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import pandas as pd
import yfinance as yf
import panel as pn
@pn.cache
def get_df(ticker, startdate , enddate , interval="1d"):
# 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['SMA50'] = DF.Close.rolling(window=50).mean()
DF = DF.reset_index()
return DF
def get_hvplot(ticker , startdate , enddate , interval):
DF = get_df(ticker , startdate=startdate , enddate=enddate , interval=interval)
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')
# # 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).opts(width=800, height=600, show_grid=True)
import panel as pn
from datetime import datetime
from datetime import date
pn.extension('bokeh', template='bootstrap')
import hvplot.pandas
tickers = ['AAPL', 'META', 'GOOG', 'IBM', 'MSFT','NKE','DLTR','DG']
ticker = pn.widgets.Select(name='Ticker', options=tickers)
window = pn.widgets.IntSlider(name='Window Size', value=50, start=1, end=200, 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(2023, 9, 1)
)
date_start.value = date(2010,1,1)
date_end.value = date.today()
pn.Row(
pn.Column( ticker, window , date_start , date_end),
# 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")) #, sizing_mode='stretch_width')
).servable(title="Under Valued Screener- Linear Regression")
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