Update app.py

app.py

@@ -288,7 +288,92 @@ st.title('Technical Analysis Indicators')
 with st.sidebar.expander("Method Selection", expanded=True):
     selected = st.radio("Select Indicator", ["Rolling Z-Score", "Rate of Change (ROC)", "Stochastic Oscillator", "Relative Strength Index (RSI)", "MACD", "Bollinger Bands", "K Reversal", "Awesome Oscillator", "Williams %R", "Aroon Oscillator"])
 
-# Sidebar for input parameters
+# Sidebar for "How to Use" instructions specific to the selected method
+with st.sidebar.expander("How to Use", expanded=True):
+    if selected == "Rolling Z-Score":
+        st.markdown("""
+        1. Enter the stock or crypto symbol (e.g., 'AAPL' for Apple or 'BTC-USD' for Bitcoin).
+        2. Choose the date range.
+        3. Set the number of days for the z-score calculation.
+        4. Set the z-score threshold.
+        5. Click 'Fetch Data' to load the data.
+        6. The chart will display the rolling z-scores and highlight buy/sell signals based on the thresholds.
+        """)
+    elif selected == "Rate of Change (ROC)":
+        st.markdown("""
+        1. Enter the stock or crypto symbol.
+        2. Choose the date range.
+        3. Set the number of days for the ROC calculation.
+        4. Click 'Fetch Data' to load the data.
+        5. The chart will display the ROC and highlight buy/sell signals based on ROC crossing zero.
+        """)
+    elif selected == "Stochastic Oscillator":
+        st.markdown("""
+        1. Enter the stock or crypto symbol.
+        2. Choose the date range.
+        3. Set the buy and sell thresholds.
+        4. Click 'Fetch Data' to load the data.
+        5. The chart will display the Stochastic Oscillator and highlight buy/sell signals.
+        """)
+    elif selected == "Relative Strength Index (RSI)":
+        st.markdown("""
+        1. Enter the stock or crypto symbol.
+        2. Choose the date range.
+        3. Set the buy and sell thresholds.
+        4. Click 'Fetch Data' to load the data.
+        5. The chart will display the RSI and highlight buy/sell signals.
+        """)
+    elif selected == "MACD":
+        st.markdown("""
+        1. Enter the stock or crypto symbol.
+        2. Choose the date range.
+        3. Click 'Fetch Data' to load the data.
+        4. The chart will display the MACD, Signal line, and Histogram, and highlight buy/sell signals based on MACD crossovers.
+        """)
+    elif selected == "Bollinger Bands":
+        st.markdown("""
+        1. Enter the stock or crypto symbol.
+        2. Choose the date range.
+        3. Click 'Fetch Data' to load the data.
+        4. The chart will display the Bollinger Bands and highlight buy/sell signals based on price crossing the bands.
+        """)
+    elif selected == "K Reversal":
+        st.markdown("""
+        1. Enter the stock or crypto symbol.
+        2. Choose the date range.
+        3. Set the period for the K Reversal calculation.
+        4. Set the buy and sell thresholds.
+        5. Click 'Fetch Data' to load the data.
+        6. The chart will display the K Reversal indicator and highlight buy/sell signals.
+        """)
+    elif selected == "Awesome Oscillator":
+        st.markdown("""
+        1. Enter the stock or crypto symbol.
+        2. Choose the date range.
+        3. Set the signal line period.
+        4. Set the buy and sell thresholds.
+        5. Click 'Fetch Data' to load the data.
+        6. The chart will display the Awesome Oscillator and highlight buy/sell signals.
+        """)
+    elif selected == "Williams %R":
+        st.markdown("""
+        1. Enter the stock or crypto symbol.
+        2. Choose the date range.
+        3. Set the look-back period.
+        4. Set the buy and sell thresholds.
+        5. Click 'Fetch Data' to load the data.
+        6. The chart will display the Williams %R and highlight buy/sell signals.
+        """)
+    elif selected == "Aroon Oscillator":
+        st.markdown("""
+        1. Enter the stock or crypto symbol.
+        2. Choose the date range.
+        3. Set the look-back period.
+        4. Click 'Fetch Data' to load the data.
+        5. The chart will display the Aroon Oscillator and highlight buy/sell signals based on thresholds.
+        """)
+
+# Sidebar for input parameters inside an expander
 with st.sidebar.expander("Input Parameters", expanded=True):
     ticker = st.text_input('Enter Stock or Crypto Symbol (e.g., AAPL or BTC-USD)', 'AAPL')
     start_date = st.date_input('Start Date', pd.to_datetime('2019-01-01'))
@@ -301,24 +386,13 @@ if 'data' not in st.session_state or st.sidebar.button('Fetch Data'):
 data = st.session_state.data
 close_prices = data['Close']
 
-# Expander for "How to" section
-with st.expander("How to Use This App", expanded=False):
-    st.markdown("""
-    1. Use the sidebar to select an indicator and input parameters.
-    2. Enter the stock or cryptocurrency symbol (e.g., 'AAPL' for Apple or 'BTC-USD' for Bitcoin).
-    3. Choose the date range for the data.
-    4. Click 'Fetch Data' to load and visualize the data.
-    5. Use the parameter settings for each method to fine-tune the analysis.
-    """)
-
 # Display results based on the selected method
 if selected == "Rolling Z-Score":
+    st.markdown("## Rolling Z-Score")
+    st.markdown("The rolling z-score method identifies overbought and oversold conditions by standardizing stock prices over different periods.")
+    
     with st.expander("Formula and Method Description", expanded=False):
         st.markdown("""
-        ### Rolling Z-Score
-        
-        The rolling z-score method identifies overbought and oversold conditions by standardizing stock prices over different periods.
-        
         **How it Works:**
         
         1. **Calculate Rolling Mean and Standard Deviation:**
@@ -334,16 +408,6 @@ if selected == "Rolling Z-Score":
         3. **Identify Signals:**
            - **Overbought Condition:** Z-score above a set threshold (e.g., 2.0) suggests the stock may be overbought.
            - **Oversold Condition:** Z-score below a set threshold (e.g., -2.0) suggests the stock may be oversold.
-        
-        **How to Use:**
-        1. Enter the stock ticker, start date, and end date.
-        2. Set the number of days for the z-score calculation.
-        3. Set the z-score threshold.
-        4. Click 'Fetch Data' to load the stock data.
-        5. The chart will display the rolling z-scores and highlight buy/sell signals based on the thresholds.
-        
-        **Results:**
-        The chart shows the close prices with highlighted buy/sell signals and their correctness percentage over a specified period.
         """)
 
     periods = st.sidebar.multiselect('Periods to Compare', options=[10, 20, 30, 40, 50], default=[20])
@@ -353,10 +417,11 @@ if selected == "Rolling Z-Score":
     st.plotly_chart(fig)
 
 elif selected == "Rate of Change (ROC)":
+    st.markdown("## Rate of Change (ROC)")
+    st.markdown("The Rate of Change (ROC) method measures the percentage change in stock prices over a specified period. It helps identify momentum and potential reversal points in the stock price.")
+    
     with st.expander("Formula and Method Description", expanded=False):
         st.markdown("""
-        ### Rate of Change (ROC)
-        The Rate of Change (ROC) method measures the percentage change in stock prices over a specified period. It helps identify momentum and potential reversal points in the stock price.
         **How it Works:**
         1. **Calculate ROC:**
            - For each day, calculate the ROC using the formula:
@@ -368,13 +433,6 @@ elif selected == "Rate of Change (ROC)":
         2. **Identify Signals:**
            - **Buy Signal:** Occurs when the ROC crosses above zero, indicating potential upward momentum.
            - **Sell Signal:** Occurs when the ROC crosses below zero, indicating potential downward momentum.
-        **How to Use:**
-        1. Enter the stock ticker, start date, and end date.
-        2. Set the number of days for the ROC calculation.
-        3. Click 'Fetch Data' to load the stock data.
-        4. The chart will display the ROC and highlight buy/sell signals based on the ROC crossing zero.
-        **Results:**
-        The chart shows the stock prices with highlighted buy/sell signals based on ROC.
         """)
 
     n_days = st.sidebar.number_input('Number of Days', min_value=1, value=14)
@@ -382,10 +440,11 @@ elif selected == "Rate of Change (ROC)":
     st.plotly_chart(fig)
 
 elif selected == "Stochastic Oscillator":
+    st.markdown("## Stochastic Oscillator")
+    st.markdown("The Stochastic Oscillator compares a stock's closing price to its price range over a specified period. It helps identify overbought and oversold conditions.")
+    
     with st.expander("Formula and Method Description", expanded=False):
         st.markdown("""
-        ### Stochastic Oscillator
-        The Stochastic Oscillator compares a stock's closing price to its price range over a specified period. It helps identify overbought and oversold conditions.
         **How it Works:**
         1. **Calculate %K and %D:**
            - The %K line is calculated as follows:
@@ -398,13 +457,6 @@ elif selected == "Stochastic Oscillator":
         2. **Identify Signals:**
            - **Buy Signal:** Occurs when %K crosses above a set threshold (e.g., 20), indicating potential upward momentum.
            - **Sell Signal:** Occurs when %K crosses below a set threshold (e.g., 80), indicating potential downward momentum.
-        **How to Use:**
-        1. Enter the stock ticker, start date, and end date.
-        2. Set the buy and sell thresholds.
-        3. Click 'Fetch Data' to load the stock data.
-        4. The chart will display the Stochastic Oscillator and highlight buy/sell signals based on thresholds.
-        **Results:**
-        The chart shows the closing prices with highlighted buy/sell signals and the %K and %D lines.
         """)
 
     buy_thresh = st.sidebar.slider('Stochastic Buy Threshold', min_value=0, max_value=100, value=5)
@@ -413,10 +465,11 @@ elif selected == "Stochastic Oscillator":
     st.plotly_chart(fig)
 
 elif selected == "Relative Strength Index (RSI)":
+    st.markdown("## Relative Strength Index (RSI)")
+    st.markdown("The RSI measures the magnitude of recent price changes to evaluate overbought or oversold conditions.")
+    
     with st.expander("Formula and Method Description", expanded=False):
         st.markdown("""
-        ### Relative Strength Index (RSI)
-        The RSI measures the magnitude of recent price changes to evaluate overbought or oversold conditions.
         **How it Works:**
         1. **Calculate RSI:**
            - Compute the average gains and average losses over a specified period (typically 14 days).
@@ -429,13 +482,6 @@ elif selected == "Relative Strength Index (RSI)":
         2. **Identify Signals:**
            - **Buy Signal:** Occurs when the RSI crosses below a set threshold (e.g., 30), indicating the stock may be oversold.
            - **Sell Signal:** Occurs when the RSI crosses above a set threshold (e.g., 70), indicating the stock may be overbought.
-        **How to Use:**
-        1. Enter the stock ticker, start date, and end date.
-        2. Set the buy and sell thresholds.
-        3. Click 'Fetch Data' to load the stock data.
-        4. The chart will display the RSI and highlight buy/sell signals based on thresholds.
-        **Results:**
-        The chart shows the stock prices with highlighted buy/sell signals and the RSI line.
         """)
 
     buy_thresh = st.sidebar.slider('RSI Buy Threshold', min_value=0, max_value=100, value=30)
@@ -444,10 +490,11 @@ elif selected == "Relative Strength Index (RSI)":
     st.plotly_chart(fig)
 
 elif selected == "MACD":
+    st.markdown("## Moving Average Convergence Divergence (MACD)")
+    st.markdown("The MACD is a trend-following momentum indicator that shows the relationship between two moving averages of a stock's price.")
+    
     with st.expander("Formula and Method Description", expanded=False):
         st.markdown("""
-        ### Moving Average Convergence Divergence (MACD)
-        The MACD is a trend-following momentum indicator that shows the relationship between two moving averages of a stock's price.
         **How it Works:**
         1. **Calculate MACD:**
            - Compute the MACD line using the formula:
@@ -461,22 +508,17 @@ elif selected == "MACD":
         2. **Identify Signals:**
            - **Buy Signal:** Occurs when the MACD line crosses above the Signal line, indicating potential upward momentum.
            - **Sell Signal:** Occurs when the MACD line crosses below the Signal line, indicating potential downward momentum.
-        **How to Use:**
-        1. Enter the stock ticker, start date, and end date.
-        2. Click 'Fetch Data' to load the stock data.
-        3. The chart will display the MACD line, Signal line, and Histogram, and highlight buy/sell signals based on MACD crossovers.
-        **Results:**
-        The chart shows the stock prices with highlighted buy/sell signals, MACD line, Signal line, and Histogram.
         """)
 
     fig = plot_macd(close_prices)
     st.plotly_chart(fig)
 
 elif selected == "Bollinger Bands":
+    st.markdown("## Bollinger Bands")
+    st.markdown("Bollinger Bands consist of a middle band (Simple Moving Average) and two outer bands (standard deviations from the SMA). They help identify overbought and oversold conditions.")
+    
     with st.expander("Formula and Method Description", expanded=False):
         st.markdown("""
-        ### Bollinger Bands
-        Bollinger Bands consist of a middle band (Simple Moving Average) and two outer bands (standard deviations from the SMA). They help identify overbought and oversold conditions.
         **How it Works:**
         1. **Calculate Bollinger Bands:**
            - Compute the middle band as the Simple Moving Average (SMA) of the stock prices over a specified period (typically 20 days).
@@ -493,22 +535,17 @@ elif selected == "Bollinger Bands":
         2. **Identify Signals:**
            - **Buy Signal:** Occurs when the stock price crosses below the lower band, indicating the stock may be oversold.
            - **Sell Signal:** Occurs when the stock price crosses above the upper band, indicating the stock may be overbought.
-        **How to Use:**
-        1. Enter the stock ticker, start date, and end date.
-        2. Click 'Fetch Data' to load the stock data.
-        3. The chart will display the Bollinger Bands and highlight buy/sell signals based on price crossing the bands.
-        **Results:**
-        The chart shows the stock prices with highlighted buy/sell signals and the Bollinger Bands.
         """)
 
     fig = plot_bollinger_bands(close_prices)
     st.plotly_chart(fig)
 
 elif selected == "K Reversal":
+    st.markdown("## K Reversal")
+    st.markdown("The K Reversal indicator helps identify potential reversal points based on the stock's high and low prices over a specified period.")
+    
     with st.expander("Formula and Method Description", expanded=False):
         st.markdown("""
-        ### K Reversal
-        The K Reversal indicator helps identify potential reversal points based on the stock's high and low prices over a specified period.
         **How it Works:**
         1. **Calculate K Reversal:**
            - Compute the highest high and lowest low over a specified period (e.g., 14 days).
@@ -521,14 +558,6 @@ elif selected == "K Reversal":
         2. **Identify Signals:**
            - **Buy Signal:** Occurs when the K value crosses below a set threshold (e.g., 20), indicating the stock may be oversold.
            - **Sell Signal:** Occurs when the K value crosses above a set threshold (e.g., 80), indicating the stock may be overbought.
-        **How to Use:**
-        1. Enter the stock ticker, start date, and end date.
-        2. Set the period for the K Reversal calculation.
-        3. Set the buy and sell thresholds.
-        4. Click 'Fetch Data' to load the stock data.
-        5. The chart will display the K Reversal indicator and highlight buy/sell signals.
-        **Results:**
-        The chart shows the stock prices with the K Reversal indicator and highlighted buy/sell signals.
         """)
 
     k_period = st.sidebar.number_input('K Reversal Period', min_value=1, value=14)
@@ -538,10 +567,11 @@ elif selected == "K Reversal":
     st.plotly_chart(fig)
 
 elif selected == "Awesome Oscillator":
+    st.markdown("## Awesome Oscillator")
+    st.markdown("The Awesome Oscillator measures market momentum by comparing the 34-period and 5-period simple moving averages of the midpoints of each candlestick.")
+    
     with st.expander("Formula and Method Description", expanded=False):
         st.markdown("""
-        ### Awesome Oscillator
-        The Awesome Oscillator measures market momentum by comparing the 34-period and 5-period simple moving averages of the midpoints of each candlestick.
         **How it Works:**
         1. **Calculate the Midpoint:**
            - For each candlestick, compute the midpoint using:
@@ -562,14 +592,6 @@ elif selected == "Awesome Oscillator":
         4. **Identify Signals:**
            - **Buy Signal:** Occurs when the AO crosses above the signal line (e.g., 0), indicating potential upward momentum.
            - **Sell Signal:** Occurs when the AO crosses below the signal line, indicating potential downward momentum.
-        **How to Use:**
-        1. Enter the stock ticker, start date, and end date.
-        2. Set the signal line period.
-        3. Set the buy and sell thresholds.
-        4. Click 'Fetch Data' to load the stock data.
-        5. The chart will display the Awesome Oscillator and highlight buy/sell signals based on thresholds.
-        **Results:**
-        The chart shows the stock prices with the Awesome Oscillator and highlighted buy/sell signals.
         """)
 
     signal_period = st.sidebar.number_input('Signal Line Period', min_value=1, value=9)
@@ -579,10 +601,11 @@ elif selected == "Awesome Oscillator":
     st.plotly_chart(fig)
 
 elif selected == "Williams %R":
+    st.markdown("## Williams %R")
+    st.markdown("The Williams %R measures overbought and oversold levels.")
+    
     with st.expander("Formula and Method Description", expanded=False):
         st.markdown("""
-        ### Williams %R
-        The Williams %R measures overbought and oversold levels.
         **How it Works:**
         1. **Calculate Williams %R:**
            - Compute the highest high and lowest low over a specified look-back period (e.g., 14 days).
@@ -595,14 +618,6 @@ elif selected == "Williams %R":
         2. **Identify Signals:**
            - **Buy Signal:** Occurs when the Williams %R crosses below a set threshold (e.g., -80), indicating the stock may be oversold.
            - **Sell Signal:** Occurs when the Williams %R crosses above a set threshold (e.g., -20), indicating the stock may be overbought.
-        **How to Use:**
-        1. Enter the stock ticker, start date, and end date.
-        2. Set the look-back period.
-        3. Set the buy and sell thresholds.
-        4. Click 'Fetch Data' to load the stock data.
-        5. The chart will display the Williams %R and highlight buy/sell signals based on thresholds.
-        **Results:**
-        The chart shows the stock prices with the Williams %R and highlighted buy/sell signals.
         """)
 
     williams_r_period = st.sidebar.number_input('Look-back Period', min_value=1, value=14)
@@ -612,10 +627,11 @@ elif selected == "Williams %R":
     st.plotly_chart(fig)
 
 elif selected == "Aroon Oscillator":
+    st.markdown("## Aroon Oscillator")
+    st.markdown("The Aroon Oscillator measures the strength of a trend in the stock's price.")
+    
     with st.expander("Formula and Method Description", expanded=False):
         st.markdown("""
-        ### Aroon Oscillator
-        The Aroon Oscillator measures the strength of a trend in the stock's price.
         **How it Works:**
         1. **Calculate Aroon Up and Aroon Down:**
            - Compute the Aroon Up, which measures the time since the highest high during the look-back period:
@@ -640,13 +656,6 @@ elif selected == "Aroon Oscillator":
         3. **Identify Signals:**
            - **Buy Signal:** Occurs when the Aroon Oscillator crosses above zero, indicating a potential upward trend.
            - **Sell Signal:** Occurs when the Aroon Oscillator crosses below zero, indicating a potential downward trend.
-        **How to Use:**
-        1. Enter the stock ticker, start date, and end date.
-        2. Set the look-back period.
-        3. Click 'Fetch Data' to load the stock data.
-        4. The chart will display the Aroon Oscillator and highlight buy/sell signals based on thresholds.
-        **Results:**
-        The chart shows the stock prices with the Aroon Oscillator and highlighted buy/sell signals.
         """)
 
     aroon_period = st.sidebar.number_input('Look-back Period', min_value=1, value=25)