backend redesign

backend.old/src/agent/tools/chart_utils_example.py

@@ -0,0 +1,154 @@
+"""
+Example usage of chart_utils.py plotting functions.
+
+This demonstrates how the LLM can use the plot_ohlc() convenience function
+in analyze_chart_data scripts to create beautiful, standard OHLC charts.
+"""
+
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+
+
+def create_sample_data(days=30):
+    """Create sample OHLCV data for testing."""
+    dates = pd.date_range(end=datetime.now(), periods=days * 24, freq='1H')
+
+    # Simulate price movement
+    np.random.seed(42)
+    close = 50000 + np.cumsum(np.random.randn(len(dates)) * 100)
+
+    data = {
+        'open': close + np.random.randn(len(dates)) * 50,
+        'high': close + np.abs(np.random.randn(len(dates))) * 100,
+        'low': close - np.abs(np.random.randn(len(dates))) * 100,
+        'close': close,
+        'volume': np.abs(np.random.randn(len(dates))) * 1000000
+    }
+
+    df = pd.DataFrame(data, index=dates)
+
+    # Ensure high is highest and low is lowest
+    df['high'] = df[['open', 'high', 'low', 'close']].max(axis=1)
+    df['low'] = df[['open', 'high', 'low', 'close']].min(axis=1)
+
+    return df
+
+
+if __name__ == "__main__":
+    from chart_utils import plot_ohlc, add_indicators_to_plot, plot_price_volume
+
+    # Create sample data
+    df = create_sample_data(days=30)
+
+    print("=" * 60)
+    print("Example 1: Basic OHLC chart with volume")
+    print("=" * 60)
+    print("\nScript the LLM would generate:")
+    print("""
+fig = plot_ohlc(df, title='BTC/USDT 1H', volume=True)
+df.tail(5)
+    """)
+
+    # Execute it
+    fig = plot_ohlc(df, title='BTC/USDT 1H', volume=True)
+    print("\n✓ Chart created successfully!")
+    print(f"  Figure size: {fig.get_size_inches()}")
+    print(f"  Number of axes: {len(fig.axes)}")
+
+
+    print("\n" + "=" * 60)
+    print("Example 2: OHLC chart with indicators")
+    print("=" * 60)
+    print("\nScript the LLM would generate:")
+    print("""
+# Calculate indicators
+df['SMA_20'] = df['close'].rolling(20).mean()
+df['SMA_50'] = df['close'].rolling(50).mean()
+df['EMA_12'] = df['close'].ewm(span=12, adjust=False).mean()
+
+# Plot with indicators
+fig = add_indicators_to_plot(
+    df,
+    indicators={
+        'SMA_20': {'color': 'blue', 'width': 1.5},
+        'SMA_50': {'color': 'red', 'width': 1.5},
+        'EMA_12': {'color': 'green', 'width': 1.0}
+    },
+    title='BTC/USDT with Moving Averages',
+    volume=True
+)
+
+df[['close', 'SMA_20', 'SMA_50', 'EMA_12']].tail(5)
+    """)
+
+    # Execute it
+    df['SMA_20'] = df['close'].rolling(20).mean()
+    df['SMA_50'] = df['close'].rolling(50).mean()
+    df['EMA_12'] = df['close'].ewm(span=12, adjust=False).mean()
+
+    fig = add_indicators_to_plot(
+        df,
+        indicators={
+            'SMA_20': {'color': 'blue', 'width': 1.5},
+            'SMA_50': {'color': 'red', 'width': 1.5},
+            'EMA_12': {'color': 'green', 'width': 1.0}
+        },
+        title='BTC/USDT with Moving Averages',
+        volume=True
+    )
+
+    print("\n✓ Chart with indicators created successfully!")
+    print(f"  Last close: ${df['close'].iloc[-1]:,.2f}")
+    print(f"  SMA 20: ${df['SMA_20'].iloc[-1]:,.2f}")
+    print(f"  SMA 50: ${df['SMA_50'].iloc[-1]:,.2f}")
+
+
+    print("\n" + "=" * 60)
+    print("Example 3: Price-only chart (no volume)")
+    print("=" * 60)
+    print("\nScript the LLM would generate:")
+    print("""
+from chart_utils import plot_price_only
+
+fig = plot_price_only(df, title='Clean Price Action')
+    """)
+
+    # Execute it
+    from chart_utils import plot_price_only
+    fig = plot_price_only(df, title='Clean Price Action')
+
+    print("\n✓ Price-only chart created successfully!")
+
+
+    print("\n" + "=" * 60)
+    print("Summary")
+    print("=" * 60)
+    print("""
+The chart_utils module provides:
+
+1. plot_ohlc() - Main function for beautiful candlestick charts
+   - Professional seaborn-inspired styling
+   - Consistent color scheme (teal up, coral down)
+   - Optional volume subplot
+   - Customizable figure size
+
+2. add_indicators_to_plot() - OHLC charts with technical indicators
+   - Overlay multiple indicators
+   - Customizable colors and line widths
+   - Proper integration with mplfinance
+
+3. Preset functions for common chart types:
+   - plot_price_volume() - Standard price + volume
+   - plot_price_only() - Candlesticks without volume
+
+Benefits:
+✓ Consistent look and feel across all charts
+✓ Less code for the LLM to generate
+✓ Professional appearance out of the box
+✓ Easy to customize when needed
+✓ Works seamlessly with analyze_chart_data tool
+
+The LLM can now simply call plot_ohlc(df) instead of writing
+custom matplotlib code for every chart request!
+    """)