Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import os
+import io
+from datetime import datetime
+from datetime import timedelta
+
+import hvplot as hv
+import holoviews as hvs
+import panel as pn
+import hvplot.pandas
+
+import yfinance as yf
+
+from datasets import load_dataset
+
+pn.extension('bokeh', template='bootstrap')
+
+def _extract_raw_data(ticker):
+  df = yf.Ticker(ticker)
+  return df.history(period="6mo", interval="1d").reset_index()
+
+def _transform_data(raw_data: pd.DataFrame):
+  data = raw_data[["Date", "Open", "High", "Low", "Close", "Volume"]].copy(deep=True).rename(columns={
+      "Date": "time",
+      "Open": "open",
+      "High": "high",
+      "Low": "low",
+      "Close": "close",
+      "Volume": "volume",
+  })
+  t_delta = timedelta(hours=1)
+  data['time_start'] = data.time - 9*t_delta # rectangles start
+  data['time_end'] = data.time + 9*t_delta    # rectangles end
+  data['positive'] = ((data.close - data.open)>0).astype(int)
+  return data
+
+def make_candle_stick(ticker):
+    raw_data = _extract_raw_data(ticker = ticker)
+    data = _transform_data(raw_data=raw_data)
+    _delta = np.median(np.diff(data.time))
+    candlestick = hvs.Segments(data, kdims=['time', 'low', 'time', 'high']) * hvs.Rectangles(data, kdims=['time_start','open', 'time_end', 'close'], vdims=['positive'])
+    candlestick = candlestick.redim.label(Low='Values')
+    candlechart = pn.Column(candlestick.opts(hvs.opts.Rectangles(color='positive', cmap=['red', 'green'], responsive=True), hvs.opts.Segments(color='black', height=400, responsive=True , show_grid=True)) , 
+                     data.hvplot(x="time", y="volume", kind="line", responsive=True, height=200).opts( show_grid=True) )
+                    #  data.hvplot(y="volume", kind="bar", responsive=True, height=200) )
+    return candlechart
+
+# Function to convert DataFrame to CSV
+def get_csv(df):
+    sio = io.StringIO()
+    df.to_csv(sio, index=False)
+    sio.seek(0)
+    return sio
+
+# Function to convert the 'Ticker' column to a comma-separated string in a text file
+def get_text(df):
+    tickers = df['Ticker'].tolist()
+    tickers_str = ','.join(tickers)
+    sio = io.StringIO()
+    sio.write(tickers_str)
+    sio.seek(0)
+    return sio
+
+dataset = load_dataset('AmirTrader/optioncharts.io', data_files='DFtickerTotal.csv')
+df = dataset['train'].to_pandas()
+
+#widget
+ticker = pn.widgets.AutocompleteInput(name='Ticker', options=list(df.Ticker) , placeholder='Write Ticker here همین جا',value='ALL', restrict=False)
+Industry = pn.widgets.CheckBoxGroup( name='Select Industry', value=list(set(df.Industry)), options=list(set(df.Industry)), inline=True)
+Sector = pn.widgets.CheckBoxGroup( name='Select Sector', value=list(set(df.Sector)), options=list(set(df.Sector)), inline=False)
+MarketCap = pn.widgets.FloatSlider(name='Market Capital (B$)', start=0, end=4000, step=1, value=1)
+
+
+def get_DF(DF,ticker, Sector,MarketCap):
+  if ticker and ticker!="ALL":
+    table1 = pn.widgets.Tabulator(DF.query("Ticker == @ticker"), height=200, widths=200, show_index=False)
+    chart1 = make_candle_stick(ticker)
+    return pn.Column(table1,chart1)
+  else:
+    # return pn.widgets.Tabulator( DF.query(" Sector in @Sector & MarketCap>@MarketCap"), height=800, widths=200, show_index=False)
+    return pn.widgets.Tabulator( DF, height=800, widths=200, show_index=False)
+
+pn.extension('tabulator')
+bound_plot = pn.bind(get_DF, DF=df,ticker=ticker, Sector=Sector ,MarketCap=MarketCap)
+
+pn.Column(pn.Row(pn.Column(ticker, MarketCap, Sector),bound_plot)).servable(title="Option Volatility View")
+