app.py

app.py

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+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 yfinance as yf
+
+@pn.cache
+def getPolygonDF(ticker , startdate , enddate , window, window2):
+    import time
+    #My Polygon API
+    mypolgonAPI = "wYB9CMZDYB6wL0ycZN33yjQ20Q0xVFlY"
+    headers = {"Authorization": f"Bearer {mypolgonAPI}"}
+
+    import pandas as pd
+    import requests
+    import json
+
+    interval = "minute"
+    period = "5"
+    limit = 50000
+
+
+    dflst = [] 
+    nexturl = f"https://api.polygon.io/v2/aggs/ticker/{ticker}/range/{period}/{interval}/{startdate}/{enddate}?adjusted=true&sort=asc&limit={limit}"
+    while True:
+        r1 = requests.get(nexturl , headers=headers)
+        df = pd.DataFrame(json.loads(r1.text)["results"])
+        df['UNIXTIME'] = pd.to_datetime(df['t'], unit='ms', utc=True).map(lambda x: x.tz_convert('America/New_York'))
+        dflst.append(df)
+        print(df.shape)
+        time.sleep(15)
+        try:
+            nexturl = json.loads(r1.text)["next_url"] 
+        except:
+            break
+    DF = pd.concat(dflst)
+    DF['SMA'] = DF.Close.rolling(window=window).mean()
+    DF['SMA2'] = DF.Close.rolling(window=window2).mean()
+    DF = DF.reset_index()
+
+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)
+
+
+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(2023, 1, 1)
+)
+
+date_end = pn.widgets.DatePicker(
+    name ="End Date",# value=datetime(2000, 1, 1),
+    description='Select a Date',
+    end= date(2023, 1, 3) #date.today() #date(2023, 9, 1)
+)
+
+date_start.value = date(2023,1,1)
+date_end.value = date(2023, 1, 3) # 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")