Create app.py

app.py

@@ -0,0 +1,138 @@
+import numpy as np 
+import pandas as pd 
+
+from datetime import datetime, date
+
+import matplotlib.pyplot as plt 
+import seaborn
+
+import yfinance as yf
+
+# import holoviews as hvs
+import panel as pn
+# import hvplot as hv
+# import hvplot.pandas
+
+pn.extension("bokeh", template="bootstrap")
+
+import os
+
+from dotenv import load_dotenv
+import holoviews as hv
+
+# from datasets import load_dataset
+from utils import load_hf_dataset  # ,upload_to_hf_dataset, download_from_hf_dataset
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Get the Hugging Face API token from the environment; either set in .env file or in the environment directly in GitHub
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Get the name of the GuruFocus dataset for TradingView, Finviz, MarketBeat and TipRanks to read from
+dataset_name_TradingView_input = os.getenv("dataset_name_TradingView_input")
+
+
+@pn.cache()
+def get_tradingview(current_datetime):
+    # Load lastest TradingView DataSet from HuggingFace Dataset which is always america.csv
+    # download_from_hf_dataset("america.csv", dataset_name_TradingView_input, HF_TOKEN)
+    return load_hf_dataset("america.csv", HF_TOKEN, dataset_name_TradingView_input)
+
+
+
+# Reading gurufocus,finviz from crawling pipelines with GitHub Action
+current_datetime = datetime.now().strftime("%Y-%m-%d")
+DF = get_tradingview(current_datetime)
+# DF.Industry.unique()
+
+def get_plot(tabtype, selected_Industry_or_Sector,  startdate , enddate):
+  # selected_Industry_or_Sector= "Major Banks"
+  if tabtype == 'Industry':
+    ticker_list = list(DF.query('Industry==@selected_Industry_or_Sector & `Market Capitalization`>100e9').Ticker)
+  else:
+    ticker_list = list(DF.query('Sector==@selected_Industry_or_Sector & `Market Capitalization`>100e9').Ticker)
+  len(ticker_list)
+  # ticker_list = ['AAPL', 'AMZN', 'GOOGL','TSLA', 'NVDA' , 'AMD']
+
+  # start = datetime(2020, 1, 1)
+  # end = datetime.now()
+  # end = datetime(2020,1,1)
+  method = 'pearson'#, 'kendall', 'spearman'
+
+  tickers=[]
+  for ticker in ticker_list:     
+    if not '/' in ticker:
+      df = yf.Ticker(ticker).history(start=startdate , end=enddate)
+      df['Ticker'] = ticker    
+      tickers.append(df)
+  DFtotal = pd.concat(tickers)
+  DFtotal = DFtotal.reset_index()
+  DFtotal = DFtotal[['Date', 'Close', 'Ticker']]
+  df_pivot = DFtotal.pivot(index='Date', columns='Ticker', values='Close').reset_index()
+  df_pivot = df_pivot.drop('Date', axis=1)
+  corr_df = df_pivot.corr(method=method)
+
+  # Convert correlation matrix to format suitable for heatmap
+  corr_reset = corr_df.reset_index()
+  melted_corr = pd.melt(corr_reset, id_vars='Ticker', var_name='Variable', value_name='Correlation')
+
+  # Create heatmap using holoviews
+  hv.extension('bokeh')
+
+  heatmap = hv.HeatMap(melted_corr, kdims=['Ticker', 'Variable'], vdims='Correlation')
+  heatmap = heatmap.opts(
+    title=f'Heat Map correlation based on {method} method for {selected_Industry_or_Sector} sector from {startdate.year} - {enddate.year}',
+    width=800,
+    height=600,
+    tools=['hover'],
+    colorbar=True,
+    cmap='RdYlGn',
+    xrotation=45,
+    labelled=[],
+    fontsize={'title': 12, 'labels': 10},
+  )
+  return heatmap
+
+# Create dropdown widget for industry selection
+industry_selector = pn.widgets.Select(
+  name='Select Industry',
+  options=list(DF.Industry.unique()),
+  value='Major Banks'
+)
+
+# Create dropdown widget for industry selection
+sector_selector = pn.widgets.Select(
+  name='Select Sector',
+  options=list(DF.Sector.unique()),
+  value='Finance'
+)
+
+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()
+
+
+tab_Sector_content = pn.Row(pn.Column(date_start, date_end, sector_selector ), pn.bind(get_plot,'Sector', sector_selector, date_start, date_end))
+tab_Industry_content = pn.Row(pn.Column(date_start, date_end, industry_selector ))#, pn.bind(get_plot,'Industry', industry_selector, date_start, date_end))
+pn.Tabs(
+    ("Tab Sector", tab_Sector_content),
+    ("Tab Industry", tab_Industry_content)
+).servable('Stock Correlation Heatmap')
+
+# pn.Row(pn.Column(date_start, date_end, industry_selector ), pn.bind(get_plot,industry_selector, date_start, date_end)).servable('US Stock Correlation Heatmap')
+
+
+
+