progress print

- Updated the method for determining the start index of the current trade to directly use the DataFrame index, enhancing clarity and performance.
- Removed the deprecated get_current_min1_end_idx method to streamline the codebase.

.gitignore

@@ -1,4 +1,7 @@
 # ---> Python
+*.json
+*.csv
+*.png
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

.python-version

@@ -0,0 +1 @@
+3.10

Dockerfile

@@ -0,0 +1,44 @@
+# Use the base image with CUDA and PyTorch
+FROM kom4cr0/cuda11.7-pytorch1.13-mamba1.1.1:1.1.1
+
+# Install NVIDIA Container Toolkit (necessary for GPU support)
+RUN apt-get update && apt-get install -y \
+    nvidia-container-runtime \
+    python3 \
+    python3-pip \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install necessary dependencies and configure NVIDIA repository
+RUN apt-get update && apt-get install -y \
+    curl \
+    gnupg \
+    lsb-release \
+    sudo \
+    && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
+    && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
+        sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
+        tee /etc/apt/sources.list.d/nvidia-container-toolkit.list \
+    && sed -i -e '/experimental/ s/^#//g' /etc/apt/sources.list.d/nvidia-container-toolkit.list \
+    && apt-get update
+
+# Install NVIDIA Container Toolkit
+RUN apt-get install -y nvidia-container-toolkit
+
+# Set the environment variables for CUDA
+ENV PATH=/usr/local/cuda-11.7/bin:$PATH
+ENV LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64:$LD_LIBRARY_PATH
+
+# Set the runtime for GPU (requires NVIDIA runtime to be installed on the host machine)
+ENV NVIDIA_VISIBLE_DEVICES=all
+ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
+
+# Set working directory to /projects
+WORKDIR /project
+
+# Install necessary Python dependencies
+# Uncomment and modify the next lines as per your project requirements
+COPY requirements.txt requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Run your Python script
+CMD ["python3", "main.py"]

cycle_detector.py

@@ -1,248 +0,0 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.signal import argrelextrema
-
-class CycleDetector:
-    def __init__(self, data, timeframe='daily'):
-        """
-        Initialize the CycleDetector with price data.
-        
-        Parameters:
-        - data: DataFrame with at least 'date' or 'datetime' and 'close' columns
-        - timeframe: 'daily', 'weekly', or 'monthly'
-        """
-        self.data = data.copy()
-        self.timeframe = timeframe
-        
-        # Ensure we have a consistent date column name
-        if 'datetime' in self.data.columns and 'date' not in self.data.columns:
-            self.data.rename(columns={'datetime': 'date'}, inplace=True)
-        
-        # Convert data to specified timeframe if needed
-        if timeframe == 'weekly' and 'date' in self.data.columns:
-            self.data = self._convert_data(self.data, 'W')
-        elif timeframe == 'monthly' and 'date' in self.data.columns:
-            self.data = self._convert_data(self.data, 'M')
-            
-        # Add columns for local minima and maxima detection
-        self._add_swing_points()
-    
-    def _convert_data(self, data, timeframe):
-        """Convert daily data to 'timeframe' timeframe."""
-        data['date'] = pd.to_datetime(data['date'])
-        data.set_index('date', inplace=True)
-        weekly = data.resample(timeframe).agg({
-            'open': 'first',
-            'high': 'max',
-            'low': 'min',
-            'close': 'last',
-            'volume': 'sum'
-        })
-        return weekly.reset_index()
-    
-    
-    def _add_swing_points(self, window=5):
-        """
-        Identify swing points (local minima and maxima).
-        
-        Parameters:
-        - window: The window size for local minima/maxima detection
-        """
-        # Set the index to make calculations easier
-        if 'date' in self.data.columns:
-            self.data.set_index('date', inplace=True)
-        
-        # Detect local minima (swing lows)
-        min_idx = argrelextrema(self.data['low'].values, np.less, order=window)[0]
-        self.data['swing_low'] = False
-        self.data.iloc[min_idx, self.data.columns.get_loc('swing_low')] = True
-        
-        # Detect local maxima (swing highs)
-        max_idx = argrelextrema(self.data['high'].values, np.greater, order=window)[0]
-        self.data['swing_high'] = False
-        self.data.iloc[max_idx, self.data.columns.get_loc('swing_high')] = True
-        
-        # Reset index
-        self.data.reset_index(inplace=True)
-    
-    def find_cycle_lows(self):
-        """Find all swing lows which represent cycle lows."""
-        swing_low_dates = self.data[self.data['swing_low']]['date'].values
-        return swing_low_dates
-    
-    def calculate_cycle_lengths(self):
-        """Calculate the lengths of each cycle between consecutive lows."""
-        swing_low_indices = np.where(self.data['swing_low'])[0]
-        cycle_lengths = np.diff(swing_low_indices)
-        return cycle_lengths
-    
-    def get_average_cycle_length(self):
-        """Calculate the average cycle length."""
-        cycle_lengths = self.calculate_cycle_lengths()
-        if len(cycle_lengths) > 0:
-            return np.mean(cycle_lengths)
-        return None
-    
-    def get_cycle_window(self, tolerance=0.10):
-        """
-        Get the cycle window with the specified tolerance.
-        
-        Parameters:
-        - tolerance: The tolerance as a percentage (default: 10%)
-        
-        Returns:
-        - tuple: (min_cycle_length, avg_cycle_length, max_cycle_length)
-        """
-        avg_length = self.get_average_cycle_length()
-        if avg_length is not None:
-            min_length = avg_length * (1 - tolerance)
-            max_length = avg_length * (1 + tolerance)
-            return (min_length, avg_length, max_length)
-        return None
-    
-    def detect_two_drives_pattern(self, lookback=10):
-        """
-        Detect 2-drives pattern: a swing low, counter trend bounce, and a lower low.
-        
-        Parameters:
-        - lookback: Number of periods to look back
-        
-        Returns:
-        - list: Indices where 2-drives patterns are detected
-        """
-        patterns = []
-        
-        for i in range(lookback, len(self.data) - 1):
-            if not self.data.iloc[i]['swing_low']:
-                continue
-                
-            # Get the segment of data to check for pattern
-            segment = self.data.iloc[i-lookback:i+1]
-            swing_lows = segment[segment['swing_low']]['low'].values
-            
-            if len(swing_lows) >= 2 and swing_lows[-1] < swing_lows[-2]:
-                # Check if there was a bounce between the two lows
-                between_lows = segment.iloc[-len(swing_lows):-1]
-                if len(between_lows) > 0 and max(between_lows['high']) > swing_lows[-2]:
-                    patterns.append(i)
-        
-        return patterns
-    
-    def detect_v_shaped_lows(self, window=5, threshold=0.02):
-        """
-        Detect V-shaped cycle lows (sharp decline followed by sharp rise).
-        
-        Parameters:
-        - window: Window to look for sharp price changes
-        - threshold: Percentage change threshold to consider 'sharp'
-        
-        Returns:
-        - list: Indices where V-shaped patterns are detected
-        """
-        patterns = []
-        
-        # Find all swing lows
-        swing_low_indices = np.where(self.data['swing_low'])[0]
-        
-        for idx in swing_low_indices:
-            # Need enough data points before and after
-            if idx < window or idx + window >= len(self.data):
-                continue
-            
-            # Get the low price at this swing low
-            low_price = self.data.iloc[idx]['low']
-            
-            # Check for sharp decline before low (at least window bars before)
-            before_segment = self.data.iloc[max(0, idx-window):idx]
-            if len(before_segment) > 0:
-                max_before = before_segment['high'].max()
-                decline = (max_before - low_price) / max_before
-                
-                # Check for sharp rise after low (at least window bars after)
-                after_segment = self.data.iloc[idx+1:min(len(self.data), idx+window+1)]
-                if len(after_segment) > 0:
-                    max_after = after_segment['high'].max()
-                    rise = (max_after - low_price) / low_price
-                    
-                    # Both decline and rise must exceed threshold to be considered V-shaped
-                    if decline > threshold and rise > threshold:
-                        patterns.append(idx)
-        
-        return patterns
-    
-    def plot_cycles(self, pattern_detection=None, title_suffix=''):
-        """
-        Plot the price data with cycle lows and detected patterns.
-        
-        Parameters:
-        - pattern_detection: 'two_drives', 'v_shape', or None
-        - title_suffix: Optional suffix for the plot title
-        """
-        plt.figure(figsize=(14, 7))
-        
-        # Determine the date column name (could be 'date' or 'datetime')
-        date_col = 'date' if 'date' in self.data.columns else 'datetime'
-        
-        # Plot price data
-        plt.plot(self.data[date_col], self.data['close'], label='Close Price')
-        
-        # Calculate a consistent vertical position for indicators based on price range
-        price_range = self.data['close'].max() - self.data['close'].min()
-        indicator_offset = price_range * 0.01  # 1% of price range
-        
-        # Plot cycle lows (now at a fixed offset below the low price)
-        swing_lows = self.data[self.data['swing_low']]
-        plt.scatter(swing_lows[date_col], swing_lows['low'] - indicator_offset, 
-                   color='green', marker='^', s=100, label='Cycle Lows')
-        
-        # Plot specific patterns if requested
-        if 'two_drives' in pattern_detection:
-            pattern_indices = self.detect_two_drives_pattern()
-            if pattern_indices:
-                patterns = self.data.iloc[pattern_indices]
-                plt.scatter(patterns[date_col], patterns['low'] - indicator_offset * 2, 
-                           color='red', marker='o', s=150, label='Two Drives Pattern')
-        
-        elif 'v_shape' in pattern_detection:
-            pattern_indices = self.detect_v_shaped_lows()
-            if pattern_indices:
-                patterns = self.data.iloc[pattern_indices]
-                plt.scatter(patterns[date_col], patterns['low'] - indicator_offset * 2, 
-                           color='purple', marker='o', s=150, label='V-Shape Pattern')
-        
-        # Add cycle lengths and averages
-        cycle_lengths = self.calculate_cycle_lengths()
-        avg_cycle = self.get_average_cycle_length()
-        cycle_window = self.get_cycle_window()
-        
-        window_text = ""
-        if cycle_window:
-            window_text = f"Tolerance Window: [{cycle_window[0]:.2f} - {cycle_window[2]:.2f}]"
-        
-        plt.title(f"Detected Cycles - {self.timeframe.capitalize()} Timeframe {title_suffix}\n"
-                 f"Average Cycle Length: {avg_cycle:.2f} periods, {window_text}")
-        
-        plt.legend()
-        plt.grid(True)
-        plt.show()
-
-# Usage example:
-# 1. Load your data
-# data = pd.read_csv('your_price_data.csv')
-
-# 2. Create cycle detector instances for different timeframes
-# weekly_detector = CycleDetector(data, timeframe='weekly')
-# daily_detector = CycleDetector(data, timeframe='daily')
-
-# 3. Analyze cycles
-# weekly_cycle_length = weekly_detector.get_average_cycle_length()
-# daily_cycle_length = daily_detector.get_average_cycle_length()
-
-# 4. Detect patterns
-# two_drives = weekly_detector.detect_two_drives_pattern()
-# v_shapes = daily_detector.detect_v_shaped_lows()
-
-# 5. Visualize
-# weekly_detector.plot_cycles(pattern_detection='two_drives')
-# daily_detector.plot_cycles(pattern_detection='v_shape')

cycles/Analysis/boillinger_band.py

@@ -0,0 +1,50 @@
+import pandas as pd
+
+class BollingerBands:
+    """
+    Calculates Bollinger Bands for given financial data.
+    """
+    def __init__(self, period: int = 20, std_dev_multiplier: float = 2.0):
+        """
+        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.
+        """
+        if period <= 0:
+            raise ValueError("Period must be a positive integer.")
+        if std_dev_multiplier <= 0:
+            raise ValueError("Standard deviation multiplier must be positive.")
+            
+        self.period = period
+        self.std_dev_multiplier = std_dev_multiplier
+
+    def calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame:
+        """
+        Calculates Bollinger Bands and adds them to the DataFrame.
+
+        Args:
+            data_df (pd.DataFrame): DataFrame with price data. Must include the price_column.
+            price_column (str): The name of the column containing the price data (e.g., 'close').
+
+        Returns:
+            pd.DataFrame: The original DataFrame with added columns:
+                          'SMA' (Simple Moving Average),
+                          'UpperBand',
+                          'LowerBand'.
+        """
+        if price_column not in data_df.columns:
+            raise ValueError(f"Price column '{price_column}' not found in DataFrame.")
+
+        # Calculate SMA
+        data_df['SMA'] = data_df[price_column].rolling(window=self.period).mean()
+
+        # Calculate Standard Deviation
+        std_dev = data_df[price_column].rolling(window=self.period).std()
+
+        # Calculate Upper and Lower Bands
+        data_df['UpperBand'] = data_df['SMA'] + (self.std_dev_multiplier * std_dev)
+        data_df['LowerBand'] = data_df['SMA'] - (self.std_dev_multiplier * std_dev)
+
+        return data_df

cycles/Analysis/rsi.py

@@ -0,0 +1,109 @@
+import pandas as pd
+import numpy as np
+
+class RSI:
+    """
+    A class to calculate the Relative Strength Index (RSI).
+    """
+    def __init__(self, period: int = 14):
+        """
+        Initializes the RSI calculator.
+
+        Args:
+            period (int): The period for RSI calculation. Default is 14.
+                          Must be a positive integer.
+        """
+        if not isinstance(period, int) or period <= 0:
+            raise ValueError("Period must be a positive integer.")
+        self.period = 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.
+
+        Args:
+            data_df (pd.DataFrame): DataFrame with historical price data.
+                                    Must contain the 'price_column'.
+            price_column (str): The name of the column containing price data.
+                                Default is 'close'.
+
+        Returns:
+            pd.DataFrame: The input DataFrame with an added 'RSI' column.
+                          Returns the original DataFrame with no 'RSI' column
+                          if the period is larger than the number of data points.
+        """
+        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:
+            print(f"Warning: Data length ({len(data_df)}) is less than RSI period ({self.period}). RSI will not be calculated.")
+            return data_df.copy()
+
+        df = data_df.copy()
+        delta = df[price_column].diff(1)
+
+        gain = delta.where(delta > 0, 0)
+        loss = -delta.where(delta < 0, 0) # Ensure loss is positive
+
+        # Calculate initial average gain and loss (SMA)
+        avg_gain = gain.rolling(window=self.period, min_periods=self.period).mean().iloc[self.period -1:self.period]
+        avg_loss = loss.rolling(window=self.period, min_periods=self.period).mean().iloc[self.period -1:self.period]
+
+
+        # 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

cycles/backtest.py

@@ -0,0 +1,230 @@
+import pandas as pd
+import numpy as np
+import time
+
+from cycles.supertrend import Supertrends
+from cycles.market_fees import MarketFees
+
+class Backtest:
+    @staticmethod
+    def run(min1_df, df, initial_usd, stop_loss_pct, 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.
+        
+        Parameters:
+        - min1_df: pandas DataFrame, 1-minute timeframe data for more accurate stop loss checking (optional)
+        - initial_usd: float, starting USD amount
+        - 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)
+        min1_timestamps = min1_df.index.values
+
+        last_print_time = time.time()
+        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]
+            price_close = _df['close'].iloc[i]
+            date = _df['timestamp'].iloc[i]
+            prev_mt = meta_trend_signal[i-1]
+            curr_mt = meta_trend_signal[i]
+            
+            # Check stop loss if in position
+            if position == 1:
+                stop_loss_result = Backtest.check_stop_loss(
+                    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
+            if balance > max_balance:
+                max_balance = balance
+            drawdown = (max_balance - balance) / 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:
+            exit_result = Backtest.handle_exit(coin, _df['close'].iloc[-1], entry_price, entry_time, _df['timestamp'].iloc[-1])
+            usd, coin, position, entry_price, trade_log_entry = exit_result
+            trade_log.append(trade_log_entry)
+
+        # Calculate statistics
+        final_balance = 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
+        max_drawdown = max(drawdowns) if drawdowns else 0
+        avg_trade = np.mean([t['exit']/t['entry']-1 for t in trade_log if t['exit'] is not None]) if trade_log else 0
+
+        trades = []
+        total_fees_usd = 0.0
+        for trade in trade_log:
+            if trade['exit'] is not None:
+                profit_pct = (trade['exit'] - trade['entry']) / trade['entry']
+            else:
+                profit_pct = 0.0
+            trades.append({
+                'entry_time': trade['entry_time'],
+                'exit_time': trade['exit_time'],
+                'entry': trade['entry'],
+                'exit': trade['exit'],
+                'profit_pct': profit_pct,
+                'type': trade.get('type', 'SELL'),
+                'fee_usd': trade.get('fee_usd')
+            })
+            fee_usd = trade.get('fee_usd')
+            total_fees_usd += fee_usd
+
+        results = {
+            "initial_usd": initial_usd,
+            "final_usd": final_balance,
+            "n_trades": n_trades,
+            "win_rate": win_rate,
+            "max_drawdown": max_drawdown,
+            "avg_trade": avg_trade,
+            "trade_log": trade_log,
+            "trades": trades,
+            "total_fees_usd": total_fees_usd,
+        }
+        if n_trades > 0:
+            results["first_trade"] = {
+                "entry_time": trade_log[0]['entry_time'],
+                "entry": trade_log[0]['entry']
+            }
+            results["last_trade"] = {
+                "exit_time": trade_log[-1]['exit_time'],
+                "exit": trade_log[-1]['exit']
+            }
+        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):
+        stop_price = entry_price * (1 - stop_loss_pct)
+
+        if current_trade_min1_start_idx is None:
+            current_trade_min1_start_idx = min1_df.index[min1_df.index >= entry_time][0]
+        current_min1_end_idx = min1_df.index[min1_df.index <= date][-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,
+            'exit': None,
+            'entry_time': entry_time,
+            'exit_time': None,
+            'fee_usd': entry_fee
+        }
+        return coin, entry_price, entry_time, usd, position, trade_log_entry
+
+    @staticmethod
+    def handle_exit(coin, price_open, entry_price, entry_time, date):
+        btc_to_sell = coin
+        usd_gross = btc_to_sell * price_open
+        exit_fee = MarketFees.calculate_okx_taker_maker_fee(usd_gross, is_maker=False)
+        usd = usd_gross - exit_fee
+        trade_log_entry = {
+            'type': 'SELL',
+            'entry': entry_price,
+            'exit': price_open,
+            'entry_time': entry_time,
+            'exit_time': date,
+            'fee_usd': exit_fee
+        }
+        coin = 0
+        position = 0
+        entry_price = 0
+        return usd, coin, position, entry_price, trade_log_entry

cycles/market_fees.py

@@ -0,0 +1,7 @@
+import pandas as pd
+
+class MarketFees:
+    @staticmethod
+    def calculate_okx_taker_maker_fee(amount, is_maker=True):
+        fee_rate = 0.0008 if is_maker else 0.0010
+        return amount * fee_rate

cycles/supertrend.py

@@ -0,0 +1,185 @@
+import pandas as pd
+import numpy as np
+import logging
+from functools import lru_cache
+
+@lru_cache(maxsize=32)
+def cached_supertrend_calculation(period, multiplier, data_tuple):
+    high = np.array(data_tuple[0])
+    low = np.array(data_tuple[1])
+    close = np.array(data_tuple[2])
+    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])
+    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))
+    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)
+    trend = np.zeros_like(close)
+    final_upper[0] = upper_band[0]
+    final_lower[0] = lower_band[0]
+    if close[0] <= upper_band[0]:
+        supertrend[0] = upper_band[0]
+        trend[0] = -1
+    else:
+        supertrend[0] = lower_band[0]
+        trend[0] = 1
+    for i in range(1, len(close)):
+        if (upper_band[i] < final_upper[i-1]) or (close[i-1] > final_upper[i-1]):
+            final_upper[i] = upper_band[i]
+        else:
+            final_upper[i] = final_upper[i-1]
+        if (lower_band[i] > final_lower[i-1]) or (close[i-1] < final_lower[i-1]):
+            final_lower[i] = lower_band[i]
+        else:
+            final_lower[i] = final_lower[i-1]
+        if supertrend[i-1] == final_upper[i-1] and close[i] <= final_upper[i]:
+            supertrend[i] = final_upper[i]
+            trend[i] = -1
+        elif supertrend[i-1] == final_upper[i-1] and close[i] > final_upper[i]:
+            supertrend[i] = final_lower[i]
+            trend[i] = 1
+        elif supertrend[i-1] == final_lower[i-1] and close[i] >= final_lower[i]:
+            supertrend[i] = final_lower[i]
+            trend[i] = 1
+        elif supertrend[i-1] == final_lower[i-1] and close[i] < final_lower[i]:
+            supertrend[i] = final_upper[i]
+            trend[i] = -1
+    return {
+        'supertrend': supertrend,
+        'trend': trend,
+        'upper_band': final_upper,
+        'lower_band': final_lower
+    }
+
+def calculate_supertrend_external(data, period, multiplier):
+    high_tuple = tuple(data['high'])
+    low_tuple = tuple(data['low'])
+    close_tuple = tuple(data['close'])
+    return cached_supertrend_calculation(period, multiplier, (high_tuple, low_tuple, close_tuple))
+
+class Supertrends:
+    def __init__(self, data, verbose=False, display=False):
+        self.data = data
+        self.verbose = verbose
+        logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
+                           format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger('TrendDetectorSimple')
+        if not isinstance(self.data, pd.DataFrame):
+            if isinstance(self.data, list):
+                self.data = pd.DataFrame({'close': self.data})
+            else:
+                raise ValueError("Data must be a pandas DataFrame or a list")
+
+    def calculate_tr(self):
+        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]
+        for i in range(1, len(close)):
+            hl_range = high[i] - low[i]
+            hc_range = abs(high[i] - close[i-1])
+            lc_range = abs(low[i] - close[i-1])
+            tr[i] = max(hl_range, hc_range, lc_range)
+        return tr
+
+    def calculate_atr(self, period=14):
+        tr = self.calculate_tr()
+        atr = np.zeros_like(tr)
+        atr[0] = tr[0]
+        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):
+        """
+        Calculate SuperTrend indicator for the price data.
+        SuperTrend is a trend-following indicator that uses ATR to determine the trend direction.
+        Parameters:
+        - period: int, the period for the ATR calculation (default: 10)
+        - multiplier: float, the multiplier for the ATR (default: 3.0)
+        Returns:
+        - Dictionary containing SuperTrend values, trend direction, and upper/lower bands
+        """
+        df = self.data.copy()
+        high = df['high'].values
+        low = df['low'].values
+        close = df['close'].values
+        atr = self.calculate_atr(period)
+        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)
+        trend = np.zeros_like(close)
+        final_upper[0] = upper_band[0]
+        final_lower[0] = lower_band[0]
+        if close[0] <= upper_band[0]:
+            supertrend[0] = upper_band[0]
+            trend[0] = -1
+        else:
+            supertrend[0] = lower_band[0]
+            trend[0] = 1
+        for i in range(1, len(close)):
+            if (upper_band[i] < final_upper[i-1]) or (close[i-1] > final_upper[i-1]):
+                final_upper[i] = upper_band[i]
+            else:
+                final_upper[i] = final_upper[i-1]
+            if (lower_band[i] > final_lower[i-1]) or (close[i-1] < final_lower[i-1]):
+                final_lower[i] = lower_band[i]
+            else:
+                final_lower[i] = final_lower[i-1]
+            if supertrend[i-1] == final_upper[i-1] and close[i] <= final_upper[i]:
+                supertrend[i] = final_upper[i]
+                trend[i] = -1
+            elif supertrend[i-1] == final_upper[i-1] and close[i] > final_upper[i]:
+                supertrend[i] = final_lower[i]
+                trend[i] = 1
+            elif supertrend[i-1] == final_lower[i-1] and close[i] >= final_lower[i]:
+                supertrend[i] = final_lower[i]
+                trend[i] = 1
+            elif supertrend[i-1] == final_lower[i-1] and close[i] < final_lower[i]:
+                supertrend[i] = final_upper[i]
+                trend[i] = -1
+        supertrend_results = {
+            'supertrend': supertrend,
+            'trend': trend,
+            'upper_band': final_upper,
+            'lower_band': final_lower
+        }
+        return supertrend_results
+
+    def calculate_supertrend_indicators(self):
+        supertrend_params = [
+            {"period": 12, "multiplier": 3.0},
+            {"period": 10, "multiplier": 1.0},
+            {"period": 11, "multiplier": 2.0}
+        ]
+        results = []
+        for p in supertrend_params:
+            result = self.calculate_supertrend(period=p["period"], multiplier=p["multiplier"])
+            results.append({
+                "results": result,
+                "params": p
+            })
+        return results

cycles/utils/data_utils.py

@@ -0,0 +1,60 @@
+import pandas as pd
+
+def aggregate_to_daily(data_df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Aggregates time-series financial data to daily OHLCV format.
+
+    The input DataFrame is expected to have a DatetimeIndex.
+    'open' will be the first 'open' price of the day.
+    'close' will be the last 'close' price of the day.
+    'high' will be the maximum 'high' price of the day.
+    'low' will be the minimum 'low' price of the day.
+    'volume' (if present) will be the sum of volumes for the day.
+
+    Args:
+        data_df (pd.DataFrame): DataFrame with a DatetimeIndex and columns
+                                like 'open', 'high', 'low', 'close', and optionally 'volume'.
+                                Column names are expected to be lowercase.
+
+    Returns:
+        pd.DataFrame: DataFrame aggregated to daily OHLCV data.
+                      The index will be a DatetimeIndex with the time set to noon (12:00:00) for each day.
+                      Returns an empty DataFrame if no relevant OHLCV columns are found.
+    
+    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 = {}
+
+    # 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
+        # For now, returning an empty DataFrame with a message might be suitable for some cases
+        print("Warning: No standard OHLCV columns (open, high, low, close, volume) found for daily aggregation.")
+        return pd.DataFrame(index=pd.to_datetime([])) # Return empty DF with datetime index
+
+    # Resample to daily frequency and apply aggregation rules
+    daily_data = data_df.resample('D').agg(agg_rules)
+    
+    # Adjust timestamps to noon if data exists
+    if not daily_data.empty and isinstance(daily_data.index, pd.DatetimeIndex):
+        daily_data.index = daily_data.index + pd.Timedelta(hours=12)
+    
+    # Remove rows where all values are NaN (these are days with no trades in the original data)
+    daily_data.dropna(how='all', inplace=True)
+
+    return daily_data

cycles/utils/gsheets.py

@@ -0,0 +1,128 @@
+import threading
+import time
+import queue
+from google.oauth2.service_account import Credentials
+import gspread
+import math
+import numpy as np
+from collections import defaultdict
+
+
+class GSheetBatchPusher(threading.Thread):
+
+    def __init__(self, queue, timestamp, spreadsheet_name, interval=60, logging=None):
+        super().__init__(daemon=True)
+        self.queue = queue
+        self.timestamp = timestamp
+        self.spreadsheet_name = spreadsheet_name
+        self.interval = interval
+        self._stop_event = threading.Event()
+        self.logging = logging
+
+    def run(self):
+        while not self._stop_event.is_set():
+            self.push_all()
+            time.sleep(self.interval)
+        # Final push on stop
+        self.push_all()
+
+    def stop(self):
+        self._stop_event.set()
+
+    def push_all(self):
+        batch_results = []
+        batch_trades = []
+        while True:
+            try:
+                results, trades = self.queue.get_nowait()
+                batch_results.extend(results)
+                batch_trades.extend(trades)
+            except queue.Empty:
+                break
+            
+        if batch_results or batch_trades:
+            self.write_results_per_combination_gsheet(batch_results, batch_trades, self.timestamp, self.spreadsheet_name)
+
+
+    def write_results_per_combination_gsheet(self, results_rows, trade_rows, timestamp, spreadsheet_name="GlimBit Backtest Results"):
+        scopes = [
+            "https://www.googleapis.com/auth/spreadsheets",
+            "https://www.googleapis.com/auth/drive"
+        ]
+        creds = Credentials.from_service_account_file('credentials/service_account.json', scopes=scopes)
+        gc = gspread.authorize(creds)
+        sh = gc.open(spreadsheet_name)
+        
+        try:
+            worksheet = sh.worksheet("Results")
+        except gspread.exceptions.WorksheetNotFound:
+            worksheet = sh.add_worksheet(title="Results", rows="1000", cols="20")
+
+        # Clear the worksheet before writing new results
+        worksheet.clear()
+
+        # Updated fieldnames to match your data rows
+        fieldnames = [
+            "timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate",
+            "max_drawdown", "avg_trade", "profit_ratio", "initial_usd", "final_usd"
+        ]
+
+        def to_native(val):
+            if isinstance(val, (np.generic, np.ndarray)):
+                val = val.item()
+            if hasattr(val, 'isoformat'):
+                return val.isoformat()
+            # Handle inf, -inf, nan
+            if isinstance(val, float):
+                if math.isinf(val):
+                    return "∞" if val > 0 else "-∞"
+                if math.isnan(val):
+                    return ""
+            return val
+
+        # Write header if sheet is empty
+        if len(worksheet.get_all_values()) == 0:
+            worksheet.append_row(fieldnames)
+
+        for row in results_rows:
+            values = [to_native(row.get(field, "")) for field in fieldnames]
+            worksheet.append_row(values)
+        
+        trades_fieldnames = [
+            "entry_time", "exit_time", "entry_price", "exit_price", "profit_pct", "type"
+        ]
+        trades_by_combo = defaultdict(list)
+
+        for trade in trade_rows:
+            tf = trade.get("timeframe")
+            sl = trade.get("stop_loss_pct")
+            trades_by_combo[(tf, sl)].append(trade)
+        
+        for (tf, sl), trades in trades_by_combo.items():
+            sl_percent = int(round(sl * 100))
+            sheet_name = f"Trades_{tf}_ST{sl_percent}%"
+
+            try:
+                trades_ws = sh.worksheet(sheet_name)
+            except gspread.exceptions.WorksheetNotFound:
+                trades_ws = sh.add_worksheet(title=sheet_name, rows="1000", cols="20")
+            
+            # Clear the trades worksheet before writing new trades
+            trades_ws.clear()
+
+            if len(trades_ws.get_all_values()) == 0:
+                trades_ws.append_row(trades_fieldnames)
+            
+            for trade in trades:
+                trade_row = [to_native(trade.get(field, "")) for field in trades_fieldnames]
+                try:
+                    trades_ws.append_row(trade_row)
+                except gspread.exceptions.APIError as e:
+                    if '429' in str(e):
+                        if self.logging is not None:
+                            self.logging.warning(f"Google Sheets API quota exceeded (429). Please wait one minute. Will retry on next batch push. Sheet: {sheet_name}")
+                        # Re-queue the failed batch for retry
+                        self.queue.put((results_rows, trade_rows))
+                        return  # Stop pushing for this batch, will retry next interval
+                    else:
+                        raise

cycles/utils/storage.py

@@ -0,0 +1,215 @@
+import os
+import json
+import pandas as pd
+import csv
+from collections import defaultdict
+
+RESULTS_DIR = "results"
+DATA_DIR = "data"
+
+class Storage:
+
+    """Storage class for storing and loading results and data"""
+    def __init__(self, logging=None, results_dir=RESULTS_DIR, data_dir=DATA_DIR):
+
+        self.results_dir = results_dir
+        self.data_dir = data_dir
+        self.logging = logging
+
+        # Create directories if they don't exist
+        os.makedirs(self.results_dir, exist_ok=True)
+        os.makedirs(self.data_dir, exist_ok=True)
+
+    def load_data(self, file_path, start_date, stop_date):
+        """Load data with optimized dtypes and filtering, supporting CSV and JSON input
+        Args:
+            file_path: path to the data file
+            start_date: start date
+            stop_date: stop date
+        Returns:
+            pandas DataFrame
+        """
+        # Determine file type
+        _, ext = os.path.splitext(file_path)
+        ext = ext.lower()
+        try:
+            if ext == ".json":
+                with open(os.path.join(self.data_dir, file_path), 'r') as f:
+                    raw = json.load(f)
+                data = pd.DataFrame(raw["Data"])
+                # Convert columns to lowercase
+                data.columns = data.columns.str.lower()
+                # Convert timestamp to datetime
+                data["timestamp"] = pd.to_datetime(data["timestamp"], unit="s")
+                # Filter by date range
+                data = data[(data["timestamp"] >= start_date) & (data["timestamp"] <= stop_date)]
+                if self.logging is not None:
+                    self.logging.info(f"Data loaded from {file_path} for date range {start_date} to {stop_date}")
+                return data.set_index("timestamp")
+            else:
+                # Define optimized dtypes
+                dtypes = {
+                    'Open': 'float32',
+                    'High': 'float32', 
+                    'Low': 'float32',
+                    'Close': 'float32',
+                    'Volume': 'float32'
+                }
+                # Read data with original capitalized column names
+                data = pd.read_csv(os.path.join(self.data_dir, file_path), dtype=dtypes)
+
+
+                # Convert timestamp to datetime
+                if 'Timestamp' in data.columns:
+                    data['Timestamp'] = pd.to_datetime(data['Timestamp'], unit='s')
+                    # Filter by date range
+                    data = data[(data['Timestamp'] >= start_date) & (data['Timestamp'] <= stop_date)]
+                    # Now convert column names to lowercase
+                    data.columns = data.columns.str.lower()
+                    if self.logging is not None:
+                        self.logging.info(f"Data loaded from {file_path} for date range {start_date} to {stop_date}")
+                    return data.set_index('timestamp')
+                else: # Attempt to use the first column if 'Timestamp' is not present
+                    data.rename(columns={data.columns[0]: 'timestamp'}, inplace=True)
+                    data['timestamp'] = pd.to_datetime(data['timestamp'], unit='s')
+                    data = data[(data['timestamp'] >= start_date) & (data['timestamp'] <= stop_date)]
+                    data.columns = data.columns.str.lower() # Ensure all other columns are lower
+                    if self.logging is not None:
+                        self.logging.info(f"Data loaded from {file_path} (using first column as timestamp) for date range {start_date} to {stop_date}")
+                    return data.set_index('timestamp')
+        except Exception as e:
+            if self.logging is not None:
+                self.logging.error(f"Error loading data from {file_path}: {e}")
+            # Return an empty DataFrame with a DatetimeIndex
+            return pd.DataFrame(index=pd.to_datetime([]))
+
+    def save_data(self, data: pd.DataFrame, file_path: str):
+        """Save processed data to a CSV file.
+        If the DataFrame has a DatetimeIndex, it's converted to float Unix timestamps
+        (seconds since epoch) before saving. The index is saved as a column named 'timestamp'.
+
+        Args:
+            data (pd.DataFrame): data to save.
+            file_path (str): path to the data file relative to the data_dir.
+        """
+        data_to_save = data.copy()
+
+        if isinstance(data_to_save.index, pd.DatetimeIndex):
+            # Convert DatetimeIndex to Unix timestamp (float seconds since epoch)
+            # and make it a column named 'timestamp'.
+            data_to_save['timestamp'] = data_to_save.index.astype('int64') / 1e9
+            # Reset index so 'timestamp' column is saved and old DatetimeIndex is not saved as a column.
+            # We want the 'timestamp' column to be the first one.
+            data_to_save.reset_index(drop=True, inplace=True)
+            # Ensure 'timestamp' is the first column if other columns exist
+            if 'timestamp' in data_to_save.columns and len(data_to_save.columns) > 1:
+                cols = ['timestamp'] + [col for col in data_to_save.columns if col != 'timestamp']
+                data_to_save = data_to_save[cols]
+        elif pd.api.types.is_numeric_dtype(data_to_save.index.dtype):
+            # If index is already numeric (e.g. float Unix timestamps from a previous save/load cycle),
+            # make it a column named 'timestamp'.
+            data_to_save['timestamp'] = data_to_save.index
+            data_to_save.reset_index(drop=True, inplace=True)
+            if 'timestamp' in data_to_save.columns and len(data_to_save.columns) > 1:
+                cols = ['timestamp'] + [col for col in data_to_save.columns if col != 'timestamp']
+                data_to_save = data_to_save[cols]
+        else:
+            # For other index types, or if no index that we want to specifically handle,
+            # save with the current index. pandas to_csv will handle it.
+            # This branch might be removed if we strictly expect either DatetimeIndex or a numeric one from previous save.
+            pass # data_to_save remains as is, to_csv will write its index if index=True
+
+        # Save to CSV, ensuring the 'timestamp' column (if created) is written, and not the DataFrame's active index.
+        full_path = os.path.join(self.data_dir, file_path)
+        data_to_save.to_csv(full_path, index=False) # index=False because timestamp is now a column
+        if self.logging is not None:
+            self.logging.info(f"Data saved to {full_path} with Unix timestamp column.")
+    
+    
+    def format_row(self, row):
+        """Format a row for a combined results CSV file
+        Args:
+            row: row to format
+        Returns:
+            formatted row
+        """
+
+        return {
+            "timeframe": row["timeframe"],
+            "stop_loss_pct": f"{row['stop_loss_pct']*100:.2f}%",
+            "n_trades": row["n_trades"],
+            "n_stop_loss": row["n_stop_loss"],
+            "win_rate": f"{row['win_rate']*100:.2f}%",
+            "max_drawdown": f"{row['max_drawdown']*100:.2f}%",
+            "avg_trade": f"{row['avg_trade']*100:.2f}%",
+            "profit_ratio": f"{row['profit_ratio']*100:.2f}%",
+            "final_usd": f"{row['final_usd']:.2f}",
+            "total_fees_usd": f"{row['total_fees_usd']:.2f}",
+        }
+    
+    def write_results_chunk(self, filename, fieldnames, rows, write_header=False, initial_usd=None):
+        """Write a chunk of results to a CSV file
+        Args:
+            filename: filename to write to
+            fieldnames: list of fieldnames
+            rows: list of rows
+            write_header: whether to write the header
+            initial_usd: initial USD
+        """
+        mode = 'w' if write_header else 'a'
+        
+        with open(filename, mode, newline="") as csvfile:
+            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+            if write_header:
+                csvfile.write(f"# initial_usd: {initial_usd}\n")
+                writer.writeheader()
+            
+            for row in rows:
+                # Only keep keys that are in fieldnames
+                filtered_row = {k: v for k, v in row.items() if k in fieldnames}
+                writer.writerow(filtered_row)
+    
+    def write_backtest_results(self, filename, fieldnames, rows, metadata_lines=None):
+        """Write a combined results to a CSV file
+        Args:
+            filename: filename to write to
+            fieldnames: list of fieldnames
+            rows: list of rows
+            metadata_lines: optional list of strings to write as header comments
+        """
+        fname = os.path.join(self.results_dir, filename)
+        with open(fname, "w", newline="") as csvfile:
+            if metadata_lines:
+                for line in metadata_lines:
+                    csvfile.write(f"{line}\n")
+            writer = csv.DictWriter(csvfile, fieldnames=fieldnames, delimiter='\t')
+            writer.writeheader()
+            for row in rows:
+                writer.writerow(self.format_row(row))
+        if self.logging is not None:
+            self.logging.info(f"Combined results written to {fname}")
+    
+    def write_trades(self, all_trade_rows, trades_fieldnames):
+        """Write trades to a CSV file
+        Args:
+            all_trade_rows: list of trade rows
+            trades_fieldnames: list of trade fieldnames
+            logging: logging object
+        """
+
+        trades_by_combo = defaultdict(list)
+        for trade in all_trade_rows:
+            tf = trade.get("timeframe")
+            sl = trade.get("stop_loss_pct")
+            trades_by_combo[(tf, sl)].append(trade)
+
+        for (tf, sl), trades in trades_by_combo.items():
+            sl_percent = int(round(sl * 100))
+            trades_filename = os.path.join(self.results_dir, f"trades_{tf}_ST{sl_percent}pct.csv")
+            with open(trades_filename, "w", newline="") as csvfile:
+                writer = csv.DictWriter(csvfile, fieldnames=trades_fieldnames)
+                writer.writeheader()
+                for trade in trades:
+                    writer.writerow({k: trade.get(k, "") for k in trades_fieldnames})
+            if self.logging is not None:
+                self.logging.info(f"Trades written to {trades_filename}")

cycles/utils/system.py

@@ -0,0 +1,19 @@
+import os
+import psutil
+
+class SystemUtils:
+
+    def __init__(self, logging=None):
+        self.logging = logging
+
+    def get_optimal_workers(self):
+        """Determine optimal number of worker processes based on system resources"""
+        cpu_count = os.cpu_count() or 4
+        memory_gb = psutil.virtual_memory().total / (1024**3)
+        # Heuristic: Use 75% of cores, but cap based on available memory
+        # Assume each worker needs ~2GB for large datasets
+        workers_by_memory = max(1, int(memory_gb / 2))
+        workers_by_cpu = max(1, int(cpu_count * 0.75))
+        if self.logging is not None:
+            self.logging.info(f"Using {min(workers_by_cpu, workers_by_memory)} workers for processing")
+        return min(workers_by_cpu, workers_by_memory)

docs/analysis.md

@@ -0,0 +1,78 @@
+# Analysis Module
+
+This document provides an overview of the `Analysis` module and its components, which are typically used for technical analysis of financial market data.
+
+## Modules
+
+The `Analysis` module includes classes for calculating common technical indicators:
+
+-   **Relative Strength Index (RSI)**: Implemented in `cycles/Analysis/rsi.py`.
+-   **Bollinger Bands**: Implemented in `cycles/Analysis/boillinger_band.py`.
+
+## Class: `RSI`
+
+Found in `cycles/Analysis/rsi.py`.
+
+Calculates the Relative Strength Index.
+### Mathematical Model  
+1. **Average Gain (AvgU)** and **Average Loss (AvgD)** over 14 periods:  
+   $$
+   \text{AvgU} = \frac{\sum \text{Upward Price Changes}}{14}, \quad \text{AvgD} = \frac{\sum \text{Downward Price Changes}}{14}
+   $$  
+2. **Relative Strength (RS)**:  
+   $$
+   RS = \frac{\text{AvgU}}{\text{AvgD}}
+   $$  
+3. **RSI**:  
+   $$
+   RSI = 100 - \frac{100}{1 + RS}
+   $$  
+
+### `__init__(self, period: int = 14)`
+
+-   **Description**: Initializes the RSI calculator.
+-   **Parameters**:
+    -   `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.
+-   **Parameters**:
+    -   `data_df` (pd.DataFrame): DataFrame with historical price data. Must contain the `price_column`.
+    -   `price_column` (str, optional): The name of the column containing price data. Defaults to 'close'.
+-   **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.
+
+## Class: `BollingerBands`
+
+Found in `cycles/Analysis/boillinger_band.py`.
+
+## **Bollinger Bands**  
+### Mathematical Model  
+1. **Middle Band**: 20-day Simple Moving Average (SMA)  
+   $$
+   \text{Middle Band} = \frac{1}{20} \sum_{i=1}^{20} \text{Close}_{t-i}
+   $$  
+2. **Upper Band**: Middle Band + 2 × 20-day Standard Deviation (σ)  
+   $$
+   \text{Upper Band} = \text{Middle Band} + 2 \times \sigma_{20}
+   $$  
+3. **Lower Band**: Middle Band − 2 × 20-day Standard Deviation (σ)  
+   $$
+   \text{Lower Band} = \text{Middle Band} - 2 \times \sigma_{20}
+   $$  
+
+
+### `__init__(self, period: int = 20, std_dev_multiplier: float = 2.0)`
+
+-   **Description**: Initializes the BollingerBands calculator.
+-   **Parameters**:
+    -   `period` (int, optional): The period for the moving average and standard deviation. Defaults to 20. Must be a positive integer.
+    -   `std_dev_multiplier` (float, optional): The number of standard deviations for the upper and lower bands. Defaults to 2.0. Must be positive.
+
+### `calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame`
+
+-   **Description**: Calculates Bollinger Bands and adds 'SMA' (Simple Moving Average), 'UpperBand', and 'LowerBand' columns to the DataFrame.
+-   **Parameters**:
+    -   `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'.

docs/utils_storage.md

@@ -0,0 +1,73 @@
+# Storage Utilities
+
+This document describes the storage utility functions found in `cycles/utils/storage.py`.
+
+## Overview
+
+The `storage.py` module provides a `Storage` class designed for handling the loading and saving of data and results. It supports operations with CSV and JSON files and integrates with pandas DataFrames for data manipulation. The class also manages the creation of necessary `results` and `data` directories.
+
+## Constants
+
+-   `RESULTS_DIR`: Defines the default directory name for storing results (default: "results").
+-   `DATA_DIR`: Defines the default directory name for storing input data (default: "data").
+
+## Class: `Storage`
+
+Handles storage operations for data and results.
+
+### `__init__(self, logging=None, results_dir=RESULTS_DIR, data_dir=DATA_DIR)`
+
+-   **Description**: Initializes the `Storage` class. It creates the results and data directories if they don't already exist.
+-   **Parameters**:
+    -   `logging` (optional): A logging instance for outputting information. Defaults to `None`.
+    -   `results_dir` (str, optional): Path to the directory for storing results. Defaults to `RESULTS_DIR`.
+    -   `data_dir` (str, optional): Path to the directory for storing data. Defaults to `DATA_DIR`.
+
+### `load_data(self, file_path, start_date, stop_date)`
+
+-   **Description**: Loads data from a specified file (CSV or JSON), performs type optimization, filters by date range, and converts column names to lowercase. The timestamp column is set as the DataFrame index.
+-   **Parameters**:
+    -   `file_path` (str): Path to the data file (relative to `data_dir`).
+    -   `start_date` (datetime-like): The start date for filtering data.
+    -   `stop_date` (datetime-like): The end date for filtering data.
+-   **Returns**: `pandas.DataFrame` - The loaded and processed data, with a `timestamp` index. Returns an empty DataFrame on error.
+
+### `save_data(self, data: pd.DataFrame, file_path: str)`
+
+-   **Description**: Saves a pandas DataFrame to a CSV file within the `data_dir`. If the DataFrame has a DatetimeIndex, it's converted to a Unix timestamp (seconds since epoch) and stored in a column named 'timestamp', which becomes the first column in the CSV. The DataFrame's active index is not saved if a 'timestamp' column is created.
+-   **Parameters**:
+    -   `data` (pd.DataFrame): The DataFrame to save.
+    -   `file_path` (str): Path to the data file (relative to `data_dir`).
+
+### `format_row(self, row)`
+
+-   **Description**: Formats a dictionary row for output to a combined results CSV file, applying specific string formatting for percentages and float values.
+-   **Parameters**:
+    -   `row` (dict): The row of data to format.
+-   **Returns**: `dict` - The formatted row.
+
+### `write_results_chunk(self, filename, fieldnames, rows, write_header=False, initial_usd=None)`
+
+-   **Description**: Writes a chunk of results (list of dictionaries) to a CSV file. Can append to an existing file or write a new one with a header. An optional `initial_usd` can be written as a comment in the header.
+-   **Parameters**:
+    -   `filename` (str): The name of the file to write to (path is absolute or relative to current working dir).
+    -   `fieldnames` (list): A list of strings representing the CSV header/column names.
+    -   `rows` (list): A list of dictionaries, where each dictionary is a row.
+    -   `write_header` (bool, optional): If `True`, writes the header. Defaults to `False`.
+    -   `initial_usd` (numeric, optional): If provided and `write_header` is `True`, this value is written as a comment in the CSV header. Defaults to `None`.
+
+### `write_results_combined(self, filename, fieldnames, rows)`
+
+-   **Description**: Writes combined results to a CSV file in the `results_dir`. Uses tab as a delimiter and formats rows using `format_row`.
+-   **Parameters**:
+    -   `filename` (str): The name of the file to write to (relative to `results_dir`).
+    -   `fieldnames` (list): A list of strings representing the CSV header/column names.
+    -   `rows` (list): A list of dictionaries, where each dictionary is a row.
+
+### `write_trades(self, all_trade_rows, trades_fieldnames)`
+
+-   **Description**: Writes trade data to separate CSV files based on timeframe and stop-loss percentage. Files are named `trades_{tf}_ST{sl_percent}pct.csv` and stored in `results_dir`.
+-   **Parameters**:
+    -   `all_trade_rows` (list): A list of dictionaries, where each dictionary represents a trade.
+    -   `trades_fieldnames` (list): A list of strings for the CSV header of trade files.
+

docs/utils_system.md

@@ -0,0 +1,49 @@
+# System Utilities
+
+This document describes the system utility functions found in `cycles/utils/system.py`.
+
+## Overview
+
+The `system.py` module provides utility functions related to system information and resource management. It currently includes a class `SystemUtils` for determining optimal configurations based on system resources.
+
+## Classes and Methods
+
+### `SystemUtils`
+
+A class to provide system-related utility methods.
+
+#### `__init__(self, logging=None)`
+
+-   **Description**: Initializes the `SystemUtils` class.
+-   **Parameters**:
+    -   `logging` (optional): A logging instance to output information. Defaults to `None`.
+
+#### `get_optimal_workers(self)`
+
+-   **Description**: Determines the optimal number of worker processes based on available CPU cores and memory.
+    The heuristic aims to use 75% of CPU cores, with a cap based on available memory (assuming each worker might need ~2GB for large datasets). It returns the minimum of the workers calculated by CPU and memory.
+-   **Parameters**: None.
+-   **Returns**: `int` - The recommended number of worker processes.
+
+## Usage Examples
+
+```python
+from cycles.utils.system import SystemUtils
+
+# Initialize (optionally with a logger)
+# import logging
+# logging.basicConfig(level=logging.INFO)
+# logger = logging.getLogger(__name__)
+# sys_utils = SystemUtils(logging=logger)
+sys_utils = SystemUtils()
+
+
+optimal_workers = sys_utils.get_optimal_workers()
+print(f"Optimal number of workers: {optimal_workers}")
+
+# This value can then be used, for example, when setting up a ThreadPoolExecutor
+# from concurrent.futures import ThreadPoolExecutor
+# with ThreadPoolExecutor(max_workers=optimal_workers) as executor:
+#     # ... submit tasks ...
+#     pass
+``` 

main.py

@@ -1,18 +1,16 @@
 import pandas as pd
 import numpy as np
-from trend_detector_macd import TrendDetectorMACD
-from trend_detector_simple import TrendDetectorSimple
-from cycle_detector import CycleDetector
-import csv
 import logging
 import concurrent.futures
 import os
-import psutil
 import datetime
-from charts import BacktestCharts
-from collections import Counter
+import argparse
+import json
+
+from cycles.utils.storage import Storage
+from cycles.utils.system import SystemUtils
+from cycles.backtest import Backtest
 
-# Set up logging
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s [%(levelname)s] %(message)s",
@@ -22,58 +20,25 @@ logging.basicConfig(
     ]
 )
 
-def get_optimal_workers():
-    """Determine optimal number of worker processes based on system resources"""
-    cpu_count = os.cpu_count() or 4
-    memory_gb = psutil.virtual_memory().total / (1024**3)
-    # Heuristic: Use 75% of cores, but cap based on available memory
-    # Assume each worker needs ~2GB for large datasets
-    workers_by_memory = max(1, int(memory_gb / 2))
-    workers_by_cpu = max(1, int(cpu_count * 0.75))
-    return min(workers_by_cpu, workers_by_memory)
-
-def load_data(file_path, start_date, stop_date):
-    """Load data with optimized dtypes and filtering"""
-    # Define optimized dtypes
-    dtypes = {
-        'Open': 'float32',
-        'High': 'float32', 
-        'Low': 'float32',
-        'Close': 'float32',
-        'Volume': 'float32'
-    }
-    
-    # Read data with original capitalized column names
-    data = pd.read_csv(file_path, dtype=dtypes)
-    
-    # Convert timestamp to datetime
-    data['Timestamp'] = pd.to_datetime(data['Timestamp'], unit='s')
-    
-    # Filter by date range
-    data = data[(data['Timestamp'] >= start_date) & (data['Timestamp'] <= stop_date)]
-    
-    # Now convert column names to lowercase
-    data.columns = data.columns.str.lower()
-    
-    return data.set_index('timestamp')
-
 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)
-    trend_detector = TrendDetectorSimple(df, verbose=False)
 
     results_rows = []
     trade_rows = []
+
     for stop_loss_pct in stop_loss_pcts:
-        results = trend_detector.backtest_meta_supertrend(
+        results = Backtest.run(
             min1_df,
+            df,
             initial_usd=initial_usd,
             stop_loss_pct=stop_loss_pct,
             debug=debug
         )
         n_trades = results["n_trades"]
         trades = results.get('trades', [])
-        n_winning_trades = sum(1 for trade in trades if trade['profit_pct'] > 0)
+        wins = [1 for t in trades if t['exit'] is not None and t['exit'] > t['entry']]
+        n_winning_trades = len(wins)
         total_profit = sum(trade['profit_pct'] for trade in trades)
         total_loss = sum(-trade['profit_pct'] for trade in trades if trade['profit_pct'] < 0)
         win_rate = n_winning_trades / n_trades if n_trades > 0 else 0
@@ -82,6 +47,7 @@ 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:
@@ -89,9 +55,14 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
             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['fee_usd'] for trade in trades)
+
         row = {
             "timeframe": rule_name,
             "stop_loss_pct": stop_loss_pct,
@@ -100,11 +71,15 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
             "win_rate": win_rate,
             "max_drawdown": max_drawdown,
             "avg_trade": avg_trade,
+            "total_profit": total_profit,
+            "total_loss": total_loss,
             "profit_ratio": profit_ratio,            
             "initial_usd": initial_usd,
             "final_usd": final_usd,
+            "total_fees_usd": total_fees_usd,
         }
         results_rows.append(row)
+
         for trade in trades:
             trade_rows.append({
                 "timeframe": rule_name,
@@ -114,9 +89,12 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
                 "entry_price": trade.get("entry"),
                 "exit_price": trade.get("exit"),
                 "profit_pct": trade.get("profit_pct"),
-                "type": trade.get("type", ""),
+                "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':
@@ -124,12 +102,16 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
             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(timeframe_info, debug=False):
-    """Process an entire timeframe (no monthly split)"""
-    rule, data_1min, stop_loss_pcts, initial_usd = timeframe_info
-    if rule == "1T":
+def process(timeframe_info, debug=False):
+    from cycles.utils.storage import Storage  # import inside function for safety
+    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":
         df = data_1min.copy()
     else:
         df = data_1min.resample(rule).agg({
@@ -141,28 +123,33 @@ def process_timeframe(timeframe_info, debug=False):
         }).dropna()
     df = df.reset_index()
 
-    # --- Add this block to check alignment ---
-    print("1-min data range:", data_1min.index.min(), "to", data_1min.index.max())
-    print(f"{rule} data range:", df['timestamp'].min(), "to", df['timestamp'].max())
-    # -----------------------------------------
+    results_rows, all_trade_rows = process_timeframe_data(data_1min, df, [stop_loss_pct], rule, initial_usd, debug=debug)
 
-    results_rows, all_trade_rows = process_timeframe_data(data_1min, df, stop_loss_pcts, rule, initial_usd, debug=debug)
-    return results_rows, all_trade_rows
-
-def write_results_chunk(filename, fieldnames, rows, write_header=False):
-    """Write a chunk of results to a CSV file"""
-    mode = 'w' if write_header else 'a'
-    
-    with open(filename, mode, newline="") as csvfile:
-        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
-        if write_header:
-            csvfile.write(f"# initial_usd: {initial_usd}\n")
+    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})
 
-        for row in rows:
-            # Only keep keys that are in fieldnames
-            filtered_row = {k: v for k, v in row.items() if k in fieldnames}
-            writer.writerow(filtered_row)
+    return results_rows, all_trade_rows
 
 def aggregate_results(all_rows):
     """Aggregate results per stop_loss_pct and per rule (timeframe)"""
@@ -175,7 +162,6 @@ 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])
@@ -185,6 +171,7 @@ def aggregate_results(all_rows):
 
         # Calculate final USD
         final_usd = np.mean([r.get('final_usd', initial_usd) for r in rows])
+        total_fees_usd = np.mean([r.get('total_fees_usd') for r in rows])
 
         summary_rows.append({
             "timeframe": rule,
@@ -197,105 +184,119 @@ def aggregate_results(all_rows):
             "profit_ratio": avg_profit_ratio,
             "initial_usd": initial_usd,
             "final_usd": final_usd,
+            "total_fees_usd": total_fees_usd,
         })
     return summary_rows
 
-def write_results_per_combination(results_rows, trade_rows, timestamp):
-    results_dir = "results"
-    os.makedirs(results_dir, exist_ok=True)
-    for row in results_rows:
-        timeframe = row["timeframe"]
-        stop_loss_pct = row["stop_loss_pct"]
-        filename = os.path.join(
-            results_dir,
-            f"{timestamp}_backtest_{timeframe}_{stop_loss_pct}.csv"
-        )
-        fieldnames = ["timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate", "max_drawdown", "avg_trade", "profit_ratio", "initial_usd", "final_usd"]
-        write_results_chunk(filename, fieldnames, [row], write_header=not os.path.exists(filename))
-    for trade in trade_rows:
-        timeframe = trade["timeframe"]
-        stop_loss_pct = trade["stop_loss_pct"]
-        trades_filename = os.path.join(
-            results_dir,
-            f"{timestamp}_trades_{timeframe}_{stop_loss_pct}.csv"
-        )
-        trades_fieldnames = [
-            "timeframe", "stop_loss_pct", "entry_time", "exit_time",
-            "entry_price", "exit_price", "profit_pct", "type"
-        ]
-        write_results_chunk(trades_filename, trades_fieldnames, [trade], write_header=not os.path.exists(trades_filename))
+def get_nearest_price(df, target_date):
+        if len(df) == 0:
+            return None, None
+        target_ts = pd.to_datetime(target_date)
+        nearest_idx = df.index.get_indexer([target_ts], method='nearest')[0]
+        nearest_time = df.index[nearest_idx]
+        price = df.iloc[nearest_idx]['close']
+        return nearest_time, price
 
 if __name__ == "__main__":
-    # Configuration
-    start_date = '2020-01-01'
-    stop_date = '2025-05-15'
-    initial_usd = 10000
-    debug = False  # Set to True to enable debug prints
-    # --- NEW: Prepare results folder and timestamp ---
-    results_dir = "results"
-    os.makedirs(results_dir, exist_ok=True)
-    timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M")
-    # --- END NEW ---
+    debug = False
 
-    # Replace the dictionary with a list of timeframe names
-    timeframes = ["15min", "1h", "6h", "1D"]
-    # timeframes = ["6h"]
+    parser = argparse.ArgumentParser(description="Run backtest with config file.")
+    parser.add_argument("config", type=str, nargs="?", help="Path to config JSON file.")
+    args = parser.parse_args()
 
-    stop_loss_pcts = [0.01, 0.02, 0.03, 0.05, 0.07, 0.10]
-    # stop_loss_pcts = [0.01]
-
-    # Load data once
-    data_1min = load_data('./data/btcusd_1-min_data.csv', start_date, stop_date)
-    logging.info(f"1min rows: {len(data_1min)}")
-    
-    # Prepare tasks
-    tasks = [
-        (name, data_1min, stop_loss_pcts, initial_usd)
-        for name in timeframes
-    ]
-    
-    # Determine optimal worker count
-    workers = get_optimal_workers()
-    logging.info(f"Using {workers} workers for processing")
-    
-    # Process tasks with optimized concurrency
-    with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as executor:
-        futures = {executor.submit(process_timeframe, task, debug): task[1] for task in tasks}
-        all_results_rows = []
-        for future in concurrent.futures.as_completed(futures):
-            #try:
-            results, trades = future.result()
-            if results or trades:
-                all_results_rows.extend(results)
-                write_results_per_combination(results, trades, timestamp)
-            #except Exception as exc:
-            #    logging.error(f"generated an exception: {exc}")
-
-    # Write all results to a single CSV file
-    combined_filename = os.path.join(results_dir, f"{timestamp}_backtest_combined.csv")
-    combined_fieldnames = [
-        "timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate",
-        "max_drawdown", "avg_trade", "profit_ratio", "final_usd"
-    ]
-
-    def format_row(row):
-        # Format percentages and floats as in your example
-        return {
-            "timeframe": row["timeframe"],
-            "stop_loss_pct": f"{row['stop_loss_pct']*100:.2f}%",
-            "n_trades": row["n_trades"],
-            "n_stop_loss": row["n_stop_loss"],
-            "win_rate": f"{row['win_rate']*100:.2f}%",
-            "max_drawdown": f"{row['max_drawdown']*100:.2f}%",
-            "avg_trade": f"{row['avg_trade']*100:.2f}%",
-            "profit_ratio": f"{row['profit_ratio']*100:.2f}%",
-            "final_usd": f"{row['final_usd']:.2f}",
+    # Default values (from config.json)
+    default_config = {
+        "start_date": "2025-05-01",
+        "stop_date": datetime.datetime.today().strftime('%Y-%m-%d'),
+        "initial_usd": 10000,
+        "timeframes": ["1D", "6h", "3h", "1h", "30m", "15m", "5m", "1m"],
+        "stop_loss_pcts": [0.01, 0.02, 0.03, 0.05],
     }
 
-    with open(combined_filename, "w", newline="") as csvfile:
-        writer = csv.DictWriter(csvfile, fieldnames=combined_fieldnames, delimiter='\t')
-        writer.writeheader()
-        for row in all_results_rows:
-            writer.writerow(format_row(row))
+    if args.config:
+        with open(args.config, 'r') as f:
+            config = json.load(f)
+    else:
+        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']
+        stop_date = input(f"Stop date [{default_config['stop_date']}]: ") or default_config['stop_date']
+
+        initial_usd_str = input(f"Initial USD [{default_config['initial_usd']}]: ") or str(default_config['initial_usd'])
+        initial_usd = float(initial_usd_str)
+
+        timeframes_str = input(f"Timeframes (comma separated) [{', '.join(default_config['timeframes'])}]: ") or ','.join(default_config['timeframes'])
+        timeframes = [tf.strip() for tf in timeframes_str.split(',') if tf.strip()]
+        
+        stop_loss_pcts_str = input(f"Stop loss pcts (comma separated) [{', '.join(str(x) for x in default_config['stop_loss_pcts'])}]: ") or ','.join(str(x) for x in default_config['stop_loss_pcts'])
+        stop_loss_pcts = [float(x.strip()) for x in stop_loss_pcts_str.split(',') if x.strip()]
+        
+        config = {
+            'start_date': start_date,
+            'stop_date': stop_date,
+            'initial_usd': initial_usd,
+            'timeframes': timeframes,
+            'stop_loss_pcts': stop_loss_pcts,
+        }
+
+    # Use config values
+    start_date = config['start_date']
+    stop_date = config['stop_date']
+    initial_usd = config['initial_usd']
+    timeframes = config['timeframes']
+    stop_loss_pcts = config['stop_loss_pcts']
+
+    timestamp = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M")
+
+    storage = Storage(logging=logging)
+    system_utils = SystemUtils(logging=logging)
+
+    data_1min = storage.load_data('btcusd_1-min_data.csv', start_date, stop_date)
+
+    nearest_start_time, start_price = get_nearest_price(data_1min, start_date)
+    nearest_stop_time, stop_price = get_nearest_price(data_1min, stop_date)
+
+    metadata_lines = [
+        f"Start date\t{start_date}\tPrice\t{start_price}",
+        f"Stop date\t{stop_date}\tPrice\t{stop_price}",
+        f"Initial USD\t{initial_usd}"
+    ]
+    
+    tasks = [
+        (name, data_1min, stop_loss_pct, initial_usd)
+        for name in timeframes
+        for stop_loss_pct in stop_loss_pcts
+    ]
+    
+    workers = system_utils.get_optimal_workers()
+
+    if debug:
+        all_results_rows = []
+        all_trade_rows = []
+
+        for task in tasks:
+            results, trades = process(task, debug)
+            if results or trades:
+                all_results_rows.extend(results)
+                all_trade_rows.extend(trades)
+    else:
+        with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as executor:
+            futures = {executor.submit(process, task, debug): task for task in tasks}
+            all_results_rows = []
+            all_trade_rows = []
+
+            for future in concurrent.futures.as_completed(futures):
+                results, trades = future.result()
+
+                if results or trades:
+                    all_results_rows.extend(results)
+                    all_trade_rows.extend(trades)
+
+    backtest_filename = os.path.join(f"{timestamp}_backtest.csv")
+    backtest_fieldnames = [
+        "timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate",
+        "max_drawdown", "avg_trade", "profit_ratio", "final_usd", "total_fees_usd"
+    ]
+    storage.write_backtest_results(backtest_filename, backtest_fieldnames, all_results_rows, metadata_lines)
+
     
-    logging.info(f"Combined results written to {combined_filename}")

main_debug.py

@@ -1,197 +0,0 @@
-import pandas as pd
-import numpy as np
-from trend_detector_simple import TrendDetectorSimple
-import os
-import datetime
-import csv
-
-def load_data(file_path, start_date, stop_date):
-    """Load and filter data by date range."""
-    data = pd.read_csv(file_path)
-    data['Timestamp'] = pd.to_datetime(data['Timestamp'], unit='s')
-    data = data[(data['Timestamp'] >= start_date) & (data['Timestamp'] <= stop_date)]
-    data.columns = data.columns.str.lower()
-    return data.set_index('timestamp')
-
-def process_month_timeframe(min1_df, month_df, stop_loss_pcts, rule_name, initial_usd):
-    """Process a single month for a given timeframe with all stop loss values."""
-    month_df = month_df.copy().reset_index(drop=True)
-    trend_detector = TrendDetectorSimple(month_df, verbose=False)
-    analysis_results = trend_detector.detect_trends()
-    signal_df = analysis_results.get('signal_df')
-
-    results_rows = []
-    trade_rows = []
-    for stop_loss_pct in stop_loss_pcts:
-        results = trend_detector.backtest_meta_supertrend(
-            min1_df,
-            initial_usd=initial_usd,
-            stop_loss_pct=stop_loss_pct
-        )
-        trades = results.get('trades', [])
-        n_trades = results["n_trades"]
-        n_winning_trades = sum(1 for trade in trades if trade['profit_pct'] > 0)
-        total_profit = sum(trade['profit_pct'] for trade in trades)
-        total_loss = sum(-trade['profit_pct'] for trade in trades if trade['profit_pct'] < 0)
-        win_rate = n_winning_trades / n_trades if n_trades > 0 else 0
-        avg_trade = total_profit / n_trades if n_trades > 0 else 0
-        profit_ratio = total_profit / total_loss if total_loss > 0 else float('inf')
-
-        # Max drawdown
-        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
-        final_usd = initial_usd
-        for trade in trades:
-            final_usd *= (1 + trade['profit_pct'])
-
-        row = {
-            "timeframe": rule_name,
-            "month": str(month_df['timestamp'].iloc[0].to_period('M')),
-            "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'),
-            "win_rate": win_rate,
-            "max_drawdown": max_drawdown,
-            "avg_trade": avg_trade,
-            "profit_ratio": profit_ratio,
-            "initial_usd": initial_usd,
-            "final_usd": final_usd,
-        }
-        results_rows.append(row)
-
-        for trade in trades:
-            trade_rows.append({
-                "timeframe": rule_name,
-                "month": str(month_df['timestamp'].iloc[0].to_period('M')),
-                "stop_loss_pct": stop_loss_pct,
-                "entry_time": trade.get("entry_time"),
-                "exit_time": trade.get("exit_time"),
-                "entry_price": trade.get("entry_price"),
-                "exit_price": trade.get("exit_price"),
-                "profit_pct": trade.get("profit_pct"),
-                "type": trade.get("type", ""),
-            })
-
-    return results_rows, trade_rows
-
-def process_timeframe(rule, data_1min, stop_loss_pcts, initial_usd):
-    """Process an entire timeframe sequentially."""
-    if rule == "1T":
-        df = data_1min.copy()
-    else:
-        df = data_1min.resample(rule).agg({
-            'open': 'first',
-            'high': 'max',
-            'low': 'min',
-            'close': 'last',
-            'volume': 'sum'
-        }).dropna()
-
-    df = df.reset_index()
-    df['month'] = df['timestamp'].dt.to_period('M')
-    results_rows = []
-    all_trade_rows = []
-
-    for month, month_df in df.groupby('month'):
-        if len(month_df) < 10:
-            continue
-        month_results, month_trades = process_month_timeframe(data_1min, month_df, stop_loss_pcts, rule, initial_usd)
-        results_rows.extend(month_results)
-        all_trade_rows.extend(month_trades)
-
-    return results_rows, all_trade_rows
-
-def aggregate_results(all_rows, initial_usd):
-    """Aggregate results per stop_loss_pct and per rule (timeframe)."""
-    from collections import defaultdict
-    grouped = defaultdict(list)
-    for row in all_rows:
-        key = (row['timeframe'], row['stop_loss_pct'])
-        grouped[key].append(row)
-
-    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])
-        avg_max_drawdown = np.mean([r['max_drawdown'] for r in rows])
-        avg_avg_trade = np.mean([r['avg_trade'] for r in rows])
-        avg_profit_ratio = np.mean([r['profit_ratio'] for r in rows])
-        final_usd = np.mean([r.get('final_usd', initial_usd) for r in rows])
-
-        summary_rows.append({
-            "timeframe": rule,
-            "stop_loss_pct": stop_loss_pct,
-            "n_trades": total_trades,
-            "n_stop_loss": total_stop_loss,
-            "win_rate": avg_win_rate,
-            "max_drawdown": avg_max_drawdown,
-            "avg_trade": avg_avg_trade,
-            "profit_ratio": avg_profit_ratio,
-            "initial_usd": initial_usd,
-            "final_usd": final_usd,
-        })
-    return summary_rows
-
-def write_results(filename, fieldnames, rows):
-    """Write results to a CSV file."""
-    with open(filename, 'w', newline="") as csvfile:
-        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
-        writer.writeheader()
-        for row in rows:
-            writer.writerow(row)
-
-if __name__ == "__main__":
-    # Config
-    start_date = '2020-01-01'
-    stop_date = '2025-05-15'
-    initial_usd = 10000
-
-    results_dir = "results"
-    os.makedirs(results_dir, exist_ok=True)
-    timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M")
-
-    timeframes = ["6h", "1D"]
-    stop_loss_pcts = [0.01, 0.02, 0.03, 0.05, 0.07, 0.10]
-
-    data_1min = load_data('./data/btcusd_1-min_data.csv', start_date, stop_date)
-    print(f"1min rows: {len(data_1min)}")
-
-    filename = os.path.join(
-        results_dir,
-        f"{timestamp}_backtest_results_{start_date}_{stop_date}_multi_timeframe_stoploss.csv"
-    )
-    fieldnames = ["timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate", "max_drawdown", "avg_trade", "profit_ratio", "initial_usd", "final_usd"]
-
-    all_results = []
-    all_trades = []
-
-    for name in timeframes:
-        print(f"Processing timeframe: {name}")
-        results, trades = process_timeframe(name, data_1min, stop_loss_pcts, initial_usd)
-        all_results.extend(results)
-        all_trades.extend(trades)
-
-    summary_rows = aggregate_results(all_results, initial_usd)
-    # write_results(filename, fieldnames, summary_rows)
-
-    trades_filename = os.path.join(
-        results_dir,
-        f"{timestamp}_backtest_trades.csv"
-    )
-    trades_fieldnames = [
-        "timeframe", "month", "stop_loss_pct", "entry_time", "exit_time",
-        "entry_price", "exit_price", "profit_pct", "type"
-    ]
-    # write_results(trades_filename, trades_fieldnames, all_trades)

pyproject.toml

@@ -0,0 +1,14 @@
+[project]
+name = "cycles"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "gspread>=6.2.1",
+    "matplotlib>=3.10.3",
+    "pandas>=2.2.3",
+    "psutil>=7.0.0",
+    "scipy>=1.15.3",
+    "seaborn>=0.13.2",
+]

test_bbrsi.py

@@ -0,0 +1,132 @@
+import logging
+import seaborn as sns
+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.boillinger_band import BollingerBands
+from cycles.Analysis.rsi import RSI
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    handlers=[
+        logging.FileHandler("backtest.log"),
+        logging.StreamHandler()
+    ]
+)
+
+config_minute = {
+    "start_date": "2022-01-01",
+    "stop_date": "2023-01-01",
+    "data_file": "btcusd_1-min_data.csv"
+}
+
+config_day = {
+    "start_date": "2022-01-01",
+    "stop_date": "2023-01-01",
+    "data_file": "btcusd_1-day_data.csv"
+}
+
+IS_DAY = True
+
+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__":
+
+    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:
+        data_daily = aggregate_to_daily(data)
+        storage.save_data(data, "btcusd_1-day_data.csv")
+        df_to_plot = data_daily
+    else:
+        df_to_plot = data
+
+    bb = BollingerBands(period=30, std_dev_multiplier=2.0)
+    data_bb = bb.calculate(df_to_plot.copy())
+
+    rsi_calculator = RSI(period=13)
+    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
+    # Ensure indices are aligned; they should be as both are from df_to_plot.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)
+        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)
+
+        # 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_ylabel('Price')
+        ax1.legend()
+        ax1.grid(True)
+
+        # 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')
+            ax2.axhline(75, color='red', linestyle='--', linewidth=0.8, label='Overbought (75)')
+            ax2.axhline(25, color='green', linestyle='--', linewidth=0.8, label='Oversold (25)')
+            # 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)
+            if not sell_signals.empty:
+                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.legend()
+            ax2.grid(True)
+        else:
+            logging.info("RSI data not available for plotting.")
+
+        plt.xlabel('Date') # Common X-axis label
+        fig.tight_layout() # Adjust layout to prevent overlapping titles/labels
+        plt.show()
+    else:
+        logging.info("No data to plot.")
+    

trend_detector_macd.py

@@ -1,287 +0,0 @@
-import pandas as pd
-import numpy as np
-import ta
-import matplotlib.pyplot as plt
-import matplotlib.dates as mdates
-import logging
-import mplfinance as mpf
-from matplotlib.patches import Rectangle
-from concurrent.futures import ProcessPoolExecutor, as_completed
-import concurrent.futures
-
-class TrendDetectorMACD:
-    def __init__(self, data, verbose=False):
-        self.data = data
-        self.verbose = verbose
-        # Configure logging
-        logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
-                           format='%(asctime)s - %(levelname)s - %(message)s')
-        self.logger = logging.getLogger('TrendDetector')
-        
-        # Convert data to pandas DataFrame if it's not already
-        if not isinstance(self.data, pd.DataFrame):
-            if isinstance(self.data, list):
-                self.logger.info("Converting list to DataFrame")
-                self.data = pd.DataFrame({'close': self.data})
-            else:
-                self.logger.error("Invalid data format provided")
-                raise ValueError("Data must be a pandas DataFrame or a list")
-
-    def detect_trends_MACD_signal(self):
-        self.logger.info("Starting trend detection")
-        if len(self.data) < 3:
-            self.logger.warning("Not enough data points for trend detection")
-            return {"error": "Not enough data points for trend detection"}
-        
-        # Create a copy of the DataFrame to avoid modifying the original
-        df = self.data.copy()
-        self.logger.info("Created copy of input data")
-        
-        # If 'close' column doesn't exist, try to use a relevant column
-        if 'close' not in df.columns and len(df.columns) > 0:
-            self.logger.info(f"'close' column not found, using {df.columns[0]} instead")
-            df['close'] = df[df.columns[0]]  # Use the first column as 'close'
-        
-        # Add trend indicators
-        self.logger.info("Calculating MACD indicators")
-        # Moving Average Convergence Divergence (MACD)
-        df['macd'] = ta.trend.macd(df['close'])
-        df['macd_signal'] = ta.trend.macd_signal(df['close'])
-        df['macd_diff'] = ta.trend.macd_diff(df['close'])
-        
-        # Directional Movement Index (DMI)
-        if all(col in df.columns for col in ['high', 'low', 'close']):
-            self.logger.info("Calculating ADX indicators")
-            df['adx'] = ta.trend.adx(df['high'], df['low'], df['close'])
-            df['adx_pos'] = ta.trend.adx_pos(df['high'], df['low'], df['close'])
-            df['adx_neg'] = ta.trend.adx_neg(df['high'], df['low'], df['close'])
-        
-        # Identify trend changes
-        self.logger.info("Identifying trend changes")
-        df['trend'] = np.where(df['macd'] > df['macd_signal'], 'up', 'down')
-        df['trend_change'] = df['trend'] != df['trend'].shift(1)
-        
-        # Generate trend segments
-        self.logger.info("Generating trend segments")
-        trends = []
-        trend_start = 0
-        
-        for i in range(1, len(df)):
-
-            if df['trend_change'].iloc[i]:
-                if i > trend_start:
-                    trends.append({
-                        "type": df['trend'].iloc[i-1],
-                        "start_index": trend_start,
-                        "end_index": i-1,
-                        "start_value": df['close'].iloc[trend_start],
-                        "end_value": df['close'].iloc[i-1]
-                    })
-                trend_start = i
-        
-        # Add the last trend
-        if trend_start < len(df):
-            trends.append({
-                "type": df['trend'].iloc[-1],
-                "start_index": trend_start,
-                "end_index": len(df)-1,
-                "start_value": df['close'].iloc[trend_start],
-                "end_value": df['close'].iloc[-1]
-            })
-        
-        self.logger.info(f"Detected {len(trends)} trend segments")
-        return trends
-        
-    def get_strongest_trend(self):
-        self.logger.info("Finding strongest trend")
-        trends = self.detect_trends_MACD_signal()
-        if isinstance(trends, dict) and "error" in trends:
-            self.logger.warning(f"Error in trend detection: {trends['error']}")
-            return trends
-            
-        if not trends:
-            self.logger.info("No significant trends detected")
-            return {"message": "No significant trends detected"}
-        
-        strongest = max(trends, key=lambda x: abs(x["end_value"] - x["start_value"]))
-        self.logger.info(f"Strongest trend: {strongest['type']} from index {strongest['start_index']} to {strongest['end_index']}")
-        return strongest
-    
-    def plot_trends(self, trends):
-        """
-        Plot price data with identified trends highlighted using candlestick charts.
-        """
-        self.logger.info("Plotting trends with candlesticks")
-        if isinstance(trends, dict) and "error" in trends:
-            self.logger.error(trends["error"])
-            print(trends["error"])
-            return
-        
-        if not trends:
-            self.logger.warning("No significant trends detected for plotting")
-            print("No significant trends detected")
-            return
-            
-        # Create a figure with 2 subplots that share the x-axis
-        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8), gridspec_kw={'height_ratios': [2, 1]}, sharex=True)
-        self.logger.info("Creating plot figure with shared x-axis")
-        
-        # Prepare data for candlestick chart
-        df = self.data.copy()
-        
-        # Ensure required columns exist for candlestick
-        required_cols = ['open', 'high', 'low', 'close']
-        if not all(col in df.columns for col in required_cols):
-            self.logger.warning("Missing required columns for candlestick. Defaulting to line chart.")
-            if 'close' in df.columns:
-                ax1.plot(df.index if 'datetime' not in df.columns else df['datetime'], 
-                        df['close'], color='black', alpha=0.7, linewidth=1, label='Price')
-            else:
-                ax1.plot(df.index if 'datetime' not in df.columns else df['datetime'], 
-                        df[df.columns[0]], color='black', alpha=0.7, linewidth=1, label='Price')
-        else:
-            # Get x values (dates if available, otherwise indices)
-            if 'datetime' in df.columns:
-                x_label = 'Date'
-                # Format date axis
-                ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
-                ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
-                fig.autofmt_xdate()
-                self.logger.info("Using datetime for x-axis")
-                
-                # For candlestick, ensure datetime is the index
-                if df.index.name != 'datetime':
-                    df = df.set_index('datetime')
-            else:
-                x_label = 'Index'
-                self.logger.info("Using index for x-axis")
-            
-            # Plot candlestick chart
-            up_color = 'green'
-            down_color = 'red'
-            
-            # Draw candlesticks manually 
-            width = 0.6
-            for i in range(len(df)):
-                # Get OHLC values for this candle
-                open_val = df['open'].iloc[i]
-                close_val = df['close'].iloc[i]
-                high_val = df['high'].iloc[i]
-                low_val = df['low'].iloc[i]
-                idx = df.index[i]
-                
-                # Determine candle color
-                color = up_color if close_val >= open_val else down_color
-                
-                # Plot candle body
-                body_height = abs(close_val - open_val)
-                bottom = min(open_val, close_val)
-                rect = Rectangle((i - width/2, bottom), width, body_height, color=color, alpha=0.8)
-                ax1.add_patch(rect)
-                
-                # Plot candle wicks
-                ax1.plot([i, i], [low_val, high_val], color='black', linewidth=1)
-            
-            # Set appropriate x-axis limits
-            ax1.set_xlim(-0.5, len(df) - 0.5)
-        
-        # Highlight each trend with a different color
-        self.logger.info("Highlighting trends on plot")
-        for trend in trends:
-            start_idx = trend['start_index']
-            end_idx = trend['end_index']
-            trend_type = trend['type']
-            
-            # Get x-coordinates for trend plotting
-            x_start = start_idx
-            x_end = end_idx
-            
-            # Get y-coordinates for trend line
-            if 'close' in df.columns:
-                y_start = df['close'].iloc[start_idx]
-                y_end = df['close'].iloc[end_idx]
-            else:
-                y_start = df[df.columns[0]].iloc[start_idx]
-                y_end = df[df.columns[0]].iloc[end_idx]
-            
-            # Choose color based on trend type
-            color = 'green' if trend_type == 'up' else 'red'
-            
-            # Plot trend line
-            ax1.plot([x_start, x_end], [y_start, y_end], color=color, linewidth=2, 
-                    label=f"{trend_type.capitalize()} Trend" if f"{trend_type.capitalize()} Trend" not in ax1.get_legend_handles_labels()[1] else "")
-            
-            # Add markers at start and end points
-            ax1.scatter(x_start, y_start, color=color, marker='o', s=50)
-            ax1.scatter(x_end, y_end, color=color, marker='s', s=50)
-        
-        # Configure first subplot
-        ax1.set_title('Price with Trends (Candlestick)', fontsize=16)
-        ax1.set_ylabel('Price', fontsize=14)
-        ax1.grid(alpha=0.3)
-        ax1.legend()
-        
-        # Create MACD in second subplot
-        self.logger.info("Creating MACD subplot")
-        
-        # Calculate MACD indicators if not already present
-        if 'macd' not in df.columns:
-            if 'close' not in df.columns and len(df.columns) > 0:
-                df['close'] = df[df.columns[0]]
-                
-            df['macd'] = ta.trend.macd(df['close'])
-            df['macd_signal'] = ta.trend.macd_signal(df['close'])
-            df['macd_diff'] = ta.trend.macd_diff(df['close'])
-        
-        # Plot MACD components on second subplot
-        x_indices = np.arange(len(df))
-        ax2.plot(x_indices, df['macd'], label='MACD', color='blue')
-        ax2.plot(x_indices, df['macd_signal'], label='Signal', color='orange')
-        
-        # Plot MACD histogram
-        for i in range(len(df)):
-            if df['macd_diff'].iloc[i] >= 0:
-                ax2.bar(i, df['macd_diff'].iloc[i], color='green', alpha=0.5, width=0.8)
-            else:
-                ax2.bar(i, df['macd_diff'].iloc[i], color='red', alpha=0.5, width=0.8)
-        
-        ax2.set_title('MACD Indicator', fontsize=16)
-        ax2.set_xlabel(x_label, fontsize=14)
-        ax2.set_ylabel('MACD', fontsize=14)
-        ax2.grid(alpha=0.3)
-        ax2.legend()
-        
-        # Enable synchronized zooming
-        plt.tight_layout()
-        plt.subplots_adjust(hspace=0.1)
-        plt.show()
-        
-        return plt
-
-    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, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
-            {"period": 10, "multiplier": 1.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
-            {"period": 11, "multiplier": 2.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN}
-        ]
-
-        def run_supertrend(params):
-            # Each thread gets its own copy of the data to avoid race conditions
-            return {
-                "results": self.calculate_supertrend(
-                    period=params["period"],
-                    multiplier=params["multiplier"]
-                ),
-                "params": params
-            }
-
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            results = list(executor.map(run_supertrend, supertrend_params))
-
-        return results

trend_detector_simple.py

@@ -1,814 +0,0 @@
-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)
-    trend = np.zeros_like(close)
-    final_upper[0] = upper_band[0]
-    final_lower[0] = lower_band[0]
-    if close[0] <= upper_band[0]:
-        supertrend[0] = upper_band[0]
-        trend[0] = -1
-    else:
-        supertrend[0] = lower_band[0]
-        trend[0] = 1
-    for i in range(1, len(close)):
-        if (upper_band[i] < final_upper[i-1]) or (close[i-1] > final_upper[i-1]):
-            final_upper[i] = upper_band[i]
-        else:
-            final_upper[i] = final_upper[i-1]
-        if (lower_band[i] > final_lower[i-1]) or (close[i-1] < final_lower[i-1]):
-            final_lower[i] = lower_band[i]
-        else:
-            final_lower[i] = final_lower[i-1]
-        if supertrend[i-1] == final_upper[i-1] and close[i] <= final_upper[i]:
-            supertrend[i] = final_upper[i]
-            trend[i] = -1
-        elif supertrend[i-1] == final_upper[i-1] and close[i] > final_upper[i]:
-            supertrend[i] = final_lower[i]
-            trend[i] = 1
-        elif supertrend[i-1] == final_lower[i-1] and close[i] >= final_lower[i]:
-            supertrend[i] = final_lower[i]
-            trend[i] = 1
-        elif supertrend[i-1] == final_lower[i-1] and close[i] < final_lower[i]:
-            supertrend[i] = final_upper[i]
-            trend[i] = -1
-    return {
-        'supertrend': supertrend,
-        'trend': trend,
-        'upper_band': final_upper,
-        'lower_band': final_lower
-    }
-
-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 TrendDetectorSimple:
-    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})
-            else:
-                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]  # 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 detect_trends(self):
-        """
-        Detect trends by identifying local minima and maxima in the price data
-        using scipy.signal.find_peaks.
-        
-        Parameters:
-        - prominence: float, required prominence of peaks (relative to the price range)
-        - width: int, required width of peaks in data points
-        
-        Returns:
-        - DataFrame with columns for timestamps, prices, and trend indicators
-        - Dictionary containing analysis results including linear regression, SMAs, and SuperTrend indicators
-        """
-        df = self.data
-        # close_prices = df['close'].values
-        
-        # max_peaks, _ = find_peaks(close_prices)
-        # min_peaks, _ = find_peaks(-close_prices)
-        
-        # df['is_min'] = False
-        # df['is_max'] = False
-        
-        # for peak in max_peaks:
-        #     df.at[peak, 'is_max'] = True
-        # for peak in min_peaks:
-        #     df.at[peak, 'is_min'] = True
-        
-        # result = df[['timestamp', 'close', 'is_min', 'is_max']].copy()
-        
-        # Perform linear regression on min_peaks and max_peaks
-        # min_prices = df['close'].iloc[min_peaks].values
-        # max_prices = df['close'].iloc[max_peaks].values
-        
-        # Linear regression for min peaks if we have at least 2 points
-        # min_slope, min_intercept, min_r_value, _, _ = stats.linregress(min_peaks, min_prices)
-        # Linear regression for max peaks if we have at least 2 points
-        # max_slope, max_intercept, max_r_value, _, _ = stats.linregress(max_peaks, max_prices)
-
-        # Calculate Simple Moving Averages (SMA) for 7 and 15 periods        
-        # sma_7 = pd.Series(close_prices).rolling(window=7, min_periods=1).mean().values
-        # sma_15 = pd.Series(close_prices).rolling(window=15, min_periods=1).mean().values
-        
-        analysis_results = {}
-        # analysis_results['linear_regression'] = {
-        #     'min': {
-        #         'slope': min_slope,
-        #         'intercept': min_intercept,
-        #         'r_squared': min_r_value ** 2
-        #     },
-        #     'max': {
-        #         'slope': max_slope,
-        #         'intercept': max_intercept,
-        #         'r_squared': max_r_value ** 2
-        #     }
-        # }
-        # analysis_results['sma'] = {
-        #     '7': sma_7,
-        #     '15': sma_15
-        # }
-        
-        # Calculate SuperTrend indicators
-        supertrend_results_list = self._calculate_supertrend_indicators()
-        analysis_results['supertrend'] = supertrend_results_list
-        
-        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, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
-            {"period": 10, "multiplier": 1.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
-            {"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 = calculate_supertrend_external(data, p["period"], p["multiplier"])
-            results.append(result)
-        
-        supertrend_results_list = []
-        for params, result in zip(supertrend_params, results):
-            supertrend_results_list.append({
-                "results": result,
-                "params": params
-            })
-        return supertrend_results_list
-
-    def plot_trends(self, trend_data, analysis_results, view="both"):
-        """
-        Plot the price data with detected trends using a candlestick chart.
-        Also plots SuperTrend indicators with three different parameter sets.
-
-        Parameters:
-        - trend_data: DataFrame, the output from detect_trends()
-        - analysis_results: Dictionary containing analysis results from detect_trends()
-        - view: str, one of 'both', 'trend', 'supertrend'; determines which plot(s) to display
-
-        Returns:
-        - None (displays the plot)
-        """
-        if not self.display:
-            return  # Do nothing if display is False
-
-        plt.style.use(self.plot_style)
-
-        if view == "both":
-            fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(self.plot_size[0]*2, self.plot_size[1]))
-        else:
-            fig, ax = plt.subplots(figsize=self.plot_size)
-            ax1 = ax2 = None
-            if view == "trend":
-                ax1 = ax
-            elif view == "supertrend":
-                ax2 = ax
-
-        fig.patch.set_facecolor(self.bg_color)
-        if ax1: ax1.set_facecolor(self.bg_color)
-        if ax2: ax2.set_facecolor(self.bg_color)
-
-        df = self.data.copy()
-
-        if ax1:
-            self._plot_trend_analysis(ax1, df, trend_data, analysis_results)
-
-        if ax2:
-            self._plot_supertrend_analysis(ax2, df, analysis_results['supertrend'])
-
-        plt.tight_layout()
-        plt.show()
-    
-    def _plot_candlesticks(self, ax, df):
-        """
-        Plot candlesticks on the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        """
-        from matplotlib.patches import Rectangle
-        
-        for i in range(len(df)):
-            # Get OHLC values for this candle
-            open_val = df['open'].iloc[i]
-            close_val = df['close'].iloc[i]
-            high_val = df['high'].iloc[i]
-            low_val = df['low'].iloc[i]
-            
-            # Determine candle color
-            color = self.candle_up_color if close_val >= open_val else self.candle_down_color
-            
-            # Plot candle body
-            body_height = abs(close_val - open_val)
-            bottom = min(open_val, close_val)
-            rect = Rectangle((i - self.candle_width/2, bottom), self.candle_width, body_height, 
-                            color=color, alpha=self.candle_alpha)
-            ax.add_patch(rect)
-            
-            # Plot candle wicks
-            ax.plot([i, i], [low_val, high_val], color=color, linewidth=self.wick_width)
-    
-    def _plot_trend_analysis(self, ax, df, trend_data, analysis_results):
-        """
-        Plot trend analysis on the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        - trend_data: pandas.DataFrame, the trend data
-        - analysis_results: dict, the analysis results
-        """
-        # Draw candlesticks
-        self._plot_candlesticks(ax, df)
-        
-        # Plot minima and maxima points
-        self._plot_min_max_points(ax, df, trend_data)
-        
-        # Plot trend lines and moving averages
-        if analysis_results:
-            self._plot_trend_lines(ax, df, analysis_results)
-        
-        # Configure the subplot
-        self._configure_subplot(ax, 'Price Chart with Trend Analysis', len(df))
-    
-    def _plot_min_max_points(self, ax, df, trend_data):
-        """
-        Plot minimum and maximum points on the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        - trend_data: pandas.DataFrame, the trend data
-        """
-        min_indices = trend_data.index[trend_data['is_min'] == True].tolist()
-        if min_indices:
-            min_y = [df['close'].iloc[i] for i in min_indices]
-            ax.scatter(min_indices, min_y, color=self.min_color, s=self.min_size, 
-                      marker=self.min_marker, label='Local Minima', zorder=self.marker_zorder)
-
-        max_indices = trend_data.index[trend_data['is_max'] == True].tolist()
-        if max_indices:
-            max_y = [df['close'].iloc[i] for i in max_indices]
-            ax.scatter(max_indices, max_y, color=self.max_color, s=self.max_size, 
-                      marker=self.max_marker, label='Local Maxima', zorder=self.marker_zorder)
-    
-    def _plot_trend_lines(self, ax, df, analysis_results):
-        """
-        Plot trend lines on the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        - analysis_results: dict, the analysis results
-        """
-        x_vals = np.arange(len(df))
-        
-        # Minima regression line (support)
-        min_slope = analysis_results['linear_regression']['min']['slope']
-        min_intercept = analysis_results['linear_regression']['min']['intercept']
-        min_line = min_slope * x_vals + min_intercept
-        ax.plot(x_vals, min_line, self.min_line_style, linewidth=self.line_width, 
-               label='Minima Regression')
-
-        # Maxima regression line (resistance)
-        max_slope = analysis_results['linear_regression']['max']['slope']
-        max_intercept = analysis_results['linear_regression']['max']['intercept']
-        max_line = max_slope * x_vals + max_intercept
-        ax.plot(x_vals, max_line, self.max_line_style, linewidth=self.line_width, 
-               label='Maxima Regression')
-
-        # SMA-7 line
-        sma_7 = analysis_results['sma']['7']
-        ax.plot(x_vals, sma_7, self.sma7_line_style, linewidth=self.line_width, 
-               label='SMA-7')
-
-        # SMA-15 line
-        sma_15 = analysis_results['sma']['15']
-        valid_idx_15 = ~np.isnan(sma_15)
-        ax.plot(x_vals[valid_idx_15], sma_15[valid_idx_15], self.sma15_line_style, 
-               linewidth=self.line_width, label='SMA-15')
-    
-    def _configure_subplot(self, ax, title, data_length):
-        """
-        Configure the subplot with title, labels, limits, and legend.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to configure
-        - title: str, the title of the subplot
-        - data_length: int, the length of the data
-        """
-        # Set title and labels
-        ax.set_title(title, fontsize=self.title_size, color=self.title_color)
-        ax.set_xlabel('Date', fontsize=self.axis_label_size, color=self.axis_label_color)
-        ax.set_ylabel('Price', fontsize=self.axis_label_size, color=self.axis_label_color)
-        
-        # Set appropriate x-axis limits
-        ax.set_xlim(-0.5, data_length - 0.5)
-        
-        # Add a legend
-        ax.legend(loc=self.legend_loc, facecolor=self.legend_bg_color)
-    
-    def _plot_supertrend_analysis(self, ax, df, supertrend_results_list=None):
-        """
-        Plot SuperTrend analysis on the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        - supertrend_results_list: list, the SuperTrend results (optional)
-        """
-        self._plot_candlesticks(ax, df)
-        self._plot_supertrend_lines(ax, df, supertrend_results_list, style='Both')
-        self._configure_subplot(ax, 'Multiple SuperTrend Indicators', len(df))
-        
-    def _plot_supertrend_lines(self, ax, df, supertrend_results_list, style="Horizontal"):
-        """
-        Plot SuperTrend lines on the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        - supertrend_results_list: list, the SuperTrend results
-        """
-        x_vals = np.arange(len(df))
-
-        if style == 'Horizontal' or style == 'Both':
-            if len(supertrend_results_list) != 3:
-                raise ValueError("Expected exactly 3 SuperTrend results for meta calculation")
-
-            trends = [st["results"]["trend"] for st in supertrend_results_list]
-
-            band_height = 0.02 * (df["high"].max() - df["low"].min())
-            y_base = df["low"].min() - band_height * 1.5
-            
-            prev_color = None
-            for i in range(1, len(x_vals)):
-                t_vals = [t[i] for t in trends]
-                up_count = t_vals.count(1)
-                down_count = t_vals.count(-1)
-
-                if down_count == 3:
-                    color = "red"
-                elif down_count == 2 and up_count == 1:
-                    color = "orange"
-                elif down_count == 1 and up_count == 2:
-                    color = "yellow"
-                elif up_count == 3:
-                    color = "green"
-                else:
-                    continue  # skip if unknown or inconsistent values
-
-                ax.add_patch(Rectangle(
-                    (x_vals[i-1], y_base),
-                    1,
-                    band_height,
-                    color=color,
-                    linewidth=0,
-                    alpha=0.6
-                ))
-                # Draw a vertical line at the change of color
-                if prev_color and prev_color != color:
-                    ax.axvline(x_vals[i-1], color="grey", alpha=0.3, linewidth=1)
-                prev_color = color
-
-            ax.set_ylim(bottom=y_base - band_height * 0.5)
-        if style == 'Curves' or style == 'Both':
-            for st in supertrend_results_list:
-                params = st["params"]
-                results = st["results"]
-                supertrend = results["supertrend"]
-                trend = results["trend"]
-                
-                # Plot SuperTrend line with color based on trend
-                for i in range(1, len(x_vals)):
-                    if trend[i] == 1:  # Uptrend
-                        ax.plot(x_vals[i-1:i+1], supertrend[i-1:i+1], params["color_up"], linewidth=self.line_width)
-                    else:  # Downtrend
-                        ax.plot(x_vals[i-1:i+1], supertrend[i-1:i+1], params["color_down"], linewidth=self.line_width)
-            self._plot_metasupertrend_lines(ax, df, supertrend_results_list)
-            self._add_supertrend_legend(ax, supertrend_results_list)
-
-    def _plot_metasupertrend_lines(self, ax, df, supertrend_results_list):
-        """
-        Plot a Meta SuperTrend line where all individual SuperTrends agree on trend.
-
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to plot on
-        - df: pandas.DataFrame, the data to plot
-        - supertrend_results_list: list, each item contains SuperTrend 'results' and 'params'
-        """
-        x_vals = np.arange(len(df))
-
-        if len(supertrend_results_list) != 3:
-            raise ValueError("Expected exactly 3 SuperTrend results for meta calculation")
-
-        trends = [st["results"]["trend"] for st in supertrend_results_list]
-        supertrends = [st["results"]["supertrend"] for st in supertrend_results_list]
-        params = supertrend_results_list[0]["params"]  # Use first config for styling
-
-        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)
-
-        for i in range(1, len(x_vals)):
-            t1, t2, t3 = trends[0][i], trends[1][i], trends[2][i]
-            if t1 == t2 == t3:
-                meta_trend = t1
-                # Average the 3 supertrend values
-                st_avg_prev = np.mean([s[i-1] for s in supertrends])
-                st_avg_curr = np.mean([s[i] for s in supertrends])
-                color = params["color_up"] if meta_trend == 1 else params["color_down"]
-                ax.plot(x_vals[i-1:i+1], [st_avg_prev, st_avg_curr], color, linewidth=self.line_width)
-
-    def _add_supertrend_legend(self, ax, supertrend_results_list):
-        """
-        Add SuperTrend legend entries to the given axis.
-        
-        Parameters:
-        - ax: matplotlib.axes.Axes, the axis to add legend entries to
-        - supertrend_results_list: list, the SuperTrend results
-        """
-        for st in supertrend_results_list:
-            params = st["params"]
-            period = params["period"]
-            multiplier = params["multiplier"]
-            color_up = params["color_up"]
-            color_down = params["color_down"]
-            
-            ax.plot([], [], color_up, linewidth=self.line_width, 
-                    label=f'ST (P:{period}, M:{multiplier}) Up')
-            ax.plot([], [], color_down, linewidth=self.line_width, 
-                    label=f'ST (P:{period}, M:{multiplier}) Down')
-    
-    def backtest_meta_supertrend(self, min1_df, initial_usd=10000, stop_loss_pct=0.05, transaction_cost=0.001, 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.
-        
-        Parameters:
-        - min1_df: pandas DataFrame, 1-minute timeframe data for more accurate stop loss checking (optional)
-        - initial_usd: float, starting USD amount
-        - stop_loss_pct: float, stop loss as a fraction (e.g. 0.05 for 5%)
-        - transaction_cost: float, transaction cost as a fraction (e.g. 0.001 for 0.1%)
-        - debug: bool, whether to print debug info
-        """
-        df = self.data.copy().reset_index(drop=True)
-        df['timestamp'] = pd.to_datetime(df['timestamp'])
-        
-        # Get meta supertrend (all three agree)
-        supertrend_results_list = self._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(df)):
-            if i % 100 == 0 and debug:
-                self.logger.debug(f"Progress: {i}/{len(df)} rows processed.")
-
-            price_open = df['open'].iloc[i]
-            price_high = df['high'].iloc[i]
-            price_low = df['low'].iloc[i]
-            price_close = df['close'].iloc[i]
-            date = df['timestamp'].iloc[i]
-            mt = meta_trend[i]
-            
-            # Check stop loss if in position
-            if position == 1:
-                stop_price = entry_price * (1 - stop_loss_pct)
-                
-                if current_trade_min1_start_idx is None:
-                    # First check after entry, find the entry point in 1-min data
-                    current_trade_min1_start_idx = min1_df.index[min1_df.index >= entry_time][0]
-                
-                # Get the end index for current check
-                current_min1_end_idx = min1_df.index[min1_df.index <= date][-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}")
-                    usd = coin * sell_price * (1 - transaction_cost)  # Apply transaction cost
-                    trade_log.append({
-                        'type': 'STOP',
-                        'entry': entry_price,
-                        'exit': sell_price,
-                        'entry_time': entry_time,
-                        'exit_time': stop_candle.name  # Use index name instead of timestamp column
-                    })
-                    coin = 0
-                    position = 0
-                    entry_price = 0
-                    current_trade_min1_start_idx = None
-                    continue
-                
-                # Update the start index for next check
-                current_trade_min1_start_idx = current_min1_end_idx
-
-            # Entry logic
-            if position == 0 and mt == 1:
-                # Buy at open, apply transaction cost
-                coin = (usd * (1 - transaction_cost)) / price_open
-                entry_price = price_open
-                entry_time = date
-                usd = 0
-                position = 1
-                current_trade_min1_start_idx = None  # Will be set on first stop loss check
-            
-            # Exit logic
-            elif position == 1 and mt == -1:
-                # Sell at open, apply transaction cost
-                usd = coin * price_open * (1 - transaction_cost)
-                trade_log.append({
-                    'type': 'SELL',
-                    'entry': entry_price,
-                    'exit': price_open,
-                    'entry_time': entry_time,
-                    'exit_time': date
-                })
-                coin = 0
-                position = 0
-                entry_price = 0
-                current_trade_min1_start_idx = None
-
-            # Track drawdown
-            balance = usd if position == 0 else coin * price_close
-            if balance > max_balance:
-                max_balance = balance
-            drawdown = (max_balance - balance) / max_balance
-            drawdowns.append(drawdown)
-
-        # If still in position at end, sell at last close
-        if position == 1:
-            usd = coin * df['close'].iloc[-1] * (1 - transaction_cost)  # Apply transaction cost
-            trade_log.append({
-                'type': 'EOD',
-                'entry': entry_price,
-                'exit': df['close'].iloc[-1],
-                'entry_time': entry_time,
-                'exit_time': df['timestamp'].iloc[-1]
-            })
-            coin = 0
-            position = 0
-            entry_price = 0
-
-        # Calculate statistics
-        final_balance = usd
-        n_trades = len(trade_log)
-        wins = [1 for t in trade_log if t['exit'] > t['entry']]
-        win_rate = len(wins) / n_trades if n_trades > 0 else 0
-        max_drawdown = max(drawdowns) if drawdowns else 0
-        avg_trade = np.mean([t['exit']/t['entry']-1 for t in trade_log]) if trade_log else 0
-
-        trades = []
-        for trade in trade_log:
-            profit_pct = (trade['exit'] - trade['entry']) / trade['entry']
-            trades.append({
-                'entry_time': trade['entry_time'],
-                'exit_time': trade['exit_time'],
-                'entry': trade['entry'],
-                'exit': trade['exit'],
-                'profit_pct': profit_pct,
-                'type': trade.get('type', 'SELL')
-            })
-
-        results = {
-            "initial_usd": initial_usd,
-            "final_usd": final_balance,
-            "n_trades": n_trades,
-            "win_rate": win_rate,
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-            "avg_trade": avg_trade,
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-            results["last_trade"] = {
-                "exit_time": trade_log[-1]['exit_time'],
-                "exit": trade_log[-1]['exit']
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-        return results
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