app.py

import pandas as pd
import panel as pn
from datetime import datetime
from datetime import date
pn.extension('bokeh', template='bootstrap')
import hvplot.pandas

import pandas as pd
import yfinance as yf
import panel as pn

@pn.cache
def get_df(ticker, startdate , enddate , interval="1d",window=50,window2=150):
    # interval="1d"
    # get_df(ticker ="PG", startdate="2000-01-01" , enddate="2023-09-01" , interval="1d")
    DF = yf.Ticker(ticker).history(start=startdate,end=enddate,interval=interval)
    DF['SMA'] = DF.Close.rolling(window=window).mean()
    DF['SMA2'] = DF.Close.rolling(window=window2).mean()
    DF = DF.reset_index()
    return DF 

def get_hvplot(ticker , startdate , enddate , interval,window,window2):
    DF = get_df(ticker , startdate=startdate , enddate=enddate , interval=interval,window=window,window2=window2)

    import hvplot.pandas  # Ensure hvplot is installed (pip install hvplot)
    from sklearn.linear_model import LinearRegression
    import holoviews as hv
    hv.extension('bokeh')
    # Assuming your dataframe is named 'df' with columns 'Date' and 'Close'
    # If not, replace 'Date' and 'Close' with your actual column names.

    # Step 1: Create a scatter plot using hvplot
    scatter_plot = DF.hvplot(x='Date', y='Close',  kind='scatter',title=f'{ticker} Close vs. Date')

    # Step 2: Fit a linear regression model
    DF['Date2'] = pd.to_numeric(DF['Date'])
    X = DF[['Date2']]
    y = DF[['Close']] #.values
    model = LinearRegression().fit(X, y)

    # # Step 3: Predict using the linear regression model
    DF['Predicted_Close'] = model.predict(X)

    # # Step 4: Create a line plot for linear regression
    line_plot = DF.hvplot(x='Date', y='Predicted_Close', kind='line',line_dash='dashed', color='red')
    line_plot_SMA = DF.hvplot(x='Date', y='SMA', kind='line',line_dash='dashed', color='orange')
    line_plot_SMA2 = DF.hvplot(x='Date', y='SMA2', kind='line',line_dash='dashed', color='orange')

    # # Step 5: Overlay scatter plot and linear regression line
    # return (scatter_plot * line_plot).opts(width=800, height=600, show_grid=True, gridstyle={ 'grid_line_color': 'gray'})
    # grid_style = {'grid_line_color': 'black'}#, 'grid_line_width': 1.5, 'ygrid_bounds': (0.3, 0.7),'minor_xgrid_line_color': 'lightgray', 'xgrid_line_dash': [4, 4]}
    return (scatter_plot * line_plot *line_plot_SMA *line_plot_SMA2).opts(width=800, height=600, show_grid=True)
def get_income_statement_df(ticker):
    yfobj = yf.Ticker(ticker)
    df= yfobj.financials.T
    df.index = pd.to_datetime(df.index, format='%Y-%m-%d')
    return df

def get_income_hvplot(ticker):
    DF = get_income_statement_df(ticker)
    plt1 = DF.hvplot.line(y='Total Revenue') * DF.hvplot.scatter(y='Total Revenue').opts(color="red") 
    plt1.opts(width=600, height=450, show_grid=True)
    plt2 = DF.hvplot.line(y='Gross Profit') * DF.hvplot.scatter(y='Gross Profit').opts(color="red") 
    plt2.opts(width=600, height=450, show_grid=True)
    plt3 = DF.hvplot.line(y='Net Income') * DF.hvplot.scatter(y='Net Income').opts(color="red")
    plt3.opts(width=600, height=450, show_grid=True)
    return pn.Column(plt1 , plt2 , plt3 )
    # return ( DF.hvplot.line(y='Net Income') * DF.hvplot.scatter(y='Net Income').opts(color="red") )+ (DF.hvplot.line(y='Gross Profit') * DF.hvplot.scatter(y='Gross Profit').opts(color="red") )+ 
    # (DF.hvplot.line(y='Total Revenue') * DF.hvplot.scatter(y='Total Revenue').opts(color="red") )

def lookup_discountedrate(betavalue):
  betavsdiscountedrate = {1: 5, 1: 6, 1.1: 6.5, 1.2: 7, 1.3: 7.5, 1.4: 8, 1.5: 8.5, 1.6: 9}
  if betavalue < 1:
    return betavsdiscountedrate[1]  # Return the value for key 1 if key is below 1
  elif betavalue > 1.6:
    return betavsdiscountedrate[1.6]  # Return the value for key 1.6 if key is above 1.6
  else:
    # Find the closest key to the given key
    closest_key = min(betavsdiscountedrate.keys(), key=lambda x: abs(x - betavalue))

    # Get the corresponding value
    value = betavsdiscountedrate[closest_key]

    return value


def calc_fairprice_CDF(ticker):
  import yfinance as yf
  yfobj = yf.Ticker(ticker)

  #calculate eps growing next 5 years
  EPSnext5Y = yfobj.get_info()['trailingPE'] / yfobj.get_info()['trailingPegRatio']

  years = 10
  #
  cashflowinitial = yfobj.get_info()['operatingCashflow']

  cashflowlst=[]
  cashflow = cashflowinitial
  for i in range(1,years+1):
    cashflow = cashflow*(1+EPSnext5Y/100)
    cashflowlst.append(cashflow)

  try:
    discountedrate = lookup_discountedrate(yfobj.get_info()['beta'])
  except:
      discountedrate = 5

  discountedfactorlst =[]
  discountedvaluelst=[]
  discountedfactor =1

  for i in range(1,years+1):
    discountedfactor =( 1 / (1+ discountedrate/100)**i)
    discountedfactorlst.append(discountedfactor)
    discountedvalue = discountedfactor * cashflowlst[i-1]
    discountedvaluelst.append(discountedvalue)

  PV10yearsCashFlow =0
  for i in range(0,years):
    PV10yearsCashFlow += discountedvaluelst[i]

  #intrinsic value before cash/debt
  intrinsicvaluebeforecashdebt = PV10yearsCashFlow / yfobj.get_info()['sharesOutstanding']

  debtpershare = yfobj.get_info()['totalDebt'] / yfobj.get_info()['sharesOutstanding']
  cashpershare = yfobj.get_info()['totalCash'] / yfobj.get_info()['sharesOutstanding']
  intrinsicvalue = intrinsicvaluebeforecashdebt + cashpershare - debtpershare

  previousClose = yfobj.get_info()['previousClose']
  deviation = 100*(intrinsicvalue - previousClose) / previousClose
  # return intrinsicvalue , previousClose , deviation
  return pn.Row(pn.widgets.StaticText(name='fairprice_CDF', value=str(round(intrinsicvalue,1)))   ,pn.widgets.StaticText(name='deviation', value=str(round(deviation,2)))    )


def calc_fairprice_DnetP(ticker):
  import yfinance as yf
  yfobj = yf.Ticker(ticker)

  #calculate eps growing next 5 years
  EPSnext5Y = yfobj.get_info()['trailingPE'] / yfobj.get_info()['trailingPegRatio']

  years = 5
  #
  cashflowinitial = yfobj.get_info()['netIncomeToCommon']

  cashflowlst=[]
  cashflow = cashflowinitial
  for i in range(1,years+1):
    cashflow = cashflow*(1+EPSnext5Y/100)
    cashflowlst.append(cashflow)

  try:
      discountedrate = lookup_discountedrate(yfobj.get_info()['beta'])
  except:
      discountedrate = 5

  discountedfactorlst =[]
  discountedvaluelst=[]
  discountedfactor =1

  for i in range(1,years+1):
    discountedfactor =( 1 / (1+ discountedrate/100)**i)
    discountedfactorlst.append(discountedfactor)
    discountedvalue = discountedfactor * cashflowlst[i-1]
    discountedvaluelst.append(discountedvalue)

  PV10yearsCashFlow =0
  for i in range(0,years):
    PV10yearsCashFlow += discountedvaluelst[i]

  #intrinsic value before cash/debt
  intrinsicvaluebeforecashdebt = PV10yearsCashFlow / yfobj.get_info()['sharesOutstanding']

  debtpershare = yfobj.get_info()['totalDebt'] / yfobj.get_info()['sharesOutstanding']
  cashpershare = yfobj.get_info()['totalCash'] / yfobj.get_info()['sharesOutstanding']
  intrinsicvalue = intrinsicvaluebeforecashdebt + cashpershare - debtpershare

  previousClose = yfobj.get_info()['previousClose']
  intrinsicvalue= intrinsicvalue + previousClose

  deviation = 100*(intrinsicvalue - previousClose) / previousClose
  # return intrinsicvalue , previousClose , deviation
  return pn.Row(pn.widgets.StaticText(name='fairprice_DnetP', value=str(round(intrinsicvalue,1)))  , pn.widgets.StaticText(name='deviation', value=str(round(deviation,2)))  ) 
    
# tickers = ['AAPL', 'META', 'GOOG', 'IBM', 'MSFT','NKE','DLTR','DG']
# ticker = pn.widgets.Select(name='Ticker', options=tickers)

tickers = pd.read_csv('tickers.csv').Ticker.to_list()
ticker = pn.widgets.AutocompleteInput(name='Ticker', options=tickers , placeholder='Write Ticker here همین جا')
ticker.value = "AAPL"
window = pn.widgets.IntSlider(name='Window Size', value=50, start=5, end=1000, step=5)
window2 = pn.widgets.IntSlider(name='Window Size2', value=150, start=5, end=1000, step=5)

# Create a DatePicker widget with a minimum date of 2000-01-01
date_start = pn.widgets.DatePicker(
    name ="Start Date",
    description='Select a Date',
    start= date(2000, 1, 1)
)

date_end = pn.widgets.DatePicker(
    name ="End Date",# value=datetime(2000, 1, 1),
    description='Select a Date',
    end= date.today() #date(2023, 9, 1)
)

date_start.value = date(2010,1,1)
date_end.value = date.today()

pn.Row(
    pn.Column( ticker, window , window2, date_start , date_end),
    # pn.bind(calc_fairprice_CDF,ticker),
    # pn.bind(calc_fairprice_DnetP,ticker)),
    # pn.panel(pn.bind(get_hvplot, ticker, "2010-01-01","2023-09-01","1d")) #, sizing_mode='stretch_width')
    pn.panel(pn.bind(get_hvplot, ticker, date_start , date_end,"1d",window,window2)), #, sizing_mode='stretch_width')
    pn.panel(pn.bind(get_income_hvplot, ticker))    #, sizing_mode='stretch_width')    
).servable(title="Under Valued Screener- Linear Regression")