Delete main.py

main.py

@@ -1,202 +0,0 @@
-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.")