candle
if taregt_hit and taregt_hit_in_first_candle:
orderbook[name]['sl'] = orderbook[name]['tg'] # I want to move my SL to target-1 level
orderbook[name]['tg'] = orderbook[name]['tg2'] # and new target level will be TARGET-2…
if sl_hit:
try:
orderbook[name]['exit_time'] = str(current_time.time())[:8]
orderbook[name]['exit_price'] = tsl.get_executed_price(orderid=orderbook[name]['sl_orderid'])
orderbook[name]['pnl'] = round((orderbook[name]['exit_price'] - orderbook[name]['entry_price'])*orderbook[name]['qty'],1)
orderbook[name]['remark'] = "Bought_SL_hit"
message = "\n".join(f"'{key}': {repr(value)}" for key, value in orderbook[name].items())
message = f"SL_HIT {name} \n\n {message}"
tsl.send_telegram_alert(message=message,receiver_chat_id=receiver_chat_id,bot_token=bot_token)
if reentry == "yes":
completed_orders.append(orderbook[name])
orderbook[name] = None
except Exception as e:
print(e)
pdb.set_trace(header = "error in sl_hit")
if tg_hit:
try:
tsl.cancel_order(OrderID=orderbook[name]['sl_orderid'])
time.sleep(2)
square_off_buy_order = tsl.order_placement(tradingsymbol=orderbook[name]['name'] ,exchange='NSE', quantity=orderbook[name]['qty'], price=0, trigger_price=0, order_type='MARKET', transaction_type='SELL', trade_type='MIS')
orderbook[name]['exit_time'] = str(current_time.time())[:8]
orderbook[name]['exit_price'] = tsl.get_executed_price(orderid=square_off_buy_order)
orderbook[name]['pnl'] = (orderbook[name]['exit_price'] - orderbook[name]['entry_price'])*orderbook[name]['qty']
orderbook[name]['remark'] = "Bought_TG_hit"
message = "\n".join(f"'{key}': {repr(value)}" for key, value in orderbook[name].items())
message = f"TG_HIT {name} \n\n {message}"
tsl.send_telegram_alert(message=message,receiver_chat_id=receiver_chat_id,bot_token=bot_token)
if reentry == "yes":
completed_orders.append(orderbook[name])
orderbook[name] = None
winsound.Beep(1500, 10000)
except Exception as e:
print(e)
pdb.set_trace(header = "error in tg_hit")
Hi @Rad_AI_Jha ,
Yes, there is api limitation to fetch historical day for 5 or 15mins time frame of last few days only. Using Day timeframe you can fetch for a year.
Hi @Kanha_Meher ,
Currently, there is no option to retrieve OHLC data for multiple stocks simultaneously.
Hi @Dhananjay_Umalkar ,
Solution Link:
Hi @Delta_Trading ,
Solution Link:
can you send the steps by which Option chain for Spread symbol can be created,
do send some screenshots also for explanation.
@Tradehull_Imran Sir,
Thanks a lot sir…
BTW, I want to send some code to you for final verification… where shud I send it…?
Reagrds
Have a nice day…
my websocket is not showing latest data. it showing aug data