import streamlit as st
import yfinance as yf
import pandas as pd
import plotly.graph_objs as go
import numpy as np
import talib
from pandas import Series
from numpy import average as npAverage, nan as npNaN, log as npLog, power as npPower, sqrt as npSqrt, zeros_like as npZeroslike
from pandas_ta.utils import get_offset, verify_series
from datetime import datetime
from matplotlib.dates import date2num

st.set_page_config(layout="wide")

st.title("Moving Averages Techniques for Price Analysis")

st.markdown("""
This app provides a detailed analysis of various moving averages:
- **Fundamental Techniques**: Includes **SMA**, **EMA**, **WMA**, **DEMA**, **TEMA**, **VAMA**, **KAMA**, **TMA**, and **HMA**.
- **Adaptive and Dynamic Approaches**: Covers methods like **FRAMA**, **ZLEMA**, **VIDYA**, **ALMA**, **MAMA**, **Adaptive Period MA**, **Rainbow MA**, **Wilders MA**, and **SMMA**.
- **Advanced Weighting Techniques**: Features **GMMA**, **LSMA**, **Welch’s MMA**, **Sin-weighted MA**, **Median MA**, **Geometric MA**, **eVWMA**, **REMA**, and **Parabolic WMA**.
- **Specialized Methods**: Includes **JMA**, **EPMA**, **CMA**, **Harmonic MA**, **McGinley Dynamic**, **Anchored MA**, and **Filtered MA**.
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("""
    - **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 the moving average method and visualize.
    """)

# Function to fetch data
@st.cache_data
def get_data(ticker, start_date, end_date):
    data = yf.download(ticker, start=start_date, end=end_date, auto_adjust=False)
    if isinstance(data.columns, pd.MultiIndex):
        data.columns = data.columns.get_level_values(0)
    if data.empty:
        raise ValueError(f"No data retrieved for {ticker}")
    if len(data) < 512:  # Ensure enough data for largest possible Rainbow MA period
        raise ValueError(f"Insufficient data points for {ticker}. Need at least 512 days.")
    return data

# Function to create the base plot with the stock price
def create_price_plot(data, ticker_symbol):
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], mode='lines', name=f'{ticker_symbol} Stock Price'))
    return fig

# Jurik Moving Average (JMA) Function
def jma(close, length=None, phase=None, offset=None, **kwargs):
    _length = int(length) if length and length > 0 else 7
    phase = float(phase) if phase and phase != 0 else 0
    close = verify_series(close, _length)
    offset = get_offset(offset)
    if close is None: return

    jma = npZeroslike(close)
    volty = npZeroslike(close)
    v_sum = npZeroslike(close)

    kv = det0 = det1 = ma2 = 0.0
    jma[0] = ma1 = uBand = lBand = close[0]

    sum_length = 10
    length = 0.5 * (_length - 1)
    pr = 0.5 if phase < -100 else 2.5 if phase > 100 else 1.5 + phase * 0.01
    length1 = max((npLog(npSqrt(length)) / npLog(2.0)) + 2.0, 0)
    pow1 = max(length1 - 2.0, 0.5)
    length2 = length1 * npSqrt(length)
    bet = length2 / (length2 + 1)
    beta = 0.45 * (_length - 1) / (0.45 * (_length - 1) + 2.0)

    m = close.shape[0]
    for i in range(1, m):
        price = close[i]

        del1 = price - uBand
        del2 = price - lBand
        volty[i] = max(abs(del1), abs(del2)) if abs(del1) != abs(del2) else 0

        v_sum[i] = v_sum[i - 1] + (volty[i] - volty[max(i - sum_length, 0)]) / sum_length
        avg_volty = npAverage(v_sum[max(i - 65, 0):i + 1])
        d_volty = 0 if avg_volty == 0 else volty[i] / avg_volty
        r_volty = max(1.0, min(npPower(length1, 1 / pow1), d_volty))

        pow2 = npPower(r_volty, pow1)
        kv = npPower(bet, npSqrt(pow2))
        uBand = price if (del1 > 0) else price - (kv * del1)
        lBand = price if (del2 < 0) else price - (kv * del2)

        power = npPower(r_volty, pow1)
        alpha = npPower(beta, power)

        ma1 = ((1 - alpha) * price) + (alpha * ma1)

        det0 = ((price - ma1) * (1 - beta)) + (beta * det0)
        ma2 = ma1 + pr * det0

        det1 = ((ma2 - jma[i - 1]) * (1 - alpha) * (1 - alpha)) + (alpha * alpha * det1)
        jma[i] = jma[i - 1] + det1

    jma[0:_length - 1] = npNaN
    jma = Series(jma, index=close.index)

    if offset != 0:
        jma = jma.shift(offset)

    if "fillna" in kwargs:
        jma.fillna(kwargs["fillna"], inplace=True)
    if "fill_method" in kwargs:
        jma.fillna(method=kwargs["fill_method"], inplace=True)

    jma.name = f"JMA_{_length}_{phase}"
    jma.category = "overlap"

    return jma

# Function to calculate Harmonic Moving Average (HMA)
def calculate_harmonic_moving_average(prices, period):
    harmonic_ma = []
    for i in range(len(prices)):
        if i < period - 1:
            harmonic_ma.append(np.nan)
        else:
            window = prices[i - period + 1: i + 1]
            harmonic_mean = period / np.sum(1.0 / window)
            harmonic_ma.append(harmonic_mean)
    return harmonic_ma

# End Point Moving Average (EPMA) Function
def calculate_EPMA(prices, period):
    epma_values = []

    for i in range(period - 1, len(prices)):
        x = np.arange(period)
        y = prices[i-period+1:i+1]

        slope, intercept = np.polyfit(x, y, 1)
        epma = slope * (period - 1) + intercept

        epma_values.append(epma)

    return [None]*(period-1) + epma_values  # Pad with None for alignment

# Chande Moving Average (CMA) Function
def calculate_CMA(prices):
    cumsum = np.cumsum(prices)
    cma = cumsum / (np.arange(len(prices)) + 1)
    return cma

# Other Moving Average Methods
# Function to calculate Parabolic Weighted Moving Average (PWMA)
def parabolic_weighted_moving_average(prices, n=14):
    weights = np.array([(n-i)**2 for i in range(n)])
    return np.convolve(prices, weights/weights.sum(), mode='valid')

# Function to calculate Regularized Exponential Moving Average (REMA)
def REMA(prices, alpha=0.1, lambda_=0.1):
    rema = [prices[0]]
    penalty = 0

    for t in range(1, len(prices)):
        second_derivative = prices[t] - 2 * prices[t-1] + prices[t-2] if t-2 >= 0 else 0
        penalty = lambda_ * second_derivative
        current_rema = alpha * prices[t] + (1 - alpha) * rema[-1] - penalty
        rema.append(current_rema)

    return rema

# Function to calculate Weighted Moving Average (WMA)
def weighted_moving_average(data, periods):
    weights = np.arange(1, periods + 1)
    wma = data.rolling(periods).apply(lambda x: np.dot(x, weights) / weights.sum(), raw=True)
    return wma

# Function to calculate Hull Moving Average (HMA)
def hull_moving_average(data, periods):
    wma_half_period = weighted_moving_average(data, int(periods / 2))
    wma_full_period = weighted_moving_average(data, periods)
    hma = weighted_moving_average(2 * wma_half_period - wma_full_period, int(np.sqrt(periods)))
    return hma

# Function to calculate Harmonic Moving Average (HMA) to avoid conflict with Hull
def harmonic_moving_average(data, period):
    def harmonic_mean(prices):
        return period / np.sum(1.0 / prices)

    hma_values = []
    for i in range(period - 1, len(data)):
        hma_values.append(harmonic_mean(data[i - period + 1:i + 1]))

    return [np.nan] * (period - 1) + hma_values

# Function to calculate Fractal Adaptive Moving Average (FRAMA)
def calculate_FRAMA(data, batch=10):
    InputPrice = data['Close'].values
    Length = len(InputPrice)
    Filt = np.array(InputPrice)

    for i in range(2 * batch, Length):
        v1 = InputPrice[i-2*batch:i - batch]
        v2 = InputPrice[i - batch:i]

        H1 = np.max(v1)
        L1 = np.min(v1)
        N1 = (H1 - L1) / batch

        H2 = np.max(v2)
        L2 = np.min(v2)
        N2 = (H2 - L2) / batch

        H = np.max([H1, H2])
        L = np.min([L1, L2])
        N3 = (H - L) / (2 * batch)

        Dimen = 0
        if N1 > 0 and N2 > 0 and N3 > 0:
            Dimen = (np.log(N1 + N2) - np.log(N3)) / np.log(2)

        alpha = np.exp(-4.6 * Dimen - 1)
        alpha = np.clip(alpha, 0.1, 1)

        Filt[i] = alpha * InputPrice[i] + (1 - alpha) * Filt[i-1]

    data['FRAMA'] = Filt
    return data

# Function to calculate Exponential Moving Average (EMA)
def calculate_EMA(prices, period):
    alpha = 2 / (period + 1)
    EMA = [prices[0]]
    for price in prices[1:]:
        EMA.append((price - EMA[-1]) * alpha + EMA[-1])
    return EMA

# Function to calculate Zero Lag Exponential Moving Average (ZLEMA)
def calculate_ZLEMA(prices, period):
    lag = period // 2
    adjusted_prices = [2 * prices[i] - (prices[i - lag] if i >= lag else prices[0]) for i in range(len(prices))]
    ZLEMA = calculate_EMA(adjusted_prices, period)
    return ZLEMA

# Function to calculate Chande Momentum Oscillator (CMO)
def calculate_CMO(prices, period):
    deltas = np.diff(prices)
    sum_gains = np.cumsum(np.where(deltas >= 0, deltas, 0))
    sum_losses = np.abs(np.cumsum(np.where(deltas < 0, deltas, 0)))

    cmo = 100 * (sum_gains - sum_losses) / (sum_gains + sum_losses)
    return np.insert(cmo, 0, 0)  # Add a zero at the beginning for alignment

# Function to calculate Variable Index Dynamic Average (VIDYA)
def calculate_VIDYA(prices, period):
    cmo_values = calculate_CMO(prices, period)
    vidya = [prices[0]]

    for i in range(1, len(prices)):
        alpha = abs(cmo_values[i]) / 100  # Normalize CMO to [0, 1]
        vidya.append((1 - alpha) * vidya[-1] + alpha * prices[i])

    return vidya

# Function to calculate Arnaud Legoux Moving Average (ALMA)
def calculate_ALMA(prices, period, offset=0.85, sigma=6):
    m = np.floor(offset * (period - 1))
    s = period / sigma
    alma = []

    for i in range(period - 1, len(prices)):
        weights = [np.exp(- (j - m)**2 / (2 * s * s)) for j in range(period)]
        sum_weights = sum(weights)
        normalized_weights = [w/sum_weights for w in weights]

        window = prices[i-period+1:i+1]
        alma_value = sum([normalized_weights[j] * window[j] for j in range(period)])
        alma.append(alma_value)

    return [None]*(period-1) + alma  # Pad the beginning with None for alignment

# Function to calculate Adaptive Period Moving Average (APMA)
def adaptive_period_moving_average(prices, min_period=5, max_period=30):
    atr = np.zeros_like(prices)
    adjusted_periods = np.zeros_like(prices)
    moving_averages = np.full_like(prices, np.nan)  # Initialize with NaN values

    for i in range(1, len(prices)):
        atr[i] = atr[i-1] + (abs(prices[i] - prices[i-1]) - atr[i-1]) / 14

        min_volatility = atr[1:i+1].min()
        max_volatility = atr[1:i+1].max()

        if max_volatility == min_volatility:
            adjusted_period = min_period
        else:
            adjusted_period = int(((max_period - min_period) / (max_volatility - min_volatility)) * (atr[i] - min_volatility) + min_period)

        adjusted_periods[i] = adjusted_period

        if i >= adjusted_period:
            moving_averages[i] = np.mean(prices[i-adjusted_period+1:i+1])

    return moving_averages

# Function to calculate Rainbow Moving Average (Rainbow EMA)
def calculate_rainbow_ema(data, lookback_periods):
    for lookback in lookback_periods:
        data[f'EMA{lookback}'] = data['Close'].ewm(span=lookback).mean()
    return data

# Function to calculate Wilders Moving Average
def wilders_moving_average(prices, period):
    wilder = [prices[0]]
    for price in prices[1:]:
        wilder_value = ((wilder[-1] * (period - 1)) + price) / period
        wilder.append(wilder_value)
    return wilder

# Function to calculate Smoothed Moving Average (SMMA)
def calculate_SMMA(prices, n):
    SMMA = [np.nan] * (n-1)  # Fill the initial n-1 values with NaN
    SMMA.append(sum(prices[:n]) / n)
    for i in range(n, len(prices)):
        smma_value = (SMMA[-1] * (n - 1) + prices[i]) / n
        SMMA.append(smma_value)
    return SMMA

# Function to calculate Least Squares Moving Average (LSMA)
def calculate_LSMA(prices, period):
    n = period
    x = np.array(range(1, n+1))

    LSMA = []
    for i in range(len(prices) - period + 1):
        y = prices[i:i+period]
        m = (n*np.sum(x*y) - np.sum(x)*np.sum(y)) / (n*np.sum(x**2) - np.sum(x)**2)
        c = (np.sum(y) - m*np.sum(x)) / n
        LSMA.append(m * n + c)  # The projected value at the end of the period

    # Padding the beginning with NaNs for alignment
    LSMA = [np.nan] * (period-1) + LSMA
    return LSMA

# Function to calculate Welch's Moving Average (Modified Moving Average, MMA)
def calculate_MMA(prices, period):
    MMA = [sum(prices[:period]) / period]  # Start with the SMA for the first value
    for t in range(period, len(prices)):
        MMA.append((prices[t] + (period - 1) * MMA[-1]) / period)
    return [None]*(period-1) + MMA  # Pad the beginning with None for alignment

# Function to calculate Sin-weighted Moving Average (SinWMA)
def calculate_SinWMA(prices, period):
    weights = [np.sin(np.pi * i / (period + 1)) for i in range(1, period+1)]
    sum_weights = sum(weights)
    normalized_weights = [w/sum_weights for w in weights]

    SinWMA = []
    for t in range(period - 1, len(prices)):
        window = prices[t-period+1:t+1]
        SinWMA.append(sum([normalized_weights[i] * window[i] for i in range(period)]))

    return [None]*(period-1) + SinWMA  # Pad the beginning with None for alignment

# Function to calculate Median Moving Average (MedMA)
def calculate_MedMA(prices, window):
    medians = []
    for i in range(len(prices)):
        if i < window - 1:
            medians.append(np.nan)
        else:
            median = np.median(prices[i - window + 1: i + 1])
            medians.append(median)
    return medians

# Function to calculate Geometric Moving Average (GMA)
def calculate_GMA(prices, window):
    gm_avg = []
    for i in range(len(prices)):
        if i < window - 1:
            gm_avg.append(np.nan)
        else:
            product = np.prod(prices[i - window + 1: i + 1])
            gma_value = product ** (1/window)
            gm_avg.append(gma_value)
    return gm_avg

# Function to calculate Elastic Volume Weighted Moving Average (eVWMA)
def calculate_eVWMA(prices, volumes, window):
    evwma_values = []
    for i in range(len(prices)):
        if i < window:
            evwma_values.append(np.nan)
        else:
            price_subset = prices[i-window+1:i+1]
            volume_subset = volumes[i-window+1:i+1]
            numerator = sum([p * v for p, v in zip(price_subset, volume_subset)])
            denominator = sum(volume_subset)
            evwma = numerator / denominator
            evwma_values.append(evwma)
    return evwma_values

# Function to calculate McGinley Dynamic (MD)
def calculate_mcginley_dynamic(prices, n):
    MD = [prices[0]]
    for i in range(1, len(prices)):
        md_value = MD[-1] + (prices[i] - MD[-1]) / (n * (prices[i] / MD[-1])**4)
        MD.append(md_value)
    return MD

# Function to calculate Anchored Moving Average (AMA)
from datetime import datetime

def calculate_AMA(prices, anchor_date, data):
    # Ensure the anchor_date is a pandas Timestamp
    anchor_date = pd.to_datetime(anchor_date)

    try:
        anchor_idx = data.index.get_loc(anchor_date)
    except KeyError:
        # If the exact date is not found, find the nearest available date
        anchor_date = data.index[data.index.get_loc(anchor_date, method='nearest')]
        anchor_idx = data.index.get_loc(anchor_date)

    AMA = []

    for i in range(len(prices)):
        if i < anchor_idx:
            AMA.append(None)
        else:
            AMA.append(sum(prices[anchor_idx:i+1]) / (i - anchor_idx + 1))

    return AMA

# Function to calculate Filtered Moving Average (FMA)
def filtered_moving_average(prices, n=14):
    # Define filter weights (for simplicity, we'll use equal weights similar to SMA)
    w = np.ones(n) / n
    return np.convolve(prices, w, mode='valid')

# Sidebar for user inputs
st.sidebar.header("Select Parameters")

# Ticker input with tooltip
ticker_symbol = st.sidebar.text_input(
    "Ticker or Crypto Pair", 
    value=st.session_state.get('ticker_symbol', 'BTC-USD'),
    help="Enter the ticker symbol (e.g., AAPL for Apple) or Cryptocurrency Pair (e.g. BTC-USD)."
)

# Date range inputs with tooltip
start_date = st.sidebar.date_input(
    "Start Date", 
    value=st.session_state.get('start_date', pd.to_datetime("2020-01-01")),
    help="Select the start date for fetching the stock data."
)
end_date = st.sidebar.date_input(
    "End Date", 
    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."
)

# Fetch Data button
if st.sidebar.button('Fetch Data'):
    try:
        # Only fetch if the ticker or date range has changed
        if (
            ticker_symbol != st.session_state.get('ticker_symbol') or 
            start_date != st.session_state.get('start_date') or 
            end_date != st.session_state.get('end_date')
        ):
            data = get_data(ticker_symbol, start_date, end_date)
            st.session_state['data'] = data
            st.session_state['ticker_symbol'] = ticker_symbol
            st.session_state['start_date'] = start_date
            st.session_state['end_date'] = end_date
            st.session_state['price_plot'] = create_price_plot(data, ticker_symbol)
            st.session_state['current_fig'] = st.session_state['price_plot']
    except Exception as e:
        st.error(f"An error occurred while fetching data: {e}")

# Check if data is fetched and stored in session state
if 'data' in st.session_state:
    data = st.session_state['data']
    
    # Moving average method selection
    with st.sidebar.expander("Simple Moving Average", expanded=False):
        use_sma = st.checkbox(
            'Simple Moving Average (SMA)', 
            value=st.session_state.get('use_sma', False),
            help="Select to apply Simple Moving Average (SMA) to the stock price."
        )
        sma_period = st.number_input(
            'SMA Period', 
            min_value=1, 
            value=st.session_state.get('sma_period', 50), 
            step=1, 
            disabled=not use_sma,
            help="Specify the period (in days) for the SMA."
        )
    
    # EMA with tooltip
    with st.sidebar.expander("Exponential Moving Average (EMA)", expanded=False):
        use_ema = st.checkbox(
            'Enable EMA', 
            value=st.session_state.get('use_ema', False),
            help="Select to apply Exponential Moving Average (EMA) to the stock price."
        )
        ema_period = st.number_input(
            'EMA Period', 
            min_value=1, 
            value=st.session_state.get('ema_period', 50), 
            step=1, 
            disabled=not use_ema,
            help="Specify the period (in days) for the EMA."
        )
    
    # WMA with tooltip
    with st.sidebar.expander("Weighted Moving Average (WMA)", expanded=False):
        use_wma = st.checkbox(
            'Enable WMA', 
            value=st.session_state.get('use_wma', False),
            help="Select to apply Weighted Moving Average (WMA) to the stock price."
        )
        wma_period = st.number_input(
            'WMA Period', 
            min_value=1, 
            value=st.session_state.get('wma_period', 50), 
            step=1, 
            disabled=not use_wma,
            help="Specify the period (in days) for the WMA."
        )
    
    # DEMA with tooltip
    with st.sidebar.expander("Double Exponential Moving Average (DEMA)", expanded=False):
        use_dema = st.checkbox(
            'Enable DEMA', 
            value=st.session_state.get('use_dema', False),
            help="Select to apply Double Exponential Moving Average (DEMA) to the stock price."
        )
        dema_period = st.number_input(
            'DEMA Period', 
            min_value=1, 
            value=st.session_state.get('dema_period', 50), 
            step=1, 
            disabled=not use_dema,
            help="Specify the period (in days) for the DEMA."
        )
    
    # TEMA with tooltip
    with st.sidebar.expander("Triple Exponential Moving Average (TEMA)", expanded=False):
        use_tema = st.checkbox(
            'Enable TEMA', 
            value=st.session_state.get('use_tema', False),
            help="Select to apply Triple Exponential Moving Average (TEMA) to the stock price."
        )
        tema_period = st.number_input(
            'TEMA Period', 
            min_value=1, 
            value=st.session_state.get('tema_period', 50), 
            step=1, 
            disabled=not use_tema,
            help="Specify the period (in days) for the TEMA."
        )
    
    # VAMA with tooltip
    with st.sidebar.expander("Volume-Adjusted Moving Average (VAMA)", expanded=False):
        use_vama = st.checkbox(
            'Enable VAMA', 
            value=st.session_state.get('use_vama', False),
            help="Select to apply Volume-Adjusted Moving Average (VAMA) to the stock price."
        )
        vama_period = st.number_input(
            'VAMA Period', 
            min_value=1, 
            value=st.session_state.get('vama_period', 50), 
            step=1, 
            disabled=not use_vama,
            help="Specify the period (in days) for the VAMA."
        )
    
    # KAMA with tooltip
    with st.sidebar.expander("Kaufman Adaptive Moving Average (KAMA)", expanded=False):
        use_kama = st.checkbox(
            'Enable KAMA', 
            value=st.session_state.get('use_kama', False),
            help="Select to apply Kaufman Adaptive Moving Average (KAMA) to the stock price."
        )
        kama_period = st.number_input(
            'KAMA Period', 
            min_value=1, 
            value=st.session_state.get('kama_period', 10), 
            step=1, 
            disabled=not use_kama,
            help="Specify the efficiency ratio period (in days) for the KAMA."
        )
        fastest_period = st.number_input(
            'Fastest SC Period', 
            min_value=1, 
            value=st.session_state.get('fastest_period', 2), 
            step=1, 
            disabled=not use_kama,
            help="Specify the fastest smoothing constant period."
        )
        slowest_period = st.number_input(
            'Slowest SC Period', 
            min_value=1, 
            value=st.session_state.get('slowest_period', 30), 
            step=1, 
            disabled=not use_kama,
            help="Specify the slowest smoothing constant period."
        )
    
    # TMA with tooltip
    with st.sidebar.expander("Triangular Moving Average (TMA)", expanded=False):
        use_tma = st.checkbox(
            'Enable TMA', 
            value=st.session_state.get('use_tma', False),
            help="Select to apply Triangular Moving Average (TMA) to the stock price."
        )
        tma_period = st.number_input(
            'TMA Period', 
            min_value=1, 
            value=st.session_state.get('tma_period', 20), 
            step=1, 
            disabled=not use_tma,
            help="Specify the period (in days) for the TMA."
        )

    # Hull MA with tooltip
    with st.sidebar.expander("Hull Moving Average (HMA)", expanded=False):
        use_hull_ma = st.checkbox(
            'Enable HMA', 
            value=st.session_state.get('use_hull_ma', False),
            help="Select to apply Hull Moving Average (HMA) to the stock price."
        )
        hull_ma_period = st.number_input(
            'HMA Period', 
            min_value=1, 
            value=st.session_state.get('hull_ma_period', 120), 
            step=1, 
            disabled=not use_hull_ma,
            help="Specify the period (in days) for the Hull Moving Average."
        )
    
    # Harmonic MA with tooltip
    with st.sidebar.expander("Harmonic Moving Average (HMA)", expanded=False):
        use_harmonic_ma = st.checkbox(
            'Enable HMA', 
            value=st.session_state.get('use_harmonic_ma', False),
            help="Select to apply Harmonic Moving Average (HMA) to the stock price."
        )
        harmonic_ma_period = st.number_input(
            'HMA Period', 
            min_value=1, 
            value=st.session_state.get('harmonic_ma_period', 120), 
            step=1, 
            disabled=not use_harmonic_ma,
            help="Specify the period (in days) for the Harmonic Moving Average."
        )
    
    # FRAMA with tooltip
    with st.sidebar.expander("Fractal Adaptive Moving Average (FRAMA)", expanded=False):
        use_frama = st.checkbox(
            'Enable FRAMA', 
            value=st.session_state.get('use_frama', False),
            help="Select to apply Fractal Adaptive Moving Average (FRAMA) to the stock price."
        )
        frama_batch = st.number_input(
            'FRAMA Batch Size', 
            min_value=1, 
            value=st.session_state.get('frama_batch', 10), 
            step=1, 
            disabled=not use_frama,
            help="Specify the batch size for FRAMA calculation."
        )
    
    # ZLEMA with tooltip
    with st.sidebar.expander("Zero Lag Exponential Moving Average (ZLEMA)", expanded=False):
        use_zlema = st.checkbox(
            'Enable ZLEMA', 
            value=st.session_state.get('use_zlema', False),
            help="Select to apply Zero Lag Exponential Moving Average (ZLEMA) to the stock price."
        )
        zlema_period = st.number_input(
            'ZLEMA Period', 
            min_value=1, 
            value=st.session_state.get('zlema_period', 28), 
            step=1, 
            disabled=not use_zlema,
            help="Specify the period (in days) for the ZLEMA."
        )
    
    # VIDYA with tooltip
    with st.sidebar.expander("Variable Index Dynamic Average (VIDYA)", expanded=False):
        use_vidya = st.checkbox(
            'Enable VIDYA', 
            value=st.session_state.get('use_vidya', False),
            help="Select to apply Variable Index Dynamic Average (VIDYA) to the stock price."
        )
        vidya_period = st.number_input(
            'VIDYA Period', 
            min_value=1, 
            value=st.session_state.get('vidya_period', 14), 
            step=1, 
            disabled=not use_vidya,
            help="Specify the period (in days) for the VIDYA."
        )
    
    # ALMA with tooltip
    with st.sidebar.expander("Arnaud Legoux Moving Average (ALMA)", expanded=False):
        use_alma = st.checkbox(
            'Enable ALMA', 
            value=st.session_state.get('use_alma', False),
            help="Select to apply Arnaud Legoux Moving Average (ALMA) to the stock price."
        )
        alma_period = st.number_input(
            'ALMA Period', 
            min_value=1, 
            value=st.session_state.get('alma_period', 36), 
            step=1, 
            disabled=not use_alma,
            help="Specify the period (in days) for the ALMA."
        )
        alma_offset = st.number_input(
            'ALMA Offset', 
            min_value=0.0, 
            max_value=1.0, 
            value=st.session_state.get('alma_offset', 0.85), 
            step=0.01, 
            disabled=not use_alma,
            help="Specify the offset for the ALMA (0 to 1)."
        )
        alma_sigma = st.number_input(
            'ALMA Sigma', 
            min_value=1, 
            value=st.session_state.get('alma_sigma', 6), 
            step=1, 
            disabled=not use_alma,
            help="Specify the sigma for the ALMA."
        )
    
    # MAMA and FAMA with tooltip
    with st.sidebar.expander("MESA Adaptive Moving Average (MAMA) & FAMA", expanded=False):
        use_mama_fama = st.checkbox(
            'Enable MAMA & FAMA', 
            value=st.session_state.get('use_mama_fama', False),
            help="Select to apply MESA Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA) to the stock price."
        )
        mama_fast_limit = st.number_input(
            'MAMA Fast Limit', 
            min_value=0.0, 
            max_value=1.0, 
            value=st.session_state.get('mama_fast_limit', 0.5), 
            step=0.01, 
            disabled=not use_mama_fama,
            help="Specify the fast limit for MAMA (0 to 1)."
        )
        mama_slow_limit = st.number_input(
            'MAMA Slow Limit', 
            min_value=0.0, 
            max_value=1.0, 
            value=st.session_state.get('mama_slow_limit', 0.05), 
            step=0.01, 
            disabled=not use_mama_fama,
            help="Specify the slow limit for MAMA (0 to 1)."
        )

    # APMA with tooltip
    with st.sidebar.expander("Adaptive Period Moving Average (APMA)", expanded=False):
        use_apma = st.checkbox(
            'Enable APMA', 
            value=st.session_state.get('use_apma', False),
            help="Select to apply Adaptive Period Moving Average (APMA) to the stock price."
        )
        apma_min_period = st.number_input(
            'APMA Min Period', 
            min_value=1, 
            value=st.session_state.get('apma_min_period', 5), 
            step=1, 
            disabled=not use_apma,
            help="Specify the minimum period for the APMA."
        )
        apma_max_period = st.number_input(
            'APMA Max Period', 
            min_value=1, 
            value=st.session_state.get('apma_max_period', 30), 
            step=1, 
            disabled=not use_apma,
            help="Specify the maximum period for the APMA."
        )
    
    # Rainbow EMA with tooltip
    with st.sidebar.expander("Rainbow Moving Average (EMA)", expanded=False):
        use_rainbow_ema = st.checkbox(
            'Enable Rainbow EMA', 
            value=st.session_state.get('use_rainbow_ema', False),
            help="Select to apply Rainbow Moving Average (EMA) with multiple lookback periods to the stock price."
        )
        rainbow_lookback_periods = st.multiselect(
            'Rainbow Lookback Periods', 
            options=[2, 4, 8, 16, 32, 64, 128, 192, 320, 512],
            default=st.session_state.get('rainbow_lookback_periods', [2, 4, 8, 16, 32, 64, 128]),
            disabled=not use_rainbow_ema,
            help="Select multiple lookback periods for the Rainbow EMA."
        )
    
    # Wilders MA with tooltip
    with st.sidebar.expander("Wilders Moving Average (Wilder's MA)", expanded=False):
        use_wilders_ma = st.checkbox(
            'Enable Wilders MA', 
            value=st.session_state.get('use_wilders_ma', False),
            help="Select to apply Wilder's Moving Average to the stock price."
        )
        wilders_ma_period = st.number_input(
            'Wilders MA Period', 
            min_value=1, 
            value=st.session_state.get('wilders_ma_period', 14), 
            step=1, 
            disabled=not use_wilders_ma,
            help="Specify the period (in days) for Wilder's Moving Average."
        )
    
    # SMMA with tooltip
    with st.sidebar.expander("Smoothed Moving Average (SMMA)", expanded=False):
        use_smma = st.checkbox(
            'Enable SMMA', 
            value=st.session_state.get('use_smma', False),
            help="Select to apply Smoothed Moving Average (SMMA) to the stock price."
        )
        smma_period = st.number_input(
            'SMMA Period', 
            min_value=1, 
            value=st.session_state.get('smma_period', 28), 
            step=1, 
            disabled=not use_smma,
            help="Specify the period (in days) for the SMMA."
        )
    
    # GMMA with tooltip
    with st.sidebar.expander("Guppy Multiple Moving Average (GMMA)", expanded=False):
        use_gmma = st.checkbox(
            'Enable GMMA', 
            value=st.session_state.get('use_gmma', False),
            help="Select to apply Guppy Multiple Moving Average (GMMA) to the stock price."
        )
        gmma_short_periods = st.multiselect(
            'GMMA Short Periods', 
            options=[3, 5, 8, 10, 12, 15],
            default=st.session_state.get('gmma_short_periods', [3, 5, 8, 10, 12, 15]),
            disabled=not use_gmma,
            help="Select the short-term periods for GMMA."
        )
        gmma_long_periods = st.multiselect(
            'GMMA Long Periods', 
            options=[30, 35, 40, 45, 50, 60],
            default=st.session_state.get('gmma_long_periods', [30, 35, 40, 45, 50, 60]),
            disabled=not use_gmma,
            help="Select the long-term periods for GMMA."
        )
    
    # LSMA with tooltip
    with st.sidebar.expander("Least Squares Moving Average (LSMA)", expanded=False):
        use_lsma = st.checkbox(
            'Enable LSMA', 
            value=st.session_state.get('use_lsma', False),
            help="Select to apply Least Squares Moving Average (LSMA) to the stock price."
        )
        lsma_period = st.number_input(
            'LSMA Period', 
            min_value=1, 
            value=st.session_state.get('lsma_period', 28), 
            step=1, 
            disabled=not use_lsma,
            help="Specify the period (in days) for the LSMA."
        )
    
    # MMA (Welch's MMA) with tooltip
    with st.sidebar.expander("Welch's Moving Average (MMA)", expanded=False):
        use_mma = st.checkbox(
            'Enable MMA', 
            value=st.session_state.get('use_mma', False),
            help="Select to apply Welch's Moving Average (Modified Moving Average) to the stock price."
        )
        mma_period = st.number_input(
            'MMA Period', 
            min_value=1, 
            value=st.session_state.get('mma_period', 14), 
            step=1, 
            disabled=not use_mma,
            help="Specify the period (in days) for the MMA."
        )
    
    # SinWMA with tooltip
    with st.sidebar.expander("Sin-weighted Moving Average (SinWMA)", expanded=False):
        use_sinwma = st.checkbox(
            'Enable SinWMA', 
            value=st.session_state.get('use_sinwma', False),
            help="Select to apply Sin-weighted Moving Average (SinWMA) to the stock price."
        )
        sinwma_period = st.number_input(
            'SinWMA Period', 
            min_value=1, 
            value=st.session_state.get('sinwma_period', 21), 
            step=1, 
            disabled=not use_sinwma,
            help="Specify the period (in days) for the SinWMA."
        )
    
    # MedMA with tooltip
    with st.sidebar.expander("Median Moving Average (MedMA)", expanded=False):
        use_medma = st.checkbox(
            'Enable MedMA', 
            value=st.session_state.get('use_medma', False),
            help="Select to apply Median Moving Average (MedMA) to the stock price."
        )
        medma_period = st.number_input(
            'MedMA Period', 
            min_value=1, 
            value=st.session_state.get('medma_period', 20), 
            step=1, 
            disabled=not use_medma,
            help="Specify the period (in days) for the MedMA."
        )
    
    # GMA with tooltip
    with st.sidebar.expander("Geometric Moving Average (GMA)", expanded=False):
        use_gma = st.checkbox(
            'Enable GMA', 
            value=st.session_state.get('use_gma', False),
            help="Select to apply Geometric Moving Average (GMA) to the stock price."
        )
        gma_period = st.number_input(
            'GMA Period', 
            min_value=1, 
            value=st.session_state.get('gma_period', 20), 
            step=1, 
            disabled=not use_gma,
            help="Specify the period (in days) for the GMA."
        )
    
    # eVWMA with tooltip
    with st.sidebar.expander("Elastic Volume Weighted Moving Average (eVWMA)", expanded=False):
        use_evwma = st.checkbox(
            'Enable eVWMA', 
            value=st.session_state.get('use_evwma', False),
            help="Select to apply Elastic Volume Weighted Moving Average (eVWMA) to the stock price."
        )
        evwma_period = st.number_input(
            'eVWMA Period', 
            min_value=1, 
            value=st.session_state.get('evwma_period', 20), 
            step=1, 
            disabled=not use_evwma,
            help="Specify the period (in days) for the eVWMA."
        )

    # REMA with tooltip
    with st.sidebar.expander("Regularized Exponential Moving Average (REMA)", expanded=False):
        use_rema = st.checkbox(
            'Enable REMA', 
            value=st.session_state.get('use_rema', False),
            help="Select to apply Regularized Exponential Moving Average (REMA) to the stock price."
        )
        rema_alpha = st.number_input(
            'REMA Alpha', 
            min_value=0.0, 
            max_value=1.0, 
            value=st.session_state.get('rema_alpha', 0.1), 
            step=0.01, 
            disabled=not use_rema,
            help="Specify the alpha value for the REMA (0 to 1)."
        )
        rema_lambda = st.number_input(
            'REMA Lambda', 
            min_value=0.0, 
            max_value=1.0, 
            value=st.session_state.get('rema_lambda', 0.1), 
            step=0.01, 
            disabled=not use_rema,
            help="Specify the lambda value for the REMA (0 to 1)."
        )
    
    # PWMA with tooltip
    with st.sidebar.expander("Parabolic Weighted Moving Average (PWMA)", expanded=False):
        use_pwma = st.checkbox(
            'Enable PWMA', 
            value=st.session_state.get('use_pwma', False),
            help="Select to apply Parabolic Weighted Moving Average (PWMA) to the stock price."
        )
        pwma_period = st.number_input(
            'PWMA Period', 
            min_value=1, 
            value=st.session_state.get('pwma_period', 14), 
            step=1, 
            disabled=not use_pwma,
            help="Specify the period (in days) for the PWMA."
        )
    
    # JMA with tooltip
    with st.sidebar.expander("Jurik Moving Average (JMA)", expanded=False):
        use_jma = st.checkbox(
            'Enable JMA', 
            value=st.session_state.get('use_jma', False),
            help="Select to apply Jurik Moving Average (JMA) to the stock price."
        )
        jma_period = st.number_input(
            'JMA Period', 
            min_value=1, 
            value=st.session_state.get('jma_period', 28), 
            step=1, 
            disabled=not use_jma,
            help="Specify the period (in days) for the JMA."
        )
        jma_phase = st.number_input(
            'JMA Phase', 
            min_value=-100.0, 
            max_value=100.0, 
            value=st.session_state.get('jma_phase', 0.0), 
            step=0.1, 
            disabled=not use_jma,
            help="Specify the phase for the JMA (-100 to 100)."
        )
    
    # EPMA with tooltip
    with st.sidebar.expander("End Point Moving Average (EPMA)", expanded=False):
        use_epma = st.checkbox(
            'Enable EPMA', 
            value=st.session_state.get('use_epma', False),
            help="Select to apply End Point Moving Average (EPMA) to the stock price."
        )
        epma_period = st.number_input(
            'EPMA Period', 
            min_value=1, 
            value=st.session_state.get('epma_period', 28), 
            step=1, 
            disabled=not use_epma,
            help="Specify the period (in days) for the EPMA."
        )
    
    # CMA with tooltip
    with st.sidebar.expander("Chande Moving Average (CMA)", expanded=False):
        use_cma = st.checkbox(
            'Enable CMA', 
            value=st.session_state.get('use_cma', False),
            help="Select to apply Chande Moving Average (CMA) to the stock price."
        )
        cma_period = len(data['Close'])  # This does not require user input.
    
    # McGinley Dynamic with tooltip
    with st.sidebar.expander("McGinley Dynamic", expanded=False):
        use_mcginley_dynamic = st.checkbox(
            'Enable McGinley Dynamic', 
            value=st.session_state.get('use_mcginley_dynamic', False),
            help="Select to apply McGinley Dynamic to the stock price."
        )
        mcginley_dynamic_period = st.number_input(
            'McGinley Dynamic Period', 
            min_value=1, 
            value=st.session_state.get('mcginley_dynamic_period', 14), 
            step=1, 
            disabled=not use_mcginley_dynamic,
            help="Specify the period (in days) for the McGinley Dynamic."
        )

    # Filtered Moving Average (FMA) with tooltip
    with st.sidebar.expander("Filtered Moving Average (FMA)", expanded=False):
        use_fma = st.checkbox(
            'Enable FMA', 
            value=st.session_state.get('use_fma', False),
            help="Select to apply Filtered Moving Average (FMA) to the stock price."
        )
        fma_period = st.number_input(
            'FMA Period', 
            min_value=1, 
            value=st.session_state.get('fma_period', 14), 
            step=1, 
            disabled=not use_fma,
            help="Specify the period (in days) for the FMA."
        )    

    # Grid toggle with tooltip
    show_grid = st.sidebar.checkbox(
        "Show Grid", 
        value=True, 
        help="Toggle to show or hide the grid on the plot."
    )
    
    # Run button to apply moving averages
    if st.sidebar.button('Run Analysis'):
        try:
            # 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_fma'] = use_fma
            st.session_state['fma_period'] = fma_period
            
            # 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 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
        except Exception as e:
            st.error(f"An error occurred while running analysis: {e}")

# 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>
    /* Adjust the width of the sidebar */
    [data-testid="stSidebar"] {
        width: 500px;  /* Change this value to set the width you want */
    }
    </style>
    """,
    unsafe_allow_html=True
)

hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)