Create app.py

app.py

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+import streamlit as st
+import yfinance as yf
+import pandas as pd
+import plotly.graph_objects as go
+
+# Function to calculate moving averages
+def calculate_moving_average(data, window_size):
+    return data['Close'].rolling(window=window_size).mean()
+
+# Function to detect support and resistance levels
+def detect_support_resistance(data):
+    # This is a simple placeholder for actual support and resistance logic
+    return data['Close'].rolling(window=20).min(), data['Close'].rolling(window=20).max()
+
+# VSA signals logic placeholder
+def vsa_signals(data):
+    # This should include real VSA calculation logic based on volume and spread
+    buy_signals = pd.Series(index=data.index, dtype='float64')
+    sell_signals = pd.Series(index=data.index, dtype='float64')
+    # Dummy logic for demonstration:
+    buy_signals[data['Volume'] > data['Volume'].rolling(20).mean()] = data['Low']
+    sell_signals[data['Volume'] < data['Volume'].rolling(20).mean()] = data['High']
+    return buy_signals, sell_signals
+
+# Streamlit sidebar options
+ticker = st.sidebar.text_input('Ticker Symbol', value='AAPL')
+start_date = st.sidebar.date_input('Start Date', pd.to_datetime('2020-01-01'))
+end_date = st.sidebar.date_input('End Date', pd.to_datetime('2020-12-31'))
+analyze_button = st.sidebar.button('Analyze')
+
+if analyze_button:
+    data = yf.download(ticker, start=start_date, end=end_date)
+    if not data.empty:
+        # Calculations
+        moving_average = calculate_moving_average(data, window_size=20)
+        support, resistance = detect_support_resistance(data)
+        buy_signals, sell_signals = vsa_signals(data)
+
+        # Plotting
+        fig = go.Figure()
+
+        # Add candlestick chart
+        fig.add_trace(go.Candlestick(x=data.index,
+                                     open=data['Open'],
+                                     high=data['High'],
+                                     low=data['Low'],
+                                     close=data['Close'], name='Market Data'))
+
+        # Add Moving Average Line
+        fig.add_trace(go.Scatter(x=data.index, y=moving_average, mode='lines', name='20-day MA'))
+
+        # Add Support and Resistance Lines
+        fig.add_trace(go.Scatter(x=data.index, y=support, mode='lines', name='Support', line=dict(color='green')))
+        fig.add_trace(go.Scatter(x=data.index, y=resistance, mode='lines', name='Resistance', line=dict(color='red')))
+
+        # Add Buy and Sell Signals
+        fig.add_trace(go.Scatter(x=buy_signals.index, y=buy_signals, mode='markers', marker=dict(color='blue', size=10), name='Buy Signal'))
+        fig.add_trace(go.Scatter(x=sell_signals.index, y=sell_signals, mode='markers', marker=dict(color='orange', size=10), name='Sell Signal'))
+
+        # Layout settings
+        fig.update_layout(title='VSA Trading Strategy Analysis', xaxis_title='Date', yaxis_title='Price', template='plotly_dark')
+
+        # Display the figure
+        st.plotly_chart(fig)
+
+        # App introduction and guide
+        st.title('VSA Trading Strategy Visualizer')
+        st.markdown('''
+        This app provides an interactive way to visualize the Volume Spread Analysis (VSA) trading strategy with buy and sell signals based on the strategy. 
+        To start, enter the ticker symbol, select the start and end dates, and then click the "Analyze" button.
+        The chart below will display the price action with overlays for moving averages, support and resistance levels, and buy/sell signals based on VSA analysis.
+        ''')
+    else:
+        st.error('No data found for the selected ticker and date range.')
+