import os import time import pandas as pd import pandas_ta as ta import logging from datetime import datetime, timedelta import pytz from thefirstock import thefirstock from typing import List import numpy as np import requests import sys class StockDataFetcher: def __init__(self, client_details: List[str]): self.client_details = client_details self.user_id = client_details[0] self.ist = pytz.timezone('Asia/Kolkata') self.logger = self.setup_logger() print("\n\n\n\n") self.logger.info("**************************") def setup_logger(self) -> logging.Logger: logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) if not logger.handlers: formatter = self.ISTFormatter('%(asctime)s - %(levelname)s - %(message)s') console_handler = logging.StreamHandler() console_handler.setFormatter(formatter) logger.addHandler(console_handler) logging.getLogger().handlers.clear() return logger class ISTFormatter(logging.Formatter): def formatTime(self, record, datefmt=None): ist = pytz.timezone('Asia/Kolkata') record_time = datetime.fromtimestamp(record.created, tz=ist) return record_time.strftime(datefmt or '%Y-%m-%d %H:%M:%S') def login(self): try: response = thefirstock.firstock_login(*self.client_details) if response.get("status") == "success": self.logger.info("Login successful") else: self.logger.error(f"Login failed: {response}") except Exception as e: self.logger.error(f"Login error: {e}") def fetch_time_price_series( self, exchange: str, trading_symbol: str, start_time: str, end_time: str, interval: str ) -> pd.DataFrame: print("\n\n") self.logger.info(f"Fetching data for {trading_symbol}") try: response = thefirstock.firstock_TimePriceSeries( userId=self.user_id, exchange=exchange, tradingSymbol=trading_symbol, startTime=start_time, endTime=end_time, interval=interval, ) if response.get("status") == "success": return pd.DataFrame(response.get("data", [])) else: self.logger.error(f"Fetch failed: {response}") return pd.DataFrame() except Exception as e: self.logger.error(f"Error fetching series: {e}") return pd.DataFrame() def getStepValueAndShortSymbol(self, symbol): tmp_symbol=symbol if symbol == 'NSE:Nifty 50': step_value = 50 tmp_symbol='NIFTY' elif symbol == 'NSE:Nifty Bank': step_value = 100 tmp_symbol='BANKNIFTY' elif symbol == 'NSE:NIFTY MID SELECT': step_value = 25 tmp_symbol='MIDCPNIFTY' elif symbol == 'NSE:Nifty Fin Service': step_value = 50 tmp_symbol='FINNIFTY' elif symbol == 'BSE:SENSEX': step_value = 100 tmp_symbol='SENSEX' else: step_value = 50 return step_value, tmp_symbol def process_symbol_data(self, symbol: str, interval: int, start_time: datetime, end_time: datetime): step_value, tmp_symbol = self.getStepValueAndShortSymbol(symbol) exchange, trading_symbol = symbol.split(":") # Fetch Data df = self.fetch_time_price_series( exchange, trading_symbol, start_time.strftime("%d/%m/%Y %H:%M:%S"), end_time.strftime("%d/%m/%Y %H:%M:%S"), str(interval), ) if df.empty: self.logger.warning(f"No data for {trading_symbol}") return # Preprocess timestamps and ensure data integrity #df['time'] = pd.to_datetime(df['time']).dt.tz_localize(self.ist) df['time'] = pd.to_datetime(df['time'], errors='coerce', dayfirst=True) if not df['time'].dt.tz: df['time'] = df['time'].dt.tz_localize(self.ist) df = df[~((df['time'].dt.hour == 9) & (df['time'].dt.minute < 15))] df = df[~(((df['time'].dt.hour == 15) & (df['time'].dt.minute >= 31)) | ((df['time'].dt.hour > 15) & (df['time'].dt.hour < 23)))] current_time = datetime.now(self.ist) df['elapsedTime'] = (current_time - df['time']).dt.total_seconds() // 60 df = df.sort_values(by='time', ascending=True) #print(df) df['intc'] = pd.to_numeric(df['intc'], errors='coerce') # Compute moving averages for support/resistance entry df['Short_MA'] = df['intc'].rolling(window=10).mean() # Short-term average df['Long_MA'] = df['intc'].rolling(window=50).mean() # Long-term average df['mean'] = df['intc'].mean() df['std'] = df['intc'].std() df['mean_dev'] = (df['intc'] - df['intc'].mean()) / df['intc'].std() df = df.dropna(subset=['Short_MA']).reset_index(drop=True) df = df.dropna(subset=['Long_MA']).reset_index(drop=True) df = df.dropna(subset=['mean_dev']).reset_index(drop=True) #print (df) threshold = 1.0 df = df[~df['mean_dev'].between(-threshold, threshold)] df = df[((df['intc'] < df['Short_MA']) & (df['Short_MA'] < df['Long_MA'])) | ((df['intc'] > df['Short_MA']) & (df['Short_MA'] > df['Long_MA']))] # Entry based on statistical threshold df.loc[ (df['intc'] < df['Short_MA']) & (df['intc'] < df['Long_MA']) & (df['mean_dev'] < -1), 'Signal' ] = 'BUY' df.loc[ (df['intc'] > df['Short_MA']) & (df['intc'] > df['Long_MA']) & (df['mean_dev'] > 1), 'Signal' ] = 'SELL' df['momentum'] = df['intc'].diff(5) df = df.dropna(subset=['momentum']).reset_index(drop=True) save_output = False if save_output: columns_to_remove = ['ssboe', 'into', 'inth', 'intl', 'intvwap', 'intv', 'intoi', 'v', 'oi', 'elapsedTime'] df_1 = df.drop(columns=columns_to_remove, errors='ignore') output_path = os.path.join("/var/www/html/ACTIONS/", f"{tmp_symbol}.csv") df_1.to_csv(output_path, index=False) # Execute only when new signal is observed #df['Signal_Change'] = df['Signal'] != df['Signal'].shift(1) #df = df[df['Signal_Change']].copy() df.loc[ (df['Signal'] == 'BUY') & ((df['momentum'] > 0) & (df['momentum'].shift(1) < 0)), 'Final_Trade' ] = 'CONFIRMED_BUY' df.loc[ (df['Signal'] == 'SELL') & ((df['momentum'] < 0) & (df['momentum'].shift(1) > 0)), 'Final_Trade' ] = 'CONFIRMED_SELL' # Log Signals & Output Results df.dropna(subset=['Final_Trade'], inplace=True) df = df.copy() # Add this line before modifying the DataFrame df.loc[:, 'instrument'] = df['intc'].apply(lambda x: str(round(x / step_value) * step_value)) # Append tmp_symbol at the beginning of the instrument df['instrument'] = tmp_symbol + df['instrument'] # Add 'CE' at the end if Final_Trade is CONFIRMED_BUY, or 'PE' if CONFIRMED_SELL df.loc[df['Final_Trade'] == 'CONFIRMED_BUY', 'instrument'] += 'CE' df.loc[df['Final_Trade'] == 'CONFIRMED_SELL', 'instrument'] += 'PE' #print(df[['time', 'intc', 'Signal', 'instrument', 'elapsedTime' ]]) print(df[['time', 'intc', 'mean', 'std', 'mean_dev', 'momentum', 'Final_Trade','instrument', 'elapsedTime' ]][-5:]) last_entry = df[['time', 'intc', 'Signal', 'momentum', 'Final_Trade','instrument', 'elapsedTime' ]][-1:] elapsedTime = int(last_entry['elapsedTime'].iloc[0]) if elapsedTime < 360: self.createEntry(last_entry) ''' else: self.createEntry(last_entry) ''' def createEntry(self, last_entry): url = "http://143.244.141.41/php/createEntry.php" # Replace with your actual URL tickTime=last_entry['time'].iloc[-1] tickTime_str = tickTime.strftime("%Y-%m-%d_%H:%M:%S") # Define the parameters params = { 'tickTime': str(tickTime_str), 'instrument': str(last_entry['instrument'].iloc[-1]), 'closePrice': str(last_entry['intc'].iloc[-1]), 'signal': str(last_entry['Signal'].iloc[-1]) } # Perform the GET request self.logger.info(f"\n\nGet Request with params: {params}\n") response = requests.get(url, params=params) if response.status_code != 200: self.logger.error(f"Request failed with status code: {response.status_code}") self.logger.error(response.text) # Print error message if available else: self.logger.info(f"Request success: {response.text}") def fetch_all_data(self): symbols = { "NSE:Nifty 50": 28, "NSE:Nifty Bank": 28, "NSE:NIFTY MID SELECT": 14, "NSE:Nifty Fin Service": 28, "BSE:SENSEX": 28 } for interval in [3]: for symbol, days in symbols.items(): time.sleep(1) start_time = datetime.now(self.ist) - timedelta(days=days) end_time = datetime.now(self.ist).replace(hour=15, minute=30) self.process_symbol_data(symbol, interval, start_time, end_time) if __name__ == "__main__": client_details = ['TT1582', 'O9i8u7y6##', '15111982', 'TT1582_API', 'ed87fa73888bc882c5d79bf832c0f9eb'] client_details = ['GA0810', 'O9i8u7y6$$', '08101994', 'GA0810_API', '83a3652534fd8232e5e6d0173a069d79'] stock_fetcher = StockDataFetcher(client_details) stock_fetcher.login() stock_fetcher.fetch_all_data()