Update core/plot.py

core/plot.py

@@ -5,49 +5,240 @@ import logging
 
 logging.basicConfig(level=logging.DEBUG, filename="debug.log", filemode="a")
 
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import pandas as pd
+import numpy as np
+import logging
+
+logging.basicConfig(level=logging.INFO)
+
 def plot_indicators(df, ticker):
     try:
-        logging.debug(f"Plotting indicators for {ticker}, df columns: {df.columns.tolist()}")
-        fig = go.Figure()
-        fig.add_trace(go.Scatter(x=df['Date'], y=df['value'], mode='lines', name='Price', line=dict(color='blue')))
-        indicators = [col for col in df.columns if col.startswith(('sma_', 'ema_', 'rsi_', 'macd_', 'bb'))]
-        for ind in indicators:
-            if ind in df.columns:
-                fig.add_trace(go.Scatter(x=df['Date'], y=df[ind], mode='lines', name=ind.upper()))
+        fig = make_subplots(
+            rows=7, cols=1, shared_xaxes=True, vertical_spacing=0.03,
+            subplot_titles=(
+                'Price & Moving Averages', 'Volume', 'MACD & RSI',
+                'Stochastic & Williams %R', 'ADX & DI', 'ATR & CCI', 'Signal Strength'
+            ),
+            row_heights=[0.4, 0.1, 0.15, 0.15, 0.15, 0.15, 0.15]
+        )
+
+        # Price and Moving Averages
+        fig.add_trace(
+            go.Candlestick(
+                x=df['Date'], open=df['Open'], high=df['High'], low=df['Low'], close=df['value'],
+                name='Price', increasing_line_color='#00CC96', decreasing_line_color='#EF553B'
+            ), row=1, col=1
+        )
+        for ma in ['sma_10', 'sma_20', 'sma_50', 'ema_12', 'ema_26', 'ema_50']:
+            if ma in df:
+                fig.add_trace(
+                    go.Scatter(x=df['Date'], y=df[ma], name=ma.upper(), line=dict(width=1.5)),
+                    row=1, col=1
+                )
+        if 'bbu_20_2' in df:
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['bbu_20_2'], name='BB Upper', line=dict(color='gray', dash='dot')),
+                row=1, col=1
+            )
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['bbm_20_2'], name='BB Middle', line=dict(color='gray')),
+                row=1, col=1
+            )
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['bbl_20_2'], name='BB Lower', line=dict(color='gray', dash='dot')),
+                row=1, col=1
+            )
+
+        # Enhanced Signal Plotting
+        buy_signals = df[df['Signal'] == 'Buy']
+        sell_signals = df[df['Signal'] == 'Sell']
+        hold_signals = df[df['Signal'] == 'Hold']
+        fig.add_trace(
+            go.Scatter(
+                x=buy_signals['Date'], y=buy_signals['value'], mode='markers+text',
+                name='Buy', marker=dict(symbol='triangle-up', size=12, color='green'),
+                text=['Buy'] * len(buy_signals), textposition='top center'
+            ), row=1, col=1
+        )
+        fig.add_trace(
+            go.Scatter(
+                x=sell_signals['Date'], y=sell_signals['value'], mode='markers+text',
+                name='Sell', marker=dict(symbol='triangle-down', size=12, color='red'),
+                text=['Sell'] * len(sell_signals), textposition='bottom center'
+            ), row=1, col=1
+        )
+        fig.add_trace(
+            go.Scatter(
+                x=hold_signals['Date'], y=hold_signals['value'], mode='markers',
+                name='Hold', marker=dict(symbol='circle', size=8, color='gray'),
+                opacity=0.5
+            ), row=1, col=1
+        )
+
+        # Position Size and Risk Annotation
+        if 'atr_14' in df:
+            atr = df['atr_14'].iloc[-1]
+            stop_distance = atr * 2
+            position_size = (10000 * 0.01) / stop_distance
+            fig.add_annotation(
+                text=f"Position Size: {position_size:.0f} shares (1% risk, ATR {atr:.2f})",
+                xref="paper", yref="paper", x=0.05, y=0.95, showarrow=False,
+                font=dict(color="black", size=12)
+            )
+
+        # Volume
+        fig.add_trace(
+            go.Bar(x=df['Date'], y=df['Volume'], name='Volume', marker_color='blue', opacity=0.5),
+            row=2, col=1
+        )
+
+        # MACD & RSI
+        if 'macd_12_26_9' in df:
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['macd_12_26_9'], name='MACD', line=dict(color='blue')),
+                row=3, col=1
+            )
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['macds_12_26_9'], name='MACD Signal', line=dict(color='orange')),
+                row=3, col=1
+            )
+            fig.add_trace(
+                go.Bar(x=df['Date'], y=df['macdh_12_26_9'], name='MACD Hist', marker_color='gray'),
+                row=3, col=1
+            )
+        if 'rsi_14' in df:
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['rsi_14'], name='RSI 14', line=dict(color='purple')),
+                row=3, col=1
+            )
+            fig.add_hline(y=70, line_dash="dash", line_color="red", row=3, col=1)
+            fig.add_hline(y=30, line_dash="dash", line_color="green", row=3, col=1)
+        if 'rsi_21' in df:
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['rsi_21'], name='RSI 21', line=dict(color='magenta', dash='dash')),
+                row=3, col=1
+            )
+        if 'rsi_50' in df:
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['rsi_50'], name='RSI 50', line=dict(color='cyan', dash='dot')),
+                row=3, col=1
+            )
+
+        # Stochastic & Williams %R
+        if 'stochk_14_3_3' in df:
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['stochk_14_3_3'], name='Stoch %K', line=dict(color='blue')),
+                row=4, col=1
+            )
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['stochd_14_3_3'], name='Stoch %D', line=dict(color='orange')),
+                row=4, col=1
+            )
+            fig.add_hline(y=80, line_dash="dash", line_color="red", row=4, col=1)
+            fig.add_hline(y=20, line_dash="dash", line_color="green", row=4, col=1)
+        if 'willr_14' in df:
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['willr_14'], name='Williams %R', line=dict(color='green')),
+                row=4, col=1
+            )
+            fig.add_hline(y=-20, line_dash="dash", line_color="red", row=4, col=1)
+            fig.add_hline(y=-80, line_dash="dash", line_color="green", row=4, col=1)
+
+        # ADX & DI
+        if 'adx_14' in df:
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['adx_14'], name='ADX', line=dict(color='blue')),
+                row=5, col=1
+            )
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df.get('pdi_14'), name='+DI', line=dict(color='green')),
+                row=5, col=1
+            )
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df.get('mdi_14'), name='-DI', line=dict(color='red')),
+                row=5, col=1
+            )
+            fig.add_hline(y=25, line_dash="dash", line_color="black", row=5, col=1)
+
+        # ATR & CCI
+        if 'atr_14' in df:
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['atr_14'], name='ATR', line=dict(color='orange')),
+                row=6, col=1
+            )
+        if 'cci_20' in df:
+            fig.add_trace(
+                go.Scatter(x=df['Date'], y=df['cci_20'], name='CCI', line=dict(color='purple')),
+                row=6, col=1
+            )
+            fig.add_hline(y=100, line_dash="dash", line_color="red", row=6, col=1)
+            fig.add_hline(y=-100, line_dash="dash", line_color="green", row=6, col=1)
+
+        # Signal Strength Plot
+        if all(col in df for col in ['RSI_Signal', 'MACD_Signal', 'ADX_Signal', 'Sentiment_Signal', 'Model_Signal']):
+            signal_strength = (
+                df['RSI_Signal'].abs() +
+                df['MACD_Signal'].abs() +
+                df['ADX_Signal'].abs() +
+                df['Sentiment_Signal'].abs() +
+                df['Model_Signal'].abs()
+            )
+            fig.add_trace(
+                go.Scatter(
+                    x=df['Date'], y=signal_strength, name='Signal Strength',
+                    line=dict(color='teal'), fill='tozeroy'
+                ), row=7, col=1
+            )
+            fig.add_hline(y=3, line_dash="dash", line_color="orange", row=7, col=1, annotation_text="Strong Signal Threshold")
+
         fig.update_layout(
             title=f"{ticker} Price and Technical Indicators",
-            xaxis_title="Date",
-            yaxis_title="Value",
-            template="plotly_dark",
-            showlegend=True
+            template="plotly_white",
+            height=2400,
+            width=1400,
+            showlegend=True,
+            xaxis_rangeslider_visible=False,
+            margin=dict(l=50, r=50, t=100, b=50),
+            xaxis=dict(tickformat="%Y-%m-%d", minor=dict(ticks="inside", showgrid=True), gridcolor="lightgrey"),
+            plot_bgcolor="white",
+            paper_bgcolor="white",
+            hovermode="x unified"
         )
-        logging.info(f"Indicators plot generated for {ticker}")
         return fig
     except Exception as e:
-        logging.error(f"Error in plot_indicators: {str(e)}")
-        return go.Figure()
+        logging.error(f"Plot indicators error: {e}")
+        return None
 
-def plot_predictions(result, df, ticker):
+# Other plotting functions remain unchanged
+def plot_forecast(result, df):
     try:
-        logging.debug(f"Plotting predictions for {ticker}")
-        actual = result.get('actual', [])
-        forecast = result.get('forecast', [])
+        actual = result.get("actual", [])
+        forecast = result.get("forecast", [])
+        if not actual or not forecast:
+            return None
         dates = df['Date'].iloc[-len(actual):]
         fig = go.Figure()
-        fig.add_trace(go.Scatter(x=dates, y=actual, mode='lines', name='Actual', line=dict(color='blue')))
-        fig.add_trace(go.Scatter(x=dates, y=forecast, mode='lines', name='Predicted', line=dict(color='orange')))
+        fig.add_trace(go.Scatter(x=dates, y=actual, name='Actual', line=dict(color='blue')))
+        fig.add_trace(go.Scatter(x=dates, y=forecast, name='Forecast', line=dict(color='orange')))
         fig.update_layout(
-            title=f"{ticker} Price Predictions",
+            title="Backtest: Actual vs Forecast",
+            template="plotly_white",
+            height=600,
             xaxis_title="Date",
             yaxis_title="Price",
-            template="plotly_dark",
-            showlegend=True
+            xaxis=dict(tickformat="%Y-%m-%d", minor=dict(ticks="inside", showgrid=True), gridcolor="lightgrey"),
+            yaxis=dict(gridcolor="lightgrey"),
+            plot_bgcolor="white",
+            paper_bgcolor="white"
         )
-        logging.info(f"Predictions plot generated for {ticker}")
         return fig
     except Exception as e:
-        logging.error(f"Error in plot_predictions: {str(e)}")
-        return go.Figure()
+        logging.error(f"Plot forecast error: {e}")
+        return None
+
+# ... (other plotting functions like plot_future_forecast, plot_metrics_r2, etc., remain as provided)
 
 def plot_metrics(result, ticker):
     try: