Update app.py

app.py

@@ -2,24 +2,51 @@ import yfinance as yf
 import pandas as pd
 import plotly.graph_objects as go
 import streamlit as st
+import numpy as np
 
-# Modify the interface to place input fields in a sidebar
+# Place input fields in the sidebar
 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"])
 
-# Continue with the spell as before
+# Download stock data
 data = yf.download(symbol, period=period)
 
-data['Local Max'] = data['Close'][(data['Close'].shift(1) < data['Close']) & (data['Close'].shift(-1) < data['Close'])]
-data['Local Min'] = data['Close'][(data['Close'].shift(1) > data['Close']) & (data['Close'].shift(-1) > data['Close'])]
+# 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()
 
+# Finding highest and lowest price for the Fibonacci Retracement Levels
+high_price = data['High'].max()
+low_price = data['Low'].min()
+
+# Calculate Fibonacci Levels
+fib_levels = [0, 0.236, 0.382, 0.5, 0.618, 0.786, 1]
+price_diff = high_price - low_price
+data['Fib_Level_0'] = high_price
+data['Fib_Level_1'] = high_price - price_diff * fib_levels[1]
+data['Fib_Level_2'] = high_price - price_diff * fib_levels[2]
+data['Fib_Level_3'] = high_price - price_diff * fib_levels[3]
+data['Fib_Level_4'] = high_price - price_diff * fib_levels[4]
+data['Fib_Level_5'] = high_price - price_diff * fib_levels[5]
+data['Fib_Level_6'] = low_price
+
+# Plotting
 fig = go.Figure()
-fig.add_trace(go.Scatter(x=data.index, y=data['Close'], name='Close'))
-fig.add_trace(go.Scatter(x=data['Local Max'].dropna().index, y=data['Local Max'].dropna(), mode='markers', name='Resistance'))
-fig.add_trace(go.Scatter(x=data['Local Min'].dropna().index, y=data['Local Min'].dropna(), mode='markers', name='Support'))
 
-table_data = pd.concat([data['Local Max'].dropna(), data['Local Min'].dropna()]).sort_index()
-st.write(table_data.to_frame(name="Level"))
+# Add traces for Close price and MAs
+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)
+
+# Note: This implementation assumes a simplistic approach to finding the high and low points for Fibonacci retracement levels. 
+# In practice, these should be determined based on significant peaks and troughs within a specific period of interest.