Create app.py

app.py

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+# Required imports
+import yfinance as yf
+import pandas as pd
+from scipy.signal import find_peaks
+import plotly.graph_objects as go
+import streamlit as st
+
+# Streamlit UI setup
+sidebar = st.sidebar
+symbol = sidebar.text_input("Enter stock symbol:", "AAPL")
+period = sidebar.selectbox("Select period:", ["1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "ytd", "max"])
+
+# Download stock data
+data = yf.download(symbol, period=period)
+
+# Calculate Moving Averages
+data['MA50'] = data['Close'].rolling(window=50).mean()
+data['MA200'] = data['Close'].rolling(window=200).mean()
+data['MA20'] = data['Close'].rolling(window=20).mean()
+
+# Detecting significant peaks and troughs
+peaks, _ = find_peaks(data['Close'], prominence=1)  # Adjust prominence as needed
+troughs, _ = find_peaks(-data['Close'], prominence=1)  # Finding troughs by inverting the data
+
+# Ensure there are peaks and troughs detected
+if len(peaks) == 0 or len(troughs) == 0:
+    st.write("No significant peaks or troughs detected in the selected period.")
+else:
+    # Using the most significant peak and trough for Fibonacci levels
+    high_price = data.iloc[peaks]['Close'].max()
+    low_price = data.iloc[troughs]['Close'].min()
+
+    # Calculate Fibonacci Levels
+    fib_levels = [0, 0.236, 0.382, 0.5, 0.618, 0.786, 1]
+    price_diff = high_price - low_price
+    for i, level in enumerate(fib_levels):
+        data[f'Fib_Level_{i}'] = high_price - price_diff * level
+
+    # Plotting
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], name='Close Price', line=dict(color='black')))
+    fig.add_trace(go.Scatter(x=data.index, y=data['MA50'], name='50-Period MA', line=dict(color='blue')))
+    fig.add_trace(go.Scatter(x=data.index, y=data['MA200'], name='200-Period MA', line=dict(color='red')))
+    fig.add_trace(go.Scatter(x=data.index, y=data['MA20'], name='20-Period MA', line=dict(color='green')))
+
+    # Add traces for Fibonacci Levels
+    for i in range(7):
+        fig.add_trace(go.Scatter(x=data.index, y=[data[f'Fib_Level_{i}'][0]]*len(data), name=f'Fib Level {fib_levels[i]*100}%', line=dict(dash='dot')))
+
+    # Display the chart
+    st.plotly_chart(fig)