Create app.py

app.py

@@ -0,0 +1,124 @@
+import gradio as gr
+import pandas as pd
+import yfinance as yf
+from datetime import timedelta,datetime
+import pytz
+import matplotlib.pyplot as plt
+from PIL import Image
+import numpy as np
+from IPython.display import display
+import functions
+
+nifty_list = ["ADANIENT","ADANIPORTS","APOLLOHOSP","ASIANPAINT","AXISBANK","BAJAJ-AUTO","BAJFINANCE","BAJAJFINSV","BPCL","BHARTIARTL","BRITANNIA","CIPLA","COALINDIA","DIVISLAB","DRREDDY","EICHERMOT","GRASIM","HCLTECH","HDFCBANK","HDFCLIFE","HEROMOTOCO","HINDALCO","HINDUNILVR","ICICIBANK","ITC","INDUSINDBK","INFY","JSWSTEEL","KOTAKBANK","LTIM","LT","M&M","MARUTI","NTPC","NESTLEIND","ONGC","POWERGRID","RELIANCE","SBILIFE","SBIN","SUNPHARMA","TCS","TATACONSUM","TATAMOTORS","TATASTEEL","TECHM","TITAN","UPL","ULTRACEMCO","WIPRO","%5ENSEI"]
+
+class Stocks:
+    def __init__(self, symbol):
+        self.symbol = symbol
+        self.data = self.fetch_data()
+
+    def fetch_data(self):
+        try:
+            # Construct the ticker symbol based on the first letter
+            ticker_symbol = self.symbol if self.symbol[0] == '%' else f"{self.symbol}.ns"
+
+            # Fetch historical data based on the constructed ticker symbol
+            data = yf.Ticker(ticker_symbol).history(period="10y", auto_adjust=True)
+            return data
+        except Exception as e:
+            print(f"Error fetching data for {self.symbol}: {e}")
+            return None
+
+    def currentdateavailability(self, curDate):
+        if curDate in self.data.index:
+            return curDate
+        else:
+            # Convert curDate to datetime and subtract one day
+            curDate_dt = datetime.strptime(curDate, "%Y-%m-%d")
+            newcDate_dt = curDate_dt - timedelta(days=1)
+
+            # Convert newcDate to string and call the method again
+            newcDate_str = newcDate_dt.strftime("%Y-%m-%d")
+            return self.currentdateavailability(newcDate_str)
+
+    def CurPrice(self, curDate=None):
+        curDate = self.currentdateavailability(curDate)
+        return self.data.loc[curDate, 'Close'] if curDate is not None else self.data.iloc[-1]['Close']
+
+    def NDayRet(self, N, curDate):
+        curDate = self.currentdateavailability(curDate)
+        NDate = self.data.index[self.data.index.get_loc(curDate) - N]
+        return self.data.loc[curDate, 'Close'] - self.data.loc[NDate, 'Close']
+
+    def DailyRet(self, curDate):
+        curDate = self.currentdateavailability(curDate)
+        return self.data.loc[curDate, 'Close'] - self.data.loc[curDate, 'Open']
+
+
+    def Last30daysPrice(self, curDate=None):
+        if curDate is not None:
+            curDate = self.currentdateavailability(curDate)
+            curDate_index = self.data.index.get_loc(curDate)
+            return self.data.iloc[curDate_index - 30:curDate_index]['Close'].values
+        else:
+            return self.data.iloc[-30:]['Close'].values
+
+
+    # This below function returns last 30 calender days close prices i.e. 30 days including holidays so less than 30 days close values are returned. Above fuction gives last 30 trading day close prices.
+    # def Last30daysPrice(self, curDate=None):
+    #     curDate = self.currentdateavailability(curDate)
+
+    #     if curDate is not None:
+    #         # Calculate date 30 days ago
+    #         curDate_dt = datetime.strptime(curDate, "%Y-%m-%d")
+    #         days_ago_30 = curDate_dt - timedelta(days=30)
+    #         thirty_days_ago_date = days_ago_30.strftime("%Y-%m-%d")
+
+    #         # Ensure the availability of 30 days ago date
+    #         thirty_days_ago_date = self.currentdateavailability(thirty_days_ago_date)
+
+    #         # Get the index of curDate and 30 days ago date in the data
+    #         curDate_index = self.data.index.get_loc(curDate)
+    #         thirty_days_ago_index = self.data.index.get_loc(thirty_days_ago_date)
+
+    #         # Return close values from 30 days ago to curDate in an array
+    #         return self.data.iloc[thirty_days_ago_index:curDate_index + 1]['Close'].values
+    #     else:
+    #         return self.data.iloc[-30:]['Close'].values
+
+
+stocks_dict = {symbol: Stocks(symbol) for symbol in nifty_list}
+
+nifty_stocks = {symbol: stocks_dict[symbol] for symbol in nifty_list[:-1]}
+
+nifty50 = {"nifty50": stocks_dict[nifty_list[-1]]}
+
+title = "Portfolio tracking Nifty50 Stocks"
+description = """
+This App Demo is made for an Assignment. This Demo takes Initial Equity, Start Date, End Date, Time Window as inputs
+"""
+
+
+iface = gr.Interface(
+    fn=final_function,
+    inputs=[
+        gr.Textbox(label="Equity"),
+        gr.Textbox(label="Start Date"),
+        gr.Textbox(label="End Date"),
+        gr.Textbox(label="N-day Window")
+    ],
+    outputs=[
+        gr.Image(type="pil"),
+        gr.Textbox(label="Strategy CAGR (%)"),
+        gr.Textbox(label="Strategy Volatility (%)"),
+        gr.Textbox(label="Strategy Sharpe Ratio"),
+        gr.Textbox(label="Benchmark CAGR (%)"),
+        gr.Textbox(label="Benchmark Volatility (%)"),
+        gr.Textbox(label="Benchmark Sharpe Ratio")
+    ],
+    title=title,
+    description=description,
+    
+)
+
+if __name__ == "__main__":
+    iface.launch()