Create app.py

app.py

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+import numpy as np 
+import pandas as pd 
+
+from datetime import datetime, date
+
+import yfinance as yf
+
+
+
+import os
+
+from dotenv import load_dotenv
+
+import holoviews as hv
+import hvplot.pandas  # noqa
+
+
+import hvplot.pandas  # Provides hvplot method for DataFrames
+import holoviews as hv
+from holoviews import opts
+import panel as pn
+
+from holoviews.plotting.links import RangeToolLink
+
+pn.extension("bokeh", template="bootstrap")
+
+
+# from datasets import load_dataset
+from utils import load_hf_dataset  # ,upload_to_hf_dataset, download_from_hf_dataset
+
+import stumpy
+# import matplotlib.pyplot as plt
+
+
+
+# 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)
+ticker_list = list(DF.query(' `Market Capitalization`>10e9').Ticker)
+
+ticker1 ="CRM"
+ticker2= "MSFT"
+
+ticker1_widget = pn.widgets.AutocompleteInput(
+    name="Ticker A",
+    options=ticker_list,
+    placeholder="Write Ticker here همین جا",
+    value=f"{ticker1}",
+    restrict=False,
+)
+
+ticker2_widget = pn.widgets.AutocompleteInput(
+    name="Ticker B",
+    options=ticker_list,
+    placeholder="Write Ticker here همین جا",
+    value=f"{ticker2}",
+    restrict=False,
+)
+
+m_widget = pn.widgets.IntSlider(
+    name='Window Size (m)',
+    value=200,
+    start=50,
+    end=400,
+    step=10
+)
+
+
+# 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(2000,1,1)
+date_end.value = date.today()
+
+def get_plot(ticker1,ticker2, m_widget, date_start , date_end):
+
+    DF1 = yf.Ticker(ticker1).history(start=date_start , end=date_end); DF1 = DF1.resample('5D').mean(); DF1['Date'] = DF1.index
+    DF2 = yf.Ticker(ticker2).history(start=date_start , end=date_end); DF2 = DF2.resample('5D').mean(); DF2['Date'] = DF2.index
+
+    m = 200
+    m = m_widget
+    varcol = 'Close'
+    mp = stumpy.stump(T_A = DF1[varcol],
+                            m = m,
+                            T_B = DF2[varcol],
+                            ignore_trivial = False)
+
+    ticker1_motif_index = mp[:, 0].argmin()
+    print(f'The motif is located at index {ticker1_motif_index} of "{ticker1}"')
+
+    ticker2_motif_index = mp[ticker1_motif_index, 1]
+    print(f'The motif is located at index {ticker2_motif_index} of "{ticker2}"')
+
+
+    plt1 = hv.Curve( DF1.iloc[ticker1_motif_index : ticker1_motif_index + m][varcol].values, label=f'{ticker1}')
+    plt2 = hv.Curve(DF2.iloc[ticker2_motif_index:ticker2_motif_index+m][varcol].values, label=f'{ticker2}')
+
+
+    plt3_1 = hv.Curve(DF1[varcol]) 
+    # plt3_2= hv.Curve(DF1.iloc[ticker1_motif_index : ticker1_motif_index + m ,-1].values , DF1.iloc[ticker1_motif_index : ticker1_motif_index + m][varcol].values)
+
+    plt4_1 = hv.Curve(DF2[varcol])
+    # plt4_2=hv.Curve(DF2.iloc[ticker2_motif_index:ticker2_motif_index+m ,-1].values , DF2.iloc[ticker2_motif_index:ticker2_motif_index+m][varcol].values)
+
+    # Plot for DF1
+    plot1 = DF1.hvplot.line(x='Date', y=varcol, title=ticker1).opts(width=500, height=400)
+    motif1 = DF1.iloc[ticker1_motif_index: ticker1_motif_index + m]
+    motif1_plot = motif1.hvplot.line( y=varcol, color='red')
+    combined_plot1 = plot1 * motif1_plot  # Overlay the motif on the main plot
+
+    # # Plot for DF2
+    plot2 = DF2.hvplot.line(x='Date', y=varcol, title=ticker2).opts(width=500, height=400)
+    motif2 = DF2.iloc[ticker2_motif_index: ticker2_motif_index + m]
+    motif2_plot = motif2.hvplot.line( y=varcol, color='red')
+    combined_plot2 = plot2 * motif2_plot  # Overlay the motif on the main plot
+
+
+    # return pn.Row(plt1*plt2 , pn.Column(plt3_1.opts(width=800, height=400) ,plt4_1.opts(width=800, height=400) ))   
+    return pn.Row(plt1*plt2 , pn.Column(combined_plot1.opts(width=800, height=400) ,combined_plot2.opts(width=800, height=400) ))   
+
+
+pn.Row( pn.Column(ticker1_widget, ticker2_widget, m_widget, date_start , date_end),
+    pn.bind(get_plot, ticker1_widget, ticker2_widget , m_widget, date_start , date_end)).servable(title="Find Similarity between two stocks")
+