Where to find all the videos of Introduction to Algo Trading for beginners.. Pls
check 3-4 posts above . you will find the you tube link
1 Like
what about 2,3,4,5, videos
We are starting from this video
This is the first one
1 Like
sir ye link open nahi ho rahi koi or tarika he
sir ye update file kahan se kilegi
Dhan_Tradehull_V2 Algo
can someone recommend my a single or combination of indicator which really gives profit on algo strategy on index options. I just need name of the indicator rest i can figure it out. Thanks in advance
Hi @HASAN_khan ,
The same topics have been covered in the new series. Kindly follow the series for detailed guidance.
Refer the link-
Hi imran
I want to make a momentam stock trading strategy. My entry and exit condition are below.
Entry condition - On the Monday 9.15 am first chek and confirm that macd line (6,13,4) is greater than macd signle (6,13,4) on nifty 50 chart. If condition setisfy than check which sector is in top 5 (from NSE 47 sectors ) within last 30 calendar day. find top 2 stock from top 5 sector within last 30 calendar day. Also check that selected stock price is must be above 10 day moving average. If condition setisfy then take trade top 2 stock from each sector. please dont take trade which stock are already in holding.
Exit condition - (1) Every day 3.15 pm check condition, If Stock goes down below 20 day moving average then exit OR
(2) Check condition On friday 3.15 pm that stock goes down from top 5 rank in each sector then exit.
Please give me python code if possible or help me to build in python.
I am following Step 7 after completing Step 6 (after rebooting) of the “Static IP for Algo Trading: Full Setup” guide, as mentioned below:
7. Connect
-
Use Remote Desktop on Windows.
-
IP: Your Droplet IP.
-
Username:
root. -
Password: The password you created in DigitalOcean credentials.
I am connecting to the Remote Desktop using these credentials.
However, after connecting and entering the server, I only see a black screen.
Kindly guide me to solve this issue.
Hi @MUKESH_PATEL
Yes we can do this Algo, a nearly similar Algo template we have used here
try this video
Hi @Jay_Das
This may be mostly due to smaller configuration size of server,
Do try increasing server config and try again
sir data api lena ho ga kya
sir about hard ha python coz not geting proper help and out come nahi aya ra ha jo chaya vo can u hel me
import pandas as pd
import numpy as np
class NiftyAlgo:
“”"
NIFTY 15-MIN RANGE + 5-MIN RETEST STRATEGY
RULES:
1. Mark first 15-min candle High/Low as range
2. Wait for breakout (close above high or below low)
3. Wait for retest back to range level
4. Look for candlestick pattern at retest:
- BUY: Hammer or Bullish Engulfing
- SELL: Inverted Hammer or Bearish Engulfing
5. Entry: Break of pattern high (buy) / Break of pattern low (sell) on NEXT candle
6. Stop Loss: Pattern candle low (buy) / Pattern candle high (sell)
7. Target: 1:2 Risk-Reward Ratio
"""
def __init__(self, rr_ratio=2.0, retest_tolerance=0.002):
self.rr_ratio = rr_ratio
self.retest_tolerance = retest_tolerance
self.all_trades = [] # Store all completed trades
self.reset_session()
def reset_session(self):
self.range_high = None
self.range_low = None
self.range_set = False
self.state = "WAITING_FOR_RANGE"
self.breakout_direction = None
self.pattern_candle = None
self.pending_entry = None
self.stop_loss = None
self.target = None
self.trades = []
# --- CANDLESTICK PATTERN DETECTORS (FIXED) ---
def is_hammer(self, o, h, l, c):
"""Hammer: Small body at top, long lower shadow, little/no upper shadow"""
body = abs(c - o)
if body == 0:
return False
upper_shadow = h - max(o, c)
lower_shadow = min(o, c) - l
# RELAXED CONDITIONS for real market data
return (lower_shadow >= 1.5 * body and # Lower shadow at least 1.5x body (was 2x)
upper_shadow <= body * 1.0 and # Upper shadow <= body (was 0.3x)
c > o) # Bullish (green candle)
def is_bullish_engulfing(self, p_o, p_h, p_l, p_c, o, h, l, c):
"""Bullish Engulfing: Current green candle completely engulfs previous red candle"""
prev_red = p_c < p_o
curr_green = c > o
return (prev_red and curr_green and
o <= p_c and c >= p_o and # Relaxed: allow equal
abs(c - o) >= abs(p_c - p_o)) # Current body >= previous body
def is_inverted_hammer(self, o, h, l, c):
"""Inverted Hammer: Small body at bottom, long upper shadow, little/no lower shadow"""
body = abs(c - o)
if body == 0:
return False
upper_shadow = h - max(o, c)
lower_shadow = min(o, c) - l
return (upper_shadow >= 1.5 * body and # Relaxed: 1.5x instead of 2x
lower_shadow <= body * 1.0 and # Relaxed: 1.0x instead of 0.3x
c < o) # Bearish (red candle)
def is_bearish_engulfing(self, p_o, p_h, p_l, p_c, o, h, l, c):
"""Bearish Engulfing: Current red candle completely engulfs previous green candle"""
prev_green = p_c > p_o
curr_red = c < o
return (prev_green and curr_red and
o >= p_c and c <= p_o and # Relaxed: allow equal
abs(c - o) >= abs(p_c - p_o)) # Current body >= previous body
# --- MAIN PROCESSING ---
def process_15min(self, candle):
"""Process first 15-min candle to set range"""
if not self.range_set:
self.range_high = candle['high']
self.range_low = candle['low']
self.range_set = True
self.state = "WAITING_FOR_BREAKOUT"
return {
"action": "RANGE_MARKED",
"high": self.range_high,
"low": self.range_low
}
return None
def process_5min(self, candle, prev_candles):
"""Process 5-min candles"""
result = {"action": "NONE", "state": self.state}
o, h, l, c = candle['open'], candle['high'], candle['low'], candle['close']
# === STATE: WAITING FOR BREAKOUT ===
if self.state == "WAITING_FOR_BREAKOUT":
if c > self.range_high:
self.breakout_direction = "UP"
self.state = "WAITING_FOR_RETEST"
result = {"action": "BREAKOUT_UP", "price": c}
elif c < self.range_low:
self.breakout_direction = "DOWN"
self.state = "WAITING_FOR_RETEST"
result = {"action": "BREAKOUT_DOWN", "price": c}
# === STATE: WAITING FOR RETEST ===
elif self.state == "WAITING_FOR_RETEST":
if self.breakout_direction == "UP":
# Price comes back to range high area
near_range = abs(c - self.range_high) / self.range_high < self.retest_tolerance
in_range = l <= self.range_high <= h
if near_range or in_range:
self.state = "WAITING_FOR_PATTERN"
result = {"action": "RETEST_UP", "price": c}
elif self.breakout_direction == "DOWN":
near_range = abs(c - self.range_low) / self.range_low < self.retest_tolerance
in_range = l <= self.range_low <= h
if near_range or in_range:
self.state = "WAITING_FOR_PATTERN"
result = {"action": "RETEST_DOWN", "price": c}
# === STATE: WAITING FOR PATTERN ===
elif self.state == "WAITING_FOR_PATTERN":
if len(prev_candles) >= 1:
prev = prev_candles[-1]
p_o, p_h, p_l, p_c = prev['open'], prev['high'], prev['low'], prev['close']
# BULLISH PATTERNS
if self.breakout_direction == "UP":
hammer = self.is_hammer(o, h, l, c)
engulfing = self.is_bullish_engulfing(p_o, p_h, p_l, p_c, o, h, l, c)
if hammer:
self.pattern_candle = candle
self.state = "WAITING_FOR_CONFIRMATION"
self.pending_entry = {
"type": "BUY",
"trigger": "HAMMER",
"ph": h,
"pl": l
}
result = {"action": "PATTERN_HAMMER", "direction": "BUY", "sl": l}
elif engulfing:
self.pattern_candle = candle
self.state = "WAITING_FOR_CONFIRMATION"
self.pending_entry = {
"type": "BUY",
"trigger": "BULLISH_ENGULFING",
"ph": h,
"pl": l
}
result = {"action": "PATTERN_BULLISH_ENGULFING", "direction": "BUY", "sl": l}
# BEARISH PATTERNS
elif self.breakout_direction == "DOWN":
inv_hammer = self.is_inverted_hammer(o, h, l, c)
engulfing = self.is_bearish_engulfing(p_o, p_h, p_l, p_c, o, h, l, c)
if inv_hammer:
self.pattern_candle = candle
self.state = "WAITING_FOR_CONFIRMATION"
self.pending_entry = {
"type": "SELL",
"trigger": "INVERTED_HAMMER",
"ph": h,
"pl": l
}
result = {"action": "PATTERN_INVERTED_HAMMER", "direction": "SELL", "sl": h}
elif engulfing:
self.pattern_candle = candle
self.state = "WAITING_FOR_CONFIRMATION"
self.pending_entry = {
"type": "SELL",
"trigger": "BEARISH_ENGULFING",
"ph": h,
"pl": l
}
result = {"action": "PATTERN_BEARISH_ENGULFING", "direction": "SELL", "sl": h}
# === STATE: WAITING FOR CONFIRMATION (Break of pattern) ===
elif self.state == "WAITING_FOR_CONFIRMATION":
entry = self.pending_entry
if entry['type'] == "BUY":
if h > entry['ph']:
self.state = "ENTRY_TRIGGERED"
self.stop_loss = entry['pl']
result = {
"action": "ENTRY_TRIGGERED",
"direction": "BUY",
"trigger_price": entry['ph'],
"stop_loss": self.stop_loss
}
elif entry['type'] == "SELL":
if l < entry['pl']:
self.state = "ENTRY_TRIGGERED"
self.stop_loss = entry['ph']
result = {
"action": "ENTRY_TRIGGERED",
"direction": "SELL",
"trigger_price": entry['pl'],
"stop_loss": self.stop_loss
}
# === STATE: ENTRY TRIGGERED → EXECUTE ON NEXT CANDLE OPEN ===
elif self.state == "ENTRY_TRIGGERED":
entry_price = o # Entry at next candle open
risk = abs(entry_price - self.stop_loss)
if self.pending_entry['type'] == "BUY":
self.target = entry_price + (risk * self.rr_ratio)
else:
self.target = entry_price - (risk * self.rr_ratio)
trade = {
"direction": self.pending_entry['type'],
"entry": entry_price,
"sl": self.stop_loss,
"target": self.target,
"pattern": self.pending_entry['trigger'],
"time": candle['timestamp']
}
self.trades.append(trade)
result = {
"action": "TRADE_EXECUTED",
"direction": self.pending_entry['type'],
"entry": entry_price,
"sl": self.stop_loss,
"target": self.target,
"risk": risk
}
self.state = "IN_TRADE"
# === STATE: IN TRADE → Monitor SL/Target ===
elif self.state == "IN_TRADE":
trade = self.trades[-1]
if trade['direction'] == "BUY":
if l <= trade['sl']:
# Save completed trade before reset
completed_trade = {
"direction": trade['direction'],
"entry": trade['entry'],
"sl": trade['sl'],
"target": trade['target'],
"pattern": trade['pattern'],
"time": trade['time'],
"exit": trade['sl'],
"pnl": trade['sl'] - trade['entry'],
"reason": "SL"
}
self.all_trades.append(completed_trade)
self.reset_session()
result = {"action": "EXIT", "reason": "SL", "pnl": completed_trade['pnl']}
elif h >= trade['target']:
completed_trade = {
"direction": trade['direction'],
"entry": trade['entry'],
"sl": trade['sl'],
"target": trade['target'],
"pattern": trade['pattern'],
"time": trade['time'],
"exit": trade['target'],
"pnl": trade['target'] - trade['entry'],
"reason": "TARGET"
}
self.all_trades.append(completed_trade)
self.reset_session()
result = {"action": "EXIT", "reason": "TARGET", "pnl": completed_trade['pnl']}
else: # SELL
if h >= trade['sl']:
completed_trade = {
"direction": trade['direction'],
"entry": trade['entry'],
"sl": trade['sl'],
"target": trade['target'],
"pattern": trade['pattern'],
"time": trade['time'],
"exit": trade['sl'],
"pnl": trade['entry'] - trade['sl'],
"reason": "SL"
}
self.all_trades.append(completed_trade)
self.reset_session()
result = {"action": "EXIT", "reason": "SL", "pnl": completed_trade['pnl']}
elif l <= trade['target']:
completed_trade = {
"direction": trade['direction'],
"entry": trade['entry'],
"sl": trade['sl'],
"target": trade['target'],
"pattern": trade['pattern'],
"time": trade['time'],
"exit": trade['target'],
"pnl": trade['entry'] - trade['target'],
"reason": "TARGET"
}
self.all_trades.append(completed_trade)
self.reset_session()
result = {"action": "EXIT", "reason": "TARGET", "pnl": completed_trade['pnl']}
return result
@Jay_Das was the server black screen solved. were you able to run the algo
Very Good Morning Sir. You have started another New series: ‘AI and Algos by Dhan’ I could not traceout the code snippets of these series. What exactly the link, if I have missed them to traceout sir. Thanks a lot sir for all your efforts at Dhan.
1 Like
IMRAN SIR , PLZ , IN DEVLOPER.DHANHQ , SOLVE THIS ERROR & IF POSSIBLE THEN MAKE A FULL VIDEO OF DEPLOYMENT OF ALGO (USING DEPENDENCIES & ENV VARIEBELS)
CODE
from Dhan_Tradehull import Tradehull
import pandas as pd
from rich import print
import time
client_id = {{CLIENT_CODE}}
access_token = {{ACCESS_TOKEN}}
tsl = Tradehull(client_id, access_token)
print(" Dhan session initialized successfully!")
watchlist = ["WIPRO","TATASTEEL", "JIOFIN"]
for name in watchlist:
chart = tsl.get_historical_data(tradingsymbol=name, exchange='NSE', timeframe="5")
completed_candle = chart.iloc[-2]
entry_price = round(completed_candle['close']*1.01, 1)
sl_price = round(completed_candle['close']*0.99, 1)
target_price = round(completed_candle['close']*1.02, 1)
orderid =tsl.place_super_order(tradingsymbol=name,
exchange="NSE",
transaction_type="BUY",
quantity=1,
order_type="LIMIT",
trade_type="MIS",
price=entry_price,
target_price=target_price,
stop_loss_price=sl_price,
trailing_jump=0.5)
print(orderid)
time.sleep(1)
ERROR:
›
Starting execution...
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Execution started, waiting for log stream...
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Waiting for log stream... (1/24)
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Log stream ready, connecting...
›
[2026-06-29 09:55:16 IST] Starting execution...
›
[2026-06-29 09:55:22 IST] Installing requirements...
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[2026-06-29 09:55:24 IST] [pip] Collecting pandas==3.0.3 (from -r /tmp/requirements.txt (line 1))
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[2026-06-29 09:55:25 IST] [pip] Downloading pandas-3.0.3-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl.metadata (79 kB)
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[2026-06-29 09:55:25 IST] [pip] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 79.5/79.5 kB 24.1 MB/s eta 0:00:00
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[2026-06-29 09:55:25 IST] [pip] Collecting Dhan-tradehull==3.3.1 (from -r /tmp/requirements.txt (line 2))
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[2026-06-29 09:55:25 IST] [pip] Downloading dhan_tradehull-3.3.1-py3-none-any.whl.metadata (46 kB)
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[2026-06-29 09:55:25 IST] [pip] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 46.5/46.5 kB 49.6 MB/s eta 0:00:00
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[2026-06-29 09:55:25 IST] [pip] Collecting dhanhq==2.2.0 (from -r /tmp/requirements.txt (line 3))
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[2026-06-29 09:55:25 IST] [pip] Downloading dhanhq-2.2.0-py3-none-any.whl.metadata (13 kB)
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[2026-06-29 09:55:25 IST] [pip] Collecting rich==15.0.0 (from -r /tmp/requirements.txt (line 4))
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[2026-06-29 09:55:25 IST] [pip] Downloading rich-15.0.0-py3-none-any.whl.metadata (18 kB)
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[2026-06-29 09:55:25 IST] [pip] Downloading pandas-3.0.3-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl (11.3 MB)
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[2026-06-29 09:55:25 IST] [pip] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 11.3/11.3 MB 272.1 MB/s eta 0:00:00
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[2026-06-29 09:55:25 IST] [pip] Downloading dhan_tradehull-3.3.1-py3-none-any.whl (38 kB)
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[2026-06-29 09:55:25 IST] [pip] Downloading dhanhq-2.2.0-py3-none-any.whl (36 kB)
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[2026-06-29 09:55:25 IST] [pip] Downloading rich-15.0.0-py3-none-any.whl (310 kB)
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[2026-06-29 09:55:25 IST] [pip] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 310.7/310.7 kB 222.1 MB/s eta 0:00:00
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[2026-06-29 09:55:25 IST] [pip] Installing collected packages: dhanhq, rich, pandas, Dhan-tradehull
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[2026-06-29 09:55:32 IST] [pip] Successfully installed Dhan-tradehull-3.3.1 dhanhq-2.2.0 pandas-3.0.3 rich-15.0.0
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[2026-06-29 09:55:32 IST] [pip]
›
[2026-06-29 09:55:32 IST] [pip] [notice] A new release of pip is available: 24.0 -> 26.1.2
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[2026-06-29 09:55:32 IST] [pip] [notice] To update, run: pip install --upgrade pip
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[2026-06-29 09:55:34 IST] [pip] Requirement already satisfied: pandas==3.0.3 in /tmp/pip/lib/python3.11/site-packages (from -r /tmp/requirements.txt (line 1)) (3.0.3)
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[2026-06-29 09:55:34 IST] [pip] Requirement already satisfied: Dhan-tradehull==3.3.1 in /tmp/pip/lib/python3.11/site-packages (from -r /tmp/requirements.txt (line 2)) (3.3.1)
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[2026-06-29 09:55:34 IST] [pip] Requirement already satisfied: dhanhq==2.2.0 in /tmp/pip/lib/python3.11/site-packages (from -r /tmp/requirements.txt (line 3)) (2.2.0)
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[2026-06-29 09:55:34 IST] [pip] Requirement already satisfied: rich==15.0.0 in /tmp/pip/lib/python3.11/site-packages (from -r /tmp/requirements.txt (line 4)) (15.0.0)
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[2026-06-29 09:55:34 IST] [pip] Collecting numpy>=1.26.0 (from pandas==3.0.3->-r /tmp/requirements.txt (line 1))
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[2026-06-29 09:55:34 IST] [pip] Downloading numpy-2.4.6-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (6.6 kB)
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[2026-06-29 09:55:34 IST] [pip] Collecting python-dateutil>=2.8.2 (from pandas==3.0.3->-r /tmp/requirements.txt (line 1))
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[2026-06-29 09:55:34 IST] [pip] Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB)
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[2026-06-29 09:55:34 IST] [pip] Collecting mibian>=0.1.3 (from Dhan-tradehull==3.3.1->-r /tmp/requirements.txt (line 2))
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[2026-06-29 09:55:34 IST] [pip] Downloading mibian-0.1.3.zip (4.3 kB)
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[2026-06-29 09:55:34 IST] [pip] Preparing metadata (setup.py): started
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[2026-06-29 09:55:35 IST] [pip] Preparing metadata (setup.py): finished with status 'done'
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[2026-06-29 09:55:35 IST] [pip] Collecting pytz>=2024.1 (from Dhan-tradehull==3.3.1->-r /tmp/requirements.txt (line 2))
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[2026-06-29 09:55:35 IST] [pip] Downloading pytz-2026.2-py2.py3-none-any.whl.metadata (22 kB)
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[2026-06-29 09:55:35 IST] [pip] Collecting requests>=2.32.3 (from Dhan-tradehull==3.3.1->-r /tmp/requirements.txt (line 2))
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[2026-06-29 09:55:35 IST] [pip] Downloading requests-2.34.2-py3-none-any.whl.metadata (4.8 kB)
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[2026-06-29 09:55:35 IST] [pip] Collecting websocket-client>=1.8.0 (from Dhan-tradehull==3.3.1->-r /tmp/requirements.txt (line 2))
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[2026-06-29 09:55:35 IST] [pip] Downloading websocket_client-1.9.0-py3-none-any.whl.metadata (8.3 kB)
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[2026-06-29 09:55:35 IST] [pip] Collecting pyotp>=2.9.0 (from Dhan-tradehull==3.3.1->-r /tmp/requirements.txt (line 2))
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[2026-06-29 09:55:35 IST] [pip] Downloading pyotp-2.10.0-py3-none-any.whl.metadata (10 kB)
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[2026-06-29 09:55:35 IST] [pip] Collecting websockets>=12.0.1 (from dhanhq==2.2.0->-r /tmp/requirements.txt (line 3))
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[2026-06-29 09:55:35 IST] [pip] Downloading websockets-16.0-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl.metadata (6.8 kB)
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[2026-06-29 09:55:35 IST] [pip] Collecting pyOpenSSL>=20.0.1 (from dhanhq==2.2.0->-r /tmp/requirements.txt (line 3))
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[2026-06-29 09:55:35 IST] [pip] Downloading pyopenssl-26.3.0-py3-none-any.whl.metadata (22 kB)
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[2026-06-29 09:55:35 IST] [pip] Collecting markdown-it-py>=2.2.0 (from rich==15.0.0->-r /tmp/requirements.txt (line 4))
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[2026-06-29 09:55:35 IST] [pip] Downloading markdown_it_py-4.2.0-py3-none-any.whl.metadata (7.4 kB)
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[2026-06-29 09:55:35 IST] [pip] Collecting pygments<3.0.0,>=2.13.0 (from rich==15.0.0->-r /tmp/requirements.txt (line 4))
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[2026-06-29 09:55:35 IST] [pip] Downloading pygments-2.20.0-py3-none-any.whl.metadata (2.5 kB)
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[2026-06-29 09:55:35 IST] [pip] Collecting mdurl~=0.1 (from markdown-it-py>=2.2.0->rich==15.0.0->-r /tmp/requirements.txt (line 4))
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[2026-06-29 09:55:35 IST] [pip] Downloading mdurl-0.1.2-py3-none-any.whl.metadata (1.6 kB)
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[2026-06-29 09:55:36 IST] [pip] Collecting cryptography<50,>=49.0.0 (from pyOpenSSL>=20.0.1->dhanhq==2.2.0->-r /tmp/requirements.txt (line 3))
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[2026-06-29 09:55:37 IST] [pip] Building wheels for collected packages: mibian
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[2026-06-29 09:55:37 IST] [pip] Building wheel for mibian (setup.py): started
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[2026-06-29 09:55:38 IST] [pip] Building wheel for mibian (setup.py): finished with status 'done'
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[2026-06-29 09:55:38 IST] [pip] Created wheel for mibian: filename=mibian-0.1.3-py3-none-any.whl size=4071 sha256=79ae29c6e120d75ab52f4d85ab55d8c23b30608671a6aeff771db074f09bfc01
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[2026-06-29 09:55:38 IST] [pip] Stored in directory: /tmp/pip-ephem-wheel-cache-1e6c3e7k/wheels/e7/5e/5c/da32a012a1a27b1f6f61a7744eac2eb20ab4854aba3143b711
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[2026-06-29 09:55:38 IST] [pip] Successfully built mibian
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[2026-06-29 09:55:38 IST] [pip] Installing collected packages: pytz, mibian, websockets, websocket-client, urllib3, typing-extensions, six, pyotp, pygments, pycparser, numpy, mdurl, idna, charset_normalizer, certifi, requests, python-dateutil, markdown-it-py, cffi, cryptography, pyOpenSSL
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[2026-06-29 09:55:44 IST] [pip] Successfully installed certifi-2026.6.17 cffi-2.0.0 charset_normalizer-3.4.7 cryptography-49.0.0 idna-3.18 markdown-it-py-4.2.0 mdurl-0.1.2 mibian-0.1.3 numpy-2.4.6 pyOpenSSL-26.3.0 pycparser-3.0 pygments-2.20.0 pyotp-2.10.0 python-dateutil-2.9.0.post0 pytz-2026.2 requests-2.34.2 six-1.17.0 typing-extensions-4.15.0 urllib3-2.7.0 websocket-client-1.9.0 websockets-16.0
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[2026-06-29 09:55:44 IST] [pip]
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[2026-06-29 09:55:44 IST] [pip] [notice] A new release of pip is available: 24.0 -> 26.1.2
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[2026-06-29 09:55:44 IST] [pip] [notice] To update, run: pip install --upgrade pip
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[2026-06-29 09:55:46 IST] ==================== SCRIPT OUTPUT START ====================
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[2026-06-29 09:55:47 IST] Mibian requires scipy to work properly
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[2026-06-29 09:55:47 IST] Codebase Version 3.3.1
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[2026-06-29 09:55:47 IST] Traceback (most recent call last):
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[2026-06-29 09:55:47 IST] File "/tmp/script.py", line 7, in <module>
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[2026-06-29 09:55:47 IST] access_token =eyJ0eXAiOiJKV1Q*****************************************************************************************************************************************************************************************************VvI6RsT0J-YnyDu18Zv-sFyg
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[2026-06-29 09:55:47 IST] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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[2026-06-29 09:55:47 IST] NameError: name 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzUxMiJ9' is not defined
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[2026-06-29 09:55:47 IST] ==================== SCRIPT OUTPUT END ====================
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[2026-06-29 09:55:47 IST] Execution failed with exit code: 1
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Execution completed.
GIVE SAME DEPENDENCIES USED BY YOU IN YOUR DEMO CODE OR EXPLAIN THAT PART DEPLY TO USE
BEACUSE AS I KNOW MY PYTHON CODE HAVE NO ERROR BUT ERROR IS COMING IN DEVLOPER.DHANHQ
I HAVE SEND SAME ERRORS & CODE IN LAST WEEK BUT I DIDN’T GET ANY RESPONSE , SO PLZ SOLVE THIS AS FAST AS POSIBLE
Hi @Ammar_parihar
yes data api subscription will be required
for this strategy
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Mark first 15-min candle High/Low as range
To get first 15 mins high low range, just call historical data, slice it, and get range -
For hammer pattern and bullish engulfing use TA-LIB : https://ta-lib.github.io/ta-lib-python/func_groups/pattern_recognition.html
-
in this code we are not calling any real data, we will need to subscribe to Data also to make live algo for it
try these changes, and let me know on the progress
for code refer this link : https://madefortrade.in/t/learn-algo-trading-with-python-codes-youtube-series/32718/3817?u=tradehull_imran