Update app.py

app.py

@@ -1,32 +1,38 @@
 import pandas as pd
 import panel as pn
 from datetime import datetime
-from datetime import date
+from datetime import date, timedelta
 pn.extension('bokeh', template='bootstrap')
 import hvplot.pandas
 import os
 import yfinance as yf
 
 @pn.cache
-def getPolygonDF(ticker , startdate , enddate , intervalperiod, window, window2):
+def getPolygonDF(ticker , startdate , enddate , intervalperiod , window, window2):
+    import json
     import time
-    #My Polygon API
-    mypolgonAPI = os.environ.get('mypolgonAPI') 
-    headers = {"Authorization": f"Bearer {mypolgonAPI}"}
-
     import pandas as pd
     import requests
-    import json
+
+    #My Polygon API (need to define in huggingface env variable)
+    mypolgonAPI = os.environ.get('mypolgonAPI') 
+    
+    #to get key from json file
+    # with open('config.json') as config_file:
+    #     config = json.load(config_file)
+    # mypolgonAPI  = config['mypolgonAPI']
+
+    headers = {"Authorization": f"Bearer {mypolgonAPI}"}
 
     interval = "minute"
     period = intervalperiod #"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)
+        print(r1)
         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)
@@ -41,33 +47,28 @@ def getPolygonDF(ticker , startdate , enddate , intervalperiod, window, window2)
     DF['SMA2'] = DF.c.rolling(window=window2).mean()
     return DF
 
-def get_hvplot(ticker , startdate , enddate , interval,window,window2):
-    DF = getPolygonDF(ticker , startdate=startdate , enddate=enddate , intervalperiod=interval,window=window,window2=window2)
+def get_hvplot(ticker , startdate ,  interval,window,window2):
+    date_end = date_start.value + timedelta(days=1) # date.today()
+    DF = getPolygonDF(ticker , startdate=startdate , enddate=date_end , intervalperiod=interval,window=window,window2=window2)
 
     import hvplot.pandas  # Ensure hvplot is installed (pip install hvplot)
 
     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
+    # Create a scatter plot using hvplot
     scatter_plot = DF.hvplot(x='UNIXTIME', y='c',  kind='scatter',title=f'{ticker} Close vs. Date')
 
-
     line_plot_SMA = DF.hvplot(x='UNIXTIME', y='SMA', kind='line',line_dash='dashed', color='orange')
     line_plot_SMA2 = DF.hvplot(x='UNIXTIME', 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_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()
+# tickers = pd.read_csv('tickers2.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)
@@ -77,23 +78,25 @@ window2 = pn.widgets.IntSlider(name='Window Size2', value=150, start=5, end=1000
 date_start = pn.widgets.DatePicker(
     name ="Start Date",
     description='Select a Date',
-    start= date(2023, 1, 1)
+    start= date.today() - timedelta(days=365 * 2)
 )
 
-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_end = pn.widgets.DatePicker(
+#     name ="End Date",# value=datetime(2000, 1, 1),
+#     description='Select a Date',
+#     end= date.today() - timedelta(days=365 * 2) + timedelta(days=1)  #date.today() #date(2023, 9, 1)
+# )
+
+date_start.value = date.today() - timedelta(days=200 * 2)
+# date_end = date_start.value + timedelta(days=1) # date.today()
 
-date_start.value = date(2023,1,1)
-date_end.value = date(2023, 1, 3) # date.today()
+menu_button = pn.widgets.Select(name='Time Frame (min)', options=['1', '5', '10'])
 
 pn.Row(
-    pn.Column( ticker, window , window2, date_start , date_end),
+    pn.Column( ticker, window , window2, date_start , menu_button),
     # 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,"5",window,window2)), #, sizing_mode='stretch_width')
+    pn.panel(pn.bind(get_hvplot, ticker, date_start , menu_button,window,window2)), #, sizing_mode='stretch_width')
     # pn.panel(pn.bind(get_income_hvplot, ticker))    #, sizing_mode='stretch_width')    
 ).servable(title="Intraday Price Action - Pattern Detection")