Update app.py

app.py

@@ -1,10 +1,8 @@
 import yfinance as yf
 import talib
 import pandas as pd
-import mplfinance as mpf
 import streamlit as st
-import matplotlib.pyplot as plt
-import matplotlib.gridspec as gridspec
+import plotly.graph_objects as go
 
 # Set page configuration
 st.set_page_config(layout="wide")
@@ -79,26 +77,22 @@ st.title('Automatic Candlestick Pattern Detection')
 
 st.write("""
 This tool automatically detects 60+ candlestick patterns in stock price data. 
-You can input the stock ticker (e.g 'AAPL') or crypto pair (e.g.'BTC-USD'), start date, and end date in the sidebar menu to analyze.
-The tool will fetch the historical data and highlight detected patterns on the charts.
-The charts display the stock price data along with vertical lines indicating where patterns were found.
-Each pattern chart shows the stock price movement around the last 5 occurrences of the pattern.
+* You can input the stock ticker (e.g. 'AAPL') or crypto pair (e.g. 'BTC-USD'), start date, and end date in the sidebar menu to analyze.
+* The tool will fetch the historical data and highlight detected patterns on the charts.
+* The charts display the stock price data along with vertical lines indicating where patterns were found.
+* If no patterns are detected, the chart for that pattern will not be included.
 """)
 
-# Sidebar inputs
-st.sidebar.header('How to Use')
-st.sidebar.write("""
-1. Enter the stock ticker or crypto pair symbol.
-2. Select the start and end dates.
-3. Select the patterns you want to detect.
-4. Click the "Run" button to generate the analysis.
-""")
 
 st.sidebar.header('Input Parameters')
-symbol = st.sidebar.text_input('Enter Stock Ticker', 'SIE.DE')
-start_date = st.sidebar.date_input('Start Date', pd.to_datetime('2020-01-01'))
+symbol = st.sidebar.text_input('Enter Asset Symbol', 'AAPL')
+start_date = st.sidebar.date_input('Start Date', pd.to_datetime('2023-06-01'))
 end_date = st.sidebar.date_input('End Date', pd.to_datetime('2025-01-01'))
 
+# Dynamic input for moving averages
+ma_short_period = st.sidebar.number_input('Short-term Moving Average Period', min_value=1, value=20)
+ma_long_period = st.sidebar.number_input('Long-term Moving Average Period', min_value=1, value=50)
+
 if st.sidebar.button('Run Analysis'):
     run_analysis = True
 else:
@@ -111,6 +105,10 @@ if run_analysis:
     data = yf.download(symbol, start=start_date, end=end_date)
     
     if not data.empty:
+        # Calculate moving averages based on user input
+        data[f'MA{ma_short_period}'] = talib.SMA(data['Close'], timeperiod=ma_short_period)
+        data[f'MA{ma_long_period}'] = talib.SMA(data['Close'], timeperiod=ma_long_period)
+        
         for pattern_name, pattern_func in pattern_funcs:
             if selected_patterns[pattern_name]:
                 data[pattern_name] = pattern_func(data['Open'], data['High'], data['Low'], data['Close'])
@@ -119,55 +117,62 @@ if run_analysis:
                 if len(pattern_dates) == 0:
                     continue
 
-                fig = plt.figure(figsize=(20, 10))
-                gs = gridspec.GridSpec(2, 4)
-
-                mc = mpf.make_marketcolors(up='g', down='r', inherit=True)
-                custom_style = mpf.make_mpf_style(marketcolors=mc)
+                fig = go.Figure()
+                
+                fig.add_trace(go.Candlestick(
+                    x=data.index,
+                    open=data['Open'],
+                    high=data['High'],
+                    low=data['Low'],
+                    close=data['Close'],
+                    name='Candlesticks',
+                    yaxis='y2'
+                ))
+
+                fig.add_trace(go.Scatter(
+                    x=data.index,
+                    y=data[f'MA{ma_short_period}'],
+                    mode='lines',
+                    line=dict(color='blue', width=1.5),
+                    name=f'{ma_short_period}-day MA',
+                    yaxis='y2'
+                ))
+
+                fig.add_trace(go.Scatter(
+                    x=data.index,
+                    y=data[f'MA{ma_long_period}'],
+                    mode='lines',
+                    line=dict(color='orange', width=1.5),
+                    name=f'{ma_long_period}-day MA',
+                    yaxis='y2'
+                ))
+
+                fig.add_trace(go.Bar(
+                    x=data.index,
+                    y=data['Volume'],
+                    name='Volume',
+                    yaxis='y',
+                    marker=dict(color='grey'),
+                    opacity=0.5
+                ))
 
-                ax1 = plt.subplot(gs[0, :3])
-                data[['Close']].plot(ax=ax1, color='blue')
                 for date in pattern_dates:
-                    ax1.axvline(date, color='red', linestyle='--', label=pattern_name if pattern_name not in [l.get_label() for l in ax1.lines] else "")
-                    ax1.annotate(date.strftime('%Y-%m-%d'), (date, data['Close'].loc[date]), xytext=(-15, 10 + 20),
-                                 textcoords='offset points', color='red', fontsize=12, rotation=90)
-
-                window = 5  # Days before and after the pattern
-                for i in range(5):
-                    if len(pattern_dates) > i:
-                        pattern_date = pattern_dates[-(i + 1)]
-
-                        start_date_window = pattern_date - pd.Timedelta(days=window)
-                        end_date_window = min(data.index[-1], pattern_date + pd.Timedelta(days=window))
-                        valid_dates = pd.date_range(start=start_date_window, end=end_date_window).intersection(data.index)
-
-                        subset = data.loc[valid_dates]
-
-                        if i == 0:
-                            ax = plt.subplot(gs[0, 3])
-                        else:
-                            ax = plt.subplot(gs[1, i - 1])
-
-                        mpf.plot(subset, type='candle', ax=ax, volume=False, show_nontrading=False, style=custom_style)
-                        ax.set_title(f'{pattern_name} Pattern {i + 1} for {symbol}')
-
-                        x_ticks = list(range(0, len(valid_dates), 1))
-                        x_labels = [date.strftime('%Y-%m-%d') for date in valid_dates]
-                        ax.set_xticks(x_ticks)
-                        ax.set_xticklabels(x_labels, rotation=90)
-
-                ax1.set_title(f"{symbol} Stock Price and {pattern_name} Pattern Detection")
-                ax1.legend(loc='best')
-                ax1.grid(True)
-                ax1.set_xlabel("Date")
-                ax1.set_ylabel("Price")
-                plt.tight_layout()
+                    fig.add_vline(x=date, line=dict(color='red', width=2, dash='dash'), name=pattern_name)
+
+                fig.update_layout(
+                    title=f"{symbol} Stock Price and {pattern_name} Pattern Detection",
+                    xaxis_title="Date",
+                    yaxis=dict(title='Volume'),
+                    yaxis2=dict(title='Price', overlaying='y', side='right'),
+                    legend_title="Legend",
+                    xaxis_rangeslider_visible=False,
+                    template='plotly_white'
+                )
                 
-                st.pyplot(fig)
+                st.plotly_chart(fig)
     else:
         st.write("No data found for the given ticker and date range.")
 
-
 hide_streamlit_style = """
 <style>
 #MainMenu {visibility: hidden;}