Create app.py

app.py

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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("Netflyp Stock and Crypto Analysis Tool")
+st.markdown("""
+Welcome to Netflyp Stock and Crypto Analysis Tool. This version includes RSI, MACD, Bollinger Bands, and volume indicators.
+
+**Features**:
+- View stock and crypto 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 or crypto symbol (as they are in yahoo finance) in the sidebar or choose from sample tickers below.
+2. Select your desired time period.
+3. Click the 'Analyze' button to load and display the data.
+""")
+
+# User Inputs
+sidebar = st.sidebar
+symbol = sidebar.text_input("Enter stock or crypto symbol", "AAPL")
+period = sidebar.selectbox("Select period", ["1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "ytd", "max"])
+
+# Sample Inputs
+sample_tickers = ["NVDA", "BTC-USD", "5258.KL", "5209.KL", "5235SS.KL"]
+ticker_buttons = st.radio("Sample tickers:", sample_tickers)
+
+# Set ticker from sample when clicked
+if ticker_buttons:
+    symbol = ticker_buttons
+    sidebar.text_input("Enter stock symbol", value=symbol, key="1")
+
+# Button to trigger analysis
+if st.button('Analyze') or st.sidebar.button('Analyze Sidebar'):
+    # 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)
+
+    # 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
+    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.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)
+    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)")
+