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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
from IPython.display import display
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('Close Price')
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
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