Create app.py

app.py

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+import yfinance as yf
+import numpy as np
+import pandas as pd
+import plotly.graph_objs as go
+import gradio as gr
+
+def plot_volatility_bands(ticker, reference_year):
+    
+    # Retrieving historical data and performing some preprocessing
+    df = yf.download(ticker)
+    df['Returns'] = df['Adj Close'].pct_change(1)
+    df['Adj Low'] = df['Low'] - (df['Close'] - df['Adj Close'])
+    df['Adj High'] = df['High'] - (df['Close'] - df['Adj Close'])
+    df['Adj Open'] = df['Open'] - (df['Close'] - df['Adj Close'])
+    df = df.fillna(0)
+    
+    # Obtaining the annualized volatility
+    T = 20
+    df['Annualized_Vol'] = np.round(df['Returns'].rolling(T).std()*np.sqrt(252), 2)
+    
+    # Calculating Bands
+    High_Band_1std = df.loc[reference_year]["Annualized_Vol"][-1]*df.loc[reference_year]["Adj Close"][-1] + df.loc[reference_year]["Adj Close"][-1]
+    Low_Band_1std = df.loc[reference_year]["Adj Close"][-1] - df.loc[reference_year]["Annualized_Vol"][-1]*df.loc[reference_year]["Adj Close"][-1]
+    High_Band_2std =  2*df.loc[reference_year]["Annualized_Vol"][-1]*df.loc[reference_year]["Adj Close"][-1] + df.loc[reference_year]["Adj Close"][-1]
+    Low_Band_2std =  df.loc[reference_year]["Adj Close"][-1] - 2*df.loc[reference_year]["Annualized_Vol"][-1]*df.loc[reference_year]["Adj Close"][-1]
+    High_Band_3std =  3*df.loc[reference_year]["Annualized_Vol"][-1]*df.loc[reference_year]["Adj Close"][-1] + df.loc[reference_year]["Adj Close"][-1]
+    Low_Band_3std =  df.loc[reference_year]["Adj Close"][-1] - 3*df.loc[reference_year]["Annualized_Vol"][-1]*df.loc[reference_year]["Adj Close"][-1]
+
+    
+    # Creating Candlestick chart
+    candlestick = go.Candlestick(x = df.loc[str(int(reference_year) + 1)].index,
+                             open = df.loc[str(int(reference_year) + 1)]['Adj Open'],
+                             high = df.loc[str(int(reference_year) + 1)]['Adj High'],
+                             low = df.loc[str(int(reference_year) + 1)]['Adj Low'],
+                             close = df.loc[str(int(reference_year) + 1)]['Adj Close'],
+                             increasing = dict(line=dict(color = 'red')),
+                             decreasing = dict(line=dict(color = 'black')),
+                             name = 'Candlesticks')
+
+
+    # Defining layout
+    layout = go.Layout(title = {'text': f'<b>Volatility-Based Supply and Demand Levels ({ticker})<br><br><sup>&nbsp;&nbsp;&nbsp;&nbsp;<i>Yearly Forecast - {str(int(reference_year) + 1)}</i></sup></b>',
+                                'x': .035, 'xanchor': 'left'},
+                       yaxis = dict(title = '<b>Price (USD)</b>',
+                                   tickfont=dict(size=16)),
+                       xaxis = dict(title = '<b>Date</b>'),
+                       template = 'seaborn',
+                       plot_bgcolor = '#F6F5F5',
+                       paper_bgcolor = '#F6F5F5',
+                       height = 850, width = 1000,
+                       showlegend=False,
+                       xaxis_rangeslider_visible = False)
+
+    fig = go.Figure(data = [candlestick], layout = layout)
+    
+    # Fixing the empty spaces in the X-Axis
+    dt_all = pd.date_range(start = df.index[0]
+                           , end = df.index[-1]
+                           , freq = "D")
+    dt_all_py = [d.to_pydatetime() for d in dt_all]
+    dt_obs_py = [d.to_pydatetime() for d in df.index]
+
+    dt_breaks = [d for d in dt_all_py if d not in dt_obs_py]
+
+    fig.update_xaxes(
+        rangebreaks = [dict(values = dt_breaks)]
+    )
+
+    # 1σ
+    fig.add_hline(y = High_Band_1std, line_width = 2, line_dash = "dot", line_color = "green")
+    fig.add_hline(y = Low_Band_1std, line_width = 2, line_dash = "dot", line_color = "red")
+
+    # 2σ
+    fig.add_hline(y = High_Band_2std, line_width = 4, line_dash = "dash", line_color = "green")
+    fig.add_hline(y = Low_Band_2std, line_width = 4, line_dash = "dash", line_color = "red")
+
+    # 3σ
+    fig.add_hline(y = High_Band_3std, line_width = 6, line_dash = "dashdot", line_color = "green")
+    fig.add_hline(y = Low_Band_3std, line_width = 6, line_dash = "dashdot", line_color = "red")
+
+    fig.show()
+    
+    # Printing Supply and Demand Levels
+    print(f"\nVolatility-Based Supply and Demand Levels for {ticker} in {int(reference_year) + 1}\n")
+    print(f"Supply Level 3σ: {High_Band_3std.round(2)}\n")
+    print(f"Supply Level 2σ: {High_Band_2std.round(2)}\n")
+    print(f"Supply Level 1σ: {High_Band_1std.round(2)}\n")
+    print('-' * 65, '\n')
+    print(f"Demand Level 1σ: {Low_Band_1std.round(2)}\n")
+    print(f"Demand Level 2σ: {Low_Band_2std.round(2)}\n")
+    print(f"Demand Level 3σ: {Low_Band_3std.round(2)}\n")
+    
+    # Creating an HTML extension of the Plotly figure
+    fig_html = fig.to_html(full_html=False, include_plotlyjs='cdn')
+    
+    # Creating the text output
+    text_info = f"""
+    Volatility-Based Supply and Demand Levels for {ticker} in {int(reference_year) + 1}\n
+    Supply Level 3σ: {High_Band_3std.round(2)}\n
+    Supply Level 2σ: {High_Band_2std.round(2)}\n
+    Supply Level 1σ: {High_Band_1std.round(2)}\n
+    -------------------------\n
+    Demand Level 1σ: {Low_Band_1std.round(2)}\n
+    Demand Level 2σ: {Low_Band_2std.round(2)}\n
+    Demand Level 3σ: {Low_Band_3std.round(2)}\n
+    """
+    
+    return fig_html, text_info
+
+def wrapper_func(ticker, reference_year):
+    fig_html, text_info = plot_volatility_bands(ticker, str(reference_year))
+    return fig_html, text_info
+
+iface = gr.Interface(
+    fn=wrapper_func, 
+    inputs=["text", "number"], 
+    outputs=["html", "text"]
+)
+
+iface.launch()