Update app.py

app.py

@@ -9,13 +9,16 @@ from numpy import average as npAverage, nan as npNaN, log as npLog, power as npP
 from pandas_ta.utils import get_offset, verify_series
 from datetime import datetime
 from matplotlib.dates import date2num
+from datetime import datetime
 
 st.set_page_config(layout="wide")
 
 st.title("Moving Averages Techniques for Price Analysis")
 
 st.markdown("""
-This tool implements various moving averages methods:
+This app provides a detailed analysis of various moving averages. You can select from a wide range of techniques to better understand asset price movement trends.
+
+### Moving Averages Categories:
 
 - **Fundamental Techniques**: Includes **SMA**, **EMA**, **WMA**, **DEMA**, **TEMA**, **VAMA**, **KAMA**, **TMA**, and **HMA**.
 
@@ -28,15 +31,14 @@ This tool implements various moving averages methods:
 For technical details on these methods, refer to [this article](https://entreprenerdly.com/top-36-moving-averages-methods-for-stock-prices-in-python/).
 """)
 
-with st.sidebar.expander("How to Use", expanded=False):
-    st.markdown("""
-    ### How to Use:
-    - **Asset Symbol**: Enter the stock symbol (e.g., `AAPL`) or Crypto Currency Pair (e.g., `BTC-USD`).
-    - **Date Range**: Select the start and end dates.
-    - **Fetch Data**: Click 'Fetch Data' to load the stock data.
-    - **Moving Averages**: Choose and customize your moving averages.
-    - **Run Analysis**: Click 'Run' to apply and visualize.
-    """)
+st.sidebar.markdown("""
+### How to Use:
+- **Asset Symbol**: Enter the stock symbol (e.g., `AAPL`) or Crypto Currency Pair (e.g., `BTC-USD`).
+- **Date Range**: Select the start and end dates.
+- **Fetch Data**: Click 'Fetch Data' to load the stock data.
+- **Moving Averages**: Choose and customize your moving averages.
+- **Run Analysis**: Click 'Run' to apply and visualize.
+""")
 
 # Function to fetch data
 @st.cache_data
@@ -458,309 +460,10 @@ start_date = st.sidebar.date_input(
 )
 end_date = st.sidebar.date_input(
     "End Date", 
-    value=st.session_state.get('end_date', pd.to_datetime("2025-01-01")),
+    value=st.session_state.get('end_date', pd.to_datetime(pd.Timestamp.now().date() + pd.Timedelta(days=1))),
     help="Select the end date for fetching the stock data."
 )
 
-# Run button to apply moving averages
-if st.sidebar.button('Run Analysis'):
-    # Save the moving average settings to session state
-    st.session_state['use_sma'] = use_sma
-    st.session_state['sma_period'] = sma_period
-    st.session_state['use_ema'] = use_ema
-    st.session_state['ema_period'] = ema_period
-    st.session_state['use_wma'] = use_wma
-    st.session_state['wma_period'] = wma_period
-    st.session_state['use_dema'] = use_dema
-    st.session_state['dema_period'] = dema_period
-    st.session_state['use_tema'] = use_tema
-    st.session_state['tema_period'] = tema_period
-    st.session_state['use_vama'] = use_vama
-    st.session_state['vama_period'] = vama_period
-    st.session_state['use_kama'] = use_kama
-    st.session_state['kama_period'] = kama_period
-    st.session_state['fastest_period'] = fastest_period
-    st.session_state['slowest_period'] = slowest_period
-    st.session_state['use_tma'] = use_tma
-    st.session_state['tma_period'] = tma_period
-    st.session_state['use_hull_ma'] = use_hull_ma
-    st.session_state['hull_ma_period'] = hull_ma_period
-    st.session_state['use_harmonic_ma'] = use_harmonic_ma
-    st.session_state['harmonic_ma_period'] = harmonic_ma_period
-    st.session_state['use_frama'] = use_frama
-    st.session_state['frama_batch'] = frama_batch
-    st.session_state['use_zlema'] = use_zlema
-    st.session_state['zlema_period'] = zlema_period
-    st.session_state['use_vidya'] = use_vidya
-    st.session_state['vidya_period'] = vidya_period
-    st.session_state['use_alma'] = use_alma
-    st.session_state['alma_period'] = alma_period
-    st.session_state['alma_offset'] = alma_offset
-    st.session_state['alma_sigma'] = alma_sigma
-    st.session_state['use_mama_fama'] = use_mama_fama
-    st.session_state['mama_fast_limit'] = mama_fast_limit
-    st.session_state['mama_slow_limit'] = mama_slow_limit
-    st.session_state['use_apma'] = use_apma
-    st.session_state['apma_min_period'] = apma_min_period
-    st.session_state['apma_max_period'] = apma_max_period
-    st.session_state['use_rainbow_ema'] = use_rainbow_ema
-    st.session_state['rainbow_lookback_periods'] = rainbow_lookback_periods
-    st.session_state['use_wilders_ma'] = use_wilders_ma
-    st.session_state['wilders_ma_period'] = wilders_ma_period
-    st.session_state['use_smma'] = use_smma
-    st.session_state['smma_period'] = smma_period
-    st.session_state['use_gmma'] = use_gmma
-    st.session_state['gmma_short_periods'] = gmma_short_periods
-    st.session_state['gmma_long_periods'] = gmma_long_periods
-    st.session_state['use_lsma'] = use_lsma
-    st.session_state['lsma_period'] = lsma_period
-    st.session_state['use_mma'] = use_mma
-    st.session_state['mma_period'] = mma_period
-    st.session_state['use_sinwma'] = use_sinwma
-    st.session_state['sinwma_period'] = sinwma_period
-    st.session_state['use_medma'] = use_medma
-    st.session_state['medma_period'] = medma_period
-    st.session_state['use_gma'] = use_gma
-    st.session_state['gma_period'] = gma_period
-    st.session_state['use_evwma'] = use_evwma
-    st.session_state['evwma_period'] = evwma_period
-    st.session_state['use_rema'] = use_rema
-    st.session_state['rema_alpha'] = rema_alpha
-    st.session_state['rema_lambda'] = rema_lambda
-    st.session_state['use_pwma'] = use_pwma
-    st.session_state['pwma_period'] = pwma_period
-    st.session_state['use_jma'] = use_jma
-    st.session_state['jma_period'] = jma_period
-    st.session_state['jma_phase'] = jma_phase
-    st.session_state['use_epma'] = use_epma
-    st.session_state['epma_period'] = epma_period
-    st.session_state['use_cma'] = use_cma
-    st.session_state['use_mcginley_dynamic'] = use_mcginley_dynamic
-    st.session_state['mcginley_dynamic_period'] = mcginley_dynamic_period
-    #st.session_state['use_ama'] = use_ama
-    #st.session_state['ama_anchor_date'] = ama_anchor_date
-    st.session_state['use_fma'] = use_fma
-    st.session_state['fma_period'] = fma_period
-    # (Save all previous moving average settings here)
-    
-    # Start with the base price plot
-    fig = go.Figure(data=st.session_state['price_plot'].data)
-    
-    # Add JMA if selected
-    if use_jma:
-        st.session_state['JMA'] = jma(data['Close'], length=jma_period, phase=jma_phase)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['JMA'], mode='lines', name=f'JMA (n={jma_period}, phase={jma_phase})', line=dict(dash='dash', color='green')))
-    
-    # Add EPMA if selected
-    if use_epma:
-        st.session_state['EPMA'] = calculate_EPMA(data['Close'].tolist(), epma_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['EPMA'], mode='lines', name=f'EPMA (n={epma_period})', line=dict(dash='dash', color='blue')))
-    
-    # Add CMA if selected
-    if use_cma:
-        st.session_state['CMA'] = calculate_CMA(data['Close'])
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['CMA'], mode='lines', name=f'CMA', line=dict(dash='dash', color='blue')))
-    
-    # Add McGinley Dynamic if selected
-    if use_mcginley_dynamic:
-        st.session_state['McGinley_Dynamic'] = calculate_mcginley_dynamic(data['Close'].tolist(), mcginley_dynamic_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['McGinley_Dynamic'], mode='lines', name=f'McGinley Dynamic (n={mcginley_dynamic_period})', line=dict(dash='dash', color='orange')))
-
-    # Add AMA if selected
-    #if use_ama:
-    #    st.session_state['AMA'] = calculate_AMA(data['Close'].tolist(), ama_anchor_date, data)
-    #    fig.add_trace(go.Scatter(x=data.index, y=st.session_state['AMA'], mode='lines', name=f'Anchored MA (Anchor Date={ama_anchor_date})', line=dict(dash='dash', color='red')))
-    #    fig.add_shape(type="line", x0=ama_anchor_date, y0=data['Close'].min(), x1=ama_anchor_date, y1=data['Close'].max(), line=dict(color="blue", width=2, dash="dash"))
-
-    # Add FMA if selected
-    if use_fma:
-        st.session_state['FMA'] = filtered_moving_average(data['Close'].values, fma_period)
-        fig.add_trace(go.Scatter(x=data.index, y=np.concatenate([np.array([np.nan]*(fma_period-1)), st.session_state['FMA']]), mode='lines', name=f'Filtered MA (n={fma_period})', line=dict(dash='dash', color='green')))
-
-    # Add SMA if selected
-    if use_sma:
-        st.session_state['SMA'] = data['Close'].rolling(window=sma_period).mean()
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['SMA'], mode='lines', name=f'{sma_period}-Day SMA', line=dict(dash='dash')))
-    
-    # Add EMA if selected
-    if use_ema:
-        st.session_state['EMA'] = data['Close'].ewm(span=ema_period, adjust=False).mean()
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['EMA'], mode='lines', name=f'{ema_period}-Day EMA', line=dict(dash='dash', color='green')))
-    
-    # Add WMA if selected
-    if use_wma:
-        weights = np.arange(1, wma_period + 1)
-        st.session_state['WMA'] = data['Close'].rolling(window=wma_period).apply(lambda prices: np.dot(prices, weights)/weights.sum(), raw=True)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['WMA'], mode='lines', name=f'{wma_period}-Day WMA', line=dict(dash='dash', color='orange')))
-    
-    # Add DEMA if selected
-    if use_dema:
-        data['EMA'] = data['Close'].ewm(span=dema_period, adjust=False).mean()
-        data['EMA2'] = data['EMA'].ewm(span=dema_period, adjust=False).mean()
-        st.session_state['DEMA'] = 2 * data['EMA'] - data['EMA2']
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['DEMA'], mode='lines', name=f'{dema_period}-Day DEMA', line=dict(dash='dash', color='red')))
-
-    # Add TEMA if selected
-    if use_tema:
-        data['EMA'] = data['Close'].ewm(span=tema_period, adjust=False).mean()
-        data['EMA2'] = data['EMA'].ewm(span=tema_period, adjust=False).mean()
-        data['EMA3'] = data['EMA2'].ewm(span=tema_period, adjust=False).mean()
-        st.session_state['TEMA'] = 3 * data['EMA'] - 3 * data['EMA2'] + data['EMA3']
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['TEMA'], mode='lines', name=f'{tema_period}-Day TEMA', line=dict(dash='dash', color='purple')))
-    
-    # Add VAMA if selected
-    if use_vama:
-        data['Volume_Price'] = data['Close'] * data['Volume']
-        st.session_state['VAMA'] = data['Volume_Price'].rolling(window=vama_period).sum() / data['Volume'].rolling(window=vama_period).sum()
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['VAMA'], mode='lines', name=f'{vama_period}-Day VAMA', line=dict(dash='dash', color='orange')))
-    
-    # Add KAMA if selected
-    if use_kama:
-        fastest_SC = 2 / (fastest_period + 1)
-        slowest_SC = 2 / (slowest_period + 1)
-        data['Change'] = abs(data['Close'] - data['Close'].shift(kama_period))
-        data['Volatility'] = data['Close'].diff().abs().rolling(window=kama_period).sum()
-        data['ER'] = data['Change'] / data['Volatility']
-        data['SC'] = (data['ER'] * (fastest_SC - slowest_SC) + slowest_SC)**2
-        data['KAMA'] = data['Close'].copy()
-        for i in range(kama_period, len(data)):
-            data['KAMA'].iloc[i] = data['KAMA'].iloc[i-1] + data['SC'].iloc[i] * (data['Close'].iloc[i] - data['KAMA'].iloc[i-1])
-        st.session_state['KAMA'] = data['KAMA']
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['KAMA'], mode='lines', name=f'KAMA (n={kama_period})', line=dict(dash='dash', color='green')))
-
-    # Add TMA if selected
-    if use_tma:
-        half_n = (tma_period + 1) // 2
-        data['Half_SMA'] = data['Close'].rolling(window=half_n).mean()
-        st.session_state['TMA'] = data['Half_SMA'].rolling(window=half_n).mean()
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['TMA'], mode='lines', name=f'TMA (n={tma_period})', line=dict(dash='dash', color='red')))
-
-    # Add Hull MA if selected
-    if use_hull_ma:
-        st.session_state['Hull_MA'] = hull_moving_average(data['Close'], hull_ma_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['Hull_MA'], mode='lines', name=f'Hull MA (n={hull_ma_period})', line=dict(dash='dash', color='green')))
-
-    # Add Harmonic MA if selected
-    if use_harmonic_ma:
-        st.session_state['Harmonic_MA'] = calculate_harmonic_moving_average(data['Close'].values, harmonic_ma_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['Harmonic_MA'], mode='lines', name=f'Harmonic MA (n={harmonic_ma_period})', line=dict(dash='dash', color='purple')))
-
-    # Add FRAMA if selected
-    if use_frama:
-        st.session_state['FRAMA'] = calculate_FRAMA(data, batch=frama_batch)['FRAMA']
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['FRAMA'], mode='lines', name=f'FRAMA (batch={frama_batch})', line=dict(dash='dash', color='green')))
-    
-    # Add ZLEMA if selected
-    if use_zlema:
-        st.session_state['ZLEMA'] = calculate_ZLEMA(data['Close'].tolist(), zlema_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['ZLEMA'], mode='lines', name=f'ZLEMA (n={zlema_period})', line=dict(dash='dash', color='red')))
-    
-    # Add VIDYA if selected
-    if use_vidya:
-        st.session_state['VIDYA'] = calculate_VIDYA(data['Close'].tolist(), vidya_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['VIDYA'], mode='lines', name=f'VIDYA (n={vidya_period})', line=dict(dash='dash', color='blue')))
-
-    # Add ALMA if selected
-    if use_alma:
-        st.session_state['ALMA'] = calculate_ALMA(data['Close'].tolist(), alma_period, offset=alma_offset, sigma=alma_sigma)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['ALMA'], mode='lines', name=f'ALMA (n={alma_period})', line=dict(dash='dash', color='purple')))
-
-    # Add MAMA and FAMA if selected
-    if use_mama_fama:
-        data['MAMA'], data['FAMA'] = talib.MAMA(data['Close'].values, fastlimit=mama_fast_limit, slowlimit=mama_slow_limit)
-        st.session_state['MAMA'] = data['MAMA']
-        st.session_state['FAMA'] = data['FAMA']
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['MAMA'], mode='lines', name=f'MAMA', line=dict(dash='dash', color='blue')))
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['FAMA'], mode='lines', name=f'FAMA', line=dict(dash='dash', color='red')))
-
-    # Add APMA if selected
-    if use_apma:
-        st.session_state['APMA'] = adaptive_period_moving_average(data['Close'].values, min_period=apma_min_period, max_period=apma_max_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['APMA'], mode='lines', name=f'APMA (min={apma_min_period}, max={apma_max_period})', line=dict(dash='dash', color='red')))
-    
-    # Add Rainbow EMA if selected
-    if use_rainbow_ema:
-        data = calculate_rainbow_ema(data, rainbow_lookback_periods)
-        colors = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet', 'black','gray','brown']
-        for i, lookback in enumerate(rainbow_lookback_periods):
-            fig.add_trace(go.Scatter(x=data.index, y=data[f'EMA{lookback}'], mode='lines', name=f'EMA {lookback}', line=dict(dash='solid', color=colors[i % len(colors)])))
-    
-    # Add Wilders MA if selected
-    if use_wilders_ma:
-        st.session_state['Wilders_MA'] = wilders_moving_average(data['Close'].tolist(), wilders_ma_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['Wilders_MA'], mode='lines', name=f'Wilders MA (n={wilders_ma_period})', line=dict(dash='dash', color='red')))
-    
-    # Add SMMA if selected
-    if use_smma:
-        st.session_state['SMMA'] = calculate_SMMA(data['Close'].tolist(), smma_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['SMMA'], mode='lines', name=f'SMMA (n={smma_period})', line=dict(dash='dash', color='red')))
-
-    # Add GMMA if selected
-    if use_gmma:
-        close_prices = data['Close'].tolist()
-        for period in gmma_short_periods:
-            data[f'EMA_{period}'] = calculate_EMA(close_prices, period)
-            fig.add_trace(go.Scatter(x=data.index, y=data[f'EMA_{period}'], mode='lines', name=f'GMMA Short EMA {period}', line=dict(dash='solid')))
-        for period in gmma_long_periods:
-            data[f'EMA_{period}'] = calculate_EMA(close_prices, period)
-            fig.add_trace(go.Scatter(x=data.index, y=data[f'EMA_{period}'], mode='lines', name=f'GMMA Long EMA {period}', line=dict(dash='dash')))
-
-    # Add LSMA if selected
-    if use_lsma:
-        st.session_state['LSMA'] = calculate_LSMA(data['Close'].tolist(), lsma_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['LSMA'], mode='lines', name=f'LSMA (n={lsma_period})', line=dict(dash='dash', color='blue')))
-
-    # Add MMA (Welch's MMA) if selected
-    if use_mma:
-        st.session_state['MMA'] = calculate_MMA(data['Close'].tolist(), mma_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['MMA'], mode='lines', name=f'MMA (n={mma_period})', line=dict(dash='dash', color='blue')))
-    
-    # Add SinWMA if selected
-    if use_sinwma:
-        st.session_state['SinWMA'] = calculate_SinWMA(data['Close'].tolist(), sinwma_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['SinWMA'], mode='lines', name=f'SinWMA (n={sinwma_period})', line=dict(dash='dash', color='green')))
-    
-    # Add MedMA if selected
-    if use_medma:
-        st.session_state['MedMA'] = calculate_MedMA(data['Close'].tolist(), medma_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['MedMA'], mode='lines', name=f'MedMA (n={medma_period})', line=dict(dash='dash', color='blue')))
-
-    # Add GMA if selected
-    if use_gma:
-        st.session_state['GMA'] = calculate_GMA(data['Close'].tolist(), gma_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['GMA'], mode='lines', name=f'GMA (n={gma_period})', line=dict(dash='dash', color='green')))
-
-    # Add eVWMA if selected
-    if use_evwma:
-        st.session_state['eVWMA'] = calculate_eVWMA(data['Close'], data['Volume'], evwma_period)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['eVWMA'], mode='lines', name=f'eVWMA (n={evwma_period})', line=dict(dash='dash', color='blue')))
-    
-    # Add REMA if selected
-    if use_rema:
-        st.session_state['REMA'] = REMA(data['Close'], alpha=rema_alpha, lambda_=rema_lambda)
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['REMA'], mode='lines', name=f'REMA (alpha={rema_alpha}, lambda={rema_lambda})', line=dict(dash='dash', color='red')))
-    
-    # Add PWMA if selected
-    if use_pwma:
-        pwma_values = parabolic_weighted_moving_average(data['Close'].values, pwma_period)
-        st.session_state['PWMA'] = np.concatenate([np.array([np.nan]*(pwma_period-1)), pwma_values])
-        fig.add_trace(go.Scatter(x=data.index, y=st.session_state['PWMA'], mode='lines', name=f'PWMA (n={pwma_period})', line=dict(dash='dash', color='red')))
-
-
-    # Update layout with grid toggle
-    fig.update_layout(
-        title=f'{ticker_symbol} Stock Price with Moving Averages',
-        xaxis_title='Date',
-        yaxis_title='Stock Price',
-        legend_title='Indicators',
-        template='plotly_white',
-        xaxis=dict(showgrid=show_grid),
-        yaxis=dict(showgrid=show_grid)
-    )
-
-    # Store the updated figure in session state
-    st.session_state['current_fig'] = fig
-
 # Fetch Data button
 if st.sidebar.button('Fetch Data'):
     # Only fetch if the ticker or date range has changed
@@ -1036,9 +739,7 @@ if 'data' in st.session_state:
     mama_fast_limit = st.sidebar.number_input(
         'MAMA Fast Limit', 
         min_value=0.0, 
-        max_value=1.0,
-
- 
+        max_value=1.0, 
         value=st.session_state.get('mama_fast_limit', 0.5), 
         step=0.01, 
         disabled=not use_mama_fama,
@@ -1369,11 +1070,312 @@ if 'data' in st.session_state:
         help="Toggle to show or hide the grid on the plot."
     )
     
+    # Run button to apply moving averages
+    if st.sidebar.button('Run Analysis'):
+        # Save the moving average settings to session state
+        st.session_state['use_sma'] = use_sma
+        st.session_state['sma_period'] = sma_period
+        st.session_state['use_ema'] = use_ema
+        st.session_state['ema_period'] = ema_period
+        st.session_state['use_wma'] = use_wma
+        st.session_state['wma_period'] = wma_period
+        st.session_state['use_dema'] = use_dema
+        st.session_state['dema_period'] = dema_period
+        st.session_state['use_tema'] = use_tema
+        st.session_state['tema_period'] = tema_period
+        st.session_state['use_vama'] = use_vama
+        st.session_state['vama_period'] = vama_period
+        st.session_state['use_kama'] = use_kama
+        st.session_state['kama_period'] = kama_period
+        st.session_state['fastest_period'] = fastest_period
+        st.session_state['slowest_period'] = slowest_period
+        st.session_state['use_tma'] = use_tma
+        st.session_state['tma_period'] = tma_period
+        st.session_state['use_hull_ma'] = use_hull_ma
+        st.session_state['hull_ma_period'] = hull_ma_period
+        st.session_state['use_harmonic_ma'] = use_harmonic_ma
+        st.session_state['harmonic_ma_period'] = harmonic_ma_period
+        st.session_state['use_frama'] = use_frama
+        st.session_state['frama_batch'] = frama_batch
+        st.session_state['use_zlema'] = use_zlema
+        st.session_state['zlema_period'] = zlema_period
+        st.session_state['use_vidya'] = use_vidya
+        st.session_state['vidya_period'] = vidya_period
+        st.session_state['use_alma'] = use_alma
+        st.session_state['alma_period'] = alma_period
+        st.session_state['alma_offset'] = alma_offset
+        st.session_state['alma_sigma'] = alma_sigma
+        st.session_state['use_mama_fama'] = use_mama_fama
+        st.session_state['mama_fast_limit'] = mama_fast_limit
+        st.session_state['mama_slow_limit'] = mama_slow_limit
+        st.session_state['use_apma'] = use_apma
+        st.session_state['apma_min_period'] = apma_min_period
+        st.session_state['apma_max_period'] = apma_max_period
+        st.session_state['use_rainbow_ema'] = use_rainbow_ema
+        st.session_state['rainbow_lookback_periods'] = rainbow_lookback_periods
+        st.session_state['use_wilders_ma'] = use_wilders_ma
+        st.session_state['wilders_ma_period'] = wilders_ma_period
+        st.session_state['use_smma'] = use_smma
+        st.session_state['smma_period'] = smma_period
+        st.session_state['use_gmma'] = use_gmma
+        st.session_state['gmma_short_periods'] = gmma_short_periods
+        st.session_state['gmma_long_periods'] = gmma_long_periods
+        st.session_state['use_lsma'] = use_lsma
+        st.session_state['lsma_period'] = lsma_period
+        st.session_state['use_mma'] = use_mma
+        st.session_state['mma_period'] = mma_period
+        st.session_state['use_sinwma'] = use_sinwma
+        st.session_state['sinwma_period'] = sinwma_period
+        st.session_state['use_medma'] = use_medma
+        st.session_state['medma_period'] = medma_period
+        st.session_state['use_gma'] = use_gma
+        st.session_state['gma_period'] = gma_period
+        st.session_state['use_evwma'] = use_evwma
+        st.session_state['evwma_period'] = evwma_period
+        st.session_state['use_rema'] = use_rema
+        st.session_state['rema_alpha'] = rema_alpha
+        st.session_state['rema_lambda'] = rema_lambda
+        st.session_state['use_pwma'] = use_pwma
+        st.session_state['pwma_period'] = pwma_period
+        st.session_state['use_jma'] = use_jma
+        st.session_state['jma_period'] = jma_period
+        st.session_state['jma_phase'] = jma_phase
+        st.session_state['use_epma'] = use_epma
+        st.session_state['epma_period'] = epma_period
+        st.session_state['use_cma'] = use_cma
+        st.session_state['use_mcginley_dynamic'] = use_mcginley_dynamic
+        st.session_state['mcginley_dynamic_period'] = mcginley_dynamic_period
+        #st.session_state['use_ama'] = use_ama
+        #st.session_state['ama_anchor_date'] = ama_anchor_date
+        st.session_state['use_fma'] = use_fma
+        st.session_state['fma_period'] = fma_period
+        # (Save all previous moving average settings here)
+        
+        # Start with the base price plot
+        fig = go.Figure(data=st.session_state['price_plot'].data)
+        
+        # Add JMA if selected
+        if use_jma:
+            st.session_state['JMA'] = jma(data['Close'], length=jma_period, phase=jma_phase)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['JMA'], mode='lines', name=f'JMA (n={jma_period}, phase={jma_phase})', line=dict(dash='dash', color='green')))
+        
+        # Add EPMA if selected
+        if use_epma:
+            st.session_state['EPMA'] = calculate_EPMA(data['Close'].tolist(), epma_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['EPMA'], mode='lines', name=f'EPMA (n={epma_period})', line=dict(dash='dash', color='blue')))
+
+        # Add CMA if selected
+        if use_cma:
+            st.session_state['CMA'] = calculate_CMA(data['Close'])
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['CMA'], mode='lines', name=f'CMA', line=dict(dash='dash', color='blue')))
+
+        # Add McGinley Dynamic if selected
+        if use_mcginley_dynamic:
+            st.session_state['McGinley_Dynamic'] = calculate_mcginley_dynamic(data['Close'].tolist(), mcginley_dynamic_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['McGinley_Dynamic'], mode='lines', name=f'McGinley Dynamic (n={mcginley_dynamic_period})', line=dict(dash='dash', color='orange')))
+
+        # Add AMA if selected
+        #if use_ama:
+        #    st.session_state['AMA'] = calculate_AMA(data['Close'].tolist(), ama_anchor_date, data)
+        #    fig.add_trace(go.Scatter(x=data.index, y=st.session_state['AMA'], mode='lines', name=f'Anchored MA (Anchor Date={ama_anchor_date})', line=dict(dash='dash', color='red')))
+        #    fig.add_shape(type="line", x0=ama_anchor_date, y0=data['Close'].min(), x1=ama_anchor_date, y1=data['Close'].max(), line=dict(color="blue", width=2, dash="dash"))
+
+        # Add FMA if selected
+        if use_fma:
+            st.session_state['FMA'] = filtered_moving_average(data['Close'].values, fma_period)
+            fig.add_trace(go.Scatter(x=data.index, y=np.concatenate([np.array([np.nan]*(fma_period-1)), st.session_state['FMA']]), mode='lines', name=f'Filtered MA (n={fma_period})', line=dict(dash='dash', color='green')))
+
+        # Add SMA if selected
+        if use_sma:
+            st.session_state['SMA'] = data['Close'].rolling(window=sma_period).mean()
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['SMA'], mode='lines', name=f'{sma_period}-Day SMA', line=dict(dash='dash')))
+        
+        # Add EMA if selected
+        if use_ema:
+            st.session_state['EMA'] = data['Close'].ewm(span=ema_period, adjust=False).mean()
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['EMA'], mode='lines', name=f'{ema_period}-Day EMA', line=dict(dash='dash', color='green')))
+        
+        # Add WMA if selected
+        if use_wma:
+            weights = np.arange(1, wma_period + 1)
+            st.session_state['WMA'] = data['Close'].rolling(window=wma_period).apply(lambda prices: np.dot(prices, weights)/weights.sum(), raw=True)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['WMA'], mode='lines', name=f'{wma_period}-Day WMA', line=dict(dash='dash', color='orange')))
+        
+        # Add DEMA if selected
+        if use_dema:
+            data['EMA'] = data['Close'].ewm(span=dema_period, adjust=False).mean()
+            data['EMA2'] = data['EMA'].ewm(span=dema_period, adjust=False).mean()
+            st.session_state['DEMA'] = 2 * data['EMA'] - data['EMA2']
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['DEMA'], mode='lines', name=f'{dema_period}-Day DEMA', line=dict(dash='dash', color='red')))
+
+        # Add TEMA if selected
+        if use_tema:
+            data['EMA'] = data['Close'].ewm(span=tema_period, adjust=False).mean()
+            data['EMA2'] = data['EMA'].ewm(span=tema_period, adjust=False).mean()
+            data['EMA3'] = data['EMA2'].ewm(span=tema_period, adjust=False).mean()
+            st.session_state['TEMA'] = 3 * data['EMA'] - 3 * data['EMA2'] + data['EMA3']
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['TEMA'], mode='lines', name=f'{tema_period}-Day TEMA', line=dict(dash='dash', color='purple')))
+        
+        # Add VAMA if selected
+        if use_vama:
+            data['Volume_Price'] = data['Close'] * data['Volume']
+            st.session_state['VAMA'] = data['Volume_Price'].rolling(window=vama_period).sum() / data['Volume'].rolling(window=vama_period).sum()
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['VAMA'], mode='lines', name=f'{vama_period}-Day VAMA', line=dict(dash='dash', color='orange')))
+        
+        # Add KAMA if selected
+        if use_kama:
+            fastest_SC = 2 / (fastest_period + 1)
+            slowest_SC = 2 / (slowest_period + 1)
+            data['Change'] = abs(data['Close'] - data['Close'].shift(kama_period))
+            data['Volatility'] = data['Close'].diff().abs().rolling(window=kama_period).sum()
+            data['ER'] = data['Change'] / data['Volatility']
+            data['SC'] = (data['ER'] * (fastest_SC - slowest_SC) + slowest_SC)**2
+            data['KAMA'] = data['Close'].copy()
+            for i in range(kama_period, len(data)):
+                data['KAMA'].iloc[i] = data['KAMA'].iloc[i-1] + data['SC'].iloc[i] * (data['Close'].iloc[i] - data['KAMA'].iloc[i-1])
+            st.session_state['KAMA'] = data['KAMA']
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['KAMA'], mode='lines', name=f'KAMA (n={kama_period})', line=dict(dash='dash', color='green')))
+
+        # Add TMA if selected
+        if use_tma:
+            half_n = (tma_period + 1) // 2
+            data['Half_SMA'] = data['Close'].rolling(window=half_n).mean()
+            st.session_state['TMA'] = data['Half_SMA'].rolling(window=half_n).mean()
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['TMA'], mode='lines', name=f'TMA (n={tma_period})', line=dict(dash='dash', color='red')))
+
+        # Add Hull MA if selected
+        if use_hull_ma:
+            st.session_state['Hull_MA'] = hull_moving_average(data['Close'], hull_ma_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['Hull_MA'], mode='lines', name=f'Hull MA (n={hull_ma_period})', line=dict(dash='dash', color='green')))
+
+        # Add Harmonic MA if selected
+        if use_harmonic_ma:
+            st.session_state['Harmonic_MA'] = calculate_harmonic_moving_average(data['Close'].values, harmonic_ma_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['Harmonic_MA'], mode='lines', name=f'Harmonic MA (n={harmonic_ma_period})', line=dict(dash='dash', color='purple')))
+
+        # Add FRAMA if selected
+        if use_frama:
+            st.session_state['FRAMA'] = calculate_FRAMA(data, batch=frama_batch)['FRAMA']
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['FRAMA'], mode='lines', name=f'FRAMA (batch={frama_batch})', line=dict(dash='dash', color='green')))
+        
+        # Add ZLEMA if selected
+        if use_zlema:
+            st.session_state['ZLEMA'] = calculate_ZLEMA(data['Close'].tolist(), zlema_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['ZLEMA'], mode='lines', name=f'ZLEMA (n={zlema_period})', line=dict(dash='dash', color='red')))
+        
+        # Add VIDYA if selected
+        if use_vidya:
+            st.session_state['VIDYA'] = calculate_VIDYA(data['Close'].tolist(), vidya_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['VIDYA'], mode='lines', name=f'VIDYA (n={vidya_period})', line=dict(dash='dash', color='blue')))
+
+        # Add ALMA if selected
+        if use_alma:
+            st.session_state['ALMA'] = calculate_ALMA(data['Close'].tolist(), alma_period, offset=alma_offset, sigma=alma_sigma)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['ALMA'], mode='lines', name=f'ALMA (n={alma_period})', line=dict(dash='dash', color='purple')))
+
+        # Add MAMA and FAMA if selected
+        if use_mama_fama:
+            data['MAMA'], data['FAMA'] = talib.MAMA(data['Close'].values, fastlimit=mama_fast_limit, slowlimit=mama_slow_limit)
+            st.session_state['MAMA'] = data['MAMA']
+            st.session_state['FAMA'] = data['FAMA']
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['MAMA'], mode='lines', name=f'MAMA', line=dict(dash='dash', color='blue')))
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['FAMA'], mode='lines', name=f'FAMA', line=dict(dash='dash', color='red')))
+
+        # Add APMA if selected
+        if use_apma:
+            st.session_state['APMA'] = adaptive_period_moving_average(data['Close'].values, min_period=apma_min_period, max_period=apma_max_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['APMA'], mode='lines', name=f'APMA (min={apma_min_period}, max={apma_max_period})', line=dict(dash='dash', color='red')))
+        
+        # Add Rainbow EMA if selected
+        if use_rainbow_ema:
+            data = calculate_rainbow_ema(data, rainbow_lookback_periods)
+            colors = ['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet', 'black','gray','brown']
+            for i, lookback in enumerate(rainbow_lookback_periods):
+                fig.add_trace(go.Scatter(x=data.index, y=data[f'EMA{lookback}'], mode='lines', name=f'EMA {lookback}', line=dict(dash='solid', color=colors[i % len(colors)])))
+        
+        # Add Wilders MA if selected
+        if use_wilders_ma:
+            st.session_state['Wilders_MA'] = wilders_moving_average(data['Close'].tolist(), wilders_ma_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['Wilders_MA'], mode='lines', name=f'Wilders MA (n={wilders_ma_period})', line=dict(dash='dash', color='red')))
+        
+        # Add SMMA if selected
+        if use_smma:
+            st.session_state['SMMA'] = calculate_SMMA(data['Close'].tolist(), smma_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['SMMA'], mode='lines', name=f'SMMA (n={smma_period})', line=dict(dash='dash', color='red')))
+
+        # Add GMMA if selected
+        if use_gmma:
+            close_prices = data['Close'].tolist()
+            for period in gmma_short_periods:
+                data[f'EMA_{period}'] = calculate_EMA(close_prices, period)
+                fig.add_trace(go.Scatter(x=data.index, y=data[f'EMA_{period}'], mode='lines', name=f'GMMA Short EMA {period}', line=dict(dash='solid')))
+            for period in gmma_long_periods:
+                data[f'EMA_{period}'] = calculate_EMA(close_prices, period)
+                fig.add_trace(go.Scatter(x=data.index, y=data[f'EMA_{period}'], mode='lines', name=f'GMMA Long EMA {period}', line=dict(dash='dash')))
+
+        # Add LSMA if selected
+        if use_lsma:
+            st.session_state['LSMA'] = calculate_LSMA(data['Close'].tolist(), lsma_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['LSMA'], mode='lines', name=f'LSMA (n={lsma_period})', line=dict(dash='dash', color='blue')))
+
+        # Add MMA (Welch's MMA) if selected
+        if use_mma:
+            st.session_state['MMA'] = calculate_MMA(data['Close'].tolist(), mma_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['MMA'], mode='lines', name=f'MMA (n={mma_period})', line=dict(dash='dash', color='blue')))
+        
+        # Add SinWMA if selected
+        if use_sinwma:
+            st.session_state['SinWMA'] = calculate_SinWMA(data['Close'].tolist(), sinwma_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['SinWMA'], mode='lines', name=f'SinWMA (n={sinwma_period})', line=dict(dash='dash', color='green')))
+        
+        # Add MedMA if selected
+        if use_medma:
+            st.session_state['MedMA'] = calculate_MedMA(data['Close'].tolist(), medma_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['MedMA'], mode='lines', name=f'MedMA (n={medma_period})', line=dict(dash='dash', color='blue')))
+
+        # Add GMA if selected
+        if use_gma:
+            st.session_state['GMA'] = calculate_GMA(data['Close'].tolist(), gma_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['GMA'], mode='lines', name=f'GMA (n={gma_period})', line=dict(dash='dash', color='green')))
+
+        # Add eVWMA if selected
+        if use_evwma:
+            st.session_state['eVWMA'] = calculate_eVWMA(data['Close'], data['Volume'], evwma_period)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['eVWMA'], mode='lines', name=f'eVWMA (n={evwma_period})', line=dict(dash='dash', color='blue')))
+        
+        # Add REMA if selected
+        if use_rema:
+            st.session_state['REMA'] = REMA(data['Close'], alpha=rema_alpha, lambda_=rema_lambda)
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['REMA'], mode='lines', name=f'REMA (alpha={rema_alpha}, lambda={rema_lambda})', line=dict(dash='dash', color='red')))
+        
+        # Add PWMA if selected
+        if use_pwma:
+            pwma_values = parabolic_weighted_moving_average(data['Close'].values, pwma_period)
+            st.session_state['PWMA'] = np.concatenate([np.array([np.nan]*(pwma_period-1)), pwma_values])
+            fig.add_trace(go.Scatter(x=data.index, y=st.session_state['PWMA'], mode='lines', name=f'PWMA (n={pwma_period})', line=dict(dash='dash', color='red')))
+
+
+        # Update layout with grid toggle
+        fig.update_layout(
+            title=f'{ticker_symbol} Stock Price with Moving Averages',
+            xaxis_title='Date',
+            yaxis_title='Stock Price',
+            legend_title='Indicators',
+            template='plotly_white',
+            xaxis=dict(showgrid=show_grid),
+            yaxis=dict(showgrid=show_grid)
+        )
+    
+        # Store the updated figure in session state
+        st.session_state['current_fig'] = fig
 
 # Display the current figure, which remains unchanged until "Run" is clicked
 if 'current_fig' in st.session_state:
     st.plotly_chart(st.session_state['current_fig'], use_container_width=True)
 
+
+
+
 st.markdown(
     """
     <style>
@@ -1394,4 +1396,4 @@ 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)