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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)
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