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Update app.py
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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
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:
ticker_symbol = self.symbol if self.symbol[0] == '%' else f"{self.symbol}.ns"
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:
curDate_dt = datetime.strptime(curDate, "%Y-%m-%d")
newcDate_dt = curDate_dt - timedelta(days=1)
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:
# 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")
# thirty_days_ago_date = self.currentdateavailability(thirty_days_ago_date)
# curDate_index = self.data.index.get_loc(curDate)
# thirty_days_ago_index = self.data.index.get_loc(thirty_days_ago_date)
# 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]]}
def dateoffset(input_date_str):
input_date_dt = datetime.strptime(input_date_str, "%Y-%m-%d")
new_date_dt = input_date_dt - timedelta(days=1)
new_date_str = new_date_dt.strftime("%Y-%m-%d")
return new_date_str
def setdates(startdate, enddate):
while startdate not in nifty50["nifty50"].data.index:
startdate = dateoffset(startdate)
while enddate not in nifty50["nifty50"].data.index:
enddate = dateoffset(enddate)
return startdate, enddate
def organisedata(startdate, enddate):
startdate, enddate = setdates(startdate, enddate)
symbols = list(nifty_stocks.keys())
common_index = nifty50["nifty50"].data.loc[startdate:enddate].index
data_frame = pd.DataFrame(index=symbols, columns=common_index)
for symbol, stock_object in nifty_stocks.items():
stock_data = stock_object.data.loc[startdate:enddate, 'Close']
data_frame.loc[symbol] = stock_data.reindex(common_index).values
return data_frame
def previoustimeframedata(n, startdate):
startdate_dt = pd.to_datetime(startdate)
ndaysagodate = startdate_dt - timedelta(days=int(n))
ndaysagodate_str = ndaysagodate.strftime("%Y-%m-%d")
startdate_str = startdate_dt.strftime("%Y-%m-%d")
return organisedata(ndaysagodate_str, startdate_str)
def portfoliooperations(equity,startdate,ndaywindow,portfolio):
startdate_dt = pd.to_datetime(startdate)
windowenddate = startdate_dt + timedelta(days=int(ndaywindow))
windowenddate_str = windowenddate.strftime("%Y-%m-%d")
startdate,windowenddate = setdates(startdate,windowenddate_str)
window_data = organisedata(startdate,windowenddate)
differences = window_data.iloc[:, -1] - window_data.iloc[:, 0]
next_portfolio = differences[differences > 0].index.tolist()
portfolio_sum = window_data.loc[portfolio, window_data.columns[0]].sum()
multiplier = equity / portfolio_sum if portfolio_sum != 0 else 0
portfolio_value = pd.DataFrame(index=window_data.columns, columns=['value'])
for date in window_data.columns:
portfolio_sum = window_data.loc[portfolio, date].sum()
portfolio_value.loc[date, 'value'] = portfolio_sum * multiplier
return next_portfolio,portfolio_value
def mainfunction (equity,startdate,enddate,ndaywindow):
pastwindow = previoustimeframedata(n=ndaywindow,startdate=startdate) # No Errors untill here
differences = pastwindow.iloc[:, -1] - pastwindow.iloc[:, 0]
portfolio = differences[differences > 0].index.tolist() # No Errors untill here
portfolio,portfolio_value = portfoliooperations(equity=equity,startdate=startdate,ndaywindow=ndaywindow,portfolio=portfolio)
enddate_tz = datetime.strptime(enddate,"%Y-%m-%d").replace(tzinfo=pytz.timezone('Asia/Kolkata'))
while portfolio_value.index[-1] < pd.to_datetime(enddate_tz) - timedelta(days=int(ndaywindow)):
portfolio,new_portfolio_value = portfoliooperations(equity=equity,startdate=startdate,ndaywindow=ndaywindow,portfolio=portfolio)
portfolio_value = pd.concat([portfolio_value, new_portfolio_value])
startdate = (pd.to_datetime(startdate)+ timedelta(days=int(ndaywindow))).strftime("%Y-%m-%d")
equity = portfolio_value.iloc[-1, 0]
return portfolio_value
def calculate_cagr(series):
total_return = (series.iloc[-1] / series.iloc[0]) - 1
num_years = len(series) / 252
cagr = (1 + total_return) ** (1 / num_years) - 1
return cagr * 100
def calculate_volatility(series):
return series.pct_change().std() * np.sqrt(252) * 100
def calculate_sharpe_ratio(series, risk_free_rate=0):
cagr = calculate_cagr(series)
volatility = calculate_volatility(series)
sharpe_ratio = (cagr - risk_free_rate) / volatility
return sharpe_ratio
def final_function(equity,startdate,enddate,ndaywindow):
equity = int(equity)
ndaywindow = int(ndaywindow)
portfolio_value = mainfunction(equity=equity,startdate=startdate,enddate=enddate,ndaywindow=ndaywindow)
nifty_data = nifty50["nifty50"].data
subset_data = nifty_data[startdate:enddate]
initial_nifty = subset_data['Close'][0]
nifty_dataseries = (equity/initial_nifty)*subset_data['Close']
plt.figure(figsize=(10, 6))
plt.plot(portfolio_value['value'], label='Strategy')
plt.plot(nifty_dataseries, label='Nifty50 as Benchmark')
plt.title('Benchmark vs Strategy')
plt.xlabel('Date')
plt.ylabel('Equity')
plt.legend()
image_path = "output_plot.png"
plt.savefig(image_path)
plt.close()
image = Image.open(image_path)
strategy_cagr = calculate_cagr(portfolio_value['value'])
strategy_volatility = calculate_volatility(portfolio_value['value'])
strategy_sharpe_ratio = calculate_sharpe_ratio(portfolio_value['value'])
benchmark_cagr = calculate_cagr(nifty_dataseries)
benchmark_volatility = calculate_volatility(nifty_dataseries)
benchmark_sharpe_ratio = calculate_sharpe_ratio(nifty_dataseries)
return image, strategy_cagr, strategy_volatility, strategy_sharpe_ratio, benchmark_cagr, benchmark_volatility, benchmark_sharpe_ratio
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. Due to COVID 19 causing the fall of almost all Stock Prices, some Dates might result in strategy falling to zero at March 2020. Hence please try other dates
"""
iface = gr.Interface(
fn=final_function,
inputs=[
gr.Textbox(label="Equity",placeholder="Enter Equity Number"),
gr.Textbox(label="Start Date",placeholder="YYYY-MM-DD"),
gr.Textbox(label="End Date",placeholder="YYYY-MM-DD"),
gr.Textbox(label="N-day Window",placeholder="Enter Window in Days")
],
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()