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 - Introduction and How to Use the App
+st.markdown("""
+# Stock Analysis Tool
+
+Welcome to our stock analysis application, lovingly crafted to provide traders and investors with key insights into market trends, moving averages, and Fibonacci retracement levels.
+
+### What It Does
+This tool allows you to:
+- View a stock's price movement over time.
+- Analyze moving averages (20, 50, 200 periods) to identify trends.
+- Utilize Fibonacci retracement levels to spot potential support and resistance areas.
+
+### How to Use
+1. Enter a stock symbol in the sidebar.
+2. Choose your desired analysis period.
+3. Review the plotted stock data and moving averages.
+4. Use the Fibonacci levels to identify support and resistance areas.
+
+**Pro Tip from a Seasoned Trader**: *Buy at support levels, and sell at resistance levels.* This strategy leverages the concept that prices tend to bounce off these key levels, offering opportunities for entry and exit.
+
+Let's dive into the analysis!
+""")
+
+# User Inputs
+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
+
+# Plot setup
+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')))
+
+# Handling Fibonacci Levels
+if len(peaks) == 0 or len(troughs) == 0:
+    fig.add_annotation(xref='paper', yref='paper', x=0.5, y=0.5, text="No significant peaks or troughs detected for Fibonacci analysis", showarrow=False, font=dict(size=20, color="red"))
+else:
+    high_price = data.iloc[peaks]['Close'].max()
+    low_price = data.iloc[troughs]['Close'].min()
+
+    # 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
+
+    # Add Fibonacci Levels to the plot
+    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)