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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.') | |