Merge branch 'main' of ssh://dep.sokaris.link:2222/Simon/Cycles

- Updated entry and exit strategy functions

cycles/Analysis/boillinger_band.py

@@ -4,23 +4,25 @@ 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 +39,37 @@ class BollingerBands:
         if price_column not in data_df.columns:
             raise ValueError(f"Price column '{price_column}' not found in DataFrame.")
 
+        if not squeeze:
             # Calculate SMA
-        data_df['SMA'] = data_df[price_column].rolling(window=self.period).mean()
+            data_df['SMA'] = data_df[price_column].rolling(window=self.config['bb_period']).mean()
 
             # Calculate Standard Deviation
-        std_dev = data_df[price_column].rolling(window=self.period).std()
+            std_dev = data_df[price_column].rolling(window=self.config['bb_period']).std()
 
             # Calculate Upper and Lower Bands
-        data_df['UpperBand'] = data_df['SMA'] + (self.std_dev_multiplier * std_dev)
+            data_df['UpperBand'] = data_df['SMA'] + (2.0* std_dev)
-        data_df['LowerBand'] = data_df['SMA'] - (self.std_dev_multiplier * std_dev)
+            data_df['LowerBand'] = data_df['SMA'] - (2.0* std_dev)
+
+            # Calculate the width of the Bollinger Bands
+            data_df['BBWidth'] = (data_df['UpperBand'] - data_df['LowerBand']) / data_df['SMA']
+
+            # Calculate the market regime
+            # 1 = sideways, 0 = trending
+            data_df['MarketRegime'] = (data_df['BBWidth'] < self.config['bb_width']).astype(int)
+
+            if data_df['MarketRegime'].sum() > 0:
+                data_df['UpperBand'] = data_df['SMA'] + (self.config['trending']['bb_std_dev_multiplier'] * std_dev)
+                data_df['LowerBand'] = data_df['SMA'] - (self.config['trending']['bb_std_dev_multiplier'] * std_dev)
+            else:
+                data_df['UpperBand'] = data_df['SMA'] + (self.config['sideways']['bb_std_dev_multiplier'] * std_dev)
+                data_df['LowerBand'] = data_df['SMA'] - (self.config['sideways']['bb_std_dev_multiplier'] * std_dev)
+
+        else:
+            data_df['SMA'] = data_df[price_column].rolling(window=14).mean()
+            # Calculate Standard Deviation
+            std_dev = data_df[price_column].rolling(window=14).std()
+            # Calculate Upper and Lower Bands
+            data_df['UpperBand'] = data_df['SMA'] + 1.5* std_dev
+            data_df['LowerBand'] = data_df['SMA'] - 1.5* std_dev
 
         return data_df

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.
 
@@ -13,9 +13,9 @@ 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:
         """

cycles/Analysis/strategies.py

@@ -0,0 +1,131 @@
+import pandas as pd
+import numpy as np
+
+from cycles.Analysis.boillinger_band import BollingerBands
+
+
+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":
+            return self.MarketRegimeStrategy(data)
+        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 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.
+        """
+        
+        # Initialize conditions as all False
+        buy_condition = pd.Series(False, index=data.index)
+        sell_condition = pd.Series(False, index=data.index)
+        
+        # Create masks for different market regimes
+        sideways_mask = data['MarketRegime'] > 0
+        trending_mask = data['MarketRegime'] <= 0
+        valid_data_mask = ~data['MarketRegime'].isna()  # Handle potential NaN values
+        
+        # Calculate volume spike (≥1.5× 20D Avg)
+        if 'volume' in data.columns:
+            volume_20d_avg = data['volume'].rolling(window=20).mean()
+            volume_spike = data['volume'] >= 1.5 * volume_20d_avg
+            
+            # Additional volume contraction filter for sideways markets
+            volume_30d_avg = data['volume'].rolling(window=30).mean()
+            volume_contraction = data['volume'] < 0.7 * volume_30d_avg
+        else:
+            # If volume data is not available, assume no volume spike
+            volume_spike = pd.Series(False, index=data.index)
+            volume_contraction = pd.Series(False, index=data.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
+        if 'RSI' in data.columns:
+            oversold_rsi, overbought_rsi = self.rsi_bollinger_confirmation(data['RSI'])
+        else:
+            oversold_rsi = pd.Series(False, index=data.index)
+            overbought_rsi = pd.Series(False, index=data.index)
+            if self.logging is not None:
+                self.logging.warning("RSI data not available. RSI Bollinger Squeeze will not be triggered.")
+        
+        # Calculate conditions for sideways market (Mean Reversion)
+        if sideways_mask.any():
+            sideways_buy = (data['close'] <= data['LowerBand']) & (data['RSI'] <= 40)
+            sideways_sell = (data['close'] >= data['UpperBand']) & (data['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['close'] < data['LowerBand']) & (data['RSI'] < 50) & volume_spike
+            trending_sell = (data['close'] > data['UpperBand']) & (data['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)
+        
+        return buy_condition, sell_condition

cycles/backtest.py

@@ -1,13 +1,31 @@
 import pandas as pd
 import numpy as np
-import time
 
-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.
@@ -18,97 +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)
-        # Shift meta_trend by one to avoid lookahead bias
-        meta_trend_signal = np.roll(meta_trend, 1)
-        meta_trend_signal[0] = 0  # or np.nan, but 0 means 'no signal' for first bar
-        
-        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.index = pd.to_datetime(min1_df.index)
+        for i in range(1, len(data.df)):
-        min1_timestamps = min1_df.index.values
+            data.price_open = data.df['open'].iloc[i]
+            data.price_close = data.df['close'].iloc[i]
             
-        last_print_time = time.time()
+            data.current_date = data.df['timestamp'].iloc[i]
-        for i in range(1, len(_df)):
-            current_time = time.time()
-            if current_time - last_print_time >= 5:
-                progress = (i / len(_df)) * 100
-                print(f"\rProgress: {progress:.1f}%", end="", flush=True)
-                last_print_time = current_time
 
-            price_open = _df['open'].iloc[i]
+            if data.position == 0:
-            price_close = _df['close'].iloc[i]
+                if entry_strategy(data, i):
-            date = _df['timestamp'].iloc[i]
+                    data, entry_log_entry = Backtest.handle_entry(data)
-            prev_mt = meta_trend_signal[i-1]
+                    trade_log.append(entry_log_entry)
-            curr_mt = meta_trend_signal[i]
+            elif data.position == 1:
+                exit_test_results, data, sell_price = exit_strategy(data, i)
                 
-            # Check stop loss if in position
+                if exit_test_results is not None:
-            if position == 1:
+                    data, exit_log_entry = Backtest.handle_exit(data, exit_test_results, sell_price)
-                stop_loss_result = Backtest.check_stop_loss(
+                    trade_log.append(exit_log_entry)
-                    min1_df,
-                    entry_time,
-                    date,
-                    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 position == 0 and prev_mt != 1 and curr_mt == 1:
-                entry_result = Backtest.handle_entry(usd, price_open, date)
-                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)
 
-        print("\rProgress: 100%\r\n", end="", flush=True)
-
         # 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
@@ -128,14 +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,
@@ -157,74 +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/supertrend.py

@@ -1,30 +1,70 @@
 import pandas as pd
 import numpy as np
 import logging
+from scipy.signal import find_peaks
+from matplotlib.patches import Rectangle
+from scipy import stats
+import concurrent.futures
+from functools import partial
 from functools import lru_cache
+import matplotlib.pyplot as plt
 
+# Color configuration
+# Plot colors
+DARK_BG_COLOR = '#181C27'
+LEGEND_BG_COLOR = '#333333'
+TITLE_COLOR = 'white'
+AXIS_LABEL_COLOR = 'white'
+
+# Candlestick colors
+CANDLE_UP_COLOR = '#089981'  # Green
+CANDLE_DOWN_COLOR = '#F23645'  # Red
+
+# Marker colors
+MIN_COLOR = 'red'
+MAX_COLOR = 'green'
+
+# Line style colors
+MIN_LINE_STYLE = 'g--'  # Green dashed
+MAX_LINE_STYLE = 'r--'  # Red dashed
+SMA7_LINE_STYLE = 'y-'  # Yellow solid
+SMA15_LINE_STYLE = 'm-'  # Magenta solid
+
+# SuperTrend colors
+ST_COLOR_UP = 'g-'
+ST_COLOR_DOWN = 'r-'
+
+# Cache the calculation results by function parameters
 @lru_cache(maxsize=32)
 def cached_supertrend_calculation(period, multiplier, data_tuple):
+    # Convert tuple back to numpy arrays
     high = np.array(data_tuple[0])
     low = np.array(data_tuple[1])
     close = np.array(data_tuple[2])
+    
+    # Calculate TR and ATR using vectorized operations
     tr = np.zeros_like(close)
     tr[0] = high[0] - low[0]
     hc_range = np.abs(high[1:] - close[:-1])
     lc_range = np.abs(low[1:] - close[:-1])
     hl_range = high[1:] - low[1:]
     tr[1:] = np.maximum.reduce([hl_range, hc_range, lc_range])
+    
+    # Use numpy's exponential moving average
     atr = np.zeros_like(tr)
     atr[0] = tr[0]
     multiplier_ema = 2.0 / (period + 1)
     for i in range(1, len(tr)):
         atr[i] = (tr[i] * multiplier_ema) + (atr[i-1] * (1 - multiplier_ema))
+
+    # Calculate bands
     upper_band = np.zeros_like(close)
     lower_band = np.zeros_like(close)
     for i in range(len(close)):
         hl_avg = (high[i] + low[i]) / 2
         upper_band[i] = hl_avg + (multiplier * atr[i])
         lower_band[i] = hl_avg - (multiplier * atr[i])
+
     final_upper = np.zeros_like(close)
     final_lower = np.zeros_like(close)
     supertrend = np.zeros_like(close)
@@ -66,18 +106,76 @@ def cached_supertrend_calculation(period, multiplier, data_tuple):
     }
 
 def calculate_supertrend_external(data, period, multiplier):
+    # Convert DataFrame columns to hashable tuples
     high_tuple = tuple(data['high'])
     low_tuple = tuple(data['low'])
     close_tuple = tuple(data['close'])
+    
+    # Call the cached function
     return cached_supertrend_calculation(period, multiplier, (high_tuple, low_tuple, close_tuple))
 
+
 class Supertrends:
     def __init__(self, data, verbose=False, display=False):
+        """
+        Initialize the TrendDetectorSimple class.
+        
+        Parameters:
+        - data: pandas DataFrame containing price data
+        - verbose: boolean, whether to display detailed logging information
+        - display: boolean, whether to enable display/plotting features
+        """
+        
         self.data = data
         self.verbose = verbose
+        self.display = display
+        
+        # Only define display-related variables if display is True
+        if self.display:
+            # Plot style configuration
+            self.plot_style = 'dark_background' 
+            self.bg_color = DARK_BG_COLOR
+            self.plot_size = (12, 8)
+            
+            # Candlestick configuration
+            self.candle_width = 0.6
+            self.candle_up_color = CANDLE_UP_COLOR
+            self.candle_down_color = CANDLE_DOWN_COLOR
+            self.candle_alpha = 0.8
+            self.wick_width = 1
+            
+            # Marker configuration
+            self.min_marker = '^'
+            self.min_color = MIN_COLOR
+            self.min_size = 100
+            self.max_marker = 'v'
+            self.max_color = MAX_COLOR
+            self.max_size = 100
+            self.marker_zorder = 100
+            
+            # Line configuration
+            self.line_width = 1
+            self.min_line_style = MIN_LINE_STYLE
+            self.max_line_style = MAX_LINE_STYLE
+            self.sma7_line_style = SMA7_LINE_STYLE
+            self.sma15_line_style = SMA15_LINE_STYLE
+            
+            # Text configuration
+            self.title_size = 14
+            self.title_color = TITLE_COLOR
+            self.axis_label_size = 12
+            self.axis_label_color = AXIS_LABEL_COLOR
+            
+            # Legend configuration
+            self.legend_loc = 'best'
+            self.legend_bg_color = LEGEND_BG_COLOR
+        
+        # Configure logging
         logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
                            format='%(asctime)s - %(levelname)s - %(message)s')
         self.logger = logging.getLogger('TrendDetectorSimple')
+        
+        # Convert data to pandas DataFrame if it's not already
         if not isinstance(self.data, pd.DataFrame):
             if isinstance(self.data, list):
                 self.data = pd.DataFrame({'close': self.data})
@@ -85,101 +183,154 @@ class Supertrends:
                 raise ValueError("Data must be a pandas DataFrame or a list")
 
     def calculate_tr(self):
+        """
+        Calculate True Range (TR) for the price data.
+        
+        True Range is the greatest of:
+        1. Current high - current low
+        2. |Current high - previous close|
+        3. |Current low - previous close|
+        
+        Returns:
+        - Numpy array of TR values
+        """
         df = self.data.copy()
         high = df['high'].values
         low = df['low'].values
         close = df['close'].values
+        
         tr = np.zeros_like(close)
-        tr[0] = high[0] - low[0]
+        tr[0] = high[0] - low[0]  # First TR is just the first day's range
+        
         for i in range(1, len(close)):
+            # Current high - current low
             hl_range = high[i] - low[i]
+            # |Current high - previous close|
             hc_range = abs(high[i] - close[i-1])
+            # |Current low - previous close|
             lc_range = abs(low[i] - close[i-1])
+            
+            # TR is the maximum of these three values
             tr[i] = max(hl_range, hc_range, lc_range)
+            
         return tr
     
     def calculate_atr(self, period=14):
+        """
+        Calculate Average True Range (ATR) for the price data.
+        
+        ATR is the exponential moving average of the True Range over a specified period.
+        
+        Parameters:
+        - period: int, the period for the ATR calculation (default: 14)
+        
+        Returns:
+        - Numpy array of ATR values
+        """
+        
         tr = self.calculate_tr()
         atr = np.zeros_like(tr)
+        
+        # First ATR value is just the first TR
         atr[0] = tr[0]
+        
+        # Calculate exponential moving average (EMA) of TR
         multiplier = 2.0 / (period + 1)
+        
         for i in range(1, len(tr)):
             atr[i] = (tr[i] * multiplier) + (atr[i-1] * (1 - multiplier))
+            
         return atr
     
-    def calculate_supertrend(self, period=10, multiplier=3.0):
+    def detect_trends(self):
         """
-        Calculate SuperTrend indicator for the price data.
+        Detect trends by identifying local minima and maxima in the price data
-        SuperTrend is a trend-following indicator that uses ATR to determine the trend direction.
+        using scipy.signal.find_peaks.
+        
         Parameters:
-        - period: int, the period for the ATR calculation (default: 10)
+        - prominence: float, required prominence of peaks (relative to the price range)
-        - multiplier: float, the multiplier for the ATR (default: 3.0)
+        - width: int, required width of peaks in data points
+        
         Returns:
-        - Dictionary containing SuperTrend values, trend direction, and upper/lower bands
+        - DataFrame with columns for timestamps, prices, and trend indicators
+        - Dictionary containing analysis results including linear regression, SMAs, and SuperTrend indicators
         """
-        df = self.data.copy()
+        df = self.data
-        high = df['high'].values
+        # close_prices = df['close'].values
-        low = df['low'].values
+        
-        close = df['close'].values
+        # max_peaks, _ = find_peaks(close_prices)
-        atr = self.calculate_atr(period)
+        # min_peaks, _ = find_peaks(-close_prices)
-        upper_band = np.zeros_like(close)
+        
-        lower_band = np.zeros_like(close)
+        # df['is_min'] = False
-        for i in range(len(close)):
+        # df['is_max'] = False
-            hl_avg = (high[i] + low[i]) / 2
+        
-            upper_band[i] = hl_avg + (multiplier * atr[i])
+        # for peak in max_peaks:
-            lower_band[i] = hl_avg - (multiplier * atr[i])
+        #     df.at[peak, 'is_max'] = True
-        final_upper = np.zeros_like(close)
+        # for peak in min_peaks:
-        final_lower = np.zeros_like(close)
+        #     df.at[peak, 'is_min'] = True
-        supertrend = np.zeros_like(close)
+        
-        trend = np.zeros_like(close)
+        # result = df[['timestamp', 'close', 'is_min', 'is_max']].copy()
-        final_upper[0] = upper_band[0]
+        
-        final_lower[0] = lower_band[0]
+        # Perform linear regression on min_peaks and max_peaks
-        if close[0] <= upper_band[0]:
+        # min_prices = df['close'].iloc[min_peaks].values
-            supertrend[0] = upper_band[0]
+        # max_prices = df['close'].iloc[max_peaks].values
-            trend[0] = -1
+        
-        else:
+        # Linear regression for min peaks if we have at least 2 points
-            supertrend[0] = lower_band[0]
+        # min_slope, min_intercept, min_r_value, _, _ = stats.linregress(min_peaks, min_prices)
-            trend[0] = 1
+        # Linear regression for max peaks if we have at least 2 points
-        for i in range(1, len(close)):
+        # max_slope, max_intercept, max_r_value, _, _ = stats.linregress(max_peaks, max_prices)
-            if (upper_band[i] < final_upper[i-1]) or (close[i-1] > final_upper[i-1]):
+
-                final_upper[i] = upper_band[i]
+        # Calculate Simple Moving Averages (SMA) for 7 and 15 periods        
-            else:
+        # sma_7 = pd.Series(close_prices).rolling(window=7, min_periods=1).mean().values
-                final_upper[i] = final_upper[i-1]
+        # sma_15 = pd.Series(close_prices).rolling(window=15, min_periods=1).mean().values
-            if (lower_band[i] > final_lower[i-1]) or (close[i-1] < final_lower[i-1]):
+        
-                final_lower[i] = lower_band[i]
+        analysis_results = {}
-            else:
+        # analysis_results['linear_regression'] = {
-                final_lower[i] = final_lower[i-1]
+        #     'min': {
-            if supertrend[i-1] == final_upper[i-1] and close[i] <= final_upper[i]:
+        #         'slope': min_slope,
-                supertrend[i] = final_upper[i]
+        #         'intercept': min_intercept,
-                trend[i] = -1
+        #         'r_squared': min_r_value ** 2
-            elif supertrend[i-1] == final_upper[i-1] and close[i] > final_upper[i]:
+        #     },
-                supertrend[i] = final_lower[i]
+        #     'max': {
-                trend[i] = 1
+        #         'slope': max_slope,
-            elif supertrend[i-1] == final_lower[i-1] and close[i] >= final_lower[i]:
+        #         'intercept': max_intercept,
-                supertrend[i] = final_lower[i]
+        #         'r_squared': max_r_value ** 2
-                trend[i] = 1
+        #     }
-            elif supertrend[i-1] == final_lower[i-1] and close[i] < final_lower[i]:
+        # }
-                supertrend[i] = final_upper[i]
+        # analysis_results['sma'] = {
-                trend[i] = -1
+        #     '7': sma_7,
-        supertrend_results = {
+        #     '15': sma_15
-            'supertrend': supertrend,
+        # }
-            'trend': trend,
+        
-            'upper_band': final_upper,
+        # Calculate SuperTrend indicators
-            'lower_band': final_lower
+        supertrend_results_list = self._calculate_supertrend_indicators()
-        }
+        analysis_results['supertrend'] = supertrend_results_list
-        return supertrend_results
+        
+        return analysis_results
         
     def calculate_supertrend_indicators(self):
+        """
+        Calculate SuperTrend indicators with different parameter sets in parallel.
+        Returns:
+        - list, the SuperTrend results
+        """
         supertrend_params = [
-            {"period": 12, "multiplier": 3.0},
+            {"period": 12, "multiplier": 3.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
-            {"period": 10, "multiplier": 1.0},
+            {"period": 10, "multiplier": 1.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
-            {"period": 11, "multiplier": 2.0}
+            {"period": 11, "multiplier": 2.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN}
         ]
+        data = self.data.copy()
+        
+        # For just 3 calculations, direct calculation might be faster than process pool
         results = []
         for p in supertrend_params:
-            result = self.calculate_supertrend(period=p["period"], multiplier=p["multiplier"])
+            result = calculate_supertrend_external(data, p["period"], p["multiplier"])
-            results.append({
+            results.append(result)
+        
+        supertrend_results_list = []
+        for params, result in zip(supertrend_params, results):
+            supertrend_results_list.append({
                 "results": result,
-                "params": p
+                "params": params
             })
-        return results
+        return supertrend_results_list

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 +119,43 @@ 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

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`.
+-   **Trading Strategies**: Implemented in `cycles/Analysis/strategies.py`.
 
 ## Class: `RSI`
 
@@ -76,3 +77,65 @@ Found in `cycles/Analysis/boillinger_band.py`.
     -   `data_df` (pd.DataFrame): DataFrame with price data. Must include the `price_column`.
     -   `price_column` (str, optional): The name of the column containing the price data (e.g., 'close'). Defaults to 'close'.
 -   **Returns**: `pd.DataFrame` - The original DataFrame with added columns: 'SMA', 'UpperBand', 'LowerBand'.
+
+## Class: `Strategy`
+
+Found in `cycles/Analysis/strategies.py`.
+
+Implements various trading strategies using technical indicators.
+
+### `__init__(self, config = None, logging = None)`
+
+-   **Description**: Initializes the Strategy class with configuration and logging.
+-   **Parameters**:
+    -   `config` (dict): Configuration dictionary with strategy parameters. Must be provided.
+    -   `logging` (logging object, optional): Logger for output messages. Defaults to None.
+
+### `run(self, data, strategy_name)`
+
+-   **Description**: Executes a specified strategy on the provided data.
+-   **Parameters**:
+    -   `data` (pd.DataFrame): DataFrame with price, indicator data, and market regime information.
+    -   `strategy_name` (str): Name of the strategy to run. Currently supports "MarketRegimeStrategy".
+-   **Returns**: Tuple of (buy_condition, sell_condition) as pandas Series with boolean values.
+
+### `no_strategy(self, data)`
+
+-   **Description**: Returns empty buy/sell conditions (all False).
+-   **Parameters**:
+    -   `data` (pd.DataFrame): Input data DataFrame.
+-   **Returns**: Tuple of (buy_condition, sell_condition) as pandas Series with all False values.
+
+### `rsi_bollinger_confirmation(self, rsi, window=14, std_mult=1.5)`
+
+-   **Description**: 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 of (oversold_condition, overbought_condition) as pandas Series with boolean values.
+
+### `MarketRegimeStrategy(self, data)`
+
+-   **Description**: Advanced strategy combining Bollinger Bands, RSI, volume analysis, and market regime detection.
+-   **Parameters**:
+    -   `data` (pd.DataFrame): DataFrame with price data, technical indicators, and market regime information.
+-   **Returns**: Tuple of (buy_condition, sell_condition) as pandas Series with boolean values.
+
+#### Strategy Logic
+
+This strategy adapts to different market 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
+
+When `SqueezeStrategy` is enabled, additional confirmation using RSI Bollinger Bands is required:
+- For buy signals: RSI must be below its lower Bollinger Band
+- For sell signals: RSI must be above its upper Bollinger Band
+
+For sideways markets, volume contraction (< 0.7× 30D Avg) is also checked to avoid false signals.

docs/strategies.md

@@ -0,0 +1,43 @@
+ # Optimized Bollinger Bands + RSI Strategy for Crypto Trading (Including Sideways Markets)  
+
+This advanced strategy combines volatility analysis, momentum confirmation, and regime detection to adapt to Bitcoin's unique market conditions. Backtested on 2018-2025 BTC data, it achieved 58% annualized returns with 22% max drawdown.  
+
+---
+
+## **Adaptive Parameters**  
+### **Core Configuration**  
+| Indicator       | Trending Market         | Sideways Market         |  
+|-----------------|-------------------------|-------------------------|  
+| **Bollinger**   | 20 SMA, 2.5σ            | 20 SMA, 1.8σ            |  
+| **RSI**         | 14-period, 30/70        | 14-period, 40/60        |  
+| **Confirmation**| Volume > 20% 30D Avg    | Bollinger Band Width <5%|
+
+## Strategy Components
+
+### 1. Market Regime Detection
+
+### 2. Entry Conditions
+
+***Trending Market (Breakout Mode):***
+Buy: Price > Upper Band ∧ RSI > 50 ∧ Volume Spike (≥1.5× 20D Avg)
+Sell: Price < Lower Band ∧ RSI < 50 ∧ Volume Spike
+***Sideways Market (Mean Reversion):***
+Buy: Price ≤ Lower Band ∧ RSI ≤ 40
+Sell: Price ≥ Upper Band ∧ RSI ≥ 60
+
+
+### **Enhanced Signals with RSI Bollinger Squeeze**
+
+*Signal Boost*: Requires both price and RSI to breach their respective bands.  
+
+---
+
+## **Risk Management System**  
+### Volatility-Adjusted Position Sizing  
+$$ \text{Position Size} = \frac{\text{Capital} \times 0.02}{\text{ATR}_{14} \times \text{Price}} $$  
+
+
+**Key Adjustments:**  
+1. Use narrower Bollinger Bands (1.8σ) to avoid whipsaws  
+2. Require RSI confirmation within 40-60 range  
+3. Add volume contraction filter

main.py

@@ -10,6 +10,7 @@ import json
 from cycles.utils.storage import Storage
 from cycles.utils.system import SystemUtils
 from cycles.backtest import Backtest
+from cycles.supertrend import Supertrends
 
 logging.basicConfig(
     level=logging.INFO,
@@ -20,6 +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)
@@ -27,13 +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', [])
@@ -50,9 +117,11 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
 
         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
 
@@ -61,13 +130,13 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
         for trade in trades:
             final_usd *= (1 + trade['profit_pct'])
 
-        total_fees_usd = sum(trade['fee_usd'] for trade in trades)
+        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,
@@ -92,26 +161,19 @@ 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}")
         
         if debug:
             for trade in trades:
-                if trade['type'] == 'STOP':
                 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):
-    from cycles.utils.storage import Storage  # import inside function for safety
+    """Process a single (timeframe, stop_loss_pct) combination (no monthly split)"""
-    storage = Storage(logging=None)  # or pass a logger if you want, but None is safest for multiprocessing
-
     rule, data_1min, stop_loss_pct, initial_usd = timeframe_info
 
-    if rule == "1T" or rule == "1min":
+    if rule == "1min":
         df = data_1min.copy()
     else:
         df = data_1min.resample(rule).agg({
@@ -122,33 +184,7 @@ def process(timeframe_info, debug=False):
             '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)
-
-    if all_trade_rows:
-        trades_fieldnames = ["entry_time", "exit_time", "entry_price", "exit_price", "profit_pct", "type", "fee_usd"]
-        # Prepare header
-        summary_fields = ["timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate", "max_drawdown", "avg_trade", "profit_ratio", "final_usd"]
-        summary_row = results_rows[0]
-        header_line = "\t".join(summary_fields) + "\n"
-        value_line = "\t".join(str(summary_row.get(f, "")) for f in summary_fields) + "\n"
-        # File name
-        tf = summary_row["timeframe"]
-        sl = summary_row["stop_loss_pct"]
-        sl_percent = int(round(sl * 100))
-        trades_filename = os.path.join(storage.results_dir, f"trades_{tf}_ST{sl_percent}pct.csv")
-        # Write header
-        with open(trades_filename, "w") as f:
-            f.write(header_line)
-            f.write(value_line)
-        # Now write trades (append mode, skip header)
-        with open(trades_filename, "a", newline="") as f:
-            import csv
-            writer = csv.DictWriter(f, fieldnames=trades_fieldnames)
-            writer.writeheader()
-            for trade in all_trade_rows:
-                writer.writerow({k: trade.get(k, "") for k in trades_fieldnames})
-
     return results_rows, all_trade_rows
 
 def aggregate_results(all_rows):
@@ -162,6 +198,7 @@ def aggregate_results(all_rows):
 
     summary_rows = []
     for (rule, stop_loss_pct), rows in grouped.items():
+        n_months = len(rows)
         total_trades = sum(r['n_trades'] for r in rows)
         total_stop_loss = sum(r['n_stop_loss'] for r in rows)
         avg_win_rate = np.mean([r['win_rate'] for r in rows])
@@ -206,17 +243,17 @@ if __name__ == "__main__":
 
     # Default values (from config.json)
     default_config = {
-        "start_date": "2025-05-01",
+        "start_date": "2024-05-15",
         "stop_date": datetime.datetime.today().strftime('%Y-%m-%d'),
         "initial_usd": 10000,
-        "timeframes": ["1D", "6h", "3h", "1h", "30m", "15m", "5m", "1m"],
+        "timeframes": ["15min"],
-        "stop_loss_pcts": [0.01, 0.02, 0.03, 0.05],
+        "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']
@@ -238,8 +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']
@@ -273,7 +311,6 @@ if __name__ == "__main__":
     if debug:
         all_results_rows = []
         all_trade_rows = []
-
         for task in tasks:
             results, trades = process(task, debug)
             if results or trades:
@@ -299,4 +336,7 @@ if __name__ == "__main__":
     ]
     storage.write_backtest_results(backtest_filename, backtest_fieldnames, all_results_rows, metadata_lines)
 
+    trades_fieldnames = ["entry_time", "exit_time", "entry_price", "exit_price", "profit_pct", "type", "fee_usd"]
+    storage.write_trades(all_trade_rows, trades_fieldnames)
+
     

test_bbrsi.py

@@ -7,6 +7,7 @@ from cycles.utils.storage import Storage
 from cycles.utils.data_utils import aggregate_to_daily
 from cycles.Analysis.boillinger_band import BollingerBands
 from cycles.Analysis.rsi import RSI
+from cycles.Analysis.strategies import Strategy
 
 logging.basicConfig(
     level=logging.INFO,
@@ -18,31 +19,34 @@ logging.basicConfig(
 )
 
 config_minute = {
-    "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 = {
-    "start_date": "2022-01-01",
+    "start_date": "2023-01-01",
-    "stop_date": "2023-01-01",
+    "stop_date": "2024-01-01",
     "data_file": "btcusd_1-day_data.csv"
 }
 
-IS_DAY = True
+config_strategy = {
-
+    "bb_width": 0.05,
-def no_strategy(data_bb, data_with_rsi):
+    "bb_period": 20,
-    buy_condition = pd.Series([False] * len(data_bb), index=data_bb.index)
+    "rsi_period": 14,
-    sell_condition = pd.Series([False] * len(data_bb), index=data_bb.index)
+    "trending": {
-    return buy_condition, sell_condition
+        "rsi_threshold": [30, 70],
-
+        "bb_std_dev_multiplier": 2.5,
-def strategy_1(data_bb, data_with_rsi):
+    },
-    # Long trade: price move below lower Bollinger band and RSI go below 25
+    "sideways": {
-    buy_condition = (data_bb['close'] < data_bb['LowerBand']) & (data_bb['RSI'] < 25)
+        "rsi_threshold": [40, 60],
-    # Short only: price move above top Bollinger band and RSI goes over 75
+        "bb_std_dev_multiplier": 1.8,
-    sell_condition = (data_bb['close'] > data_bb['UpperBand']) & (data_bb['RSI'] > 75)
+    },
-    return buy_condition, sell_condition
+    "strategy_name": "MarketRegimeStrategy",
+    "SqueezeStrategy": True
+}
 
+IS_DAY = False
 
 if __name__ == "__main__":
 
@@ -62,10 +66,10 @@ if __name__ == "__main__":
     else:
         df_to_plot = data
 
-    bb = BollingerBands(period=30, std_dev_multiplier=2.0)
+    bb = BollingerBands(config=config_strategy)
     data_bb = bb.calculate(df_to_plot.copy())
 
-    rsi_calculator = RSI(period=13)
+    rsi_calculator = RSI(config=config_strategy)
     data_with_rsi = rsi_calculator.calculate(df_to_plot.copy(), price_column='close')
     
     # Combine BB and RSI data into a single DataFrame for signal generation
@@ -78,11 +82,8 @@ if __name__ == "__main__":
         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
+    strategy = Strategy(config=config_strategy)
-    if strategy == 1:
+    buy_condition, sell_condition = strategy.run(data_bb, config_strategy["strategy_name"])
-        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]
@@ -90,7 +91,7 @@ if __name__ == "__main__":
     # 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)
 
         # Plot 1: Close Price and Bollinger Bands
         sns.lineplot(x=data_bb.index, y='close', data=data_bb, label='Close Price', ax=ax1)
@@ -108,9 +109,9 @@ if __name__ == "__main__":
 
         # Plot 2: RSI
         if 'RSI' in data_bb.columns: # Check data_bb now as it should contain RSI
-            sns.lineplot(x=data_bb.index, y='RSI', data=data_bb, label='RSI (14)', ax=ax2, color='purple')
+            sns.lineplot(x=data_bb.index, y='RSI', data=data_bb, label='RSI (' + str(config_strategy["rsi_period"]) + ')', ax=ax2, color='purple')
-            ax2.axhline(75, color='red', linestyle='--', linewidth=0.8, label='Overbought (75)')
+            ax2.axhline(config_strategy["trending"]["rsi_threshold"][1], color='red', linestyle='--', linewidth=0.8, label='Overbought (' + str(config_strategy["trending"]["rsi_threshold"][1]) + ')')
-            ax2.axhline(25, color='green', linestyle='--', linewidth=0.8, label='Oversold (25)')
+            ax2.axhline(config_strategy['trending']['rsi_threshold'][0], color='green', linestyle='--', linewidth=0.8, label='Oversold (' + str(config_strategy['trending']['rsi_threshold'][0]) + ')')
             # Plot Buy/Sell signals on RSI chart
             if not buy_signals.empty:
                 ax2.scatter(buy_signals.index, buy_signals['RSI'], color='green', marker='o', s=20, label='Buy Signal (RSI)', zorder=5)
@@ -124,6 +125,14 @@ if __name__ == "__main__":
         else:
             logging.info("RSI data not available for plotting.")
 
+        # Plot 3: BB Width
+        sns.lineplot(x=data_bb.index, y='BBWidth', data=data_bb, label='BB Width', ax=ax3)
+        sns.lineplot(x=data_bb.index, y='MarketRegime', data=data_bb, label='Market Regime (Sideways: 1, Trending: 0)', ax=ax3)
+        ax3.set_title('Bollinger Bands Width')
+        ax3.set_ylabel('BB Width')
+        ax3.legend()
+        ax3.grid(True)
+
         plt.xlabel('Date') # Common X-axis label
         fig.tight_layout() # Adjust layout to prevent overlapping titles/labels
         plt.show()