Update app.py

app.py

@@ -4,10 +4,11 @@ import numpy as np
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 import streamlit as st
+from datetime import datetime, timedelta
 
-# Helper function to fetch stock data
+# Helper function to fetch stock or crypto data
 def fetch_stock_data(ticker: str, start_date: str, end_date: str) -> pd.DataFrame:
-    """Fetch stock data from Yahoo Finance."""
+    """Fetch stock or crypto data from Yahoo Finance."""
     return yf.download(ticker, start=start_date, end=end_date)
 
 # Function to estimate probability and plot
@@ -28,7 +29,7 @@ def estimate_probability(data, n_days, initial_price, up_target, down_target):
 
     # Plotting
     fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.1,
-                        subplot_titles=(f'Distribution of {n_days}-day percentage changes', f'Stock Price for {ticker}'),
+                        subplot_titles=(f'Distribution of {n_days}-day percentage changes', f'Price for {ticker}'),
                         specs=[[{"secondary_y": False}], [{"secondary_y": False}]])
 
     fig.add_trace(go.Histogram(x=data[f'{n_days}d_pct_change'], nbinsx=100, name='Percentage Change'), row=1, col=1)
@@ -41,58 +42,50 @@ def estimate_probability(data, n_days, initial_price, up_target, down_target):
     fig.add_trace(go.Scatter(x=up_instances.index, y=up_instances['Adj Close'], mode='markers', marker=dict(color='blue', symbol='triangle-up', size=10), name=f"{up_threshold * 100:.2f}% increase"), row=2, col=1)
     fig.add_trace(go.Scatter(x=down_instances.index, y=down_instances['Adj Close'], mode='markers', marker=dict(color='red', symbol='triangle-down', size=10), name=f"{down_threshold * 100:.2f}% decrease"), row=2, col=1)
 
-    fig.update_layout(title_text=f"Probability Estimation and Stock Price for {ticker}", xaxis_title='Date', yaxis_title='Adjusted Close Price')
+    fig.update_layout(title_text=f"Probability Estimation and Price for {ticker}", xaxis_title='Date', yaxis_title='Adjusted Close Price')
 
     return fig, up_frequency, down_frequency
 
 # Streamlit app
-st.set_page_config(page_title="Stock Probability Analysis", layout="wide")
-st.title('Stock Probability Analysis')
+st.set_page_config(page_title="Price Probability Analysis", layout="wide")
+st.title('Price Probability Analysis for Stocks and Cryptocurrencies')
 
 # Sidebar for method selection
-st.sidebar.header("Input Parameters")
-ticker = st.sidebar.text_input('Enter Stock Ticker', 'SAP.DE')
-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('2025-12-02'))
+st.sidebar.header("How to Use")
+st.sidebar.markdown("""
+1. **Enter Symbol:** Input the stock or cryptocurrency symbol you want to analyze.
+2. **Select Date Range:** Choose the start and end dates for the historical data.
+3. **Set Parameters:** Adjust the number of days, initial price, and targets for the analysis.
+4. **Run Analysis:** Click the 'Run Analysis' button to generate the results.
+""")
+
+# Sidebar for user inputs with collapsible sections
+with st.sidebar:
+    st.header("Input Parameters")
+    
+    with st.expander("Symbol and Date Range", expanded=True):
+        ticker = st.text_input('Enter Symbol', 'BTC-USD', help="Enter the stock or cryptocurrency symbol, e.g., AAPL, BTC-USD")
+        start_date = st.date_input('Start Date', value=pd.to_datetime('2020-01-01'), help="Select the start date for the analysis.")
+        end_date = st.date_input('End Date', value=datetime.now() + timedelta(days=1), help="Select the end date for the analysis.")
+    
+    with st.expander("Parameter Settings", expanded=True):
+        n_days = st.slider('Number of Days', min_value=1, max_value=100, value=30, step=1, help="Number of days over which to calculate percentage change.")
+        initial_price = st.number_input('Initial Price', value=0.0, help="The initial price to use for target estimation.")
+        up_target = st.number_input('Up Target', value=0.0, help="The target price for an upward move.")
+        down_target = st.number_input('Down Target', value=0.0, help="The target price for a downward move.")
 
 # Fetch data to set default values
 if 'data' not in st.session_state:
     st.session_state.data = fetch_stock_data(ticker, start_date, end_date)
 data = st.session_state.data
 
-# Set default values
-default_initial_price = data['Adj Close'].iloc[-1]
-default_up_target = default_initial_price + 10
-default_down_target = default_initial_price - 10
-
-# Sidebar for dynamic inputs
-n_days = st.sidebar.slider('Number of Days', min_value=1, max_value=100, value=30, step=1)
-initial_price = st.sidebar.number_input('Initial Price', value=default_initial_price)
-up_target = st.sidebar.number_input('Up Target', value=default_up_target)
-down_target = st.sidebar.number_input('Down Target', value=default_down_target)
-
-# Explanation and instructions
-st.markdown("""
-### Explanation of the Analysis
-
-This app estimates the probability of a stock reaching certain price targets within a specified number of days based on historical data.
-
-- **Distribution of Percentage Changes**: The histogram shows the distribution of percentage changes over the selected number of days.
-- **Price Targets**: The vertical lines indicate the price targets for upward and downward moves. The frequencies of reaching these targets are annotated.
-- **Stock Price Plot**: The line chart shows the historical adjusted close prices with markers indicating instances where the price targets were met.
-
-### How to Use
-
-1. **Enter Stock Ticker**: Input the stock symbol you want to analyze.
-2. **Select Date Range**: Choose the start and end dates for the historical data.
-3. **Set Parameters**:
-   - **Number of Days (n_days)**: Define the period over which you want to calculate the percentage change.
-   - **Initial Price**: The starting price for your analysis.
-   - **Up Target**: The target price for an upward move.
-   - **Down Target**: The target price for a downward move.
-4. **Run Analysis**: Click the 'Run Analysis' button to generate the results.
-
-""")
+# Set default values for initial price, up target, and down target
+if initial_price == 0.0:
+    initial_price = data['Adj Close'].iloc[-1]
+if up_target == 0.0:
+    up_target = initial_price + 10
+if down_target == 0.0:
+    down_target = initial_price - 10
 
 # Run button
 run_button = st.sidebar.button('Run Analysis')
@@ -102,24 +95,15 @@ if run_button:
     st.session_state.data = fetch_stock_data(ticker, start_date, end_date)
     data = st.session_state.data
 
-    if initial_price == 0.0:
-        initial_price = data['Adj Close'].iloc[-1]
-    if up_target == 0.0:
-        up_target = initial_price + 10
-    if down_target == 0.0:
-        down_target = initial_price - 10
-
     fig, up_frequency, down_frequency = estimate_probability(data, n_days, initial_price, up_target, down_target)
     st.plotly_chart(fig)
 
     st.markdown(f"""
     ### Results
-
     **Probability of Reaching Targets:**
     - Probability of reaching the up target ({up_target:.2f}) in {n_days} days: **{up_frequency * 100:.2f}%**
     - Probability of reaching the down target ({down_target:.2f}) in {n_days} days: **{down_frequency * 100:.2f}%**
-
-    This analysis helps in understanding the historical likelihood of the stock price reaching certain targets within a specified number of days.
+    This analysis helps in understanding the historical likelihood of the price reaching certain targets within a specified number of days.
     """)
 
 hide_streamlit_style = """
@@ -128,4 +112,4 @@ hide_streamlit_style = """
 footer {visibility: hidden;}
 </style>
 """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)