Update app.py

app.py

@@ -54,7 +54,7 @@ def plot_distributions(bootstrap_simulations, data, thresholds, bootstrap_probab
     fig.add_vline(x=latest_price, line=dict(color='green', dash='dash'), name=f'Latest Price: {latest_price:.2f}')
     fig.add_vrect(x0=ci_68_bootstrap[0], x1=ci_68_bootstrap[1], fillcolor='yellow', opacity=0.2, layer="below", line_width=0, annotation_text="68% CI", annotation_position="top left")
     fig.add_vrect(x0=ci_95_bootstrap[0], x1=ci_95_bootstrap[1], fillcolor='grey', opacity=0.2, layer="below", line_width=0, annotation_text="95% CI", annotation_position="top left")
-    
+
     max_freq = np.histogram(final_bootstrap_prices, bins=50)[0].max()
 
     # Calculate positions based on a fraction of the max frequency
@@ -110,20 +110,44 @@ def plot_price_with_cones(data, bootstrap_percentiles, days, thresholds, bootstr
 
     fig.update_layout(title='Bootstrapping Simulation Cone', xaxis_title='Date', yaxis_title='Price', showlegend=True)
     fig.update_xaxes(type='date')
-    
+
     return fig
 
 # Streamlit app
 st.set_page_config(layout="wide")
-st.title('Future Stock Price Bootstrap Simulation')
+st.title('Future Stock/Crypto Price Bootstrap Simulation')
 
 st.sidebar.header('Input Parameters')
 
+# How to use (in sidebar, closed by default)
+with st.sidebar.expander("How to Use", expanded=False):
+    st.write("""
+    1. Enter the stock ticker or crypto pair (e.g., 'AAPL' or 'BTC-USD') in the 'Ticker' field.
+    2. Set the start and end dates for historical data.
+    3. Adjust the number of days for simulation and number of iterations if desired.
+    4. Set price thresholds for probability calculations.
+    5. Click 'Run Simulation' to start the bootstrapping simulation.
+    6. Analyze the resulting charts and statistics.
+    """)
+
+# Ticker and dates in an expander (open by default)
+with st.sidebar.expander("Ticker and Dates", expanded=True):
+    ticker = st.text_input('Enter Ticker', 'ASML.AS', help="Enter a stock ticker (e.g., AAPL) or a crypto pair (e.g., BTC-USD)")
+    start_date = st.date_input('Start Date', pd.to_datetime('2020-01-01'), help="Select the start date for historical data")
+    end_date = st.date_input('End Date', pd.to_datetime('2025-01-01'), help="Select the end date for historical data")
+
+# Other parameters
+days = st.sidebar.number_input('Number of Days for Simulation', min_value=1, max_value=365, value=30, help="Number of days to simulate into the future")
+n_iterations = st.sidebar.number_input('Number of Simulations', min_value=100, max_value=100000, value=10000, help="Number of bootstrap iterations to run")
+threshold1 = st.sidebar.number_input('Threshold 1', min_value=0, value=850, help="Lower price threshold for probability calculations")
+threshold2 = st.sidebar.number_input('Threshold 2', min_value=0, value=1050, help="Upper price threshold for probability calculations")
+thresholds = [threshold1, threshold2]
+
 st.write("""
 ### Description
-This application simulates future stock prices using bootstrapping simulation methods.
-You can specify the stock ticker, the date range, the number of simulation days, the number of simulations, and price thresholds.
-The simulation results will show the probability of the stock price falling below, between, or above the specified thresholds.
+This application simulates future stock or cryptocurrency prices using bootstrapping simulation methods.
+You can specify the stock ticker or crypto pair, the date range, the number of simulation days, the number of simulations, and price thresholds.
+The simulation results will show the probability of the price falling below, between, or above the specified thresholds.
 
 **Background and Concept**
 
@@ -131,7 +155,7 @@ The concept of bootstrapping was introduced by Bradley Efron in 1979. The primar
 
 **Steps in Bootstrapping:**
 
-Given a dataset \( X = \{x_1, x_2, ..., x_n\} \), we aim to estimate the statistic \( \theta \) (e.g., the mean return of a stock).
+Given a dataset \( X = \{x_1, x_2, ..., x_n\} \), we aim to estimate the statistic \( \theta \) (e.g., the mean return of a stock or cryptocurrency).
 
 1. **Resampling**: Create a resample \( X^* \) by drawing \( n \) observations from \( X \) with replacement. This means that each data point can be selected multiple times in a single resample:
 """)
@@ -147,27 +171,12 @@ st.latex(r'\{\theta_1^*, \theta_2^*, ..., \theta_B^*\}')
 st.write("""
 4. **Estimate**: Use the bootstrap statistics to estimate the mean, standard error, and confidence intervals of \( \theta \).
 
-**How to use:**
-1. Enter the stock ticker, start date, and end date.
-2. Set the number of days for the simulation and the number of iterations.
-3. Enter the price thresholds.
-4. Click 'Run Simulation' to start the bootstrapping simulation.
-
 **Results:**
 The app will display two charts:
 1. The distribution of the final simulated prices with key statistical measures.
-2. The historical stock prices with simulated future price cones and the specified thresholds.
+2. The historical prices with simulated future price cones and the specified thresholds.
 """)
 
-ticker = st.sidebar.text_input('Enter Stock Ticker', 'ASML.AS')
-start_date = st.sidebar.date_input('Start Date', pd.to_datetime('2020-01-01'))
-end_date = st.sidebar.date_input('End Date', pd.to_datetime('2025-01-01'))
-days = st.sidebar.number_input('Number of Days for Simulation', min_value=1, max_value=365, value=30)
-n_iterations = st.sidebar.number_input('Number of Simulations', min_value=100, max_value=100000, value=10000)
-threshold1 = st.sidebar.number_input('Threshold 1', min_value=0, value=850)
-threshold2 = st.sidebar.number_input('Threshold 2', min_value=0, value=1050)
-thresholds = [threshold1, threshold2]
-
 if st.sidebar.button('Run Simulation'):
     data = get_stock_data(ticker, start_date, end_date)
 
@@ -199,11 +208,11 @@ if st.sidebar.button('Run Simulation'):
     - **Bootstrap 95% CI:** The 95% confidence interval for the simulated future prices.
     - **Threshold 1 and Threshold 2:** {threshold1:.2f}, {threshold2:.2f}
     - **Probability Annotations:**
-      - The probability of the stock price being below Threshold 1: {bootstrap_probabilities["below"]:.2%}
-      - The probability of the stock price being between Threshold 1 and Threshold 2: {bootstrap_probabilities["between"]:.2%}
-      - The probability of the stock price being above Threshold 2: {bootstrap_probabilities["above"]:.2%}
+      - The probability of the price being below Threshold 1: {bootstrap_probabilities["below"]:.2%}
+      - The probability of the price being between Threshold 1 and Threshold 2: {bootstrap_probabilities["between"]:.2%}
+      - The probability of the price being above Threshold 2: {bootstrap_probabilities["above"]:.2%}
 
-    These results help in understanding the potential future movements of the stock price based on historical data and bootstrapping simulation.
+    These results help in understanding the potential future movements of the stock or cryptocurrency price based on historical data and bootstrapping simulation.
     """)
 
 hide_streamlit_style = """
@@ -212,4 +221,4 @@ hide_streamlit_style = """
 footer {visibility: hidden;}
 </style>
 """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+st.markdown(hide_streamlit_style