Create app.py

app.py

@@ -0,0 +1,202 @@
+import streamlit as st
+import plotly.graph_objects as go
+from datetime import datetime, timedelta
+import pandas as pd
+import numpy as np
+
+from utils.patterns import identify_patterns, calculate_technical_indicators
+from utils.predictions import predict_movement
+from utils.trading import fetch_market_data, is_market_open
+
+# Page configuration
+st.set_page_config(
+    page_title="Trading Pattern Analysis",
+    page_icon="📈",
+    layout="wide"
+)
+
+# Load custom CSS
+with open('styles/custom.css') as f:
+    st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
+# Pattern descriptions from the uploaded file
+PATTERN_DESCRIPTIONS = {
+    'HAMMER': 'Small body near the top with long lower wick, indicating buying pressure overcoming selling pressure.',
+    'INVERTED_HAMMER': 'Small body with long upper wick after downtrend, indicating resistance but potential upward movement.',
+    'PIERCING_LINE': 'Two-candlestick pattern where second closes above midpoint of first, signaling bullish shift.',
+    'BULLISH_ENGULFING': 'Small bearish candle followed by larger bullish candle that engulfs previous one.',
+    'MORNING_STAR': 'Three-candlestick pattern with bearish, small-bodied, and bullish candle indicating reversal.',
+    'THREE_WHITE_SOLDIERS': 'Three consecutive long bullish candles with small/no wicks, showing strong buying pressure.',
+    'BULLISH_HARAMI': 'Small bullish candle within body of preceding large bearish candle.',
+    'HANGING_MAN': 'Small body at top with long lower wick, signaling potential reversal.',
+    'DARK_CLOUD_COVER': 'Two-candlestick pattern with bearish closing below midpoint of previous bullish.',
+    'BEARISH_ENGULFING': 'Small bullish candle followed by larger bearish candle that engulfs it.',
+    'EVENING_STAR': 'Three-candlestick pattern with bullish, small-bodied, and bearish candle.',
+    'THREE_BLACK_CROWS': 'Three consecutive bearish candles showing strong selling.',
+    'SHOOTING_STAR': 'Small body with long upper wick, signaling resistance.',
+    'DOJI': 'Small body with wicks, showing market indecision.',
+    'DRAGONFLY_DOJI': 'Doji with long lower wick, showing buying pressure at bottom.',
+    'GRAVESTONE_DOJI': 'Doji with long upper wick, showing selling pressure at top.'
+}
+
+# Sidebar
+st.sidebar.title("Trading Controls")
+
+# Market Status Indicator
+market_open = is_market_open()
+status_color = "🟢" if market_open else "🔴"
+market_status = "Market Open" if market_open else "Market Closed"
+st.sidebar.write(f"{status_color} {market_status}")
+
+symbol = st.sidebar.text_input("Symbol", value="AAPL", help="Enter a valid stock symbol (e.g., AAPL, MSFT)")
+timeframe = st.sidebar.selectbox(
+    "Timeframe",
+    ["30m", "1h", "2h", "4h"],
+    index=0,
+    help="Select analysis timeframe (each candle represents 15 minutes)"
+)
+
+# Add auto-refresh option
+auto_refresh = st.sidebar.checkbox("Auto-refresh data", value=True)
+if auto_refresh:
+    st.sidebar.write("Updates every minute")
+    st.rerun()  # Use st.rerun() instead of experimental_rerun()
+
+# Main content
+st.title("Trading Pattern Analysis")
+
+try:
+    # Fetch and process data
+    with st.spinner('Fetching market data...'):
+        df = fetch_market_data(symbol, period='1d', interval='15m')
+
+        if len(df) >= 2:
+            df = calculate_technical_indicators(df)
+            patterns = identify_patterns(df)
+
+            # Create candlestick chart
+            fig = go.Figure(data=[go.Candlestick(
+                x=df.index,
+                open=df['Open'],
+                high=df['High'],
+                low=df['Low'],
+                close=df['Close']
+            )])
+
+            # Update layout for dark theme
+            fig.update_layout(
+                template="plotly_dark",
+                plot_bgcolor="#252525",
+                paper_bgcolor="#252525",
+                xaxis_rangeslider_visible=False,
+                height=600,
+                title=f"{symbol} - Live Market Data ({timeframe} timeframe)"
+            )
+
+            # Display chart
+            st.plotly_chart(fig, use_container_width=True)
+
+            # Pattern Analysis
+            col1, col2 = st.columns(2)
+
+            with col1:
+                st.subheader("Pattern Analysis")
+                if not patterns.empty and len(patterns) > 0:
+                    latest_patterns = patterns.iloc[-1]
+                    detected_patterns = latest_patterns[latest_patterns == 1].index.tolist()
+
+                    if detected_patterns:
+                        st.write("Detected Patterns:")
+                        for pattern in detected_patterns:
+                            st.markdown(f"""
+                            <div class="pattern-container">
+                                <h4>• {pattern.replace('_', ' ')}</h4>
+                                <p>{PATTERN_DESCRIPTIONS.get(pattern, '')}</p>
+                            </div>
+                            """, unsafe_allow_html=True)
+                    else:
+                        st.info("No patterns detected in current timeframe")
+                else:
+                    st.write("No pattern data available")
+
+            with col2:
+                st.subheader("Prediction")
+                if len(df) >= 30:
+                    prediction, probability = predict_movement(df)
+
+                    if prediction is not None and probability is not None:
+                        direction = "Upward" if prediction else "Downward"
+                        confidence = probability[1] if prediction else probability[0]
+
+                        direction_class = "profit" if direction == "Upward" else "loss"
+                        st.markdown(f"""
+                        <div class="prediction-container">
+                            <h3 class="{direction_class}">Predicted Movement: {direction}</h3>
+                            <p>Confidence: {confidence:.2%}</p>
+                            <p>(Next 15-minute prediction)</p>
+                        </div>
+                        """, unsafe_allow_html=True)
+                    else:
+                        st.write("Could not generate prediction")
+                else:
+                    st.write("Insufficient data for prediction")
+
+            # Technical Indicators
+            st.subheader("Technical Indicators")
+            col3, col4, col5 = st.columns(3)
+
+            with col3:
+                last_rsi = df['RSI'].iloc[-1] if 'RSI' in df else None
+                prev_rsi = df['RSI'].iloc[-2] if 'RSI' in df and len(df) > 1 else None
+
+                if last_rsi is not None and prev_rsi is not None:
+                    delta = last_rsi - prev_rsi
+                    delta_color = "profit" if delta > 0 else "loss"
+                    st.markdown(f"""
+                    <div class="metric-container">
+                        <h4>RSI</h4>
+                        <p>{last_rsi:.2f}</p>
+                        <p class="{delta_color}">({delta:+.2f})</p>
+                    </div>
+                    """, unsafe_allow_html=True)
+
+            with col4:
+                last_macd = df['MACD'].iloc[-1] if 'MACD' in df else None
+                prev_macd = df['MACD'].iloc[-2] if 'MACD' in df and len(df) > 1 else None
+
+                if last_macd is not None and prev_macd is not None:
+                    delta = last_macd - prev_macd
+                    delta_color = "profit" if delta > 0 else "loss"
+                    st.markdown(f"""
+                    <div class="metric-container">
+                        <h4>MACD</h4>
+                        <p>{last_macd:.2f}</p>
+                        <p class="{delta_color}">({delta:+.2f})</p>
+                    </div>
+                    """, unsafe_allow_html=True)
+
+            with col5:
+                last_sma = df['SMA_20'].iloc[-1] if 'SMA_20' in df else None
+                last_close = df['Close'].iloc[-1] if len(df) > 0 else None
+
+                if last_sma is not None and last_close is not None:
+                    delta = last_close - last_sma
+                    delta_color = "profit" if delta > 0 else "loss"
+                    st.markdown(f"""
+                    <div class="metric-container">
+                        <h4>15-min SMA</h4>
+                        <p>{last_sma:.2f}</p>
+                        <p class="{delta_color}">({delta:+.2f})</p>
+                    </div>
+                    """, unsafe_allow_html=True)
+        else:
+            st.warning("Insufficient data points. This could be because the market is closed or the selected timeframe is too short.")
+
+except Exception as e:
+    st.error(f"Error: {str(e)}")
+    if "Connection Error" in str(e):
+        st.warning("Unable to connect to market data. Please check your internet connection and try again.")
+    elif "not found" in str(e):
+        st.warning("Invalid symbol. Please enter a valid stock symbol.")
+    else:
+        st.info("If the market is closed, you can still view the most recent trading data.")