Update app.py

app.py

@@ -6,7 +6,7 @@ import persim
 import plotly.graph_objs as go
 import warnings
 
-# Function to fetch stock data
+# Function to fetch stock or crypto data
 def fetch_data(ticker_name, start_date, end_date):
     raw_data = yf.download(ticker_name, start=start_date, end=end_date)
     adjusted_close = raw_data['Adj Close'].dropna()
@@ -33,34 +33,38 @@ def compute_wasserstein_distances(log_returns, window_size, rips):
 
     return distances
 
-# Streamlit app
+# Streamlit app configuration
 st.set_page_config(layout="wide")
 
 st.title("Market Crash Analysis with Topology")
 st.write("""
 This application analyzes asset price data using Wasserstein distances to detect changes in price dynamics over time.
-Wasserstein distances, derived from persistence diagrams in Topological Data Analysis (TDA), help identify significant shifts in stock price behaviors.
+Wasserstein distances, derived from persistence diagrams in Topological Data Analysis (TDA), help identify significant shifts in asset price behaviors for both stocks and cryptocurrencies.
 """)
 
-st.sidebar.title('Input Parameters')
-
-# Input fields for user
-ticker_name = st.sidebar.text_input('Enter Ticker Symbol', '^GSPC')
-start_date_string = st.sidebar.text_input('Start Date (YYYY-MM-DD)', '2020-01-01')
-end_date_string = st.sidebar.text_input('End Date (YYYY-MM-DD)', '2025-01-01')
-window_size = st.sidebar.slider('Window Size', min_value=5, max_value=50, value=20)
-threshold = st.sidebar.slider('Alert Threshold', min_value=0.05, max_value=0.2, value=0.075, step=0.005)
-
-st.sidebar.write("""
-**How to use:**
-1. Enter the stock ticker symbol (e.g., `^GSPC` for S&P 500).
-2. Specify the start and end dates for the analysis period.
-3. Adjust the window size for the sliding window analysis.
-4. Set the alert threshold for detecting significant changes.
-5. Click 'Run Analysis' to start.
-""")
+# Sidebar for "How to Use" instructions inside an expander, closed by default
+with st.sidebar.expander("How to Use", expanded=False):
+    st.write("""
+    **How to use this app:**
+    1. Enter the stock or crypto ticker symbol (e.g., `^GSPC` for S&P 500 or `BTC-USD` for Bitcoin).
+    2. Specify the start and end dates for the analysis period.
+    3. Adjust the window size for the sliding window analysis.
+    4. Set the alert threshold for detecting significant changes in price dynamics.
+    5. Click 'Run Analysis' to start.
+    """)
+
+# Input parameters inside an expander, open by default
+with st.expander("Input Parameters", expanded=True):
+    ticker_name = st.text_input('Enter Stock or Crypto Symbol (e.g., AAPL or BTC-USD)', '^GSPC', help="Enter the ticker symbol for the stock or cryptocurrency you want to analyze.")
+    start_date_string = st.date_input('Start Date', pd.to_datetime('2020-01-01'), help="Select the start date for the data range.")
+    end_date_string = st.date_input('End Date', pd.to_datetime(pd.Timestamp.now().date() + pd.Timedelta(days=1)), help="Select the end date for the data range.")
+
+# Parameters for the selected method inside an expander, open by default
+with st.expander("Parameters", expanded=True):
+    window_size = st.slider('Window Size', min_value=5, max_value=50, value=20, help="Set the window size for the sliding window analysis.")
+    threshold = st.slider('Alert Threshold', min_value=0.05, max_value=0.2, value=0.075, step=0.005, help="Set the threshold for detecting significant changes in price dynamics.")
 
-if st.sidebar.button('Run Analysis'):
+if st.button('Run Analysis'):
     st.write(f"Analyzing {ticker_name} from {start_date_string} to {end_date_string} with window size {window_size} and threshold {threshold}")
 
     # Fetch data
@@ -92,12 +96,10 @@ if st.sidebar.button('Run Analysis'):
     fig.update_layout(title='Wasserstein Distances Over Time', xaxis_title='Date', yaxis_title='Wasserstein Distance')
     st.plotly_chart(fig, use_container_width=True)
 
-    # Interpretation of results
-    st.subheader("Interpretation of Results")
-    
+    # Explanation of the Wasserstein Distance method
+    st.subheader("Wasserstein Distance Methodology")
     st.write("""
-    **Wasserstein Distance Analysis:**
-    The Wasserstein distance measures the difference between two distributions. In this context, it quantifies changes in the log returns of stock prices over time.
+    The Wasserstein distance is a measure from optimal transport theory, used here to compare distributions of log returns in different time windows.
     A high Wasserstein distance indicates a significant change in the price dynamics, which might suggest a market event or shift in investor sentiment.
     """)
 
@@ -109,25 +111,29 @@ if st.sidebar.button('Run Analysis'):
     - Where \( W(P, Q) \) is the Wasserstein distance between distributions \( P \) and \( Q \).
     - \( d(x, y) \) is the distance between points \( x \) and \( y \).
     - \( \gamma \) is a joint distribution with marginals \( P \) and \( Q \).
-    
-    **Alert Threshold:**
-    The alert threshold is set to identify significant changes in the Wasserstein distances. Alerts are triggered when the distance exceeds the threshold.
     """)
+
+    # Interpretation of results
+    st.subheader("Interpretation of Results")
+    st.write("""
+    **Wasserstein Distance Analysis:**
+    The Wasserstein distance quantifies changes in the log returns of asset prices over time.
+    A high distance indicates a significant shift in price dynamics, potentially due to a market event or a change in investor behavior.
+    """)
+
     st.write(f"Threshold: {threshold}")
 
     st.write("""
     **Plot Interpretation:**
-    - The first plot shows the stock price over time with alerts marked in red.
+    - The first plot shows the asset price over time with alerts marked in red.
     - The second plot displays the Wasserstein distances over time, with the threshold indicated by a dashed red line. Peaks above this line represent significant changes in price dynamics.
     """)
 
-
-
-
+# Hide the default Streamlit menu and footer
 hide_streamlit_style = """
 <style>
 #MainMenu {visibility: hidden;}
 footer {visibility: hidden;}
 </style>
 """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)