refactored code. before cleaning

lib/pt_strategy/models.py

@@ -27,10 +27,9 @@ class ZScoreOLSModel(PairsTradingModel):
 
         assert zscore_df is not None
         return Prediction(
-            tstamp_=pair.market_data_.index[-1],
+            tstamp=pair.market_data_.iloc[-1]["tstamp"],
-            disequilibrium_=self.training_df_["dis-equilibrium"].iloc[-1],
+            disequilibrium=self.training_df_["dis-equilibrium"].iloc[-1],
-            scaled_disequilibrium_=self.training_df_["scaled_dis-equilibrium"].iloc[-1],
+            scaled_disequilibrium=self.training_df_["scaled_dis-equilibrium"].iloc[-1],
-            pair_=pair,
         )
 
     def _fit_zscore(self, pair: TradingPair) -> pd.DataFrame:

lib/pt_strategy/pt_model.py

@@ -9,12 +9,15 @@ import pandas as pd
 
 from pt_strategy.trading_pair import TradingPair
 
-@dataclass
 class Prediction:
     tstamp_: pd.Timestamp
     disequilibrium_: float
     scaled_disequilibrium_: float
-    pair_: TradingPair
+
+    def __init__(self, tstamp: pd.Timestamp, disequilibrium: float, scaled_disequilibrium: float):
+        self.tstamp_ = tstamp
+        self.disequilibrium_ = disequilibrium
+        self.scaled_disequilibrium_ = scaled_disequilibrium
 
     def to_dict(self) -> Dict[str, Any]:
         return {
@@ -22,10 +25,10 @@ class Prediction:
             "disequilibrium": self.disequilibrium_,
             "signed_scaled_disequilibrium": self.scaled_disequilibrium_,
             "scaled_disequilibrium": abs(self.scaled_disequilibrium_),
-            "pair": self.pair_,
+            # "pair": self.pair_,
         }
-    def to_pd_series(self) -> pd.Series:
+    def to_df(self) -> pd.DataFrame:
-        return pd.DataFrame([self.to_dict()]).iloc[0]
+        return pd.DataFrame([self.to_dict()])
     
 class PairsTradingModel(ABC):
 

lib/pt_strategy/trading_strategy.py

@@ -26,6 +26,7 @@ class PtResearchStrategy:
     pt_mkt_data_: PtMarketData
 
     trades_: List[pd.DataFrame]
+    predictions_: pd.DataFrame
 
     def __init__(
         self,
@@ -41,6 +42,7 @@ class PtResearchStrategy:
         self.trades_ = []
         self.trading_pair_ = TradingPair(config=config, instruments=instruments)
         self.model_data_policy_ = ModelDataPolicy.create(config)
+        self.predictions_ = pd.DataFrame()
 
         import copy
 
@@ -83,6 +85,7 @@ class PtResearchStrategy:
             prediction = self.trading_pair_.run(
                 market_data_df, self.model_data_policy_.advance()
             )
+            self.predictions_ = pd.concat([self.predictions_, prediction.to_df()], ignore_index=True)
             assert prediction is not None
 
             trades = self._create_trades(

lib/tools/viz/viz_prices.py

@@ -0,0 +1,79 @@
+from pt_strategy.trading_strategy import PtResearchStrategy
+
+
+def visualize_prices(strategy: PtResearchStrategy, trading_date: str) -> None: 
+    # Plot raw price data
+    import matplotlib.pyplot as plt
+    # Set plotting style
+    import seaborn as sns
+
+    pair = strategy.trading_pair_
+    SYMBOL_A = pair.symbol_a_
+    SYMBOL_B = pair.symbol_b_
+    TRD_DATE = f"{trading_date[0:4]}-{trading_date[4:6]}-{trading_date[6:8]}"
+
+    plt.style.use('seaborn-v0_8')
+    sns.set_palette("husl")
+    plt.rcParams['figure.figsize'] = (15, 10)
+
+    # Get column names for the trading pair
+    colname_a, colname_b = pair.colnames()
+    price_data = strategy.pt_mkt_data_.market_data_df_.copy()
+
+    # Create separate subplots for better visibility
+    fig_price, price_axes = plt.subplots(2, 1, figsize=(18, 10))
+
+    # Plot SYMBOL_A
+    price_axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, 
+                        label=f'{SYMBOL_A}', linewidth=1, color='blue')
+    price_axes[0].set_title(f'{SYMBOL_A} Price Data ({TRD_DATE})')
+    price_axes[0].set_ylabel(f'{SYMBOL_A} Price')
+    price_axes[0].legend()
+    price_axes[0].grid(True)
+
+    # Plot SYMBOL_B
+    price_axes[1].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, 
+                        label=f'{SYMBOL_B}', linewidth=1, color='red')
+    price_axes[1].set_title(f'{SYMBOL_B} Price Data ({TRD_DATE})')
+    price_axes[1].set_ylabel(f'{SYMBOL_B} Price')
+    price_axes[1].set_xlabel('Time')
+    price_axes[1].legend()
+    price_axes[1].grid(True)
+
+    plt.tight_layout()
+    plt.show()
+        
+
+    # Plot individual prices
+    fig, axes = plt.subplots(2, 1, figsize=(18, 12))
+
+    # Normalized prices for comparison
+    norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]
+    norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]
+
+    axes[0].plot(price_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8, linewidth=1)
+    axes[0].plot(price_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8, linewidth=1)
+    axes[0].set_title(f'Normalized Price Comparison (Base = 1.0)  ({TRD_DATE})')
+    axes[0].set_ylabel('Normalized Price')
+    axes[0].legend()
+    axes[0].grid(True)
+
+    # Price ratio
+    price_ratio = price_data[colname_a] / price_data[colname_b]
+    axes[1].plot(price_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8, linewidth=1)
+    axes[1].set_title(f'Price Ratio Px({SYMBOL_A})/Px({SYMBOL_B}) ({TRD_DATE})')
+    axes[1].set_ylabel('Ratio')
+    axes[1].set_xlabel('Time')
+    axes[1].legend()
+    axes[1].grid(True)
+
+    plt.tight_layout()
+    plt.show()
+
+    # Print basic statistics
+    print(f"\nPrice Statistics:")
+    print(f"  {SYMBOL_A}: Mean=${price_data[colname_a].mean():.2f}, Std=${price_data[colname_a].std():.2f}")
+    print(f"  {SYMBOL_B}: Mean=${price_data[colname_b].mean():.2f}, Std=${price_data[colname_b].std():.2f}")
+    print(f"  Price Ratio: Mean={price_ratio.mean():.2f}, Std={price_ratio.std():.2f}")
+    print(f"  Correlation: {price_data[colname_a].corr(price_data[colname_b]):.4f}")
+

lib/tools/viz/viz_trades.py

@@ -0,0 +1,507 @@
+from __future__ import annotations
+
+import os
+from typing import Any, Dict
+
+from pt_strategy.results import (PairResearchResult, create_result_database,
+                                 store_config_in_database)
+from pt_strategy.trading_strategy import PtResearchStrategy
+from tools.filetools import resolve_datafiles
+from tools.instruments import get_instruments
+
+
+def visualize_trades(strategy: PtResearchStrategy, results: PairResearchResult, trading_date: str) -> None: 
+
+    import pandas as pd
+    import plotly.express as px
+    import plotly.graph_objects as go
+    import plotly.offline as pyo
+    from IPython.display import HTML
+    from plotly.subplots import make_subplots
+
+
+    pair = strategy.trading_pair_
+    trades = results.trades_[trading_date].copy()
+    origin_mkt_data_df = strategy.pt_mkt_data_.origin_mkt_data_df_
+    mkt_data_df = strategy.pt_mkt_data_.market_data_df_
+    TRD_DATE = f"{trading_date[0:4]}-{trading_date[4:6]}-{trading_date[6:8]}"
+    SYMBOL_A = pair.symbol_a_
+    SYMBOL_B = pair.symbol_b_
+
+    
+    print(f"\nCreated trading pair: {pair}")
+    print(f"Market data shape: {pair.market_data_.shape}")
+    print(f"Column names: {pair.colnames()}")
+
+    # Configure plotly for offline mode
+    pyo.init_notebook_mode(connected=True)
+
+    # Strategy-specific interactive visualization
+    assert strategy.config_ is not None
+
+    print("=== SLIDING FIT INTERACTIVE VISUALIZATION ===")
+    print("Note: Rolling Fit strategy visualization with interactive plotly charts")
+    
+
+    # Create consistent timeline - superset of timestamps from both dataframes
+    all_timestamps = sorted(set(mkt_data_df['tstamp']))
+
+
+    # Create a unified timeline dataframe for consistent plotting
+    timeline_df = pd.DataFrame({'tstamp': all_timestamps})
+
+    # Merge with predicted data to get dis-equilibrium values
+    timeline_df = timeline_df.merge(strategy.predictions_[['tstamp', 'disequilibrium', 'scaled_disequilibrium', 'signed_scaled_disequilibrium']], 
+                                    on='tstamp', how='left')
+
+    # Get Symbol_A and Symbol_B market data
+    colname_a, colname_b = pair.colnames()
+    symbol_a_data = mkt_data_df[['tstamp', colname_a]].copy()
+    symbol_b_data = mkt_data_df[['tstamp', colname_b]].copy()
+
+    norm_a = symbol_a_data[colname_a] / symbol_a_data[colname_a].iloc[0]
+    norm_b = symbol_b_data[colname_b] / symbol_b_data[colname_b].iloc[0]
+
+    print(f"Using consistent timeline with {len(timeline_df)} timestamps")
+    print(f"Timeline range: {timeline_df['tstamp'].min()} to {timeline_df['tstamp'].max()}")
+
+    # Create subplots with price charts at bottom
+    fig = make_subplots(
+        rows=4, cols=1,
+        row_heights=[0.3, 0.4, 0.15, 0.15],
+        subplot_titles=[
+            f'Dis-equilibrium with Trading Thresholds ({TRD_DATE})',
+            f'Normalized Price Comparison with BUY/SELL Signals - {SYMBOL_A}&{SYMBOL_B} ({TRD_DATE})',
+            f'{SYMBOL_A} Market Data with Trading Signals ({TRD_DATE})',
+            f'{SYMBOL_B} Market Data with Trading Signals ({TRD_DATE})',
+        ],
+        vertical_spacing=0.06,
+        specs=[[{"secondary_y": False}],
+                [{"secondary_y": False}],
+                [{"secondary_y": False}],
+                [{"secondary_y": False}]]
+    )
+
+    # 1. Scaled dis-equilibrium with thresholds - using consistent timeline
+    fig.add_trace(
+        go.Scatter(
+            x=timeline_df['tstamp'],
+            y=timeline_df['scaled_disequilibrium'],
+            name='Absolute Scaled Dis-equilibrium',
+            line=dict(color='green', width=2),
+            opacity=0.8
+        ),
+        row=1, col=1
+    )
+
+    fig.add_trace(
+        go.Scatter(
+            x=timeline_df['tstamp'],
+            y=timeline_df['signed_scaled_disequilibrium'],
+            name='Scaled Dis-equilibrium',
+            line=dict(color='darkmagenta', width=2),
+            opacity=0.8
+        ),
+        row=1, col=1
+    )
+
+    # Add threshold lines to first subplot
+    fig.add_shape(
+        type="line",
+        x0=timeline_df['tstamp'].min(),
+        x1=timeline_df['tstamp'].max(),
+        y0=strategy.config_['dis-equilibrium_open_trshld'],
+        y1=strategy.config_['dis-equilibrium_open_trshld'],
+        line=dict(color="purple", width=2, dash="dot"),
+        opacity=0.7,
+        row=1, col=1
+    )
+
+    fig.add_shape(
+        type="line",
+        x0=timeline_df['tstamp'].min(),
+        x1=timeline_df['tstamp'].max(),
+        y0=-strategy.config_['dis-equilibrium_open_trshld'],
+        y1=-strategy.config_['dis-equilibrium_open_trshld'],
+        line=dict(color="purple", width=2, dash="dot"),
+        opacity=0.7,
+        row=1, col=1
+    )
+
+    fig.add_shape(
+        type="line",
+        x0=timeline_df['tstamp'].min(),
+        x1=timeline_df['tstamp'].max(),
+        y0=strategy.config_['dis-equilibrium_close_trshld'],
+        y1=strategy.config_['dis-equilibrium_close_trshld'],
+        line=dict(color="brown", width=2, dash="dot"),
+        opacity=0.7,
+        row=1, col=1
+    )
+
+    fig.add_shape(
+        type="line",
+        x0=timeline_df['tstamp'].min(),
+        x1=timeline_df['tstamp'].max(),
+        y0=-strategy.config_['dis-equilibrium_close_trshld'],
+        y1=-strategy.config_['dis-equilibrium_close_trshld'],
+        line=dict(color="brown", width=2, dash="dot"),
+        opacity=0.7,
+        row=1, col=1
+    )
+
+    fig.add_shape(
+        type="line",
+        x0=timeline_df['tstamp'].min(),
+        x1=timeline_df['tstamp'].max(),
+        y0=0,
+        y1=0,
+        line=dict(color="black", width=1, dash="solid"),
+        opacity=0.5,
+        row=1, col=1
+    )
+
+    # Add normalized price lines
+    fig.add_trace(
+        go.Scatter(
+            x=mkt_data_df['tstamp'],
+            y=norm_a,
+            name=f'{SYMBOL_A} (Normalized)',
+            line=dict(color='blue', width=2),
+            opacity=0.8
+        ),
+        row=2, col=1
+    )
+
+    fig.add_trace(
+        go.Scatter(
+            x=mkt_data_df['tstamp'],
+            y=norm_b,
+            name=f'{SYMBOL_B} (Normalized)',
+            line=dict(color='orange', width=2),
+            opacity=0.8,
+        ),
+        row=2, col=1
+    )
+
+    # Add BUY and SELL signals if available
+    if trades is not None and len(trades) > 0:
+        # Define signal groups to avoid legend repetition
+        signal_groups = {}
+        
+        # Process all trades and group by signal type (ignore OPEN/CLOSE status)
+        for _, trade in trades.iterrows():
+            symbol = trade['symbol']
+            side = trade['side']
+            # status = trade['status']
+            action = trade['action']
+            
+            # Create signal group key (without status to combine OPEN/CLOSE)
+            signal_key = f"{symbol} {side} {action}"
+            
+            # Find normalized price for this trade
+            trade_time = trade['time']
+            if symbol == SYMBOL_A:
+                closest_idx = mkt_data_df['tstamp'].searchsorted(trade_time)
+                if closest_idx < len(norm_a):
+                    norm_price = norm_a.iloc[closest_idx]
+                else:
+                    norm_price = norm_a.iloc[-1]
+            else:  # SYMBOL_B
+                closest_idx = mkt_data_df['tstamp'].searchsorted(trade_time)
+                if closest_idx < len(norm_b):
+                    norm_price = norm_b.iloc[closest_idx]
+                else:
+                    norm_price = norm_b.iloc[-1]
+            
+            # Initialize group if not exists
+            if signal_key not in signal_groups:
+                signal_groups[signal_key] = {
+                    'times': [],
+                    'prices': [],
+                    'actual_prices': [],
+                    'symbol': symbol,
+                    'side': side,
+                    # 'status': status,
+                    'action': trade['action']
+                }
+            
+            # Add to group
+            signal_groups[signal_key]['times'].append(trade_time)
+            signal_groups[signal_key]['prices'].append(norm_price)
+            signal_groups[signal_key]['actual_prices'].append(trade['price'])
+        
+        # Add each signal group as a single trace
+        for signal_key, group_data in signal_groups.items():
+            symbol = group_data['symbol']
+            side = group_data['side']
+            # status = group_data['status']
+            
+            # Determine marker properties (same for all OPEN/CLOSE of same side)
+            is_close: bool = (group_data['action'] == "CLOSE")
+            
+            if 'BUY' in side:
+                marker_color = 'green'
+                marker_symbol = 'triangle-up'
+                marker_size = 14
+            else:  # SELL
+                marker_color = 'red'
+                marker_symbol = 'triangle-down'
+                marker_size = 14
+            
+            # Create hover text for each point in the group
+            hover_texts = []
+            for i, (time, norm_price, actual_price) in enumerate(zip(group_data['times'], 
+                                                                    group_data['prices'], 
+                                                                    group_data['actual_prices'])):
+                # Find the corresponding trade to get the status for hover text
+                trade_info = trades[(trades['time'] == time) & 
+                                    (trades['symbol'] == symbol) & 
+                                    (trades['side'] == side)]
+                if len(trade_info) > 0:
+                    action = trade_info.iloc[0]['action']
+                    hover_texts.append(f'<b>{signal_key} {action}</b><br>' +
+                                    f'Time: {time}<br>' +
+                                    f'Normalized Price: {norm_price:.4f}<br>' +
+                                    f'Actual Price: ${actual_price:.2f}')
+                else:
+                    hover_texts.append(f'<b>{signal_key}</b><br>' +
+                                    f'Time: {time}<br>' +
+                                    f'Normalized Price: {norm_price:.4f}<br>' +
+                                    f'Actual Price: ${actual_price:.2f}')
+            
+            fig.add_trace(
+                go.Scatter(
+                    x=group_data['times'],
+                    y=group_data['prices'],
+                    mode='markers',
+                    name=signal_key,
+                    marker=dict(
+                        color=marker_color,
+                        size=marker_size,
+                        symbol=marker_symbol,
+                        line=dict(width=2, color='black') if is_close else None
+                    ),
+                    showlegend=True,
+                    hovertemplate='%{text}<extra></extra>',
+                    text=hover_texts
+                ),
+                row=2, col=1
+            )
+
+    # -----------------------------                
+        
+        fig.add_trace(
+            go.Scatter(
+                x=symbol_a_data['tstamp'],
+                y=symbol_a_data[colname_a],
+                name=f'{SYMBOL_A} Price',
+                line=dict(color='blue', width=2),
+                opacity=0.8
+            ),
+            row=3, col=1
+        )
+
+        # Filter trades for Symbol_A
+        symbol_a_trades = trades[trades['symbol'] == SYMBOL_A]
+        print(f"\nSymbol_A trades:\n{symbol_a_trades}")
+        
+        if len(symbol_a_trades) > 0:
+            # Separate trades by action and status for different colors
+            buy_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) & 
+                                                (symbol_a_trades['action'].str.contains('OPEN', na=False))]
+            buy_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) & 
+                                                (symbol_a_trades['action'].str.contains('CLOSE', na=False))]
+            
+            sell_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) & 
+                                                (symbol_a_trades['action'].str.contains('OPEN', na=False))]
+            sell_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) & 
+                                                (symbol_a_trades['action'].str.contains('CLOSE', na=False))]
+            
+            # Add BUY OPEN signals
+            if len(buy_open_trades) > 0:
+                fig.add_trace(
+                    go.Scatter(
+                        x=buy_open_trades['time'],
+                        y=buy_open_trades['price'],
+                        mode='markers',
+                        name=f'{SYMBOL_A} BUY OPEN',
+                        marker=dict(color='green', size=12, symbol='triangle-up'),
+                        showlegend=True
+                    ),
+                    row=3, col=1
+                )
+            
+            # Add BUY CLOSE signals
+            if len(buy_close_trades) > 0:
+                fig.add_trace(
+                    go.Scatter(
+                        x=buy_close_trades['time'],
+                        y=buy_close_trades['price'],
+                        mode='markers',
+                        name=f'{SYMBOL_A} BUY CLOSE',
+                        marker=dict(color='green', size=12, symbol='triangle-up'),
+                        line=dict(width=2, color='black'),
+                        showlegend=True
+                    ),
+                    row=3, col=1
+                )
+            
+            # Add SELL OPEN signals
+            if len(sell_open_trades) > 0:
+                fig.add_trace(
+                    go.Scatter(
+                        x=sell_open_trades['time'],
+                        y=sell_open_trades['price'],
+                        mode='markers',
+                        name=f'{SYMBOL_A} SELL OPEN',
+                        marker=dict(color='red', size=12, symbol='triangle-down'),
+                        showlegend=True
+                    ),
+                    row=3, col=1
+                )
+            
+            # Add SELL CLOSE signals
+            if len(sell_close_trades) > 0:
+                fig.add_trace(
+                    go.Scatter(
+                        x=sell_close_trades['time'],
+                        y=sell_close_trades['price'],
+                        mode='markers',
+                        name=f'{SYMBOL_A} SELL CLOSE',
+                        marker=dict(color='red', size=12, symbol='triangle-down'),
+                        line=dict(width=2, color='black'),
+                        showlegend=True
+                    ),
+                    row=3, col=1
+                )
+                    
+            # 4. Symbol_B Market Data with Trading Signals
+        fig.add_trace(
+            go.Scatter(
+                x=symbol_b_data['tstamp'],
+                y=symbol_b_data[colname_b],
+                name=f'{SYMBOL_B} Price',
+                line=dict(color='orange', width=2),
+                opacity=0.8
+            ),
+            row=4, col=1
+        )
+        
+        # Add trading signals for Symbol_B if available
+        symbol_b_trades = trades[trades['symbol'] == SYMBOL_B]
+        print(f"\nSymbol_B trades:\n{symbol_b_trades}")
+        
+        if len(symbol_b_trades) > 0:
+            # Separate trades by action and status for different colors
+            buy_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) & 
+                                                (symbol_b_trades['action'].str.startswith('OPEN', na=False))]
+            buy_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) & 
+                                                (symbol_b_trades['action'].str.startswith('CLOSE', na=False))]
+            
+            sell_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) & 
+                                                (symbol_b_trades['action'].str.contains('OPEN', na=False))]
+            sell_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) & 
+                                                (symbol_b_trades['action'].str.contains('CLOSE', na=False))]
+            
+            # Add BUY OPEN signals
+            if len(buy_open_trades) > 0:
+                fig.add_trace(
+                    go.Scatter(
+                        x=buy_open_trades['time'],
+                        y=buy_open_trades['price'],
+                        mode='markers',
+                        name=f'{SYMBOL_B} BUY OPEN',
+                        marker=dict(color='darkgreen', size=12, symbol='triangle-up'),
+                        showlegend=True
+                    ),
+                    row=4, col=1
+                )
+            
+            # Add BUY CLOSE signals
+            if len(buy_close_trades) > 0:
+                fig.add_trace(
+                    go.Scatter(
+                        x=buy_close_trades['time'],
+                        y=buy_close_trades['price'],
+                        mode='markers',
+                        name=f'{SYMBOL_B} BUY CLOSE',
+                        marker=dict(color='green', size=12, symbol='triangle-up'),
+                        line=dict(width=2, color='black'),
+                        showlegend=True
+                    ),
+                    row=4, col=1
+                )
+            
+            # Add SELL OPEN signals
+            if len(sell_open_trades) > 0:
+                fig.add_trace(
+                    go.Scatter(
+                        x=sell_open_trades['time'],
+                        y=sell_open_trades['price'],
+                        mode='markers',
+                        name=f'{SYMBOL_B} SELL OPEN',
+                        marker=dict(color='red', size=12, symbol='triangle-down'),
+                        showlegend=True
+                    ),
+                    row=4, col=1
+                )
+            
+            # Add SELL CLOSE signals
+            if len(sell_close_trades) > 0:
+                fig.add_trace(
+                    go.Scatter(
+                        x=sell_close_trades['time'],
+                        y=sell_close_trades['price'],
+                        mode='markers',
+                        name=f'{SYMBOL_B} SELL CLOSE',
+                        marker=dict(color='red', size=12, symbol='triangle-down'),
+                        line=dict(width=2, color='black'),
+                        showlegend=True
+                    ),
+                    row=4, col=1
+                )
+    
+        # Update layout
+        fig.update_layout(
+            height=1600,
+            title_text=f"Strategy Analysis - {SYMBOL_A} & {SYMBOL_B} ({TRD_DATE})",
+            showlegend=True,
+            template="plotly_white",
+            plot_bgcolor='lightgray',
+        )
+        
+        # Update y-axis labels
+        fig.update_yaxes(title_text="Scaled Dis-equilibrium", row=1, col=1)
+        fig.update_yaxes(title_text=f"{SYMBOL_A} Price ($)", row=2, col=1)
+        fig.update_yaxes(title_text=f"{SYMBOL_B} Price ($)", row=3, col=1)
+        fig.update_yaxes(title_text="Normalized Price (Base = 1.0)", row=4, col=1)
+        
+        # Update x-axis labels and ensure consistent time range
+        time_range = [timeline_df['tstamp'].min(), timeline_df['tstamp'].max()]
+        fig.update_xaxes(range=time_range, row=1, col=1)
+        fig.update_xaxes(range=time_range, row=2, col=1)
+        fig.update_xaxes(range=time_range, row=3, col=1)
+        fig.update_xaxes(title_text="Time", range=time_range, row=4, col=1)
+        
+        # Display using plotly offline mode
+        # pyo.iplot(fig)
+        fig.show()
+        
+    else:
+        print("No interactive visualization data available - strategy may not have run successfully")
+
+    print(f"\nChart shows:")
+    print(f"- {SYMBOL_A} and {SYMBOL_B} prices normalized to start at 1.0")
+    print(f"- BUY signals shown as green triangles pointing up")
+    print(f"- SELL signals shown as orange triangles pointing down")
+    print(f"- All BUY signals per symbol grouped together, all SELL signals per symbol grouped together")
+    print(f"- Hover over markers to see individual trade details (OPEN/CLOSE status)")
+
+    if trades is not None and len(trades) > 0:
+        print(f"- Total signals displayed: {len(trades)}")
+        print(f"- {SYMBOL_A} signals: {len(trades[trades['symbol'] == SYMBOL_A])}")
+        print(f"- {SYMBOL_B} signals: {len(trades[trades['symbol'] == SYMBOL_B])}")
+    else:
+        print("- No trading signals to display")
+

research/viz_test.py

@@ -0,0 +1,111 @@
+from __future__ import annotations
+
+import os
+from typing import Any, Dict
+
+from pt_strategy.results import (PairResearchResult, create_result_database,
+                                 store_config_in_database)
+from pt_strategy.trading_strategy import PtResearchStrategy
+from tools.filetools import resolve_datafiles
+from tools.instruments import get_instruments
+from tools.viz.viz_trades import visualize_trades
+
+
+def main() -> None:
+    import argparse
+
+    from tools.config import expand_filename, load_config
+
+    parser = argparse.ArgumentParser(description="Run pairs trading backtest.")
+    parser.add_argument(
+        "--config", type=str, required=True, help="Path to the configuration file."
+    )
+    parser.add_argument(
+        "--date_pattern",
+        type=str,
+        required=True,
+        help="Date YYYYMMDD, allows * and ? wildcards",
+    )
+    parser.add_argument(
+        "--instruments",
+        type=str,
+        required=True,
+        help="Comma-separated list of instrument symbols (e.g., COIN:EQUITY,GBTC:CRYPTO)",
+    )
+    parser.add_argument(
+        "--result_db",
+        type=str,
+        required=False,
+        default="NONE",
+        help="Path to SQLite database for storing results. Use 'NONE' to disable database output.",
+    )
+
+    args = parser.parse_args()
+
+    config: Dict = load_config(args.config)
+
+    # Resolve data files (CLI takes priority over config)
+    instruments = get_instruments(args, config)
+    datafiles = resolve_datafiles(config, args.date_pattern, instruments)
+
+    days = list(set([day for day, _ in datafiles]))
+    print(f"Found {len(datafiles)} data files to process:")
+    for df in datafiles:
+        print(f"  - {df}")
+
+    # Create result database if needed
+    if args.result_db.upper() != "NONE":
+        args.result_db = expand_filename(args.result_db)
+        create_result_database(args.result_db)
+
+    # Initialize a dictionary to store all trade results
+    all_results: Dict[str, Dict[str, Any]] = {}
+    is_config_stored = False
+    # Process each data file
+
+    results = PairResearchResult(config=config)
+    for day in sorted(days):
+        md_datafiles = [datafile for md_day, datafile in datafiles if md_day == day]
+        if not all([os.path.exists(datafile) for datafile in md_datafiles]):
+            print(f"WARNING: insufficient data files: {md_datafiles}")
+            continue
+        print(f"\n====== Processing {day} ======")
+
+        if not is_config_stored:
+            store_config_in_database(
+                db_path=args.result_db,
+                config_file_path=args.config,
+                config=config,
+                datafiles=datafiles,
+                instruments=instruments,
+            )
+            is_config_stored = True
+
+        pt_strategy = PtResearchStrategy(
+            config=config, datafiles=md_datafiles, instruments=instruments
+        )
+        pt_strategy.run()
+        results.add_day_results(
+            day=day,
+            trades=pt_strategy.day_trades(),
+            outstanding_positions=pt_strategy.outstanding_positions(),
+        )
+
+
+    results.analyze_pair_performance()
+    
+    
+    visualize_trades(pt_strategy, results, day)
+
+
+    if args.result_db.upper() != "NONE":
+        print(f"\nResults stored in database: {args.result_db}")
+    else:
+        print("No results to display.")
+
+
+
+
+
+if __name__ == "__main__":
+    main()