From 1b6b5e57359c05831e87ffa6a5dd0e556f4789e6 Mon Sep 17 00:00:00 2001 From: Oleg Sheynin Date: Wed, 30 Jul 2025 20:11:25 +0000 Subject: [PATCH] refactored code. before cleaning --- lib/pt_strategy/models.py | 7 +- lib/pt_strategy/pt_model.py | 13 +- lib/pt_strategy/trading_strategy.py | 3 + lib/tools/viz/viz_prices.py | 79 + lib/tools/viz/viz_trades.py | 507 ++ .../notebooks/pair_trading_test_NEW.ipynb | 6645 +++++++++++++++-- research/viz_test.py | 111 + 7 files changed, 6729 insertions(+), 636 deletions(-) create mode 100644 lib/tools/viz/viz_prices.py create mode 100644 lib/tools/viz/viz_trades.py create mode 100644 research/viz_test.py diff --git a/lib/pt_strategy/models.py b/lib/pt_strategy/models.py index 373f28a..24e819e 100644 --- a/lib/pt_strategy/models.py +++ b/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], - disequilibrium_=self.training_df_["dis-equilibrium"].iloc[-1], - scaled_disequilibrium_=self.training_df_["scaled_dis-equilibrium"].iloc[-1], - pair_=pair, + tstamp=pair.market_data_.iloc[-1]["tstamp"], + disequilibrium=self.training_df_["dis-equilibrium"].iloc[-1], + scaled_disequilibrium=self.training_df_["scaled_dis-equilibrium"].iloc[-1], ) def _fit_zscore(self, pair: TradingPair) -> pd.DataFrame: diff --git a/lib/pt_strategy/pt_model.py b/lib/pt_strategy/pt_model.py index c23ea30..1f817f9 100644 --- a/lib/pt_strategy/pt_model.py +++ b/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: - return pd.DataFrame([self.to_dict()]).iloc[0] + def to_df(self) -> pd.DataFrame: + return pd.DataFrame([self.to_dict()]) class PairsTradingModel(ABC): diff --git a/lib/pt_strategy/trading_strategy.py b/lib/pt_strategy/trading_strategy.py index 3d851be..0119cc4 100644 --- a/lib/pt_strategy/trading_strategy.py +++ b/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( diff --git a/lib/tools/viz/viz_prices.py b/lib/tools/viz/viz_prices.py new file mode 100644 index 0000000..2575176 --- /dev/null +++ b/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}") + diff --git a/lib/tools/viz/viz_trades.py b/lib/tools/viz/viz_trades.py new file mode 100644 index 0000000..209bb39 --- /dev/null +++ b/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'{signal_key} {action}
' + + f'Time: {time}
' + + f'Normalized Price: {norm_price:.4f}
' + + f'Actual Price: ${actual_price:.2f}') + else: + hover_texts.append(f'{signal_key}
' + + f'Time: {time}
' + + f'Normalized Price: {norm_price:.4f}
' + + 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}', + 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") + diff --git a/research/notebooks/pair_trading_test_NEW.ipynb b/research/notebooks/pair_trading_test_NEW.ipynb index 2a44221..eb54281 100644 --- a/research/notebooks/pair_trading_test_NEW.ipynb +++ b/research/notebooks/pair_trading_test_NEW.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -160,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -213,12 +213,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "\n", "def prepare_config() -> None:\n", + " from typing import Dict, List, Any\n", + " import os\n", + " \n", " global PT_BT_CONFIG\n", " global CONFIG_FILE\n", " global SYMBOL_A\n", @@ -226,6 +229,7 @@ " global TRD_DATE\n", " global DATA_FILES\n", " global FIT_MODEL\n", + " \n", "\n", " print(f\"Trading Parameters:\")\n", " print(f\" Configuration: {CONFIG_FILE}\")\n", @@ -240,17 +244,10 @@ "\n", " if PT_BT_CONFIG:\n", " print(f\"✓ Successfully loaded configuration\")\n", - " # print(f\" Data directory: {PT_BT_CONFIG['data_directory']}\")\n", - " # print(f\" Database table: {PT_BT_CONFIG['db_table_name']}\")\n", - " # print(f\" Exchange: {PT_BT_CONFIG['exchange_id']}\")\n", " print(f\" Training window: {PT_BT_CONFIG['training_size']} minutes\")\n", " print(f\" Open threshold: {PT_BT_CONFIG['dis-equilibrium_open_trshld']}\")\n", " print(f\" Close threshold: {PT_BT_CONFIG['dis-equilibrium_close_trshld']}\")\n", " \n", - " # Instantiate strategy from config\n", - " # FIT_MODEL = instantiate_fit_method_from_config(PT_BT_CONFIG)\n", - " # print(f\" Fit Method: {type(FIT_MODEL).__name__}\")\n", - " \n", " # Automatically construct data file name based on date and config type\n", " DATA_FILE = f\"{TRADING_DATE}.mktdata.ohlcv.db\"\n", " data_directory_a = PT_BT_CONFIG[\"market_data_loading\"][INSTRUMENTS[\"A\"][\"instrument_type\"]][\"data_directory\"]\n", @@ -297,16 +294,13 @@ "source": [ "\n", "def run_strategy() -> None: # Load market data\n", + " from typing import Dict, Any\n", " global PT_BT_CONFIG\n", " global INSTRUMENTS\n", - " global DATA_FILE\n", - " global SYMBOL_A\n", - " global SYMBOL_B\n", - " global pair\n", - " global DB_TABLE_NAME\n", " global PT_RESEARCH_STRATEGY\n", " global PT_RESULTS\n", "\n", + " \n", " import pandas as pd\n", " from tools.data_loader import load_market_data\n", " from pt_strategy.trading_pair import TradingPair\n", @@ -349,615 +343,22 @@ }, { "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "raw" - } - }, + "metadata": {}, "source": [ - "## Visualize Raw Price Data\n" + "## Visualize" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "def visualize_prices() -> None:\n", - " # Plot raw price data\n", - " global price_data\n", - " \n", - " import matplotlib.pyplot as plt\n", - " # Set plotting style\n", - " import seaborn as sns\n", - "\n", - " plt.style.use('seaborn-v0_8')\n", - " sns.set_palette(\"husl\")\n", - " plt.rcParams['figure.figsize'] = (15, 10)\n", - "\n", - " # Get column names for the trading pair\n", - " colname_a, colname_b = pair.colnames()\n", - " price_data = pair.market_data_.copy()\n", - "\n", - " # # 1. Price data - separate plots for each symbol\n", - " # colname_a, colname_b = pair.colnames()\n", - " # price_data = pair.market_data_.copy()\n", - "\n", - " # Create separate subplots for better visibility\n", - " fig_price, price_axes = plt.subplots(2, 1, figsize=(18, 10))\n", - "\n", - " # Plot SYMBOL_A\n", - " price_axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, \n", - " label=f'{SYMBOL_A}', linewidth=1, color='blue')\n", - " price_axes[0].set_title(f'{SYMBOL_A} Price Data ({TRD_DATE})')\n", - " price_axes[0].set_ylabel(f'{SYMBOL_A} Price')\n", - " price_axes[0].legend()\n", - " price_axes[0].grid(True)\n", - "\n", - " # Plot SYMBOL_B\n", - " price_axes[1].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, \n", - " label=f'{SYMBOL_B}', linewidth=1, color='red')\n", - " price_axes[1].set_title(f'{SYMBOL_B} Price Data ({TRD_DATE})')\n", - " price_axes[1].set_ylabel(f'{SYMBOL_B} Price')\n", - " price_axes[1].set_xlabel('Time')\n", - " price_axes[1].legend()\n", - " price_axes[1].grid(True)\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - " \n", - "\n", - " # Plot individual prices\n", - " fig, axes = plt.subplots(2, 1, figsize=(18, 12))\n", - "\n", - " # Normalized prices for comparison\n", - " norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]\n", - " norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]\n", - "\n", - " axes[0].plot(price_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8, linewidth=1)\n", - " axes[0].plot(price_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8, linewidth=1)\n", - " axes[0].set_title(f'Normalized Price Comparison (Base = 1.0) ({TRD_DATE})')\n", - " axes[0].set_ylabel('Normalized Price')\n", - " axes[0].legend()\n", - " axes[0].grid(True)\n", - "\n", - " # Price ratio\n", - " price_ratio = price_data[colname_a] / price_data[colname_b]\n", - " axes[1].plot(price_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8, linewidth=1)\n", - " axes[1].set_title(f'Price Ratio Px({SYMBOL_A})/Px({SYMBOL_B}) ({TRD_DATE})')\n", - " axes[1].set_ylabel('Ratio')\n", - " axes[1].set_xlabel('Time')\n", - " axes[1].legend()\n", - " axes[1].grid(True)\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - " # Print basic statistics\n", - " print(f\"\\nPrice Statistics:\")\n", - " print(f\" {SYMBOL_A}: Mean=${price_data[colname_a].mean():.2f}, Std=${price_data[colname_a].std():.2f}\")\n", - " print(f\" {SYMBOL_B}: Mean=${price_data[colname_b].mean():.2f}, Std=${price_data[colname_b].std():.2f}\")\n", - " print(f\" Price Ratio: Mean={price_ratio.mean():.2f}, Std={price_ratio.std():.2f}\")\n", - " print(f\" Correlation: {price_data[colname_a].corr(price_data[colname_b]):.4f}\")\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualization" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def visualize_trades() -> None:\n", - " # global price_data\n", - " # global pair_trades\n", - " global PT_BT_CONFIG\n", - " global SYMBOL_A\n", - " global SYMBOL_B\n", - " global TRD_DATE\n", - " global PREDICTED_RESULT\n", - " global PT_RESEARCH_STRATEGY\n", - " global PT_RESULTS\n", - "\n", - " import plotly.graph_objects as go\n", - " from plotly.subplots import make_subplots\n", - " import plotly.express as px\n", - " import plotly.offline as pyo\n", - " from IPython.display import HTML\n", - " import pandas as pd\n", - "\n", - "\n", - " pair = PT_RESEARCH_STRATEGY.trading_pair_\n", - " pair_trades = PT_RESULTS.trades_[TRADING_DATE]\n", - " outstanding_positions = PT_RESULTS.outstanding_positions_[TRADING_DATE]\n", - "\n", - " print(f\"\\nCreated trading pair: {pair}\")\n", - " print(f\"Market data shape: {pair.market_data_.shape}\")\n", - " print(f\"Column names: {pair.colnames()}\")\n", - "\n", - " # Configure plotly for offline mode\n", - " pyo.init_notebook_mode(connected=True)\n", - "\n", - " # Strategy-specific interactive visualization\n", - " assert PT_BT_CONFIG is not None\n", - "\n", - " print(\"=== SLIDING FIT INTERACTIVE VISUALIZATION ===\")\n", - " print(\"Note: Rolling Fit strategy visualization with interactive plotly charts\")\n", - "\n", - " # Create consistent timeline - superset of timestamps from both dataframes\n", - " market_timestamps = set(pair.market_data_['tstamp'])\n", - " predicted_timestamps = set(PREDICTED_RESULT['tstamp'])\n", - "\n", - " # Create superset of all timestamps\n", - " all_timestamps = sorted(market_timestamps.union(predicted_timestamps))\n", - "\n", - " # Create a unified timeline dataframe for consistent plotting\n", - " timeline_df = pd.DataFrame({'tstamp': all_timestamps})\n", - "\n", - " # Merge with predicted data to get dis-equilibrium values\n", - " timeline_df = timeline_df.merge(PREDICTED_RESULT[['tstamp', 'disequilibrium', 'scaled_disequilibrium', 'signed_scaled_disequilibrium']], \n", - " on='tstamp', how='left')\n", - "\n", - " # Get Symbol_A and Symbol_B market data\n", - " colname_a, colname_b = pair.colnames()\n", - " symbol_a_data = pair.original_market_data_[['tstamp', colname_a]].copy()\n", - " symbol_b_data = pair.original_market_data_[['tstamp', colname_b]].copy()\n", - "\n", - " norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]\n", - " norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]\n", - "\n", - " print(f\"Using consistent timeline with {len(timeline_df)} timestamps\")\n", - " print(f\"Timeline range: {timeline_df['tstamp'].min()} to {timeline_df['tstamp'].max()}\")\n", - "\n", - " # Create subplots with price charts at bottom\n", - " fig = make_subplots(\n", - " rows=4, cols=1,\n", - " row_heights=[0.3, 0.4, 0.15, 0.15],\n", - " subplot_titles=[\n", - " f'Dis-equilibrium with Trading Thresholds ({TRD_DATE})',\n", - " f'Normalized Price Comparison with BUY/SELL Signals - {SYMBOL_A}&{SYMBOL_B} ({TRD_DATE})',\n", - " f'{SYMBOL_A} Market Data with Trading Signals ({TRD_DATE})',\n", - " f'{SYMBOL_B} Market Data with Trading Signals ({TRD_DATE})',\n", - " ],\n", - " vertical_spacing=0.06,\n", - " specs=[[{\"secondary_y\": False}],\n", - " [{\"secondary_y\": False}],\n", - " [{\"secondary_y\": False}],\n", - " [{\"secondary_y\": False}]]\n", - " )\n", - "\n", - " # 1. Scaled dis-equilibrium with thresholds - using consistent timeline\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=timeline_df['tstamp'],\n", - " y=timeline_df['scaled_disequilibrium'],\n", - " name='Absolute Scaled Dis-equilibrium',\n", - " line=dict(color='green', width=2),\n", - " opacity=0.8\n", - " ),\n", - " row=1, col=1\n", - " )\n", - "\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=timeline_df['tstamp'],\n", - " y=timeline_df['signed_scaled_disequilibrium'],\n", - " name='Scaled Dis-equilibrium',\n", - " line=dict(color='darkmagenta', width=2),\n", - " opacity=0.8\n", - " ),\n", - " row=1, col=1\n", - " )\n", - "\n", - " # Add threshold lines to first subplot\n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=timeline_df['tstamp'].min(),\n", - " x1=timeline_df['tstamp'].max(),\n", - " y0=PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n", - " y1=PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n", - " line=dict(color=\"purple\", width=2, dash=\"dot\"),\n", - " opacity=0.7,\n", - " row=1, col=1\n", - " )\n", - "\n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=timeline_df['tstamp'].min(),\n", - " x1=timeline_df['tstamp'].max(),\n", - " y0=-PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n", - " y1=-PT_BT_CONFIG['dis-equilibrium_open_trshld'],\n", - " line=dict(color=\"purple\", width=2, dash=\"dot\"),\n", - " opacity=0.7,\n", - " row=1, col=1\n", - " )\n", - "\n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=timeline_df['tstamp'].min(),\n", - " x1=timeline_df['tstamp'].max(),\n", - " y0=PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n", - " y1=PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n", - " line=dict(color=\"brown\", width=2, dash=\"dot\"),\n", - " opacity=0.7,\n", - " row=1, col=1\n", - " )\n", - "\n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=timeline_df['tstamp'].min(),\n", - " x1=timeline_df['tstamp'].max(),\n", - " y0=-PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n", - " y1=-PT_BT_CONFIG['dis-equilibrium_close_trshld'],\n", - " line=dict(color=\"brown\", width=2, dash=\"dot\"),\n", - " opacity=0.7,\n", - " row=1, col=1\n", - " )\n", - "\n", - " fig.add_shape(\n", - " type=\"line\",\n", - " x0=timeline_df['tstamp'].min(),\n", - " x1=timeline_df['tstamp'].max(),\n", - " y0=0,\n", - " y1=0,\n", - " line=dict(color=\"black\", width=1, dash=\"solid\"),\n", - " opacity=0.5,\n", - " row=1, col=1\n", - " )\n", - "\n", - " # Add normalized price lines\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=price_data['tstamp'],\n", - " y=norm_a,\n", - " name=f'{SYMBOL_A} (Normalized)',\n", - " line=dict(color='blue', width=2),\n", - " opacity=0.8\n", - " ),\n", - " row=2, col=1\n", - " )\n", - "\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=price_data['tstamp'],\n", - " y=norm_b,\n", - " name=f'{SYMBOL_B} (Normalized)',\n", - " line=dict(color='orange', width=2),\n", - " opacity=0.8,\n", - " ),\n", - " row=2, col=1\n", - " )\n", - "\n", - " # Add BUY and SELL signals if available\n", - " if pair_trades is not None and len(pair_trades) > 0:\n", - " # Define signal groups to avoid legend repetition\n", - " signal_groups = {}\n", - " \n", - " # Process all trades and group by signal type (ignore OPEN/CLOSE status)\n", - " for _, trade in pair_trades.iterrows():\n", - " symbol = trade['symbol']\n", - " side = trade['side']\n", - " # status = trade['status']\n", - " action = trade['action']\n", - " \n", - " # Create signal group key (without status to combine OPEN/CLOSE)\n", - " signal_key = f\"{symbol} {side} {action}\"\n", - " \n", - " # Find normalized price for this trade\n", - " trade_time = trade['time']\n", - " if symbol == SYMBOL_A:\n", - " closest_idx = price_data['tstamp'].searchsorted(trade_time)\n", - " if closest_idx < len(norm_a):\n", - " norm_price = norm_a.iloc[closest_idx]\n", - " else:\n", - " norm_price = norm_a.iloc[-1]\n", - " else: # SYMBOL_B\n", - " closest_idx = price_data['tstamp'].searchsorted(trade_time)\n", - " if closest_idx < len(norm_b):\n", - " norm_price = norm_b.iloc[closest_idx]\n", - " else:\n", - " norm_price = norm_b.iloc[-1]\n", - " \n", - " # Initialize group if not exists\n", - " if signal_key not in signal_groups:\n", - " signal_groups[signal_key] = {\n", - " 'times': [],\n", - " 'prices': [],\n", - " 'actual_prices': [],\n", - " 'symbol': symbol,\n", - " 'side': side,\n", - " # 'status': status,\n", - " 'action': trade['action']\n", - " }\n", - " \n", - " # Add to group\n", - " signal_groups[signal_key]['times'].append(trade_time)\n", - " signal_groups[signal_key]['prices'].append(norm_price)\n", - " signal_groups[signal_key]['actual_prices'].append(trade['price'])\n", - " \n", - " # Add each signal group as a single trace\n", - " for signal_key, group_data in signal_groups.items():\n", - " symbol = group_data['symbol']\n", - " side = group_data['side']\n", - " # status = group_data['status']\n", - " \n", - " # Determine marker properties (same for all OPEN/CLOSE of same side)\n", - " is_close: bool = (group_data['action'] == \"CLOSE\")\n", - " \n", - " if 'BUY' in side:\n", - " marker_color = 'green'\n", - " marker_symbol = 'triangle-up'\n", - " marker_size = 14\n", - " else: # SELL\n", - " marker_color = 'red'\n", - " marker_symbol = 'triangle-down'\n", - " marker_size = 14\n", - " \n", - " # Create hover text for each point in the group\n", - " hover_texts = []\n", - " for i, (time, norm_price, actual_price) in enumerate(zip(group_data['times'], \n", - " group_data['prices'], \n", - " group_data['actual_prices'])):\n", - " # Find the corresponding trade to get the status for hover text\n", - " trade_info = pair_trades[(pair_trades['time'] == time) & \n", - " (pair_trades['symbol'] == symbol) & \n", - " (pair_trades['side'] == side)]\n", - " if len(trade_info) > 0:\n", - " action = trade_info.iloc[0]['action']\n", - " hover_texts.append(f'{signal_key} {action}
' +\n", - " f'Time: {time}
' +\n", - " f'Normalized Price: {norm_price:.4f}
' +\n", - " f'Actual Price: ${actual_price:.2f}')\n", - " else:\n", - " hover_texts.append(f'{signal_key}
' +\n", - " f'Time: {time}
' +\n", - " f'Normalized Price: {norm_price:.4f}
' +\n", - " f'Actual Price: ${actual_price:.2f}')\n", - " \n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=group_data['times'],\n", - " y=group_data['prices'],\n", - " mode='markers',\n", - " name=signal_key,\n", - " marker=dict(\n", - " color=marker_color,\n", - " size=marker_size,\n", - " symbol=marker_symbol,\n", - " line=dict(width=2, color='black') if is_close else None\n", - " ),\n", - " showlegend=True,\n", - " hovertemplate='%{text}',\n", - " text=hover_texts\n", - " ),\n", - " row=2, col=1\n", - " )\n", - "\n", - " # ----------------------------- \n", - " \n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=symbol_a_data['tstamp'],\n", - " y=symbol_a_data[colname_a],\n", - " name=f'{SYMBOL_A} Price',\n", - " line=dict(color='blue', width=2),\n", - " opacity=0.8\n", - " ),\n", - " row=3, col=1\n", - " )\n", - "\n", - " # Filter trades for Symbol_A\n", - " symbol_a_trades = pair_trades[pair_trades['symbol'] == SYMBOL_A]\n", - " print(f\"\\nSymbol_A trades:\\n{symbol_a_trades}\")\n", - " \n", - " if len(symbol_a_trades) > 0:\n", - " # Separate trades by action and status for different colors\n", - " buy_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) & \n", - " (symbol_a_trades['action'].str.contains('OPEN', na=False))]\n", - " buy_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('BUY', na=False)) & \n", - " (symbol_a_trades['action'].str.contains('CLOSE', na=False))]\n", - " \n", - " sell_open_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) & \n", - " (symbol_a_trades['action'].str.contains('OPEN', na=False))]\n", - " sell_close_trades = symbol_a_trades[(symbol_a_trades['side'].str.contains('SELL', na=False)) & \n", - " (symbol_a_trades['action'].str.contains('CLOSE', na=False))]\n", - " \n", - " # Add BUY OPEN signals\n", - " if len(buy_open_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=buy_open_trades['time'],\n", - " y=buy_open_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_A} BUY OPEN',\n", - " marker=dict(color='green', size=12, symbol='triangle-up'),\n", - " showlegend=True\n", - " ),\n", - " row=3, col=1\n", - " )\n", - " \n", - " # Add BUY CLOSE signals\n", - " if len(buy_close_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=buy_close_trades['time'],\n", - " y=buy_close_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_A} BUY CLOSE',\n", - " marker=dict(color='green', size=12, symbol='triangle-up'),\n", - " line=dict(width=2, color='black'),\n", - " showlegend=True\n", - " ),\n", - " row=3, col=1\n", - " )\n", - " \n", - " # Add SELL OPEN signals\n", - " if len(sell_open_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=sell_open_trades['time'],\n", - " y=sell_open_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_A} SELL OPEN',\n", - " marker=dict(color='red', size=12, symbol='triangle-down'),\n", - " showlegend=True\n", - " ),\n", - " row=3, col=1\n", - " )\n", - " \n", - " # Add SELL CLOSE signals\n", - " if len(sell_close_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=sell_close_trades['time'],\n", - " y=sell_close_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_A} SELL CLOSE',\n", - " marker=dict(color='red', size=12, symbol='triangle-down'),\n", - " line=dict(width=2, color='black'),\n", - " showlegend=True\n", - " ),\n", - " row=3, col=1\n", - " )\n", - " \n", - " # 4. Symbol_B Market Data with Trading Signals\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=symbol_b_data['tstamp'],\n", - " y=symbol_b_data[colname_b],\n", - " name=f'{SYMBOL_B} Price',\n", - " line=dict(color='orange', width=2),\n", - " opacity=0.8\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Add trading signals for Symbol_B if available\n", - " symbol_b_trades = pair_trades[pair_trades['symbol'] == SYMBOL_B]\n", - " print(f\"\\nSymbol_B trades:\\n{symbol_b_trades}\")\n", - " \n", - " if len(symbol_b_trades) > 0:\n", - " # Separate trades by action and status for different colors\n", - " buy_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) & \n", - " (symbol_b_trades['action'].str.startswith('OPEN', na=False))]\n", - " buy_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('BUY', na=False)) & \n", - " (symbol_b_trades['action'].str.startswith('CLOSE', na=False))]\n", - " \n", - " sell_open_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) & \n", - " (symbol_b_trades['action'].str.contains('OPEN', na=False))]\n", - " sell_close_trades = symbol_b_trades[(symbol_b_trades['side'].str.contains('SELL', na=False)) & \n", - " (symbol_b_trades['action'].str.contains('CLOSE', na=False))]\n", - " \n", - " # Add BUY OPEN signals\n", - " if len(buy_open_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=buy_open_trades['time'],\n", - " y=buy_open_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_B} BUY OPEN',\n", - " marker=dict(color='darkgreen', size=12, symbol='triangle-up'),\n", - " showlegend=True\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Add BUY CLOSE signals\n", - " if len(buy_close_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=buy_close_trades['time'],\n", - " y=buy_close_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_B} BUY CLOSE',\n", - " marker=dict(color='green', size=12, symbol='triangle-up'),\n", - " line=dict(width=2, color='black'),\n", - " showlegend=True\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Add SELL OPEN signals\n", - " if len(sell_open_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=sell_open_trades['time'],\n", - " y=sell_open_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_B} SELL OPEN',\n", - " marker=dict(color='red', size=12, symbol='triangle-down'),\n", - " showlegend=True\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Add SELL CLOSE signals\n", - " if len(sell_close_trades) > 0:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=sell_close_trades['time'],\n", - " y=sell_close_trades['price'],\n", - " mode='markers',\n", - " name=f'{SYMBOL_B} SELL CLOSE',\n", - " marker=dict(color='red', size=12, symbol='triangle-down'),\n", - " line=dict(width=2, color='black'),\n", - " showlegend=True\n", - " ),\n", - " row=4, col=1\n", - " )\n", - " \n", - " # Update layout\n", - " fig.update_layout(\n", - " height=1600,\n", - " title_text=f\"Strategy Analysis - {SYMBOL_A} & {SYMBOL_B} ({TRD_DATE})\",\n", - " showlegend=True,\n", - " template=\"plotly_white\",\n", - " plot_bgcolor='lightgray',\n", - " )\n", - " \n", - " # Update y-axis labels\n", - " fig.update_yaxes(title_text=\"Scaled Dis-equilibrium\", row=1, col=1)\n", - " fig.update_yaxes(title_text=f\"{SYMBOL_A} Price ($)\", row=2, col=1)\n", - " fig.update_yaxes(title_text=f\"{SYMBOL_B} Price ($)\", row=3, col=1)\n", - " fig.update_yaxes(title_text=\"Normalized Price (Base = 1.0)\", row=4, col=1)\n", - " \n", - " # Update x-axis labels and ensure consistent time range\n", - " time_range = [timeline_df['tstamp'].min(), timeline_df['tstamp'].max()]\n", - " fig.update_xaxes(range=time_range, row=1, col=1)\n", - " fig.update_xaxes(range=time_range, row=2, col=1)\n", - " fig.update_xaxes(range=time_range, row=3, col=1)\n", - " fig.update_xaxes(title_text=\"Time\", range=time_range, row=4, col=1)\n", - " \n", - " # Display using plotly offline mode\n", - " # pyo.iplot(fig)\n", - " fig.show()\n", - " \n", - " else:\n", - " print(\"No interactive visualization data available - strategy may not have run successfully\")\n", - "\n", - " print(f\"\\nChart shows:\")\n", - " print(f\"- {SYMBOL_A} and {SYMBOL_B} prices normalized to start at 1.0\")\n", - " print(f\"- BUY signals shown as green triangles pointing up\")\n", - " print(f\"- SELL signals shown as orange triangles pointing down\")\n", - " print(f\"- All BUY signals per symbol grouped together, all SELL signals per symbol grouped together\")\n", - " print(f\"- Hover over markers to see individual trade details (OPEN/CLOSE status)\")\n", - "\n", - " if pair_trades is not None and len(pair_trades) > 0:\n", - " print(f\"- Total signals displayed: {len(pair_trades)}\")\n", - " print(f\"- {SYMBOL_A} signals: {len(pair_trades[pair_trades['symbol'] == SYMBOL_A])}\")\n", - " print(f\"- {SYMBOL_B} signals: {len(pair_trades[pair_trades['symbol'] == SYMBOL_B])}\")\n", - " else:\n", - " print(\"- No trading signals to display\")\n", - "\n", - "# visualization()\n" + "def visualize() -> None:\n", + " from tools.viz.viz_prices import visualize_prices\n", + " visualize_prices(strategy=PT_RESEARCH_STRATEGY, trading_date=TRADING_DATE)\n", + " from tools.viz.viz_trades import visualize_trades\n", + " visualize_trades(strategy=PT_RESEARCH_STRATEGY, results=PT_RESULTS, trading_date=TRADING_DATE)" ] }, { @@ -973,15 +374,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def summary() -> None:\n", " global PT_RESULTS\n", - " \n", - " PT_RESULTS.analyze_pair_performance()\n", - " \n" + " PT_RESULTS.analyze_pair_performance()" ] }, { @@ -993,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1050,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1087,16 +486,6008 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setup complete!\n", + "Current working directory: /home/oleg\n", + "Trading Parameters:\n", + " Configuration: /home/oleg/develop/pairs_trading/configuration/new_zscore.cfg\n", + " Symbol A: ADA-USDT\n", + " Symbol B: SOL-USDT\n", + " Trading Date: 2025-06-05\n", + "\n", + "Loading /home/oleg/develop/pairs_trading/configuration/new_zscore.cfg configuration using HJSON...\n", + "✓ Successfully loaded configuration\n", + " Training window: 120 minutes\n", + " Open threshold: 2\n", + " Close threshold: 0.5\n", + "\n", + "Data Configuration:\n", + " Data File: 20250605.mktdata.ohlcv.db\n", + " ✓ Data file found: ./data/crypto/20250605.mktdata.ohlcv.db\n", + "OPEN_TRADES: 2025-06-05 13:32:00 scaled_disequilibrium=-2.4460555867838187\n", + "OPEN TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 13:32:00 OPEN ADA-USDT BUY 0.682562 -2.446056 2.446056 -2.446056 OPEN\n", + "1 2025-06-05 13:32:00 OPEN SOL-USDT SELL 153.597967 -2.446056 2.446056 -2.446056 OPEN\n", + "CLOSE TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 13:41:00 CLOSE ADA-USDT SELL 0.683371 -0.329524 0.329524 -0.329524 CLOSE\n", + "1 2025-06-05 13:41:00 CLOSE SOL-USDT BUY 153.047927 -0.329524 0.329524 -0.329524 CLOSE\n", + "OPEN_TRADES: 2025-06-05 14:36:00 scaled_disequilibrium=-2.0656672663903457\n", + "OPEN TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 14:36:00 OPEN ADA-USDT BUY 0.670864 -2.065667 2.065667 -2.065667 OPEN\n", + "1 2025-06-05 14:36:00 OPEN SOL-USDT SELL 149.937010 -2.065667 2.065667 -2.065667 OPEN\n", + "CLOSE TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 14:47:00 CLOSE ADA-USDT SELL 0.677909 0.449403 0.449403 0.449403 CLOSE\n", + "1 2025-06-05 14:47:00 CLOSE SOL-USDT BUY 151.278572 0.449403 0.449403 0.449403 CLOSE\n", + "OPEN_TRADES: 2025-06-05 14:55:00 scaled_disequilibrium=-2.0134100228666436\n", + "OPEN TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 14:55:00 OPEN ADA-USDT BUY 0.673174 -2.01341 2.01341 -2.01341 OPEN\n", + "1 2025-06-05 14:55:00 OPEN SOL-USDT SELL 150.812932 -2.01341 2.01341 -2.01341 OPEN\n", + "CLOSE TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 15:22:00 CLOSE ADA-USDT SELL 0.675208 -0.182443 0.182443 -0.182443 CLOSE\n", + "1 2025-06-05 15:22:00 CLOSE SOL-USDT BUY 150.984110 -0.182443 0.182443 -0.182443 CLOSE\n", + "OPEN_TRADES: 2025-06-05 16:03:00 scaled_disequilibrium=-2.109009416680833\n", + "OPEN TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 16:03:00 OPEN ADA-USDT BUY 0.671655 -2.109009 2.109009 -2.109009 OPEN\n", + "1 2025-06-05 16:03:00 OPEN SOL-USDT SELL 150.844181 -2.109009 2.109009 -2.109009 OPEN\n", + "CLOSE TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 16:27:00 CLOSE ADA-USDT SELL 0.663392 -0.340001 0.340001 -0.340001 CLOSE\n", + "1 2025-06-05 16:27:00 CLOSE SOL-USDT BUY 149.116981 -0.340001 0.340001 -0.340001 CLOSE\n", + "OPEN_TRADES: 2025-06-05 16:40:00 scaled_disequilibrium=-2.104262608340289\n", + "OPEN TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 16:40:00 OPEN ADA-USDT BUY 0.660896 -2.104263 2.104263 -2.104263 OPEN\n", + "1 2025-06-05 16:40:00 OPEN SOL-USDT SELL 149.051567 -2.104263 2.104263 -2.104263 OPEN\n", + "CLOSE TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 16:47:00 CLOSE ADA-USDT SELL 0.660624 -0.448926 0.448926 -0.448926 CLOSE\n", + "1 2025-06-05 16:47:00 CLOSE SOL-USDT BUY 148.878845 -0.448926 0.448926 -0.448926 CLOSE\n", + "OPEN_TRADES: 2025-06-05 17:17:00 scaled_disequilibrium=-2.363830112535802\n", + "OPEN TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 17:17:00 OPEN ADA-USDT BUY 0.663076 -2.36383 2.36383 -2.36383 OPEN\n", + "1 2025-06-05 17:17:00 OPEN SOL-USDT SELL 149.702783 -2.36383 2.36383 -2.36383 OPEN\n", + "CLOSE TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 17:41:00 CLOSE ADA-USDT SELL 0.663472 -0.201384 0.201384 -0.201384 CLOSE\n", + "1 2025-06-05 17:41:00 CLOSE SOL-USDT BUY 149.425867 -0.201384 0.201384 -0.201384 CLOSE\n", + "OPEN_TRADES: 2025-06-05 18:02:00 scaled_disequilibrium=-2.242955179241669\n", + "OPEN TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 18:02:00 OPEN ADA-USDT BUY 0.660815 -2.242955 2.242955 -2.242955 OPEN\n", + "1 2025-06-05 18:02:00 OPEN SOL-USDT SELL 149.455464 -2.242955 2.242955 -2.242955 OPEN\n", + "CLOSE TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 18:36:00 CLOSE ADA-USDT SELL 0.663326 -0.409435 0.409435 -0.409435 CLOSE\n", + "1 2025-06-05 18:36:00 CLOSE SOL-USDT BUY 149.863517 -0.409435 0.409435 -0.409435 CLOSE\n", + "OPEN_TRADES: 2025-06-05 19:03:00 scaled_disequilibrium=-2.5413086373281155\n", + "OPEN TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 19:03:00 OPEN ADA-USDT BUY 0.657905 -2.541309 2.541309 -2.541309 OPEN\n", + "1 2025-06-05 19:03:00 OPEN SOL-USDT SELL 149.240433 -2.541309 2.541309 -2.541309 OPEN\n", + "CLOSE TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 19:19:00 CLOSE ADA-USDT SELL 0.638560 -0.044278 0.044278 -0.044278 CLOSE\n", + "1 2025-06-05 19:19:00 CLOSE SOL-USDT BUY 144.915997 -0.044278 0.044278 -0.044278 CLOSE\n", + "OPEN_TRADES: 2025-06-05 20:02:00 scaled_disequilibrium=-2.0824957059190754\n", + "OPEN TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 20:02:00 OPEN ADA-USDT BUY 0.641034 -2.082496 2.082496 -2.082496 OPEN\n", + "1 2025-06-05 20:02:00 OPEN SOL-USDT SELL 146.100694 -2.082496 2.082496 -2.082496 OPEN\n", + "CLOSE TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 20:13:00 CLOSE ADA-USDT SELL 0.637171 -0.443037 0.443037 -0.443037 CLOSE\n", + "1 2025-06-05 20:13:00 CLOSE SOL-USDT BUY 145.392057 -0.443037 0.443037 -0.443037 CLOSE\n", + "OPEN_TRADES: 2025-06-05 21:24:00 scaled_disequilibrium=-2.2040356379182975\n", + "OPEN TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 21:24:00 OPEN ADA-USDT BUY 0.623426 -2.204036 2.204036 -2.204036 OPEN\n", + "1 2025-06-05 21:24:00 OPEN SOL-USDT SELL 143.260214 -2.204036 2.204036 -2.204036 OPEN\n", + "CLOSE TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 21:46:00 CLOSE ADA-USDT SELL 0.627175 -0.353691 0.353691 -0.353691 CLOSE\n", + "1 2025-06-05 21:46:00 CLOSE SOL-USDT BUY 143.784983 -0.353691 0.353691 -0.353691 CLOSE\n", + "OPEN_TRADES: 2025-06-05 22:13:00 scaled_disequilibrium=-2.3383272565498383\n", + "OPEN TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 22:13:00 OPEN ADA-USDT BUY 0.622229 -2.338327 2.338327 -2.338327 OPEN\n", + "1 2025-06-05 22:13:00 OPEN SOL-USDT SELL 143.277122 -2.338327 2.338327 -2.338327 OPEN\n", + ": *** Position is NOT CLOSED. ***\n", + "CLOSE_POSITION TRADES:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 22:27:00 CLOSE ADA-USDT SELL 0.623272 0.0 0.0 0.0 CLOSE_POSITION\n", + "1 2025-06-05 22:27:00 CLOSE SOL-USDT BUY 143.996271 0.0 0.0 0.0 CLOSE_POSITION\n", + "\n", + "Created trading pair: \n", + "Market data shape: (120, 7)\n", + "Column names: ['close_ADA-USDT', 'close_SOL-USDT']\n", + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Price Statistics:\n", + " ADA-USDT: Mean=$0.66, Std=$0.02\n", + " SOL-USDT: Mean=$148.99, Std=$3.42\n", + " Price Ratio: Mean=0.00, Std=0.00\n", + " Correlation: 0.9902\n", + "\n", + "Created trading pair: \n", + "Market data shape: (120, 7)\n", + "Column names: ['close_ADA-USDT', 'close_SOL-USDT']\n" + ] + }, + { + "data": { + "text/html": [ + " \n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== SLIDING FIT INTERACTIVE VISUALIZATION ===\n", + "Note: Rolling Fit strategy visualization with interactive plotly charts\n", + "Using consistent timeline with 659 timestamps\n", + "Timeline range: 2025-06-05 11:30:00 to 2025-06-05 22:29:00\n", + "\n", + "Symbol_A trades:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "0 2025-06-05 13:32:00 OPEN ADA-USDT BUY 0.682562 -2.446056 2.446056 -2.446056 OPEN\n", + "2 2025-06-05 13:41:00 CLOSE ADA-USDT SELL 0.683371 -0.329524 0.329524 -0.329524 CLOSE\n", + "4 2025-06-05 14:36:00 OPEN ADA-USDT BUY 0.670864 -2.065667 2.065667 -2.065667 OPEN\n", + "6 2025-06-05 14:47:00 CLOSE ADA-USDT SELL 0.677909 0.449403 0.449403 0.449403 CLOSE\n", + "8 2025-06-05 14:55:00 OPEN ADA-USDT BUY 0.673174 -2.013410 2.013410 -2.013410 OPEN\n", + "10 2025-06-05 15:22:00 CLOSE ADA-USDT SELL 0.675208 -0.182443 0.182443 -0.182443 CLOSE\n", + "12 2025-06-05 16:03:00 OPEN ADA-USDT BUY 0.671655 -2.109009 2.109009 -2.109009 OPEN\n", + "14 2025-06-05 16:27:00 CLOSE ADA-USDT SELL 0.663392 -0.340001 0.340001 -0.340001 CLOSE\n", + "16 2025-06-05 16:40:00 OPEN ADA-USDT BUY 0.660896 -2.104263 2.104263 -2.104263 OPEN\n", + "18 2025-06-05 16:47:00 CLOSE ADA-USDT SELL 0.660624 -0.448926 0.448926 -0.448926 CLOSE\n", + "20 2025-06-05 17:17:00 OPEN ADA-USDT BUY 0.663076 -2.363830 2.363830 -2.363830 OPEN\n", + "22 2025-06-05 17:41:00 CLOSE ADA-USDT SELL 0.663472 -0.201384 0.201384 -0.201384 CLOSE\n", + "24 2025-06-05 18:02:00 OPEN ADA-USDT BUY 0.660815 -2.242955 2.242955 -2.242955 OPEN\n", + "26 2025-06-05 18:36:00 CLOSE ADA-USDT SELL 0.663326 -0.409435 0.409435 -0.409435 CLOSE\n", + "28 2025-06-05 19:03:00 OPEN ADA-USDT BUY 0.657905 -2.541309 2.541309 -2.541309 OPEN\n", + "30 2025-06-05 19:19:00 CLOSE ADA-USDT SELL 0.638560 -0.044278 0.044278 -0.044278 CLOSE\n", + "32 2025-06-05 20:02:00 OPEN ADA-USDT BUY 0.641034 -2.082496 2.082496 -2.082496 OPEN\n", + "34 2025-06-05 20:13:00 CLOSE ADA-USDT SELL 0.637171 -0.443037 0.443037 -0.443037 CLOSE\n", + "36 2025-06-05 21:24:00 OPEN ADA-USDT BUY 0.623426 -2.204036 2.204036 -2.204036 OPEN\n", + "38 2025-06-05 21:46:00 CLOSE ADA-USDT SELL 0.627175 -0.353691 0.353691 -0.353691 CLOSE\n", + "40 2025-06-05 22:13:00 OPEN ADA-USDT BUY 0.622229 -2.338327 2.338327 -2.338327 OPEN\n", + "42 2025-06-05 22:27:00 CLOSE ADA-USDT SELL 0.623272 0.000000 0.000000 0.000000 CLOSE_POSITION\n", + "\n", + "Symbol_B trades:\n", + " time action symbol side price disequilibrium scaled_disequilibrium signed_scaled_disequilibrium status\n", + "1 2025-06-05 13:32:00 OPEN SOL-USDT SELL 153.597967 -2.446056 2.446056 -2.446056 OPEN\n", + "3 2025-06-05 13:41:00 CLOSE SOL-USDT BUY 153.047927 -0.329524 0.329524 -0.329524 CLOSE\n", + "5 2025-06-05 14:36:00 OPEN SOL-USDT SELL 149.937010 -2.065667 2.065667 -2.065667 OPEN\n", + "7 2025-06-05 14:47:00 CLOSE SOL-USDT BUY 151.278572 0.449403 0.449403 0.449403 CLOSE\n", + "9 2025-06-05 14:55:00 OPEN SOL-USDT SELL 150.812932 -2.013410 2.013410 -2.013410 OPEN\n", + "11 2025-06-05 15:22:00 CLOSE SOL-USDT BUY 150.984110 -0.182443 0.182443 -0.182443 CLOSE\n", + "13 2025-06-05 16:03:00 OPEN SOL-USDT SELL 150.844181 -2.109009 2.109009 -2.109009 OPEN\n", + "15 2025-06-05 16:27:00 CLOSE SOL-USDT BUY 149.116981 -0.340001 0.340001 -0.340001 CLOSE\n", + "17 2025-06-05 16:40:00 OPEN SOL-USDT SELL 149.051567 -2.104263 2.104263 -2.104263 OPEN\n", + "19 2025-06-05 16:47:00 CLOSE SOL-USDT BUY 148.878845 -0.448926 0.448926 -0.448926 CLOSE\n", + "21 2025-06-05 17:17:00 OPEN SOL-USDT SELL 149.702783 -2.363830 2.363830 -2.363830 OPEN\n", + "23 2025-06-05 17:41:00 CLOSE SOL-USDT BUY 149.425867 -0.201384 0.201384 -0.201384 CLOSE\n", + "25 2025-06-05 18:02:00 OPEN SOL-USDT SELL 149.455464 -2.242955 2.242955 -2.242955 OPEN\n", + "27 2025-06-05 18:36:00 CLOSE SOL-USDT BUY 149.863517 -0.409435 0.409435 -0.409435 CLOSE\n", + "29 2025-06-05 19:03:00 OPEN SOL-USDT SELL 149.240433 -2.541309 2.541309 -2.541309 OPEN\n", + "31 2025-06-05 19:19:00 CLOSE SOL-USDT BUY 144.915997 -0.044278 0.044278 -0.044278 CLOSE\n", + "33 2025-06-05 20:02:00 OPEN SOL-USDT SELL 146.100694 -2.082496 2.082496 -2.082496 OPEN\n", + "35 2025-06-05 20:13:00 CLOSE SOL-USDT BUY 145.392057 -0.443037 0.443037 -0.443037 CLOSE\n", + "37 2025-06-05 21:24:00 OPEN SOL-USDT SELL 143.260214 -2.204036 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+ " SOL-USDT: SELL @ $153.60 → BUY @ $153.05 | Return: +0.36% | Shares: 6.51\n", + " Disequilibrium: Open: -2.4461, Close: -0.3295\n", + " Pair Return: +0.48% | Close Condition: CLOSE\n", + "\n", + " 14:36:00-14:47:00\n", + " ADA-USDT: BUY @ $0.67 → SELL @ $0.68 | Return: +1.05% | Shares: 1490.62\n", + " SOL-USDT: SELL @ $149.94 → BUY @ $151.28 | Return: -0.89% | Shares: 6.67\n", + " Disequilibrium: Open: -2.0657, Close: 0.4494\n", + " Pair Return: +0.16% | Close Condition: CLOSE\n", + "\n", + " 14:55:00-15:22:00\n", + " ADA-USDT: BUY @ $0.67 → SELL @ $0.68 | Return: +0.30% | Shares: 1485.50\n", + " SOL-USDT: SELL @ $150.81 → BUY @ $150.98 | Return: -0.11% | Shares: 6.63\n", + " Disequilibrium: Open: -2.0134, Close: -0.1824\n", + " Pair Return: +0.19% | Close Condition: CLOSE\n", + "\n", + " 16:03:00-16:27:00\n", + " ADA-USDT: BUY @ $0.67 → SELL @ $0.66 | Return: -1.23% | Shares: 1488.86\n", + " SOL-USDT: SELL @ $150.84 → BUY @ $149.12 | Return: +1.15% | Shares: 6.63\n", + " Disequilibrium: Open: -2.1090, Close: -0.3400\n", + " Pair Return: -0.09% | Close Condition: CLOSE\n", + "\n", + " 16:40:00-16:47:00\n", + " ADA-USDT: BUY @ $0.66 → SELL @ $0.66 | Return: -0.04% | Shares: 1513.10\n", + " SOL-USDT: SELL @ $149.05 → BUY @ $148.88 | Return: +0.12% | Shares: 6.71\n", + " Disequilibrium: Open: -2.1043, Close: -0.4489\n", + " Pair Return: +0.07% | Close Condition: CLOSE\n", + "\n", + " 17:17:00-17:41:00\n", + " ADA-USDT: BUY @ $0.66 → SELL @ $0.66 | Return: +0.06% | Shares: 1508.12\n", + " SOL-USDT: SELL @ $149.70 → BUY @ $149.43 | Return: +0.18% | Shares: 6.68\n", + " Disequilibrium: Open: -2.3638, Close: -0.2014\n", + " Pair Return: +0.24% | Close Condition: CLOSE\n", + "\n", + " 18:02:00-18:36:00\n", + " ADA-USDT: BUY @ $0.66 → SELL @ $0.66 | Return: +0.38% | Shares: 1513.28\n", + " SOL-USDT: SELL @ $149.46 → BUY @ $149.86 | Return: -0.27% | Shares: 6.69\n", + " Disequilibrium: Open: -2.2430, Close: -0.4094\n", + " Pair Return: +0.11% | Close Condition: CLOSE\n", + "\n", + " 19:03:00-19:19:00\n", + " ADA-USDT: BUY @ $0.66 → SELL @ $0.64 | Return: -2.94% | Shares: 1519.98\n", + " SOL-USDT: SELL @ $149.24 → BUY @ $144.92 | Return: +2.90% | Shares: 6.70\n", + " Disequilibrium: Open: -2.5413, Close: -0.0443\n", + " Pair Return: -0.04% | Close Condition: CLOSE\n", + "\n", + " 20:02:00-20:13:00\n", + " ADA-USDT: BUY @ $0.64 → SELL @ $0.64 | Return: -0.60% | Shares: 1559.98\n", + " SOL-USDT: SELL @ $146.10 → BUY @ $145.39 | Return: +0.49% | Shares: 6.84\n", + " Disequilibrium: Open: -2.0825, Close: -0.4430\n", + " Pair Return: -0.12% | Close Condition: CLOSE\n", + "\n", + " 21:24:00-21:46:00\n", + " ADA-USDT: BUY @ $0.62 → SELL @ $0.63 | Return: +0.60% | Shares: 1604.04\n", + " SOL-USDT: SELL @ $143.26 → BUY @ $143.78 | Return: -0.37% | Shares: 6.98\n", + " Disequilibrium: Open: -2.2040, Close: -0.3537\n", + " Pair Return: +0.24% | Close Condition: CLOSE\n", + "\n", + " 22:13:00-22:27:00\n", + " ADA-USDT: BUY @ $0.62 → SELL @ $0.62 | Return: +0.17% | Shares: 1607.13\n", + " SOL-USDT: SELL @ $143.28 → BUY @ $144.00 | Return: -0.50% | Shares: 6.98\n", + " Disequilibrium: Open: -2.3383, Close: 0.0000\n", + " Pair Return: -0.33% | Close Condition: CLOSE_POSITION\n", + "\n", + " Day Total Return: +0.90% (11 pairs)\n", + "\n", + "====== TOTAL RETURN ACROSS ALL DAYS ======\n", + "Total Return: +0.90%\n", + "Total Days: 1\n", + "Average Daily Return: +0.90%\n", + "\n", + "====== NO OUTSTANDING POSITIONS ======\n", + "\n", + "====== PAIR RESEARCH GRAND TOTALS ======\n", + "Total Return: +0.90%\n", + "Total Days Traded: 1\n", + "Total Pair Trades: 11\n", + "Average Daily Return: +0.90%\n", + "Best Day: 20250605 (+0.90%)\n", + "Worst Day: 20250605 (+0.90%)\n", + "\n", + "====== ADDITIONAL METRICS ======\n", + "Winning Days: 1/1 (100.0%)\n", + "Average Symbol Return: +0.04%\n", + "Average Pair Return: +0.04%\n", + "Daily Return Range: +0.90% to +0.90%\n" + ] + } + ], "source": [ "setup()\n", "load_config_from_file()\n", "prepare_config()\n", "run_strategy()\n", - "visualize_prices()\n", - "visualize_trades()\n", + "visualize()\n", "summary() \n" ] } diff --git a/research/viz_test.py b/research/viz_test.py new file mode 100644 index 0000000..e10d31b --- /dev/null +++ b/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()