SIR CAN I PLACE A PRICE ALART IN DHAN USING PYTHON CODE @Tradehull_Imran
@Tradehull_Imran
Hi Imran,
I am on 3 episode of Algo Trading course and tried to fetch previous_hist_data and intraday_hist_data but i am getting the error in attached print screenshot. could you please guide me why i am unable to fetch the data.
sir talib is not installing
if this statement is used in terminal pip install talib
out put is given below
(.venv) PS C:\Users\Intel\PycharmProjects\dhanhq> pip install talib
ERROR: Could not find a version that satisfies the requirement talib (from versions: none)
ERROR: No matching distribution found for talib
(.venv) PS C:\Users\Intel\PycharmProjects\dhanhq>
please share your whatsapp no sir
I think its a version issue,
do use this upgraded codebase file : Dhan_Tradehull_V2.py - Google Drive
Note : The library will update when necessary, but the link to the codebase file remains same
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Hi @s_nandi
Yes Price or Condition based alerts can be made using dhanhq,
we do use telegram api / gmail api to create alerts
1 Like
Hi @Avi_Shetty
Some upgrades were made in dhan-tradehull codebase, regarding historical data
Apply this solution: Learn Algo Trading with Python | Codes | Youtube Series - #952 by Tradehull_Imran
1 Like
See this session for installation process : https://www.youtube.com/watch?v=YAyIoDJYorA&list=PLnuHyqUCoJsPA4l9KRLrNpWLIfZ9ucLxx&index=8
Talib is installed after we run install_libraries.bat
and install_libraries uses TA_Lib-0.4.24-cp38-cp38-win_amd64.whl to install Talib
1 Like
sir, giving below error
Sell Signal Formed
exception got in ce_pe_option_df ‘<’ not supported between instances of ‘int’ and ‘str’
Algo is working 21:31:36.459860
Thanks Sir, Also update get_intraday_data for data fetching for specific date period like between from date and to date, although i have modified it for me, but it will be helpful & useful for all if same can be updated in Dhan_Tradehull_V2 from your side
def get_intraday_datanew(self, tradingsymbol, exchange, timeframe, from_date=None, to_date=None, debug="NO"):
try:
tradingsymbol = tradingsymbol.upper()
exchange = exchange.upper()
instrument_df = self.instrument_df.copy()
# Map available timeframes
available_frames = {
2: '2T', # 2 minutes
3: '3T', # 3 minutes
5: '5T', # 5 minutes
10: '10T', # 10 minutes
15: '15T', # 15 minutes
30: '30T', # 30 minutes
60: '60T' # 60 minutes
}
# Handle default date range (last 5 days)
if from_date is None:
from_date = (datetime.datetime.now() - datetime.timedelta(days=5)).strftime('%Y-%m-%d')
if to_date is None:
to_date = datetime.datetime.now().strftime('%Y-%m-%d')
# Exchange mappings
script_exchange = {
"NSE": self.Dhan.NSE,
"NFO": self.Dhan.FNO,
"BFO": "BSE_FNO",
"CUR": self.Dhan.CUR,
"BSE": self.Dhan.BSE,
"MCX": self.Dhan.MCX,
"INDEX": self.Dhan.INDEX
}
instrument_exchange = {
'NSE': "NSE",
'BSE': "BSE",
'NFO': 'NSE',
'BFO': 'BSE',
'MCX': 'MCX',
'CUR': 'NSE'
}
index_exchange = {
"NIFTY": 'NSE',
"BANKNIFTY": "NSE",
"FINNIFTY": "NSE",
"MIDCPNIFTY": "NSE",
"BANKEX": "BSE",
"SENSEX": "BSE"
}
# Handle index exceptions
if tradingsymbol in index_exchange:
exchange = index_exchange[tradingsymbol]
# Fetch security ID and instrument type
if tradingsymbol in self.commodity_step_dict.keys():
security_check = instrument_df[(instrument_df['SEM_EXM_EXCH_ID'] == 'MCX') &
(instrument_df['SM_SYMBOL_NAME'] == tradingsymbol.upper()) &
(instrument_df['SEM_INSTRUMENT_NAME'] == 'FUTCOM')]
if security_check.empty:
raise Exception("Check the Tradingsymbol")
security_id = security_check.sort_values(by='SEM_EXPIRY_DATE').iloc[0]['SEM_SMST_SECURITY_ID']
else:
security_check = instrument_df[
((instrument_df['SEM_TRADING_SYMBOL'] == tradingsymbol) |
(instrument_df['SEM_CUSTOM_SYMBOL'] == tradingsymbol)) &
(instrument_df['SEM_EXM_EXCH_ID'] == instrument_exchange[exchange])
]
if security_check.empty:
raise Exception("Check the Tradingsymbol")
security_id = security_check.iloc[-1]['SEM_SMST_SECURITY_ID']
instrument_type = security_check.iloc[-1]['SEM_INSTRUMENT_NAME']
exchange_segment = script_exchange[exchange]
# Fetch OHLC data from Dhan API
ohlc = self.Dhan.intraday_minute_data(
str(security_id), exchange_segment, instrument_type, from_date, to_date, int(1)
)
if debug.upper() == "YES":
print(ohlc)
if ohlc['status'] != 'failure':
df = pd.DataFrame(ohlc['data'])
if not df.empty:
df['timestamp'] = df['timestamp'].apply(lambda x: self.convert_to_date_time(x))
df = df.dropna(subset=['open', 'high', 'low', 'close'])
# Filter for valid trading days
df['timestamp'] = pd.to_datetime(df['timestamp'])
df = df[(df['timestamp'].dt.date >= datetime.datetime.strptime(from_date, "%Y-%m-%d").date()) &
(df['timestamp'].dt.date <= datetime.datetime.strptime(to_date, "%Y-%m-%d").date())]
# Resample to desired timeframe
if timeframe == 1:
return df
df = self.resample_timeframe(df, available_frames[timeframe])
return df
else:
return df
else:
raise Exception(ohlc)
except Exception as e:
print(e)
self.logger.exception(f"Exception in Getting OHLC data as {e}")
traceback.print_exc()
1 Like
Hi, tradehall@imran,
in session 8,
$$$$$$$ cc_5 = chart_5.iloc[-1] # pandas completed candle $$$$$$$
we used ilock[-2] for last completed candle in session 6
but ilock[-1] used for last completed candle in session 8 ---- I request u pls explain it . why are u use iloc[-1] ?
The code below is for Back testing (Paper Trade), working for me , coded for option trading in focus, you can modify it as per your requirement. Waiting for suggestions and remarks. Thank you.
import pdb
from Dhan_Tradehull_V2 import Tradehull
import pandas as pd
import talib
import time
import datetime
client_code = "put your client_code"
token_id = "put your toekn_id"
tsl = Tradehull(client_code,token_id)
traded_watchlist = []
tradelist = []
############## Simulation Input #############
lot_size = 15
lots_number = 100 #How many lots you want to trade for
watchlist = {'Script':'BANKNIFTY 30 JAN 51200 PUT','Selected_date': '2025-01-03', 'exchange': 'NFO'}
#############################################
print("\nSimulation started for selected_date.....", datetime.datetime.now().strftime("%H:%M:%S"))
try:
data_1min = tsl.get_historical_data(watchlist['Script'], watchlist['exchange'], '1')
data_1min = pd.DataFrame(data_1min)
data_1min['timestamp'] = pd.to_datetime(data_1min['timestamp'])
data_1min.set_index('timestamp', inplace=True)
selected_date = pd.Timestamp(watchlist['Selected_date'])
# Filter data for simulation
data_1min_filtered = data_1min[data_1min.index.date < selected_date.date()] # Data till
final_data_1min = data_1min[data_1min.index.date == selected_date.date()] # Data for
def filter_market_hours(data):
market_open = datetime.time(9, 15)
market_close = datetime.time(15, 30)
return data.between_time(market_open, market_close)
def update_5min_data(data_1min_filtered):
resampled = data_1min_filtered.resample('5T').agg({
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'volume': 'sum'
})
resampled = resampled.dropna(how='any')
return filter_market_hours(resampled)
def update_15min_data(data_1min_filtered):
resampled = data_1min_filtered.resample('15T').agg({
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last',
'volume': 'sum'
})
resampled = resampled.dropna(how='any')
return filter_market_hours(resampled)
data_5min_filtered = update_5min_data(data_1min_filtered)
data_15min_filtered = update_15min_data(data_1min_filtered)
for index, row in final_data_1min.iterrows():
row_df = pd.DataFrame([row], index=[index])
data_1min_filtered = pd.concat([data_1min_filtered, row_df]).sort_index()
data_5min_filtered = update_5min_data(data_1min_filtered)
data_15min_filtered = update_15min_data(data_1min_filtered)
tradingsymbol = watchlist['Script']
chart_1 = data_1min_filtered
chart_5 = data_5min_filtered
chart_15 = data_15min_filtered #use it if u need
chart_1['rsi'] = talib.RSI(chart_1['close'], timeperiod=14)
chart_5['rsi'] = talib.RSI(chart_5['close'], timeperiod=14)
max_order_limit = len(traded_watchlist)
if (max_order_limit < 1):
bc_5 = (chart_5['rsi'].iloc[-2]) < chart_5['rsi'].iloc[-1]
ltp = chart_1['close'].iloc[-1]
# Buy conditions
if (bc_5):
if (chart_1['rsi'].iloc[-1] > 60):
buy_time = chart_1.index[-1].strftime("%H:%M:%S")
buy_price = ltp
sl_price = chart_1['low'].iloc[-2]
traded_watchlist.append({"Script":tradingsymbol, "Buy": buy_price, "Buy_Time": buy_time, "SL_price": sl_price})
#Selling
max_order_limit = len(traded_watchlist)
if (max_order_limit > 0):
for wstock_name in traded_watchlist:
tradingsymbol = wstock_name["Script"]
sl_price = wstock_name["SL_price"]
buy_price = wstock_name["Buy"]
buy_time = wstock_name["Buy_Time"]
wltp = chart_1['close'].iloc[-1]
sell_time = chart_1.index[-1].strftime("%H:%M:%S")
#Sell conditions
if ((sl_price > wltp) or (chart_5['low'].iloc[-2] > wltp)): #Modify the selling condition as per your requirment
sell_price = wltp
quantity = lot_size * lots_number
Trade_point = round((sell_price - buy_price),2)
Trade_TO = sell_price + buy_price
Trade_PNL = round((Trade_point*quantity - (40 + Trade_TO*quantity*0.0011)),2) #Broker and other charges included roughly
tradelist.append({"SN": len(tradelist) + 1, "Script":tradingsymbol, "Buy": buy_price, "Buy_Time": buy_time, "SL_price": sl_price, "Sell": sell_price, "Sell_Time": sell_time, "Trade_point":Trade_point, "Trade_PNL":Trade_PNL})
traded_watchlist[:] = [item for item in traded_watchlist if item['Script'] != tradingsymbol]
except KeyboardInterrupt:
print("\nProcess interrupted by user (Ctrl+C).")
except Exception as e:
print("\nAn unexpected error occurred:", e)
finally:
print("\nSimulation ended.....", datetime.datetime.now().strftime("%H:%M:%S"))
print(f"\nFinal Trade Details for {watchlist['Selected_date']}:")
if tradelist:
for trade in tradelist:
trade['Buy_Time'] = trade['Buy_Time'].strftime("%H:%M:%S") if isinstance(trade['Buy_Time'], datetime.time) else trade['Buy_Time']
trade['Sell_Time'] = trade['Sell_Time'].strftime("%H:%M:%S") if isinstance(trade['Sell_Time'], datetime.time) else trade['Sell_Time']
headers = tradelist[0].keys()
column_widths = {header: max(len(str(header)), *(len(str(row[header])) for row in tradelist)) for header in headers}
header_row = " ".join(f"{header:<{column_widths[header]}}" for header in headers)
print(header_row)
for trade in tradelist:
row = " ".join(f"{str(value):<{column_widths[header]}}" for header, value in trade.items())
print(row)
Final_PNL = round(sum(trade['Trade_PNL'] for trade in tradelist), 2)
Final_point = round(sum(trade['Trade_point'] for trade in tradelist), 2)
print(f"\nFinal_point of 1 unit: {Final_point}")
print(f"Final_PNL of 1 lot: Rs.{Final_PNL}/-")
else:
print("No trades to display.")
2 Likes
@Tradehull_Imran - getting below error “ModuleNotFoundError: No module named ‘Dhan_Tradehull_V2’”.
And while installing pip install Dhan_Tradehull_V2, getting below error, any idea sir?
“ERROR: Could not find a version that satisfies the requirement Dhan_Tradehull_V2 (from versions: none)
ERROR: No matching distribution found for Dhan_Tradehull_V2”
@Tradehull_Imran sir Can You Share Strategy Backtesting Code
Hello sir Im Nimesh Guliana
Use API of Angel one
Algo for swing trading
Algo start time 9:15 end time 15:15
Algo for Equity time 1W chart
Strategy SME 50 for buy and SME 100 for sell ( just for try not for real )
Add
SL on last candel
in a day total trade will 4
no ripped trade
And more learn from you Sir
i hope you will make one video on it
1 Like
index_chart = tsl.get_historical_data(tradingsymbol='NIFTY JAN FUT', exchange='NFO', timeframe="1")
Dear sir,
For retrieving historical data, previously we were getting 5 trading days data as mentioned in ‘DHANHQ API documentation’, but now i getting for only two days. I am using the latest version of Tradehull_V2. Is this error appearing for all of us? Kindly verify.
Hello Sir, I am not able to understand, whether Vote is registered OR not?
How to confirm my Vote Registration, Please.
VBR Prasad
Oh, That’s a great Initiative…
2 Likes
Hello everyone,
I would like to propose a backtest of a scalping strategy that I’ve been studying. This strategy uses a combination of moving averages and the Average Directional Index (ADX) to identify strong trends and potential trade entries.
Scalping Strategy Overview:
Indicators Used:
- Simple Moving Average (SMA) – A basic lagging indicator.
- Exponential Moving Average (EMA) – More responsive to recent price changes.
- Jurik Moving Average (JMA) – A leading indicator that reacts faster than both SMA and EMA.
Why Three Moving Averages?
- SMA is lagging, EMA is more reactive, and JMA leads them all. This combination helps in filtering out noise and identifying trends more accurately.
Trend Identification Logic:
- Buy Signal: If JMA > EMA > SMA, the market is in an uptrend.
- Sell Signal: If JMA < EMA < SMA, the market is in a downtrend.
Additional Confirmation:
- ADX Indicator: We only consider trades when the ADX is above 25, which indicates a strong trend.
Entry Conditions:
For a Buy Trade:
- Trend Check: Ensure JMA > EMA > SMA.
- ADX Confirmation: ADX value must be above 25.
- Entry Point: Enter the trade after a pullback when the price bounces upward.
For a Sell Trade:
- Trend Check: Ensure JMA < EMA < SMA.
- ADX Confirmation: ADX value must be above 25.
- Entry Point: Enter the trade after a retracement when the price drops downward.
Risk Management: Stop-Loss and Take Profit
- Stop-Loss:
- Place your stop-loss at the high of the entry candle for a sell trade or the low of the entry candle for a buy trade.
- Take Profit:
- Aim for a 1:1.5 risk-to-reward ratio initially.
- Book 70% of your profit at this level.
- For the remaining position, aim for a 1:2 risk-to-reward ratio and exit the trade completely at this point.
I believe this strategy could be effective for fast-paced intraday trades, especially in volatile markets. Backtesting this could give us valuable insights into its success rate and areas of improvement.
Looking forward to hearing your thoughts!
4 Likes
Sir Installation done,
certutil -generateSSTFromWU roots.sst
certutil -addstore -f root roots.sst
i tested run working fine sir,
thankyou very much sir
1 Like