Enhance README and documentation for Cycles framework

- Expanded the README.md to provide a comprehensive overview of the Cycles framework, including features, quick start instructions, and configuration examples.
- Updated strategies documentation to detail the architecture, available strategies, and their configurations, emphasizing the new multi-timeframe capabilities.
- Added a new timeframe system documentation to explain the strategy-controlled timeframe management and automatic data resampling.
- Improved the strategy manager documentation to clarify its role in orchestrating multiple strategies and combining signals effectively.
- Adjusted configuration examples to reflect recent changes in strategy parameters and usage.

.gitignore

@@ -1,5 +1,4 @@
 # ---> Python
-*.json
 *.csv
 *.png
 # Byte-compiled / optimized / DLL files

README.md

@@ -1 +1,177 @@
-# Cycles
+# Cycles - Advanced Trading Strategy Backtesting Framework
+
+A sophisticated Python framework for backtesting cryptocurrency trading strategies with multi-timeframe analysis, strategy combination, and advanced signal processing.
+
+## Features
+
+- **Multi-Strategy Architecture**: Combine multiple trading strategies with configurable weights and rules
+- **Multi-Timeframe Analysis**: Strategies can operate on different timeframes (1min, 5min, 15min, 1h, etc.)
+- **Advanced Strategies**:
+  - **Default Strategy**: Meta-trend analysis using multiple Supertrend indicators
+  - **BBRS Strategy**: Bollinger Bands + RSI with market regime detection
+- **Flexible Signal Combination**: Weighted consensus, majority voting, any/all combinations
+- **Precise Stop-Loss**: 1-minute precision for accurate risk management
+- **Comprehensive Backtesting**: Detailed performance metrics and trade analysis
+- **Data Visualization**: Interactive charts and performance plots
+
+## Quick Start
+
+### Prerequisites
+
+- Python 3.8+
+- [uv](https://github.com/astral-sh/uv) package manager (recommended)
+
+### Installation
+
+```bash
+# Clone the repository
+git clone <repository-url>
+cd Cycles
+
+# Install dependencies with uv
+uv sync
+
+# Or install with pip
+pip install -r requirements.txt
+```
+
+### Running Backtests
+
+Use the `uv run` command to execute backtests with different configurations:
+
+```bash
+# Run default strategy on 5-minute timeframe
+uv run .\main.py .\configs\config_default_5min.json
+
+# Run default strategy on 15-minute timeframe
+uv run .\main.py .\configs\config_default.json
+
+# Run BBRS strategy with market regime detection
+uv run .\main.py .\configs\config_bbrs.json
+
+# Run combined strategies
+uv run .\main.py .\configs\config_combined.json
+```
+
+### Configuration Examples
+
+#### Default Strategy (5-minute timeframe)
+```bash
+uv run .\main.py .\configs\config_default_5min.json
+```
+
+#### BBRS Strategy with Multi-timeframe Analysis
+```bash
+uv run .\main.py .\configs\config_bbrs_multi_timeframe.json
+```
+
+#### Combined Strategies with Weighted Consensus
+```bash
+uv run .\main.py .\configs\config_combined.json
+```
+
+## Configuration
+
+Strategies are configured using JSON files in the `configs/` directory:
+
+```json
+{
+    "start_date": "2024-01-01",
+    "stop_date": "2024-01-31",
+    "initial_usd": 10000,
+    "timeframes": ["15min"],
+    "stop_loss_pcts": [0.03, 0.05],
+    "strategies": [
+        {
+            "name": "default",
+            "weight": 1.0,
+            "params": {
+                "timeframe": "15min"
+            }
+        }
+    ],
+    "combination_rules": {
+        "entry": "any",
+        "exit": "any",
+        "min_confidence": 0.5
+    }
+}
+```
+
+### Available Strategies
+
+1. **Default Strategy**: Meta-trend analysis using Supertrend indicators
+2. **BBRS Strategy**: Bollinger Bands + RSI with market regime detection
+
+### Combination Rules
+
+- **Entry**: `any`, `all`, `majority`, `weighted_consensus`
+- **Exit**: `any`, `all`, `priority` (prioritizes stop-loss signals)
+
+## Project Structure
+
+```
+Cycles/
+├── configs/                 # Configuration files
+├── cycles/                  # Core framework
+│   ├── strategies/         # Strategy implementation
+│   │   ├── base.py        # Base strategy classes
+│   │   ├── default_strategy.py
+│   │   ├── bbrs_strategy.py
+│   │   └── manager.py     # Strategy manager
+│   ├── Analysis/          # Technical analysis
+│   ├── utils/             # Utilities
+│   └── charts.py          # Visualization
+├── docs/                   # Documentation
+├── data/                   # Market data
+├── results/               # Backtest results
+└── main.py               # Main entry point
+```
+
+## Documentation
+
+Detailed documentation is available in the `docs/` directory:
+
+- **[Strategy Manager](./docs/strategy_manager.md)** - Multi-strategy orchestration and signal combination
+- **[Strategies](./docs/strategies.md)** - Individual strategy implementations and usage
+- **[Timeframe System](./docs/timeframe_system.md)** - Advanced timeframe management and multi-timeframe strategies
+- **[Analysis](./docs/analysis.md)** - Technical analysis components
+- **[Storage Utils](./docs/utils_storage.md)** - Data storage and retrieval
+- **[System Utils](./docs/utils_system.md)** - System utilities
+
+## Examples
+
+### Single Strategy Backtest
+```bash
+# Test default strategy on different timeframes
+uv run .\main.py .\configs\config_default.json      # 15min
+uv run .\main.py .\configs\config_default_5min.json # 5min
+```
+
+### Multi-Strategy Backtest
+```bash
+# Combine multiple strategies with different weights
+uv run .\main.py .\configs\config_combined.json
+```
+
+### Custom Configuration
+Create your own configuration file and run:
+```bash
+uv run .\main.py .\configs\your_config.json
+```
+
+## Output
+
+Backtests generate:
+- **CSV Results**: Detailed performance metrics per timeframe/strategy
+- **Trade Log**: Individual trade records with entry/exit details
+- **Performance Charts**: Visual analysis of strategy performance (in debug mode)
+- **Log Files**: Detailed execution logs
+
+## License
+
+[Add your license information here]
+
+## Contributing
+
+[Add contributing guidelines here]

configs/config_bbrs.json

@@ -0,0 +1,29 @@
+{
+    "start_date": "2025-03-01",
+    "stop_date": "2025-03-15",
+    "initial_usd": 10000,
+    "timeframes": ["1min"],
+    "stop_loss_pcts": [0.05],
+    "strategies": [
+        {
+            "name": "bbrs",
+            "weight": 1.0,
+            "params": {
+                "bb_width": 0.05,
+                "bb_period": 20,
+                "rsi_period": 14,
+                "trending_rsi_threshold": [30, 70],
+                "trending_bb_multiplier": 2.5,
+                "sideways_rsi_threshold": [40, 60],
+                "sideways_bb_multiplier": 1.8,
+                "strategy_name": "MarketRegimeStrategy",
+                "SqueezeStrategy": true
+            }
+        }
+    ],
+    "combination_rules": {
+        "entry": "any",
+        "exit": "any",
+        "min_confidence": 0.5
+    }
+} 

configs/config_bbrs_multi_timeframe.json

@@ -0,0 +1,29 @@
+{
+    "start_date": "2024-01-01",
+    "stop_date": "2024-01-31",
+    "initial_usd": 10000,
+    "timeframes": ["1min"],
+    "stop_loss_pcts": [0.05],
+    "strategies": [
+        {
+            "name": "bbrs",
+            "weight": 1.0,
+            "params": {
+                "bb_width": 0.05,
+                "bb_period": 20,
+                "rsi_period": 14,
+                "trending_rsi_threshold": [30, 70],
+                "trending_bb_multiplier": 2.5,
+                "sideways_rsi_threshold": [40, 60],
+                "sideways_bb_multiplier": 1.8,
+                "strategy_name": "MarketRegimeStrategy",
+                "SqueezeStrategy": true
+            }
+        }
+    ],
+    "combination_rules": {
+        "entry": "any",
+        "exit": "any",
+        "min_confidence": 0.5
+    }
+} 

configs/config_combined.json

@@ -0,0 +1,29 @@
+{
+    "start_date": "2025-03-01",
+    "stop_date": "2025-03-15",
+    "initial_usd": 10000,
+    "timeframes": ["15min"],
+    "stop_loss_pcts": [0.04],
+    "strategies": [
+        {
+            "name": "default",
+            "weight": 0.6,
+            "params": {}
+        },
+        {
+            "name": "bbrs",
+            "weight": 0.4,
+            "params": {
+                "bb_width": 0.05,
+                "bb_period": 20,
+                "rsi_period": 14,
+                "strategy_name": "MarketRegimeStrategy"
+            }
+        }
+    ],
+    "combination_rules": {
+        "entry": "weighted_consensus",
+        "exit": "any",
+        "min_confidence": 0.6
+    }
+} 

configs/config_default.json

@@ -0,0 +1,19 @@
+{
+    "start_date": "2025-03-01",
+    "stop_date": "2025-03-15",
+    "initial_usd": 10000,
+    "timeframes": ["15min"],
+    "stop_loss_pcts": [0.03, 0.05],
+    "strategies": [
+        {
+            "name": "default",
+            "weight": 1.0,
+            "params": {}
+        }
+    ],
+    "combination_rules": {
+        "entry": "any",
+        "exit": "any",
+        "min_confidence": 0.5
+    }
+} 

configs/config_default_5min.json

@@ -0,0 +1,21 @@
+{
+    "start_date": "2024-01-01",
+    "stop_date": "2024-01-31", 
+    "initial_usd": 10000,
+    "timeframes": ["5min"],
+    "stop_loss_pcts": [0.03, 0.05],
+    "strategies": [
+        {
+            "name": "default",
+            "weight": 1.0,
+            "params": {
+                "timeframe": "5min"
+            }
+        }
+    ],
+    "combination_rules": {
+        "entry": "any",
+        "exit": "any", 
+        "min_confidence": 0.5
+    }
+} 

cycles/Analysis/boillinger_band.py

@@ -37,12 +37,13 @@ class BollingerBands:
                           'UpperBand',
                           'LowerBand'.
         """
-        if price_column not in data_df.columns:
-            raise ValueError(f"Price column '{price_column}' not found in DataFrame.")
 
         # Work on a copy to avoid modifying the original DataFrame passed to the function
         data_df = data_df.copy()
 
+        if price_column not in data_df.columns:
+            raise ValueError(f"Price column '{price_column}' not found in DataFrame.")
+
         if not squeeze:
             period = self.config['bb_period']
             bb_width_threshold = self.config['bb_width']

cycles/Analysis/strategies.py

@@ -14,7 +14,56 @@ class Strategy:
         self.config = config
         self.logging = logging
 
+    def _ensure_datetime_index(self, data):
+        """
+        Ensure the DataFrame has a DatetimeIndex for proper time-series operations.
+        If the DataFrame has a 'timestamp' column but not a DatetimeIndex, convert it.
+        
+        Args:
+            data (DataFrame): Input DataFrame
+            
+        Returns:
+            DataFrame: DataFrame with proper DatetimeIndex
+        """
+        if data.empty:
+            return data
+            
+        # Check if we have a DatetimeIndex already
+        if isinstance(data.index, pd.DatetimeIndex):
+            return data
+            
+        # Check if we have a 'timestamp' column that we can use as index
+        if 'timestamp' in data.columns:
+            data_copy = data.copy()
+            # Convert timestamp column to datetime if it's not already
+            if not pd.api.types.is_datetime64_any_dtype(data_copy['timestamp']):
+                data_copy['timestamp'] = pd.to_datetime(data_copy['timestamp'])
+            # Set timestamp as index and drop the column
+            data_copy = data_copy.set_index('timestamp')
+            if self.logging:
+                self.logging.info("Converted 'timestamp' column to DatetimeIndex for strategy processing.")
+            return data_copy
+            
+        # If we have a regular index but it might be datetime strings, try to convert
+        try:
+            if data.index.dtype == 'object':
+                data_copy = data.copy()
+                data_copy.index = pd.to_datetime(data_copy.index)
+                if self.logging:
+                    self.logging.info("Converted index to DatetimeIndex for strategy processing.")
+                return data_copy
+        except:
+            pass
+            
+        # If we can't create a proper DatetimeIndex, warn and return as-is
+        if self.logging:
+            self.logging.warning("Could not create DatetimeIndex for strategy processing. Time-based operations may fail.")
+        return data
+
     def run(self, data, strategy_name):
+        # Ensure proper DatetimeIndex before processing
+        data = self._ensure_datetime_index(data)
+        
         if strategy_name == "MarketRegimeStrategy":
             result = self.MarketRegimeStrategy(data)
             return self.standardize_output(result, strategy_name)
@@ -126,8 +175,8 @@ class Strategy:
             DataFrame: A unified DataFrame containing original data, BB, RSI, and signals.
         """
 
-        # data = aggregate_to_hourly(data, 4)
-        data = aggregate_to_daily(data)
+        data = aggregate_to_hourly(data, 1)
+        # data = aggregate_to_daily(data)
         
         # Calculate Bollinger Bands
         bb_calculator = BollingerBands(config=self.config)
@@ -263,6 +312,8 @@ class Strategy:
             if self.logging:
                 self.logging.warning("CryptoTradingStrategy: Input data is empty or missing 'close'/'volume' columns.")
             return pd.DataFrame() # Return empty DataFrame if essential data is missing
+        
+        print(f"data: {data.head()}")
 
         # Aggregate data
         data_15m = aggregate_to_minutes(data.copy(), 15)

cycles/charts.py

@@ -68,4 +68,386 @@ class BacktestCharts:
 
         plt.tight_layout(h_pad=0.1)
         plt.show()
-        
+
+    @staticmethod
+    def format_strategy_data_with_trades(strategy_data, backtest_results):
+        """
+        Format strategy data for universal plotting with actual executed trades.
+        Converts strategy output into the expected column format: "x_type_name"
+        
+        Args:
+            strategy_data (DataFrame): Output from strategy with columns like 'close', 'UpperBand', 'LowerBand', 'RSI'
+            backtest_results (dict): Results from backtest.run() containing actual executed trades
+            
+        Returns:
+            DataFrame: Formatted data ready for plot_data function
+        """
+        formatted_df = pd.DataFrame(index=strategy_data.index)
+        
+        # Plot 1: Price data with Bollinger Bands and actual trade signals
+        if 'close' in strategy_data.columns:
+            formatted_df['1_line_close'] = strategy_data['close']
+        
+        # Bollinger Bands area (prefer standard names, fallback to timeframe-specific)
+        upper_band_col = None
+        lower_band_col = None
+        sma_col = None
+        
+        # Check for standard BB columns first
+        if 'UpperBand' in strategy_data.columns and 'LowerBand' in strategy_data.columns:
+            upper_band_col = 'UpperBand'
+            lower_band_col = 'LowerBand'
+        # Check for 15m BB columns
+        elif 'UpperBand_15m' in strategy_data.columns and 'LowerBand_15m' in strategy_data.columns:
+            upper_band_col = 'UpperBand_15m'
+            lower_band_col = 'LowerBand_15m'
+        
+        if upper_band_col and lower_band_col:
+            formatted_df['1_area_bb_upper'] = strategy_data[upper_band_col]
+            formatted_df['1_area_bb_lower'] = strategy_data[lower_band_col]
+        
+        # SMA/Moving Average line
+        if 'SMA' in strategy_data.columns:
+            sma_col = 'SMA'
+        elif 'SMA_15m' in strategy_data.columns:
+            sma_col = 'SMA_15m'
+            
+        if sma_col:
+            formatted_df['1_line_sma'] = strategy_data[sma_col]
+        
+        # Strategy buy/sell signals (all signals from strategy) as smaller scatter points
+        if 'BuySignal' in strategy_data.columns and 'close' in strategy_data.columns:
+            strategy_buy_points = strategy_data['close'].where(strategy_data['BuySignal'], np.nan)
+            formatted_df['1_scatter_strategy_buy'] = strategy_buy_points
+            
+        if 'SellSignal' in strategy_data.columns and 'close' in strategy_data.columns:
+            strategy_sell_points = strategy_data['close'].where(strategy_data['SellSignal'], np.nan)
+            formatted_df['1_scatter_strategy_sell'] = strategy_sell_points
+        
+        # Actual executed trades from backtest results (larger, more prominent)
+        if 'trades' in backtest_results and backtest_results['trades']:
+            # Create series for buy and sell points
+            buy_points = pd.Series(np.nan, index=strategy_data.index)
+            sell_points = pd.Series(np.nan, index=strategy_data.index)
+            
+            for trade in backtest_results['trades']:
+                entry_time = trade.get('entry_time')
+                exit_time = trade.get('exit_time')
+                entry_price = trade.get('entry')
+                exit_price = trade.get('exit')
+                
+                # Find closest index for entry time
+                if entry_time is not None and entry_price is not None:
+                    try:
+                        if isinstance(entry_time, str):
+                            entry_time = pd.to_datetime(entry_time)
+                        # Find the closest index to entry_time
+                        closest_entry_idx = strategy_data.index.get_indexer([entry_time], method='nearest')[0]
+                        if closest_entry_idx >= 0:
+                            buy_points.iloc[closest_entry_idx] = entry_price
+                    except (ValueError, IndexError, TypeError):
+                        pass  # Skip if can't find matching time
+                
+                # Find closest index for exit time
+                if exit_time is not None and exit_price is not None:
+                    try:
+                        if isinstance(exit_time, str):
+                            exit_time = pd.to_datetime(exit_time)
+                        # Find the closest index to exit_time
+                        closest_exit_idx = strategy_data.index.get_indexer([exit_time], method='nearest')[0]
+                        if closest_exit_idx >= 0:
+                            sell_points.iloc[closest_exit_idx] = exit_price
+                    except (ValueError, IndexError, TypeError):
+                        pass  # Skip if can't find matching time
+            
+            formatted_df['1_scatter_actual_buy'] = buy_points
+            formatted_df['1_scatter_actual_sell'] = sell_points
+        
+        # Stop Loss and Take Profit levels
+        if 'StopLoss' in strategy_data.columns:
+            formatted_df['1_line_stop_loss'] = strategy_data['StopLoss']
+        if 'TakeProfit' in strategy_data.columns:
+            formatted_df['1_line_take_profit'] = strategy_data['TakeProfit']
+        
+        # Plot 2: RSI
+        rsi_col = None
+        if 'RSI' in strategy_data.columns:
+            rsi_col = 'RSI'
+        elif 'RSI_15m' in strategy_data.columns:
+            rsi_col = 'RSI_15m'
+            
+        if rsi_col:
+            formatted_df['2_line_rsi'] = strategy_data[rsi_col]
+            # Add RSI overbought/oversold levels
+            formatted_df['2_line_rsi_overbought'] = 70
+            formatted_df['2_line_rsi_oversold'] = 30
+        
+        # Plot 3: Volume (if available)
+        if 'volume' in strategy_data.columns:
+            formatted_df['3_bar_volume'] = strategy_data['volume']
+        
+        # Add volume moving average if available
+        if 'VolumeMA_15m' in strategy_data.columns:
+            formatted_df['3_line_volume_ma'] = strategy_data['VolumeMA_15m']
+        
+        return formatted_df
+
+    @staticmethod
+    def format_strategy_data(strategy_data):
+        """
+        Format strategy data for universal plotting (without trade signals).
+        Converts strategy output into the expected column format: "x_type_name"
+        
+        Args:
+            strategy_data (DataFrame): Output from strategy with columns like 'close', 'UpperBand', 'LowerBand', 'RSI'
+            
+        Returns:
+            DataFrame: Formatted data ready for plot_data function
+        """
+        formatted_df = pd.DataFrame(index=strategy_data.index)
+        
+        # Plot 1: Price data with Bollinger Bands
+        if 'close' in strategy_data.columns:
+            formatted_df['1_line_close'] = strategy_data['close']
+        
+        # Bollinger Bands area (prefer standard names, fallback to timeframe-specific)
+        upper_band_col = None
+        lower_band_col = None
+        sma_col = None
+        
+        # Check for standard BB columns first
+        if 'UpperBand' in strategy_data.columns and 'LowerBand' in strategy_data.columns:
+            upper_band_col = 'UpperBand'
+            lower_band_col = 'LowerBand'
+        # Check for 15m BB columns
+        elif 'UpperBand_15m' in strategy_data.columns and 'LowerBand_15m' in strategy_data.columns:
+            upper_band_col = 'UpperBand_15m'
+            lower_band_col = 'LowerBand_15m'
+        
+        if upper_band_col and lower_band_col:
+            formatted_df['1_area_bb_upper'] = strategy_data[upper_band_col]
+            formatted_df['1_area_bb_lower'] = strategy_data[lower_band_col]
+        
+        # SMA/Moving Average line
+        if 'SMA' in strategy_data.columns:
+            sma_col = 'SMA'
+        elif 'SMA_15m' in strategy_data.columns:
+            sma_col = 'SMA_15m'
+            
+        if sma_col:
+            formatted_df['1_line_sma'] = strategy_data[sma_col]
+        
+        # Stop Loss and Take Profit levels
+        if 'StopLoss' in strategy_data.columns:
+            formatted_df['1_line_stop_loss'] = strategy_data['StopLoss']
+        if 'TakeProfit' in strategy_data.columns:
+            formatted_df['1_line_take_profit'] = strategy_data['TakeProfit']
+        
+        # Plot 2: RSI
+        rsi_col = None
+        if 'RSI' in strategy_data.columns:
+            rsi_col = 'RSI'
+        elif 'RSI_15m' in strategy_data.columns:
+            rsi_col = 'RSI_15m'
+            
+        if rsi_col:
+            formatted_df['2_line_rsi'] = strategy_data[rsi_col]
+            # Add RSI overbought/oversold levels
+            formatted_df['2_line_rsi_overbought'] = 70
+            formatted_df['2_line_rsi_oversold'] = 30
+        
+        # Plot 3: Volume (if available)
+        if 'volume' in strategy_data.columns:
+            formatted_df['3_bar_volume'] = strategy_data['volume']
+        
+        # Add volume moving average if available
+        if 'VolumeMA_15m' in strategy_data.columns:
+            formatted_df['3_line_volume_ma'] = strategy_data['VolumeMA_15m']
+        
+        return formatted_df
+
+    @staticmethod
+    def plot_data(df):
+        """
+        Universal plot function for any formatted data.
+        - df: DataFrame with column names in format "x_type_name" where:
+              x = plot number (subplot)
+              type = plot type (line, area, scatter, bar, etc.)
+              name = descriptive name for the data series
+        """
+        if df.empty:
+            print("No data to plot")
+            return
+            
+        # Parse all columns
+        plot_info = []
+        for column in df.columns:
+            parts = column.split('_', 2)  # Split into max 3 parts
+            if len(parts) < 3:
+                print(f"Warning: Skipping column '{column}' - invalid format. Expected 'x_type_name'")
+                continue
+                
+            try:
+                plot_number = int(parts[0])
+                plot_type = parts[1].lower()
+                plot_name = parts[2]
+                plot_info.append((plot_number, plot_type, plot_name, column))
+            except ValueError:
+                print(f"Warning: Skipping column '{column}' - invalid plot number")
+                continue
+
+        if not plot_info:
+            print("No valid columns found for plotting")
+            return
+
+        # Group by plot number
+        plots = {}
+        for plot_num, plot_type, plot_name, column in plot_info:
+            if plot_num not in plots:
+                plots[plot_num] = []
+            plots[plot_num].append((plot_type, plot_name, column))
+
+        # Sort plot numbers
+        plot_numbers = sorted(plots.keys())
+        n_plots = len(plot_numbers)
+
+        # Create subplots
+        fig, axs = plt.subplots(n_plots, 1, figsize=(16, 6 * n_plots), sharex=True)
+        if n_plots == 1:
+            axs = [axs]  # Ensure axs is always a list
+
+        # Plot each subplot
+        for i, plot_num in enumerate(plot_numbers):
+            ax = axs[i]
+            plot_items = plots[plot_num]
+            
+            # Handle Bollinger Bands area first (needs special handling)
+            bb_upper = None
+            bb_lower = None
+            
+            for plot_type, plot_name, column in plot_items:
+                if plot_type == 'area' and 'bb_upper' in plot_name:
+                    bb_upper = df[column]
+                elif plot_type == 'area' and 'bb_lower' in plot_name:
+                    bb_lower = df[column]
+            
+            # Plot Bollinger Bands area if both bounds exist
+            if bb_upper is not None and bb_lower is not None:
+                ax.fill_between(df.index, bb_upper, bb_lower, alpha=0.2, color='gray', label='Bollinger Bands')
+            
+            # Plot other items
+            for plot_type, plot_name, column in plot_items:
+                if plot_type == 'area' and ('bb_upper' in plot_name or 'bb_lower' in plot_name):
+                    continue  # Already handled above
+                    
+                data = df[column].dropna()  # Remove NaN values for cleaner plots
+                
+                if plot_type == 'line':
+                    color = None
+                    linestyle = '-'
+                    alpha = 1.0
+                    
+                    # Special styling for different line types
+                    if 'overbought' in plot_name:
+                        color = 'red'
+                        linestyle = '--'
+                        alpha = 0.7
+                    elif 'oversold' in plot_name:
+                        color = 'green'
+                        linestyle = '--'
+                        alpha = 0.7
+                    elif 'stop_loss' in plot_name:
+                        color = 'red'
+                        linestyle = ':'
+                        alpha = 0.8
+                    elif 'take_profit' in plot_name:
+                        color = 'green'
+                        linestyle = ':'
+                        alpha = 0.8
+                    elif 'sma' in plot_name:
+                        color = 'orange'
+                        alpha = 0.8
+                    elif 'volume_ma' in plot_name:
+                        color = 'purple'
+                        alpha = 0.7
+                    
+                    ax.plot(data.index, data, label=plot_name.replace('_', ' ').title(), 
+                           color=color, linestyle=linestyle, alpha=alpha)
+                    
+                elif plot_type == 'scatter':
+                    color = 'green' if 'buy' in plot_name else 'red' if 'sell' in plot_name else 'blue'
+                    marker = '^' if 'buy' in plot_name else 'v' if 'sell' in plot_name else 'o'
+                    size = 100 if 'buy' in plot_name or 'sell' in plot_name else 50
+                    alpha = 0.8
+                    zorder = 5
+                    label_name = plot_name.replace('_', ' ').title()
+                    
+                    # Special styling for different signal types
+                    if 'actual_buy' in plot_name:
+                        color = 'darkgreen'
+                        marker = '^'
+                        size = 120
+                        alpha = 1.0
+                        zorder = 10  # Higher z-order to appear on top
+                        label_name = 'Actual Buy Trades'
+                    elif 'actual_sell' in plot_name:
+                        color = 'darkred'
+                        marker = 'v'
+                        size = 120
+                        alpha = 1.0
+                        zorder = 10  # Higher z-order to appear on top
+                        label_name = 'Actual Sell Trades'
+                    elif 'strategy_buy' in plot_name:
+                        color = 'lightgreen'
+                        marker = '^'
+                        size = 60
+                        alpha = 0.6
+                        zorder = 3  # Lower z-order to appear behind actual trades
+                        label_name = 'Strategy Buy Signals'
+                    elif 'strategy_sell' in plot_name:
+                        color = 'lightcoral'
+                        marker = 'v'
+                        size = 60
+                        alpha = 0.6
+                        zorder = 3  # Lower z-order to appear behind actual trades
+                        label_name = 'Strategy Sell Signals'
+                    
+                    ax.scatter(data.index, data, label=label_name, 
+                             color=color, marker=marker, s=size, alpha=alpha, zorder=zorder)
+                    
+                elif plot_type == 'area':
+                    ax.fill_between(data.index, data, alpha=0.5, label=plot_name.replace('_', ' ').title())
+                    
+                elif plot_type == 'bar':
+                    ax.bar(data.index, data, alpha=0.7, label=plot_name.replace('_', ' ').title())
+                    
+                else:
+                    print(f"Warning: Plot type '{plot_type}' not supported for column '{column}'")
+
+            # Customize subplot
+            ax.grid(True, alpha=0.3)
+            ax.legend()
+            
+            # Set titles and labels
+            if plot_num == 1:
+                ax.set_title('Price Chart with Bollinger Bands and Signals')
+                ax.set_ylabel('Price')
+            elif plot_num == 2:
+                ax.set_title('RSI Indicator')
+                ax.set_ylabel('RSI')
+                ax.set_ylim(0, 100)
+            elif plot_num == 3:
+                ax.set_title('Volume')
+                ax.set_ylabel('Volume')
+            else:
+                ax.set_title(f'Plot {plot_num}')
+
+        # Set x-axis label only on the bottom subplot
+        axs[-1].set_xlabel('Time')
+        
+        plt.tight_layout()
+        plt.show()
+                
+                
+                
+            

cycles/strategies/__init__.py

@@ -0,0 +1,40 @@
+"""
+Strategies Module
+
+This module contains the strategy management system for trading strategies.
+It provides a flexible framework for implementing, combining, and managing multiple trading strategies.
+
+Components:
+- StrategyBase: Abstract base class for all strategies
+- DefaultStrategy: Meta-trend based strategy
+- BBRSStrategy: Bollinger Bands + RSI strategy  
+- StrategyManager: Orchestrates multiple strategies
+- StrategySignal: Represents trading signals with confidence levels
+
+Usage:
+    from cycles.strategies import StrategyManager, create_strategy_manager
+    
+    # Create strategy manager from config
+    strategy_manager = create_strategy_manager(config)
+    
+    # Or create individual strategies
+    from cycles.strategies import DefaultStrategy, BBRSStrategy
+    default_strategy = DefaultStrategy(weight=1.0, params={})
+"""
+
+from .base import StrategyBase, StrategySignal
+from .default_strategy import DefaultStrategy
+from .bbrs_strategy import BBRSStrategy
+from .manager import StrategyManager, create_strategy_manager
+
+__all__ = [
+    'StrategyBase',
+    'StrategySignal', 
+    'DefaultStrategy',
+    'BBRSStrategy',
+    'StrategyManager',
+    'create_strategy_manager'
+]
+
+__version__ = '1.0.0'
+__author__ = 'TCP Cycles Team' 

cycles/strategies/base.py

@@ -0,0 +1,250 @@
+"""
+Base classes for the strategy management system.
+
+This module contains the fundamental building blocks for all trading strategies:
+- StrategySignal: Represents trading signals with confidence and metadata
+- StrategyBase: Abstract base class that all strategies must inherit from
+"""
+
+import pandas as pd
+from abc import ABC, abstractmethod
+from typing import Dict, Optional, List, Union
+
+
+class StrategySignal:
+    """
+    Represents a trading signal from a strategy.
+    
+    A signal encapsulates the strategy's recommendation along with confidence
+    level, optional price target, and additional metadata.
+    
+    Attributes:
+        signal_type (str): Type of signal - "ENTRY", "EXIT", or "HOLD"
+        confidence (float): Confidence level from 0.0 to 1.0
+        price (Optional[float]): Optional specific price for the signal
+        metadata (Dict): Additional signal data and context
+    
+    Example:
+        # Entry signal with high confidence
+        signal = StrategySignal("ENTRY", confidence=0.8)
+        
+        # Exit signal with stop loss price
+        signal = StrategySignal("EXIT", confidence=1.0, price=50000, 
+                              metadata={"type": "STOP_LOSS"})
+    """
+    
+    def __init__(self, signal_type: str, confidence: float = 1.0, 
+                 price: Optional[float] = None, metadata: Optional[Dict] = None):
+        """
+        Initialize a strategy signal.
+        
+        Args:
+            signal_type: Type of signal ("ENTRY", "EXIT", "HOLD")
+            confidence: Confidence level (0.0 to 1.0)
+            price: Optional specific price for the signal
+            metadata: Additional signal data and context
+        """
+        self.signal_type = signal_type
+        self.confidence = max(0.0, min(1.0, confidence))  # Clamp to [0,1]
+        self.price = price
+        self.metadata = metadata or {}
+    
+    def __repr__(self) -> str:
+        """String representation of the signal."""
+        return (f"StrategySignal(type={self.signal_type}, "
+                f"confidence={self.confidence:.2f}, "
+                f"price={self.price}, metadata={self.metadata})")
+
+
+class StrategyBase(ABC):
+    """
+    Abstract base class for all trading strategies.
+    
+    This class defines the interface that all strategies must implement:
+    - get_timeframes(): Specify required timeframes for the strategy
+    - initialize(): Setup strategy with backtester data
+    - get_entry_signal(): Generate entry signals
+    - get_exit_signal(): Generate exit signals
+    - get_confidence(): Optional confidence calculation
+    
+    Attributes:
+        name (str): Strategy name
+        weight (float): Strategy weight for combination
+        params (Dict): Strategy parameters
+        initialized (bool): Whether strategy has been initialized
+        timeframes_data (Dict): Resampled data for different timeframes
+    
+    Example:
+        class MyStrategy(StrategyBase):
+            def get_timeframes(self):
+                return ["15min"]  # This strategy works on 15-minute data
+            
+            def initialize(self, backtester):
+                # Setup strategy indicators using self.timeframes_data["15min"]
+                self.initialized = True
+            
+            def get_entry_signal(self, backtester, df_index):
+                # Return StrategySignal based on analysis
+                if should_enter:
+                    return StrategySignal("ENTRY", confidence=0.7)
+                return StrategySignal("HOLD", confidence=0.0)
+    """
+    
+    def __init__(self, name: str, weight: float = 1.0, params: Optional[Dict] = None):
+        """
+        Initialize the strategy base.
+        
+        Args:
+            name: Strategy name/identifier
+            weight: Strategy weight for combination (default: 1.0)
+            params: Strategy-specific parameters
+        """
+        self.name = name
+        self.weight = weight
+        self.params = params or {}
+        self.initialized = False
+        self.timeframes_data = {}  # Will store resampled data for each timeframe
+    
+    def get_timeframes(self) -> List[str]:
+        """
+        Get the list of timeframes required by this strategy.
+        
+        Override this method to specify which timeframes your strategy needs.
+        The base class will automatically resample the 1-minute data to these timeframes
+        and make them available in self.timeframes_data.
+        
+        Returns:
+            List[str]: List of timeframe strings (e.g., ["1min", "15min", "1h"])
+        
+        Example:
+            def get_timeframes(self):
+                return ["15min"]  # Strategy needs 15-minute data
+                
+            def get_timeframes(self):
+                return ["5min", "15min", "1h"]  # Multi-timeframe strategy
+        """
+        return ["1min"]  # Default to 1-minute data
+    
+    def _resample_data(self, original_data: pd.DataFrame) -> None:
+        """
+        Resample the original 1-minute data to all required timeframes.
+        
+        This method is called automatically during initialization to create
+        resampled versions of the data for each timeframe the strategy needs.
+        
+        Args:
+            original_data: Original 1-minute OHLCV data with DatetimeIndex
+        """
+        self.timeframes_data = {}
+        
+        for timeframe in self.get_timeframes():
+            if timeframe == "1min":
+                # For 1-minute data, just use the original
+                self.timeframes_data[timeframe] = original_data.copy()
+            else:
+                # Resample to the specified timeframe
+                resampled = original_data.resample(timeframe).agg({
+                    'open': 'first',
+                    'high': 'max',
+                    'low': 'min',
+                    'close': 'last',
+                    'volume': 'sum'
+                }).dropna()
+                
+                self.timeframes_data[timeframe] = resampled
+    
+    def get_data_for_timeframe(self, timeframe: str) -> Optional[pd.DataFrame]:
+        """
+        Get resampled data for a specific timeframe.
+        
+        Args:
+            timeframe: Timeframe string (e.g., "15min", "1h")
+            
+        Returns:
+            pd.DataFrame: Resampled OHLCV data or None if timeframe not available
+        """
+        return self.timeframes_data.get(timeframe)
+    
+    def get_primary_timeframe_data(self) -> pd.DataFrame:
+        """
+        Get data for the primary (first) timeframe.
+        
+        Returns:
+            pd.DataFrame: Data for the first timeframe in get_timeframes() list
+        """
+        primary_timeframe = self.get_timeframes()[0]
+        return self.timeframes_data[primary_timeframe]
+    
+    @abstractmethod
+    def initialize(self, backtester) -> None:
+        """
+        Initialize strategy with backtester data.
+        
+        This method is called once before backtesting begins.
+        The original 1-minute data will already be resampled to all required timeframes
+        and available in self.timeframes_data.
+        
+        Strategies should setup indicators, validate data, and
+        set self.initialized = True when complete.
+        
+        Args:
+            backtester: Backtest instance with data and configuration
+        """
+        pass
+    
+    @abstractmethod 
+    def get_entry_signal(self, backtester, df_index: int) -> StrategySignal:
+        """
+        Generate entry signal for the given data index.
+        
+        The df_index refers to the index in the backtester's working dataframe,
+        which corresponds to the primary timeframe data.
+        
+        Args:
+            backtester: Backtest instance with current state
+            df_index: Current index in the primary timeframe dataframe
+            
+        Returns:
+            StrategySignal: Entry signal with confidence level
+        """
+        pass
+    
+    @abstractmethod
+    def get_exit_signal(self, backtester, df_index: int) -> StrategySignal:
+        """
+        Generate exit signal for the given data index.
+        
+        The df_index refers to the index in the backtester's working dataframe,
+        which corresponds to the primary timeframe data.
+        
+        Args:
+            backtester: Backtest instance with current state
+            df_index: Current index in the primary timeframe dataframe
+            
+        Returns:
+            StrategySignal: Exit signal with confidence level
+        """
+        pass
+    
+    def get_confidence(self, backtester, df_index: int) -> float:
+        """
+        Get strategy confidence for the current market state.
+        
+        Default implementation returns 1.0. Strategies can override
+        this to provide dynamic confidence based on market conditions.
+        
+        Args:
+            backtester: Backtest instance with current state
+            df_index: Current index in the primary timeframe dataframe
+            
+        Returns:
+            float: Confidence level (0.0 to 1.0)
+        """
+        return 1.0
+    
+    def __repr__(self) -> str:
+        """String representation of the strategy."""
+        timeframes = self.get_timeframes()
+        return (f"{self.__class__.__name__}(name={self.name}, "
+                f"weight={self.weight}, timeframes={timeframes}, "
+                f"initialized={self.initialized})") 

cycles/strategies/bbrs_strategy.py

@@ -0,0 +1,344 @@
+"""
+Bollinger Bands + RSI Strategy (BBRS)
+
+This module implements a sophisticated trading strategy that combines Bollinger Bands
+and RSI indicators with market regime detection. The strategy adapts its parameters
+based on whether the market is trending or moving sideways.
+
+Key Features:
+- Dynamic parameter adjustment based on market regime
+- Bollinger Band squeeze detection
+- RSI overbought/oversold conditions
+- Market regime-specific thresholds
+- Multi-timeframe analysis support
+"""
+
+import pandas as pd
+import numpy as np
+import logging
+from typing import Tuple, Optional, List
+
+from .base import StrategyBase, StrategySignal
+
+
+class BBRSStrategy(StrategyBase):
+    """
+    Bollinger Bands + RSI Strategy implementation.
+    
+    This strategy uses Bollinger Bands and RSI indicators with market regime detection
+    to generate trading signals. It adapts its parameters based on whether the market
+    is in a trending or sideways regime.
+    
+    The strategy works with 1-minute data as input and lets the underlying Strategy class
+    handle internal resampling to the timeframes it needs (typically 15min and 1h).
+    Stop-loss execution uses 1-minute precision.
+    
+    Parameters:
+        bb_width (float): Bollinger Band width threshold (default: 0.05)
+        bb_period (int): Bollinger Band period (default: 20)
+        rsi_period (int): RSI calculation period (default: 14)
+        trending_rsi_threshold (list): RSI thresholds for trending market [low, high]
+        trending_bb_multiplier (float): BB multiplier for trending market
+        sideways_rsi_threshold (list): RSI thresholds for sideways market [low, high]
+        sideways_bb_multiplier (float): BB multiplier for sideways market
+        strategy_name (str): Strategy implementation name ("MarketRegimeStrategy" or "CryptoTradingStrategy")
+        SqueezeStrategy (bool): Enable squeeze strategy
+        stop_loss_pct (float): Stop loss percentage (default: 0.05)
+    
+    Example:
+        params = {
+            "bb_width": 0.05,
+            "bb_period": 20,
+            "rsi_period": 14,
+            "strategy_name": "MarketRegimeStrategy",
+            "SqueezeStrategy": true
+        }
+        strategy = BBRSStrategy(weight=1.0, params=params)
+    """
+    
+    def __init__(self, weight: float = 1.0, params: Optional[dict] = None):
+        """
+        Initialize the BBRS strategy.
+        
+        Args:
+            weight: Strategy weight for combination (default: 1.0)
+            params: Strategy parameters for Bollinger Bands and RSI
+        """
+        super().__init__("bbrs", weight, params)
+    
+    def get_timeframes(self) -> List[str]:
+        """
+        Get the timeframes required by the BBRS strategy.
+        
+        BBRS strategy uses 1-minute data as input and lets the Strategy class
+        handle internal resampling to the timeframes it needs (15min, 1h, etc.).
+        We still include 1min for stop-loss precision.
+        
+        Returns:
+            List[str]: List of timeframes needed for the strategy
+        """
+        # BBRS strategy works with 1-minute data and lets Strategy class handle resampling
+        return ["1min"]
+    
+    def initialize(self, backtester) -> None:
+        """
+        Initialize BBRS strategy with signal processing.
+        
+        Sets up the strategy by:
+        1. Using 1-minute data directly (Strategy class handles internal resampling)
+        2. Running the BBRS strategy processing on 1-minute data
+        3. Creating signals aligned with backtester expectations
+        
+        Args:
+            backtester: Backtest instance with OHLCV data
+        """
+        # Resample to get 1-minute data (which should be the original data)
+        self._resample_data(backtester.original_df)
+        
+        # Get 1-minute data for strategy processing - Strategy class will handle internal resampling
+        min1_data = self.get_data_for_timeframe("1min")
+        
+        # Initialize empty signal series for backtester compatibility
+        # Note: These will be populated after strategy processing
+        backtester.strategies["buy_signals"] = pd.Series(False, index=range(len(min1_data)))
+        backtester.strategies["sell_signals"] = pd.Series(False, index=range(len(min1_data)))
+        backtester.strategies["stop_loss_pct"] = self.params.get("stop_loss_pct", 0.05)
+        backtester.strategies["primary_timeframe"] = "1min"
+        
+        # Run strategy processing on 1-minute data
+        self._run_strategy_processing(backtester)
+        
+        self.initialized = True
+    
+    def _run_strategy_processing(self, backtester) -> None:
+        """
+        Run the actual BBRS strategy processing.
+        
+        Uses the Strategy class from cycles.Analysis.strategies to process
+        the 1-minute data. The Strategy class will handle internal resampling
+        to the timeframes it needs (15min, 1h, etc.) and generate buy/sell signals.
+        
+        Args:
+            backtester: Backtest instance with timeframes_data available
+        """
+        from cycles.Analysis.strategies import Strategy
+        
+        # Get 1-minute data for strategy processing - let Strategy class handle resampling
+        strategy_data = self.get_data_for_timeframe("1min")
+        
+        # Configure strategy parameters with defaults
+        config_strategy = {
+            "bb_width": self.params.get("bb_width", 0.05),
+            "bb_period": self.params.get("bb_period", 20),
+            "rsi_period": self.params.get("rsi_period", 14),
+            "trending": {
+                "rsi_threshold": self.params.get("trending_rsi_threshold", [30, 70]),
+                "bb_std_dev_multiplier": self.params.get("trending_bb_multiplier", 2.5),
+            },
+            "sideways": {
+                "rsi_threshold": self.params.get("sideways_rsi_threshold", [40, 60]),
+                "bb_std_dev_multiplier": self.params.get("sideways_bb_multiplier", 1.8),
+            },
+            "strategy_name": self.params.get("strategy_name", "MarketRegimeStrategy"),
+            "SqueezeStrategy": self.params.get("SqueezeStrategy", True)
+        }
+        
+        # Run strategy processing on 1-minute data - Strategy class handles internal resampling
+        strategy = Strategy(config=config_strategy, logging=logging)
+        processed_data = strategy.run(strategy_data, config_strategy["strategy_name"])
+        
+        # Store processed data for plotting and analysis
+        backtester.processed_data = processed_data
+        
+        if processed_data.empty:
+            # If strategy processing failed, keep empty signals
+            return
+        
+        # Extract signals from processed data
+        buy_signals_raw = processed_data.get('BuySignal', pd.Series(False, index=processed_data.index)).astype(bool)
+        sell_signals_raw = processed_data.get('SellSignal', pd.Series(False, index=processed_data.index)).astype(bool)
+        
+        # The processed_data will be on whatever timeframe the Strategy class outputs
+        # We need to map these signals back to 1-minute resolution for backtesting
+        original_1min_data = self.get_data_for_timeframe("1min")
+        
+        # Reindex signals to 1-minute resolution using forward-fill
+        buy_signals_1min = buy_signals_raw.reindex(original_1min_data.index, method='ffill').fillna(False)
+        sell_signals_1min = sell_signals_raw.reindex(original_1min_data.index, method='ffill').fillna(False)
+        
+        # Convert to integer index to match backtester expectations
+        backtester.strategies["buy_signals"] = pd.Series(buy_signals_1min.values, index=range(len(buy_signals_1min)))
+        backtester.strategies["sell_signals"] = pd.Series(sell_signals_1min.values, index=range(len(sell_signals_1min)))
+    
+    def get_entry_signal(self, backtester, df_index: int) -> StrategySignal:
+        """
+        Generate entry signal based on BBRS buy signals.
+        
+        Entry occurs when the BBRS strategy processing has generated
+        a buy signal based on Bollinger Bands and RSI conditions on
+        the primary timeframe.
+        
+        Args:
+            backtester: Backtest instance with current state
+            df_index: Current index in the primary timeframe dataframe
+            
+        Returns:
+            StrategySignal: Entry signal if buy condition met, hold otherwise
+        """
+        if not self.initialized:
+            return StrategySignal("HOLD", confidence=0.0)
+        
+        if df_index >= len(backtester.strategies["buy_signals"]):
+            return StrategySignal("HOLD", confidence=0.0)
+        
+        if backtester.strategies["buy_signals"].iloc[df_index]:
+            # High confidence for BBRS buy signals
+            confidence = self._calculate_signal_confidence(backtester, df_index, "entry")
+            return StrategySignal("ENTRY", confidence=confidence)
+        
+        return StrategySignal("HOLD", confidence=0.0)
+    
+    def get_exit_signal(self, backtester, df_index: int) -> StrategySignal:
+        """
+        Generate exit signal based on BBRS sell signals or stop loss.
+        
+        Exit occurs when:
+        1. BBRS strategy generates a sell signal
+        2. Stop loss is triggered based on price movement
+        
+        Args:
+            backtester: Backtest instance with current state
+            df_index: Current index in the primary timeframe dataframe
+            
+        Returns:
+            StrategySignal: Exit signal with type and price, or hold signal
+        """
+        if not self.initialized:
+            return StrategySignal("HOLD", confidence=0.0)
+        
+        if df_index >= len(backtester.strategies["sell_signals"]):
+            return StrategySignal("HOLD", confidence=0.0)
+        
+        # Check for sell signal
+        if backtester.strategies["sell_signals"].iloc[df_index]:
+            confidence = self._calculate_signal_confidence(backtester, df_index, "exit")
+            return StrategySignal("EXIT", confidence=confidence, 
+                                metadata={"type": "SELL_SIGNAL"})
+        
+        # Check for stop loss using 1-minute data for precision
+        stop_loss_result, sell_price = self._check_stop_loss(backtester)
+        if stop_loss_result:
+            return StrategySignal("EXIT", confidence=1.0, price=sell_price,
+                                metadata={"type": "STOP_LOSS"})
+        
+        return StrategySignal("HOLD", confidence=0.0)
+    
+    def get_confidence(self, backtester, df_index: int) -> float:
+        """
+        Get strategy confidence based on signal strength and market conditions.
+        
+        Confidence can be enhanced by analyzing multiple timeframes and
+        market regime consistency.
+        
+        Args:
+            backtester: Backtest instance with current state
+            df_index: Current index in the primary timeframe dataframe
+            
+        Returns:
+            float: Confidence level (0.0 to 1.0)
+        """
+        if not self.initialized:
+            return 0.0
+        
+        # Check for active signals
+        has_buy_signal = (df_index < len(backtester.strategies["buy_signals"]) and 
+                         backtester.strategies["buy_signals"].iloc[df_index])
+        has_sell_signal = (df_index < len(backtester.strategies["sell_signals"]) and 
+                          backtester.strategies["sell_signals"].iloc[df_index])
+        
+        if has_buy_signal or has_sell_signal:
+            signal_type = "entry" if has_buy_signal else "exit"
+            return self._calculate_signal_confidence(backtester, df_index, signal_type)
+        
+        # Moderate confidence during neutral periods
+        return 0.5
+    
+    def _calculate_signal_confidence(self, backtester, df_index: int, signal_type: str) -> float:
+        """
+        Calculate confidence level for a signal based on multiple factors.
+        
+        Can consider multiple timeframes, market regime, volatility, etc.
+        
+        Args:
+            backtester: Backtest instance
+            df_index: Current index
+            signal_type: "entry" or "exit"
+            
+        Returns:
+            float: Confidence level (0.0 to 1.0)
+        """
+        base_confidence = 1.0
+        
+        # TODO: Implement multi-timeframe confirmation
+        # For now, return high confidence for primary signals
+        # Future enhancements could include:
+        # - Checking confirmation from additional timeframes
+        # - Analyzing market regime consistency
+        # - Considering volatility levels
+        # - RSI and BB position analysis
+        
+        return base_confidence
+    
+    def _check_stop_loss(self, backtester) -> Tuple[bool, Optional[float]]:
+        """
+        Check if stop loss is triggered using 1-minute data for precision.
+        
+        Uses 1-minute data regardless of primary timeframe to ensure
+        accurate stop loss execution.
+        
+        Args:
+            backtester: Backtest instance with current trade state
+            
+        Returns:
+            Tuple[bool, Optional[float]]: (stop_loss_triggered, sell_price)
+        """
+        # Calculate stop loss price
+        stop_price = backtester.entry_price * (1 - backtester.strategies["stop_loss_pct"])
+        
+        # Use 1-minute data for precise stop loss checking
+        min1_data = self.get_data_for_timeframe("1min")
+        if min1_data is None:
+            # Fallback to original_df if 1min timeframe not available
+            min1_data = backtester.original_df if hasattr(backtester, 'original_df') else backtester.min1_df
+        
+        min1_index = min1_data.index
+        
+        # Find data range from entry to current time
+        start_candidates = min1_index[min1_index >= backtester.entry_time]
+        if len(start_candidates) == 0:
+            return False, None
+            
+        backtester.current_trade_min1_start_idx = start_candidates[0]
+        end_candidates = min1_index[min1_index <= backtester.current_date]
+        
+        if len(end_candidates) == 0:
+            return False, None
+            
+        backtester.current_min1_end_idx = end_candidates[-1]
+        
+        # Check if any candle in the range triggered stop loss
+        min1_slice = min1_data.loc[backtester.current_trade_min1_start_idx:backtester.current_min1_end_idx]
+        
+        if (min1_slice['low'] <= stop_price).any():
+            # Find the first candle that triggered stop loss
+            stop_candle = min1_slice[min1_slice['low'] <= stop_price].iloc[0]
+            
+            # Use open price if it gapped below stop, otherwise use stop price
+            if stop_candle['open'] < stop_price:
+                sell_price = stop_candle['open']
+            else:
+                sell_price = stop_price
+                
+            return True, sell_price
+        
+        return False, None 

cycles/strategies/default_strategy.py

@@ -0,0 +1,254 @@
+"""
+Default Meta-Trend Strategy
+
+This module implements the default trading strategy based on meta-trend analysis
+using multiple Supertrend indicators. The strategy enters when trends align
+and exits on trend reversal or stop loss.
+
+The meta-trend is calculated by comparing three Supertrend indicators:
+- Entry: When meta-trend changes from != 1 to == 1  
+- Exit: When meta-trend changes to -1 or stop loss is triggered
+"""
+
+import numpy as np
+from typing import Tuple, Optional, List
+
+from .base import StrategyBase, StrategySignal
+
+
+class DefaultStrategy(StrategyBase):
+    """
+    Default meta-trend strategy implementation.
+    
+    This strategy uses multiple Supertrend indicators to determine market direction.
+    It generates entry signals when all three Supertrend indicators align in an 
+    upward direction, and exit signals when they reverse or stop loss is triggered.
+    
+    The strategy works best on 15-minute timeframes but can be configured for other timeframes.
+    
+    Parameters:
+        stop_loss_pct (float): Stop loss percentage (default: 0.03)
+        timeframe (str): Preferred timeframe for analysis (default: "15min")
+    
+    Example:
+        strategy = DefaultStrategy(weight=1.0, params={"stop_loss_pct": 0.05, "timeframe": "15min"})
+    """
+    
+    def __init__(self, weight: float = 1.0, params: Optional[dict] = None):
+        """
+        Initialize the default strategy.
+        
+        Args:
+            weight: Strategy weight for combination (default: 1.0)
+            params: Strategy parameters including stop_loss_pct and timeframe
+        """
+        super().__init__("default", weight, params)
+    
+    def get_timeframes(self) -> List[str]:
+        """
+        Get the timeframes required by the default strategy.
+        
+        The default strategy works on a single timeframe (typically 15min)
+        but also needs 1min data for precise stop-loss execution.
+        
+        Returns:
+            List[str]: List containing primary timeframe and 1min for stop-loss
+        """
+        primary_timeframe = self.params.get("timeframe", "15min")
+        
+        # Always include 1min for stop-loss precision, avoid duplicates
+        timeframes = [primary_timeframe]
+        if primary_timeframe != "1min":
+            timeframes.append("1min")
+            
+        return timeframes
+    
+    def initialize(self, backtester) -> None:
+        """
+        Initialize meta trend calculation using Supertrend indicators.
+        
+        Calculates the meta-trend by comparing three Supertrend indicators.
+        When all three agree on direction, meta-trend follows that direction.
+        Otherwise, meta-trend is neutral (0).
+        
+        Args:
+            backtester: Backtest instance with OHLCV data
+        """
+        from cycles.Analysis.supertrend import Supertrends
+        
+        # First, resample the original 1-minute data to required timeframes
+        self._resample_data(backtester.original_df)
+        
+        # Get the primary timeframe data for strategy calculations
+        primary_timeframe = self.get_timeframes()[0]
+        strategy_data = self.get_data_for_timeframe(primary_timeframe)
+        
+        # Calculate Supertrend indicators on the primary timeframe
+        supertrends = Supertrends(strategy_data, verbose=False)
+        supertrend_results_list = supertrends.calculate_supertrend_indicators()
+        
+        # Extract trend arrays from each Supertrend
+        trends = [st['results']['trend'] for st in supertrend_results_list]
+        trends_arr = np.stack(trends, axis=1)
+        
+        # Calculate meta-trend: all three must agree for direction signal
+        meta_trend = np.where(
+            (trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]), 
+            trends_arr[:,0], 
+            0  # Neutral when trends don't agree
+        )
+        
+        # Store in backtester for access during trading
+        # Note: backtester.df should now be using our primary timeframe
+        backtester.strategies["meta_trend"] = meta_trend
+        backtester.strategies["stop_loss_pct"] = self.params.get("stop_loss_pct", 0.03)
+        backtester.strategies["primary_timeframe"] = primary_timeframe
+        
+        self.initialized = True
+    
+    def get_entry_signal(self, backtester, df_index: int) -> StrategySignal:
+        """
+        Generate entry signal based on meta-trend direction change.
+        
+        Entry occurs when meta-trend changes from != 1 to == 1, indicating
+        all Supertrend indicators now agree on upward direction.
+        
+        Args:
+            backtester: Backtest instance with current state
+            df_index: Current index in the primary timeframe dataframe
+            
+        Returns:
+            StrategySignal: Entry signal if trend aligns, hold signal otherwise
+        """
+        if not self.initialized:
+            return StrategySignal("HOLD", 0.0)
+        
+        if df_index < 1:
+            return StrategySignal("HOLD", 0.0)
+        
+        # Check for meta-trend entry condition
+        prev_trend = backtester.strategies["meta_trend"][df_index - 1]
+        curr_trend = backtester.strategies["meta_trend"][df_index]
+        
+        if prev_trend != 1 and curr_trend == 1:
+            # Strong confidence when all indicators align for entry
+            return StrategySignal("ENTRY", confidence=1.0)
+        
+        return StrategySignal("HOLD", confidence=0.0)
+    
+    def get_exit_signal(self, backtester, df_index: int) -> StrategySignal:
+        """
+        Generate exit signal based on meta-trend reversal or stop loss.
+        
+        Exit occurs when:
+        1. Meta-trend changes to -1 (trend reversal)
+        2. Stop loss is triggered based on price movement
+        
+        Args:
+            backtester: Backtest instance with current state
+            df_index: Current index in the primary timeframe dataframe
+            
+        Returns:
+            StrategySignal: Exit signal with type and price, or hold signal
+        """
+        if not self.initialized:
+            return StrategySignal("HOLD", 0.0)
+        
+        if df_index < 1:
+            return StrategySignal("HOLD", 0.0)
+        
+        # Check for meta-trend exit signal
+        prev_trend = backtester.strategies["meta_trend"][df_index - 1]
+        curr_trend = backtester.strategies["meta_trend"][df_index]
+        
+        if prev_trend != 1 and curr_trend == -1:
+            return StrategySignal("EXIT", confidence=1.0, 
+                                metadata={"type": "META_TREND_EXIT_SIGNAL"})
+        
+        # Check for stop loss using 1-minute data for precision
+        stop_loss_result, sell_price = self._check_stop_loss(backtester)
+        if stop_loss_result:
+            return StrategySignal("EXIT", confidence=1.0, price=sell_price,
+                                metadata={"type": "STOP_LOSS"})
+        
+        return StrategySignal("HOLD", confidence=0.0)
+    
+    def get_confidence(self, backtester, df_index: int) -> float:
+        """
+        Get strategy confidence based on meta-trend strength.
+        
+        Higher confidence when meta-trend is strongly directional,
+        lower confidence during neutral periods.
+        
+        Args:
+            backtester: Backtest instance with current state
+            df_index: Current index in the primary timeframe dataframe
+            
+        Returns:
+            float: Confidence level (0.0 to 1.0)
+        """
+        if not self.initialized or df_index >= len(backtester.strategies["meta_trend"]):
+            return 0.0
+        
+        curr_trend = backtester.strategies["meta_trend"][df_index]
+        
+        # High confidence for strong directional signals
+        if curr_trend == 1 or curr_trend == -1:
+            return 1.0
+        
+        # Low confidence for neutral trend
+        return 0.3
+    
+    def _check_stop_loss(self, backtester) -> Tuple[bool, Optional[float]]:
+        """
+        Check if stop loss is triggered based on price movement.
+        
+        Uses 1-minute data for precise stop loss checking regardless of
+        the primary timeframe used for strategy signals.
+        
+        Args:
+            backtester: Backtest instance with current trade state
+            
+        Returns:
+            Tuple[bool, Optional[float]]: (stop_loss_triggered, sell_price)
+        """
+        # Calculate stop loss price
+        stop_price = backtester.entry_price * (1 - backtester.strategies["stop_loss_pct"])
+        
+        # Use 1-minute data for precise stop loss checking
+        min1_data = self.get_data_for_timeframe("1min")
+        if min1_data is None:
+            # Fallback to original_df if 1min timeframe not available
+            min1_data = backtester.original_df if hasattr(backtester, 'original_df') else backtester.min1_df
+        
+        min1_index = min1_data.index
+        
+        # Find data range from entry to current time
+        start_candidates = min1_index[min1_index >= backtester.entry_time]
+        if len(start_candidates) == 0:
+            return False, None
+            
+        backtester.current_trade_min1_start_idx = start_candidates[0]
+        end_candidates = min1_index[min1_index <= backtester.current_date]
+        
+        if len(end_candidates) == 0:
+            return False, None
+            
+        backtester.current_min1_end_idx = end_candidates[-1]
+        
+        # Check if any candle in the range triggered stop loss
+        min1_slice = min1_data.loc[backtester.current_trade_min1_start_idx:backtester.current_min1_end_idx]
+        
+        if (min1_slice['low'] <= stop_price).any():
+            # Find the first candle that triggered stop loss
+            stop_candle = min1_slice[min1_slice['low'] <= stop_price].iloc[0]
+            
+            # Use open price if it gapped below stop, otherwise use stop price
+            if stop_candle['open'] < stop_price:
+                sell_price = stop_candle['open']
+            else:
+                sell_price = stop_price
+                
+            return True, sell_price
+        
+        return False, None 

cycles/strategies/manager.py

@@ -0,0 +1,394 @@
+"""
+Strategy Manager
+
+This module contains the StrategyManager class that orchestrates multiple trading strategies
+and combines their signals using configurable aggregation rules.
+
+The StrategyManager supports various combination methods for entry and exit signals:
+- Entry: any, all, majority, weighted_consensus
+- Exit: any, all, priority (with stop loss prioritization)
+"""
+
+from typing import Dict, List, Tuple, Optional
+import logging
+
+from .base import StrategyBase, StrategySignal
+from .default_strategy import DefaultStrategy
+from .bbrs_strategy import BBRSStrategy
+
+
+class StrategyManager:
+    """
+    Manages multiple strategies and combines their signals.
+    
+    The StrategyManager loads multiple strategies from configuration,
+    initializes them with backtester data, and combines their signals
+    using configurable aggregation rules.
+    
+    Attributes:
+        strategies (List[StrategyBase]): List of loaded strategies
+        combination_rules (Dict): Rules for combining signals
+        initialized (bool): Whether manager has been initialized
+    
+    Example:
+        config = {
+            "strategies": [
+                {"name": "default", "weight": 0.6, "params": {}},
+                {"name": "bbrs", "weight": 0.4, "params": {"bb_width": 0.05}}
+            ],
+            "combination_rules": {
+                "entry": "weighted_consensus",
+                "exit": "any",
+                "min_confidence": 0.6
+            }
+        }
+        manager = StrategyManager(config["strategies"], config["combination_rules"])
+    """
+    
+    def __init__(self, strategies_config: List[Dict], combination_rules: Optional[Dict] = None):
+        """
+        Initialize the strategy manager.
+        
+        Args:
+            strategies_config: List of strategy configurations
+            combination_rules: Rules for combining signals
+        """
+        self.strategies = self._load_strategies(strategies_config)
+        self.combination_rules = combination_rules or {
+            "entry": "weighted_consensus",
+            "exit": "any",
+            "min_confidence": 0.5
+        }
+        self.initialized = False
+    
+    def _load_strategies(self, strategies_config: List[Dict]) -> List[StrategyBase]:
+        """
+        Load strategies from configuration.
+        
+        Creates strategy instances based on configuration and registers
+        them with the manager. Supports extensible strategy registration.
+        
+        Args:
+            strategies_config: List of strategy configurations
+            
+        Returns:
+            List[StrategyBase]: List of instantiated strategies
+            
+        Raises:
+            ValueError: If unknown strategy name is specified
+        """
+        strategies = []
+        
+        for config in strategies_config:
+            name = config.get("name", "").lower()
+            weight = config.get("weight", 1.0)
+            params = config.get("params", {})
+            
+            if name == "default":
+                strategies.append(DefaultStrategy(weight, params))
+            elif name == "bbrs":
+                strategies.append(BBRSStrategy(weight, params))
+            else:
+                raise ValueError(f"Unknown strategy: {name}. "
+                               f"Available strategies: default, bbrs")
+        
+        return strategies
+    
+    def initialize(self, backtester) -> None:
+        """
+        Initialize all strategies with backtester data.
+        
+        Calls the initialize method on each strategy, allowing them
+        to set up indicators, validate data, and prepare for trading.
+        Each strategy will handle its own timeframe resampling.
+        
+        Args:
+            backtester: Backtest instance with OHLCV data
+        """
+        for strategy in self.strategies:
+            try:
+                strategy.initialize(backtester)
+                
+                # Log strategy timeframe information
+                timeframes = strategy.get_timeframes()
+                logging.info(f"Initialized strategy: {strategy.name} with timeframes: {timeframes}")
+                
+            except Exception as e:
+                logging.error(f"Failed to initialize strategy {strategy.name}: {e}")
+                raise
+        
+        self.initialized = True
+        logging.info(f"Strategy manager initialized with {len(self.strategies)} strategies")
+        
+        # Log summary of all timeframes being used
+        all_timeframes = set()
+        for strategy in self.strategies:
+            all_timeframes.update(strategy.get_timeframes())
+        logging.info(f"Total unique timeframes in use: {sorted(all_timeframes)}")
+    
+    def get_entry_signal(self, backtester, df_index: int) -> bool:
+        """
+        Get combined entry signal from all strategies.
+        
+        Collects entry signals from all strategies and combines them
+        according to the configured combination rules.
+        
+        Args:
+            backtester: Backtest instance with current state
+            df_index: Current index in the dataframe
+            
+        Returns:
+            bool: True if combined signal suggests entry, False otherwise
+        """
+        if not self.initialized:
+            return False
+        
+        # Collect signals from all strategies
+        signals = {}
+        for strategy in self.strategies:
+            try:
+                signal = strategy.get_entry_signal(backtester, df_index)
+                signals[strategy.name] = {
+                    "signal": signal,
+                    "weight": strategy.weight,
+                    "confidence": signal.confidence
+                }
+            except Exception as e:
+                logging.warning(f"Strategy {strategy.name} entry signal failed: {e}")
+                signals[strategy.name] = {
+                    "signal": StrategySignal("HOLD", 0.0),
+                    "weight": strategy.weight,
+                    "confidence": 0.0
+                }
+        
+        return self._combine_entry_signals(signals)
+    
+    def get_exit_signal(self, backtester, df_index: int) -> Tuple[Optional[str], Optional[float]]:
+        """
+        Get combined exit signal from all strategies.
+        
+        Collects exit signals from all strategies and combines them
+        according to the configured combination rules.
+        
+        Args:
+            backtester: Backtest instance with current state
+            df_index: Current index in the dataframe
+            
+        Returns:
+            Tuple[Optional[str], Optional[float]]: (exit_type, exit_price) or (None, None)
+        """
+        if not self.initialized:
+            return None, None
+        
+        # Collect signals from all strategies
+        signals = {}
+        for strategy in self.strategies:
+            try:
+                signal = strategy.get_exit_signal(backtester, df_index)
+                signals[strategy.name] = {
+                    "signal": signal,
+                    "weight": strategy.weight,
+                    "confidence": signal.confidence
+                }
+            except Exception as e:
+                logging.warning(f"Strategy {strategy.name} exit signal failed: {e}")
+                signals[strategy.name] = {
+                    "signal": StrategySignal("HOLD", 0.0),
+                    "weight": strategy.weight,
+                    "confidence": 0.0
+                }
+        
+        return self._combine_exit_signals(signals)
+    
+    def _combine_entry_signals(self, signals: Dict) -> bool:
+        """
+        Combine entry signals based on combination rules.
+        
+        Supports multiple combination methods:
+        - any: Enter if ANY strategy signals entry
+        - all: Enter only if ALL strategies signal entry
+        - majority: Enter if majority of strategies signal entry
+        - weighted_consensus: Enter based on weighted average confidence
+        
+        Args:
+            signals: Dictionary of strategy signals with weights and confidence
+            
+        Returns:
+            bool: Combined entry decision
+        """
+        method = self.combination_rules.get("entry", "weighted_consensus")
+        min_confidence = self.combination_rules.get("min_confidence", 0.5)
+        
+        # Filter for entry signals above minimum confidence
+        entry_signals = [
+            s for s in signals.values() 
+            if s["signal"].signal_type == "ENTRY" and s["signal"].confidence >= min_confidence
+        ]
+        
+        if not entry_signals:
+            return False
+        
+        if method == "any":
+            # Enter if any strategy signals entry
+            return len(entry_signals) > 0
+        
+        elif method == "all":
+            # Enter only if all strategies signal entry
+            return len(entry_signals) == len(self.strategies)
+        
+        elif method == "majority":
+            # Enter if majority of strategies signal entry
+            return len(entry_signals) > len(self.strategies) / 2
+        
+        elif method == "weighted_consensus":
+            # Enter based on weighted average confidence
+            total_weight = sum(s["weight"] for s in entry_signals)
+            if total_weight == 0:
+                return False
+            
+            weighted_confidence = sum(
+                s["signal"].confidence * s["weight"] 
+                for s in entry_signals
+            ) / total_weight
+            
+            return weighted_confidence >= min_confidence
+        
+        else:
+            logging.warning(f"Unknown entry combination method: {method}, using 'any'")
+            return len(entry_signals) > 0
+    
+    def _combine_exit_signals(self, signals: Dict) -> Tuple[Optional[str], Optional[float]]:
+        """
+        Combine exit signals based on combination rules.
+        
+        Supports multiple combination methods:
+        - any: Exit if ANY strategy signals exit (recommended for risk management)
+        - all: Exit only if ALL strategies agree on exit
+        - priority: Exit based on priority order (STOP_LOSS > SELL_SIGNAL > others)
+        
+        Args:
+            signals: Dictionary of strategy signals with weights and confidence
+            
+        Returns:
+            Tuple[Optional[str], Optional[float]]: (exit_type, exit_price) or (None, None)
+        """
+        method = self.combination_rules.get("exit", "any")
+        
+        # Filter for exit signals
+        exit_signals = [
+            s for s in signals.values() 
+            if s["signal"].signal_type == "EXIT"
+        ]
+        
+        if not exit_signals:
+            return None, None
+        
+        if method == "any":
+            # Exit if any strategy signals exit (first one found)
+            for signal_data in exit_signals:
+                signal = signal_data["signal"]
+                exit_type = signal.metadata.get("type", "EXIT")
+                return exit_type, signal.price
+        
+        elif method == "all":
+            # Exit only if all strategies agree on exit
+            if len(exit_signals) == len(self.strategies):
+                signal = exit_signals[0]["signal"]
+                exit_type = signal.metadata.get("type", "EXIT")
+                return exit_type, signal.price
+        
+        elif method == "priority":
+            # Priority order: STOP_LOSS > SELL_SIGNAL > others
+            stop_loss_signals = [
+                s for s in exit_signals 
+                if s["signal"].metadata.get("type") == "STOP_LOSS"
+            ]
+            if stop_loss_signals:
+                signal = stop_loss_signals[0]["signal"]
+                return "STOP_LOSS", signal.price
+            
+            sell_signals = [
+                s for s in exit_signals 
+                if s["signal"].metadata.get("type") == "SELL_SIGNAL"
+            ]
+            if sell_signals:
+                signal = sell_signals[0]["signal"]
+                return "SELL_SIGNAL", signal.price
+            
+            # Return first available exit signal
+            signal = exit_signals[0]["signal"]
+            exit_type = signal.metadata.get("type", "EXIT")
+            return exit_type, signal.price
+        
+        else:
+            logging.warning(f"Unknown exit combination method: {method}, using 'any'")
+            # Fallback to 'any' method
+            signal = exit_signals[0]["signal"]
+            exit_type = signal.metadata.get("type", "EXIT")
+            return exit_type, signal.price
+        
+        return None, None
+    
+    def get_strategy_summary(self) -> Dict:
+        """
+        Get summary of loaded strategies and their configuration.
+        
+        Returns:
+            Dict: Summary of strategies, weights, combination rules, and timeframes
+        """
+        return {
+            "strategies": [
+                {
+                    "name": strategy.name,
+                    "weight": strategy.weight,
+                    "params": strategy.params,
+                    "timeframes": strategy.get_timeframes(),
+                    "initialized": strategy.initialized
+                }
+                for strategy in self.strategies
+            ],
+            "combination_rules": self.combination_rules,
+            "total_strategies": len(self.strategies),
+            "initialized": self.initialized,
+            "all_timeframes": list(set().union(*[strategy.get_timeframes() for strategy in self.strategies]))
+        }
+    
+    def __repr__(self) -> str:
+        """String representation of the strategy manager."""
+        strategy_names = [s.name for s in self.strategies]
+        return (f"StrategyManager(strategies={strategy_names}, "
+                f"initialized={self.initialized})")
+
+
+def create_strategy_manager(config: Dict) -> StrategyManager:
+    """
+    Factory function to create StrategyManager from configuration.
+    
+    Provides a convenient way to create a StrategyManager instance
+    from a configuration dictionary.
+    
+    Args:
+        config: Configuration dictionary with strategies and combination_rules
+        
+    Returns:
+        StrategyManager: Configured strategy manager instance
+        
+    Example:
+        config = {
+            "strategies": [
+                {"name": "default", "weight": 1.0, "params": {}}
+            ],
+            "combination_rules": {
+                "entry": "any",
+                "exit": "any"
+            }
+        }
+        manager = create_strategy_manager(config)
+    """
+    strategies_config = config.get("strategies", [])
+    combination_rules = config.get("combination_rules", {})
+    
+    if not strategies_config:
+        raise ValueError("No strategies specified in configuration")
+    
+    return StrategyManager(strategies_config, combination_rules) 

docs/strategies.md

@@ -1,98 +1,405 @@
-# Trading Strategies (`cycles/Analysis/strategies.py`)
+# Strategies Documentation
 
-This document outlines the trading strategies implemented within the `Strategy` class. These strategies utilize technical indicators calculated by other classes in the `Analysis` module.
+## Overview
 
-## Class: `Strategy`
+The Cycles framework implements advanced trading strategies with sophisticated timeframe management, signal processing, and multi-strategy combination capabilities. Each strategy can operate on its preferred timeframes while maintaining precise execution control.
 
-Manages and executes different trading strategies.
+## Architecture
 
-### `__init__(self, config: dict = None, logging = None)`
+### Strategy System Components
 
--   **Description**: Initializes the Strategy class.
--   **Parameters**:
-    -   `config` (dict, optional): Configuration dictionary containing parameters for various indicators and strategy settings. Must be provided if strategies requiring config are used.
-    -   `logging` (logging.Logger, optional): Logger object for outputting messages. Defaults to `None`.
+1. **StrategyBase**: Abstract base class with timeframe management
+2. **Individual Strategies**: DefaultStrategy, BBRSStrategy implementations  
+3. **StrategyManager**: Multi-strategy orchestration and signal combination
+4. **Timeframe System**: Automatic data resampling and signal mapping
 
-### `run(self, data: pd.DataFrame, strategy_name: str) -> pd.DataFrame`
+### New Timeframe Management
 
--   **Description**: Executes a specified trading strategy on the input data.
--   **Parameters**:
-    -   `data` (pd.DataFrame): Input DataFrame containing at least price data (e.g., 'close', 'volume'). Specific strategies might require other columns or will calculate them.
-    -   `strategy_name` (str): The name of the strategy to run. Supported names include:
-        -   `"MarketRegimeStrategy"`
-        -   `"CryptoTradingStrategy"`
-        -   `"no_strategy"` (or any other unrecognized name will default to this)
--   **Returns**: `pd.DataFrame` - A DataFrame containing the original data augmented with indicator values, and `BuySignal` and `SellSignal` (boolean) columns specific to the executed strategy. The structure of the DataFrame (e.g., daily, 15-minute) depends on the strategy.
+Each strategy now controls its own timeframe requirements:
 
-### `no_strategy(self, data: pd.DataFrame) -> pd.DataFrame`
+```python
+class MyStrategy(StrategyBase):
+    def get_timeframes(self):
+        return ["15min", "1h"]  # Strategy specifies needed timeframes
+    
+    def initialize(self, backtester):
+        # Framework automatically resamples data
+        self._resample_data(backtester.original_df)
+        
+        # Access resampled data
+        data_15m = self.get_data_for_timeframe("15min")
+        data_1h = self.get_data_for_timeframe("1h")
+```
 
--   **Description**: A default strategy that generates no trading signals. It can serve as a baseline or placeholder.
--   **Parameters**:
-    -   `data` (pd.DataFrame): Input data DataFrame.
--   **Returns**: `pd.DataFrame` - The input DataFrame with `BuySignal` and `SellSignal` columns added, both containing all `False` values.
+## Available Strategies
 
----
+### 1. Default Strategy (Meta-Trend Analysis)
 
-## Implemented Strategies
+**Purpose**: Meta-trend analysis using multiple Supertrend indicators
 
-### 1. `MarketRegimeStrategy`
+**Timeframe Behavior**:
+- **Configurable Primary Timeframe**: Set via `params["timeframe"]` (default: "15min")
+- **1-Minute Precision**: Always includes 1min data for precise stop-loss execution
+- **Example Timeframes**: `["15min", "1min"]` or `["5min", "1min"]`
 
--   **Description**: An adaptive strategy that combines Bollinger Bands and RSI, adjusting its parameters based on detected market regimes (trending vs. sideways). It operates on daily aggregated data (aggregation is performed internally).
--   **Core Logic**:
-    -   Calculates Bollinger Bands (using `BollingerBands` class) with adaptive standard deviation multipliers based on `MarketRegime` (derived from `BBWidth`).
-    -   Calculates RSI (using `RSI` class).
-    -   **Trending Market (Breakout Mode)**:
-        -   Buy: Price < Lower Band ∧ RSI < 50 ∧ Volume Spike.
-        -   Sell: Price > Upper Band ∧ RSI > 50 ∧ Volume Spike.
-    -   **Sideways Market (Mean Reversion)**:
-        -   Buy: Price ≤ Lower Band ∧ RSI ≤ 40.
-        -   Sell: Price ≥ Upper Band ∧ RSI ≥ 60.
-    -   **Squeeze Confirmation** (if `config["SqueezeStrategy"]` is `True`):
-        -   Requires additional confirmation from RSI Bollinger Bands (calculated by `rsi_bollinger_confirmation` helper method).
-        -   Sideways markets also check for volume contraction.
--   **Key Configuration Parameters (from `config` dict)**:
-    -   `bb_period`, `bb_width`
-    -   `trending['bb_std_dev_multiplier']`, `trending['rsi_threshold']`
-    -   `sideways['bb_std_dev_multiplier']`, `sideways['rsi_threshold']`
-    -   `rsi_period`
-    -   `SqueezeStrategy` (boolean)
--   **Output DataFrame Columns (Daily)**: Includes input columns plus `SMA`, `UpperBand`, `LowerBand`, `BBWidth`, `MarketRegime`, `RSI`, `BuySignal`, `SellSignal`.
+**Configuration**:
+```json
+{
+    "name": "default",
+    "weight": 1.0,
+    "params": {
+        "timeframe": "15min",    // Configurable: "5min", "15min", "1h", etc.
+        "stop_loss_pct": 0.03    // Stop loss percentage
+    }
+}
+```
 
-#### `rsi_bollinger_confirmation(self, rsi: pd.Series, window: int = 14, std_mult: float = 1.5) -> tuple`
+**Algorithm**:
+1. Calculate 3 Supertrend indicators with different parameters on primary timeframe
+2. Determine meta-trend: all three must agree for directional signal
+3. **Entry**: Meta-trend changes from != 1 to == 1 (all trends align upward)
+4. **Exit**: Meta-trend changes to -1 (trend reversal) or stop-loss triggered
+5. **Stop-Loss**: 1-minute precision using percentage-based threshold
 
--   **Description** (Helper for `MarketRegimeStrategy`): Calculates Bollinger Bands on RSI values for signal confirmation.
--   **Parameters**:
-    -   `rsi` (pd.Series): Series containing RSI values.
-    -   `window` (int, optional): The period for the moving average. Defaults to 14.
-    -   `std_mult` (float, optional): Standard deviation multiplier for bands. Defaults to 1.5.
--   **Returns**: `tuple` - (oversold_condition, overbought_condition) as pandas Series (boolean).
+**Strengths**:
+- Robust trend following with multiple confirmations
+- Configurable for different market timeframes
+- Precise risk management
+- Low false signals in trending markets
 
-### 2. `CryptoTradingStrategy`
+**Best Use Cases**:
+- Medium to long-term trend following
+- Markets with clear directional movements
+- Risk-conscious trading with defined exits
 
--   **Description**: A multi-timeframe strategy primarily designed for volatile assets like cryptocurrencies. It aggregates input data into 15-minute and 1-hour intervals for analysis.
--   **Core Logic**:
-    -   Aggregates data to 15-minute (`data_15m`) and 1-hour (`data_1h`) resolutions using `aggregate_to_minutes` and `aggregate_to_hourly` from `data_utils.py`.
-    -   Calculates 15-minute Bollinger Bands (20-period, 2 std dev) and 15-minute EMA-smoothed RSI (14-period) using `BollingerBands.calculate_custom_bands` and `RSI.calculate_custom_rsi`.
-    -   Calculates 1-hour Bollinger Bands (50-period, 1.8 std dev) using `BollingerBands.calculate_custom_bands`.
-    -   **Signal Generation (on 15m timeframe)**:
-        -   Buy Signal: Price ≤ Lower 15m Band ∧ Price ≤ Lower 1h Band ∧ RSI_15m < 35 ∧ Volume Confirmation.
-        -   Sell Signal: Price ≥ Upper 15m Band ∧ Price ≥ Upper 1h Band ∧ RSI_15m > 65 ∧ Volume Confirmation.
-    -   **Volume Confirmation**: Current 15m volume > 1.5 × 20-period MA of 15m volume.
-    -   **Risk Management**: Calculates `StopLoss` and `TakeProfit` levels based on a simplified ATR (standard deviation of 15m close prices over the last 4 periods).
-        -   Buy: SL = Price - 2 * ATR; TP = Price + 4 * ATR
-        -   Sell: SL = Price + 2 * ATR; TP = Price - 4 * ATR
--   **Key Configuration Parameters**: While this strategy uses fixed parameters for its core indicator calculations, the `config` object passed to the `Strategy` class might be used by helper functions or for future extensions (though not heavily used by the current `CryptoTradingStrategy` logic itself for primary indicator settings).
--   **Output DataFrame Columns (15-minute)**: Includes resampled 15m OHLCV, plus `UpperBand_15m`, `SMA_15m`, `LowerBand_15m`, `RSI_15m`, `VolumeMA_15m`, `UpperBand_1h` (forward-filled), `LowerBand_1h` (forward-filled), `BuySignal`, `SellSignal`, `StopLoss`, `TakeProfit`.
+### 2. BBRS Strategy (Bollinger Bands + RSI)
 
----
+**Purpose**: Market regime-adaptive strategy combining Bollinger Bands and RSI
 
-## General Strategy Concepts (from previous high-level notes)
+**Timeframe Behavior**:
+- **1-Minute Input**: Strategy receives 1-minute data
+- **Internal Resampling**: Underlying Strategy class handles resampling to 15min/1h
+- **No Double-Resampling**: Avoids conflicts with existing resampling logic
+- **Signal Mapping**: Results mapped back to 1-minute resolution
 
-While the specific implementations above have their own detailed logic, some general concepts that often inspire trading strategies include:
+**Configuration**:
+```json
+{
+    "name": "bbrs",
+    "weight": 1.0,
+    "params": {
+        "bb_width": 0.05,                           // Bollinger Band width threshold
+        "bb_period": 20,                            // Bollinger Band period
+        "rsi_period": 14,                           // RSI calculation period
+        "trending_rsi_threshold": [30, 70],         // RSI thresholds for trending market
+        "trending_bb_multiplier": 2.5,              // BB multiplier for trending market
+        "sideways_rsi_threshold": [40, 60],         // RSI thresholds for sideways market
+        "sideways_bb_multiplier": 1.8,              // BB multiplier for sideways market
+        "strategy_name": "MarketRegimeStrategy",    // Implementation variant
+        "SqueezeStrategy": true,                    // Enable squeeze detection
+        "stop_loss_pct": 0.05                      // Stop loss percentage
+    }
+}
+```
 
--   **Adaptive Parameters**: Adjusting indicator settings (like Bollinger Band width or RSI thresholds) based on market conditions (e.g., trending vs. sideways).
--   **Multi-Timeframe Analysis**: Confirming signals on one timeframe with trends or levels on another (e.g., 15-minute signals confirmed by 1-hour context).
--   **Volume Confirmation**: Using volume spikes or contractions to validate price-based signals.
--   **Volatility-Adjusted Risk Management**: Using measures like ATR (Average True Range) to set stop-loss and take-profit levels, or to size positions dynamically.
+**Algorithm**:
 
-These concepts are partially reflected in the implemented strategies, particularly in `MarketRegimeStrategy` (adaptive parameters) and `CryptoTradingStrategy` (multi-timeframe, volume confirmation, ATR-based risk levels).
+**MarketRegimeStrategy** (Primary Implementation):
+1. **Market Regime Detection**: Determines if market is trending or sideways
+2. **Adaptive Parameters**: Adjusts BB/RSI thresholds based on market regime
+3. **Trending Market Entry**: Price < Lower Band ∧ RSI < 50 ∧ Volume Spike
+4. **Sideways Market Entry**: Price ≤ Lower Band ∧ RSI ≤ 40  
+5. **Exit Conditions**: Opposite band touch, RSI reversal, or stop-loss
+6. **Volume Confirmation**: Requires 1.5× average volume for trending signals
+
+**CryptoTradingStrategy** (Alternative Implementation):
+1. **Multi-Timeframe Analysis**: Combines 15-minute and 1-hour Bollinger Bands
+2. **Entry**: Price ≤ both 15m & 1h lower bands + RSI < 35 + Volume surge
+3. **Exit**: 2:1 risk-reward ratio with ATR-based stops
+4. **Adaptive Volatility**: Uses ATR for dynamic stop-loss/take-profit
+
+**Strengths**:
+- Adapts to different market regimes
+- Multiple timeframe confirmation (internal)
+- Volume analysis for signal quality
+- Sophisticated entry/exit conditions
+
+**Best Use Cases**:
+- Volatile cryptocurrency markets
+- Markets with alternating trending/sideways periods
+- Short to medium-term trading
+
+## Strategy Combination
+
+### Multi-Strategy Architecture
+
+The StrategyManager allows combining multiple strategies with configurable rules:
+
+```json
+{
+    "strategies": [
+        {
+            "name": "default",
+            "weight": 0.6,
+            "params": {"timeframe": "15min"}
+        },
+        {
+            "name": "bbrs", 
+            "weight": 0.4,
+            "params": {"strategy_name": "MarketRegimeStrategy"}
+        }
+    ],
+    "combination_rules": {
+        "entry": "weighted_consensus",
+        "exit": "any",
+        "min_confidence": 0.6
+    }
+}
+```
+
+### Signal Combination Methods
+
+**Entry Combinations**:
+- **`any`**: Enter if ANY strategy signals entry
+- **`all`**: Enter only if ALL strategies signal entry
+- **`majority`**: Enter if majority of strategies signal entry  
+- **`weighted_consensus`**: Enter based on weighted confidence average
+
+**Exit Combinations**:
+- **`any`**: Exit if ANY strategy signals exit (recommended for risk management)
+- **`all`**: Exit only if ALL strategies agree
+- **`priority`**: Prioritized exit (STOP_LOSS > SELL_SIGNAL > others)
+
+## Performance Characteristics
+
+### Default Strategy Performance
+
+**Strengths**:
+- **Trend Accuracy**: High accuracy in strong trending markets
+- **Risk Management**: Defined stop-losses with 1-minute precision
+- **Low Noise**: Multiple Supertrend confirmation reduces false signals
+- **Adaptable**: Works across different timeframes
+
+**Weaknesses**:
+- **Sideways Markets**: May generate false signals in ranging markets
+- **Lag**: Multiple confirmations can delay entry/exit signals
+- **Whipsaws**: Vulnerable to rapid trend reversals
+
+**Optimal Conditions**:
+- Clear trending markets
+- Medium to low volatility trending
+- Sufficient data history for Supertrend calculation
+
+### BBRS Strategy Performance
+
+**Strengths**:
+- **Market Adaptation**: Automatically adjusts to market regime
+- **Volume Confirmation**: Reduces false signals with volume analysis
+- **Multi-Timeframe**: Internal analysis across multiple timeframes
+- **Volatility Handling**: Designed for cryptocurrency volatility
+
+**Weaknesses**:
+- **Complexity**: More parameters to optimize
+- **Market Noise**: Can be sensitive to short-term noise
+- **Volume Dependency**: Requires reliable volume data
+
+**Optimal Conditions**:
+- High-volume cryptocurrency markets
+- Markets with clear regime shifts
+- Sufficient data for regime detection
+
+## Usage Examples
+
+### Single Strategy Backtests
+
+```bash
+# Default strategy on 15-minute timeframe
+uv run .\main.py .\configs\config_default.json
+
+# Default strategy on 5-minute timeframe  
+uv run .\main.py .\configs\config_default_5min.json
+
+# BBRS strategy with market regime detection
+uv run .\main.py .\configs\config_bbrs.json
+```
+
+### Multi-Strategy Backtests
+
+```bash
+# Combined strategies with weighted consensus
+uv run .\main.py .\configs\config_combined.json
+```
+
+### Custom Configurations
+
+**Aggressive Default Strategy**:
+```json
+{
+    "name": "default",
+    "params": {
+        "timeframe": "5min",      // Faster signals
+        "stop_loss_pct": 0.02     // Tighter stop-loss
+    }
+}
+```
+
+**Conservative BBRS Strategy**:
+```json
+{
+    "name": "bbrs", 
+    "params": {
+        "bb_width": 0.03,         // Tighter BB width
+        "stop_loss_pct": 0.07,    // Wider stop-loss
+        "SqueezeStrategy": false  // Disable squeeze for simplicity
+    }
+}
+```
+
+## Development Guidelines
+
+### Creating New Strategies
+
+1. **Inherit from StrategyBase**:
+```python
+from cycles.strategies.base import StrategyBase, StrategySignal
+
+class NewStrategy(StrategyBase):
+    def __init__(self, weight=1.0, params=None):
+        super().__init__("new_strategy", weight, params)
+```
+
+2. **Specify Timeframes**:
+```python
+def get_timeframes(self):
+    return ["1h"]  # Specify required timeframes
+```
+
+3. **Implement Core Methods**:
+```python
+def initialize(self, backtester):
+    self._resample_data(backtester.original_df)
+    # Calculate indicators...
+    self.initialized = True
+
+def get_entry_signal(self, backtester, df_index):
+    # Entry logic...
+    return StrategySignal("ENTRY", confidence=0.8)
+
+def get_exit_signal(self, backtester, df_index):
+    # Exit logic...
+    return StrategySignal("EXIT", confidence=1.0)
+```
+
+4. **Register Strategy**:
+```python
+# In StrategyManager._load_strategies()
+elif name == "new_strategy":
+    strategies.append(NewStrategy(weight, params))
+```
+
+### Timeframe Best Practices
+
+1. **Minimize Timeframe Requirements**:
+```python
+def get_timeframes(self):
+    return ["15min"]  # Only what's needed
+```
+
+2. **Include 1min for Stop-Loss**:
+```python
+def get_timeframes(self):
+    primary_tf = self.params.get("timeframe", "15min")
+    timeframes = [primary_tf]
+    if "1min" not in timeframes:
+        timeframes.append("1min")
+    return timeframes
+```
+
+3. **Handle Multi-Timeframe Synchronization**:
+```python
+def get_entry_signal(self, backtester, df_index):
+    # Get current timestamp from primary timeframe
+    primary_data = self.get_primary_timeframe_data()
+    current_time = primary_data.index[df_index]
+    
+    # Map to other timeframes
+    hourly_data = self.get_data_for_timeframe("1h")
+    h1_idx = hourly_data.index.get_indexer([current_time], method='ffill')[0]
+```
+
+## Testing and Validation
+
+### Strategy Testing Workflow
+
+1. **Individual Strategy Testing**:
+   - Test each strategy independently
+   - Validate on different timeframes
+   - Check edge cases and data sufficiency
+
+2. **Multi-Strategy Testing**:
+   - Test strategy combinations
+   - Validate combination rules
+   - Monitor for signal conflicts
+
+3. **Timeframe Validation**:
+   - Ensure consistent behavior across timeframes
+   - Validate data alignment
+   - Check memory usage with large datasets
+
+### Performance Monitoring
+
+```python
+# Get strategy summary
+summary = strategy_manager.get_strategy_summary()
+print(f"Strategies: {[s['name'] for s in summary['strategies']]}")
+print(f"Timeframes: {summary['all_timeframes']}")
+
+# Monitor individual strategy performance
+for strategy in strategy_manager.strategies:
+    print(f"{strategy.name}: {strategy.get_timeframes()}")
+```
+
+## Advanced Topics
+
+### Multi-Timeframe Strategy Development
+
+For strategies requiring multiple timeframes:
+
+```python
+class MultiTimeframeStrategy(StrategyBase):
+    def get_timeframes(self):
+        return ["5min", "15min", "1h"]
+    
+    def get_entry_signal(self, backtester, df_index):
+        # Analyze multiple timeframes
+        data_5m = self.get_data_for_timeframe("5min")
+        data_15m = self.get_data_for_timeframe("15min") 
+        data_1h = self.get_data_for_timeframe("1h")
+        
+        # Synchronize across timeframes
+        current_time = data_5m.index[df_index]
+        idx_15m = data_15m.index.get_indexer([current_time], method='ffill')[0]
+        idx_1h = data_1h.index.get_indexer([current_time], method='ffill')[0]
+        
+        # Multi-timeframe logic
+        short_signal = self._analyze_5min(data_5m, df_index)
+        medium_signal = self._analyze_15min(data_15m, idx_15m)
+        long_signal = self._analyze_1h(data_1h, idx_1h)
+        
+        # Combine signals with appropriate confidence
+        if short_signal and medium_signal and long_signal:
+            return StrategySignal("ENTRY", confidence=0.9)
+        elif short_signal and medium_signal:
+            return StrategySignal("ENTRY", confidence=0.7)
+        else:
+            return StrategySignal("HOLD", confidence=0.0)
+```
+
+### Strategy Optimization
+
+1. **Parameter Optimization**: Systematic testing of strategy parameters
+2. **Timeframe Optimization**: Finding optimal timeframes for each strategy
+3. **Combination Optimization**: Optimizing weights and combination rules
+4. **Market Regime Adaptation**: Adapting strategies to different market conditions
+
+For detailed timeframe system documentation, see [Timeframe System](./timeframe_system.md).

docs/strategy_manager.md

@@ -0,0 +1,390 @@
+# Strategy Manager Documentation
+
+## Overview
+
+The Strategy Manager is a sophisticated orchestration system that enables the combination of multiple trading strategies with configurable signal aggregation rules. It supports multi-timeframe analysis, weighted consensus voting, and flexible signal combination methods.
+
+## Architecture
+
+### Core Components
+
+1. **StrategyBase**: Abstract base class defining the strategy interface
+2. **StrategySignal**: Encapsulates trading signals with confidence levels
+3. **StrategyManager**: Orchestrates multiple strategies and combines signals
+4. **Strategy Implementations**: DefaultStrategy, BBRSStrategy, etc.
+
+### New Timeframe System
+
+The framework now supports strategy-level timeframe management:
+
+- **Strategy-Controlled Timeframes**: Each strategy specifies its required timeframes
+- **Automatic Data Resampling**: Framework automatically resamples 1-minute data to strategy needs
+- **Multi-Timeframe Support**: Strategies can use multiple timeframes simultaneously
+- **Precision Stop-Loss**: All strategies maintain 1-minute data for precise execution
+
+```python
+class MyStrategy(StrategyBase):
+    def get_timeframes(self):
+        return ["15min", "1h"]  # Strategy needs both timeframes
+    
+    def initialize(self, backtester):
+        # Access resampled data
+        data_15m = self.get_data_for_timeframe("15min")
+        data_1h = self.get_data_for_timeframe("1h")
+        # Setup indicators...
+```
+
+## Strategy Interface
+
+### StrategyBase Class
+
+All strategies must inherit from `StrategyBase` and implement:
+
+```python
+from cycles.strategies.base import StrategyBase, StrategySignal
+
+class MyStrategy(StrategyBase):
+    def get_timeframes(self) -> List[str]:
+        """Specify required timeframes"""
+        return ["15min"]
+    
+    def initialize(self, backtester) -> None:
+        """Setup strategy with data"""
+        self._resample_data(backtester.original_df)
+        # Calculate indicators...
+        self.initialized = True
+    
+    def get_entry_signal(self, backtester, df_index: int) -> StrategySignal:
+        """Generate entry signals"""
+        if condition_met:
+            return StrategySignal("ENTRY", confidence=0.8)
+        return StrategySignal("HOLD", confidence=0.0)
+    
+    def get_exit_signal(self, backtester, df_index: int) -> StrategySignal:
+        """Generate exit signals"""
+        if exit_condition:
+            return StrategySignal("EXIT", confidence=1.0, 
+                                metadata={"type": "SELL_SIGNAL"})
+        return StrategySignal("HOLD", confidence=0.0)
+```
+
+### StrategySignal Class
+
+Encapsulates trading signals with metadata:
+
+```python
+# Entry signal with high confidence
+entry_signal = StrategySignal("ENTRY", confidence=0.9)
+
+# Exit signal with specific price
+exit_signal = StrategySignal("EXIT", confidence=1.0, price=50000,
+                           metadata={"type": "STOP_LOSS"})
+
+# Hold signal
+hold_signal = StrategySignal("HOLD", confidence=0.0)
+```
+
+## Available Strategies
+
+### 1. Default Strategy
+
+Meta-trend analysis using multiple Supertrend indicators.
+
+**Features:**
+- Uses 3 Supertrend indicators with different parameters
+- Configurable timeframe (default: 15min)
+- Entry when all trends align upward
+- Exit on trend reversal or stop-loss
+
+**Configuration:**
+```json
+{
+    "name": "default",
+    "weight": 1.0,
+    "params": {
+        "timeframe": "15min",
+        "stop_loss_pct": 0.03
+    }
+}
+```
+
+**Timeframes:**
+- Primary: Configurable (default 15min)
+- Stop-loss: Always includes 1min for precision
+
+### 2. BBRS Strategy
+
+Bollinger Bands + RSI with market regime detection.
+
+**Features:**
+- Market regime detection (trending vs sideways)
+- Adaptive parameters based on market conditions
+- Volume analysis and confirmation
+- Multi-timeframe internal analysis (1min → 15min/1h)
+
+**Configuration:**
+```json
+{
+    "name": "bbrs",
+    "weight": 1.0,
+    "params": {
+        "bb_width": 0.05,
+        "bb_period": 20,
+        "rsi_period": 14,
+        "strategy_name": "MarketRegimeStrategy",
+        "stop_loss_pct": 0.05
+    }
+}
+```
+
+**Timeframes:**
+- Input: 1min (Strategy class handles internal resampling)
+- Internal: 15min, 1h (handled by underlying Strategy class)
+- Output: Mapped back to 1min for backtesting
+
+## Signal Combination
+
+### Entry Signal Combination
+
+```python
+combination_rules = {
+    "entry": "weighted_consensus",  # or "any", "all", "majority"
+    "min_confidence": 0.6
+}
+```
+
+**Methods:**
+- **`any`**: Enter if ANY strategy signals entry
+- **`all`**: Enter only if ALL strategies signal entry  
+- **`majority`**: Enter if majority of strategies signal entry
+- **`weighted_consensus`**: Enter based on weighted average confidence
+
+### Exit Signal Combination
+
+```python
+combination_rules = {
+    "exit": "priority"  # or "any", "all"
+}
+```
+
+**Methods:**
+- **`any`**: Exit if ANY strategy signals exit (recommended for risk management)
+- **`all`**: Exit only if ALL strategies agree
+- **`priority`**: Prioritized exit (STOP_LOSS > SELL_SIGNAL > others)
+
+## Configuration
+
+### Basic Strategy Manager Setup
+
+```json
+{
+    "strategies": [
+        {
+            "name": "default",
+            "weight": 0.6,
+            "params": {
+                "timeframe": "15min",
+                "stop_loss_pct": 0.03
+            }
+        },
+        {
+            "name": "bbrs", 
+            "weight": 0.4,
+            "params": {
+                "bb_width": 0.05,
+                "strategy_name": "MarketRegimeStrategy"
+            }
+        }
+    ],
+    "combination_rules": {
+        "entry": "weighted_consensus",
+        "exit": "any",
+        "min_confidence": 0.5
+    }
+}
+```
+
+### Timeframe Examples
+
+**Single Timeframe Strategy:**
+```json
+{
+    "name": "default",
+    "params": {
+        "timeframe": "5min"  # Strategy works on 5-minute data
+    }
+}
+```
+
+**Multi-Timeframe Strategy (Future Enhancement):**
+```json
+{
+    "name": "multi_tf_strategy",
+    "params": {
+        "timeframes": ["5min", "15min", "1h"],  # Multiple timeframes
+        "primary_timeframe": "15min"
+    }
+}
+```
+
+## Usage Examples
+
+### Create Strategy Manager
+
+```python
+from cycles.strategies import create_strategy_manager
+
+config = {
+    "strategies": [
+        {"name": "default", "weight": 1.0, "params": {"timeframe": "15min"}}
+    ],
+    "combination_rules": {
+        "entry": "any",
+        "exit": "any"
+    }
+}
+
+strategy_manager = create_strategy_manager(config)
+```
+
+### Initialize and Use
+
+```python
+# Initialize with backtester
+strategy_manager.initialize(backtester)
+
+# Get signals during backtesting
+entry_signal = strategy_manager.get_entry_signal(backtester, df_index)
+exit_signal, exit_price = strategy_manager.get_exit_signal(backtester, df_index)
+
+# Get strategy summary
+summary = strategy_manager.get_strategy_summary()
+print(f"Loaded strategies: {[s['name'] for s in summary['strategies']]}")
+print(f"All timeframes: {summary['all_timeframes']}")
+```
+
+## Extending the System
+
+### Adding New Strategies
+
+1. **Create Strategy Class:**
+```python
+class NewStrategy(StrategyBase):
+    def get_timeframes(self):
+        return ["1h"]  # Specify required timeframes
+    
+    def initialize(self, backtester):
+        self._resample_data(backtester.original_df)
+        # Setup indicators...
+        self.initialized = True
+    
+    def get_entry_signal(self, backtester, df_index):
+        # Implement entry logic
+        pass
+    
+    def get_exit_signal(self, backtester, df_index):
+        # Implement exit logic
+        pass
+```
+
+2. **Register in StrategyManager:**
+```python
+# In StrategyManager._load_strategies()
+elif name == "new_strategy":
+    strategies.append(NewStrategy(weight, params))
+```
+
+### Multi-Timeframe Strategy Development
+
+For strategies requiring multiple timeframes:
+
+```python
+class MultiTimeframeStrategy(StrategyBase):
+    def get_timeframes(self):
+        return ["5min", "15min", "1h"]
+    
+    def initialize(self, backtester):
+        self._resample_data(backtester.original_df)
+        
+        # Access different timeframes
+        data_5m = self.get_data_for_timeframe("5min")
+        data_15m = self.get_data_for_timeframe("15min") 
+        data_1h = self.get_data_for_timeframe("1h")
+        
+        # Calculate indicators on each timeframe
+        # ...
+    
+    def _calculate_signal_confidence(self, backtester, df_index):
+        # Analyze multiple timeframes for confidence
+        primary_signal = self._get_primary_signal(df_index)
+        confirmation = self._get_timeframe_confirmation(df_index)
+        
+        return primary_signal * confirmation
+```
+
+## Performance Considerations
+
+### Timeframe Management
+
+- **Efficient Resampling**: Each strategy resamples data once during initialization
+- **Memory Usage**: Only required timeframes are kept in memory
+- **Signal Mapping**: Efficient mapping between timeframes using pandas reindex
+
+### Strategy Combination
+
+- **Lazy Evaluation**: Signals calculated only when needed
+- **Error Handling**: Individual strategy failures don't crash the system
+- **Logging**: Comprehensive logging for debugging and monitoring
+
+## Best Practices
+
+1. **Strategy Design:**
+   - Specify minimal required timeframes
+   - Include 1min for stop-loss precision
+   - Use confidence levels effectively
+
+2. **Signal Combination:**
+   - Use `any` for exits (risk management)
+   - Use `weighted_consensus` for entries
+   - Set appropriate minimum confidence levels
+
+3. **Error Handling:**
+   - Implement robust initialization checks
+   - Handle missing data gracefully
+   - Log strategy-specific warnings
+
+4. **Testing:**
+   - Test strategies individually before combining
+   - Validate timeframe requirements
+   - Monitor memory usage with large datasets
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Timeframe Mismatches:**
+   - Ensure strategy specifies correct timeframes
+   - Check data availability for all timeframes
+
+2. **Signal Conflicts:**
+   - Review combination rules
+   - Adjust confidence thresholds
+   - Monitor strategy weights
+
+3. **Performance Issues:**
+   - Minimize timeframe requirements
+   - Optimize indicator calculations
+   - Use efficient pandas operations
+
+### Debugging Tips
+
+- Enable detailed logging: `logging.basicConfig(level=logging.DEBUG)`
+- Use strategy summary: `manager.get_strategy_summary()`
+- Test individual strategies before combining
+- Monitor signal confidence levels
+
+---
+
+**Version**: 1.0.0  
+**Last Updated**: January 2025  
+**TCP Cycles Project**

docs/timeframe_system.md

@@ -0,0 +1,488 @@
+# Timeframe System Documentation
+
+## Overview
+
+The Cycles framework features a sophisticated timeframe management system that allows strategies to operate on their preferred timeframes while maintaining precise execution control. This system supports both single-timeframe and multi-timeframe strategies with automatic data resampling and intelligent signal mapping.
+
+## Architecture
+
+### Core Concepts
+
+1. **Strategy-Controlled Timeframes**: Each strategy specifies its required timeframes
+2. **Automatic Resampling**: Framework resamples 1-minute data to strategy needs
+3. **Precision Execution**: All strategies maintain 1-minute data for accurate stop-loss execution
+4. **Signal Mapping**: Intelligent mapping between different timeframe resolutions
+
+### Data Flow
+
+```
+Original 1min Data
+        ↓
+Strategy.get_timeframes() → ["15min", "1h"]
+        ↓
+Automatic Resampling
+        ↓
+Strategy Logic (15min + 1h analysis)
+        ↓
+Signal Generation
+        ↓
+Map to Working Timeframe
+        ↓
+Backtesting Engine
+```
+
+## Strategy Timeframe Interface
+
+### StrategyBase Methods
+
+All strategies inherit timeframe capabilities from `StrategyBase`:
+
+```python
+class MyStrategy(StrategyBase):
+    def get_timeframes(self) -> List[str]:
+        """Specify required timeframes for this strategy"""
+        return ["15min", "1h"]  # Strategy needs both timeframes
+    
+    def initialize(self, backtester) -> None:
+        # Automatic resampling happens here
+        self._resample_data(backtester.original_df)
+        
+        # Access resampled data
+        data_15m = self.get_data_for_timeframe("15min")
+        data_1h = self.get_data_for_timeframe("1h")
+        
+        # Calculate indicators on each timeframe
+        self.indicators_15m = self._calculate_indicators(data_15m)
+        self.indicators_1h = self._calculate_indicators(data_1h)
+        
+        self.initialized = True
+```
+
+### Data Access Methods
+
+```python
+# Get data for specific timeframe
+data_15m = strategy.get_data_for_timeframe("15min")
+
+# Get primary timeframe data (first in list)
+primary_data = strategy.get_primary_timeframe_data()
+
+# Check available timeframes
+timeframes = strategy.get_timeframes()
+```
+
+## Supported Timeframes
+
+### Standard Timeframes
+
+- **`"1min"`**: 1-minute bars (original resolution)
+- **`"5min"`**: 5-minute bars
+- **`"15min"`**: 15-minute bars
+- **`"30min"`**: 30-minute bars
+- **`"1h"`**: 1-hour bars
+- **`"4h"`**: 4-hour bars
+- **`"1d"`**: Daily bars
+
+### Custom Timeframes
+
+Any pandas-compatible frequency string is supported:
+- **`"2min"`**: 2-minute bars
+- **`"10min"`**: 10-minute bars
+- **`"2h"`**: 2-hour bars
+- **`"12h"`**: 12-hour bars
+
+## Strategy Examples
+
+### Single Timeframe Strategy
+
+```python
+class SingleTimeframeStrategy(StrategyBase):
+    def get_timeframes(self):
+        return ["15min"]  # Only needs 15-minute data
+    
+    def initialize(self, backtester):
+        self._resample_data(backtester.original_df)
+        
+        # Work with 15-minute data
+        data = self.get_primary_timeframe_data()
+        self.indicators = self._calculate_indicators(data)
+        self.initialized = True
+    
+    def get_entry_signal(self, backtester, df_index):
+        # df_index refers to 15-minute data
+        if self.indicators['signal'][df_index]:
+            return StrategySignal("ENTRY", confidence=0.8)
+        return StrategySignal("HOLD", confidence=0.0)
+```
+
+### Multi-Timeframe Strategy
+
+```python
+class MultiTimeframeStrategy(StrategyBase):
+    def get_timeframes(self):
+        return ["15min", "1h", "4h"]  # Multiple timeframes
+    
+    def initialize(self, backtester):
+        self._resample_data(backtester.original_df)
+        
+        # Access different timeframes
+        self.data_15m = self.get_data_for_timeframe("15min")
+        self.data_1h = self.get_data_for_timeframe("1h")
+        self.data_4h = self.get_data_for_timeframe("4h")
+        
+        # Calculate indicators on each timeframe
+        self.trend_4h = self._calculate_trend(self.data_4h)
+        self.momentum_1h = self._calculate_momentum(self.data_1h)
+        self.entry_signals_15m = self._calculate_entries(self.data_15m)
+        
+        self.initialized = True
+    
+    def get_entry_signal(self, backtester, df_index):
+        # Primary timeframe is 15min (first in list)
+        # Map df_index to other timeframes for confirmation
+        
+        # Get current 15min timestamp
+        current_time = self.data_15m.index[df_index]
+        
+        # Find corresponding indices in other timeframes
+        h1_idx = self.data_1h.index.get_indexer([current_time], method='ffill')[0]
+        h4_idx = self.data_4h.index.get_indexer([current_time], method='ffill')[0]
+        
+        # Multi-timeframe confirmation
+        trend_ok = self.trend_4h[h4_idx] > 0
+        momentum_ok = self.momentum_1h[h1_idx] > 0.5
+        entry_signal = self.entry_signals_15m[df_index]
+        
+        if trend_ok and momentum_ok and entry_signal:
+            confidence = 0.9  # High confidence with all timeframes aligned
+            return StrategySignal("ENTRY", confidence=confidence)
+        
+        return StrategySignal("HOLD", confidence=0.0)
+```
+
+### Configurable Timeframe Strategy
+
+```python
+class ConfigurableStrategy(StrategyBase):
+    def get_timeframes(self):
+        # Strategy timeframe configurable via parameters
+        primary_tf = self.params.get("timeframe", "15min")
+        return [primary_tf, "1min"]  # Primary + 1min for stop-loss
+    
+    def initialize(self, backtester):
+        self._resample_data(backtester.original_df)
+        
+        primary_tf = self.get_timeframes()[0]
+        self.data = self.get_data_for_timeframe(primary_tf)
+        
+        # Indicator parameters can also be timeframe-dependent
+        if primary_tf == "5min":
+            self.ma_period = 20
+        elif primary_tf == "15min":
+            self.ma_period = 14
+        else:
+            self.ma_period = 10
+            
+        self.indicators = self._calculate_indicators(self.data)
+        self.initialized = True
+```
+
+## Built-in Strategy Timeframe Behavior
+
+### Default Strategy
+
+**Timeframes**: Configurable primary + 1min for stop-loss
+
+```python
+# Configuration
+{
+    "name": "default",
+    "params": {
+        "timeframe": "5min"  # Configurable timeframe
+    }
+}
+
+# Resulting timeframes: ["5min", "1min"]
+```
+
+**Features**:
+- Supertrend analysis on configured timeframe
+- 1-minute precision for stop-loss execution
+- Optimized for 15-minute default, but works on any timeframe
+
+### BBRS Strategy
+
+**Timeframes**: 1min input (internal resampling)
+
+```python
+# Configuration
+{
+    "name": "bbrs",
+    "params": {
+        "strategy_name": "MarketRegimeStrategy"
+    }
+}
+
+# Resulting timeframes: ["1min"]
+```
+
+**Features**:
+- Uses 1-minute data as input
+- Internal resampling to 15min/1h by Strategy class
+- Signals mapped back to 1-minute resolution
+- No double-resampling issues
+
+## Advanced Features
+
+### Timeframe Synchronization
+
+When working with multiple timeframes, synchronization is crucial:
+
+```python
+def _get_synchronized_signals(self, df_index, primary_timeframe="15min"):
+    """Get signals synchronized across timeframes"""
+    
+    # Get timestamp from primary timeframe
+    primary_data = self.get_data_for_timeframe(primary_timeframe)
+    current_time = primary_data.index[df_index]
+    
+    signals = {}
+    for tf in self.get_timeframes():
+        if tf == primary_timeframe:
+            signals[tf] = df_index
+        else:
+            # Find corresponding index in other timeframe
+            tf_data = self.get_data_for_timeframe(tf)
+            tf_idx = tf_data.index.get_indexer([current_time], method='ffill')[0]
+            signals[tf] = tf_idx
+    
+    return signals
+```
+
+### Dynamic Timeframe Selection
+
+Strategies can adapt timeframes based on market conditions:
+
+```python
+class AdaptiveStrategy(StrategyBase):
+    def get_timeframes(self):
+        # Fixed set of timeframes strategy might need
+        return ["5min", "15min", "1h"]
+    
+    def _select_active_timeframe(self, market_volatility):
+        """Select timeframe based on market conditions"""
+        if market_volatility > 0.8:
+            return "5min"   # High volatility -> shorter timeframe
+        elif market_volatility > 0.4:
+            return "15min"  # Medium volatility -> medium timeframe
+        else:
+            return "1h"     # Low volatility -> longer timeframe
+    
+    def get_entry_signal(self, backtester, df_index):
+        # Calculate market volatility
+        volatility = self._calculate_volatility(df_index)
+        
+        # Select appropriate timeframe
+        active_tf = self._select_active_timeframe(volatility)
+        
+        # Generate signal on selected timeframe
+        return self._generate_signal_for_timeframe(active_tf, df_index)
+```
+
+## Configuration Examples
+
+### Single Timeframe Configuration
+
+```json
+{
+    "strategies": [
+        {
+            "name": "default",
+            "weight": 1.0,
+            "params": {
+                "timeframe": "15min",
+                "stop_loss_pct": 0.03
+            }
+        }
+    ]
+}
+```
+
+### Multi-Timeframe Configuration
+
+```json
+{
+    "strategies": [
+        {
+            "name": "multi_timeframe_strategy",
+            "weight": 1.0,
+            "params": {
+                "primary_timeframe": "15min",
+                "confirmation_timeframes": ["1h", "4h"],
+                "signal_timeframe": "5min"
+            }
+        }
+    ]
+}
+```
+
+### Mixed Strategy Configuration
+
+```json
+{
+    "strategies": [
+        {
+            "name": "default",
+            "weight": 0.6,
+            "params": {
+                "timeframe": "15min"
+            }
+        },
+        {
+            "name": "bbrs",
+            "weight": 0.4,
+            "params": {
+                "strategy_name": "MarketRegimeStrategy"
+            }
+        }
+    ]
+}
+```
+
+## Performance Considerations
+
+### Memory Usage
+
+- Only required timeframes are resampled and stored
+- Original 1-minute data shared across all strategies
+- Efficient pandas resampling with minimal memory overhead
+
+### Processing Speed
+
+- Resampling happens once during initialization
+- No repeated resampling during backtesting
+- Vectorized operations on pre-computed timeframes
+
+### Data Alignment
+
+- All timeframes aligned to original 1-minute timestamps
+- Forward-fill resampling ensures data availability
+- Intelligent handling of missing data points
+
+## Best Practices
+
+### 1. Minimize Timeframe Requirements
+
+```python
+# Good - minimal timeframes
+def get_timeframes(self):
+    return ["15min"]
+
+# Less optimal - unnecessary timeframes
+def get_timeframes(self):
+    return ["1min", "5min", "15min", "1h", "4h", "1d"]
+```
+
+### 2. Use Appropriate Timeframes for Strategy Logic
+
+```python
+# Good - timeframe matches strategy logic
+class TrendStrategy(StrategyBase):
+    def get_timeframes(self):
+        return ["1h"]  # Trend analysis works well on hourly data
+
+class ScalpingStrategy(StrategyBase):
+    def get_timeframes(self):
+        return ["1min", "5min"]  # Scalping needs fine-grained data
+```
+
+### 3. Include 1min for Stop-Loss Precision
+
+```python
+def get_timeframes(self):
+    primary_tf = self.params.get("timeframe", "15min")
+    timeframes = [primary_tf]
+    
+    # Always include 1min for precise stop-loss
+    if "1min" not in timeframes:
+        timeframes.append("1min")
+    
+    return timeframes
+```
+
+### 4. Handle Timeframe Edge Cases
+
+```python
+def get_entry_signal(self, backtester, df_index):
+    # Check bounds for all timeframes
+    if df_index >= len(self.get_primary_timeframe_data()):
+        return StrategySignal("HOLD", confidence=0.0)
+    
+    # Robust timeframe indexing
+    try:
+        signal = self._calculate_signal(df_index)
+        return signal
+    except IndexError:
+        return StrategySignal("HOLD", confidence=0.0)
+```
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Index Out of Bounds**
+   ```python
+   # Problem: Different timeframes have different lengths
+   # Solution: Always check bounds
+   if df_index < len(self.data_1h):
+       signal = self.data_1h[df_index]
+   ```
+
+2. **Timeframe Misalignment**
+   ```python
+   # Problem: Assuming same index across timeframes
+   # Solution: Use timestamp-based alignment
+   current_time = primary_data.index[df_index]
+   h1_idx = hourly_data.index.get_indexer([current_time], method='ffill')[0]
+   ```
+
+3. **Memory Issues with Large Datasets**
+   ```python
+   # Solution: Only include necessary timeframes
+   def get_timeframes(self):
+       # Return minimal set
+       return ["15min"]  # Not ["1min", "5min", "15min", "1h"]
+   ```
+
+### Debugging Tips
+
+```python
+# Log timeframe information
+def initialize(self, backtester):
+    self._resample_data(backtester.original_df)
+    
+    for tf in self.get_timeframes():
+        data = self.get_data_for_timeframe(tf)
+        print(f"Timeframe {tf}: {len(data)} bars, "
+              f"from {data.index[0]} to {data.index[-1]}")
+    
+    self.initialized = True
+```
+
+## Future Enhancements
+
+### Planned Features
+
+1. **Dynamic Timeframe Switching**: Strategies adapt timeframes based on market conditions
+2. **Timeframe Confidence Weighting**: Different confidence levels per timeframe
+3. **Cross-Timeframe Signal Validation**: Automatic signal confirmation across timeframes
+4. **Optimized Memory Management**: Lazy loading and caching for large datasets
+
+### Extension Points
+
+The timeframe system is designed for easy extension:
+
+- Custom resampling methods
+- Alternative timeframe synchronization strategies
+- Market-specific timeframe preferences
+- Real-time timeframe adaptation 

main.py

@@ -12,6 +12,8 @@ from cycles.utils.system import SystemUtils
 from cycles.backtest import Backtest
 from cycles.Analysis.supertrend import Supertrends
 from cycles.charts import BacktestCharts
+from cycles.Analysis.strategies import Strategy
+from cycles.strategies import StrategyManager, create_strategy_manager
 
 logging.basicConfig(
     level=logging.INFO,
@@ -23,6 +25,8 @@ logging.basicConfig(
 )
 
 def default_init_strategy(backtester: Backtest):
+    """Calculate meta trend
+    """
     supertrends = Supertrends(backtester.df, verbose=False)
 
     supertrend_results_list = supertrends.calculate_supertrend_indicators()
@@ -33,10 +37,158 @@ def default_init_strategy(backtester: Backtest):
 
     backtester.strategies["meta_trend"] = meta_trend
 
+def bbrs_init_strategy(backtester: Backtest):
+    """BBRs entry strategy initialization - just setup basic structure"""
+    # Initialize empty strategies
+    backtester.strategies["buy_signals"] = pd.Series(False, index=range(len(backtester.df)))
+    backtester.strategies["sell_signals"] = pd.Series(False, index=range(len(backtester.df)))
+    return backtester
+
+def run_bbrs_strategy_processing(backtester: Backtest, original_df):
+    """Run the actual strategy processing after backtest is initialized"""
+    config_strategy = {
+        "bb_width": 0.05,
+        "bb_period": 20,
+        "rsi_period": 14,
+        "trending": {
+            "rsi_threshold": [30, 70],
+            "bb_std_dev_multiplier": 2.5,
+        },
+        "sideways": {
+            "rsi_threshold": [40, 60],
+            "bb_std_dev_multiplier": 1.8,
+        },
+        "strategy_name": "MarketRegimeStrategy", # "MarketRegimeStrategy", # CryptoTradingStrategy
+        "SqueezeStrategy": True
+    }
+
+    strategy = Strategy(config=config_strategy, logging=logging)
+    processed_data = strategy.run(original_df, config_strategy["strategy_name"])
+    print(f"processed_data: {processed_data.head()}")
+
+    # Store processed data for plotting
+    backtester.processed_data = processed_data
+
+    if processed_data.empty:
+        # If strategy processing failed, create empty signals aligned with backtest DataFrame
+        buy_condition = pd.Series(False, index=range(len(backtester.df)))
+        sell_condition = pd.Series(False, index=range(len(backtester.df)))
+    else:
+        # Get original signals from processed data
+        buy_signals_raw = processed_data.get('BuySignal', pd.Series(False, index=processed_data.index)).astype(bool)
+        sell_signals_raw = processed_data.get('SellSignal', pd.Series(False, index=processed_data.index)).astype(bool)
+        
+        # Get the DatetimeIndex from the original 1-minute data
+        original_datetime_index = original_df.index
+        
+        # Reindex signals from 15-minute to 1-minute resolution using forward-fill
+        # This maps each 15-minute signal to the corresponding 1-minute timestamps
+        buy_signals_1min = buy_signals_raw.reindex(original_datetime_index, method='ffill').fillna(False)
+        sell_signals_1min = sell_signals_raw.reindex(original_datetime_index, method='ffill').fillna(False)
+        
+        # Convert to integer index to match backtest DataFrame
+        buy_condition = pd.Series(buy_signals_1min.values, index=range(len(buy_signals_1min)))
+        sell_condition = pd.Series(sell_signals_1min.values, index=range(len(sell_signals_1min)))
+        
+        # Ensure same length as backtest DataFrame (should be same now, but safety check)
+        if len(buy_condition) != len(backtester.df):
+            target_length = len(backtester.df)
+            if len(buy_condition) > target_length:
+                buy_condition = buy_condition[:target_length]
+                sell_condition = sell_condition[:target_length]
+            else:
+                # Pad with False if shorter
+                buy_values = buy_condition.values
+                sell_values = sell_condition.values
+                buy_values = np.pad(buy_values, (0, target_length - len(buy_values)), constant_values=False)
+                sell_values = np.pad(sell_values, (0, target_length - len(sell_values)), constant_values=False)
+                buy_condition = pd.Series(buy_values, index=range(target_length))
+                sell_condition = pd.Series(sell_values, index=range(target_length))
+
+    backtester.strategies["buy_signals"] = buy_condition
+    backtester.strategies["sell_signals"] = sell_condition
+    # backtester.strategies["buy_signals"] = sell_condition
+    # backtester.strategies["sell_signals"] = buy_condition
+
+    print(f"buy_signals length: {len(backtester.strategies['buy_signals'])}, backtest df length: {len(backtester.df)}")
+
+def bbrs_entry_strategy(backtester: Backtest, df_index):
+    """BBRs entry strategy
+    Entry when buy signal is true
+    """
+    return backtester.strategies["buy_signals"].iloc[df_index]
+
+def bbrs_exit_strategy(backtester: Backtest, df_index):
+    """BBRs exit strategy
+    Exit when sell signal is true or stop loss is triggered
+    """
+    if backtester.strategies["sell_signals"].iloc[df_index]:
+        return "SELL_SIGNAL", backtester.df.iloc[df_index]['close']
+    
+    # Check for stop loss using BBRs-specific stop loss strategy
+    stop_loss_result, sell_price = bbrs_stop_loss_strategy(backtester)
+    if stop_loss_result:
+        backtester.strategies["current_trade_min1_start_idx"] = \
+            backtester.current_trade_min1_start_idx
+        return "STOP_LOSS", sell_price
+    
+    return None, None
+
+def bbrs_stop_loss_strategy(backtester: Backtest):
+    """BBRs stop loss strategy
+    Calculate stop loss price based on 5% loss
+    Find the first min1 candle that is below the stop loss price
+    If the stop loss price is below the open price, use the open price as the stop loss price
+    """
+    # Use 5% stop loss as requested
+    stop_loss_pct = 0.05
+    stop_price = backtester.entry_price * (1 - stop_loss_pct)
+
+    # Use the original min1 dataframe that has datetime index
+    min1_df = backtester.original_df if hasattr(backtester, 'original_df') else backtester.min1_df
+    min1_index = min1_df.index
+    
+    # Find candles from entry time to current time
+    start_candidates = min1_index[min1_index >= backtester.entry_time]
+    if len(start_candidates) == 0:
+        return False, None
+        
+    backtester.current_trade_min1_start_idx = start_candidates[0]
+    end_candidates = min1_index[min1_index <= backtester.current_date]
+
+    if len(end_candidates) == 0:
+        print("Warning: no end candidate here. Need to be checked")
+        return False, None
+    backtester.current_min1_end_idx = end_candidates[-1]
+
+    # Get the slice of data between entry and current time
+    min1_slice = min1_df.loc[backtester.current_trade_min1_start_idx:backtester.current_min1_end_idx]
+    
+    # Check if any candle's low price hits the stop loss
+    if (min1_slice['low'] <= stop_price).any():
+        stop_candle = min1_slice[min1_slice['low'] <= stop_price].iloc[0]
+
+        # If the candle opened below stop price, use open price; otherwise use stop price
+        if stop_candle['open'] < stop_price:
+            sell_price = stop_candle['open']
+        else:
+            sell_price = stop_price
+        return True, sell_price
+
+    return False, None
+
 def default_entry_strategy(backtester: Backtest, df_index):
+    """Entry strategy
+    Entry when meta trend is 1
+    """
     return backtester.strategies["meta_trend"][df_index - 1] != 1 and backtester.strategies["meta_trend"][df_index] == 1
 
 def stop_loss_strategy(backtester: Backtest):
+    """Stop loss strategy
+    Calculate stop loss price
+    Find the first min1 candle that is below the stop loss price
+    If the stop loss price is below the open price, use the open price as the stop loss price
+    """
     stop_price = backtester.entry_price * (1 - backtester.strategies["stop_loss_pct"])
 
     min1_index = backtester.min1_df.index
@@ -78,24 +230,96 @@ def default_exit_strategy(backtester: Backtest, df_index):
     
     return None, None
 
-def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd, debug=False):
-    """Process the entire timeframe with all stop loss values (no monthly split)"""
-    df = df.copy().reset_index(drop=True)
+def strategy_manager_init(backtester: Backtest):
+    """Strategy Manager initialization function"""
+    # This will be called by Backtest.__init__, but actual initialization 
+    # happens in strategy_manager.initialize()
+    pass
+
+def strategy_manager_entry(backtester: Backtest, df_index: int):
+    """Strategy Manager entry function"""
+    return backtester.strategy_manager.get_entry_signal(backtester, df_index)
+
+def strategy_manager_exit(backtester: Backtest, df_index: int):
+    """Strategy Manager exit function"""
+    return backtester.strategy_manager.get_exit_signal(backtester, df_index)
+
+def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd, strategy_config=None, debug=False):
+    """Process the entire timeframe with all stop loss values using Strategy Manager"""
 
     results_rows = []
     trade_rows = []
 
-    min1_df['timestamp'] = pd.to_datetime(min1_df.index) # need ?
-
     for stop_loss_pct in stop_loss_pcts:
-        backtester = Backtest(initial_usd, df, min1_df, default_init_strategy)
-        backtester.strategies["stop_loss_pct"] = stop_loss_pct
+        # Create and initialize strategy manager
+        if strategy_config:
+            # Use provided strategy configuration
+            strategy_manager = create_strategy_manager(strategy_config)
+        else:
+            # Default to single default strategy for backward compatibility
+            default_strategy_config = {
+                "strategies": [
+                    {
+                        "name": "default",
+                        "weight": 1.0,
+                        "params": {"stop_loss_pct": stop_loss_pct}
+                    }
+                ],
+                "combination_rules": {
+                    "entry": "any",
+                    "exit": "any",
+                    "min_confidence": 0.5
+                }
+            }
+            strategy_manager = create_strategy_manager(default_strategy_config)
+        
+        # Inject stop_loss_pct into all strategy params if not present
+        for strategy in strategy_manager.strategies:
+            if "stop_loss_pct" not in strategy.params:
+                strategy.params["stop_loss_pct"] = stop_loss_pct
+        
+        # Get the primary timeframe from the first strategy for backtester setup
+        primary_strategy = strategy_manager.strategies[0]
+        primary_timeframe = primary_strategy.get_timeframes()[0]
+        
+        # For BBRS strategy, it works with 1-minute data directly and handles internal resampling
+        # For other strategies, use their preferred timeframe
+        if primary_strategy.name == "bbrs":
+            # BBRS strategy processes 1-minute data and outputs signals on its internal timeframes
+            # Use 1-minute data for backtester working dataframe
+            working_df = min1_df.copy()
+        else:
+            # Other strategies specify their preferred timeframe
+            # Create backtester working data from the primary strategy's primary timeframe
+            temp_backtester = type('temp', (), {})()
+            temp_backtester.original_df = min1_df
+            
+            # Let the primary strategy resample the data to get the working dataframe
+            primary_strategy._resample_data(min1_df)
+            working_df = primary_strategy.get_primary_timeframe_data()
+        
+        # Prepare working dataframe for backtester (ensure timestamp column)
+        working_df_for_backtest = working_df.copy().reset_index()
+        if 'index' in working_df_for_backtest.columns:
+            working_df_for_backtest = working_df_for_backtest.rename(columns={'index': 'timestamp'})
+        
+        # Initialize backtest with strategy manager initialization
+        backtester = Backtest(initial_usd, working_df_for_backtest, working_df_for_backtest, strategy_manager_init)
+        
+        # Store original min1_df for strategy processing
+        backtester.original_df = min1_df
+        
+        # Attach strategy manager to backtester and initialize
+        backtester.strategy_manager = strategy_manager
+        strategy_manager.initialize(backtester)
 
+        # Run backtest with strategy manager functions
         results = backtester.run(
-            default_entry_strategy,
-            default_exit_strategy,
+            strategy_manager_entry,
+            strategy_manager_exit,
             debug
         )
+
         n_trades = results["n_trades"]
         trades = results.get('trades', [])
         wins = [1 for t in trades if t['exit'] is not None and t['exit'] > t['entry']]
@@ -126,8 +350,9 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
 
         total_fees_usd = sum(trade.get('fee_usd', 0.0) for trade in trades)
 
+        # Update row to include timeframe information
         row = {
-            "timeframe": rule_name,
+            "timeframe": f"{rule_name}({primary_timeframe})",  # Show actual timeframe used
             "stop_loss_pct": stop_loss_pct,
             "n_trades": n_trades,
             "n_stop_loss": sum(1 for trade in trades if 'type' in trade and trade['type'] == 'STOP_LOSS'),
@@ -145,7 +370,7 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
 
         for trade in trades:
             trade_rows.append({
-                "timeframe": rule_name,
+                "timeframe": f"{rule_name}({primary_timeframe})",
                 "stop_loss_pct": stop_loss_pct,
                 "entry_time": trade.get("entry_time"),
                 "exit_time": trade.get("exit_time"),
@@ -155,34 +380,48 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
                 "type": trade.get("type"),
                 "fee_usd": trade.get("fee_usd"),
             })
-        logging.info(f"Timeframe: {rule_name}, Stop Loss: {stop_loss_pct}, Trades: {n_trades}")
+        
+        # Log strategy summary
+        strategy_summary = strategy_manager.get_strategy_summary()
+        logging.info(f"Timeframe: {rule_name}({primary_timeframe}), Stop Loss: {stop_loss_pct}, "
+                    f"Trades: {n_trades}, Strategies: {[s['name'] for s in strategy_summary['strategies']]}")
         
         if debug:
             # Plot after each backtest run
             try:
-                meta_trend = backtester.strategies["meta_trend"]
-                BacktestCharts.plot(df, meta_trend)
+                # Check if any strategy has processed_data for universal plotting
+                processed_data = None
+                for strategy in strategy_manager.strategies:
+                    if hasattr(backtester, 'processed_data') and backtester.processed_data is not None:
+                        processed_data = backtester.processed_data
+                        break
+                
+                if processed_data is not None and not processed_data.empty:
+                    # Format strategy data with actual executed trades for universal plotting
+                    formatted_data = BacktestCharts.format_strategy_data_with_trades(processed_data, results)
+                    # Plot using universal function
+                    BacktestCharts.plot_data(formatted_data)
+                else:
+                    # Fallback to meta_trend plot if available
+                    if "meta_trend" in backtester.strategies:
+                        meta_trend = backtester.strategies["meta_trend"]
+                        # Use the working dataframe for plotting
+                        BacktestCharts.plot(working_df, meta_trend)
+                    else:
+                        print("No plotting data available")
             except Exception as e:
                 print(f"Plotting failed: {e}")
 
     return results_rows, trade_rows
 
 def process(timeframe_info, debug=False):
-    """Process a single (timeframe, stop_loss_pct) combination (no monthly split)"""
-    rule, data_1min, stop_loss_pct, initial_usd = timeframe_info
+    """Process a single (timeframe, stop_loss_pct) combination with strategy config"""
+    rule, data_1min, stop_loss_pct, initial_usd, strategy_config = timeframe_info
 
-    if rule == "1min":
-        df = data_1min.copy()
-    else:
-        df = data_1min.resample(rule).agg({
-            'open': 'first',
-            'high': 'max',
-            'low': 'min',
-            'close': 'last',
-            'volume': 'sum'
-        }).dropna()
-    df = df.reset_index()
-    results_rows, all_trade_rows = process_timeframe_data(data_1min, df, [stop_loss_pct], rule, initial_usd, debug=debug)
+    # Pass the original 1-minute data - strategies will handle their own timeframe resampling
+    results_rows, all_trade_rows = process_timeframe_data(
+        data_1min, data_1min, [stop_loss_pct], rule, initial_usd, strategy_config, debug=debug
+    )
     return results_rows, all_trade_rows
 
 def aggregate_results(all_rows):
@@ -241,11 +480,23 @@ if __name__ == "__main__":
 
     # Default values (from config.json)
     default_config = {
-        "start_date": "2025-05-01",
+        "start_date": "2025-03-01",
         "stop_date": datetime.datetime.today().strftime('%Y-%m-%d'),
         "initial_usd": 10000,
         "timeframes": ["15min"],
         "stop_loss_pcts": [0.03],
+        "strategies": [
+            {
+                "name": "default",
+                "weight": 1.0,
+                "params": {}
+            }
+        ],
+        "combination_rules": {
+            "entry": "any",
+            "exit": "any",
+            "min_confidence": 0.5
+        }
     }
 
     if args.config:
@@ -272,6 +523,8 @@ if __name__ == "__main__":
             'initial_usd': initial_usd,
             'timeframes': timeframes,
             'stop_loss_pcts': stop_loss_pcts,
+            'strategies': default_config['strategies'],
+            'combination_rules': default_config['combination_rules']
         }
     else:
         config = default_config
@@ -281,6 +534,12 @@ if __name__ == "__main__":
     initial_usd = config['initial_usd']
     timeframes = config['timeframes']
     stop_loss_pcts = config['stop_loss_pcts']
+    
+    # Extract strategy configuration
+    strategy_config = {
+        "strategies": config.get('strategies', default_config['strategies']),
+        "combination_rules": config.get('combination_rules', default_config['combination_rules'])
+    }
 
     timestamp = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M")
 
@@ -298,8 +557,9 @@ if __name__ == "__main__":
         f"Initial USD\t{initial_usd}"
     ]
     
+    # Create tasks for each (timeframe, stop_loss_pct) combination
     tasks = [
-        (name, data_1min, stop_loss_pct, initial_usd)
+        (name, data_1min, stop_loss_pct, initial_usd, strategy_config)
         for name in timeframes
         for stop_loss_pct in stop_loss_pcts
     ]

test_bbrsi.py

@@ -2,6 +2,7 @@ import logging
 import seaborn as sns
 import matplotlib.pyplot as plt
 import pandas as pd
+import datetime
 
 from cycles.utils.storage import Storage
 from cycles.Analysis.strategies import Strategy
@@ -16,8 +17,8 @@ logging.basicConfig(
 )
 
 config = {
-    "start_date": "2023-01-01",
-    "stop_date": "2024-01-01",
+     "start_date": "2025-03-01",
+    "stop_date": datetime.datetime.today().strftime('%Y-%m-%d'),
     "data_file": "btcusd_1-min_data.csv"
 }