Update app.py

app.py

@@ -6,6 +6,9 @@ import streamlit as st
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
 
+# Set page configuration
+st.set_page_config(layout="wide")
+
 # Define pattern functions
 pattern_funcs = [
     ("Two Crows", talib.CDL2CROWS),
@@ -72,69 +75,103 @@ pattern_funcs = [
 ]
 
 # Streamlit app setup
-st.title('Candlestick Pattern Detection')
+st.title('Candlestick Pattern Detection App')
+
+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 app 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.
+""")
 
 # Sidebar inputs
+st.sidebar.header('How to Use')
+st.sidebar.write("""
+1. Enter the stock ticker 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'))
 end_date = st.sidebar.date_input('End Date', pd.to_datetime('2024-01-01'))
 
 if st.sidebar.button('Run'):
+    run_analysis = True
+else:
+    run_analysis = False
+
+st.sidebar.header('Candlestick Patterns')
+selected_patterns = {name: st.sidebar.checkbox(name, value=True) for name, func in pattern_funcs}
+
+if run_analysis:
     data = yf.download(symbol, start=start_date, end=end_date)
     
     if not data.empty:
         for pattern_name, pattern_func in pattern_funcs:
-            data[pattern_name] = pattern_func(data['Open'], data['High'], data['Low'], data['Close'])
-            pattern_dates = data[data[pattern_name] != 0].index
-
-            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)
-
-            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()
-            
-            st.pyplot(fig)
+            if selected_patterns[pattern_name]:
+                data[pattern_name] = pattern_func(data['Open'], data['High'], data['Low'], data['Close'])
+                pattern_dates = data[data[pattern_name] != 0].index
+
+                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)
+
+                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()
+                
+                st.pyplot(fig)
     else:
-        st.write("No data found for the given ticker and date range.")
+        st.write("No data found for the given ticker and date range.")
+
+
+hide_streamlit_style = """
+<style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style>
+"""
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)