Enhance strategy output standardization and improve plotting logic

- Introduced a new method to standardize output column names across different strategies, ensuring consistency in data handling and plotting.
- Updated plotting logic in test_bbrsi.py to utilize standardized column names, improving clarity and maintainability.
- Enhanced error handling for missing data in plots and adjusted visual elements for better representation of trading signals.
- Improved the overall structure of strategy implementations to support additional indicators and metadata for better analysis.

cycles/Analysis/boillinger_band.py

@@ -1,26 +1,29 @@
 import pandas as pd
+import numpy as np
 
 class BollingerBands:
     """
     Calculates Bollinger Bands for given financial data.
     """
-    def __init__(self, period: int = 20, std_dev_multiplier: float = 2.0):
+    def __init__(self, config):
         """
         Initializes the BollingerBands calculator.
 
         Args:
             period (int): The period for the moving average and standard deviation.
             std_dev_multiplier (float): The number of standard deviations for the upper and lower bands.
+            bb_width (float): The width of the Bollinger Bands.
         """
-        if period <= 0:
+        if config['bb_period'] <= 0:
             raise ValueError("Period must be a positive integer.")
-        if std_dev_multiplier <= 0:
+        if config['trending']['bb_std_dev_multiplier'] <= 0 or config['sideways']['bb_std_dev_multiplier'] <= 0:
             raise ValueError("Standard deviation multiplier must be positive.")
+        if config['bb_width'] <= 0:
+            raise ValueError("BB width must be positive.")
             
-        self.period = period
+        self.config = config
-        self.std_dev_multiplier = std_dev_multiplier
 
-    def calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame:
+    def calculate(self, data_df: pd.DataFrame, price_column: str = 'close', squeeze = False) -> pd.DataFrame:
         """
         Calculates Bollinger Bands and adds them to the DataFrame.
 
@@ -37,14 +40,105 @@ class BollingerBands:
         if price_column not in data_df.columns:
             raise ValueError(f"Price column '{price_column}' not found in DataFrame.")
 
-        # Calculate SMA
+        # Work on a copy to avoid modifying the original DataFrame passed to the function
-        data_df['SMA'] = data_df[price_column].rolling(window=self.period).mean()
+        data_df = data_df.copy()
 
-        # Calculate Standard Deviation
+        if not squeeze:
-        std_dev = data_df[price_column].rolling(window=self.period).std()
+            period = self.config['bb_period']
+            bb_width_threshold = self.config['bb_width']
+            trending_std_multiplier = self.config['trending']['bb_std_dev_multiplier']
+            sideways_std_multiplier = self.config['sideways']['bb_std_dev_multiplier']
             
-        # Calculate Upper and Lower Bands
+            # Calculate SMA
-        data_df['UpperBand'] = data_df['SMA'] + (self.std_dev_multiplier * std_dev)
+            data_df['SMA'] = data_df[price_column].rolling(window=period).mean()
-        data_df['LowerBand'] = data_df['SMA'] - (self.std_dev_multiplier * std_dev)
+
+            # Calculate Standard Deviation
+            std_dev = data_df[price_column].rolling(window=period).std()
+
+            # Calculate reference Upper and Lower Bands for BBWidth calculation (e.g., using 2.0 std dev)
+            # This ensures BBWidth is calculated based on a consistent band definition before applying adaptive multipliers.
+            ref_upper_band = data_df['SMA'] + (2.0 * std_dev)
+            ref_lower_band = data_df['SMA'] - (2.0 * std_dev)
+
+            # Calculate the width of the Bollinger Bands
+            # Avoid division by zero or NaN if SMA is zero or NaN by replacing with np.nan
+            data_df['BBWidth'] = np.where(data_df['SMA'] != 0, (ref_upper_band - ref_lower_band) / data_df['SMA'], np.nan)
+
+            # Calculate the market regime (1 = sideways, 0 = trending)
+            # Handle NaN in BBWidth: if BBWidth is NaN, MarketRegime should also be NaN or a default (e.g. trending)
+            data_df['MarketRegime'] = np.where(data_df['BBWidth'].isna(), np.nan, 
+                                             (data_df['BBWidth'] < bb_width_threshold).astype(float)) # Use float for NaN compatibility
+
+            # Determine the std dev multiplier for each row based on its market regime
+            conditions = [
+                data_df['MarketRegime'] == 1,  # Sideways market
+                data_df['MarketRegime'] == 0   # Trending market
+            ]
+            choices = [
+                sideways_std_multiplier, 
+                trending_std_multiplier
+            ]
+            # Default multiplier if MarketRegime is NaN (e.g., use trending or a neutral default like 2.0)
+            # For now, let's use trending_std_multiplier as default if MarketRegime is NaN.
+            # This can be adjusted based on desired behavior for periods where regime is undetermined.
+            row_specific_std_multiplier = np.select(conditions, choices, default=trending_std_multiplier) 
+
+            # Calculate final Upper and Lower Bands using the row-specific multiplier
+            data_df['UpperBand'] = data_df['SMA'] + (row_specific_std_multiplier * std_dev)
+            data_df['LowerBand'] = data_df['SMA'] - (row_specific_std_multiplier * std_dev)
+
+        else: # squeeze is True
+            price_series = data_df[price_column]
+            # Use the static method for the squeeze case with fixed parameters
+            upper_band, sma, lower_band = self.calculate_custom_bands(
+                price_series, 
+                window=14, 
+                num_std=1.5, 
+                min_periods=14 # Match typical squeeze behavior where bands appear after full period
+            )
+            data_df['SMA'] = sma
+            data_df['UpperBand'] = upper_band
+            data_df['LowerBand'] = lower_band
+            # BBWidth and MarketRegime are not typically calculated/used in a simple squeeze context by this method
+            # If needed, they could be added, but the current structure implies they are part of the non-squeeze path.
+            data_df['BBWidth'] = np.nan
+            data_df['MarketRegime'] = np.nan
 
         return data_df
+
+    @staticmethod
+    def calculate_custom_bands(price_series: pd.Series, window: int = 20, num_std: float = 2.0, min_periods: int = None) -> tuple[pd.Series, pd.Series, pd.Series]:
+        """
+        Calculates Bollinger Bands with specified window and standard deviation multiplier.
+
+        Args:
+            price_series (pd.Series): Series of prices.
+            window (int): The period for the moving average and standard deviation.
+            num_std (float): The number of standard deviations for the upper and lower bands.
+            min_periods (int, optional): Minimum number of observations in window required to have a value.
+                                         Defaults to `window` if None.
+
+        Returns:
+            tuple[pd.Series, pd.Series, pd.Series]: Upper band, SMA, Lower band.
+        """
+        if not isinstance(price_series, pd.Series):
+            raise TypeError("price_series must be a pandas Series.")
+        if not isinstance(window, int) or window <= 0:
+            raise ValueError("window must be a positive integer.")
+        if not isinstance(num_std, (int, float)) or num_std <= 0:
+            raise ValueError("num_std must be a positive number.")
+        if min_periods is not None and (not isinstance(min_periods, int) or min_periods <= 0):
+            raise ValueError("min_periods must be a positive integer if provided.")
+
+        actual_min_periods = window if min_periods is None else min_periods
+
+        sma = price_series.rolling(window=window, min_periods=actual_min_periods).mean()
+        std = price_series.rolling(window=window, min_periods=actual_min_periods).std()
+        
+        # Replace NaN std with 0 to avoid issues if sma is present but std is not (e.g. constant price in window)
+        std = std.fillna(0)
+        
+        upper_band = sma + (std * num_std)
+        lower_band = sma - (std * num_std)
+        
+        return upper_band, sma, lower_band

cycles/Analysis/rsi.py

@@ -5,7 +5,7 @@ class RSI:
     """
     A class to calculate the Relative Strength Index (RSI).
     """
-    def __init__(self, period: int = 14):
+    def __init__(self, config):
         """
         Initializes the RSI calculator.
 
@@ -13,13 +13,13 @@ class RSI:
             period (int): The period for RSI calculation. Default is 14.
                           Must be a positive integer.
         """
-        if not isinstance(period, int) or period <= 0:
+        if not isinstance(config['rsi_period'], int) or config['rsi_period'] <= 0:
             raise ValueError("Period must be a positive integer.")
-        self.period = period
+        self.period = config['rsi_period']
 
     def calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame:
         """
-        Calculates the RSI and adds it as a column to the input DataFrame.
+        Calculates the RSI (using Wilder's smoothing) and adds it as a column to the input DataFrame.
 
         Args:
             data_df (pd.DataFrame): DataFrame with historical price data.
@@ -35,75 +35,79 @@ class RSI:
         if price_column not in data_df.columns:
             raise ValueError(f"Price column '{price_column}' not found in DataFrame.")
 
-        if len(data_df) < self.period:
+        # Check if data is sufficient for calculation (need period + 1 for one diff calculation)
-            print(f"Warning: Data length ({len(data_df)}) is less than RSI period ({self.period}). RSI will not be calculated.")
+        if len(data_df) < self.period + 1:
-            return data_df.copy()
+            print(f"Warning: Data length ({len(data_df)}) is less than RSI period ({self.period}) + 1. RSI will not be calculated meaningfully.")
+            df_copy = data_df.copy()
+            df_copy['RSI'] = np.nan # Add an RSI column with NaNs
+            return df_copy
 
-        df = data_df.copy()
+        df = data_df.copy() # Work on a copy
-        delta = df[price_column].diff(1)
         
-        gain = delta.where(delta > 0, 0)
+        price_series = df[price_column]
-        loss = -delta.where(delta < 0, 0) # Ensure loss is positive
         
-        # Calculate initial average gain and loss (SMA)
+        # Call the static custom RSI calculator, defaulting to EMA for Wilder's smoothing
-        avg_gain = gain.rolling(window=self.period, min_periods=self.period).mean().iloc[self.period -1:self.period]
+        rsi_series = self.calculate_custom_rsi(price_series, window=self.period, smoothing='EMA')
-        avg_loss = loss.rolling(window=self.period, min_periods=self.period).mean().iloc[self.period -1:self.period]
         
-
+        df['RSI'] = rsi_series
-        # Calculate subsequent average gains and losses (EMA-like)
-        # Pre-allocate lists for gains and losses to avoid repeated appending to Series
-        gains = [0.0] * len(df)
-        losses = [0.0] * len(df)
-
-        if not avg_gain.empty:
-            gains[self.period -1] = avg_gain.iloc[0]
-        if not avg_loss.empty:
-            losses[self.period -1] = avg_loss.iloc[0]
-
-
-        for i in range(self.period, len(df)):
-            gains[i] = ((gains[i-1] * (self.period - 1)) + gain.iloc[i]) / self.period
-            losses[i] = ((losses[i-1] * (self.period - 1)) + loss.iloc[i]) / self.period
-        
-        df['avg_gain'] = pd.Series(gains, index=df.index)
-        df['avg_loss'] = pd.Series(losses, index=df.index)
-
-        # Calculate RS
-        # Handle division by zero: if avg_loss is 0, RS is undefined or infinite.
-        # If avg_loss is 0 and avg_gain is also 0, RSI is conventionally 50.
-        # If avg_loss is 0 and avg_gain > 0, RSI is conventionally 100.
-        rs = df['avg_gain'] / df['avg_loss']
-        
-        # Calculate RSI
-        # RSI = 100 - (100 / (1 + RS))
-        # If avg_loss is 0:
-        #   If avg_gain > 0, RS -> inf, RSI -> 100
-        #   If avg_gain == 0, RS -> NaN (0/0), RSI -> 50 (conventionally, or could be 0 or 100 depending on interpretation)
-        # We will use a common convention where RSI is 100 if avg_loss is 0 and avg_gain > 0,
-        # and RSI is 0 if avg_loss is 0 and avg_gain is 0 (or 50, let's use 0 to indicate no strength if both are 0).
-        # However, to avoid NaN from 0/0, it's better to calculate RSI directly with conditions.
-
-        rsi_values = []
-        for i in range(len(df)):
-            avg_g = df['avg_gain'].iloc[i]
-            avg_l = df['avg_loss'].iloc[i]
-
-            if i < self.period -1 : # Not enough data for initial SMA
-                 rsi_values.append(np.nan)
-                 continue
-
-            if avg_l == 0:
-                if avg_g == 0:
-                    rsi_values.append(50) # Or 0, or np.nan depending on how you want to treat this. 50 implies neutrality.
-                else:
-                    rsi_values.append(100) # Max strength
-            else:
-                rs_val = avg_g / avg_l
-                rsi_values.append(100 - (100 / (1 + rs_val)))
-        
-        df['RSI'] = pd.Series(rsi_values, index=df.index)
-        
-        # Remove intermediate columns if desired, or keep them for debugging
-        # df.drop(columns=['avg_gain', 'avg_loss'], inplace=True)
         
         return df
+
+    @staticmethod
+    def calculate_custom_rsi(price_series: pd.Series, window: int = 14, smoothing: str = 'SMA') -> pd.Series:
+        """
+        Calculates RSI with specified window and smoothing (SMA or EMA).
+
+        Args:
+            price_series (pd.Series): Series of prices.
+            window (int): The period for RSI calculation. Must be a positive integer.
+            smoothing (str): Smoothing method, 'SMA' or 'EMA'. Defaults to 'SMA'.
+
+        Returns:
+            pd.Series: Series containing the RSI values.
+        """
+        if not isinstance(price_series, pd.Series):
+            raise TypeError("price_series must be a pandas Series.")
+        if not isinstance(window, int) or window <= 0:
+            raise ValueError("window must be a positive integer.")
+        if smoothing not in ['SMA', 'EMA']:
+            raise ValueError("smoothing must be either 'SMA' or 'EMA'.")
+        if len(price_series) < window + 1: # Need at least window + 1 prices for one diff
+            # print(f"Warning: Data length ({len(price_series)}) is less than RSI window ({window}) + 1. RSI will be all NaN.")
+            return pd.Series(np.nan, index=price_series.index)
+
+        delta = price_series.diff()
+        # The first delta is NaN. For gain/loss calculations, it can be treated as 0.
+        # However, subsequent rolling/ewm will handle NaNs appropriately if min_periods is set.
+
+        gain = delta.where(delta > 0, 0.0)
+        loss = -delta.where(delta < 0, 0.0) # Ensure loss is positive
+
+        # Ensure gain and loss Series have the same index as price_series for rolling/ewm
+        # This is important if price_series has missing dates/times
+        gain = gain.reindex(price_series.index, fill_value=0.0)
+        loss = loss.reindex(price_series.index, fill_value=0.0)
+
+        if smoothing == 'EMA':
+            # adjust=False for Wilder's smoothing used in RSI
+            avg_gain = gain.ewm(alpha=1/window, adjust=False, min_periods=window).mean()
+            avg_loss = loss.ewm(alpha=1/window, adjust=False, min_periods=window).mean()
+        else:  # SMA
+            avg_gain = gain.rolling(window=window, min_periods=window).mean()
+            avg_loss = loss.rolling(window=window, min_periods=window).mean()
+        
+        # Handle division by zero for RS calculation
+        # If avg_loss is 0, RS can be considered infinite (if avg_gain > 0) or undefined (if avg_gain also 0)
+        rs = avg_gain / avg_loss.replace(0, 1e-9) # Replace 0 with a tiny number to avoid direct division by zero warning
+
+        rsi = 100 - (100 / (1 + rs))
+        
+        # Correct RSI values for edge cases where avg_loss was 0
+        # If avg_loss is 0 and avg_gain is > 0, RSI is 100.
+        # If avg_loss is 0 and avg_gain is 0, RSI is 50 (neutral).
+        rsi[avg_loss == 0] = np.where(avg_gain[avg_loss == 0] > 0, 100, 50)
+        
+        # Ensure RSI is NaN where avg_gain or avg_loss is NaN (due to min_periods)
+        rsi[avg_gain.isna() | avg_loss.isna()] = np.nan
+        
+        return rsi

cycles/Analysis/strategies.py

@@ -0,0 +1,364 @@
+import pandas as pd
+import numpy as np
+
+from cycles.Analysis.boillinger_band import BollingerBands
+from cycles.Analysis.rsi import RSI
+from cycles.utils.data_utils import aggregate_to_daily, aggregate_to_hourly, aggregate_to_minutes
+
+
+class Strategy:
+
+    def __init__(self, config = None, logging = None):
+        if config is None:
+            raise ValueError("Config must be provided.")
+        self.config = config
+        self.logging = logging
+
+    def run(self, data, strategy_name):
+        if strategy_name == "MarketRegimeStrategy":
+            result = self.MarketRegimeStrategy(data)
+            return self.standardize_output(result, strategy_name)
+        elif strategy_name == "CryptoTradingStrategy":
+            result = self.CryptoTradingStrategy(data)
+            return self.standardize_output(result, strategy_name)
+        else:
+            if self.logging is not None:
+                self.logging.warning(f"Strategy {strategy_name} not found. Using no_strategy instead.")
+            return self.no_strategy(data)
+
+    def standardize_output(self, data, strategy_name):
+        """
+        Standardize column names across different strategies to ensure consistent plotting and analysis
+        
+        Args:
+            data (DataFrame): Strategy output DataFrame
+            strategy_name (str): Name of the strategy that generated this data
+            
+        Returns:
+            DataFrame: Data with standardized column names
+        """
+        if data.empty:
+            return data
+            
+        # Create a copy to avoid modifying the original
+        standardized = data.copy()
+        
+        # Standardize column names based on strategy
+        if strategy_name == "MarketRegimeStrategy":
+            # MarketRegimeStrategy already has standard column names for most fields
+            # Just ensure all standard columns exist
+            pass
+        elif strategy_name == "CryptoTradingStrategy":
+            # Map strategy-specific column names to standard names
+            column_mapping = {
+                'UpperBand_15m': 'UpperBand',
+                'LowerBand_15m': 'LowerBand',
+                'SMA_15m': 'SMA',
+                'RSI_15m': 'RSI',
+                'VolumeMA_15m': 'VolumeMA',
+                # Keep StopLoss and TakeProfit as they are
+            }
+            
+            # Add standard columns from mapped columns
+            for old_col, new_col in column_mapping.items():
+                if old_col in standardized.columns and new_col not in standardized.columns:
+                    standardized[new_col] = standardized[old_col]
+                    
+            # Add additional strategy-specific data as metadata columns
+            if 'UpperBand_1h' in standardized.columns:
+                standardized['UpperBand_1h_meta'] = standardized['UpperBand_1h']
+            if 'LowerBand_1h' in standardized.columns:
+                standardized['LowerBand_1h_meta'] = standardized['LowerBand_1h']
+        
+        # Ensure all strategies have BBWidth if possible
+        if 'BBWidth' not in standardized.columns and 'UpperBand' in standardized.columns and 'LowerBand' in standardized.columns:
+            standardized['BBWidth'] = (standardized['UpperBand'] - standardized['LowerBand']) / standardized['SMA'] if 'SMA' in standardized.columns else np.nan
+            
+        return standardized
+
+    def no_strategy(self, data):
+        """No strategy: returns False for both buy and sell conditions"""
+        buy_condition = pd.Series([False] * len(data), index=data.index)
+        sell_condition = pd.Series([False] * len(data), index=data.index)
+        return buy_condition, sell_condition
+
+    def rsi_bollinger_confirmation(self, rsi, window=14, std_mult=1.5):
+        """Calculate RSI Bollinger Bands for confirmation
+        
+        Args:
+            rsi (Series): RSI values
+            window (int): Rolling window for SMA
+            std_mult (float): Standard deviation multiplier
+            
+        Returns:
+            tuple: (oversold condition, overbought condition)
+        """
+        valid_rsi = ~rsi.isna()
+        if not valid_rsi.any():
+            # Return empty Series if no valid RSI data
+            return pd.Series(False, index=rsi.index), pd.Series(False, index=rsi.index)
+            
+        rsi_sma = rsi.rolling(window).mean()
+        rsi_std = rsi.rolling(window).std()
+        upper_rsi_band = rsi_sma + std_mult * rsi_std
+        lower_rsi_band = rsi_sma - std_mult * rsi_std
+        
+        return (rsi < lower_rsi_band), (rsi > upper_rsi_band)
+
+    def MarketRegimeStrategy(self, data):
+        """Optimized Bollinger Bands + RSI Strategy for Crypto Trading (Including Sideways Markets)
+        with adaptive Bollinger Bands
+        
+        This advanced strategy combines volatility analysis, momentum confirmation, and regime detection
+        to adapt to Bitcoin's unique market conditions.
+        
+        Entry Conditions:
+        - Trending Market (Breakout Mode):
+            Buy: Price < Lower Band ∧ RSI < 50 ∧ Volume Spike (≥1.5× 20D Avg)
+            Sell: Price > Upper Band ∧ RSI > 50 ∧ Volume Spike
+        - Sideways Market (Mean Reversion):
+            Buy: Price ≤ Lower Band ∧ RSI ≤ 40
+            Sell: Price ≥ Upper Band ∧ RSI ≥ 60
+            
+        Enhanced with RSI Bollinger Squeeze for signal confirmation when enabled.
+        
+        Returns:
+            DataFrame: A unified DataFrame containing original data, BB, RSI, and signals.
+        """
+
+        # data = aggregate_to_hourly(data, 4)
+        data = aggregate_to_daily(data)
+        
+        # Calculate Bollinger Bands
+        bb_calculator = BollingerBands(config=self.config)
+        # Ensure we are working with a copy to avoid modifying the original DataFrame upstream
+        data_bb = bb_calculator.calculate(data.copy())
+
+        # Calculate RSI
+        rsi_calculator = RSI(config=self.config)
+        # Use the original data's copy for RSI calculation as well, to maintain index integrity
+        data_with_rsi = rsi_calculator.calculate(data.copy(), price_column='close')
+
+        # Combine BB and RSI data into a single DataFrame for signal generation
+        # Ensure indices are aligned; they should be as both are from data.copy()
+        if 'RSI' in data_with_rsi.columns:
+            data_bb['RSI'] = data_with_rsi['RSI']
+        else:
+            # If RSI wasn't calculated (e.g., not enough data), create a dummy column with NaNs
+            # to prevent errors later, though signals won't be generated.
+            data_bb['RSI'] = pd.Series(index=data_bb.index, dtype=float)
+            if self.logging:
+                self.logging.warning("RSI column not found or not calculated. Signals relying on RSI may not be generated.")
+
+        # Initialize conditions as all False
+        buy_condition = pd.Series(False, index=data_bb.index)
+        sell_condition = pd.Series(False, index=data_bb.index)
+        
+        # Create masks for different market regimes
+        # MarketRegime is expected to be in data_bb from BollingerBands calculation
+        sideways_mask = data_bb['MarketRegime'] > 0
+        trending_mask = data_bb['MarketRegime'] <= 0
+        valid_data_mask = ~data_bb['MarketRegime'].isna()  # Handle potential NaN values
+        
+        # Calculate volume spike (≥1.5× 20D Avg)
+        # 'volume' column should be present in the input 'data', and thus in 'data_bb'
+        if 'volume' in data_bb.columns:
+            volume_20d_avg = data_bb['volume'].rolling(window=20).mean()
+            volume_spike = data_bb['volume'] >= 1.5 * volume_20d_avg
+            
+            # Additional volume contraction filter for sideways markets
+            volume_30d_avg = data_bb['volume'].rolling(window=30).mean()
+            volume_contraction = data_bb['volume'] < 0.7 * volume_30d_avg
+        else:
+            # If volume data is not available, assume no volume spike
+            volume_spike = pd.Series(False, index=data_bb.index)
+            volume_contraction = pd.Series(False, index=data_bb.index)
+            if self.logging is not None:
+                self.logging.warning("Volume data not available. Volume conditions will not be triggered.")
+        
+        # Calculate RSI Bollinger Squeeze confirmation
+        # RSI column is now part of data_bb
+        if 'RSI' in data_bb.columns and not data_bb['RSI'].isna().all():
+            oversold_rsi, overbought_rsi = self.rsi_bollinger_confirmation(data_bb['RSI'])
+        else:
+            oversold_rsi = pd.Series(False, index=data_bb.index)
+            overbought_rsi = pd.Series(False, index=data_bb.index)
+            if self.logging is not None and ('RSI' not in data_bb.columns or data_bb['RSI'].isna().all()):
+                self.logging.warning("RSI data not available or all NaN. RSI Bollinger Squeeze will not be triggered.")
+        
+        # Calculate conditions for sideways market (Mean Reversion)
+        if sideways_mask.any():
+            sideways_buy = (data_bb['close'] <= data_bb['LowerBand']) & (data_bb['RSI'] <= 40)
+            sideways_sell = (data_bb['close'] >= data_bb['UpperBand']) & (data_bb['RSI'] >= 60)
+            
+            # Add enhanced confirmation for sideways markets
+            if self.config.get("SqueezeStrategy", False):
+                sideways_buy = sideways_buy & oversold_rsi & volume_contraction
+                sideways_sell = sideways_sell & overbought_rsi & volume_contraction
+            
+            # Apply only where market is sideways and data is valid
+            buy_condition = buy_condition | (sideways_buy & sideways_mask & valid_data_mask)
+            sell_condition = sell_condition | (sideways_sell & sideways_mask & valid_data_mask)
+        
+        # Calculate conditions for trending market (Breakout Mode)
+        if trending_mask.any():
+            trending_buy = (data_bb['close'] < data_bb['LowerBand']) & (data_bb['RSI'] < 50) & volume_spike
+            trending_sell = (data_bb['close'] > data_bb['UpperBand']) & (data_bb['RSI'] > 50) & volume_spike
+            
+            # Add enhanced confirmation for trending markets
+            if self.config.get("SqueezeStrategy", False):
+                trending_buy = trending_buy & oversold_rsi
+                trending_sell = trending_sell & overbought_rsi
+            
+            # Apply only where market is trending and data is valid
+            buy_condition = buy_condition | (trending_buy & trending_mask & valid_data_mask)
+            sell_condition = sell_condition | (trending_sell & trending_mask & valid_data_mask)
+        
+        # Add buy/sell conditions as columns to the DataFrame
+        data_bb['BuySignal'] = buy_condition
+        data_bb['SellSignal'] = sell_condition
+        
+        return data_bb
+
+    # Helper functions for CryptoTradingStrategy
+    def _volume_confirmation_crypto(self, current_volume, volume_ma):
+        """Check volume surge against moving average for crypto strategy"""
+        if pd.isna(current_volume) or pd.isna(volume_ma) or volume_ma == 0:
+            return False
+        return current_volume > 1.5 * volume_ma
+
+    def _multi_timeframe_signal_crypto(self, current_price, rsi_value, 
+                                     lower_band_15m, lower_band_1h,
+                                     upper_band_15m, upper_band_1h):
+        """Generate signals with multi-timeframe confirmation for crypto strategy"""
+        # Ensure all inputs are not NaN before making comparisons
+        if any(pd.isna(val) for val in [current_price, rsi_value, lower_band_15m, lower_band_1h, upper_band_15m, upper_band_1h]):
+            return False, False
+            
+        buy_signal = (current_price <= lower_band_15m and 
+                     current_price <= lower_band_1h and
+                     rsi_value < 35)
+        
+        sell_signal = (current_price >= upper_band_15m and 
+                      current_price >= upper_band_1h and
+                      rsi_value > 65)
+        
+        return buy_signal, sell_signal
+
+    def CryptoTradingStrategy(self, data):
+        """Core trading algorithm with risk management
+        - Multi-Timeframe Confirmation: Combines 15-minute and 1-hour Bollinger Bands 
+        - Adaptive Volatility Filtering: Uses ATR for dynamic stop-loss/take-profit 
+        - Volume Spike Detection: Requires 1.5× average volume for confirmation 
+        - EMA-Smoothed RSI: Reduces false signals in choppy markets 
+        - Regime-Adaptive Parameters:
+            - Trending: 2σ bands, RSI 35/65 thresholds
+            - Sideways: 1.8σ bands, RSI 40/60 thresholds 
+        - Strategy Logic:
+            - Long Entry: Price ≤ both 15m & 1h lower bands + RSI < 35 + Volume surge
+            - Short Entry: Price ≥ both 15m & 1h upper bands + RSI > 65 + Volume surge
+            - Exit: 2:1 risk-reward ratio with ATR-based stops 
+        """
+        if data.empty or 'close' not in data.columns or 'volume' not in data.columns:
+            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
+
+        # Aggregate data
+        data_15m = aggregate_to_minutes(data.copy(), 15)
+        data_1h = aggregate_to_hourly(data.copy(), 1)
+
+        if data_15m.empty or data_1h.empty:
+            if self.logging:
+                self.logging.warning("CryptoTradingStrategy: Not enough data for 15m or 1h aggregation.")
+            return pd.DataFrame() # Return original data if aggregation fails
+        
+        # --- Calculate indicators for 15m timeframe ---
+        # Ensure 'close' and 'volume' exist before trying to access them
+        if 'close' not in data_15m.columns or 'volume' not in data_15m.columns:
+            if self.logging: self.logging.warning("CryptoTradingStrategy: 15m data missing close or volume.")
+            return data # Or an empty DF
+
+        price_data_15m = data_15m['close']
+        volume_data_15m = data_15m['volume']
+
+        upper_15m, sma_15m, lower_15m = BollingerBands.calculate_custom_bands(price_data_15m, window=20, num_std=2, min_periods=1)
+        # Use the static method from RSI class
+        rsi_15m = RSI.calculate_custom_rsi(price_data_15m, window=14, smoothing='EMA')
+        volume_ma_15m = volume_data_15m.rolling(window=20, min_periods=1).mean()
+
+        # Add 15m indicators to data_15m DataFrame
+        data_15m['UpperBand_15m'] = upper_15m
+        data_15m['SMA_15m'] = sma_15m
+        data_15m['LowerBand_15m'] = lower_15m
+        data_15m['RSI_15m'] = rsi_15m
+        data_15m['VolumeMA_15m'] = volume_ma_15m
+
+        # --- Calculate indicators for 1h timeframe ---
+        if 'close' not in data_1h.columns:
+            if self.logging: self.logging.warning("CryptoTradingStrategy: 1h data missing close.")
+            return data_15m # Return 15m data as 1h failed
+            
+        price_data_1h = data_1h['close']
+        # Use the static method from BollingerBands class, setting min_periods to 1 explicitly
+        upper_1h, _, lower_1h = BollingerBands.calculate_custom_bands(price_data_1h, window=50, num_std=1.8, min_periods=1)
+        
+        # Add 1h indicators to a temporary DataFrame to be merged
+        df_1h_indicators = pd.DataFrame(index=data_1h.index)
+        df_1h_indicators['UpperBand_1h'] = upper_1h
+        df_1h_indicators['LowerBand_1h'] = lower_1h
+        
+        # Merge 1h indicators into 15m DataFrame
+        # Use reindex and ffill to propagate 1h values to 15m intervals
+        data_15m = pd.merge(data_15m, df_1h_indicators, left_index=True, right_index=True, how='left')
+        data_15m['UpperBand_1h'] = data_15m['UpperBand_1h'].ffill()
+        data_15m['LowerBand_1h'] = data_15m['LowerBand_1h'].ffill()
+
+        # --- Generate Signals ---
+        buy_signals = pd.Series(False, index=data_15m.index)
+        sell_signals = pd.Series(False, index=data_15m.index)
+        stop_loss_levels = pd.Series(np.nan, index=data_15m.index)
+        take_profit_levels = pd.Series(np.nan, index=data_15m.index)
+
+        # ATR calculation needs a rolling window, apply to 'high', 'low', 'close' if available
+        # Using a simplified ATR for now: std of close prices over the last 4 15-min periods (1 hour)
+        if 'close' in data_15m.columns:
+            atr_series = price_data_15m.rolling(window=4, min_periods=1).std()
+        else:
+            atr_series = pd.Series(0, index=data_15m.index) # No ATR if close is missing
+
+        for i in range(len(data_15m)):
+            if i == 0: continue # Skip first row for volume_ma_15m[i-1]
+
+            current_price = data_15m['close'].iloc[i]
+            current_volume = data_15m['volume'].iloc[i]
+            rsi_val = data_15m['RSI_15m'].iloc[i]
+            lb_15m = data_15m['LowerBand_15m'].iloc[i]
+            ub_15m = data_15m['UpperBand_15m'].iloc[i]
+            lb_1h = data_15m['LowerBand_1h'].iloc[i]
+            ub_1h = data_15m['UpperBand_1h'].iloc[i]
+            vol_ma = data_15m['VolumeMA_15m'].iloc[i-1] # Use previous period's MA
+            atr = atr_series.iloc[i]
+
+            vol_confirm = self._volume_confirmation_crypto(current_volume, vol_ma)
+            buy_signal, sell_signal = self._multi_timeframe_signal_crypto(
+                current_price, rsi_val, lb_15m, lb_1h, ub_15m, ub_1h
+            )
+
+            if buy_signal and vol_confirm:
+                buy_signals.iloc[i] = True
+                if not pd.isna(atr) and atr > 0:
+                    stop_loss_levels.iloc[i] = current_price - 2 * atr
+                    take_profit_levels.iloc[i] = current_price + 4 * atr
+            elif sell_signal and vol_confirm:
+                sell_signals.iloc[i] = True
+                if not pd.isna(atr) and atr > 0:
+                    stop_loss_levels.iloc[i] = current_price + 2 * atr
+                    take_profit_levels.iloc[i] = current_price - 4 * atr
+        
+        data_15m['BuySignal'] = buy_signals
+        data_15m['SellSignal'] = sell_signals
+        data_15m['StopLoss'] = stop_loss_levels
+        data_15m['TakeProfit'] = take_profit_levels
+
+        return data_15m

cycles/backtest.py

@@ -1,12 +1,31 @@
 import pandas as pd
 import numpy as np
 
-from cycles.supertrend import Supertrends
 from cycles.market_fees import MarketFees
 
 class Backtest:
+    class Data:
+        def __init__(self, initial_usd, df, min1_df, init_strategy_fields) -> None:
+            self.initial_usd = initial_usd
+            self.usd = initial_usd
+            self.max_balance = initial_usd
+            self.coin = 0
+            self.position = 0
+            self.entry_price = 0
+            self.entry_time = None
+            self.current_trade_min1_start_idx = None
+            self.current_min1_end_idx = None
+            self.price_open = None
+            self.price_close = None
+            self.current_date = None
+            self.strategies = {}
+            self.df = df
+            self.min1_df = min1_df
+
+            self = init_strategy_fields(self)
+
     @staticmethod
-    def run(min1_df, df, initial_usd, stop_loss_pct, debug=False):
+    def run(data, entry_strategy, exit_strategy, debug=False):
         """
         Backtest a simple strategy using the meta supertrend (all three supertrends agree).
         Buys when meta supertrend is positive, sells when negative, applies a percentage stop loss.
@@ -17,84 +36,43 @@ class Backtest:
         - stop_loss_pct: float, stop loss as a fraction (e.g. 0.05 for 5%)
         - debug: bool, whether to print debug info
         """
-        _df = df.copy().reset_index(drop=True)
-        _df['timestamp'] = pd.to_datetime(_df['timestamp'])
-        
-        supertrends = Supertrends(_df, verbose=False)
-
-        supertrend_results_list = supertrends.calculate_supertrend_indicators()
-        trends = [st['results']['trend'] for st in supertrend_results_list]
-        trends_arr = np.stack(trends, axis=1)
-        meta_trend = np.where((trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]), 
-                                trends_arr[:,0], 0)
-        
-        position = 0  # 0 = no position, 1 = long
-        entry_price = 0
-        usd = initial_usd
-        coin = 0
         trade_log = []
-        max_balance = initial_usd
         drawdowns = []
         trades = []
-        entry_time = None
-        current_trade_min1_start_idx = None
 
-        min1_df['timestamp'] = pd.to_datetime(min1_df.index)
+        for i in range(1, len(data.df)):
+            data.price_open = data.df['open'].iloc[i]
+            data.price_close = data.df['close'].iloc[i]
             
-        for i in range(1, len(_df)):
+            data.current_date = data.df['timestamp'].iloc[i]
-            price_open = _df['open'].iloc[i]
-            price_close = _df['close'].iloc[i]
-            date = _df['timestamp'].iloc[i]
-            prev_mt = meta_trend[i-1]
-            curr_mt = meta_trend[i]
 
-            # Check stop loss if in position
+            if data.position == 0:
-            if position == 1:
+                if entry_strategy(data, i):
-                stop_loss_result = Backtest.check_stop_loss(
+                    data, entry_log_entry = Backtest.handle_entry(data)
-                    min1_df,
+                    trade_log.append(entry_log_entry)
-                    entry_time,
+            elif data.position == 1:
-                    date,
+                exit_test_results, data, sell_price = exit_strategy(data, i)
-                    entry_price,
-                    stop_loss_pct,
-                    coin,
-                    usd,
-                    debug,
-                    current_trade_min1_start_idx
-                )
-                if stop_loss_result is not None:
-                    trade_log_entry, current_trade_min1_start_idx, position, coin, entry_price = stop_loss_result
-                    trade_log.append(trade_log_entry)
-                    continue
-                # Update the start index for next check
-                current_trade_min1_start_idx = min1_df.index[min1_df.index <= date][-1]
                 
-            # Entry: only if not in position and signal changes to 1
+                if exit_test_results is not None:
-            if position == 0 and prev_mt != 1 and curr_mt == 1:
+                    data, exit_log_entry = Backtest.handle_exit(data, exit_test_results, sell_price)
-                entry_result = Backtest.handle_entry(usd, price_open, date)
+                    trade_log.append(exit_log_entry)
-                coin, entry_price, entry_time, usd, position, trade_log_entry = entry_result
-                trade_log.append(trade_log_entry)
-            
-            # Exit: only if in position and signal changes from 1 to -1
-            elif position == 1 and prev_mt == 1 and curr_mt == -1:
-                exit_result = Backtest.handle_exit(coin, price_open, entry_price, entry_time, date)
-                usd, coin, position, entry_price, trade_log_entry = exit_result
-                trade_log.append(trade_log_entry)
 
             # Track drawdown
-            balance = usd if position == 0 else coin * price_close
+            balance = data.usd if data.position == 0 else data.coin * data.price_close
-            if balance > max_balance:
+
-                max_balance = balance
+            if balance > data.max_balance:
-            drawdown = (max_balance - balance) / max_balance
+                data.max_balance = balance
+
+            drawdown = (data.max_balance - balance) / data.max_balance
             drawdowns.append(drawdown)
 
         # If still in position at end, sell at last close
-        if position == 1:
+        if data.position == 1:
-            exit_result = Backtest.handle_exit(coin, _df['close'].iloc[-1], entry_price, entry_time, _df['timestamp'].iloc[-1])
+            data, exit_log_entry = Backtest.handle_exit(data, "EOD", None)
-            usd, coin, position, entry_price, trade_log_entry = exit_result
+            trade_log.append(exit_log_entry)
-            trade_log.append(trade_log_entry)
 
         # Calculate statistics
-        final_balance = usd
+        final_balance = data.usd
         n_trades = len(trade_log)
         wins = [1 for t in trade_log if t['exit'] is not None and t['exit'] > t['entry']]
         win_rate = len(wins) / n_trades if n_trades > 0 else 0
@@ -114,14 +92,14 @@ class Backtest:
                 'entry': trade['entry'],
                 'exit': trade['exit'],
                 'profit_pct': profit_pct,
-                'type': trade.get('type', 'SELL'),
+                'type': trade['type'],
-                'fee_usd': trade.get('fee_usd')
+                'fee_usd': trade['fee_usd']
             })
             fee_usd = trade.get('fee_usd')
             total_fees_usd += fee_usd
 
         results = {
-            "initial_usd": initial_usd,
+            "initial_usd": data.initial_usd,
             "final_usd": final_balance,
             "n_trades": n_trades,
             "win_rate": win_rate,
@@ -143,74 +121,45 @@ class Backtest:
         return results
 
     @staticmethod
-    def check_stop_loss(min1_df, entry_time, date, entry_price, stop_loss_pct, coin, usd, debug, current_trade_min1_start_idx):
+    def handle_entry(data):
-        stop_price = entry_price * (1 - stop_loss_pct)
+        entry_fee = MarketFees.calculate_okx_taker_maker_fee(data.usd, is_maker=False)
+        usd_after_fee = data.usd - entry_fee
         
-        if current_trade_min1_start_idx is None:
+        data.coin = usd_after_fee / data.price_open
-            current_trade_min1_start_idx = min1_df.index[min1_df.index >= entry_time][0]
+        data.entry_price = data.price_open
-        current_min1_end_idx = min1_df.index[min1_df.index <= date][-1]
+        data.entry_time = data.current_date
+        data.usd = 0
+        data.position = 1
         
-        # Check all 1-minute candles in between for stop loss
-        min1_slice = min1_df.loc[current_trade_min1_start_idx:current_min1_end_idx]
-        if (min1_slice['low'] <= stop_price).any():
-            # Stop loss triggered, find the exact candle
-            stop_candle = min1_slice[min1_slice['low'] <= stop_price].iloc[0]
-            # More realistic fill: if open < stop, fill at open, else at stop
-            if stop_candle['open'] < stop_price:
-                sell_price = stop_candle['open']
-            else:
-                sell_price = stop_price
-            if debug:
-                print(f"STOP LOSS triggered: entry={entry_price}, stop={stop_price}, sell_price={sell_price}, entry_time={entry_time}, stop_time={stop_candle.name}")
-            btc_to_sell = coin
-            usd_gross = btc_to_sell * sell_price
-            exit_fee = MarketFees.calculate_okx_taker_maker_fee(usd_gross, is_maker=False)
-            trade_log_entry = {
-                'type': 'STOP',
-                'entry': entry_price,
-                'exit': sell_price,
-                'entry_time': entry_time,
-                'exit_time': stop_candle.name,
-                'fee_usd': exit_fee
-            }
-            # After stop loss, reset position and entry
-            return trade_log_entry, None, 0, 0, 0
-        return None
-
-    @staticmethod
-    def handle_entry(usd, price_open, date):
-        entry_fee = MarketFees.calculate_okx_taker_maker_fee(usd, is_maker=False)
-        usd_after_fee = usd - entry_fee
-        coin = usd_after_fee / price_open
-        entry_price = price_open
-        entry_time = date
-        usd = 0
-        position = 1
         trade_log_entry = {
             'type': 'BUY',
-            'entry': entry_price,
+            'entry': data.entry_price,
             'exit': None,
-            'entry_time': entry_time,
+            'entry_time': data.entry_time,
             'exit_time': None,
             'fee_usd': entry_fee
         }
-        return coin, entry_price, entry_time, usd, position, trade_log_entry
+        return data, trade_log_entry
 
     @staticmethod
-    def handle_exit(coin, price_open, entry_price, entry_time, date):
+    def handle_exit(data, exit_reason, sell_price):
-        btc_to_sell = coin
+        btc_to_sell = data.coin
-        usd_gross = btc_to_sell * price_open
+        exit_price = sell_price if sell_price is not None else data.price_open
+        usd_gross = btc_to_sell * exit_price
         exit_fee = MarketFees.calculate_okx_taker_maker_fee(usd_gross, is_maker=False)
-        usd = usd_gross - exit_fee
+
-        trade_log_entry = {
+        data.usd = usd_gross - exit_fee
-            'type': 'SELL',
+
-            'entry': entry_price,
+        exit_log_entry = {
-            'exit': price_open,
+            'type': exit_reason,
-            'entry_time': entry_time,
+            'entry': data.entry_price,
-            'exit_time': date,
+            'exit': exit_price,
+            'entry_time': data.entry_time,
+            'exit_time': data.current_date,
             'fee_usd': exit_fee
         }
-        coin = 0
+        data.coin = 0
-        position = 0
+        data.position = 0
-        entry_price = 0
+        data.entry_price = 0
-        return usd, coin, position, entry_price, trade_log_entry
+
+        return data, exit_log_entry

cycles/market_fees.py

@@ -2,6 +2,6 @@ import pandas as pd
 
 class MarketFees:
     @staticmethod
-    def calculate_okx_taker_maker_fee(amount, is_maker=True):
+    def calculate_okx_taker_maker_fee(amount, is_maker=True) -> float:
         fee_rate = 0.0008 if is_maker else 0.0010
         return amount * fee_rate

cycles/utils/data_utils.py

@@ -1,5 +1,80 @@
 import pandas as pd
 
+def check_data(data_df: pd.DataFrame) -> bool:
+    """
+    Checks if the input DataFrame has a DatetimeIndex.
+
+    Args:
+        data_df (pd.DataFrame): DataFrame to check.
+
+    Returns:
+        bool: True if the DataFrame has a DatetimeIndex, False otherwise.
+    """
+
+    if not isinstance(data_df.index, pd.DatetimeIndex):
+        print("Warning: Input DataFrame must have a DatetimeIndex.")
+        return False
+    
+    agg_rules = {}
+
+    # Define aggregation rules based on available columns
+    if 'open' in data_df.columns:
+        agg_rules['open'] = 'first'
+    if 'high' in data_df.columns:
+        agg_rules['high'] = 'max'
+    if 'low' in data_df.columns:
+        agg_rules['low'] = 'min'
+    if 'close' in data_df.columns:
+        agg_rules['close'] = 'last'
+    if 'volume' in data_df.columns:
+        agg_rules['volume'] = 'sum'
+
+    if not agg_rules:
+        print("Warning: No standard OHLCV columns (open, high, low, close, volume) found for daily aggregation.")
+        return False
+    
+    return agg_rules
+
+def aggregate_to_weekly(data_df: pd.DataFrame, weeks: int = 1) -> pd.DataFrame:
+    """
+    Aggregates time-series financial data to weekly OHLCV format.
+
+    The input DataFrame is expected to have a DatetimeIndex.
+    'open' will be the first 'open' price of the week.
+    'close' will be the last 'close' price of the week.
+    'high' will be the maximum 'high' price of the week.
+    'low' will be the minimum 'low' price of the week.
+    'volume' (if present) will be the sum of volumes for the week.
+
+    Args:
+        data_df (pd.DataFrame): DataFrame with a DatetimeIndex and columns
+                                like 'open', 'high', 'low', 'close', and optionally 'volume'.
+        weeks (int): The number of weeks to aggregate to. Default is 1.
+
+    Returns:
+        pd.DataFrame: DataFrame aggregated to weekly OHLCV data.
+                      The index will be a DatetimeIndex with the time set to the start of the week.
+                      Returns an empty DataFrame if no relevant OHLCV columns are found.
+    """
+    
+    agg_rules = check_data(data_df)
+
+    if not agg_rules:
+        print("Warning: No standard OHLCV columns (open, high, low, close, volume) found for weekly aggregation.")
+        return pd.DataFrame(index=pd.to_datetime([]))
+
+    # Resample to weekly frequency and apply aggregation rules
+    weekly_data = data_df.resample(f'{weeks}W').agg(agg_rules)
+
+    weekly_data.dropna(how='all', inplace=True)
+
+    # Adjust timestamps to the start of the week    
+    if not weekly_data.empty and isinstance(weekly_data.index, pd.DatetimeIndex):
+        weekly_data.index = weekly_data.index.floor('W')
+
+    return weekly_data
+
+
 def aggregate_to_daily(data_df: pd.DataFrame) -> pd.DataFrame:
     """
     Aggregates time-series financial data to daily OHLCV format.
@@ -24,22 +99,8 @@ def aggregate_to_daily(data_df: pd.DataFrame) -> pd.DataFrame:
     Raises:
         ValueError: If the input DataFrame does not have a DatetimeIndex.
     """
-    if not isinstance(data_df.index, pd.DatetimeIndex):
-        raise ValueError("Input DataFrame must have a DatetimeIndex.")
     
-    agg_rules = {}
+    agg_rules = check_data(data_df)
-
-    # Define aggregation rules based on available columns
-    if 'open' in data_df.columns:
-        agg_rules['open'] = 'first'
-    if 'high' in data_df.columns:
-        agg_rules['high'] = 'max'
-    if 'low' in data_df.columns:
-        agg_rules['low'] = 'min'
-    if 'close' in data_df.columns:
-        agg_rules['close'] = 'last'
-    if 'volume' in data_df.columns:
-        agg_rules['volume'] = 'sum'
 
     if not agg_rules:
         # Log a warning or raise an error if no relevant columns are found
@@ -58,3 +119,81 @@ def aggregate_to_daily(data_df: pd.DataFrame) -> pd.DataFrame:
     daily_data.dropna(how='all', inplace=True)
 
     return daily_data
+
+
+def aggregate_to_hourly(data_df: pd.DataFrame, hours: int = 1) -> pd.DataFrame:
+    """
+    Aggregates time-series financial data to hourly OHLCV format.
+
+    The input DataFrame is expected to have a DatetimeIndex.
+    'open' will be the first 'open' price of the hour.
+    'close' will be the last 'close' price of the hour.
+    'high' will be the maximum 'high' price of the hour.
+    'low' will be the minimum 'low' price of the hour.
+    'volume' (if present) will be the sum of volumes for the hour.
+
+    Args:
+        data_df (pd.DataFrame): DataFrame with a DatetimeIndex and columns
+                                like 'open', 'high', 'low', 'close', and optionally 'volume'.
+        hours (int): The number of hours to aggregate to. Default is 1.
+
+    Returns:
+        pd.DataFrame: DataFrame aggregated to hourly OHLCV data.
+                      The index will be a DatetimeIndex with the time set to the start of the hour.
+                      Returns an empty DataFrame if no relevant OHLCV columns are found.
+    """
+
+    agg_rules = check_data(data_df)
+
+    if not agg_rules:
+        print("Warning: No standard OHLCV columns (open, high, low, close, volume) found for hourly aggregation.")
+        return pd.DataFrame(index=pd.to_datetime([]))
+
+    # Resample to hourly frequency and apply aggregation rules  
+    hourly_data = data_df.resample(f'{hours}h').agg(agg_rules)
+
+    hourly_data.dropna(how='all', inplace=True)
+
+    # Adjust timestamps to the start of the hour
+    if not hourly_data.empty and isinstance(hourly_data.index, pd.DatetimeIndex):
+        hourly_data.index = hourly_data.index.floor('h')
+
+    return hourly_data
+
+
+def aggregate_to_minutes(data_df: pd.DataFrame, minutes: int) -> pd.DataFrame:
+    """
+    Aggregates time-series financial data to N-minute OHLCV format.
+
+    The input DataFrame is expected to have a DatetimeIndex.
+    'open' will be the first 'open' price of the N-minute interval.
+    'close' will be the last 'close' price of the N-minute interval.
+    'high' will be the maximum 'high' price of the N-minute interval.
+    'low' will be the minimum 'low' price of the N-minute interval.
+    'volume' (if present) will be the sum of volumes for the N-minute interval.
+
+    Args:
+        data_df (pd.DataFrame): DataFrame with a DatetimeIndex and columns
+                                like 'open', 'high', 'low', 'close', and optionally 'volume'.
+        minutes (int): The number of minutes to aggregate to.
+
+    Returns:
+        pd.DataFrame: DataFrame aggregated to N-minute OHLCV data.
+                      The index will be a DatetimeIndex.
+                      Returns an empty DataFrame if no relevant OHLCV columns are found or
+                      if the input DataFrame does not have a DatetimeIndex.
+    """
+    agg_rules_obj = check_data(data_df) # check_data returns rules or False
+
+    if not agg_rules_obj:
+        # check_data already prints a warning if index is not DatetimeIndex or no OHLCV columns
+        # Ensure an empty DataFrame with a DatetimeIndex is returned for consistency
+        return pd.DataFrame(index=pd.to_datetime([]))
+
+    # Resample to N-minute frequency and apply aggregation rules
+    # Using .agg(agg_rules_obj) where agg_rules_obj is the dict from check_data
+    resampled_data = data_df.resample(f'{minutes}min').agg(agg_rules_obj)
+    
+    resampled_data.dropna(how='all', inplace=True)
+
+    return resampled_data

docs/analysis.md

@@ -8,6 +8,7 @@ The `Analysis` module includes classes for calculating common technical indicato
 
 -   **Relative Strength Index (RSI)**: Implemented in `cycles/Analysis/rsi.py`.
 -   **Bollinger Bands**: Implemented in `cycles/Analysis/boillinger_band.py`.
+-   Note: Trading strategies are detailed in `strategies.md`.
 
 ## Class: `RSI`
 
@@ -15,64 +16,91 @@ Found in `cycles/Analysis/rsi.py`.
 
 Calculates the Relative Strength Index.
 ### Mathematical Model  
-1. **Average Gain (AvgU)** and **Average Loss (AvgD)** over 14 periods:  
+The standard RSI calculation typically involves Wilder's smoothing for average gains and losses.
+1. **Price Change (Delta)**: Difference between consecutive closing prices.
+2. **Gain and Loss**: Separate positive (gain) and negative (loss, expressed as positive) price changes.
+3. **Average Gain (AvgU)** and **Average Loss (AvgD)**: Smoothed averages of gains and losses over the RSI period. Wilder's smoothing is a specific type of exponential moving average (EMA):
+   - Initial AvgU/AvgD: Simple Moving Average (SMA) over the first `period` values.
+   - Subsequent AvgU: `(Previous AvgU * (period - 1) + Current Gain) / period`
+   - Subsequent AvgD: `(Previous AvgD * (period - 1) + Current Loss) / period`
+4. **Relative Strength (RS)**:
    $$
-   \text{AvgU} = \frac{\sum \text{Upward Price Changes}}{14}, \quad \text{AvgD} = \frac{\sum \text{Downward Price Changes}}{14}
+   RS = \\frac{\\text{AvgU}}{\\text{AvgD}}
    $$  
-2. **Relative Strength (RS)**:  
+5. **RSI**:  
    $$
-   RS = \frac{\text{AvgU}}{\text{AvgD}}
+   RSI = 100 - \\frac{100}{1 + RS}
-   $$  
-3. **RSI**:  
-   $$
-   RSI = 100 - \frac{100}{1 + RS}
    $$
+   Special conditions:
+   - If AvgD is 0: RSI is 100 if AvgU > 0, or 50 if AvgU is also 0 (neutral).
 
-### `__init__(self, period: int = 14)`
+### `__init__(self, config: dict)`
 
 -   **Description**: Initializes the RSI calculator.
--   **Parameters**:
+-   **Parameters**:\n    -   `config` (dict): Configuration dictionary. Must contain an `'rsi_period'` key with a positive integer value (e.g., `{'rsi_period': 14}`).
-    -   `period` (int, optional): The period for RSI calculation. Defaults to 14. Must be a positive integer.
 
 ### `calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame`
 
--   **Description**: Calculates the RSI and adds it as an 'RSI' column to the input DataFrame. Handles cases where data length is less than the period by returning the original DataFrame with a warning.
+-   **Description**: Calculates the RSI (using Wilder's smoothing by default) and adds it as an 'RSI' column to the input DataFrame. This method utilizes `calculate_custom_rsi` internally with `smoothing='EMA'`.
+-   **Parameters**:\n    -   `data_df` (pd.DataFrame): DataFrame with historical price data. Must contain the `price_column`.\n    -   `price_column` (str, optional): The name of the column containing price data. Defaults to 'close'.
+-   **Returns**: `pd.DataFrame` - A copy of the input DataFrame with an added 'RSI' column. If data length is insufficient for the period, the 'RSI' column will contain `np.nan`.
+
+### `calculate_custom_rsi(price_series: pd.Series, window: int = 14, smoothing: str = 'SMA') -> pd.Series` (Static Method)
+
+-   **Description**: Calculates RSI with a specified window and smoothing method (SMA or EMA). This is the core calculation engine.
 -   **Parameters**:
-    -   `data_df` (pd.DataFrame): DataFrame with historical price data. Must contain the `price_column`.
+    -   `price_series` (pd.Series): Series of prices.
-    -   `price_column` (str, optional): The name of the column containing price data. Defaults to 'close'.
+    -   `window` (int, optional): The period for RSI calculation. Defaults to 14. Must be a positive integer.
--   **Returns**: `pd.DataFrame` - The input DataFrame with an added 'RSI' column (containing `np.nan` for initial periods where RSI cannot be calculated). Returns a copy of the original DataFrame if the period is larger than the number of data points.
+    -   `smoothing` (str, optional): Smoothing method, can be 'SMA' (Simple Moving Average) or 'EMA' (Exponential Moving Average, specifically Wilder's smoothing when `alpha = 1/window`). Defaults to 'SMA'.
+-   **Returns**: `pd.Series` - Series containing the RSI values. Returns a series of NaNs if data length is insufficient.
 
 ## Class: `BollingerBands`
 
 Found in `cycles/Analysis/boillinger_band.py`.
 
-## **Bollinger Bands**  
+Calculates Bollinger Bands.
 ### Mathematical Model  
-1. **Middle Band**: 20-day Simple Moving Average (SMA)  
+1. **Middle Band**: Simple Moving Average (SMA) over `period`.
    $$
-   \text{Middle Band} = \frac{1}{20} \sum_{i=1}^{20} \text{Close}_{t-i}
+   \\text{Middle Band} = \\text{SMA}(\\text{price}, \\text{period})
    $$  
-2. **Upper Band**: Middle Band + 2 × 20-day Standard Deviation (σ)  
+2. **Standard Deviation (σ)**: Standard deviation of price over `period`.
+3. **Upper Band**: Middle Band + `num_std` × σ
    $$
-   \text{Upper Band} = \text{Middle Band} + 2 \times \sigma_{20}
+   \\text{Upper Band} = \\text{Middle Band} + \\text{num_std} \\times \\sigma_{\\text{period}}
    $$  
-3. **Lower Band**: Middle Band − 2 × 20-day Standard Deviation (σ)  
+4. **Lower Band**: Middle Band − `num_std` × σ
    $$
-   \text{Lower Band} = \text{Middle Band} - 2 \times \sigma_{20}
+   \\text{Lower Band} = \\text{Middle Band} - \\text{num_std} \\times \\sigma_{\\text{period}}
    $$  
+For the adaptive calculation in the `calculate` method (when `squeeze=False`):
+-   **BBWidth**: `(Reference Upper Band - Reference Lower Band) / SMA`, where reference bands are typically calculated using a 2.0 standard deviation multiplier.
+-   **MarketRegime**: Determined by comparing `BBWidth` to a threshold from the configuration. `1` for sideways, `0` for trending.
+-   The `num_std` used for the final Upper and Lower Bands then varies based on this `MarketRegime` and the `bb_std_dev_multiplier` values for "trending" and "sideways" markets from the configuration, applied row-wise.
 
-
+### `__init__(self, config: dict)`
-### `__init__(self, period: int = 20, std_dev_multiplier: float = 2.0)`
 
 -   **Description**: Initializes the BollingerBands calculator.
--   **Parameters**:
+-   **Parameters**:\n    -   `config` (dict): Configuration dictionary. It must contain:
-    -   `period` (int, optional): The period for the moving average and standard deviation. Defaults to 20. Must be a positive integer.
+        -   `'bb_period'` (int): Positive integer for the moving average and standard deviation period.
-    -   `std_dev_multiplier` (float, optional): The number of standard deviations for the upper and lower bands. Defaults to 2.0. Must be positive.
+        -   `'trending'` (dict): Containing `'bb_std_dev_multiplier'` (float, positive) for trending markets.
+        -   `'sideways'` (dict): Containing `'bb_std_dev_multiplier'` (float, positive) for sideways markets.
+        -   `'bb_width'` (float): Positive float threshold for determining market regime.
 
-### `calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame`
+### `calculate(self, data_df: pd.DataFrame, price_column: str = 'close', squeeze: bool = False) -> pd.DataFrame`
 
--   **Description**: Calculates Bollinger Bands and adds 'SMA' (Simple Moving Average), 'UpperBand', and 'LowerBand' columns to the DataFrame.
+-   **Description**: Calculates Bollinger Bands and adds relevant columns to the DataFrame.
+    -   If `squeeze` is `False` (default): Calculates adaptive Bollinger Bands. It determines the market regime (trending/sideways) based on `BBWidth` and applies different standard deviation multipliers (from the `config`) on a row-by-row basis. Adds 'SMA', 'UpperBand', 'LowerBand', 'BBWidth', and 'MarketRegime' columns.
+    -   If `squeeze` is `True`: Calculates simpler Bollinger Bands with a fixed window of 14 and a standard deviation multiplier of 1.5 by calling `calculate_custom_bands`. Adds 'SMA', 'UpperBand', 'LowerBand' columns; 'BBWidth' and 'MarketRegime' will be `NaN`.
+-   **Parameters**:\n    -   `data_df` (pd.DataFrame): DataFrame with price data. Must include the `price_column`.\n    -   `price_column` (str, optional): The name of the column containing the price data. Defaults to 'close'.\n    -   `squeeze` (bool, optional): If `True`, calculates bands with fixed parameters (window 14, std 1.5). Defaults to `False`.
+-   **Returns**: `pd.DataFrame` - A copy of the original DataFrame with added Bollinger Band related columns.
+
+### `calculate_custom_bands(price_series: pd.Series, window: int = 20, num_std: float = 2.0, min_periods: int = None) -> tuple[pd.Series, pd.Series, pd.Series]` (Static Method)
+
+-   **Description**: Calculates Bollinger Bands with a specified window, standard deviation multiplier, and minimum periods.
 -   **Parameters**:
-    -   `data_df` (pd.DataFrame): DataFrame with price data. Must include the `price_column`.
+    -   `price_series` (pd.Series): Series of prices.
-    -   `price_column` (str, optional): The name of the column containing the price data (e.g., 'close'). Defaults to 'close'.
+    -   `window` (int, optional): The period for the moving average and standard deviation. Defaults to 20.
--   **Returns**: `pd.DataFrame` - The original DataFrame with added columns: 'SMA', 'UpperBand', 'LowerBand'.
+    -   `num_std` (float, optional): The number of standard deviations for the upper and lower bands. Defaults to 2.0.
+    -   `min_periods` (int, optional): Minimum number of observations in window required to have a value. Defaults to `window` if `None`.
+-   **Returns**: `tuple[pd.Series, pd.Series, pd.Series]` - A tuple containing the Upper band, SMA, and Lower band series.

docs/strategies.md

@@ -0,0 +1,98 @@
+# Trading Strategies (`cycles/Analysis/strategies.py`)
+
+This document outlines the trading strategies implemented within the `Strategy` class. These strategies utilize technical indicators calculated by other classes in the `Analysis` module.
+
+## Class: `Strategy`
+
+Manages and executes different trading strategies.
+
+### `__init__(self, config: dict = None, logging = None)`
+
+-   **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`.
+
+### `run(self, data: pd.DataFrame, strategy_name: str) -> pd.DataFrame`
+
+-   **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.
+
+### `no_strategy(self, data: pd.DataFrame) -> pd.DataFrame`
+
+-   **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.
+
+---
+
+## Implemented Strategies
+
+### 1. `MarketRegimeStrategy`
+
+-   **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`.
+
+#### `rsi_bollinger_confirmation(self, rsi: pd.Series, window: int = 14, std_mult: float = 1.5) -> tuple`
+
+-   **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).
+
+### 2. `CryptoTradingStrategy`
+
+-   **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`.
+
+---
+
+## General Strategy Concepts (from previous high-level notes)
+
+While the specific implementations above have their own detailed logic, some general concepts that often inspire trading strategies include:
+
+-   **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.
+
+These concepts are partially reflected in the implemented strategies, particularly in `MarketRegimeStrategy` (adaptive parameters) and `CryptoTradingStrategy` (multi-timeframe, volume confirmation, ATR-based risk levels).

main.py

@@ -6,11 +6,11 @@ import os
 import datetime
 import argparse
 import json
-import ast
 
 from cycles.utils.storage import Storage
 from cycles.utils.system import SystemUtils
 from cycles.backtest import Backtest
+from cycles.Analysis.supertrend import Supertrends
 
 logging.basicConfig(
     level=logging.INFO,
@@ -21,6 +21,68 @@ logging.basicConfig(
     ]
 )
 
+def default_init_strategy(data: Backtest.Data) -> Backtest.Data:
+    supertrends = Supertrends(data.df, verbose=False)
+
+    supertrend_results_list = supertrends.calculate_supertrend_indicators()
+    trends = [st['results']['trend'] for st in supertrend_results_list]
+    trends_arr = np.stack(trends, axis=1)
+    meta_trend = np.where((trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]), 
+                            trends_arr[:,0], 0)
+
+    data.strategies["meta_trend"] = meta_trend
+    
+    return data
+
+def default_entry_strategy(data, df_index):
+    return data.strategies["meta_trend"][df_index - 1] != 1 and data.strategies["meta_trend"][df_index] == 1
+
+def stop_loss_strategy(data):
+    stop_price = data.entry_price * (1 - data.strategies["stop_loss_pct"])
+
+    # Ensure index is sorted and is a DatetimeIndex
+    min1_index = data.min1_df.index
+
+    # Find the first index >= entry_time
+    start_candidates = min1_index[min1_index >= data.entry_time]
+    data.current_trade_min1_start_idx = start_candidates[0]
+
+    # Find the last index <= current_date
+    end_candidates = min1_index[min1_index <= data.current_date]
+    if len(end_candidates) == 0:
+        print("Warning: no end candidate here. Need to be checked")
+        return False, None
+    data.current_min1_end_idx = end_candidates[-1]
+
+    min1_slice = data.min1_df.loc[data.current_trade_min1_start_idx:data.current_min1_end_idx]
+    
+    # print(f"lowest low in that range: {min1_slice['low'].min()}, count: {len(min1_slice)}")
+    # print(f"slice start: {min1_slice.index[0]}, slice end: {min1_slice.index[-1]}")
+
+    if (min1_slice['low'] <= stop_price).any():
+        stop_candle = min1_slice[min1_slice['low'] <= stop_price].iloc[0]
+
+        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_exit_strategy(data: Backtest.Data, df_index):
+    if data.strategies["meta_trend"][df_index - 1] != 1 and \
+       data.strategies["meta_trend"][df_index] == -1:
+        return "META_TREND_EXIT_SIGNAL", data, None
+
+    stop_loss_result, sell_price = stop_loss_strategy(data)
+    if stop_loss_result:
+        data.strategies["current_trade_min1_start_idx"] = \
+            data.min1_df.index[data.min1_df.index <= data.current_date][-1]
+        return "STOP_LOSS", data, sell_price
+    
+    return None, data, 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)
@@ -28,13 +90,17 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
     results_rows = []
     trade_rows = []
 
+    min1_df['timestamp'] = pd.to_datetime(min1_df.index) # need ?    
+
     for stop_loss_pct in stop_loss_pcts:
+        data = Backtest.Data(initial_usd, df, min1_df, default_init_strategy)
+        data.strategies["stop_loss_pct"] = stop_loss_pct
+
         results = Backtest.run(
-            min1_df,
+            data,
-            df,
+            default_entry_strategy,
-            initial_usd=initial_usd,
+            default_exit_strategy,
-            stop_loss_pct=stop_loss_pct,
+            debug
-            debug=debug
         )
         n_trades = results["n_trades"]
         trades = results.get('trades', [])
@@ -48,22 +114,29 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
         cumulative_profit = 0
         max_drawdown = 0
         peak = 0
+
         for trade in trades:
             cumulative_profit += trade['profit_pct']
+
             if cumulative_profit > peak:
                 peak = cumulative_profit
             drawdown = peak - cumulative_profit
+
             if drawdown > max_drawdown:
                 max_drawdown = drawdown
+
         final_usd = initial_usd
+
         for trade in trades:
             final_usd *= (1 + trade['profit_pct'])
+
         total_fees_usd = sum(trade.get('fee_usd', 0.0) for trade in trades)
+
         row = {
             "timeframe": rule_name,
             "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'),
+            "n_stop_loss": sum(1 for trade in trades if 'type' in trade and trade['type'] == 'STOP_LOSS'),
             "win_rate": win_rate,
             "max_drawdown": max_drawdown,
             "avg_trade": avg_trade,
@@ -75,6 +148,7 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
             "total_fees_usd": total_fees_usd,
         }
         results_rows.append(row)
+
         for trade in trades:
             trade_rows.append({
                 "timeframe": rule_name,
@@ -88,20 +162,18 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
                 "fee_usd": trade.get("fee_usd"),
             })
         logging.info(f"Timeframe: {rule_name}, Stop Loss: {stop_loss_pct}, Trades: {n_trades}")
+        
         if debug:
             for trade in trades:
-                if trade['type'] == 'STOP':
+                print(trade)
-                    print(trade)
+
-            for trade in trades:
-                if trade['profit_pct'] < -0.09:  # or whatever is close to -0.10
-                    print("Large loss trade:", trade)
     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
 
-    if rule == "1T":
+    if rule == "1min":
         df = data_1min.copy()
     else:
         df = data_1min.resample(rule).agg({
@@ -163,7 +235,7 @@ def get_nearest_price(df, target_date):
         return nearest_time, price
 
 if __name__ == "__main__":
-    debug = True
+    debug = False
 
     parser = argparse.ArgumentParser(description="Run backtest with config file.")
     parser.add_argument("config", type=str, nargs="?", help="Path to config JSON file.")
@@ -174,14 +246,14 @@ if __name__ == "__main__":
         "start_date": "2024-05-15",
         "stop_date": datetime.datetime.today().strftime('%Y-%m-%d'),
         "initial_usd": 10000,
-        "timeframes": ["1D"],
+        "timeframes": ["15min"],
-        "stop_loss_pcts": [0.01, 0.02, 0.03],
+        "stop_loss_pcts": [0.03],
     }
 
     if args.config:
         with open(args.config, 'r') as f:
             config = json.load(f)
-    else:
+    elif not debug:
         print("No config file provided. Please enter the following values (press Enter to use default):")
 
         start_date = input(f"Start date [{default_config['start_date']}]: ") or default_config['start_date']
@@ -203,8 +275,9 @@ if __name__ == "__main__":
             'timeframes': timeframes,
             'stop_loss_pcts': stop_loss_pcts,
         }
+    else:
+        config = default_config
 
-    # Use config values
     start_date = config['start_date']
     stop_date = config['stop_date']
     initial_usd = config['initial_usd']

test_bbrsi.py

@@ -4,9 +4,7 @@ import matplotlib.pyplot as plt
 import pandas as pd
 
 from cycles.utils.storage import Storage
-from cycles.utils.data_utils import aggregate_to_daily
+from cycles.Analysis.strategies import Strategy
-from cycles.Analysis.boillinger_band import BollingerBands
-from cycles.Analysis.rsi import RSI
 
 logging.basicConfig(
     level=logging.INFO,
@@ -17,115 +15,145 @@ logging.basicConfig(
     ]
 )
 
-config_minute = {
+config = {
-    "start_date": "2022-01-01",
+    "start_date": "2023-01-01",
-    "stop_date": "2023-01-01",
+    "stop_date": "2024-01-01",
     "data_file": "btcusd_1-min_data.csv"
 }
 
-config_day = {
+config_strategy = {
-    "start_date": "2022-01-01",
+    "bb_width": 0.05,
-    "stop_date": "2023-01-01",
+    "bb_period": 20,
-    "data_file": "btcusd_1-day_data.csv"
+    "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", # CryptoTradingStrategy
+    "SqueezeStrategy": True
 }
 
-IS_DAY = True
+IS_DAY = False
-
-def no_strategy(data_bb, data_with_rsi):
-    buy_condition = pd.Series([False] * len(data_bb), index=data_bb.index)
-    sell_condition = pd.Series([False] * len(data_bb), index=data_bb.index)
-    return buy_condition, sell_condition
-
-def strategy_1(data_bb, data_with_rsi):
-    # Long trade: price move below lower Bollinger band and RSI go below 25
-    buy_condition = (data_bb['close'] < data_bb['LowerBand']) & (data_bb['RSI'] < 25)
-    # Short only: price move above top Bollinger band and RSI goes over 75
-    sell_condition = (data_bb['close'] > data_bb['UpperBand']) & (data_bb['RSI'] > 75)
-    return buy_condition, sell_condition
-
 
 if __name__ == "__main__":
 
+    # Load data
     storage = Storage(logging=logging)
-
-    if IS_DAY:
-        config = config_day
-    else:
-        config = config_minute
-
     data = storage.load_data(config["data_file"], config["start_date"], config["stop_date"])
 
-    if not IS_DAY:
+    # Run strategy
-        data_daily = aggregate_to_daily(data)
+    strategy = Strategy(config=config_strategy, logging=logging)
-        storage.save_data(data, "btcusd_1-day_data.csv")
+    processed_data = strategy.run(data.copy(), config_strategy["strategy_name"])
-        df_to_plot = data_daily
-    else:
-        df_to_plot = data
 
-    bb = BollingerBands(period=30, std_dev_multiplier=2.0)
+    # Get buy and sell signals
-    data_bb = bb.calculate(df_to_plot.copy())
+    buy_condition = processed_data.get('BuySignal', pd.Series(False, index=processed_data.index)).astype(bool)
+    sell_condition = processed_data.get('SellSignal', pd.Series(False, index=processed_data.index)).astype(bool)
 
-    rsi_calculator = RSI(period=13)
+    buy_signals = processed_data[buy_condition]
-    data_with_rsi = rsi_calculator.calculate(df_to_plot.copy(), price_column='close')
+    sell_signals = processed_data[sell_condition]
     
-    # Combine BB and RSI data into a single DataFrame for signal generation
+    # Plot the data with seaborn library
-    # Ensure indices are aligned; they should be as both are from df_to_plot.copy()
+    if processed_data is not None and not processed_data.empty:
-    if 'RSI' in data_with_rsi.columns:
-        data_bb['RSI'] = data_with_rsi['RSI']
-    else:
-        # If RSI wasn't calculated (e.g., not enough data), create a dummy column with NaNs
-        # to prevent errors later, though signals won't be generated.
-        data_bb['RSI'] = pd.Series(index=data_bb.index, dtype=float)
-        logging.warning("RSI column not found or not calculated. Signals relying on RSI may not be generated.")
-
-    strategy = 1
-    if strategy == 1:
-        buy_condition, sell_condition = strategy_1(data_bb, data_with_rsi)
-    else:
-        buy_condition, sell_condition = no_strategy(data_bb, data_with_rsi)
-
-    buy_signals = data_bb[buy_condition]
-    sell_signals = data_bb[sell_condition]
-    
-    # plot the data with seaborn library
-    if df_to_plot is not None and not df_to_plot.empty:
         # Create a figure with two subplots, sharing the x-axis
-        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(16, 8), sharex=True)
+        fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(16, 8), sharex=True)
+
+        strategy_name = config_strategy["strategy_name"]
+
+        # Plot 1: Close Price and Strategy-Specific Bands/Levels
+        sns.lineplot(x=processed_data.index, y='close', data=processed_data, label='Close Price', ax=ax1)
+        
+        # Use standardized column names for bands
+        if 'UpperBand' in processed_data.columns and 'LowerBand' in processed_data.columns:
+            # Instead of lines, shade the area between upper and lower bands
+            ax1.fill_between(processed_data.index, 
+                           processed_data['LowerBand'],
+                           processed_data['UpperBand'], 
+                           alpha=0.1, color='blue', label='Bollinger Bands')
+        else:
+            logging.warning(f"{strategy_name}: UpperBand or LowerBand not found for plotting.")
+            
+        # Add strategy-specific extra indicators if available
+        if strategy_name == "CryptoTradingStrategy":
+            if 'StopLoss' in processed_data.columns:
+                sns.lineplot(x=processed_data.index, y='StopLoss', data=processed_data, label='Stop Loss', ax=ax1, linestyle='--', color='orange')
+            if 'TakeProfit' in processed_data.columns:
+                sns.lineplot(x=processed_data.index, y='TakeProfit', data=processed_data, label='Take Profit', ax=ax1, linestyle='--', color='purple')
         
-        # Plot 1: Close Price and Bollinger Bands
-        sns.lineplot(x=data_bb.index, y='close', data=data_bb, label='Close Price', ax=ax1)
-        sns.lineplot(x=data_bb.index, y='UpperBand', data=data_bb, label='Upper Band (BB)', ax=ax1)
-        sns.lineplot(x=data_bb.index, y='LowerBand', data=data_bb, label='Lower Band (BB)', ax=ax1)
         # Plot Buy/Sell signals on Price chart
         if not buy_signals.empty:
             ax1.scatter(buy_signals.index, buy_signals['close'], color='green', marker='o', s=20, label='Buy Signal', zorder=5)
         if not sell_signals.empty:
             ax1.scatter(sell_signals.index, sell_signals['close'], color='red', marker='o', s=20, label='Sell Signal', zorder=5)
-        ax1.set_title('Price and Bollinger Bands with Signals')
+        ax1.set_title(f'Price and Signals ({strategy_name})')
         ax1.set_ylabel('Price')
         ax1.legend()
         ax1.grid(True)
 
-        # Plot 2: RSI
+        # Plot 2: RSI and Strategy-Specific Thresholds
-        if 'RSI' in data_bb.columns: # Check data_bb now as it should contain RSI
+        if 'RSI' in processed_data.columns: 
-            sns.lineplot(x=data_bb.index, y='RSI', data=data_bb, label='RSI (14)', ax=ax2, color='purple')
+            sns.lineplot(x=processed_data.index, y='RSI', data=processed_data, label=f'RSI (' + str(config_strategy.get("rsi_period", 14)) + ')', ax=ax2, color='purple')
-            ax2.axhline(75, color='red', linestyle='--', linewidth=0.8, label='Overbought (75)')
+            if strategy_name == "MarketRegimeStrategy":
-            ax2.axhline(25, color='green', linestyle='--', linewidth=0.8, label='Oversold (25)')
+                # Get threshold values
+                upper_threshold = config_strategy.get("trending", {}).get("rsi_threshold", [30,70])[1]
+                lower_threshold = config_strategy.get("trending", {}).get("rsi_threshold", [30,70])[0]
+                
+                # Shade overbought area (upper)
+                ax2.fill_between(processed_data.index, upper_threshold, 100, 
+                                alpha=0.1, color='red', label=f'Overbought (>{upper_threshold})')
+                
+                # Shade oversold area (lower)
+                ax2.fill_between(processed_data.index, 0, lower_threshold, 
+                                alpha=0.1, color='green', label=f'Oversold (<{lower_threshold})')
+                
+            elif strategy_name == "CryptoTradingStrategy":
+                # Shade overbought area (upper)
+                ax2.fill_between(processed_data.index, 65, 100, 
+                                alpha=0.1, color='red', label='Overbought (>65)')
+                
+                # Shade oversold area (lower)
+                ax2.fill_between(processed_data.index, 0, 35, 
+                                alpha=0.1, color='green', label='Oversold (<35)')
+
             # Plot Buy/Sell signals on RSI chart
-            if not buy_signals.empty:
+            if not buy_signals.empty and 'RSI' in buy_signals.columns:
                 ax2.scatter(buy_signals.index, buy_signals['RSI'], color='green', marker='o', s=20, label='Buy Signal (RSI)', zorder=5)
-            if not sell_signals.empty:
+            if not sell_signals.empty and 'RSI' in sell_signals.columns:
                 ax2.scatter(sell_signals.index, sell_signals['RSI'], color='red', marker='o', s=20, label='Sell Signal (RSI)', zorder=5)
             ax2.set_title('Relative Strength Index (RSI) with Signals')
             ax2.set_ylabel('RSI Value')
-            ax2.set_ylim(0, 100) # RSI is typically bounded between 0 and 100
+            ax2.set_ylim(0, 100) 
             ax2.legend()
             ax2.grid(True)
         else:
             logging.info("RSI data not available for plotting.")
 
-        plt.xlabel('Date') # Common X-axis label
+        # Plot 3: Strategy-Specific Indicators
-        fig.tight_layout() # Adjust layout to prevent overlapping titles/labels
+        ax3.clear() # Clear previous plot content if any
+        if 'BBWidth' in processed_data.columns:
+            sns.lineplot(x=processed_data.index, y='BBWidth', data=processed_data, label='BB Width', ax=ax3)
+            
+        if strategy_name == "MarketRegimeStrategy":
+            if 'MarketRegime' in processed_data.columns:
+                 sns.lineplot(x=processed_data.index, y='MarketRegime', data=processed_data, label='Market Regime (Sideways: 1, Trending: 0)', ax=ax3)
+            ax3.set_title('Bollinger Bands Width & Market Regime')
+            ax3.set_ylabel('Value')
+        elif strategy_name == "CryptoTradingStrategy":
+            if 'VolumeMA' in processed_data.columns:
+                sns.lineplot(x=processed_data.index, y='VolumeMA', data=processed_data, label='Volume MA', ax=ax3)
+            if 'volume' in processed_data.columns:
+                sns.lineplot(x=processed_data.index, y='volume', data=processed_data, label='Volume', ax=ax3, alpha=0.5)
+            ax3.set_title('Volume Analysis')
+            ax3.set_ylabel('Volume')
+        
+        ax3.legend()
+        ax3.grid(True)
+
+        plt.xlabel('Date') 
+        fig.tight_layout() 
         plt.show()
     else:
         logging.info("No data to plot.")