Update app.py

app.py

@@ -3,68 +3,58 @@ import yfinance as yf
 import pandas as pd
 import plotly.graph_objs as go
 
-def fetch_data(ticker, start_date, end_date):
-    data = yf.download(ticker, start=start_date, end=end_date)
-    return data
-
-def calculate_indicators(data, short_ema, long_ema):
-    # Exponential Moving Averages
-    data['EMA_Short'] = data['Close'].ewm(span=short_ema, adjust=False).mean()
-    data['EMA_Long'] = data['Close'].ewm(span=long_ema, adjust=False).mean()
-    return data
+# Function to calculate EMA
+def calculate_ema(data, span):
+    return data.ewm(span=span, adjust=False).mean()
 
-def identify_signals(data):
-    data['Position'] = (data['EMA_Short'] > data['EMA_Long']).astype(int)
-    data['Signal'] = data['Position'].diff()
-    data['Buy Signal'] = data['Signal'] == 1
-    data['Sell Signal'] = data['Signal'] == -1
+# Function to get data and calculate EMAs
+def get_data_with_ema(ticker, start_date, end_date, short_ema, long_ema):
+    data = yf.download(ticker, start=start_date, end=end_date)
+    data['Short_EMA'] = calculate_ema(data['Close'], short_ema)
+    data['Long_EMA'] = calculate_ema(data['Close'], long_ema)
+    data['Signal'] = 0
+    data['Signal'][short_ema:] = \
+        (data['Short_EMA'][short_ema:] > data['Long_EMA'][short_ema:]).astype(int)
+    data['Position'] = data['Signal'].diff()
     return data
 
-def plot_data(data):
+# Streamlit interface
+st.sidebar.title("2 EMA Crossover Trading Strategy")
+ticker = st.sidebar.text_input("Enter the ticker symbol", "AAPL")
+short_ema = st.sidebar.number_input("Short EMA length", min_value=5, max_value=50, value=20)
+long_ema = st.sidebar.number_input("Long EMA length", min_value=10, max_value=200, value=50)
+start_date = st.sidebar.date_input("Start Date", pd.to_datetime("2020-01-01"))
+end_date = st.sidebar.date_input("End Date", pd.to_datetime("today"))
+
+if st.sidebar.button("Analyze"):
+    data = get_data_with_ema(ticker, start_date, end_date, short_ema, long_ema)
+    # Plotting
     fig = go.Figure()
-
-    # Adding Close price trace
-    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], name='Close Price', line=dict(color='blue', width=2)))
-
-    # Adding EMAs
-    fig.add_trace(go.Scatter(x=data.index, y=data['EMA_Short'], name=f'EMA {short_ema}', line=dict(color='green', width=1.5)))
-    fig.add_trace(go.Scatter(x=data.index, y=data['EMA_Long'], name=f'EMA {long_ema}', line=dict(color='red', width=1.5)))
-
-    # Adding Buy and Sell signals
-    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=12, color='green')))
-    fig.add_trace(go.Scatter(x=sells.index, y=sells['Close'], mode='markers', name='Sell Signal', marker=dict(symbol='triangle-down', size=12, color='red')))
-
-    # Layout updates
-    fig.update_layout(title='2 EMA Crossover Trading Strategy Visualization', xaxis_title='Date', yaxis_title='Price', template='plotly_dark')
-    fig.update_xaxes(rangeslider_visible=True)
-    
-    return fig
-
-def main():
-    st.sidebar.title("Settings")
-    ticker = st.sidebar.text_input("Enter the ticker symbol, e.g., 'AAPL'", 'AAPL')
-    start_date = st.sidebar.date_input("Select the start date")
-    end_date = st.sidebar.date_input("Select the end date")
-    short_ema = st.sidebar.number_input("Enter the shorter EMA period", min_value=1, value=12)
-    long_ema = st.sidebar.number_input("Enter the longer EMA period", min_value=1, value=26)
-
-    st.title("2 EMA Crossover Trading Strategy")
-    st.markdown("""
-        ## Description
-        This application visualizes a 2 Exponential Moving Average (EMA) Crossover Strategy on historical stock data. 
-        The strategy involves two EMAs: a shorter period EMA and a longer period EMA. 
-        A **buy signal** is generated when the shorter EMA crosses above the longer EMA, and a **sell signal** is generated when the shorter EMA crosses below the longer EMA.
-        """)
-
-    if st.sidebar.button("Analyze"):
-        data = fetch_data(ticker, start_date, end_date)
-        data = calculate_indicators(data, short_ema, long_ema)
-        data = identify_signals(data)
-        fig = plot_data(data)
-        st.plotly_chart(fig, use_container_width=True)
-
-if __name__ == "__main__":
-    main()
-    
+    fig.add_trace(go.Candlestick(x=data.index,
+                                 open=data['Open'], high=data['High'],
+                                 low=data['Low'], close=data['Close'], name='Candlestick'))
+    fig.add_trace(go.Scatter(x=data.index, y=data['Short_EMA'], line=dict(color='blue', width=1.5), name='Short EMA'))
+    fig.add_trace(go.Scatter(x=data.index, y=data['Long_EMA'], line=dict(color='red', width=1.5), name='Long EMA'))
+    # Buy signals
+    buys = data[data['Position'] == 1]
+    sells = data[data['Position'] == -1]
+    fig.add_trace(go.Scatter(x=buys.index, y=buys['Close'], mode='markers', marker=dict(color='green', size=10), name='Buy Signal'))
+    fig.add_trace(go.Scatter(x=sells.index, y=sells['Close'], mode='markers', marker=dict(color='red', size=10), name='Sell Signal'))
+
+    fig.update_layout(title=f"2 EMA Crossover Trading Analysis for {ticker}",
+                      xaxis_title="Date",
+                      yaxis_title="Price",
+                      xaxis_rangeslider_visible=False)
+    fig.update_xaxes(type='category')
+    st.plotly_chart(fig, use_container_width=True)
+
+st.title("2 EMA Crossover Trading Strategy App")
+st.markdown("""
+This app visualizes the 2 EMA Crossover Trading Strategy for any stock ticker available on Yahoo Finance.
+Select the ticker symbol, EMA lengths, start and end date for the analysis in the sidebar and click analyze to see the results.
+**Instructions:**
+1. Enter the stock ticker symbol (e.g., AAPL, GOOGL).
+2. Choose the lengths for the Short and Long EMAs.
+3. Set the desired time period for analysis.
+4. Click the 'Analyze' button to view the interactive chart with buy and sell signals.
+""")