update

.gitignore

@@ -1,4 +1,5 @@
 /.venv/
 .venv
 /.vscode/
-.vscode
+.vscode
+.obsidian

deleteme.md

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+[Fetching data](#Fetching%20data)
+
+# Fetching data
+fdfd

docs.md

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+## MCP
+
+### Local vbtpro mcp server
+main code in `vectorbtpro.mcp_server` can run either in `stdio` or in `streamable-http mode`
+
+Run mcp server in streamable-http mode (currently manually in terminal as single process for all local mcp clients)
+```bash
+cd /Users/davidbrazda/Documents/Development/python/vectorbt.pro-2025.6.24
+source .venv/bin/activate 
+python -m vectorbtpro.mcp_server --transport streamable-http
+```
+
+Claude desktop (currently doesnt support remotes) can use it by `mcp-remote`
+
+```json
+      "vectorbtpro_server": {
+        "command": "npx",
+        "args": [
+          "-y", 
+          "mcp-remote", 
+          "http://localhost:8000/mcp"
+        ]
+      }
+```
+
+Other MCPs (Claude code, Gemini CLI)
+
+```json
+{
+  "mcpServers": {
+    "vectorbtpro_server": {
+      "transport": "http",
+      "url": "http://localhost:8000"
+    }
+  }
+}
+```
+
+
+## Claude Desktop
+
+**Settings:**
+
+ `/Users/davidbrazda/Library/Application Support/Claude/claude_desktop_config.json`
+ 
+**Logs:**
+
+ `/Users/davidbrazda/Library/Logs/Claude` (main.log,mcp.log, mcp_server-name.log)
+
+## Claude code
+[Doc](https://docs.anthropic.com/en/docs/claude-code/mcp)
+
+**Settings:**
+
+- User/global settings (`~/.claude.json`)
+- Project-specific MCP file (`.mcp.json`)
+
+
+**Dedicated MCP file:** ~/.claude/mcp_servers.json 
+
+**Logs:**
+
+## Claude Code Router
+[doc](https://github.com/musistudio/claude-code-router)
+
+**Settings:**
+
+To set models for each tasks
+
+`~/.claude-code-router/config.json`
+
+**Logs:**
+
+`~/.claude-code-router/claude-code-router.log`
+

features_targets.md

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+Here goes the target features
+
+
+> [!NOTE] Poznámka
+> Contents
+
+
+> [!note]- poznám ka
+> neco
+> neco
+
+> [!example]- Graph: voaltility averagae slope
+> ![[Pasted image 20250630140635.png]]
+
+
+> [!example]- Graph: volatility average slope across 1d to 30d range of windows
+> ![[Volatility_average_slope.png]]
+
+# Things to try
+
+TODO:
+* lepsi labeling
+* continue here https://claude.ai/chat/b3ee78b6-9662-4f25-95f0-ecac4a78a41b
+* try model with other symbols
+* rey different retraining options (even hourly)
+
+Features:
+- add datetime features (useful for rush hour model)
+- add MT features as columns
+- use convolutional networks to create features (https://www.youtube.com/watch?v=6wK4q8QvsV4)
+Enhance model:
+* multi target see xgb doc
+* use SL with target price, with validy for few seconds
+* how handle imbalanced datase https://xgboost.readthedocs.io/en/stable/tutorials/param_tuning.html
+
+
+Target:
+- maybe add manual labeling
+
+# Features
+
+```python
+
+    def prepare_features(self, df: pd.DataFrame) -> tuple[pd.DataFrame, list]:
+        """Prepare enhanced features from input df with focus on predictive potential"""
+        features = pd.DataFrame(index=df.index)
+
+        # Original ohlcv added to features
+        features['close'] = df['close']
+        features['volume'] = df['volume']
+        features['trades_count'] = df['trades']
+        features['buy_volume'] = df['buyvolume']
+        features['sell_volume'] = df['sellvolume']
+        features['high'] = df['high']
+        features['low'] = df['low']
+        # features['log_return'] = np.log(features['close'] / features['close'].shift(1))
+        # features['returns_1'] = features['close'].pct_change()
+        # features['returns_5'] = features['close'].pct_change(5)
+        # features['returns_20'] = features['close'].pct_change(20)
+
+        def get_fib_windows():
+            """
+            #TODO based on real time (originally for 1s bars)
+
+            Generate Fibonacci sequence windows up to ~1 hour (3600 seconds)
+            Returns sequence: 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584
+            """
+            fib_windows = [3, 5]
+            while fib_windows[-1] < 3600/60:
+                next_fib = fib_windows[-1] + fib_windows[-2]
+                if next_fib > 3600/60:
+                    break
+                fib_windows.append(next_fib)
+            return fib_windows
+
+        fib_windows = get_fib_windows()
+        
+        # Base price and returns
+        features['log_return'] = np.log(features['close'] / features['close'].shift(1))
+        features['price_velocity'] = (features['close'] - features['close'].shift(1)) / 1.0  # per second
+        features['price_acceleration'] = features['price_velocity'] - features['price_velocity'].shift(1)
+        
+        # Fibonacci-based features
+        for window in fib_windows:
+            # Price features
+            features[f'log_return_{window}s'] = np.log(features['close'] / features['close'].shift(window))
+            features[f'volatility_{window}s'] = features['log_return'].rolling(window).std()
+            features[f'range_{window}s'] = (features['high'].rolling(window).max() - 
+                                        features['low'].rolling(window).min()) / features['close']
+            
+            # Volume features
+            features[f'volume_momentum_{window}s'] = (
+                features['volume'].rolling(window).mean() / 
+                features['volume'].rolling(window * 2).mean()
+            )
+            
+            features[f'buy_volume_momentum_{window}s'] = (
+                features['buy_volume'].rolling(window).mean() / 
+                features['buy_volume'].rolling(window * 2).mean()
+            )
+            
+            features[f'sell_volume_momentum_{window}s'] = (
+                features['sell_volume'].rolling(window).mean() / 
+                features['sell_volume'].rolling(window * 2).mean()
+            )
+            
+            # Trade features
+            features[f'trade_intensity_{window}s'] = (
+                features['trades_count'].rolling(window).mean() / 
+                features['trades_count'].rolling(window * 2).mean()
+            )
+            
+            features[f'avg_trade_size_{window}s'] = (
+                features['volume'].rolling(window).sum() / 
+                features['trades_count'].rolling(window).sum()
+            )
+            
+            # Order flow features
+            features[f'cum_volume_delta_{window}s'] = (
+                features['buy_volume'] - features['sell_volume']
+            ).rolling(window).sum()
+            
+            features[f'volume_pressure_{window}s'] = (
+                features['buy_volume'].rolling(window).sum() / 
+                features['sell_volume'].rolling(window).sum()
+            )
+            
+            # Price efficiency
+            features[f'price_efficiency_{window}s'] = (
+                np.abs(features['close'] - features['close'].shift(window)) /
+                (features['high'].rolling(window).max() - features['low'].rolling(window).min())
+            )
+            
+            # Moving averages and their crosses
+            features[f'sma_{window}s'] = features['close'].rolling(window).mean()
+            if window > 5:  # Create MA crosses with shorter timeframe
+                features[f'ma_cross_5_{window}s'] = (
+                    features['close'].rolling(5).mean() - 
+                    features['close'].rolling(window).mean()
+                )
+        
+        # MA-based features
+        ma_lengths = [5, 10, 20, 50]
+        for length in ma_lengths:
+            # Regular MAs
+            features[f'ma_{length}'] = features['close'].rolling(length).mean()
+            
+            # MA slopes (rate of change)
+            features[f'ma_{length}_slope'] = features[f'ma_{length}'].pct_change(3)
+            
+            # Price distance from MA
+            features[f'price_ma_{length}_dist'] = (features['close'] - features[f'ma_{length}']) / features[f'ma_{length}']
+            
+            # MA crossovers
+            if length > 5:
+                features[f'ma_5_{length}_cross'] = (features['ma_5'] - features[f'ma_{length}']) / features[f'ma_{length}']
+        
+        # MA convergence/divergence
+        features['ma_convergence'] = ((features['ma_5'] - features['ma_20']).abs() / 
+                                    features['ma_20'].rolling(10).mean())
+        
+        # Volatility features using MAs
+        features['ma_volatility'] = features['ma_5'].rolling(10).std() / features['ma_20']
+        
+        # MA momentum
+        features['ma_momentum'] = (features['ma_5'] / features['ma_5'].shift(5) - 1) * 100
+          
+        
+        # Cleanup and feature selection
+        features = features.replace([np.inf, -np.inf], np.nan)
+        
+        lookback = 1000
+        if len(features) > lookback:
+            rolling_corr = features.iloc[-lookback:].corr().abs()
+            upper = rolling_corr.where(np.triu(np.ones(rolling_corr.shape), k=1).astype(bool))
+            to_drop = [column for column in upper.columns if any(upper[column] > 0.95)]
+            print(f"Column highly correlated - maybe drop? {to_drop} ")
+            #features = features.drop(columns=to_drop)
+        
+        feature_columns = list(features.columns)
+        print(f"Features shape before dropna: {features.shape}")
+        
+        return features.dropna(), feature_columns
+```
+
+
+
+# Targets
+## Unbalanced classes
+
+```python
+from xgboost import XGBClassifier
+
+# Compute scale_pos_weight
+n_0 = sum(y_train == 0)
+n_1 = sum(y_train == 1)
+scale_pos_weight = n_0 / n_1
+
+model = XGBClassifier(scale_pos_weight=scale_pos_weight, ...)
+```
+
+
+```python
+    def create_target_regressor(self, df: pd.DataFrame) -> pd.Series:
+        """
+        https://claude.ai/chat/8e7fe81c-ddbe-4e64-9af0-2bc4764fc5f0
+
+        Creates enhanced target variable using adaptive returns based on market conditions.
+        Key improvements:
+        1. Multi-timeframe momentum approach
+        2. Volume-volatility regime adaptation
+        3. Trend-following vs mean-reversion regime detection
+        4. Noise reduction through sophisticated filtering
+        
+        Parameters:
+        -----------
+        df : pd.DataFrame
+            Features df containing required columns: 'close', 'volume', volatility features
+        
+        Returns:
+        --------
+        pd.Series
+            Enhanced target variable with cross-day targets removed
+        """
+
+        future_bars= self.config.forward_bars
+
+        future_ma_fast = df['close'].shift(-future_bars).rolling(5).mean()
+        
+        # Calculate forward returns (original approach)
+        forward_returns = df['close'].shift(-future_bars) / df['close'] - 1
+        
+        target =  forward_returns
+
+       # 6. Advanced noise reduction
+        # Use exponential moving standard deviation for dynamic thresholds
+        target_std = target.ewm(span=50, min_periods=20).std()
+        
+        # Adaptive thresholds based on rolling standard deviation
+        upper_clip = 2.5 * target_std
+        lower_clip = -2.5 * target_std
+        
+        # Apply soft clipping using hyperbolic tangent
+        target = target_std * np.tanh(target / target_std)
+        
+        # Final hard clips for extreme outliers
+        target = target.clip(lower=lower_clip, upper=upper_clip)
+
+
+        # 7. Remove cross-day targets and intraday seasonality
+        target = self.remove_crossday_targets(target, df, future_bars)
+
+        #only 10% of extreme values from both sides are kept
+        #target = target.where((target > target.quantile(0.9)) | (target < target.quantile(0.1)), 0)
+
+        print("after target generation", target.index[[0, -1]])
+        
+        return target
+```

ml-snippets.md

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+- [Features](#features)
+  - [Features analysis](#features-analysis)
+    - [Target to classes](#target-to-classes)
+  - [Features importance](#features-importance)
+  - [Features selection](#features-selection)
+- [Prediction](#prediction)
+  - [evaluation](#evaluation)
+    - [calculated returns based on various probability prediction thresholda](#calculated-returns-based-on-various-probability-prediction-thresholda)
+    - [cumulative returns bases od prob predictions](#cumulative-returns-bases-od-prob-predictions)
+  - [charts](#charts)
+
+
+
+
+
+# Features
+
+## Features analysis
+
+```python
+# Calculate different percentiles
+percentiles = [1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 99]
+print("\nPercentiles:")
+for p in percentiles:
+    print(f"{p}th percentile: {df['target'].quantile(p/100):.6f}")
+
+# Plot distribution
+plt.figure(figsize=(15, 10))
+
+# Plot 1: Overall distribution
+plt.subplot(2, 2, 1)
+sns.histplot(df['target'], bins=100)
+plt.title('Distribution of Returns')
+plt.axvline(x=0, color='r', linestyle='--', alpha=0.5)
+
+# Plot 2: Distribution with potential thresholds
+plt.subplot(2, 2, 2)
+sns.histplot(df['target'], bins=100)
+plt.title('Distribution with Potential Thresholds')
+
+# Add lines for different standard deviations
+std = df['target'].std()
+mean = df['target'].mean()
+for i in [0.5, 1.0, 1.5]:
+    plt.axvline(x=mean + i*std, color='g', linestyle='--', alpha=0.3, label=f'+{i} std')
+    plt.axvline(x=mean - i*std, color='r', linestyle='--', alpha=0.3, label=f'-{i} std')
+plt.legend()
+
+# Let's try different threshold approaches
+# Approach 1: Standard deviation based
+std_multiplier = 0.2
+std_threshold = std_multiplier * std
+labels_std = np.where(df['target'] > std_threshold, 1,
+                     np.where(df['target'] < -std_threshold, -1, 0))
+
+# Approach 2: Percentile based
+percentile_threshold = 0.2  # top/bottom 20%
+top_threshold = df['target'].quantile(1 - percentile_threshold)
+bottom_threshold = df['target'].quantile(percentile_threshold)
+labels_percentile = np.where(df['target'] > top_threshold, 1,
+                           np.where(df['target'] < bottom_threshold, -1, 0))
+
+# Plot 3: Distribution of STD-based classes
+plt.subplot(2, 2, 3)
+sns.histplot(data=pd.DataFrame({'return': df['target'], 'class': labels_std}), 
+            x='return', hue='class', bins=100)
+plt.title(f'Classes Based on {std_multiplier} Standard Deviation')
+plt.axvline(x=std_threshold, color='g', linestyle='--', alpha=0.5)
+plt.axvline(x=-std_threshold, color='r', linestyle='--', alpha=0.5)
+
+# Plot 4: Distribution of Percentile-based classes
+plt.subplot(2, 2, 4)
+sns.histplot(data=pd.DataFrame({'return': df['target'], 'class': labels_percentile}), 
+            x='return', hue='class', bins=100)
+plt.title(f'Classes Based on {percentile_threshold*100}th Percentiles')
+plt.axvline(x=top_threshold, color='g', linestyle='--', alpha=0.5)
+plt.axvline(x=bottom_threshold, color='r', linestyle='--', alpha=0.5)
+
+plt.tight_layout()
+plt.show()
+
+# Print class distributions
+print("\nClass Distribution (STD-based):")
+print(pd.Series(labels_std).value_counts(normalize=True))
+
+print("\nClass Distribution (Percentile-based):")
+print(pd.Series(labels_percentile).value_counts(normalize=True))
+
+# Calculate mean return for each class
+print("\nMean Return by Class (STD-based):")
+std_df = pd.DataFrame({'return': df['target'], 'class': labels_std})
+print(std_df.groupby('class')['return'].mean())
+
+print("\nMean Return by Class (Percentile-based):")
+perc_df = pd.DataFrame({'return': df['target'], 'class': labels_percentile})
+print(perc_df.groupby('class')['return'].mean())
+```
+<img src="image-1.png" alt="Target distributions" width="300"/>
+
+### Target to classes
+
+Based on std dev
+
+```python
+# Read and prepare the data
+df = pd.read_csv('model_data.csv')
+df = df.drop('ts_event', axis=1)
+
+# Separate features and target
+X = df.drop('target', axis=1)
+y = df['target']
+
+# Split the data first so we only use train data statistics for thresholds
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Calculate threshold based on training data only
+train_std = y_train.std()
+threshold = 0.2 * train_std
+
+# Transform targets into classes (update this function) instead of -1,0,1 do 0,1,2
+def create_labels(y, threshold):
+    return np.where(y > threshold, 2,
+                   np.where(y < -threshold, 0, 1))
+
+y_train_classes = create_labels(y_train, threshold)
+y_test_classes = create_labels(y_test, threshold)
+
+# Print class distribution
+print("Training Class Distribution:")
+print(pd.Series(y_train_classes).value_counts(normalize=True))
+print("\nTest Class Distribution:")
+print(pd.Series(y_test_classes).value_counts(normalize=True))
+```
+
+based on percentile/threshold
+
+## Features importance
+
+```python
+#XGB top 20 feature importance
+feature_importance = pd.DataFrame({
+    'feature': X.columns,
+    'importance': xgb_model.feature_importances_
+})
+feature_importance = feature_importance.sort_values('importance', ascending=False).head(20)
+
+plt.figure(figsize=(12, 6))
+sns.barplot(x='importance', y='feature', data=feature_importance)
+plt.title('Top 20 Most Important Features')
+plt.xlabel('Feature Importance')
+plt.tight_layout()
+plt.show()
+
+```
+
+## Features selection
+
+# Prediction
+
+## evaluation
+
+```python
+# Calculate directional accuracy
+directional_accuracy = (np.sign(y_pred) == np.sign(y_test)).mean()
+print(f"Directional Accuracy: {directional_accuracy:.4f}")
+
+#confusion matrix
+from sklearn.metrics import confusion_matrix
+# Plot confusion matrix
+plt.figure(figsize=(10, 8))
+cm = confusion_matrix(y_test_classes, y_pred)
+sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
+plt.title('Confusion Matrix')
+plt.ylabel('True Label')
+plt.xlabel('Predicted Label')
+plt.show()
+```
+### calculated returns based on various probability prediction thresholda
+
+```python
+# .predict_proba() gives the probabilities for each class
+print("Predicted probabilities:", model.predict_proba(X_test))
+# Output example:
+# [
+#  [0.35, 0.65],  # 35% not spam, 65% spam
+#  [0.70, 0.30],  # 70% not spam, 30% spam
+#  [0.45, 0.55],  # 45% not spam, 55% spam
+# ]
+```
+
+Chart probabilities
+
+```python
+# Predict probabilities for each class
+probabilities = model.predict_proba(X_test)  # Shape: (n_samples, n_classes)
+results_df = pd.DataFrame({
+    'Date': dates_test,
+    'Short Probability': probabilities[:, 0],   # Probability of class 0 (short)
+    'Neutral Probability': probabilities[:, 1], # Probability of class 1 (neutral)
+    'Long Probability': probabilities[:, 2]     # Probability of class 2 (long)
+}).sort_values(by='Date')  # Sort by date for time series plotting
+
+fig = go.Figure()
+
+# Add lines for each class probability
+fig.add_trace(go.Scatter(
+    x=results_df['Date'], y=results_df['Short Probability'],
+    mode='lines', name='Short (Class 0)', line=dict(color='red')
+))
+
+fig.add_trace(go.Scatter(
+    x=results_df['Date'], y=results_df['Neutral Probability'],
+    mode='lines', name='Neutral (Class 1)', line=dict(color='orange')
+))
+
+fig.add_trace(go.Scatter(
+    x=results_df['Date'], y=results_df['Long Probability'],
+    mode='lines', name='Long (Class 2)', line=dict(color='green')
+))
+
+# Add title and labels
+fig.update_layout(
+    title="Time Series of Predicted Class Probabilities",
+    xaxis_title="Date",
+    yaxis_title="Probability",
+    legend_title="Class"
+)
+
+fig.show()
+
+```
+
+### cumulative returns bases od prob predictions
+```python
+# Calculate returns based on probablity predictions
+def calculate_returns(predictions, actual_returns, confidence_threshold=0.0):
+    pred_probs = final_model.predict_proba(X_test_selected)
+    max_probs = np.max(pred_probs, axis=1)
+    
+    # Only take positions when confidence exceeds threshold
+    positions = np.zeros_like(predictions, dtype=float)
+    confident_mask = max_probs > confidence_threshold
+    
+    # Convert predictions 0->-1, 2->1 for returns calculation
+    adj_predictions = np.where(predictions == 2, 1, np.where(predictions == 0, -1, 0))
+    positions[confident_mask] = adj_predictions[confident_mask]
+    
+    returns = positions * actual_returns
+    return returns, np.mean(confident_mask)
+
+# Test different confidence thresholds
+confidence_thresholds = [0.4, 0.5, 0.6, 0.7, 0.8]
+results = []
+
+for conf_threshold in confidence_thresholds:
+    returns, coverage = calculate_returns(y_pred, y_test.values, conf_threshold)
+    
+    # Calculate metrics
+    sharpe = np.sqrt(252) * returns.mean() / returns.std()
+    accuracy = accuracy_score(y_test_classes[returns != 0], 
+                            y_pred[returns != 0])
+    
+    results.append({
+        'confidence_threshold': conf_threshold,
+        'sharpe': sharpe,
+        'accuracy': accuracy,
+        'coverage': coverage
+    })
+
+##Plot difference confidence threshodls
+# Plot cumulative returns
+plt.figure(figsize=(12, 6))
+for th in confidence_thresholds:
+    returns, _ = calculate_returns(y_pred, y_test.values, th)  # Using 0.6 confidence threshold
+    cumulative_returns = (1 + returns).cumprod()
+    plt.plot(cumulative_returns)
+plt.title('Cumulative Returns (0.6 confidence threshold)')
+plt.xlabel('Trade Number')
+plt.ylabel('Cumulative Return')
+plt.grid(True)
+plt.show()
+
+
+results_df = pd.DataFrame(results)
+print("\nPerformance at different confidence thresholds:")
+print(results_df)
+
+# Plot feature importance
+importance_df = pd.DataFrame({
+    'feature': selected_features,
+    'importance': final_model.feature_importances_
+})
+importance_df = importance_df.sort_values('importance', ascending=False)
+
+plt.figure(figsize=(12, 6))
+sns.barplot(x='importance', y='feature', data=importance_df)
+plt.title('Feature Importance')
+plt.xlabel('Importance')
+plt.tight_layout()
+plt.show()
+
+```
+
+
+## charts
+
+```python
+# Actual vs predicted values
+plt.figure(figsize=(10, 6))
+plt.scatter(y_test, y_pred, alpha=0.5)
+plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)
+plt.xlabel('Actual Returns')
+plt.ylabel('Predicted Returns')
+plt.title('Actual vs Predicted Returns')
+plt.tight_layout()
+plt.show()
+```

pandas-snippets.md

@@ -0,0 +1,9 @@
+- [Basics](#basics)
+
+# Basics
+
+```python
+df.loc[:, "D"] = np.array([5] * len(df))
+df.loc[:, ["A", "B"]]
+df.sort_index(axis=1, ascending=False) #axis=1 sort columns by labels, axis=0 sort by index
+```

vbt-snippets.md

@@ -1,6 +1,7 @@
-- [FETCHING DATA](#fetching-data)
+- [DEBUGGING](#debugging)
+- [Fetching Data](#fetching-data)
   - [REINDEX to main session](#reindex-to-main-session)
-  - [indexing](#indexing)
+  - [Smart indexing](#smart-indexing)
   - [Data manipulation](#data-manipulation)
 - [DISCOVERY](#discovery)
 - [DATA/WRAPPER](#datawrapper)
@@ -8,8 +9,11 @@
 - [RESAMPLING](#resampling)
   - [config](#config)
 - [REALIGN](#realign)
-  - [REALIGN\_CLOSING accessors](#realign_closing-accessors)
+  - [REALIGN\_CLOSING accessors](#realignclosing-accessors)
 - [SIGNALS](#signals)
+  - [Comparing](#comparing)
+  - [GENERATE SIGNALS IRERATIVELY (numba)](#generate-signals-ireratively-numba)
+  - [or as indicators](#or-as-indicators)
   - [ENTRIES/EXITS time based](#entriesexits-time-based)
   - [STOPS](#stops)
   - [OHLCSTX Module](#ohlcstx-module)
@@ -24,18 +28,43 @@
 - [Stoploss/Takeprofit](#stoplosstakeprofit)
   - [SL - ATR based](#sl---atr-based)
   - [EXIT after time](#exit-after-time)
-  - [CALLBACKS -](#callbacks--)
+  - [CALLBACKS -](#callbacks)
     - [MEMORY](#memory)
 - [Portfolio](#portfolio)
+  - [from signals](#from-signals)
+  - [CALLBACKS](#callbacks-1)
+    - [Access running total return from sim](#access-running-total-return-from-sim)
+    - [Staticization](#staticization)
+  - [Grouping](#grouping)
+- [Portfolio analysis](#portfolio-analysis)
+  - [pf.trades analysis](#pftrades-analysis)
+    - [PnL by hour of the day (BOXPLOT)](#pnl-by-hour-of-the-day-boxplot)
+  - [PF resampling](#pf-resampling)
+  - [PF Plotting](#pf-plotting)
+  - [Key Portfolio Analysis Methods \& Properties](#key-portfolio-analysis-methods--properties)
+    - [1. **Basic Portfolio Metrics**](#1-basic-portfolio-metrics)
+    - [2. **Comprehensive Stats Method**](#2-comprehensive-stats-method)
+    - [3. **Trade Analysis**](#3-trade-analysis)
+    - [4. **Drawdown Analysis**](#4-drawdown-analysis)
+    - [5. **Order Analysis**](#5-order-analysis)
+    - [6. **Custom Metrics**](#6-custom-metrics)
+    - [7. **Visualization Methods**](#7-visualization-methods)
+    - [8. **Advanced Analysis Examples**](#8-advanced-analysis-examples)
+  - [Entries/exits visual analysis](#entriesexits-visual-analysis)
+  - [Configuration](#configuration)
+- [Optimalization](#optimalization)
+  - [Param configuration](#param-configuration)
+  - [Pipeline](#pipeline)
 - [INDICATORS DEV](#indicators-dev)
   - [Custom ind](#custom-ind)
     - [register custom ind](#register-custom-ind)
     - [VWAP anchored example](#vwap-anchored-example)
     - [Use ttols indicators](#use-ttols-indicators)
 - [FAV INDICATORS](#fav-indicators)
-- [GROUPING](#grouping)
+- [GROUPING](#grouping-1)
 - [SPLITTING](#splitting)
 - [CHARTING](#charting)
+  - [standard vbt plot](#standard-vbt-plot)
 - [MULTIACCOUNT](#multiaccount)
 - [CUSTOM SIMULATION](#custom-simulation)
 - [ANALYSIS](#analysis)
@@ -43,7 +72,6 @@
 - [UTILS](#utils)
 - [Market calendar](#market-calendar)
 
-
 ```python
 import vectorbtpro as vbt
 from lightweight_charts import Panel, chart, PlotDFAccessor, PlotSRAccessor
@@ -56,8 +84,36 @@ if not hasattr(pd.DataFrame, 'lw'):
     pd.api.extensions.register_dataframe_accessor("lw")(PlotDFAccessor)
 ```
 
+# DEBUGGING
 
-# FETCHING DATA  
+```python
+vbt.pprint(pf.entry_trades) #pretty print of instance
+vbt.pdir(pf.entry_trades) #available methods/properties
+vbt.phelp(ollcov.run) #input/output attribnuttes of the method
+```
+
+prints which arguments are being passed to apply_func.
+
+```python
+def apply_func(*args, **kwargs):
+    for i, arg in enumerate(args):
+        print("arg {}: {}".format(i, type(arg)))
+    for k, v in kwargs.items():
+        print("kwarg {}: {}".format(k, type(v)))
+    raise NotImplementedError
+
+RollCov = vbt.IF(
+    class_name='RollCov',
+    input_names=['ts1', 'ts2'],
+    param_names=['w'],
+    output_names=['rollcov'],
+).with_apply_func(apply_func, select_params=False)
+
+ollCov.run(ts1, ts2, [2, 3], some_arg="some_value")
+```
+
+
+# Fetching Data
 ```python
  #fetching from remote db
 from lib.db import Connection
@@ -85,7 +141,35 @@ basic_data = vbt.Data.from_data(vbt.symbol_dict({"BAC": ohlcv_df}), tz_convert=z
 basic_data.wrapper.index.normalize().nunique() #numdays
 
 #Fetching Trades and Aggregating custom OHLCV
-TBD
+from ttools import load_data
+#This is how to call LOAD function
+symbol = ["SPY", "BAC"]
+#datetime in zoneNY 
+day_start = datetime(2024, 1, 15, 9, 30, 0)
+day_stop = datetime(2024, 10, 20, 16, 0, 0)
+day_start = zoneNY.localize(day_start)
+day_stop = zoneNY.localize(day_stop)
+
+#requested AGG
+resolution = 1 #12s bars
+agg_type = AggType.OHLCV #other types AggType.OHLCV_VOL, AggType.OHLCV_DOL, AggType.OHLCV_RENKO
+exclude_conditions = ['C','O','4','B','7','V','P','W','U','Z','F','9','M','6'] #None to defaults
+minsize = 100 #min trade size to include
+main_session_only = False
+force_remote = False
+
+data = load_data(symbol = symbol,
+                     agg_type = agg_type,
+                     resolution = resolution,
+                     start_date = day_start,
+                     end_date = day_stop,
+                     #exclude_conditions = None,
+                     minsize = minsize,
+                     main_session_only = main_session_only,
+                     force_remote = force_remote,
+                     return_vbt = True, #returns vbt object
+                     verbose = True
+                     )
 ```
 
 ## REINDEX to main session
@@ -114,10 +198,12 @@ testData = vbt.YFData.fetch(['MSFT'], start=start, end=end, timeframe=timeframe,
 # of market hours)
 testData = testData.transform(lambda x: x.reindex(market_klines))
 ```
-## indexing
+## Smart indexing
 ```python
+signal.vbt.xloc["04-26-2024":"04-29-2024"].get() #pdseries or df timeindex
+signal.vbt.xloc[("BAC", "04-26-2024"):("BAC","04-29-2024")].get() #multiindex
+entries.vbt.xloc["04-16-2024"].get() #one day
 entries.vbt.xloc[slice("2024-08-01","2024-08-03")].obj.info()
-
 data.xloc[slice("9:30","10:00")] #targeting only morning rush
 ```
 
@@ -217,7 +303,8 @@ _feature_config: tp.ClassVar[Config] = HybridConfig(
 
 basic_data._feature_config = _feature_config
 ```
-ddd
+
+```python
 #1s to 1T
 t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','trades','sellvolume']].resample("1T")
 t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())
@@ -226,6 +313,7 @@ t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())
 resampler_s = vbt.Resampler(target_data.index, source_data.index, source_freq="1T", target_freq="1s")
 basic_data.resample(resampler_s)
 
+```
 
 # REALIGN
 
@@ -255,7 +343,15 @@ t15close_realigned_with_resampler = t1data.data["BAC"].realign_closing(resampler
 
 
 # SIGNALS
+## Comparing
 ```python
+dvla = np.round(div_vwap_lin_angle.real,4) #ROUNDING to 4 decimals
+
+long_entries = tts.isrisingc(dvla,3).vbt & div_vwap_cum.div_below(0) #strictly rising for 3 bars
+short_entries = tts.isfalling(dvla,3).vbt & div_vwap_cum.div_above(0) #strictly falling for 3 bars
+long_entries = tts.isrising(dvla,3)#rising for 3 bars including equal values
+short_entries = tts.isfalling(dvla,3)#falling for 3 bars including equal values
+
 cond1 = data.get("Low") < bb.lowerband
 #comparing with previous value
 cond2 = bandwidth > bandwidth.shift(1)
@@ -263,8 +359,116 @@ cond2 = bandwidth > bandwidth.shift(1)
 cond2 = bandwidth > bandwidth.vbt.ago("7d")
 mask = cond1 & cond2
 mask.sum()
+
+#creating
+bandwidth = (bb.upperband - bb.lowerband) / bb.middleband
+mask = bandwidth.vbt > vbt.Param([0.15, 0.3], name="threshold")  #broadcasts and create combinations (for scalar params only)
+
+#same but for arrays
+mask = bandwidth.vbt.combine(
+    [0.15, 0.3],  #values elements (scalars or array)
+    combine_func=np.greater, 
+    keys=pd.Index([0.15, 0.3], name="threshold")  #keys for the multiindex
+)
+mask.sum()
 ```
 
+## GENERATE SIGNALS IRERATIVELY (numba)
+
+Used for 1D. For multiple symbol create own indicator instead.
+```python
+@njit  
+def generate_mask_1d_nb(  #required arrays as inputs
+    high, low,  
+    uband, mband, lband,  
+    cond2_th, cond4_th  
+):
+    out = np.full(high.shape, False)  
+    
+    for i in range(high.shape[0]):  
+        
+        bandwidth = (uband[i] - lband[i]) / mband[i]
+        cond1 = low[i] < lband[i]
+        cond2 = bandwidth > cond2_th
+        cond3 = high[i] > uband[i]
+        cond4 = bandwidth < cond4_th
+        signal = (cond1 and cond2) or (cond3 and cond4)  
+        out[i] = signal  
+        
+    return out
+
+mask = generate_mask_1d_nb(
+    data.get("High")["BTCUSDT"].values,  
+    data.get("Low")["BTCUSDT"].values,
+    bb.upperband["BTCUSDT"].values,
+    bb.middleband["BTCUSDT"].values,
+    bb.lowerband["BTCUSDT"].values,
+    0.30,
+    0.15
+)
+symbol_wrapper = data.get_symbol_wrapper()
+mask = symbol_wrapper["BTCUSDT"].wrap(mask)  
+mask.sum()
+```
+
+or create extra numba function to iterate over columns
+
+```python
+@njit
+def generate_mask_nb(  
+    high, low,
+    uband, mband, lband,
+    cond2_th, cond4_th
+):
+    out = np.empty(high.shape, dtype=np.bool_)  
+    
+    for col in range(high.shape[1]):  
+        out[:, col] = generate_mask_1d_nb(  
+            high[:, col], low[:, col],
+            uband[:, col], mband[:, col], lband[:, col],
+            cond2_th, cond4_th
+        )
+        
+    return out
+
+mask = generate_mask_nb(
+    vbt.to_2d_array(data.get("High")),  
+    vbt.to_2d_array(data.get("Low")),
+    vbt.to_2d_array(bb.upperband),
+    vbt.to_2d_array(bb.middleband),
+    vbt.to_2d_array(bb.lowerband),
+    0.30,
+    0.15
+)
+mask = symbol_wrapper.wrap(mask)
+mask.sum()
+```
+
+
+## or as indicators
+
+Works on columns.
+
+```python
+MaskGenerator = vbt.IF(  
+    input_names=["high", "low", "uband", "mband", "lband"],
+    param_names=["cond2_th", "cond4_th"],
+    output_names=["mask"]
+).with_apply_func(generate_mask_1d_nb, takes_1d=True)  
+mask_generator = MaskGenerator.run(  
+    data.get("High"),
+    data.get("Low"),
+    bb.upperband,
+    bb.middleband,
+    bb.lowerband,
+    [0.3, 0.4],
+    [0.1, 0.2],
+    param_product=True  
+)
+mask_generator.mask.sum()
+```
+
+
 ## ENTRIES/EXITS time based
 ```python
 #create entries/exits based on open of first symbol
@@ -485,7 +689,515 @@ Usecases:
 # Portfolio
 
 group_by=True to put all columns to the same group and cash_sharing=True to share capital among them
+## from signals
 
+```python
+pf = vbt.Portfolio.from_signals(
+    close=s12_data.close,
+    entries=long_entries_cln,
+    exits=long_exits,
+    short_entries=short_entries_cln,
+    short_exits=short_exits,
+    size=1, 
+    size_type=vbt.pf_enums.SizeType.Amount # Value, Percent, TargetAmount
+    price="nextopen" #where the fill is happening. Default is "close" of current bar, can be also  multiparameter vbt.Param(["close", "nextopen"]) 
+    sl_stop=0.3,
+    tp_stop = 0.4,
+    delta_format = vbt.pf_enums.DeltaFormat.Percent100, #(Absolute, Percent, Percent100, Target)
+    fees=0.0167/100,
+    freq="12s") #sl_stop=sl_stop, tp_stop = sl_stop,, tsl_stop
+```
+
+[SizeType enums](http://5.161.179.223:8000/vbt-doc/api/portfolio/enums/index.html#vectorbtpro.portfolio.enums.SizeType)
+
+[DeltaFormat enums](http://5.161.179.223:8000/vbt-doc/api/portfolio/enums/index.html#vectorbtpro.portfolio.enums.DeltaFormat)
+
+[Other PF enums](http://5.161.179.223:8000/vbt-doc/api/portfolio/enums/index.html)
+
+## CALLBACKS
+
+Callbacks functions can be used to place/alter entries/exits and various other things dynamically based on simulation status.
+All of them contain [SignalContext](http://5.161.179.223:8000/vbt-doc/api/portfolio/enums/#vectorbtpro.portfolio.enums.SignalContext) and also can include custom Memory.
+
+Importan SignalContact attributes:
+* `c.i` - current index
+* `c.index` - time index numpy
+* `c.last_pos_info[c.col] ` - named tuple of last position info 
+`{'names': ['id', 'col', 'size', 'entry_order_id', 'entry_idx', 'entry_price', 'entry_fees', 'exit_order_id', 'exit_idx', 'exit_price', 'exit_fees', 'pnl', 'return', 'direction', 'status', 'parent_id']`
+
+Callback functions:
+- signal_func_nb - place/alter entries/exits
+- adjust_sl_func_nb - adjust SL at each time stamp
+- adjust_func_nb - adjust size 
+- post_segment_func_nb
+
+More on callbacks in [cookbook](http://5.161.179.223:8000/vbt-doc/cookbook/portfolio/index.html#callbacks).
+
+For exit dependent entries, the entries can be preprocessed in `signal_func_nb` see [callbacks](http://5.161.179.223:8000/vbt-doc/cookbook/portfolio/index.html#callbacks) in cookbok or [signal function](http://5.161.179.223:8000/vbt-doc/documentation/portfolio/from-signals/index.html#signal-function)in doc
+
+```python
+@njit
+def signal_func_nb(c, entries, exits, short_entries, short_exits, cooldown_time, cooldown_bars):
+    entry = vbt.pf_nb.select_nb(c, entries) #get current value
+    exit = vbt.pf_nb.select_nb(c, exits)
+    short_entry = vbt.pf_nb.select_nb(c, short_entries)
+    short_exit = vbt.pf_nb.select_nb(c, short_exits)
+    if not vbt.pf_nb.in_position_nb(c): # short for c.last_position == 0
+        if vbt.pf_nb.has_orders_nb(c):  
+            if c.last_pos_info[c.col]["pnl"] < 0:  #current index is c.i
+                last_exit_idx = c.last_pos_info[c.col]["exit_idx"] # exit index from last_pos_info named tuple
+                if cooldown_time is not None and c.index[c.i] - c.index[last_exit_idx] < cooldown_time:
+                    return False, exit, False, short_exit #disable entry
+                elif cooldown_bars is not None and last_exit_idx + cooldown_bars > c.i:
+                    return False, exit, False, short_exit #disable entry
+    return entry, exit, short_entry, short_exit
+
+cooldown_time = vbt.dt.to_ns(vbt.timedelta("1m"))
+cooldown_bars = 3
+
+pf = vbt.Portfolio.from_signals(
+    close=s12_data.close,
+    bm_close=data.data["SPY"].close, #explicit benchmark used in pf, ie. pf.plot_cum_returns().show()
+    entries=long_entries_cln,
+    exits=long_exits,
+    short_entries=short_entries_cln,
+    short_exits=short_exits,
+    signal_func_nb="signal_func_nb.py",
+    signal_args=(
+        vbt.Rep("entries"), 
+        vbt.Rep("exits"),
+        vbt.Rep("short_entries"),
+        vbt.Rep("short_exits"),
+        cooldown_time, # cooldown in timedelta in ns after exit
+        cooldown_bars  #cooldown in number of bars after exit
+    ),
+    sl_stop=0.3,
+    tp_stop = 0.4,
+    delta_format = vbt.pf_enums.DeltaFormat.Percent100, #(Absolute, Percent, Percent100, Target)
+    fees=0.0167/100,
+    freq="12s",
+    #staticized=True
+    #jitted=False
+    ) #sl_stop=sl_stop, tp_stop = sl_stop,, tsl_stop
+```
+
+Tips:  
+- To avoid waiting for the compilation, remove the `@njit` decorator from `signal_func_nb` and pass `jitted=False` to from_signals in order to disable Numba
+
+### Access running total return from sim
+
+create an empty array for cumulative returns and populate it inside the post_segment_func_nb callback. The same array accessed by other callbacks can be used to get the total return at any time step.
+
+```python
+@njit
+def adjust_func_nb(c, cum_return):
+    if c.cash_sharing:
+        total_return = cum_return[c.group] - 1
+    else:
+        total_return = cum_return[c.col] - 1
+    ...  
+
+@njit
+def post_segment_func_nb(c, cum_return):
+    if c.cash_sharing:
+        cum_return[c.group] *= 1 + c.last_return[c.group]
+    else:
+        for col in range(c.from_col, c.to_col):
+            cum_return[col] *= 1 + c.last_return[col]
+
+cum_return = None
+def init_cum_return(wrapper):
+    global cum_return
+    if cum_return is None:
+        cum_return = np.full(wrapper.shape_2d[1], 1.0)
+    return cum_return
+
+pf = vbt.PF.from_signals(
+    ...,
+    adjust_func_nb=adjust_func_nb,
+    adjust_args=(vbt.RepFunc(init_cum_return),),
+    post_segment_func_nb=post_segment_func_nb,
+    post_segment_args=(vbt.RepFunc(init_cum_return),),
+)
+
+```
+
+### Staticization
+Callbacks make function uncacheable, 
+to overcome that
+- define the callback in external file `signal_func_nb.py`
+
+```python
+@njit
+def signal_func_nb(c, fast_sma, slow_sma):  
+    long = vbt.pf_nb.iter_crossed_above_nb(c, fast_sma, slow_sma)
+    short = vbt.pf_nb.iter_crossed_below_nb(c, fast_sma, slow_sma)
+    return long, False, short, False
+```
+
+and then use use `staticized=True`
+
+```python
+data = vbt.YFData.pull("BTC-USD")
+pf = vbt.PF.from_signals(
+    data,
+    signal_func_nb="signal_func_nb.py",  
+    signal_args=(vbt.Rep("fast_sma"), vbt.Rep("slow_sma")),
+    broadcast_named_args=dict(
+        fast_sma=data.run("sma", 20, hide_params=True, unpack=True), 
+        slow_sma=data.run("sma", 50, hide_params=True, unpack=True)
+    ),
+    staticized=True  
+)
+```
+
+## Grouping
+
+Grouping in [signal function](http://5.161.179.223:8000/vbt-doc/documentation/portfolio/from-signals/index.html#signal-function).
+
+# Portfolio analysis
+
+[Portfolio base doc](http://5.161.179.223:8000/vbt-doc/api/portfolio/base/)
+
+```python
+pf.orders.readable
+pf.entry_trades.readable
+pf.exit_trades.readable
+pf.trades.readable
+pf.positions.readable
+pf.trade_history #human readable df expanding trades with metrics
+
+dd = pf.get_drawdowns().records_readable
+dd[dd["Status"] == "Active"] #Recovered
+pf.metrics #get available metrics and its short names&function 
+
+#trades
+vbt.pdir(pf.trades) # available methods/properties
+
+#orders
+pf.orders.side_buy.count() # pf.order.attribute_value.COUNT()
+pf.orders.stats(group_by=True)
+
+#daily returns
+pf.daily_returns.sort_values([(2, 'BAC')], ascending=True) #sorting values in levels
+pf.daily_returns.sort_values(pf.daily_returns.columns[0], ascending=True) #same with first level
+pf.daily_returns.cumsum()
+```
+## pf.trades analysis
+[pf.trades.plot()](http://5.161.179.223:8000/vbt-doc/api/portfolio/trades/#vectorbtpro.portfolio.trades.Trades.plot) doc - various options.
+
+```python
+fig = pf.trades.plot()
+fig.auto_rangebreaks()
+fig.show()
+
+df = pf.trades.readable
+```
+```python
+df["Direction"].value_counts() #count of trades for each Direction
+df.groupby("Direction")["PnL"].sum() #sum of pnl for each Direction (Short vs Long) .vbt.barplot() -to plot
+
+#daily PnL
+df.groupby(df['Exit Index'].dt.date)['PnL'].sum().sort_index(ascending=False) #daily PnL
+
+#daily PnL for each Direction
+df.groupby([df['Exit Index'].dt.date, 'Direction'])['PnL'].sum().sort_index(ascending=False) #daily PnL for each Direction
+#same but unstack, wehere long/short values become columns - for better charting
+df = df.groupby([df['Exit Index'].dt.date, 'Direction'])['PnL'].sum().sort_index(ascending=False).unstack()
+#df.vbt.barplot() or
+df.plot(kind="bar", stacked=True)
+
+
+#hourly PnL for each Direction, by Exit
+df = df.groupby([df['Exit Index'].dt.hour, 'Direction'])['PnL'].sum().sort_index(ascending=False).unstack()
+#df.vbt.barplot()
+df.plot(kind="bar", stacked=True)
+```
+
+```python
+#PnL by Day of the Week and Direction
+# Group by day of the week and direction, then sum PnL
+pnl_by_day_and_direction_week = df.groupby([df['Exit Index'].dt.day_name(), 'Direction'])['PnL'].sum().unstack()
+fig = pnl_by_day_and_direction_week.vbt.barplot()
+fig.update_layout(
+    barmode='stack',  # Stack/group/overlay/relative the bars
+    title='Profit by Day of the Week and Direction',
+    xaxis_title='Day of the Week',
+    yaxis_title='Cumulative Profit'
+)
+```
+
+### PnL by hour of the day (BOXPLOT)
+![alt text](image.png)
+```python
+a = df.groupby([df['Exit Index'].dt.day_name(), df['Exit Index'].dt.hour])['PnL'].sum().unstack()
+fig = a.vbt.boxplot()
+fig.update_layout(
+    #barmode='stack',  # Stack/group/overlay/relative the bars
+    title='Profit by hour of the day',
+    xaxis_title='Hour of the day',
+    yaxis_title='Cumulative Profit'
+)
+```
+
+
+```python
+##Profit/Loss (PnL) vs. Trade Duration
+# Calculate trade duration in minutes
+df['Trade Duration'] = (df['Exit Index'] - df['Entry Index']).dt.total_seconds() / 60
+
+# Scatter plot of PnL vs Trade Duration
+plt.style.use('dark_background')
+colors = {'Short': 'lightcyan', 'Long': 'yellow'}
+plt.scatter(df['Trade Duration'], df['PnL'], c=df['Direction'].map(colors))
+
+# Adding labels and title
+plt.title('Trade Duration vs. Profit/Loss')
+plt.xlabel('Duration (Minutes)')
+plt.ylabel('Profit/Loss')
+
+# Create a legend
+handles = [plt.Line2D([0], [0], marker='o', color='w', label='Short', markerfacecolor='lightcyan', markersize=10),
+           plt.Line2D([0], [0], marker='o', color='w', label='Long', markerfacecolor='yellow', markersize=10)]
+plt.legend(title='Type', handles=handles)
+
+plt.tight_layout()
+plt.show()
+
+##Cumulative profits vs benchmark
+pf.plot_cum_returns().show()
+
+
+```
+## PF resampling
+```python
+monthly_returns = pf.returns_acc.resample("M").get() 
+daily_returns = pf.resample("D").returns #alternative
+fig = monthly_returns.vbt.boxplot() #box plot of monthly returns
+fig = monthly_returns.vbt.heatmap()   #heatmap of time vs  monthly returns 
+fig = monthly_returns.vbt.ts_heatmap() #heatmap of  returns vs time
+
+```
+
+## PF Plotting
+```python
+pf.plot_trade_signals().show() #plot long short entries/exits
+pf.plot_cum_returns().show() #cum returns vs benchmark
+
+##whether returns are distirbuted normally
+pf.returns.vbt.qqplot()
+
+#TRADES
+pf.trades.plot_mae_returns().show()#MAE/MFE - identify max loss/profit during the trade
+pf.trades.plot_expanding_mfe_returns().show() #expanding mea/mfe returns
+```
+[Plot Edge ratio](http://5.161.179.223:8000/static/js/vbt/features/analysis/index.html#edge-ratio) `pf.trades.plot_running_edge_ratio()`
+
+
+I'll research VectorBT PRO's portfolio analysis capabilities for you. Let me search for information about analyzing Portfolio objects, particularly those created by `Portfolio.from_signal`.Let me search for the correct Portfolio creation methods and analysis capabilities:Now let me search specifically for Portfolio analysis methods and attributes:Let me search for more specific information about Portfolio methods and examples:Let me search for specific Portfolio methods that provide analysis capabilities:Now let me find information about the Portfolio.from_signals method:Let me run some code to test the Portfolio analysis capabilities:Let me search for more specific Portfolio analysis examples and metrics:Based on my research of VectorBT PRO's Portfolio analysis capabilities, here's a comprehensive overview of how to analyze Portfolio objects (including those created by `Portfolio.from_signals`):
+
+## Key Portfolio Analysis Methods & Properties
+
+### 1. **Basic Portfolio Metrics**
+The Portfolio object provides numerous built-in properties for analysis:
+
+- **Returns & Performance:**
+  - `pf.returns` - Portfolio returns time series
+  - `pf.total_return` - Total return percentage
+  - `pf.annualized_return` - Annualized return
+  - `pf.cumulative_returns` - Cumulative returns time series
+
+- **Risk Metrics:**
+  - `pf.sharpe_ratio` - Sharpe ratio
+  - `pf.sortino_ratio` - Sortino ratio
+  - `pf.max_drawdown` - Maximum drawdown
+  - `pf.annualized_volatility` - Annualized volatility
+  - `pf.value_at_risk` - Value at Risk
+  - `pf.cond_value_at_risk` - Conditional Value at Risk
+
+- **Portfolio Value & Cash:**
+  - `pf.value` - Portfolio value time series
+  - `pf.final_value` - Final portfolio value
+  - `pf.cash` - Cash holdings over time
+  - `pf.asset_value` - Asset value over time
+
+### 2. **Comprehensive Stats Method**
+The most powerful analysis tool is the `stats()` method:
+
+```python
+# Get default statistics
+pf.stats()
+
+# Get specific metrics
+pf.stats(['total_return', 'sharpe_ratio', 'max_dd', 'total_trades'])
+
+# Get all available metrics
+pf.stats('all')
+```
+
+**Common metric names for `stats()`:**
+- `'total_return'` - Total return percentage
+- `'total_trades'` - Number of trades
+- `'win_rate'` - Winning trade percentage
+- `'sharpe_ratio'` - Sharpe ratio
+- `'sortino_ratio'` - Sortino ratio
+- `'max_dd'` - Maximum drawdown (note: `max_dd`, not `max_drawdown`)
+- `'calmar_ratio'` - Calmar ratio
+- `'omega_ratio'` - Omega ratio
+- `'expectancy'` - Expected value per trade
+- `'profit_factor'` - Profit factor
+- `'best_trade'` - Best trade return
+- `'worst_trade'` - Worst trade return
+- `'avg_winning_trade'` - Average winning trade
+- `'avg_losing_trade'` - Average losing trade
+
+### 3. **Trade Analysis**
+Access detailed trade information through the `trades` property:
+
+```python
+# Trade statistics
+pf.trades.stats()
+pf.trades.count()  # Total number of trades
+pf.trades.win_rate  # Win rate
+pf.trades.profit_factor  # Profit factor
+
+# Direction-specific analysis
+pf.trades.direction_long.count()  # Number of long trades
+pf.trades.direction_short.count()  # Number of short trades
+pf.trades.direction_long.pnl.sum()  # Total long P&L
+pf.trades.direction_short.pnl.sum()  # Total short P&L
+
+# Trade records
+pf.trades.records_readable  # Human-readable trade records
+pf.trade_history  # Detailed trade history DataFrame
+```
+
+### 4. **Drawdown Analysis**
+Analyze drawdowns using the `drawdowns` property:
+
+```python
+pf.drawdowns.stats()  # Drawdown statistics
+pf.drawdowns.max_duration  # Maximum drawdown duration
+pf.drawdown  # Drawdown time series
+```
+
+### 5. **Order Analysis**
+Examine order execution details:
+
+```python
+pf.orders.stats()  # Order statistics
+pf.orders.records_readable  # Human-readable order records
+```
+
+### 6. **Custom Metrics**
+You can add custom metrics to the Portfolio class:
+
+```python
+# Add custom metrics
+vbt.Portfolio.metrics["skew"] = dict(title="Skew", calc_func="returns.skew")
+vbt.Portfolio.metrics["kurtosis"] = dict(title="Kurtosis", calc_func="returns.kurtosis")
+
+# Then use in stats
+pf.stats(['skew', 'kurtosis'])
+```
+
+### 7. **Visualization Methods**
+Portfolio objects have built-in plotting capabilities:
+
+```python
+pf.plot()  # Default portfolio plot
+pf.plot_value()  # Portfolio value over time
+pf.plot_cumulative_returns()  # Cumulative returns
+pf.plot_drawdowns()  # Drawdown plot
+pf.plot_underwater()  # Underwater plot
+pf.trades.plot()  # Trade analysis plots
+```
+
+### 8. **Advanced Analysis Examples**
+
+**Multi-parameter analysis:**
+```python
+# Combine multiple metrics into DataFrame
+metrics = [
+    pf.total_return,
+    pf.trades.win_rate, 
+    pf.sharpe_ratio,
+    pf.max_drawdown,
+    pf.trades.count()
+]
+keys = ['total_return', 'win_rate', 'sharpe_ratio', 'max_drawdown', 'trade_count']
+combined_stats = pd.concat(metrics, axis=1, keys=keys)
+```
+
+**Performance optimization:**
+- Use `save_returns=True` in `Portfolio.from_signals()` to pre-calculate returns and speed up metric calculations
+- Enable caching for repeated analysis
+
+This comprehensive analysis framework allows you to thoroughly evaluate portfolio performance from multiple angles - returns, risk, trade efficiency, and drawdown characteristics.
+
+Would you like me to help you implement any specific analysis workflow, or do you have questions about particular metrics or methods?
+
+## Entries/exits visual analysis
+
+```python
+#display entry exits for visual analysis
+import ttools as tts
+trade_entries, trade_exits = tts.trades2entries_exits(pf) #helper to extract info from trades and orders with texts to markers (notext=True can be used)
+
+Panel(
+    ohlcv=(s12_data.ohlcv.data["BAC"],),
+    right=[(s12_data.close, "close", trade_entries, trade_exits)],
+    middle1=[(pf.returns.cumsum(), "returns")],
+    ).chart(precision=4)
+
+#or alternative display just markers with no text
+trade_entries = pd.Series(index=pf.trades.readable["Entry Index"], dtype=bool, data=True)
+trade_exits = pd.Series(index=pf.trades.readable["Exit Index"], dtype=bool, data=True)
+#then call Panel same as above
+```
+
+## Configuration
+Changing year freq for stocks
+```python
+vbt.settings.returns.year_freq = pd.Timedelta(hours=6.5) * 252 
+```
+
+# Optimalization
+
+## Param configuration
+
+```python
+tp_stop = vbt.Param(tp_stop, condition="tp_stop > sl_stop") #conditional hyper parameters
+```
+```python
+tp_stop = vbt.Param(tp_stop, condition="tp_stop > sl_stop") #conditional hyper parameters
+```
+## Pipeline
+```python
+bt.parameterized(merge_func="concat")  
+def sma_crossover_perf(data, fast_window, slow_window):
+    fast_sma = data.run("sma", fast_window, short_name="fast_sma")  
+    slow_sma = data.run("sma", slow_window, short_name="slow_sma")
+    entries = fast_sma.real_crossed_above(slow_sma)
+    exits = fast_sma.real_crossed_below(slow_sma)
+    pf = vbt.Portfolio.from_signals(
+        data, entries, exits, direction="both")  
+    return pf.sharpe_ratio
+
+#Let's test a grid of fast_window and slow_window combinations on one year of that data:
+
+perf = sma_crossover_perf(  
+    data["2020":"2020"],  
+    vbt.Param(np.arange(5, 50), condition="x < slow_window"),  
+    vbt.Param(np.arange(5, 50)),  
+    _execute_kwargs=dict(  
+        show_progress=True,
+        clear_cache=50,  
+        collect_garbage=50
+    )
+)
+perf
+```
 
 # INDICATORS DEV
 
@@ -813,6 +1525,26 @@ pane2 = Panel(....)
 ch = chart([pane1, pane2], size="s")
 ```
 
+## standard vbt plot
+
+```python
+#skip gaps automatically
+vbt.settings.plotting.auto_rangebreaks = True
+vbt.settings.set_theme("dark")
+
+data.plot(symbol="SPY", yaxis=dict(type="log")).show()
+
+#skip non-business hours and weekends
+fig = df.vbt.plot()
+fig.update_xaxes(
+    rangebreaks=[
+        dict(bounds=['sat', 'mon']),
+        dict(bounds=[16, 9.5], pattern='hour'),
+        
+    ]
+)
+```
+
 
 # MULTIACCOUNT
 Simultaneous LONG and short (hedging)
@@ -834,6 +1566,8 @@ pf_stats.sort_values(by='Sharpe Ratio', ascending=False).iloc[::-1].vbt.heatmap(
 # UTILS
 
 ```python
+#use plotly resampler
+vbt.settings.plotting["use_resampler"] = True
 
 #RELOAD module in ipynb
 %load_ext autoreload
@@ -855,6 +1589,7 @@ vbt.print_cache_stats(vbt.PF)
 
 vbt.flush() #clear cache and collect garbage
 vbt.clear_cache(pf) #of specific
+vbt.clear_pycache()
 
 
 #TIMING
@@ -889,3 +1624,4 @@ nyse = get_calendar('NYSE')
 # Create a CustomBusinessDay object using the NYSE trading calendar
 custom_bd = CustomBusinessDay(holidays=nyse.holidays().holidays, weekmask=nyse.weekmask, calendar=nyse)
 ```
+

vbt-topics/vbt-custom-metrics-analysis.md

@@ -0,0 +1,893 @@
+Custom metrics in VectorBT PRO, which is a powerful feature for extending portfolio analysis beyond the built-in metrics.Now let me provide a comprehensive elaboration on custom metrics in VectorBT PRO:
+
+# Custom Metrics in VectorBT PRO
+
+Custom metrics in VectorBT PRO provide a powerful way to extend portfolio analysis beyond the built-in metrics. They allow you to calculate domain-specific metrics, implement proprietary performance measures, or create metrics tailored to your specific trading strategies.
+
+## 1. Understanding the Metrics System
+
+### Built-in Metrics Structure
+VectorBT PRO uses a configuration-based approach where metrics are stored in `Portfolio.metrics` as a `HybridConfig`:
+
+```python
+# View all available metrics
+print(vbt.Portfolio.metrics)
+
+# Get specific metric configuration
+print(vbt.Portfolio.metrics['sharpe_ratio'])
+```
+
+### Metric Configuration Structure
+Each metric is defined as a dictionary with specific keys:
+
+```python
+metric_config = {
+    'title': 'My Custom Metric',        # Display name
+    'calc_func': calculation_function,   # Function to calculate the metric
+    'tags': ['custom', 'risk'],         # Tags for filtering
+    'apply_to_timedelta': False,        # Whether to convert to timedelta
+    'agg_func': None,                   # Aggregation function
+    'resolve_calc_func': True,          # Whether to resolve attributes
+    # ... other configuration options
+}
+```
+
+## 2. Creating Custom Metrics
+
+### Method 1: Simple Function-Based Metrics
+
+```python
+# Add a simple custom metric
+vbt.Portfolio.metrics['total_bars'] = dict(
+    title='Total Bars',
+    calc_func=lambda self: len(self.wrapper.index)
+)
+
+# Add skewness and kurtosis
+vbt.Portfolio.metrics['skew'] = dict(
+    title='Skew', 
+    calc_func='returns.skew'
+)
+vbt.Portfolio.metrics['kurtosis'] = dict(
+    title='Kurtosis', 
+    calc_func='returns.kurtosis'
+)
+```
+
+### Method 2: Complex Custom Calculations
+
+```python
+# Custom metric with multiple parameters
+def total_return_no_fees(self, orders):
+    """Calculate total return without fees"""
+    return (self.total_profit + orders.fees.sum()) / self.get_init_cash() * 100
+
+vbt.Portfolio.metrics['total_return_no_fees'] = dict(
+    title='Total Return (No Fees) [%]',
+    calc_func=total_return_no_fees,
+    resolve_orders=True  # Automatically resolve orders parameter
+)
+```
+
+### Method 3: Using Lambda Functions with Settings
+
+```python
+# PnL in dollar terms (for futures trading)
+vbt.Portfolio.metrics['pnl_dollars'] = dict(
+    title='PnL ($)',
+    calc_func=lambda self, settings: (self.value.iloc[-1] - self.value.iloc[0]) * 50,
+    resolve_calc_func=False  # Don't resolve attributes automatically
+)
+```
+
+## 3. Advanced Custom Metrics
+
+### Quantile-Based Metrics
+```python
+def value_at_risk_custom(returns, confidence_level=0.05):
+    """Custom VaR calculation"""
+    return returns.quantile(confidence_level)
+
+vbt.Portfolio.metrics['custom_var'] = dict(
+    title='Custom VaR (5%)',
+    calc_func=value_at_risk_custom,
+    resolve_returns=True,
+    confidence_level=0.05
+)
+```
+
+### Multi-Component Metrics
+```python
+def comprehensive_trade_stats(trades):
+    """Return multiple trade statistics"""
+    return {
+        'long_trades': trades.direction_long.count(),
+        'short_trades': trades.direction_short.count(),
+        'long_pnl': trades.direction_long.pnl.sum(),
+        'short_pnl': trades.direction_short.pnl.sum(),
+        'avg_trade_duration': trades.duration.mean()
+    }
+
+vbt.Portfolio.metrics['trade_breakdown'] = dict(
+    title='Trade Breakdown',
+    calc_func=comprehensive_trade_stats,
+    resolve_trades=True
+)
+```
+
+### Time-Based Metrics
+```python
+def monthly_returns_volatility(returns):
+    """Calculate monthly returns volatility"""
+    monthly_returns = returns.resample('M').sum()
+    return monthly_returns.std() * np.sqrt(12)
+
+vbt.Portfolio.metrics['monthly_vol'] = dict(
+    title='Monthly Volatility',
+    calc_func=monthly_returns_volatility,
+    resolve_returns=True
+)
+```
+
+## 4. Metric Resolution and Parameters
+
+### Automatic Parameter Resolution
+VectorBT PRO can automatically resolve portfolio attributes as parameters:
+
+```python
+# These parameters will be automatically resolved:
+vbt.Portfolio.metrics['custom_metric'] = dict(
+    title='Custom Metric',
+    calc_func=lambda returns, trades, orders: calculation_logic(returns, trades, orders),
+    resolve_returns=True,    # Passes self.returns
+    resolve_trades=True,     # Passes self.trades
+    resolve_orders=True      # Passes self.orders
+)
+```
+
+### Common Resolvable Parameters
+- `self` - The portfolio instance
+- `returns` - Portfolio returns
+- `trades` - Trade records
+- `orders` - Order records
+- `drawdowns` - Drawdown records
+- `value` - Portfolio value
+- `close` - Close prices
+- `init_cash` - Initial cash
+- `total_profit` - Total profit
+- `wrapper` - Array wrapper (for index/column info)
+
+## 5. Global vs Instance-Level Metrics
+
+### Global Metrics (Class-Level)
+```python
+# Add to all future Portfolio instances
+vbt.Portfolio.metrics['my_metric'] = metric_config
+
+# Or modify settings globally
+vbt.settings.portfolio['stats']['metrics'] = list(vbt.Portfolio.metrics.items()) + [
+    ('my_metric', metric_config)
+]
+```
+
+### Instance-Level Metrics
+```python
+# Add to specific portfolio instance
+pf._metrics['my_metric'] = metric_config
+
+# Then use it
+pf.stats(['my_metric'])
+```
+
+## 6. Using Custom Metrics
+
+### Basic Usage
+```python
+# Calculate specific custom metrics
+pf.stats(['total_bars', 'skew', 'kurtosis'])
+
+# Calculate all metrics including custom ones
+pf.stats('all')
+
+# Filter by tags
+pf.stats(tags=['custom'])
+```
+
+### Advanced Usage with Settings
+```python
+# Use custom metrics in optimization
+results = []
+for param in parameter_combinations:
+    pf = vbt.Portfolio.from_signals(close, entries, exits, **param)
+    stats = pf.stats(['total_return', 'sharpe_ratio', 'my_custom_metric'])
+    results.append(stats)
+
+# Create comparison DataFrame
+comparison_df = pd.DataFrame(results)
+```
+
+## 7. Real-World Examples
+
+### Futures Trading Metrics
+```python
+# Point-based P&L for futures
+vbt.Portfolio.metrics['pnl_points'] = dict(
+    title='P&L (Points)',
+    calc_func=lambda self: (self.value.iloc[-1] - self.value.iloc[0]) / self.close.iloc[0] * 10000
+)
+
+# Risk-adjusted return for futures
+vbt.Portfolio.metrics['risk_adjusted_return'] = dict(
+    title='Risk Adjusted Return',
+    calc_func=lambda self, returns: self.total_return / returns.std() * np.sqrt(252),
+    resolve_returns=True
+)
+```
+
+### Intraday Strategy Metrics
+```python
+# Time-of-day analysis
+def intraday_performance(orders):
+    """Analyze performance by hour of day"""
+    order_df = orders.records_readable
+    order_df['hour'] = order_df.index.hour
+    return order_df.groupby('hour')['PnL'].mean()
+
+vbt.Portfolio.metrics['hourly_performance'] = dict(
+    title='Hourly Performance',
+    calc_func=intraday_performance,
+    resolve_orders=True
+)
+```
+
+### Market Regime Metrics
+```python
+def regime_performance(returns, benchmark_returns):
+    """Performance in different market regimes"""
+    bull_mask = benchmark_returns > benchmark_returns.quantile(0.6)
+    bear_mask = benchmark_returns < benchmark_returns.quantile(0.4)
+    
+    return {
+        'bull_return': returns[bull_mask].mean(),
+        'bear_return': returns[bear_mask].mean(),
+        'bull_sharpe': returns[bull_mask].mean() / returns[bull_mask].std() * np.sqrt(252),
+        'bear_sharpe': returns[bear_mask].mean() / returns[bear_mask].std() * np.sqrt(252)
+    }
+
+vbt.Portfolio.metrics['regime_analysis'] = dict(
+    title='Market Regime Analysis',
+    calc_func=regime_performance,
+    resolve_returns=True,
+    resolve_bm_returns=True
+)
+```
+
+## 8. Best Practices
+
+### 1. Naming Conventions
+- Use descriptive names: `monthly_volatility` instead of `mv`
+- Include units in title: `'Max Drawdown [%]'`
+- Use consistent naming patterns
+
+### 2. Error Handling
+```python
+def robust_metric(returns):
+    """Metric with error handling"""
+    try:
+        if len(returns) < 2:
+            return np.nan
+        return returns.std() * np.sqrt(252)
+    except Exception as e:
+        print(f"Error calculating metric: {e}")
+        return np.nan
+```
+
+### 3. Performance Optimization
+```python
+# Use vectorized operations
+def efficient_metric(returns):
+    """Efficient vectorized calculation"""
+    return returns.rolling(30).std().mean()
+
+# Avoid loops when possible
+def inefficient_metric(returns):
+    """Avoid this approach"""
+    results = []
+    for i in range(len(returns)):
+        results.append(some_calculation(returns.iloc[i]))
+    return np.mean(results)
+```
+
+### 4. Documentation
+```python
+vbt.Portfolio.metrics['documented_metric'] = dict(
+    title='Well Documented Metric',
+    calc_func=lambda returns: returns.std() * np.sqrt(252),
+    resolve_returns=True,
+    tags=['custom', 'risk', 'volatility'],
+    # Add description in comments or docstrings
+)
+```
+
+## 9. Common Pitfalls and Solutions
+
+### Pitfall 1: Metric Not Available After Creation
+```python
+# ❌ Wrong: Metric added after portfolio creation
+pf = vbt.Portfolio.from_signals(...)
+vbt.Portfolio.metrics['my_metric'] = metric_config
+pf.stats(['my_metric'])  # KeyError!
+
+# ✅ Correct: Add metric before portfolio creation
+vbt.Portfolio.metrics['my_metric'] = metric_config
+pf = vbt.Portfolio.from_signals(...)
+pf.stats(['my_metric'])  # Works!
+```
+
+### Pitfall 2: Incorrect Parameter Resolution
+```python
+# ❌ Wrong: Using external variables
+portfolio_instance = some_portfolio
+vbt.Portfolio.metrics['bad_metric'] = dict(
+    calc_func=lambda self: portfolio_instance.total_return  # External reference
+)
+
+# ✅ Correct: Using self parameter
+vbt.Portfolio.metrics['good_metric'] = dict(
+    calc_func=lambda self: self.total_return  # Self reference
+)
+```
+
+### Pitfall 3: Missing Error Handling
+```python
+# ❌ Wrong: No error handling
+def risky_metric(trades):
+    return trades.pnl.sum() / trades.duration.mean()  # Division by zero possible
+
+# ✅ Correct: With error handling
+def safe_metric(trades):
+    if len(trades) == 0 or trades.duration.mean() == 0:
+        return np.nan
+    return trades.pnl.sum() / trades.duration.mean()
+```
+
+Custom metrics in VectorBT PRO provide unlimited flexibility to analyze your trading strategies exactly how you need. They integrate seamlessly with the existing stats system and can be used in optimization, comparison, and reporting workflows.
+
+I'll provide a comprehensive analysis of VectorBT PRO's `pf.trades` analysis capabilities, with a focus on the specific metrics you mentioned.# Comprehensive VectorBT PRO `pf.trades` Analysis
+
+The `pf.trades` object in VectorBT PRO provides extensive capabilities for analyzing trading performance. Here's a comprehensive guide focusing on directional analysis, temporal patterns, and advanced trade analytics.
+
+## 1. Basic Trade Analysis
+
+### Trade Counts by Direction
+```python
+# Basic trade counts
+total_trades = pf.trades.count()
+long_trades = pf.trades.direction_long.count()
+short_trades = pf.trades.direction_short.count()
+
+print(f"Total trades: {total_trades}")
+print(f"Long trades: {long_trades}")
+print(f"Short trades: {short_trades}")
+
+# Alternative using records
+trade_records = pf.trades.records_readable
+direction_counts = trade_records['Direction'].value_counts()
+print(f"\nDirection breakdown:\n{direction_counts}")
+```
+
+### P&L Analysis by Direction
+```python
+# Total P&L by direction
+long_pnl = pf.trades.direction_long.pnl.sum()
+short_pnl = pf.trades.direction_short.pnl.sum()
+total_pnl = pf.trades.pnl.sum()
+
+print(f"Long P&L: {long_pnl:.2f}")
+print(f"Short P&L: {short_pnl:.2f}")
+print(f"Total P&L: {total_pnl:.2f}")
+
+# P&L statistics by direction
+long_stats = pf.trades.direction_long.pnl.describe()
+short_stats = pf.trades.direction_short.pnl.describe()
+```
+
+## 2. Daily P&L Analysis
+
+### Daily P&L Calculation
+```python
+# Method 1: Using trade records with date grouping
+trade_records = pf.trades.records_readable
+trade_records['exit_date'] = trade_records.index.date
+
+# Daily P&L overall
+daily_pnl = trade_records.groupby('exit_date')['PnL'].sum()
+
+# Daily P&L by direction
+daily_pnl_by_direction = trade_records.groupby(['exit_date', 'Direction'])['PnL'].sum().unstack(fill_value=0)
+
+print("Daily P&L by Direction:")
+print(daily_pnl_by_direction.head())
+```
+
+### Daily P&L for Each Direction
+```python
+# Separate long and short daily P&L
+long_trades_records = trade_records[trade_records['Direction'] == 'Long']
+short_trades_records = trade_records[trade_records['Direction'] == 'Short']
+
+daily_long_pnl = long_trades_records.groupby('exit_date')['PnL'].sum()
+daily_short_pnl = short_trades_records.groupby('exit_date')['PnL'].sum()
+
+# Combine into comprehensive daily analysis
+daily_analysis = pd.DataFrame({
+    'Total_PnL': daily_pnl,
+    'Long_PnL': daily_long_pnl,
+    'Short_PnL': daily_short_pnl,
+    'Long_Trades': long_trades_records.groupby('exit_date').size(),
+    'Short_Trades': short_trades_records.groupby('exit_date').size()
+}).fillna(0)
+
+print("Daily Trade Analysis:")
+print(daily_analysis.head())
+```
+
+## 3. Hourly P&L Analysis by Exit Time
+
+### Hourly P&L by Direction
+```python
+# Extract hour from exit time
+trade_records = pf.trades.records_readable
+trade_records['exit_hour'] = trade_records.index.hour
+
+# Hourly P&L analysis
+hourly_pnl_analysis = trade_records.groupby(['exit_hour', 'Direction']).agg({
+    'PnL': ['sum', 'mean', 'count'],
+    'Return': ['mean', 'std']
+}).round(4)
+
+print("Hourly P&L Analysis by Direction:")
+print(hourly_pnl_analysis)
+
+# Separate analysis for each direction
+hourly_long_pnl = trade_records[trade_records['Direction'] == 'Long'].groupby('exit_hour')['PnL'].agg(['sum', 'mean', 'count'])
+hourly_short_pnl = trade_records[trade_records['Direction'] == 'Short'].groupby('exit_hour')['PnL'].agg(['sum', 'mean', 'count'])
+
+print("\nHourly Long P&L:")
+print(hourly_long_pnl)
+
+print("\nHourly Short P&L:")
+print(hourly_short_pnl)
+```
+
+### Advanced Hourly Analysis
+```python
+# Create comprehensive hourly performance matrix
+def hourly_performance_analysis(trades_records):
+    """Comprehensive hourly performance analysis"""
+    
+    # Add time components
+    trades_records['exit_hour'] = trades_records.index.hour
+    trades_records['entry_hour'] = pd.to_datetime(trades_records['Entry Index']).dt.hour
+    
+    # Hourly exit analysis
+    hourly_stats = trades_records.groupby(['exit_hour', 'Direction']).agg({
+        'PnL': ['sum', 'mean', 'count', 'std'],
+        'Return': ['mean', 'std'],
+        'Size': 'mean'
+    }).round(4)
+    
+    return hourly_stats
+
+hourly_performance = hourly_performance_analysis(trade_records)
+```
+
+## 4. Day of Week Analysis
+
+### P&L by Day of Week and Direction
+```python
+# Add day of week analysis
+trade_records['exit_day_of_week'] = trade_records.index.day_name()
+trade_records['exit_weekday'] = trade_records.index.weekday  # 0=Monday, 6=Sunday
+
+# Day of week P&L analysis
+dow_analysis = trade_records.groupby(['exit_day_of_week', 'Direction']).agg({
+    'PnL': ['sum', 'mean', 'count'],
+    'Return': ['mean', 'std'],
+    'Size': 'mean'
+}).round(4)
+
+print("Day of Week Analysis:")
+print(dow_analysis)
+
+# Pivot for easier viewing
+dow_pivot = trade_records.pivot_table(
+    index='exit_day_of_week',
+    columns='Direction',
+    values='PnL',
+    aggfunc=['sum', 'mean', 'count'],
+    fill_value=0
+)
+
+print("\nDay of Week Pivot Analysis:")
+print(dow_pivot)
+```
+
+### Advanced Day of Week Patterns
+```python
+# Create comprehensive day of week analysis
+def day_of_week_analysis(trades_records):
+    """Comprehensive day of week performance analysis"""
+    
+    # Ensure we have day names in proper order
+    day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
+    
+    trades_records['exit_day_name'] = trades_records.index.day_name()
+    
+    # Group by day and direction
+    dow_stats = trades_records.groupby(['exit_day_name', 'Direction']).agg({
+        'PnL': ['sum', 'mean', 'count', 'std'],
+        'Return': ['mean', 'std'],
+        'Size': 'mean',
+        'Entry Fees': 'mean',
+        'Exit Fees': 'mean'
+    }).round(4)
+    
+    # Reorder by day
+    dow_stats = dow_stats.reindex(day_order, level=0)
+    
+    return dow_stats
+
+dow_comprehensive = day_of_week_analysis(trade_records)
+```
+
+## 5. Advanced Temporal Analysis
+
+### Combined Time Pattern Analysis
+```python
+# Create comprehensive time pattern analysis
+def comprehensive_time_analysis(pf):
+    """Complete temporal analysis of trades"""
+    
+    trades_records = pf.trades.records_readable
+    
+    # Add all time components
+    trades_records['exit_hour'] = trades_records.index.hour
+    trades_records['exit_day_name'] = trades_records.index.day_name()
+    trades_records['exit_month'] = trades_records.index.month
+    trades_records['exit_date'] = trades_records.index.date
+    
+    # 1. Hourly analysis
+    hourly_stats = trades_records.groupby(['exit_hour', 'Direction']).agg({
+        'PnL': ['sum', 'mean', 'count'],
+        'Return': ['mean', 'std']
+    }).round(4)
+    
+    # 2. Daily analysis
+    daily_stats = trades_records.groupby(['exit_day_name', 'Direction']).agg({
+        'PnL': ['sum', 'mean', 'count'],
+        'Return': ['mean', 'std']
+    }).round(4)
+    
+    # 3. Monthly analysis
+    monthly_stats = trades_records.groupby(['exit_month', 'Direction']).agg({
+        'PnL': ['sum', 'mean', 'count'],
+        'Return': ['mean', 'std']
+    }).round(4)
+    
+    # 4. Combined hour-day analysis
+    hour_day_stats = trades_records.groupby(['exit_day_name', 'exit_hour', 'Direction']).agg({
+        'PnL': ['sum', 'mean', 'count']
+    }).round(4)
+    
+    return {
+        'hourly': hourly_stats,
+        'daily': daily_stats,
+        'monthly': monthly_stats,
+        'hour_day': hour_day_stats
+    }
+
+# Execute comprehensive analysis
+time_analysis = comprehensive_time_analysis(pf)
+
+# Display results
+print("=== HOURLY ANALYSIS ===")
+print(time_analysis['hourly'])
+print("\n=== DAILY ANALYSIS ===")
+print(time_analysis['daily'])
+print("\n=== MONTHLY ANALYSIS ===")
+print(time_analysis['monthly'])
+```
+
+## 6. Custom Metrics for Trade Analysis
+
+### Custom Trade Metrics
+```python
+# Add custom metrics to Portfolio for directional analysis
+vbt.Portfolio.metrics['long_trade_count'] = dict(
+    title='Long Trade Count',
+    calc_func=lambda trades: trades.direction_long.count(),
+    resolve_trades=True
+)
+
+vbt.Portfolio.metrics['short_trade_count'] = dict(
+    title='Short Trade Count',
+    calc_func=lambda trades: trades.direction_short.count(),
+    resolve_trades=True
+)
+
+vbt.Portfolio.metrics['long_pnl_total'] = dict(
+    title='Long P&L Total',
+    calc_func=lambda trades: trades.direction_long.pnl.sum(),
+    resolve_trades=True
+)
+
+vbt.Portfolio.metrics['short_pnl_total'] = dict(
+    title='Short P&L Total',
+    calc_func=lambda trades: trades.direction_short.pnl.sum(),
+    resolve_trades=True
+)
+
+# Temporal metrics
+vbt.Portfolio.metrics['best_hour_pnl'] = dict(
+    title='Best Hour P&L',
+    calc_func=lambda trades: trades.records_readable.groupby(trades.records_readable.index.hour)['PnL'].sum().max(),
+    resolve_trades=True
+)
+
+vbt.Portfolio.metrics['worst_hour_pnl'] = dict(
+    title='Worst Hour P&L',
+    calc_func=lambda trades: trades.records_readable.groupby(trades.records_readable.index.hour)['PnL'].sum().min(),
+    resolve_trades=True
+)
+```
+
+## 7. Performance Analysis Functions
+
+### Comprehensive Trade Performance Function
+```python
+def analyze_trade_performance(pf):
+    """Comprehensive trade performance analysis"""
+    
+    trades = pf.trades
+    records = trades.records_readable
+    
+    # Basic directional statistics
+    direction_stats = {
+        'Long': {
+            'count': trades.direction_long.count(),
+            'total_pnl': trades.direction_long.pnl.sum(),
+            'avg_pnl': trades.direction_long.pnl.mean(),
+            'win_rate': trades.direction_long.win_rate,
+            'profit_factor': trades.direction_long.profit_factor
+        },
+        'Short': {
+            'count': trades.direction_short.count(),
+            'total_pnl': trades.direction_short.pnl.sum(),
+            'avg_pnl': trades.direction_short.pnl.mean(),
+            'win_rate': trades.direction_short.win_rate,
+            'profit_factor': trades.direction_short.profit_factor
+        }
+    }
+    
+    # Temporal analysis
+    records['exit_hour'] = records.index.hour
+    records['exit_day'] = records.index.day_name()
+    records['exit_date'] = records.index.date
+    
+    # Hourly P&L by direction
+    hourly_pnl = records.groupby(['exit_hour', 'Direction'])['PnL'].agg(['sum', 'mean', 'count'])
+    
+    # Daily P&L by direction
+    daily_pnl = records.groupby(['exit_day', 'Direction'])['PnL'].agg(['sum', 'mean', 'count'])
+    
+    # Date-based P&L
+    date_pnl = records.groupby(['exit_date', 'Direction'])['PnL'].agg(['sum', 'mean', 'count'])
+    
+    # Best/worst performing times
+    best_hours = records.groupby(['exit_hour', 'Direction'])['PnL'].sum().groupby('Direction').idxmax()
+    worst_hours = records.groupby(['exit_hour', 'Direction'])['PnL'].sum().groupby('Direction').idxmin()
+    
+    return {
+        'direction_stats': direction_stats,
+        'hourly_pnl': hourly_pnl,
+        'daily_pnl': daily_pnl,
+        'date_pnl': date_pnl,
+        'best_hours': best_hours,
+        'worst_hours': worst_hours
+    }
+
+# Execute analysis
+performance_analysis = analyze_trade_performance(pf)
+```
+
+## 8. Visualization Functions
+
+### Trade Performance Visualization
+```python
+def plot_trade_performance(pf):
+    """Create comprehensive trade performance plots"""
+    
+    import plotly.graph_objects as go
+    from plotly.subplots import make_subplots
+    
+    records = pf.trades.records_readable
+    records['exit_hour'] = records.index.hour
+    records['exit_day'] = records.index.day_name()
+    
+    # Create subplots
+    fig = make_subplots(
+        rows=2, cols=2,
+        subplot_titles=['Hourly P&L by Direction', 'Daily P&L by Direction', 
+                       'P&L Distribution', 'Cumulative P&L by Direction'],
+        specs=[[{"secondary_y": True}, {"secondary_y": True}],
+               [{"secondary_y": False}, {"secondary_y": False}]]
+    )
+    
+    # Hourly P&L
+    hourly_long = records[records['Direction'] == 'Long'].groupby('exit_hour')['PnL'].sum()
+    hourly_short = records[records['Direction'] == 'Short'].groupby('exit_hour')['PnL'].sum()
+    
+    fig.add_trace(go.Bar(x=hourly_long.index, y=hourly_long.values, name='Long Hourly', marker_color='green'), row=1, col=1)
+    fig.add_trace(go.Bar(x=hourly_short.index, y=hourly_short.values, name='Short Hourly', marker_color='red'), row=1, col=1)
+    
+    # Daily P&L
+    day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
+    daily_long = records[records['Direction'] == 'Long'].groupby('exit_day')['PnL'].sum().reindex(day_order, fill_value=0)
+    daily_short = records[records['Direction'] == 'Short'].groupby('exit_day')['PnL'].sum().reindex(day_order, fill_value=0)
+    
+    fig.add_trace(go.Bar(x=daily_long.index, y=daily_long.values, name='Long Daily', marker_color='lightgreen'), row=1, col=2)
+    fig.add_trace(go.Bar(x=daily_short.index, y=daily_short.values, name='Short Daily', marker_color='lightcoral'), row=1, col=2)
+    
+    # P&L Distribution
+    fig.add_trace(go.Histogram(x=records[records['Direction'] == 'Long']['PnL'], name='Long Distribution', opacity=0.7), row=2, col=1)
+    fig.add_trace(go.Histogram(x=records[records['Direction'] == 'Short']['PnL'], name='Short Distribution', opacity=0.7), row=2, col=1)
+    
+    # Cumulative P&L
+    long_cumulative = records[records['Direction'] == 'Long']['PnL'].cumsum()
+    short_cumulative = records[records['Direction'] == 'Short']['PnL'].cumsum()
+    
+    fig.add_trace(go.Scatter(y=long_cumulative.values, mode='lines', name='Long Cumulative', line=dict(color='green')), row=2, col=2)
+    fig.add_trace(go.Scatter(y=short_cumulative.values, mode='lines', name='Short Cumulative', line=dict(color='red')), row=2, col=2)
+    
+    fig.update_layout(height=800, title_text="Comprehensive Trade Analysis")
+    return fig
+
+# Create visualization
+# trade_plot = plot_trade_performance(pf)
+# trade_plot.show()
+```
+
+## 9. Advanced Analytics
+
+### Trade Streaks and Patterns
+```python
+def analyze_trade_patterns(pf):
+    """Analyze trade patterns and streaks"""
+    
+    trades = pf.trades
+    records = trades.records_readable
+    
+    # Winning and losing streaks
+    winning_streaks = trades.winning_streak.records_readable
+    losing_streaks = trades.losing_streak.records_readable
+    
+    # Pattern analysis
+    patterns = {
+        'longest_winning_streak': winning_streaks['Duration'].max() if len(winning_streaks) > 0 else 0,
+        'longest_losing_streak': losing_streaks['Duration'].max() if len(losing_streaks) > 0 else 0,
+        'avg_winning_streak': winning_streaks['Duration'].mean() if len(winning_streaks) > 0 else 0,
+        'avg_losing_streak': losing_streaks['Duration'].mean() if len(losing_streaks) > 0 else 0,
+    }
+    
+    # Direction-specific patterns
+    long_patterns = analyze_direction_patterns(trades.direction_long)
+    short_patterns = analyze_direction_patterns(trades.direction_short)
+    
+    return {
+        'overall_patterns': patterns,
+        'long_patterns': long_patterns,
+        'short_patterns': short_patterns
+    }
+
+def analyze_direction_patterns(direction_trades):
+    """Analyze patterns for specific direction"""
+    
+    if direction_trades.count() == 0:
+        return {}
+    
+    return {
+        'total_trades': direction_trades.count(),
+        'win_rate': direction_trades.win_rate,
+        'profit_factor': direction_trades.profit_factor,
+        'avg_winner': direction_trades.winning.pnl.mean() if direction_trades.winning.count() > 0 else 0,
+        'avg_loser': direction_trades.losing.pnl.mean() if direction_trades.losing.count() > 0 else 0,
+        'largest_winner': direction_trades.pnl.max(),
+        'largest_loser': direction_trades.pnl.min(),
+        'total_pnl': direction_trades.pnl.sum()
+    }
+
+# Execute pattern analysis
+pattern_analysis = analyze_trade_patterns(pf)
+```
+
+## 10. Summary Report Function
+
+### Comprehensive Trade Report
+```python
+def generate_trade_report(pf):
+    """Generate comprehensive trade analysis report"""
+    
+    print("="*80)
+    print("COMPREHENSIVE TRADE ANALYSIS REPORT")
+    print("="*80)
+    
+    # Basic Statistics
+    trades = pf.trades
+    total_trades = trades.count()
+    long_trades = trades.direction_long.count()
+    short_trades = trades.direction_short.count()
+    
+    print(f"\n📊 BASIC STATISTICS")
+    print(f"Total Trades: {total_trades}")
+    print(f"Long Trades: {long_trades} ({long_trades/total_trades*100:.1f}%)")
+    print(f"Short Trades: {short_trades} ({short_trades/total_trades*100:.1f}%)")
+    
+    # P&L Analysis
+    print(f"\n💰 P&L ANALYSIS")
+    print(f"Total P&L: ${trades.pnl.sum():.2f}")
+    print(f"Long P&L: ${trades.direction_long.pnl.sum():.2f}")
+    print(f"Short P&L: ${trades.direction_short.pnl.sum():.2f}")
+    print(f"Average P&L per Trade: ${trades.pnl.mean():.2f}")
+    
+    # Temporal Analysis
+    records = trades.records_readable
+    records['exit_hour'] = records.index.hour
+    records['exit_day'] = records.index.day_name()
+    
+    print(f"\n⏰ TEMPORAL ANALYSIS")
+    
+    # Best/Worst Hours
+    hourly_pnl = records.groupby('exit_hour')['PnL'].sum()
+    best_hour = hourly_pnl.idxmax()
+    worst_hour = hourly_pnl.idxmin()
+    
+    print(f"Best Hour: {best_hour}:00 (${hourly_pnl[best_hour]:.2f})")
+    print(f"Worst Hour: {worst_hour}:00 (${hourly_pnl[worst_hour]:.2f})")
+    
+    # Best/Worst Days
+    daily_pnl = records.groupby('exit_day')['PnL'].sum()
+    best_day = daily_pnl.idxmax()
+    worst_day = daily_pnl.idxmin()
+    
+    print(f"Best Day: {best_day} (${daily_pnl[best_day]:.2f})")
+    print(f"Worst Day: {worst_day} (${daily_pnl[worst_day]:.2f})")
+    
+    # Direction Performance
+    print(f"\n📈 DIRECTION PERFORMANCE")
+    if long_trades > 0:
+        print(f"Long Win Rate: {trades.direction_long.win_rate:.2%}")
+        print(f"Long Profit Factor: {trades.direction_long.profit_factor:.2f}")
+    
+    if short_trades > 0:
+        print(f"Short Win Rate: {trades.direction_short.win_rate:.2%}")
+        print(f"Short Profit Factor: {trades.direction_short.profit_factor:.2f}")
+    
+    print("="*80)
+
+# Generate report
+generate_trade_report(pf)
+```
+
+This comprehensive analysis framework provides all the tools needed to analyze `pf.trades` with particular focus on:
+
+1. **Direction-specific analysis** - Separate analysis for long and short trades
+2. **Daily P&L patterns** - Understanding daily performance patterns
+3. **Hourly P&L by direction** - Identifying optimal trading hours for each direction
+4. **Day of week analysis** - Finding the best/worst days for different directions
+5. **Custom metrics** - Extending the analysis with domain-specific metrics
+6. **Visualization tools** - Creating comprehensive performance visualizations
+7. **Pattern recognition** - Identifying winning/losing streaks and patterns
+8. **Comprehensive reporting** - Generating detailed performance reports
+
+The framework is designed to be modular, allowing you to pick and choose the specific analyses most relevant to your trading strategy evaluation needs.