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Update app.py

77d4950 verified about 2 years ago

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# FB Page: https://www.facebook.com/AIsparking

import streamlit as st
import yfinance as yf
import pandas as pd
import numpy as np
from ta.trend import MACD
from ta.momentum import RSIIndicator, StochasticOscillator
from ta.volatility import AverageTrueRange, BollingerBands
from ta.volume import VolumeWeightedAveragePrice
import plotly.graph_objects as go
from datetime import datetime, timedelta
import plotly.subplots as sp
from ta.trend import IchimokuIndicator

# Set page config
st.set_page_config(layout="wide", page_title="Stock Technical Analysis")

# Functions from previous implementations remain the same
# Add error handling wrapper
def safe_execute(func):
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            st.error(f"Error: {str(e)}")
            return None
    return wrapper

@safe_execute
def get_stock_data(symbol, market, nYear):
    if market == 'HK':
        symbol = f'{symbol}.HK'
    
    end_date = datetime.now()
    start_date = end_date - timedelta(days=nYear*365)
    
    df = yf.download(symbol, start=start_date, end=end_date)
    if df.empty:
        raise ValueError("No data found for this symbol")
    return df

# Combine all technical indicators
@safe_execute

def generate_recommendation(df):
    last_row = df.iloc[-1]
    signals = []
    
    # RSI signals
    if last_row['RSI'] < 30:
        signals.append(('BUY', 'RSI oversold'))
    elif last_row['RSI'] > 70:
        signals.append(('SELL', 'RSI overbought'))
    
    # Stochastic signals
    if last_row['%K'] < 20 and last_row['%D'] < 20:
        signals.append(('BUY', 'Stochastic oversold'))
    elif last_row['%K'] > 80 and last_row['%D'] > 80:
        signals.append(('SELL', 'Stochastic overbought'))
    
    # MACD signals
    if last_row['MACD'] > last_row['MACD_Signal']:
        signals.append(('BUY', 'MACD crossover'))
    elif last_row['MACD'] < last_row['MACD_Signal']:
        signals.append(('SELL', 'MACD crossunder'))
    
    return signals

def calculate_indicators(df):
    # Calculate RSI
    rsi = RSIIndicator(df['Close'])
    df['RSI'] = rsi.rsi()
    
    # Calculate Stochastic
    stoch = StochasticOscillator(df['High'], df['Low'], df['Close'])
    df['%K'] = stoch.stoch()
    df['%D'] = stoch.stoch_signal()
    
    # Calculate MACD
    macd = MACD(df['Close'])
    df['MACD'] = macd.macd()
    df['MACD_Signal'] = macd.macd_signal()
    
    # Calculate ATR
    atr = AverageTrueRange(df['High'], df['Low'], df['Close'])
    df['ATR'] = atr.average_true_range()
    
    # Add multiple SMAs
    df['SMA20'] = df['Close'].rolling(window=20).mean()
    df['SMA50'] = df['Close'].rolling(window=50).mean()
    df['SMA100'] = df['Close'].rolling(window=100).mean()
    df['SMA200'] = df['Close'].rolling(window=200).mean()
    
    # Calculate ATR
    atr = AverageTrueRange(df['High'], df['Low'], df['Close'])
    df['ATR'] = atr.average_true_range()
    
    # Calculate VWAP
    vwap = VolumeWeightedAveragePrice(high=df['High'], 
                                     low=df['Low'], 
                                     close=df['Close'], 
                                     volume=df['Volume'])
    df['VWAP'] = vwap.volume_weighted_average_price()
    
    return df

def calculate_additional_indicators(df):
    # Add Bollinger Bands
    bb = BollingerBands(df['Close'])
    df['BB_upper'] = bb.bollinger_hband()
    df['BB_lower'] = bb.bollinger_lband()
    df['BB_middle'] = bb.bollinger_mavg()
    
    # Add Moving Averages
    df['MA50'] = df['Close'].rolling(window=50).mean()
    df['MA200'] = df['Close'].rolling(window=200).mean()
    
    # Add VWAP
    vwap = VolumeWeightedAveragePrice(high=df['High'], low=df['Low'], 
                                     close=df['Close'], volume=df['Volume'])
    df['VWAP'] = vwap.volume_weighted_average_price()
    
    return df

def calculate_ema(df):
    # Calculate EMAs
    df['EMA9'] = df['Close'].ewm(span=9, adjust=False).mean()
    df['EMA21'] = df['Close'].ewm(span=21, adjust=False).mean()
    return df

def calculate_obv(df):
    # Calculate OBV
    obv = []
    prev_obv = 0
    for i in range(len(df)):
        if i == 0:
            obv.append(prev_obv)
            continue
        if df['Close'].iloc[i] > df['Close'].iloc[i-1]:
            current_obv = prev_obv + df['Volume'].iloc[i]
        elif df['Close'].iloc[i] < df['Close'].iloc[i-1]:
            current_obv = prev_obv - df['Volume'].iloc[i]
        else:
            current_obv = prev_obv
        obv.append(current_obv)
        prev_obv = current_obv
    df['OBV'] = obv
    return df

def calculate_ichimoku(df):
    ichimoku = IchimokuIndicator(high=df['High'], low=df['Low'])
    df['ichimoku_a'] = ichimoku.ichimoku_a()
    df['ichimoku_b'] = ichimoku.ichimoku_b()
    df['ichimoku_base'] = ichimoku.ichimoku_base_line()
    df['ichimoku_conversion'] = ichimoku.ichimoku_conversion_line()
    return df

def calculate_all_indicators(df):
    df = calculate_indicators(df)
    df = calculate_additional_indicators(df)
    df = calculate_ema(df)
    df = calculate_obv(df)
    df = calculate_ichimoku(df)
    return df

def enhanced_recommendation(df):
    last_row = df.iloc[-1]
    prev_row = df.iloc[-2]
    signals = []
    
    # Add EMA signals
    if last_row['EMA9'] > last_row['EMA21']:
        signals.append(('BUY', 'EMA9 crossed above EMA21'))
    elif last_row['EMA9'] < last_row['EMA21']:
        signals.append(('SELL', 'EMA9 crossed below EMA21'))
    
    # Add Ichimoku signals
    if (last_row['ichimoku_conversion'] > last_row['ichimoku_base'] and 
        last_row['Close'] > last_row['ichimoku_a']):
        signals.append(('BUY', 'Ichimoku bullish signal'))
    elif (last_row['ichimoku_conversion'] < last_row['ichimoku_base'] and 
          last_row['Close'] < last_row['ichimoku_b']):
        signals.append(('SELL', 'Ichimoku bearish signal'))
    
    # Add OBV signals
    if df['OBV'].iloc[-1] > df['OBV'].iloc[-2]:
        signals.append(('BUY', 'OBV increasing'))
    else:
        signals.append(('SELL', 'OBV decreasing'))

    # Add SMA signals
    if (last_row['SMA20'] > last_row['SMA50'] and 
        prev_row['SMA20'] <= prev_row['SMA50']):
        signals.append(('BUY', 'SMA20 crossed above SMA50'))
    elif (last_row['SMA20'] < last_row['SMA50'] and 
          prev_row['SMA20'] >= prev_row['SMA50']):
        signals.append(('SELL', 'SMA20 crossed below SMA50'))
    
    # Add VWAP signals
    if last_row['Close'] > last_row['VWAP']:
        signals.append(('BUY', 'Price above VWAP'))
    else:
        signals.append(('SELL', 'Price below VWAP'))
    
    # Add ATR-based volatility signals
    atr_threshold = df['ATR'].mean() * 1.5
    if last_row['ATR'] > atr_threshold:
        signals.append(('HOLD', 'High volatility detected by ATR'))
    
    return signals


# Main app layout
st.title('Advanced Stock Technical Analysis')

# Sidebar for inputs
with st.sidebar:
    st.header('Input Parameters')
    market = st.selectbox('Select Market', ['HK','US'])
    symbol = st.text_input('Enter Stock Symbol (e.g. 0700 for HK):')
    
    # Add analysis timeframe option
    timeframe = st.selectbox('Select Timeframe', ['1y','2y','3y','5y','8y','10y'])
    nYear = int(timeframe.split("y")[0])

if st.sidebar.button('Analyze'):
    if symbol:
        with st.spinner('Fetching and analyzing data...'):
            df = get_stock_data(symbol, market,nYear)
            
            if df is not None:
                df = calculate_all_indicators(df)
                
                # Create tabs for different analyses
                tab1, tab2, tab3 = st.tabs(['Price Analysis', 'Technical Indicators', 'Technical Analysis'])
                
                with tab1:
                    # Main price chart with volume
                    fig = sp.make_subplots(rows=2, cols=1, shared_xaxes=True, 
                                         vertical_spacing=0.03, row_heights=[0.7, 0.3])
                    
                    fig.add_trace(go.Candlestick(x=df.index, open=df['Open'],
                                                high=df['High'], low=df['Low'],
                                                close=df['Close'], name='Price'),
                                 row=1, col=1)
                    
                    # Add Moving Averages
                    fig.add_trace(go.Scatter(x=df.index, y=df['MA50'],
                                           name='MA50', line=dict(color='orange')),
                                 row=1, col=1)
                    fig.add_trace(go.Scatter(x=df.index, y=df['MA200'],
                                           name='MA200', line=dict(color='blue')),
                                 row=1, col=1)
                    
                    # Add volume bars
                    colors = ['red' if row['Open'] > row['Close'] else 'green' 
                             for index, row in df.iterrows()]
                    fig.add_trace(go.Bar(x=df.index, y=df['Volume'],
                                       marker_color=colors, name='Volume'),
                                 row=2, col=1)
                    
                    fig.update_layout(height=800)
                    st.plotly_chart(fig, use_container_width=True)
                
                with tab2:
                    col1, col2 = st.columns(2)
                    
                    with col1:
                        # RSI Plot
                        fig_rsi = go.Figure()
                        fig_rsi.add_trace(go.Scatter(x=df.index, y=df['RSI'],
                                                    name='RSI'))
                        fig_rsi.add_hline(y=70, line_dash="dash", line_color="red")
                        fig_rsi.add_hline(y=30, line_dash="dash", line_color="green")
                        fig_rsi.update_layout(title='RSI Indicator')
                        st.plotly_chart(fig_rsi)
                        
                        # MACD Plot
                        fig_macd = go.Figure()
                        fig_macd.add_trace(go.Scatter(x=df.index, y=df['MACD'],
                                                     name='MACD'))
                        fig_macd.add_trace(go.Scatter(x=df.index, y=df['MACD_Signal'],
                                                     name='Signal'))
                        fig_macd.update_layout(title='MACD Indicator')
                        st.plotly_chart(fig_macd)

                        # EMA Plot
                        fig_ema = go.Figure()
                        fig_ema.add_trace(go.Scatter(x=df.index, y=df['EMA9'], name='EMA9'))
                        fig_ema.add_trace(go.Scatter(x=df.index, y=df['EMA21'], name='EMA21'))
                        fig_ema.update_layout(title='EMA Indicators')
                        st.plotly_chart(fig_ema)
                        
                        # OBV Plot
                        fig_obv = go.Figure()
                        fig_obv.add_trace(go.Scatter(x=df.index, y=df['OBV'], name='OBV'))
                        fig_obv.update_layout(title='On-Balance Volume (OBV)')
                        st.plotly_chart(fig_obv)

                        # VWAP Plot
                        fig_vwap = go.Figure()
                        fig_vwap.add_trace(go.Scatter(x=df.index, y=df['Close'], name='Price'))
                        fig_vwap.add_trace(go.Scatter(x=df.index, y=df['VWAP'], 
                                                     name='VWAP', line=dict(color='orange')))
                        fig_vwap.update_layout(title='Volume Weighted Average Price (VWAP)')
                        st.plotly_chart(fig_vwap)                        

                    
                    with col2:
                        # Stochastic Plot
                        fig_stoch = go.Figure()
                        fig_stoch.add_trace(go.Scatter(x=df.index, y=df['%K'],
                                                      name='%K'))
                        fig_stoch.add_trace(go.Scatter(x=df.index, y=df['%D'],
                                                      name='%D'))
                        fig_stoch.update_layout(title='Stochastic Oscillator')
                        st.plotly_chart(fig_stoch)
                        
                        # Bollinger Bands
                        fig_bb = go.Figure()
                        fig_bb.add_trace(go.Scatter(x=df.index, y=df['BB_upper'],
                                                   name='Upper Band'))
                        fig_bb.add_trace(go.Scatter(x=df.index, y=df['Close'],
                                                   name='Price'))
                        fig_bb.add_trace(go.Scatter(x=df.index, y=df['BB_lower'],
                                                   name='Lower Band'))
                        fig_bb.update_layout(title='Bollinger Bands')
                        st.plotly_chart(fig_bb)

                        # Ichimoku Cloud
                        fig_ichimoku = go.Figure()
                        fig_ichimoku.add_trace(go.Scatter(x=df.index, y=df['ichimoku_a'], name='Senkou Span A'))
                        fig_ichimoku.add_trace(go.Scatter(x=df.index, y=df['ichimoku_b'], name='Senkou Span B'))
                        fig_ichimoku.add_trace(go.Scatter(x=df.index, y=df['ichimoku_base'], name='Kijun-sen'))
                        fig_ichimoku.add_trace(go.Scatter(x=df.index, y=df['ichimoku_conversion'], name='Tenkan-sen'))
                        fig_ichimoku.update_layout(title='Ichimoku Cloud')
                        st.plotly_chart(fig_ichimoku)

                        # SMA Plot
                        fig_sma = go.Figure()
                        fig_sma.add_trace(go.Scatter(x=df.index, y=df['Close'], name='Price'))
                        fig_sma.add_trace(go.Scatter(x=df.index, y=df['SMA20'], name='SMA20'))
                        fig_sma.add_trace(go.Scatter(x=df.index, y=df['SMA50'], name='SMA50'))
                        fig_sma.add_trace(go.Scatter(x=df.index, y=df['SMA100'], name='SMA100'))
                        fig_sma.add_trace(go.Scatter(x=df.index, y=df['SMA200'], name='SMA200'))
                        fig_sma.update_layout(title='Simple Moving Averages (SMA)')
                        st.plotly_chart(fig_sma)
                        
                        # ATR Plot
                        fig_atr = go.Figure()
                        fig_atr.add_trace(go.Scatter(x=df.index, y=df['ATR'], name='ATR'))
                        fig_atr.update_layout(title='Average True Range (ATR)')
                        st.plotly_chart(fig_atr)


                with tab3:
                    st.subheader('Technical Analysis')
                    
                    # Combine all signals
                    signals = generate_recommendation(df)
                    enhanced_signals = enhanced_recommendation(df)
                    all_signals = signals + enhanced_signals
                    
                    # Count buy and sell signals
                    buy_signals = len([s for s in all_signals if s[0] == 'BUY'])
                    sell_signals = len([s for s in all_signals if s[0] == 'SELL'])
                    
                    # Display recommendation summary
                    col1, col2, col3 = st.columns(3)
                    with col1:
                        st.metric("Buy Signals", buy_signals)
                    with col2:
                        st.metric("Sell Signals", sell_signals)
                    with col3:
                        overall_rec = "BUY" if buy_signals > sell_signals else "SELL" if sell_signals > buy_signals else "HOLD"
                        if overall_rec == "BUY":
                            st.success(f"Overall: {overall_rec} 📈")
                        elif overall_rec == "SELL":
                            st.error(f"Overall: {overall_rec} 📉")
                        else:
                            st.warning(f"Overall: {overall_rec} ↔️")
                    
                    # Display detailed signals
                    st.subheader("Detailed Signals:")
                    for signal, reason in all_signals:
                        if signal == 'BUY':
                            st.success(f"🟢 {signal}: {reason}")
                        else:
                            st.error(f"🔴 {signal}: {reason}")

                    # Technical Indicators Summary
                    st.subheader("Technical Indicators Summary")
                    
                    # Create expandable section for current price levels
                    with st.expander("📊 Current Price Levels", expanded=True):
                        current_price = df['Close'].iloc[-1]
                        prev_close = df['Close'].iloc[-2]
                        price_change = ((current_price - prev_close) / prev_close) * 100
                        
                        # Price information with colored indicators
                        if price_change > 0:
                            st.success(f"📈 Current Price: ${current_price:.2f} (+{price_change:.2f}%)")
                        else:
                            st.error(f"📉 Current Price: ${current_price:.2f} ({price_change:.2f}%)")
                        
                        # Display technical levels in a more organized way
                        col1, col2, col3 = st.columns(3)
                        with col1:
                            st.info(f"💹 VWAP\n${df['VWAP'].iloc[-1]:.2f}")
                        with col2:
                            st.info(f"📏 ATR\n${df['ATR'].iloc[-1]:.2f}")
                        with col3:
                            st.info(f"📈 SMA50\n${df['SMA50'].iloc[-1]:.2f}")

                    # Moving Averages Analysis
                    with st.expander("📈 Moving Averages Analysis", expanded=True):
                        sma_status = "Bullish" if (df['SMA20'].iloc[-1] > df['SMA50'].iloc[-1]) else "Bearish"
                        sma_icon = "🟢" if sma_status == "Bullish" else "🔴"
                        st.write(f"{sma_icon} SMA20 vs SMA50: {sma_status}")
                        
                        # Add more SMA comparisons
                        sma_100_status = "Bullish" if (df['Close'].iloc[-1] > df['SMA100'].iloc[-1]) else "Bearish"
                        sma_100_icon = "🟢" if sma_100_status == "Bullish" else "🔴"
                        st.write(f"{sma_100_icon} Price vs SMA100: {sma_100_status}")
                        
                        sma_200_status = "Bullish" if (df['Close'].iloc[-1] > df['SMA200'].iloc[-1]) else "Bearish"
                        sma_200_icon = "🟢" if sma_200_status == "Bullish" else "🔴"
                        st.write(f"{sma_200_icon} Price vs SMA200: {sma_200_status}")

                    # Volatility Analysis
                    with st.expander("📊 Volatility Analysis", expanded=True):
                        atr_avg = df['ATR'].mean()
                        current_atr = df['ATR'].iloc[-1]
                        atr_ratio = current_atr / atr_avg
                        
                        if atr_ratio > 1.5:
                            volatility = "High"
                            vol_icon = "⚠️"
                            st.warning(f"{vol_icon} Volatility: {volatility}")
                        elif atr_ratio < 0.5:
                            volatility = "Low"
                            vol_icon = "💤"
                            st.info(f"{vol_icon} Volatility: {volatility}")
                        else:
                            volatility = "Normal"
                            vol_icon = "✅"
                            st.success(f"{vol_icon} Volatility: {volatility}")
                        
                        st.write(f"ATR Ratio: {atr_ratio:.2f}x average")

                    # VWAP Analysis
                    with st.expander("💹 VWAP Analysis", expanded=True):
                        vwap_diff = ((df['Close'].iloc[-1] - df['VWAP'].iloc[-1]) / df['VWAP'].iloc[-1]) * 100
                        if vwap_diff > 0:
                            st.success(f"🟢 Price is ABOVE VWAP by {abs(vwap_diff):.2f}%")
                        else:
                            st.error(f"🔴 Price is BELOW VWAP by {abs(vwap_diff):.2f}%")
                    
                    # Add risk warning
                    st.warning("⚠️ Disclaimer: Above analysis is for AI Research and Learning purposes only. DO NOT make any investment decisions according to the displayed information and analysis result.")
            
            else:
                st.error('Error fetching stock data. Please check the symbol.')
    else:
        st.warning('Please enter a stock symbol.')


with st.sidebar:
    # Add a horizontal line
    st.markdown("---")

    # Add text at the bottom
    st.warning("⚠️ Disclaimer: This is for AI Research and Learning purposes only. DO NOT make any investment decisions according to the displayed information and analysis result.")

    st.markdown("---")
    st.write("https://www.facebook.com/AIsparking")

	# FB Page: https://www.facebook.com/AIsparking

	import streamlit as st
	import yfinance as yf
	import pandas as pd
	import numpy as np
	from ta.trend import MACD
	from ta.momentum import RSIIndicator, StochasticOscillator
	from ta.volatility import AverageTrueRange, BollingerBands
	from ta.volume import VolumeWeightedAveragePrice
	import plotly.graph_objects as go
	from datetime import datetime, timedelta
	import plotly.subplots as sp
	from ta.trend import IchimokuIndicator

	# Set page config
	st.set_page_config(layout="wide", page_title="Stock Technical Analysis")

	# Functions from previous implementations remain the same
	# Add error handling wrapper
	def safe_execute(func):
	def wrapper(args, *kwargs):
	try:
	return func(args, *kwargs)
	except Exception as e:
	st.error(f"Error: {str(e)}")
	return None
	return wrapper

	@safe_execute
	def get_stock_data(symbol, market, nYear):
	if market == 'HK':
	symbol = f'{symbol}.HK'

	end_date = datetime.now()
	start_date = end_date - timedelta(days=nYear*365)

	df = yf.download(symbol, start=start_date, end=end_date)
	if df.empty:
	raise ValueError("No data found for this symbol")
	return df

	# Combine all technical indicators
	@safe_execute

	def generate_recommendation(df):
	last_row = df.iloc[-1]
	signals = []

	# RSI signals
	if last_row['RSI'] < 30:
	signals.append(('BUY', 'RSI oversold'))
	elif last_row['RSI'] > 70:
	signals.append(('SELL', 'RSI overbought'))

	# Stochastic signals
	if last_row['%K'] < 20 and last_row['%D'] < 20:
	signals.append(('BUY', 'Stochastic oversold'))
	elif last_row['%K'] > 80 and last_row['%D'] > 80:
	signals.append(('SELL', 'Stochastic overbought'))

	# MACD signals
	if last_row['MACD'] > last_row['MACD_Signal']:
	signals.append(('BUY', 'MACD crossover'))
	elif last_row['MACD'] < last_row['MACD_Signal']:
	signals.append(('SELL', 'MACD crossunder'))

	return signals

	def calculate_indicators(df):
	# Calculate RSI
	rsi = RSIIndicator(df['Close'])
	df['RSI'] = rsi.rsi()

	# Calculate Stochastic
	stoch = StochasticOscillator(df['High'], df['Low'], df['Close'])
	df['%K'] = stoch.stoch()
	df['%D'] = stoch.stoch_signal()

	# Calculate MACD
	macd = MACD(df['Close'])
	df['MACD'] = macd.macd()
	df['MACD_Signal'] = macd.macd_signal()

	# Calculate ATR
	atr = AverageTrueRange(df['High'], df['Low'], df['Close'])
	df['ATR'] = atr.average_true_range()

	# Add multiple SMAs
	df['SMA20'] = df['Close'].rolling(window=20).mean()
	df['SMA50'] = df['Close'].rolling(window=50).mean()
	df['SMA100'] = df['Close'].rolling(window=100).mean()
	df['SMA200'] = df['Close'].rolling(window=200).mean()

	# Calculate ATR
	atr = AverageTrueRange(df['High'], df['Low'], df['Close'])
	df['ATR'] = atr.average_true_range()

	# Calculate VWAP
	vwap = VolumeWeightedAveragePrice(high=df['High'],
	low=df['Low'],
	close=df['Close'],
	volume=df['Volume'])
	df['VWAP'] = vwap.volume_weighted_average_price()

	return df

	def calculate_additional_indicators(df):
	# Add Bollinger Bands
	bb = BollingerBands(df['Close'])
	df['BB_upper'] = bb.bollinger_hband()
	df['BB_lower'] = bb.bollinger_lband()
	df['BB_middle'] = bb.bollinger_mavg()

	# Add Moving Averages
	df['MA50'] = df['Close'].rolling(window=50).mean()
	df['MA200'] = df['Close'].rolling(window=200).mean()

	# Add VWAP
	vwap = VolumeWeightedAveragePrice(high=df['High'], low=df['Low'],
	close=df['Close'], volume=df['Volume'])
	df['VWAP'] = vwap.volume_weighted_average_price()

	return df

	def calculate_ema(df):
	# Calculate EMAs
	df['EMA9'] = df['Close'].ewm(span=9, adjust=False).mean()
	df['EMA21'] = df['Close'].ewm(span=21, adjust=False).mean()
	return df

	def calculate_obv(df):
	# Calculate OBV
	obv = []
	prev_obv = 0
	for i in range(len(df)):
	if i == 0:
	obv.append(prev_obv)
	continue
	if df['Close'].iloc[i] > df['Close'].iloc[i-1]:
	current_obv = prev_obv + df['Volume'].iloc[i]
	elif df['Close'].iloc[i] < df['Close'].iloc[i-1]:
	current_obv = prev_obv - df['Volume'].iloc[i]
	else:
	current_obv = prev_obv
	obv.append(current_obv)
	prev_obv = current_obv
	df['OBV'] = obv
	return df

	def calculate_ichimoku(df):
	ichimoku = IchimokuIndicator(high=df['High'], low=df['Low'])
	df['ichimoku_a'] = ichimoku.ichimoku_a()
	df['ichimoku_b'] = ichimoku.ichimoku_b()
	df['ichimoku_base'] = ichimoku.ichimoku_base_line()
	df['ichimoku_conversion'] = ichimoku.ichimoku_conversion_line()
	return df

	def calculate_all_indicators(df):
	df = calculate_indicators(df)
	df = calculate_additional_indicators(df)
	df = calculate_ema(df)
	df = calculate_obv(df)
	df = calculate_ichimoku(df)
	return df

	def enhanced_recommendation(df):
	last_row = df.iloc[-1]
	prev_row = df.iloc[-2]
	signals = []

	# Add EMA signals
	if last_row['EMA9'] > last_row['EMA21']:
	signals.append(('BUY', 'EMA9 crossed above EMA21'))
	elif last_row['EMA9'] < last_row['EMA21']:
	signals.append(('SELL', 'EMA9 crossed below EMA21'))

	# Add Ichimoku signals
	if (last_row['ichimoku_conversion'] > last_row['ichimoku_base'] and
	last_row['Close'] > last_row['ichimoku_a']):
	signals.append(('BUY', 'Ichimoku bullish signal'))
	elif (last_row['ichimoku_conversion'] < last_row['ichimoku_base'] and
	last_row['Close'] < last_row['ichimoku_b']):
	signals.append(('SELL', 'Ichimoku bearish signal'))

	# Add OBV signals
	if df['OBV'].iloc[-1] > df['OBV'].iloc[-2]:
	signals.append(('BUY', 'OBV increasing'))
	else:
	signals.append(('SELL', 'OBV decreasing'))

	# Add SMA signals
	if (last_row['SMA20'] > last_row['SMA50'] and
	prev_row['SMA20'] <= prev_row['SMA50']):
	signals.append(('BUY', 'SMA20 crossed above SMA50'))
	elif (last_row['SMA20'] < last_row['SMA50'] and
	prev_row['SMA20'] >= prev_row['SMA50']):
	signals.append(('SELL', 'SMA20 crossed below SMA50'))

	# Add VWAP signals
	if last_row['Close'] > last_row['VWAP']:
	signals.append(('BUY', 'Price above VWAP'))
	else:
	signals.append(('SELL', 'Price below VWAP'))

	# Add ATR-based volatility signals
	atr_threshold = df['ATR'].mean() * 1.5
	if last_row['ATR'] > atr_threshold:
	signals.append(('HOLD', 'High volatility detected by ATR'))

	return signals


	# Main app layout
	st.title('Advanced Stock Technical Analysis')

	# Sidebar for inputs
	with st.sidebar:
	st.header('Input Parameters')
	market = st.selectbox('Select Market', ['HK','US'])
	symbol = st.text_input('Enter Stock Symbol (e.g. 0700 for HK):')

	# Add analysis timeframe option
	timeframe = st.selectbox('Select Timeframe', ['1y','2y','3y','5y','8y','10y'])
	nYear = int(timeframe.split("y")[0])

	if st.sidebar.button('Analyze'):
	if symbol:
	with st.spinner('Fetching and analyzing data...'):
	df = get_stock_data(symbol, market,nYear)

	if df is not None:
	df = calculate_all_indicators(df)

	# Create tabs for different analyses
	tab1, tab2, tab3 = st.tabs(['Price Analysis', 'Technical Indicators', 'Technical Analysis'])

	with tab1:
	# Main price chart with volume
	fig = sp.make_subplots(rows=2, cols=1, shared_xaxes=True,
	vertical_spacing=0.03, row_heights=[0.7, 0.3])

	fig.add_trace(go.Candlestick(x=df.index, open=df['Open'],
	high=df['High'], low=df['Low'],
	close=df['Close'], name='Price'),
	row=1, col=1)

	# Add Moving Averages
	fig.add_trace(go.Scatter(x=df.index, y=df['MA50'],
	name='MA50', line=dict(color='orange')),
	row=1, col=1)
	fig.add_trace(go.Scatter(x=df.index, y=df['MA200'],
	name='MA200', line=dict(color='blue')),
	row=1, col=1)

	# Add volume bars
	colors = ['red' if row['Open'] > row['Close'] else 'green'
	for index, row in df.iterrows()]
	fig.add_trace(go.Bar(x=df.index, y=df['Volume'],
	marker_color=colors, name='Volume'),
	row=2, col=1)

	fig.update_layout(height=800)
	st.plotly_chart(fig, use_container_width=True)

	with tab2:
	col1, col2 = st.columns(2)

	with col1:
	# RSI Plot
	fig_rsi = go.Figure()
	fig_rsi.add_trace(go.Scatter(x=df.index, y=df['RSI'],
	name='RSI'))
	fig_rsi.add_hline(y=70, line_dash="dash", line_color="red")
	fig_rsi.add_hline(y=30, line_dash="dash", line_color="green")
	fig_rsi.update_layout(title='RSI Indicator')
	st.plotly_chart(fig_rsi)

	# MACD Plot
	fig_macd = go.Figure()
	fig_macd.add_trace(go.Scatter(x=df.index, y=df['MACD'],
	name='MACD'))
	fig_macd.add_trace(go.Scatter(x=df.index, y=df['MACD_Signal'],
	name='Signal'))
	fig_macd.update_layout(title='MACD Indicator')
	st.plotly_chart(fig_macd)

	# EMA Plot
	fig_ema = go.Figure()
	fig_ema.add_trace(go.Scatter(x=df.index, y=df['EMA9'], name='EMA9'))
	fig_ema.add_trace(go.Scatter(x=df.index, y=df['EMA21'], name='EMA21'))
	fig_ema.update_layout(title='EMA Indicators')
	st.plotly_chart(fig_ema)

	# OBV Plot
	fig_obv = go.Figure()
	fig_obv.add_trace(go.Scatter(x=df.index, y=df['OBV'], name='OBV'))
	fig_obv.update_layout(title='On-Balance Volume (OBV)')
	st.plotly_chart(fig_obv)

	# VWAP Plot
	fig_vwap = go.Figure()
	fig_vwap.add_trace(go.Scatter(x=df.index, y=df['Close'], name='Price'))
	fig_vwap.add_trace(go.Scatter(x=df.index, y=df['VWAP'],
	name='VWAP', line=dict(color='orange')))
	fig_vwap.update_layout(title='Volume Weighted Average Price (VWAP)')
	st.plotly_chart(fig_vwap)


	with col2:
	# Stochastic Plot
	fig_stoch = go.Figure()
	fig_stoch.add_trace(go.Scatter(x=df.index, y=df['%K'],
	name='%K'))
	fig_stoch.add_trace(go.Scatter(x=df.index, y=df['%D'],
	name='%D'))
	fig_stoch.update_layout(title='Stochastic Oscillator')
	st.plotly_chart(fig_stoch)

	# Bollinger Bands
	fig_bb = go.Figure()
	fig_bb.add_trace(go.Scatter(x=df.index, y=df['BB_upper'],
	name='Upper Band'))
	fig_bb.add_trace(go.Scatter(x=df.index, y=df['Close'],
	name='Price'))
	fig_bb.add_trace(go.Scatter(x=df.index, y=df['BB_lower'],
	name='Lower Band'))
	fig_bb.update_layout(title='Bollinger Bands')
	st.plotly_chart(fig_bb)

	# Ichimoku Cloud
	fig_ichimoku = go.Figure()
	fig_ichimoku.add_trace(go.Scatter(x=df.index, y=df['ichimoku_a'], name='Senkou Span A'))
	fig_ichimoku.add_trace(go.Scatter(x=df.index, y=df['ichimoku_b'], name='Senkou Span B'))
	fig_ichimoku.add_trace(go.Scatter(x=df.index, y=df['ichimoku_base'], name='Kijun-sen'))
	fig_ichimoku.add_trace(go.Scatter(x=df.index, y=df['ichimoku_conversion'], name='Tenkan-sen'))
	fig_ichimoku.update_layout(title='Ichimoku Cloud')
	st.plotly_chart(fig_ichimoku)

	# SMA Plot
	fig_sma = go.Figure()
	fig_sma.add_trace(go.Scatter(x=df.index, y=df['Close'], name='Price'))
	fig_sma.add_trace(go.Scatter(x=df.index, y=df['SMA20'], name='SMA20'))
	fig_sma.add_trace(go.Scatter(x=df.index, y=df['SMA50'], name='SMA50'))
	fig_sma.add_trace(go.Scatter(x=df.index, y=df['SMA100'], name='SMA100'))
	fig_sma.add_trace(go.Scatter(x=df.index, y=df['SMA200'], name='SMA200'))
	fig_sma.update_layout(title='Simple Moving Averages (SMA)')
	st.plotly_chart(fig_sma)

	# ATR Plot
	fig_atr = go.Figure()
	fig_atr.add_trace(go.Scatter(x=df.index, y=df['ATR'], name='ATR'))
	fig_atr.update_layout(title='Average True Range (ATR)')
	st.plotly_chart(fig_atr)


	with tab3:
	st.subheader('Technical Analysis')

	# Combine all signals
	signals = generate_recommendation(df)
	enhanced_signals = enhanced_recommendation(df)
	all_signals = signals + enhanced_signals

	# Count buy and sell signals
	buy_signals = len([s for s in all_signals if s[0] == 'BUY'])
	sell_signals = len([s for s in all_signals if s[0] == 'SELL'])

	# Display recommendation summary
	col1, col2, col3 = st.columns(3)
	with col1:
	st.metric("Buy Signals", buy_signals)
	with col2:
	st.metric("Sell Signals", sell_signals)
	with col3:
	overall_rec = "BUY" if buy_signals > sell_signals else "SELL" if sell_signals > buy_signals else "HOLD"
	if overall_rec == "BUY":
	st.success(f"Overall: {overall_rec} 📈")
	elif overall_rec == "SELL":
	st.error(f"Overall: {overall_rec} 📉")
	else:
	st.warning(f"Overall: {overall_rec} ↔️")

	# Display detailed signals
	st.subheader("Detailed Signals:")
	for signal, reason in all_signals:
	if signal == 'BUY':
	st.success(f"🟢 {signal}: {reason}")
	else:
	st.error(f"🔴 {signal}: {reason}")

	# Technical Indicators Summary
	st.subheader("Technical Indicators Summary")

	# Create expandable section for current price levels
	with st.expander("📊 Current Price Levels", expanded=True):
	current_price = df['Close'].iloc[-1]
	prev_close = df['Close'].iloc[-2]
	price_change = ((current_price - prev_close) / prev_close) * 100

	# Price information with colored indicators
	if price_change > 0:
	st.success(f"📈 Current Price: ${current_price:.2f} (+{price_change:.2f}%)")
	else:
	st.error(f"📉 Current Price: ${current_price:.2f} ({price_change:.2f}%)")

	# Display technical levels in a more organized way
	col1, col2, col3 = st.columns(3)
	with col1:
	st.info(f"💹 VWAP\n${df['VWAP'].iloc[-1]:.2f}")
	with col2:
	st.info(f"📏 ATR\n${df['ATR'].iloc[-1]:.2f}")
	with col3:
	st.info(f"📈 SMA50\n${df['SMA50'].iloc[-1]:.2f}")

	# Moving Averages Analysis
	with st.expander("📈 Moving Averages Analysis", expanded=True):
	sma_status = "Bullish" if (df['SMA20'].iloc[-1] > df['SMA50'].iloc[-1]) else "Bearish"
	sma_icon = "🟢" if sma_status == "Bullish" else "🔴"
	st.write(f"{sma_icon} SMA20 vs SMA50: {sma_status}")

	# Add more SMA comparisons
	sma_100_status = "Bullish" if (df['Close'].iloc[-1] > df['SMA100'].iloc[-1]) else "Bearish"
	sma_100_icon = "🟢" if sma_100_status == "Bullish" else "🔴"
	st.write(f"{sma_100_icon} Price vs SMA100: {sma_100_status}")

	sma_200_status = "Bullish" if (df['Close'].iloc[-1] > df['SMA200'].iloc[-1]) else "Bearish"
	sma_200_icon = "🟢" if sma_200_status == "Bullish" else "🔴"
	st.write(f"{sma_200_icon} Price vs SMA200: {sma_200_status}")

	# Volatility Analysis
	with st.expander("📊 Volatility Analysis", expanded=True):
	atr_avg = df['ATR'].mean()
	current_atr = df['ATR'].iloc[-1]
	atr_ratio = current_atr / atr_avg

	if atr_ratio > 1.5:
	volatility = "High"
	vol_icon = "⚠️"
	st.warning(f"{vol_icon} Volatility: {volatility}")
	elif atr_ratio < 0.5:
	volatility = "Low"
	vol_icon = "💤"
	st.info(f"{vol_icon} Volatility: {volatility}")
	else:
	volatility = "Normal"
	vol_icon = "✅"
	st.success(f"{vol_icon} Volatility: {volatility}")

	st.write(f"ATR Ratio: {atr_ratio:.2f}x average")

	# VWAP Analysis
	with st.expander("💹 VWAP Analysis", expanded=True):
	vwap_diff = ((df['Close'].iloc[-1] - df['VWAP'].iloc[-1]) / df['VWAP'].iloc[-1]) * 100
	if vwap_diff > 0:
	st.success(f"🟢 Price is ABOVE VWAP by {abs(vwap_diff):.2f}%")
	else:
	st.error(f"🔴 Price is BELOW VWAP by {abs(vwap_diff):.2f}%")

	# Add risk warning
	st.warning("⚠️ Disclaimer: Above analysis is for AI Research and Learning purposes only. DO NOT make any investment decisions according to the displayed information and analysis result.")

	else:
	st.error('Error fetching stock data. Please check the symbol.')
	else:
	st.warning('Please enter a stock symbol.')


	with st.sidebar:
	# Add a horizontal line
	st.markdown("---")

	# Add text at the bottom
	st.warning("⚠️ Disclaimer: This is for AI Research and Learning purposes only. DO NOT make any investment decisions according to the displayed information and analysis result.")

	st.markdown("---")
	st.write("https://www.facebook.com/AIsparking")