import logging import sys import time from datetime import datetime, timedelta, time from typing import List import pytz import pandas as pd import requests import json import math from thefirstock import thefirstock from ta.trend import ADXIndicator def get_client_details(): try: url = 'http://143.244.141.41/php/getUserDetails.php' response = requests.get(url) response.raise_for_status() data = response.json() if not data.get('success', False): raise ValueError("getUserDetails endpoint returned unsuccessful response") user_data = data['data'] return [user_data['field1'], user_data['field2'], user_data['field3'], user_data['field4'], user_data['field5']] except requests.exceptions.RequestException as e: print(f"HTTP Request failed: {e}") return None except (json.JSONDecodeError, KeyError) as e: print(f"Failed to parse response: {e}") return None class StockDataFetcher: def __init__(self, client_details: List[str], createEntries: bool = False): self.client_details = client_details self.user_id = client_details[0] self.ist = pytz.timezone('Asia/Kolkata') self.logger = self.setup_logger() self.createEntries = createEntries self.transactionsCounter = 0 self.lotCount = 1 self.tick_interval = 1 self.order_allowed_steps = 1 self.total_pl = 0.0 self.debug_data = '' self.open_positions_price = {'CE': [], 'PE': []} # (instrument, buyPrice) 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 addLogDataDebug(self, text): self.logger.debug(f'{text}') self.debug_data += text + '\n' def addLogDataInfo(self, text): self.logger.info(f'{text}') self.debug_data += text + '\n' def setup_logger(self) -> logging.Logger: logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) 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 def login(self): try: self.addLogDataInfo(f"Attempting login for {self.client_details[0]}") response = thefirstock.firstock_login(*self.client_details) if response.get("status") == "success": self.addLogDataInfo("Login successful") else: self.addLogDataInfo(f"Login failed: {response}") sys.exit() except Exception as e: self.addLogDataInfo(f"Login error: {e}") sys.exit() def process_symbol_data(self, symbol: str, interval: int, start_time: datetime, end_time: datetime): exchange, trading_symbol = symbol.split(":") 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), ) numeric_cols = ['intc', 'intvwap', 'oi', 'intoi'] for col in numeric_cols: df[col] = pd.to_numeric(df[col], errors='coerce') df = df.sort_values(by='time', ascending=True) df['cost'] = (df['intc'] * df['oi']) / 10000000 df['cost'] = df['cost'].round(2) return df def createEntry(self, tickTimeStr, instrument, closePrice, signal, lotCount): instrument = 'SENSEX' + str(instrument) self.transactionsCounter += 1 if self.createEntries: url = "http://143.244.141.41/php/createMoneyHeistEntry.php" tickTimeStr += ':' + str(self.transactionsCounter).zfill(3) params = { 'tickTime': str(tickTimeStr), 'instrument': str(instrument), 'closePrice': str(closePrice), 'signal': str(signal), 'orderType': str(lotCount) } response = requests.get(url, params=params) if response.status_code != 200: self.addLogDataDebug(f"Request failed with status code: {response.status_code}") if response.text != '': self.addLogDataDebug(response.text + "\n") def fetch_time_price_series(self, exchange: str, trading_symbol: str, start_time: str, end_time: str, interval: str) -> pd.DataFrame: self.addLogDataDebug(f"Fetching data for {trading_symbol} from {start_time} to {end_time}. interval:{interval}") 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", [])) self.addLogDataDebug(f"Fetch failed: {response}") return pd.DataFrame() except Exception as e: self.addLogDataDebug(f"Error fetching series: {e}") return pd.DataFrame() def getSensexStrikePriceGivenCurrentValue(self, closePrice, type): strike_prices = [] try: price = float(closePrice) remainder = price % 100 base_strike = int(price + (100 - remainder)) if remainder > 50 else int(price - remainder) except (ValueError, TypeError): raise ValueError("Invalid closePrice input. Must be a number or numeric string.") if base_strike < price and type == 'PE': base_strike += 100 elif base_strike > price and type == 'CE': base_strike -= 100 for step in range(self.order_allowed_steps): if type == 'CE': strike_prices.append(base_strike - 100 * step) else: strike_prices.append(base_strike + 100 * step) return strike_prices def fetch_all_data(self, elapsed: int): elapsed = int(elapsed) elapsedStart = elapsed + 1 # Handle Monday special case if datetime.now(self.ist).weekday() == 0: elapsedStart = elapsed + 3 # Set time range start_time = datetime.now(self.ist).replace(hour=14, minute=0, second=0) - timedelta(days=elapsedStart) end_time = datetime.now(self.ist).replace(hour=15, minute=31, second=0) - timedelta(days=elapsed) interval = self.tick_interval sensexdf = self.process_symbol_data('BSE:SENSEX', interval, start_time, end_time) df = sensexdf[['time', 'intc', 'inth', 'intl']].rename(columns={ 'intc': 'close', 'inth': 'high', 'intl': 'low' }).copy() # Convert types df['close'] = pd.to_numeric(df['close'], errors='coerce') df['high'] = pd.to_numeric(df['high'], errors='coerce') df['low'] = pd.to_numeric(df['low'], errors='coerce') df['time'] = pd.to_datetime(df['time'], format='%d-%m-%Y %H:%M:%S') df = df.sort_values('time').reset_index(drop=True) df = df[df['time'].dt.time >= time(9, 15)] df = df[df['time'].dt.time <= time(15, 30)] df = df.reset_index(drop=True) # === ADX using ta library === period = 14 adx = ADXIndicator(high=df['high'], low=df['low'], close=df['close'], window=period) df['+DI'] = adx.adx_pos() df['-DI'] = adx.adx_neg() df['ADX'] = adx.adx() df['histogram'] = df['+DI'] - df['-DI'] df = df[df['histogram'].notna()].reset_index(drop=True) # Filter for current day and after 9:15 AM target_date = pd.to_datetime(end_time).date() df = df[df['time'].dt.date == target_date] df = df[df['time'].dt.time >= time(9, 15)] df = df.reset_index(drop=True) # === Signal Generation === signals = [] signal_types = [] state = 'neutral' final_closePrice = df.iloc[-1]['close'] if len(df) > 0 else 0.0 for i in range(len(df)): _time = df.loc[i, 'time'] hist = df.loc[i, 'histogram'] final_closePrice = df.loc[i, 'close'] #print(_time, hist) if state == 'neutral': if hist > 35.3: signals.append(i) signal_types.append('PE') # SELL signal state = 'waiting_sell_exit' elif hist < -35.3: signals.append(i) signal_types.append('CE') # BUY signal state = 'waiting_buy_exit' elif state == 'waiting_sell_exit' and hist < 15: state = 'neutral' elif state == 'waiting_buy_exit' and hist > -15: state = 'neutral' # Create signals DataFrame df_signals = pd.DataFrame() if signals: df_signals = df.loc[signals].reset_index(drop=True) df_signals['signal'] = signal_types self.addLogDataInfo("ADX Histogram Signals:") open_positions = {'CE': set(), 'PE': set()} for index, row in df_signals.iterrows(): timeStr = str(row['time']) closePrice = row['close'] signal_type = row['signal'] opposite_type = 'PE' if signal_type == 'CE' else 'CE' # Close opposite positions for open_instr in open_positions[opposite_type].copy(): instrument_key = open_instr.split('-')[0] action = 'SELL' matches = [(i, bp) for i, (iname, bp) in enumerate(self.open_positions_price[opposite_type]) if iname == instrument_key] for i, buy_price in matches: pl = round((closePrice - buy_price) * self.lotCount, 2) if opposite_type == 'CE' \ else round((buy_price - closePrice) * self.lotCount, 2) self.addLogDataInfo(f"closeEntry <= time: {timeStr}, instrument: {instrument_key}, " f"Price: {closePrice}, action: {action}, P/L: {pl}") self.createEntry(timeStr, instrument_key, closePrice, action, self.lotCount) self.total_pl += pl self.open_positions_price[opposite_type] = [ (iname, bp) for (iname, bp) in self.open_positions_price[opposite_type] if iname != instrument_key ] open_positions[opposite_type].discard(open_instr) # Open new positions strikePrices = self.getSensexStrikePriceGivenCurrentValue(closePrice, signal_type) for strikePrice in strikePrices: instrument = f"{strikePrice}{signal_type}" action = 'BUY' self.addLogDataInfo(f"createEntry => time: {timeStr}, instrument: {instrument}, " f"Price: {closePrice}, action: {action}") self.createEntry(timeStr, instrument, closePrice, action, self.lotCount) entry = f"{instrument}-{index}" open_positions[signal_type].add(entry) self.open_positions_price[signal_type].append((instrument, closePrice)) # Final summary self.addLogDataInfo("") self.addLogDataInfo(f"current close price: {final_closePrice}") self.addLogDataInfo(f"Total Realized P/L: ₹{round(self.total_pl, 2)}") self.addLogDataInfo("\n") if __name__ == "__main__": client_details = get_client_details() elapsed = float(sys.argv[1]) if len(sys.argv) > 1 else 0 createEntries = len(sys.argv) > 2 stock_fetcher = StockDataFetcher(client_details, createEntries) stock_fetcher.login() stock_fetcher.fetch_all_data(elapsed)