Create utils.py

utils.py

@@ -0,0 +1,222 @@
+import requests
+import pandas as pd
+import datetime
+import pytz
+import numpy as np
+import math
+import ta
+
+class StockDataFetcher:
+    def __init__(self):
+
+        self.base_url = "https://groww.in/v1/api/charting_service/v3/chart/exchange/NSE/segment/CASH/"
+        self.base_fno_url = "https://groww.in/v1/api/stocks_fo_data/v3/charting_service/chart/exchange/NSE/segment/FNO/"
+        self.latest_stock_price = "https://groww.in/v1/api/stocks_data/v1/tr_live_prices/exchange/NSE/segment/CASH/"
+        self.latest_option_price = "https://groww.in/v1/api/stocks_fo_data/v1/tr_live_prices/exchange/NSE/segment/FNO/"
+        self.option_chain = "https://groww.in/v1/api/option_chain_service/v1/option_chain/derivatives/"
+        self.search_url = "https://groww.in/v1/api/search/v1/entity"
+        self.news_url = "https://groww.in/v1/api/stocks_company_master/v1/company_news/groww_contract_id/"
+        self.all_stocks_url = "https://groww.in/v1/api/stocks_data/v1/all_stocks"
+
+        self.indian_timezone = pytz.timezone('Asia/Kolkata')
+        self.utc_timezone = pytz.timezone('UTC')
+        self.headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0'
+        }
+
+    def _get_time_range(self, days=7):
+        current_time = datetime.datetime.now(self.indian_timezone)
+        start_time = current_time - datetime.timedelta(days=days)
+        start_time_utc = start_time.astimezone(pytz.utc)
+        current_time_utc = current_time.astimezone(pytz.utc)
+        start_time_millis = int(start_time_utc.timestamp() * 1000)
+        end_time_millis = int(current_time_utc.timestamp() * 1000)
+        return start_time_millis, end_time_millis
+
+    def fetch_stock_data(self, symbol, interval=15, days=7):
+        start_time, end_time = self._get_time_range(days)
+        params = {
+            'endTimeInMillis': end_time,
+            'intervalInMinutes': interval,
+            'startTimeInMillis': start_time,
+        }
+        try:
+            print("Downloading data of", symbol.upper())
+            if symbol[-2:].upper() == "PE" or symbol[-2:].upper() == "CE" or symbol[-3:].upper() == "FUT":
+              response = requests.get(self.base_fno_url + symbol.upper(), params=params, headers=self.headers)
+            else:
+              response = requests.get(self.base_url + symbol.upper(), params=params, headers=self.headers)
+            response.raise_for_status()
+            data = response.json()
+            columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
+            for row in data['candles']:
+                row[0] = datetime.datetime.utcfromtimestamp(row[0])
+            df = pd.DataFrame(data['candles'], columns=columns)
+            df['Date'] = pd.to_datetime(df['Date'])
+            df['Date'] = df['Date'].dt.tz_localize(self.utc_timezone).dt.tz_convert(self.indian_timezone)
+            return df
+        except requests.exceptions.RequestException as e:
+            print(f"Error during API request: {e}")
+            return None
+
+    def fetch_latest_price(self, symbol):
+      try:
+        if symbol[-2:].upper() == "PE" or symbol[-2:].upper() == "CE" or symbol[-3:].upper() == "FUT":
+          response = requests.get(self.latest_option_price + symbol.upper() + "/latest", headers=self.headers)
+        else:
+          response = requests.get(self.latest_stock_price + symbol.upper() + "/latest", headers=self.headers)
+        if response.status_code == 200:
+            data = response.json()
+            latest_price = data.get('ltp')
+            print(symbol, 'Price: ', latest_price)
+            return latest_price
+        else:
+            print(f"Failed to fetch data. Status code: {response.status_code}")
+            return None
+      except Exception as e:
+          print(f"An error occurred: {e}")
+          return None
+
+    def fetch_option_chain(self, symbol):
+      response = requests.get(self.option_chain + symbol, headers=self.headers)
+      data = response.json()['optionChain']['optionChains']
+      ltp = response.json()['livePrice']['value']
+
+      chain = []
+      for i in range(len(data)):
+        chain.append({"Symbol_CE": data[i]["callOption"]['growwContractId'], "OI_CALL": data[i]["callOption"]['openInterest'] , "CALL": data[i]["callOption"]['ltp'], "strikePrice": data[i]['strikePrice']/100, "PUT": data[i]["putOption"]['ltp'], "OI_PUT": data[i]["putOption"]['openInterest'], "Symbol_PE": data[i]["putOption"]['growwContractId']}
+        )
+
+      chain = pd.DataFrame(chain)
+      index = chain[(chain['strikePrice'] >= ltp)].head(1).index[0]
+      print(response.json()['livePrice'])
+      chain = chain[index-6:index+7].reset_index(drop=True)
+      optin_exp = chain['Symbol_CE'][0][:-7]
+      return chain, optin_exp
+
+    def search_entity(self, symbol, entity=None, page=0, size=1, app=False):
+      params = {
+          'app': app,
+          'entity_type': entity,
+          'page': page,
+          'q': f"{symbol}",
+          'size': size
+      }
+      try:
+          response = requests.get(self.search_url, params=params, headers=self.headers)
+          response.raise_for_status()
+          data = response.json()
+          entity = data['content'][0]
+          return {"ID": entity['id'], "title": entity['title'], "NSE_Symbol": entity['nse_scrip_code'], "contract_id" : entity["groww_contract_id"]}
+      except requests.exceptions.RequestException as e:
+          print(f"Error during API request: {e}")
+          return None
+
+    def fetch_stock_news(self, symbol, page=1, size=1):
+      params = {
+          "page" : page,
+          "size" : size
+      }
+      try:
+        symbol_id = self.search_entity(symbol.upper())['contract_id']
+        response = requests.get(self.news_url + symbol_id, headers=self.headers, params=params).json()['results']
+        print(response)
+        news = []
+        for i in range(len(response)):
+          Title = response[i]['title']
+          Summary = response[i]['summary']
+          Url = response[i]['url']
+          Date = response[i]['pubDate']
+          Source = response[i]['source']
+          CompanyName = response[i]['companies'][0]['companyName']
+          ScripCode = response[i]['companies'][0]['nseScripCode']
+          BlogUrl = response[i]['companies'][0]['blogUrl']
+          Topics = response[i]['topics'][0]
+
+          news.append({
+              'title': Title,
+              'summary': Summary,
+              'url': Url,
+              'pubDate': Date,
+              'source': Source,
+              'companyName': CompanyName,
+              'symbol': ScripCode,
+              'blogUrl': BlogUrl,
+              'topics': Topics
+          })
+
+        news_table = pd.DataFrame(news)
+        return news_table
+      except:
+        print("Something went wrong")
+        return None
+
+    def fetch_all_stock(self):
+      try:
+        params = {
+            'listFilters': {'INDUSTRY': [], 'INDEX': []},
+            'INDEX': ["BSE 100", "Nifty 100", "Nifty Bank", "Nifty Next 50", "Nifty Midcap 100", "SENSEX", "Nifty 50"],
+            'INDUSTRY': [],
+            'objFilters': {'CLOSE_PRICE': {'max': 100000, 'min': 0}, 'MARKET_CAP': {'min': 0, 'max': 2000000000000000}},
+            'CLOSE_PRICE': {'max': 100000, 'min': 0},
+            'MARKET_CAP': {'min': 0, 'max': 2000000000000000},
+            'size': "1000",
+            'sortBy': "NA",
+            'sortType': "ASC"
+        }
+
+        all_data = []
+        page = 0
+        while True:
+            params['page'] = str(page)
+            response = requests.post(self.all_stocks_url, headers=self.headers, json=params)
+            data = response.json()
+            records = data.get('records', [])
+            if not records:
+                break
+            all_data.extend(records)
+            page += 1
+
+        df = pd.DataFrame(all_data)
+        live_price_df = pd.json_normalize(df['livePriceDto'])
+        df = pd.concat([df, live_price_df], axis=1)
+        df = df.drop(columns=['livePriceDto'])
+        return df
+      except:
+        return None
+
+    def realtime_signal(self, symbol, intervals=15, days=10):
+
+      rounding_value=None
+
+      if symbol.upper() == "NIFTY":
+        index_symbol = "NIFTY"
+        rounding_value = 50
+
+      elif symbol.upper() == "NIFTY-BANK":
+        index_symbol = "BANKNIFTY"
+        rounding_value = 100
+
+      else:
+        pass
+
+      stock_data = self.fetch_stock_data(index_symbol, intervals, days)
+      chain, exp = self.fetch_option_chain(symbol.upper())
+      stock_data['RSI'] = ta.momentum.rsi(stock_data['Close'], window=14)
+      stock_data = stock_data.drop(columns=['Volume'])
+      stock_data['Prev_RSI'] = stock_data['RSI'].shift(1)
+      stock_data['Signal'] = 0
+      call_condition = (stock_data['RSI'] > 60) & (stock_data['Prev_RSI'] < 60)
+      put_condition = (stock_data['RSI'] < 40) & (stock_data['Prev_RSI'] > 40)
+      stock_data.loc[call_condition, 'Signal'] = 1
+      stock_data.loc[put_condition, 'Signal'] = 2
+      stock_data = stock_data.dropna().reset_index(drop=True)
+
+      def floor_to_nearest(value, nearest):
+          return math.ceil(value / nearest) * nearest
+
+      stock_data['Option'] = stock_data['Close'].apply(lambda x: floor_to_nearest(x, rounding_value))
+
+      stock_data['direction'] = np.where(stock_data['Signal'] == 2, "PE", np.where(stock_data['Signal'] == 1, "CE", ""))
+      stock_data['symbol'] = exp + stock_data['Option'].astype(str) + stock_data['direction']
+      return stock_data