Update app.py

app.py

@@ -3,6 +3,12 @@ import yfinance as yf
 import pandas as pd
 import plotly.graph_objects as go
 import streamlit as st
+from plotly.subplots import make_subplots
+from itertools import product
+import warnings
+from datetime import datetime
+
+warnings.filterwarnings("ignore")
 
 st.set_page_config(page_title="Expected Stock Price Movement Using Volatility Multipliers", layout="wide")
 st.title('Expected Stock Price Movement Using Volatility Multipliers')
@@ -11,7 +17,6 @@ st.title('Expected Stock Price Movement Using Volatility Multipliers')
 st.sidebar.title('Input Parameters')
 
 with st.sidebar.expander("How to use:", expanded=False):
-    #st.subheader('How to Use')
     st.markdown("""
     1. **Input Parameters**: Enter the stock ticker or cryptocurrency pair, date range, time horizon, standard deviation multipliers, and rolling window period.
     2. **Run the Analysis**: Click the "Run" button to perform the analyses and visualize the results.
@@ -26,10 +31,9 @@ with st.sidebar.expander("Ticker and Date Settings", expanded=True):
 # Wrapping parameter settings in an expander
 with st.sidebar.expander("Parameter Settings", expanded=True):
     time_horizon = st.slider('Time Horizon (Days)', min_value=1, max_value=60, value=30, help="Set the number of days into the future for which you want to estimate asset prices.")
-    std_multipliers = st.multiselect('Select Std Multipliers', [1, 1.25, 1.5, 1.75, 2,2.25,2.5,2.75,3], default=[1, 1.25, 1.5, 1.75], help="Choose the standard deviation multipliers to calculate future price ranges.")
+    std_multipliers = st.multiselect('Select Std Multipliers', [1, 1.25, 1.5, 1.75, 2, 2.25, 2.5, 2.75, 3], default=[1, 1.25, 1.5, 1.75], help="Choose the standard deviation multipliers to calculate future price ranges.")
     rolling_window = st.slider('Rolling Window (Days)', min_value=10, max_value=90, value=30, step=5, help="Set the number of days to use for calculating rolling volatility.")
 
-
 st.write("""
 This tool estimates the potential price movement of a selected stock or cryptocurrency pair over a specified time horizon. 
 The predictions are based on historical volatility, calculated from the asset's daily returns. 
@@ -47,20 +51,24 @@ with st.expander("Click here to read more about the methodology", expanded=False
     - **σ (sigma)**: Standard deviation of the stock's returns, representing volatility
     - **t**: Time horizon in days
     - **z**: Multiplier corresponding to the desired confidence level, which adjusts for standard deviation
-
     To read more about the methodologies, visit [this link](https://entreprenerdly.com/expected-stock-price-movement-with-volatility-multipliers/).
     """)
 
 if st.sidebar.button('Run Analysis'):
-    stock_data = yf.download(ticker, start=start_date, end=end_date)
+    # Updated get_data: use "Close" and squeeze the result to ensure a 1D Series.
+    def get_data(ticker, start, end):
+        data = yf.download(ticker, start=start, end=end)
+        return data['Close'].squeeze()
+    
+    stock_data = get_data(ticker, start_date, end_date)
     
     if not stock_data.empty:
-        stock_data['Returns'] = stock_data['Close'].pct_change()
-        current_price = stock_data.iloc[-1]['Close']
+        stock_data['Returns'] = stock_data.pct_change() if isinstance(stock_data, pd.Series) else stock_data['Close'].pct_change()
+        current_price = stock_data.iloc[-1]
 
         # Method 1: Volatility over dynamic periods
         fig1 = go.Figure()
-        plot_data = stock_data[-rolling_window:]['Close']
+        plot_data = stock_data[-rolling_window:]
         date_range = pd.date_range(plot_data.index[-1] + pd.DateOffset(1), periods=time_horizon, freq='D')
         
         st.markdown("""
@@ -77,9 +85,9 @@ if st.sidebar.button('Run Analysis'):
 
             for i in range(time_horizon):
                 if i == 0:
-                    volatility = stock_data['Returns'].iloc[-rolling_window:].std()
+                    volatility = stock_data.iloc[-rolling_window:].pct_change().std()
                 else:
-                    volatility = stock_data['Returns'].iloc[-(rolling_window + i):-i].std()
+                    volatility = stock_data.iloc[-(rolling_window + i):-i].pct_change().std()
                 expected_price_movement = current_price * volatility * np.sqrt(i + 1) * std_multiplier
                 expected_upper_bounds.iloc[i] = current_price + expected_price_movement
                 expected_lower_bounds.iloc[i] = current_price - expected_price_movement
@@ -89,11 +97,11 @@ if st.sidebar.button('Run Analysis'):
             fig1.add_trace(go.Scatter(x=date_range, y=expected_upper_bounds, fill='tonexty', fillcolor='rgba(128, 128, 128, 0.3)', mode='none', showlegend=False))
 
         fig1.update_layout(title=f'{ticker} - Dynamic Volatility Expected Price Movement',
-                          xaxis_title='Date',
-                          yaxis_title='Price',
-                          legend_title='Legend',
-                          width=1600,
-                          height=800)
+                           xaxis_title='Date',
+                           yaxis_title='Price',
+                           legend_title='Legend',
+                           width=1600,
+                           height=800)
 
         # Method 2: Single volatility measure over the period
         fig2 = go.Figure()
@@ -106,7 +114,7 @@ if st.sidebar.button('Run Analysis'):
             expected_lower_bounds = pd.Series(index=date_range)
 
             for i in range(time_horizon):
-                volatility = stock_data['Returns'].std() * std_multiplier
+                volatility = stock_data.pct_change().std() * std_multiplier
                 expected_price_movement = current_price * volatility * np.sqrt(i + 1)
                 expected_upper_bounds.iloc[i] = current_price + expected_price_movement
                 expected_lower_bounds.iloc[i] = current_price - expected_price_movement
@@ -116,11 +124,11 @@ if st.sidebar.button('Run Analysis'):
             fig2.add_trace(go.Scatter(x=date_range, y=expected_upper_bounds, fill='tonexty', fillcolor='rgba(128, 128, 128, 0.3)', mode='none', showlegend=False))
 
         fig2.update_layout(title=f'{ticker} - Single Volatility Measure Expected Price Movement',
-                          xaxis_title='Date',
-                          yaxis_title='Price',
-                          legend_title='Legend',
-                          width=1600,
-                          height=800)
+                           xaxis_title='Date',
+                           yaxis_title='Price',
+                           legend_title='Legend',
+                           width=1600,
+                           height=800)
 
         st.plotly_chart(fig1)
         
@@ -128,7 +136,6 @@ if st.sidebar.button('Run Analysis'):
         ### Method 2: Single Volatility Measure
         This method calculates stock price movement based on a single, constant measure of volatility derived from the entire historical data set available.
         """)
-
         st.plotly_chart(fig2)
     else:
         st.write("No data found for the given ticker and date range.")