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# Required imports
import yfinance as yf
import pandas as pd
import numpy as np
from scipy.signal import find_peaks
import plotly.graph_objects as go
import plotly.express as px
import streamlit as st
from plotly.subplots import make_subplots
# Define functions for technical indicators
def compute_macd(data, slow=26, fast=12, smooth=9):
exp1 = data['Close'].ewm(span=fast, adjust=False).mean()
exp2 = data['Close'].ewm(span=slow, adjust=False).mean()
macd = exp1 - exp2
signal = macd.ewm(span=smooth, adjust=False).mean()
return macd, signal
def compute_rsi(data, window=14):
delta = data['Close'].diff()
gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
rs = gain / loss
return 100 - (100 / (1 + rs))
def compute_bollinger(data, window=20, num_std=2):
rolling_mean = data['Close'].rolling(window=window).mean()
rolling_std = data['Close'].rolling(window=window).std()
upper_band = rolling_mean + (rolling_std * num_std)
lower_band = rolling_mean - (rolling_std * num_std)
return upper_band, lower_band
# Streamlit UI - Introduction and How to Use the App
st.title("Advanced Stock Analysis Tool")
st.markdown("""
Welcome to the advanced stock analysis application designed for both beginner and seasoned traders. This version includes RSI, MACD, Bollinger Bands, and volume indicators, along with the original features.
**Features**:
- View stock price movements and indicators over time.
- Analyze technical indicators such as RSI, MACD, and Bollinger Bands.
- Identify and visualize MA crossovers which are significant for trading strategies.
- Download charts for offline analysis.
**Instructions**:
1. Enter a stock symbol in the sidebar.
2. Select your desired time period.
3. Explore the indicators and make informed trading decisions.
""")
# 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"])
# Fetch stock data
data = yf.download(symbol, period=period)
# Calculate technical indicators
data['MA20'] = data['Close'].rolling(window=20).mean()
data['MA50'] = data['Close'].rolling(window=50).mean()
data['MA200'] = data['Close'].rolling(window=200).mean()
data['RSI'] = compute_rsi(data)
data['MACD'], data['Signal'] = compute_macd(data)
data['Upper_Band'], data['Lower_Band'] = compute_bollinger(data)
# Detect MA crossovers
cross_above = (data['MA20'].shift(14) < data['MA50'].shift(14)) & (data['MA20'] > data['MA50'])
cross_below = (data['MA20'].shift(14) > data['MA50'].shift(14)) & (data['MA20'] < data['MA50'])
# Plot setup
fig = make_subplots(rows=3, cols=1, shared_xaxes=True, vertical_spacing=0.02,
subplot_titles=('Stock Price and Moving Averages', 'Volume and Bollinger Bands', 'RSI and MACD'))
# Add traces for price and moving averages
fig.add_trace(go.Scatter(x=data.index, y=data['Close'], name='Close Price'), row=1, col=1)
fig.add_trace(go.Scatter(x=data.index, y=data['MA20'], name='20-Period MA', line=dict(color='green')), row=1, col=1)
fig.add_trace(go.Scatter(x=data.index, y=data['MA50'], name='50-Period MA', line=dict(color='blue')), row=1, col=1)
fig.add_trace(go.Scatter(x=data.index, y=data['MA200'], name='200-Period MA', line=dict(color='red')), row=1, col=1)
fig.add_trace(go.Scatter(x=data.index[cross_above], y=data['MA20'][cross_above], mode='markers', marker=dict(color='green', size=10), name='Golden Cross'), row=1, col=1)
fig.add_trace(go.Scatter(x=data.index[cross_below], y=data['MA20'][cross_below], mode='markers', marker=dict(color='red', size=10), name='Death Cross'), row=1, col=1)
# Add traces for Bollinger Bands and volume
fig.add_trace(go.Bar(x=data.index, y=data['Volume'], name='Volume'), row=2, col=1)
fig.add_trace(go.Scatter(x=data.index, y=data['Upper_Band'], name='Upper Bollinger Band', line=dict(color='purple')), row=2, col=1)
fig.add_trace(go.Scatter(x=data.index, y=data['Lower_Band'], name='Lower Bollinger Band', line=dict(color='purple')), row=2, col=1)
# Add traces for RSI and MACD
fig.add_trace(go.Scatter(x=data.index, y=data['RSI'], name='RSI', line=dict(color='orange')), row=3, col=1)
fig.add_trace(go.Scatter(x=data.index, y=data['MACD'], name='MACD', line=dict(color='pink')), row=3, col=1)
fig.add_trace(go.Scatter(x=data.index, y=data['Signal'], name='MACD Signal', line=dict(color='cyan')), row=3, col=1)
# Layout adjustments
fig.update_layout(height=1200, width=800, showlegend=True)
fig.update_yaxes(title_text="Price", row=1, col=1)
fig.update_yaxes(title_text="Volume", row=2, col=1)
fig.update_yaxes(title_text="Index Value", row=3, col=1)
# Display the chart with a download button
st.plotly_chart(fig)
fig.write_html("stock_analysis_chart.html")
st.markdown("[Download Chart](stock_analysis_chart.html)")
# The code provides a detailed analysis and visual representation of the stock data with added technical indicators, fulfilling the advanced requirements of both beginner and seasoned traders.
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