Welcome to Free Python Codes Library 🚀
Here you can find ready-to-use Python scripts for:
✔ Algo Trading
✔ Telegram Bots
✔ Historical Data Codes
✔ Automation & APIs… etc.
⚠️ Important Notice & Disclaimer
The Python codes shared on this page are provided free of charge for educational and learning purposes only.
While every effort is made to ensure accuracy and proper functionality, we do not guarantee that the code will produce the same results on every system or environment. Differences in system configuration, Python versions, libraries, APIs, or external data sources may affect the output.
Users are strongly advised to review, test, and cross-check all code outputs before using them in live environments, trading systems, or production setups.
By using these codes, you acknowledge that you are solely responsible for any results, outcomes, or losses, and we shall not be held liable for incorrect values, errors, or unintended behavior.
Telegram Bot Python Codes
import requests
import logging
import creds as cr
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# ========================== Configuration ==========================
bot_token = cr.bot_token
chat_id = cr.chat_id
# ========================== Telegram Sender ==========================
def send_telegram_message(bot_token, chat_id, message):
url = f"https://api.telegram.org/bot{bot_token}/sendMessage"
payload = {
'chat_id': chat_id,
'text': message
}
try:
response = requests.post(url, data=payload)
return response.json()
except Exception as e:
logging.error(f"Telegram Error: {e}")
return None
send_telegram_message(bot_token, chat_id, "Hi.....This is Python")
send_telegram_message(bot_token, chat_id, f"Credentials:\n Bot_Token: {bot_token} \n💥\n Chat_ID: {chat_id} \n💥")
Fetching Session Token – Flattrade
# ========================== Initial Necessary Codes ==========================
file = open('flattrade_session_token.txt', 'r')
token = file.readline()
print (token)
import creds as cr
from api_helper import NorenApiPy
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
#start of our program
api = NorenApiPy()
usersession=token
ret = api.set_session(userid=cr.user_id, password=cr.pws, usertoken=usersession)
Historical Intra Day Data – Flattrade
# ========================== Initial Necessary Codes ==========================
with open('flattrade_session_token.txt', 'r') as file:
token = file.readline().strip()
print (token)
import creds as cr
from api_helper import NorenApiPy
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
#start of our program
api = NorenApiPy()
usersession=token
ret = api.set_session(userid=cr.user_id, password=cr.pws, usertoken=usersession)
# ========================== Historical data Codes ==========================
from api_helper import NorenApiPy
import logging
from datetime import datetime, timedelta
import pandas as pd
exchange = 'NSE' # 'NFO' for F&O
Time_Frame = 5 # enter the timeframe in which candle data you want
# ========================== Fetch Historical Option Data ==========================
def historical_data(token):
try:
start_time = (datetime.today() - timedelta(days=4)).replace(hour=9, minute=15, second=0, microsecond=0)
ret = api.get_time_price_series(exchange=exchange, token=token, starttime=start_time.timestamp(), interval=Time_Frame)
if not ret:
return None
df = pd.DataFrame(ret)
df['intc'] = pd.to_numeric(df['intc'], errors='coerce')
df['inth'] = pd.to_numeric(df['inth'], errors='coerce')
df['intl'] = pd.to_numeric(df['intl'], errors='coerce')
df['intv'] = pd.to_numeric(df['intv'], errors='coerce')
return df
except Exception as e:
logging.error(f"Error fetching historical data for token {token}: {e}")
return None
his_data = historical_data(token='22')
print (his_data)
Buy / Sell Trigger Code (In Function) – Flattrade
exchange = 'NSE' # 'BSE' 'NFO' 'BFO'
# ========================== Orders ==========================
def place_buy_order(Script, qty):
try:
ret = api.place_order(buy_or_sell='B', product_type='I',
exchange=exchange, tradingsymbol=Script,
quantity=qty, discloseqty=0, price_type='MKT', price=0, trigger_price=None,
retention='DAY', remarks='Buy_Triggered')
return ret
except Exception as e:
logging.error(f"Error placing buy order for {Script}: {e}")
return None
def place_sell_order(Script, qty):
try:
ret = api.place_order(buy_or_sell='S', product_type='I',
exchange=exchange, tradingsymbol=Script,
quantity=qty, discloseqty=0,price_type='MKT', price=0, trigger_price=None,
retention='DAY', remarks='Square-Off')
return ret
except Exception as e:
logging.error(f"Error placing sell order for {Script}: {e}")
return None
Market Time | Python Codes
import time
def myalgo():
print (" my code is running")
while True:
now = time.localtime()
if now.tm_hour < 9 or (now.tm_hour == 9 and now.tm_min < 15):
print (" waiting for market to start")
time.sleep(60)
continue
elif now.tm_hour > 15 or (now.tm_hour == 15 and now.tm_min >= 30):
print ("Market is off now.. bye bye see you tomarrow")
break
myalgo()
time.sleep(5)
SMA vs EMA Indicator | Python Codes
df = his_data[::-1] df['SMA20'] = round(df.intc.rolling(window=20).mean(), 2) df['EMA09'] = round(df.intc.ewm(span=9, adjust=False).mean(), 2) df[::-1]
EMA Manual | Python Codes
df = his_data[::-1]
# Calculate EMA...
def Ema(df):
try:
period = 9
alpha = 2 / (period + 1)
# Initialize EMA list
ema = []
# Loop to calculate EMA
for i in range(len(df)):
if i < period:
# Not enough data to calculate EMA yet
ema.append(None)
elif i == period:
# First EMA is the SMA of the first 'period' values
sma = df['intc'].iloc[i - period:i].mean()
ema.append(sma)
else:
# Apply EMA formula
prev_ema = ema[-1]
price = df['intc'].iloc[i]
ema_val = alpha * price + (1 - alpha) * prev_ema
ema.append(ema_val)
# Assign to new column in df
df['EMA_09'] = ema
return df
except Exception as e:
logging.error(f"Error calculating EMA: {e}")
return df
df_ema = Ema(df)
df_ema['EMA_09'] = round(df_ema['EMA_09'], 2)
df = df_ema[::-1]
df
SUPERTrend Indicator | Python Codes
import numpy as np
df = his_data[::-1]
def Supertrend(df, atr_period=10, multiplier=3.0):
"""
Calculate the Supertrend indicator.
"""
try:
high = df['inth']
low = df['intl']
close = df['intc']
# Calculate ATR using ewm like original code
price_diffs = [high - low,
high - close.shift(),
close.shift() - low]
true_range = pd.concat(price_diffs, axis=1)
true_range = true_range.abs().max(axis=1)
atr = true_range.ewm(alpha=1/atr_period, min_periods=atr_period).mean()
hl2 = (high + low) / 2
final_upperband = upperband = hl2 + (multiplier * atr)
final_lowerband = lowerband = hl2 - (multiplier * atr)
# Initialize supertrend array with boolean values like original code
supertrend = [True] * len(df)
for i in range(1, len(df.index)):
curr, prev = i, i - 1
if close.iloc[curr] > final_upperband.iloc[prev]:
supertrend[curr] = True
elif close.iloc[curr] < final_lowerband.iloc[prev]:
supertrend[curr] = False
else:
supertrend[curr] = supertrend[prev]
if supertrend[curr] == True and final_lowerband.iloc[curr] < final_lowerband.iloc[prev]:
final_lowerband.iat[curr] = final_lowerband.iat[prev]
if supertrend[curr] == False and final_upperband.iloc[curr] > final_upperband.iloc[prev]:
final_upperband.iat[curr] = final_upperband.iat[prev]
if supertrend[curr] == True:
final_upperband.iat[curr] = np.nan
else:
final_lowerband.iat[curr] = np.nan
# Add the calculated values to the DataFrame
df['supertrend'] = supertrend
df['final_lowerband'] = round(final_lowerband, 2)
df['final_upperband'] = round(final_upperband, 2)
return df
except Exception as e:
logging.error(f"Error calculating Supertrend: {e}")
return df
df_sup = Supertrend(df)
df = df_sup[::-1]
sheet['B2'].value = df
df
ATR Indicator | Python Codes
def atr(df, window=14):
# True Range
high_low = df['inth'] - df['intl']
high_close = (df['inth'] - df['intc'].shift(1)).abs()
low_close = (df['intl'] - df['intc'].shift(1)).abs()
tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
# Wilder ATR (Real ATR)
df['ATR'] = tr.ewm(alpha=1/window, adjust=False).mean()
return df
VWAP Indicator (for Intraday)| Python Codes
def intraday_vwap(df):
df = df.copy()
# Convert time column
df['time'] = pd.to_datetime(df['time'], format="%d-%m-%Y %H:%M:%S")
# Filter only latest trading day
latest_date = df['time'].dt.date.iloc[-1]
df = df[df['time'].dt.date == latest_date].copy()
# Calculate VWAP
df['VWAP'] = (
((df['inth'] + df['intl'] + df['intc']) / 3) *
df['intv']
).cumsum() / df['intv'].cumsum()
return df
Fetch Trade Data in Excel | Python Codes
def append_trade_row(sheet, row_data):
last_row = sheet.range("A" + str(sheet.cells.last_cell.row)).end("up").row + 1
sheet.range(f"A{last_row}").value = row_data
Trailing Stop-Loss | Python Codes (Must Watch Related Video on YouTube Channel) link below:
# ========================== Trailing Stop Loss Buy ==========================
class TrailingStopLoss_Buy:
def __init__(self, initial_price, trailing_stop_percentage):
self.initial_price = initial_price
self.trailing_stop_percentage = trailing_stop_percentage
self.trailing_stop_price = int(initial_price * (1 - trailing_stop_percentage / 100))
def update(self, current_price):
if current_price > self.initial_price:
self.initial_price = current_price
self.trailing_stop_price = int(current_price * (1 - self.trailing_stop_percentage / 100))
def should_sell(self, current_price):
return current_price <= self.trailing_stop_price
# ========================== Trailing Stop Loss Sell ==========================
class TrailingStopLoss_Sell:
def __init__(self, initial_price, trailing_stop_percentage):
self.initial_price = initial_price
self.trailing_stop_percentage = trailing_stop_percentage
self.trailing_stop_price = int(initial_price * (1 + trailing_stop_percentage / 100))
def update(self, current_price):
if current_price < self.initial_price:
self.initial_price = current_price
self.trailing_stop_price = int(current_price * (1 + self.trailing_stop_percentage / 100))
def should_buy(self, current_price):
return current_price >= self.trailing_stop_price
Get/Fetch Data with Google Sheet | Python Codes
import gspread
import logging
# ========================== Data Exchange with Google Sheet ==================
gs = gspread.service_account(filename="google.json")
sh = gs.open_by_url("https://docs.google.com/spreadsheets/xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx") #Enter your google sheet url
def update_google_sheet(sheet_name, cell_no, data):
try:
sheet = sh.worksheet(sheet_name) # ✅ correct
sheet.update(cell_no, [[data]]) # ✅ correct format
logging.info(f"Updated {sheet_name} {cell_no} with {data}")
except Exception as e:
logging.error(f"Google Sheets Error: {e}")
def read_google_sheet(sheet_name, cell_no):
try:
sheet = sh.worksheet(sheet_name)
value = sheet.acell(cell_no).value # ✅ get single cell
return value
except Exception as e:
logging.error(f"Read Error: {e}")
return None
def get_range_values(sheet_name, cell_range):
try:
sheet = sh.worksheet(sheet_name)
data = sheet.get(cell_range) # get range
# Flatten + remove empty values
values = [item for sublist in data for item in sublist if item]
return values
except Exception as e:
logging.error(f"Read Range Error: {e}")
return []
def append_trade_row(sheet_name, row_data):
try:
sheet = sh.worksheet(sheet_name)
sheet.append_row(row_data) # ✅ auto adds to next row
logging.info("Trade row appended successfully.")
except Exception as e:
logging.error(f"Append Error: {e}")
# ======= Just as an Examples Update as per your need ===============
# values read from Google Sheet
trade_type = read_google_sheet("Config", "B2")
# Fetch Data in Google Sheet
update_google_sheet(sheet_name="Confit", cell_no="B5", data=cash)
# Get Range Data from Google Sheet
stocks = get_range_values("Config", "F2:F6")
# Apeend row Data in Google Sheet
append_trade_row(
"Paper Trades", # sheet name
[
orderbook[stock]['Date'],
orderbook[stock]['Entry_Time'],
orderbook[stock]['Side'],
orderbook[stock]['stock_name'],
orderbook[stock]['buy_entry_price'],
orderbook[stock]['Exit_time'],
orderbook[stock]['Exit_Price'],
orderbook[stock]['Qty'],
orderbook[stock]['PnL'],
orderbook[stock]['Exit_Reason'],
orderbook[stock]['traded'],
orderbook[stock]['Charges'],
orderbook[stock]['FPnL']
]
)