|
import streamlit as st |
|
import yfinance as yf |
|
import pandas as pd |
|
import plotly.graph_objs as go |
|
|
|
|
|
stock_exchanges = { |
|
"Kuala Lumpur Stock Exchange (KLSE)": { |
|
"CIMB Group Holdings Bhd - CIMB": "1023.KL", |
|
"RHB Bank Bhd - RHBBANK": "1066.KL", |
|
"Hong Leong Financial Group Bhd - HLFG": "1082.KL", |
|
"Malayan Banking Bhd - MAYBANK": "1155.KL", |
|
"Public Bank Bhd - PBBANK": "1295.KL", |
|
"IOI Corporation Bhd - IOICORP": "1961.KL", |
|
"Kuala Lumpur Kepong Bhd - KLK": "2445.KL", |
|
"Genting Bhd - GENTING": "3182.KL", |
|
"MISC Bhd - MISC": "3816.KL", |
|
"PPB Group Bhd - PPB": "4065.KL", |
|
"Sime Darby Bhd - SIME": "4197.KL", |
|
"Telekom Malaysia Bhd - TM": "4863.KL", |
|
"Tenaga Nasional Bhd - TENAGA": "5278.KL", |
|
"Top Glove Corporation Bhd - TOPGLOV": "7113.KL", |
|
"AirAsia X Bhd - AAX": "5238.KL", |
|
"Ramssol Group Bhd - RAMSSOL": "0236.KL", |
|
"Uzma Bhd - UZMA": "7250.KL", |
|
"WZ Satu Bhd - WZSATU": "7245.KL", |
|
"Systech Bhd - SYSTECH": "0050.KL", |
|
"Yong Tai Bhd - YONGTAI": "7066.KL", |
|
}, |
|
"Euronext": { |
|
"LVMH Moet Hennessy Louis Vuitton SE - LVMH": "MC.PA", |
|
"TotalEnergies SE - TotalEnergies": "TTE.PA", |
|
"Sanofi SA - Sanofi": "SAN.PA", |
|
"Air Liquide SA - Air Liquide": "AI.PA", |
|
"Schneider Electric SE - Schneider Electric": "SU.PA", |
|
"Kering SA - Kering": "KER.PA", |
|
"BNP Paribas SA - BNP Paribas": "BNP.PA", |
|
"Hermès International SCA - Hermès": "RMS.PA", |
|
"L'Oréal SA - L'Oréal": "OR.PA", |
|
"AXA SA - AXA": "CS.PA", |
|
"Vinci SA - Vinci": "DG.PA", |
|
"Dassault Systèmes SE - Dassault Systèmes": "DSY.PA", |
|
"Engie SA - Engie": "ENGI.PA", |
|
"Société Générale SA - Société Générale": "GLE.PA", |
|
"Pernod Ricard SA - Pernod Ricard": "RI.PA", |
|
"Safran SA - Safran": "SAF.PA", |
|
"ArcelorMittal SA - ArcelorMittal": "MT.AS", |
|
"Saint-Gobain SA - Saint-Gobain": "SGO.PA", |
|
"Capgemini SE - Capgemini": "CAP.PA", |
|
"Danone SA - Danone": "BN.PA", |
|
}, |
|
"London Stock Exchange (LSE)": { |
|
"HSBC Holdings plc - HSBC": "HSBA.L", |
|
"Royal Dutch Shell plc - Shell": "RDSA.L", |
|
"BP plc - BP": "BP.L", |
|
"GlaxoSmithKline plc - GlaxoSmithKline": "GSK.L", |
|
"AstraZeneca plc - AstraZeneca": "AZN.L", |
|
"Unilever plc - Unilever": "ULVR.L", |
|
"British American Tobacco plc - British American Tobacco": "BATS.L", |
|
"Diageo plc - Diageo": "DGE.L", |
|
"Barclays plc - Barclays": "BARC.L", |
|
"Lloyds Banking Group plc - Lloyds": "LLOY.L", |
|
"Vodafone Group plc - Vodafone": "VOD.L", |
|
"Rio Tinto plc - Rio Tinto": "RIO.L", |
|
"Reckitt Benckiser Group plc - Reckitt Benckiser": "RKT.L", |
|
"Tesco plc - Tesco": "TSCO.L", |
|
"Glencore plc - Glencore": "GLEN.L", |
|
"National Grid plc - National Grid": "NG.L", |
|
"BT Group plc - BT Group": "BT-A.L", |
|
"Aviva plc - Aviva": "AV.L", |
|
"Imperial Brands plc - Imperial Brands": "IMB.L", |
|
"Rolls-Royce Holdings plc - Rolls-Royce": "RR.L", |
|
}, |
|
"NYSE": { |
|
"Berkshire Hathaway Inc. (Class B) - BRK-B": "BRK-B", |
|
"Johnson & Johnson - JNJ": "JNJ", |
|
"JPMorgan Chase & Co. - JPM": "JPM", |
|
"Procter & Gamble Co. - PG": "PG", |
|
"Visa Inc. (Class A) - V": "V", |
|
"Walmart Inc. - WMT": "WMT", |
|
"Mastercard Incorporated (Class A) - MA": "MA", |
|
"The Home Depot, Inc. - HD": "HD", |
|
"Bank of America Corporation - BAC": "BAC", |
|
"Walt Disney Company (The) - DIS": "DIS", |
|
"Pfizer Inc. - PFE": "PFE", |
|
"Chevron Corporation - CVX": "CVX", |
|
"Coca-Cola Company (The) - KO": "KO", |
|
"Exxon Mobil Corporation - XOM": "XOM", |
|
"AbbVie Inc. - ABBV": "ABBV", |
|
"Merck & Co., Inc. - MRK": "MRK", |
|
"AT&T Inc. - T": "T", |
|
"Verizon Communications Inc. - VZ": "VZ", |
|
"Morgan Stanley - MS": "MS", |
|
"Goldman Sachs Group, Inc. (The) - GS": "GS", |
|
}, |
|
"NASDAQ": { |
|
"Apple Inc. - AAPL": "AAPL", |
|
"Microsoft Corporation - MSFT": "MSFT", |
|
"Amazon.com, Inc. - AMZN": "AMZN", |
|
"Tesla, Inc. - TSLA": "TSLA", |
|
"Alphabet Inc. (Class A) - GOOGL": "GOOGL", |
|
"Alphabet Inc. (Class C) - GOOG": "GOOG", |
|
"NVIDIA Corporation - NVDA": "NVDA", |
|
"Meta Platforms, Inc. - META": "META", |
|
"Netflix, Inc. - NFLX": "NFLX", |
|
"Intel Corporation - INTC": "INTC", |
|
"Adobe Inc. - ADBE": "ADBE", |
|
"Cisco Systems, Inc. - CSCO": "CSCO", |
|
"PepsiCo, Inc. - PEP": "PEP", |
|
"Comcast Corporation - CMCSA": "CMCSA", |
|
"Advanced Micro Devices, Inc. - AMD": "AMD", |
|
"Broadcom Inc. - AVGO": "AVGO", |
|
"Charter Communications, Inc. - CHTR": "CHTR", |
|
"PayPal Holdings, Inc. - PYPL": "PYPL", |
|
"Starbucks Corporation - SBUX": "SBUX", |
|
"Booking Holdings Inc. - BKNG": "BKNG", |
|
}, |
|
} |
|
|
|
def fetch_data(ticker, start_date, end_date): |
|
data = yf.download(ticker, start=start_date, end=end_date) |
|
return data |
|
|
|
def fetch_weekly_data(ticker, start_date, end_date): |
|
data = yf.download(ticker, start=start_date, end=end_date, interval='1wk') |
|
return data |
|
|
|
def calculate_indicators(data): |
|
|
|
data['Middle Band'] = data['Close'].rolling(window=20).mean() |
|
data['Upper Band'] = data['Middle Band'] + 1.96 * data['Close'].rolling(window=20).std() |
|
data['Lower Band'] = data['Middle Band'] - 1.96 * data['Close'].rolling(window=20).std() |
|
|
|
|
|
data['MA5'] = data['Close'].rolling(window=5).mean() |
|
data['MA10'] = data['Close'].rolling(window=10).mean() |
|
|
|
return data |
|
|
|
def identify_signals(data): |
|
|
|
data['Buy Signal'] = ((data['Close'] < data['Lower Band']) & (data['Close'].shift(1) > data['Lower Band'])) | \ |
|
((data['Close'] > data['MA5']) & (data['Close'].shift(1) < data['MA5'])) |
|
data['Sell Signal'] = ((data['Close'] > data['Upper Band']) & (data['Close'].shift(1) < data['Upper Band'])) | \ |
|
((data['Close'] < data['MA5']) & (data['Close'].shift(1) > data['MA5'])) |
|
|
|
|
|
avg_volume = data['Volume'].rolling(window=20).mean() |
|
data['Buy Signal'] = data['Buy Signal'] & (data['Volume'] > avg_volume) |
|
data['Sell Signal'] = data['Sell Signal'] & (data['Volume'] > avg_volume) |
|
|
|
return data |
|
|
|
def identify_weekly_signals(weekly_data): |
|
weekly_data['Buy Signal'] = ((weekly_data['Close'] < weekly_data['Lower Band']) & (weekly_data['Close'].shift(1) > weekly_data['Lower Band'])) | \ |
|
((weekly_data['Close'] > weekly_data['MA5']) & (weekly_data['Close'].shift(1) < weekly_data['MA5'])) |
|
weekly_data['Sell Signal'] = ((weekly_data['Close'] > weekly_data['Upper Band']) & (weekly_data['Close'].shift(1) < weekly_data['Upper Band'])) | \ |
|
((weekly_data['Close'] < weekly_data['MA5']) & (weekly_data['Close'].shift(1) > weekly_data['MA5'])) |
|
return weekly_data |
|
|
|
def confirm_signals_with_weekly(data, weekly_data): |
|
weekly_data = calculate_indicators(weekly_data) |
|
weekly_data = identify_weekly_signals(weekly_data) |
|
data['Weekly Buy Signal'] = weekly_data['Buy Signal'].reindex(data.index, method='ffill') |
|
data['Weekly Sell Signal'] = weekly_data['Sell Signal'].reindex(data.index, method='ffill') |
|
data['Buy Signal'] = data['Buy Signal'] & data['Weekly Buy Signal'] |
|
data['Sell Signal'] = data['Sell Signal'] & data['Weekly Sell Signal'] |
|
|
|
return data |
|
|
|
def plot_data(data): |
|
fig = go.Figure() |
|
|
|
|
|
fig.add_trace(go.Scatter(x=data.index, y=data['Close'], name='Close Price', line=dict(color='blue', width=2))) |
|
|
|
|
|
fig.add_trace(go.Scatter(x=data.index, y=data['Upper Band'], name='Upper Bollinger Band', line=dict(color='red', dash='dash'))) |
|
fig.add_trace(go.Scatter(x=data.index, y=data['Middle Band'], name='Middle Bollinger Band', line=dict(color='white', dash='dash'))) |
|
fig.add_trace(go.Scatter(x=data.index, y=data['Lower Band'], name='Lower Bollinger Band', line=dict(color='red', dash='dash'))) |
|
|
|
|
|
fig.add_trace(go.Scatter(x=data.index, y=data['MA5'], name='5-Day MA', line=dict(color='green', dash='dot'))) |
|
fig.add_trace(go.Scatter(x=data.index, y=data['MA10'], name='10-Day MA', line=dict(color='orange', dash='dot'))) |
|
|
|
|
|
buys = data[data['Buy Signal']] |
|
sells = data[data['Sell Signal']] |
|
fig.add_trace(go.Scatter(x=buys.index, y=buys['Close'], mode='markers', name='Buy Signal', marker=dict(symbol='triangle-up', size=10, color='green'))) |
|
fig.add_trace(go.Scatter(x=sells.index, y=sells['Close'], mode='markers', name='Sell Signal', marker=dict(symbol='triangle-down', size=10, color='red'))) |
|
|
|
|
|
fig.update_layout(title='Stock Price and Trading Signals', xaxis_title='Date', yaxis_title='Price', template='plotly_dark') |
|
fig.update_xaxes(rangeslider_visible=True) |
|
|
|
return fig |
|
|
|
def main(): |
|
st.title("OMA Ally BBMA Trading Signal") |
|
|
|
|
|
st.sidebar.title("Select Ticker Symbol") |
|
exchange = st.sidebar.selectbox("Select Stock Exchange", list(stock_exchanges.keys())) |
|
ticker_symbols = stock_exchanges[exchange] |
|
ticker = st.sidebar.selectbox("Select Ticker Symbol", list(ticker_symbols.keys())) |
|
ticker_symbol = ticker_symbols[ticker] |
|
|
|
st.markdown(""" |
|
## App Explanation |
|
This app visualizes the Oma Ally BBMA (Bollinger Bands and Moving Averages) trading strategy for the top 20 stocks from global stock exchanges. It helps identify potential buy and sell signals based on the trading strategy. |
|
|
|
## How to Use the App |
|
1. **Select Stock Exchange:** Use the dropdown menu in the sidebar to choose the stock exchange. |
|
2. **Select Ticker Symbol:** Choose the ticker symbol of the stock you want to analyze from the dropdown menu in the sidebar. |
|
3. **Select Date Range:** Pick the start and end dates for the period you want to analyze. |
|
4. **Analyze:** Click the 'Analyze' button to fetch the data, calculate indicators, and plot the results. |
|
""") |
|
|
|
start_date = st.date_input("Select the start date") |
|
end_date = st.date_input("Select the end date") |
|
|
|
if st.button("Analyze"): |
|
data = fetch_data(ticker_symbol, start_date, end_date) |
|
weekly_data = fetch_weekly_data(ticker_symbol, start_date, end_date) |
|
data = calculate_indicators(data) |
|
data = identify_signals(data) |
|
data = confirm_signals_with_weekly(data, weekly_data) |
|
fig = plot_data(data) |
|
st.plotly_chart(fig, use_container_width=True) |
|
|
|
if __name__ == "__main__": |
|
main() |
|
|
