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.

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/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)

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/taxes.py

@@ -1,25 +0,0 @@
-import pandas as pd
-
-class Taxes:
-    def __init__(self, tax_rate=0.20):
-        """
-        tax_rate: flat tax rate on positive profits (e.g., 0.20 for 20%)
-        """
-        self.tax_rate = tax_rate
-
-    def add_taxes_to_results_csv(self, input_csv, output_csv=None, profit_col='final_usd'):
-        """
-        Reads a backtest results CSV, adds tax columns, and writes to a new CSV.
-        - input_csv: path to the input CSV file
-        - output_csv: path to the output CSV file (if None, overwrite input)
-        - profit_col: column name for profit (default: 'final_usd')
-        """
-        df = pd.read_csv(input_csv, delimiter=None)
-        # Compute tax only on positive profits
-        df['tax_paid'] = df[profit_col].apply(lambda x: self.tax_rate * x if x > 0 else 0)
-        df['net_profit_after_tax'] = df[profit_col] - df['tax_paid']
-        df['cumulative_tax_paid'] = df['tax_paid'].cumsum()
-        if not output_csv:
-            output_csv = input_csv
-        df.to_csv(output_csv, index=False)
-        return output_csv

cycles/trend_detector_simple.py

@@ -1,849 +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]
-            prev_mt = meta_trend[i-1]
-            curr_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}")
-                    btc_to_sell = coin
-                    fee_btc = btc_to_sell * transaction_cost
-                    btc_after_fee = btc_to_sell - fee_btc
-                    usd = btc_after_fee * sell_price
-                    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
-                        'fee_btc': fee_btc
-                    })
-                    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: only if not in position and signal changes to 1
-            if position == 0 and prev_mt != 1 and curr_mt == 1:
-                # Buy at open, fee is charged in BTC (base currency)
-                gross_btc = usd / price_open
-                fee_btc = gross_btc * transaction_cost
-                coin = gross_btc - fee_btc
-                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
-                trade_log.append({
-                    'type': 'BUY',
-                    'entry': entry_price,
-                    'exit': None,
-                    'entry_time': entry_time,
-                    'exit_time': None,
-                    'fee_btc': fee_btc
-                })
-            
-            # Exit: only if in position and signal changes from 1 to -1
-            elif position == 1 and prev_mt == 1 and curr_mt == -1:
-                # Sell at open, fee is charged in BTC (base currency)
-                btc_to_sell = coin
-                fee_btc = btc_to_sell * transaction_cost
-                btc_after_fee = btc_to_sell - fee_btc
-                usd = btc_after_fee * price_open
-                trade_log.append({
-                    'type': 'SELL',
-                    'entry': entry_price,
-                    'exit': price_open,
-                    'entry_time': entry_time,
-                    'exit_time': date,
-                    'fee_btc': fee_btc
-                })
-                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:
-            btc_to_sell = coin
-            fee_btc = btc_to_sell * transaction_cost
-            btc_after_fee = btc_to_sell - fee_btc
-            usd = btc_after_fee * df['close'].iloc[-1]
-            trade_log.append({
-                'type': 'EOD',
-                'entry': entry_price,
-                'exit': df['close'].iloc[-1],
-                'entry_time': entry_time,
-                'exit_time': df['timestamp'].iloc[-1],
-                'fee_btc': fee_btc
-            })
-            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'] 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_btc = 0.0
-        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')
-            })
-            # Sum up BTC fees and their USD equivalent (use exit price if available)
-            fee_btc = trade.get('fee_btc', 0.0)
-            total_fees_btc += fee_btc
-            if fee_btc and trade.get('exit') is not None:
-                total_fees_usd += fee_btc * trade['exit']
-
-        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_btc": total_fees_btc,
-            "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
-    

cycles/utils/apply_taxes_to_file.py

@@ -1,23 +0,0 @@
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-from taxes import Taxes
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print("Usage: python apply_taxes_to_file.py <input_csv> [profit_col]")
-        sys.exit(1)
-
-    input_csv = sys.argv[1]
-    profit_col = sys.argv[2] if len(sys.argv) > 2 else 'final_usd'
-
-    if not os.path.isfile(input_csv):
-        print(f"File not found: {input_csv}")
-        sys.exit(1)
-
-    base, ext = os.path.splitext(input_csv)
-    output_csv = f"{base}_taxed.csv"
-
-    taxes = Taxes()  # Default 20% tax rate
-    taxes.add_taxes_to_results_csv(input_csv, output_csv, profit_col=profit_col)
-    print(f"Taxed file saved as: {output_csv}")

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/storage.py

@@ -57,7 +57,10 @@ class Storage:
                 }
                 # 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)]
@@ -66,10 +69,62 @@ class Storage:
                     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 None 
+            # 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
@@ -89,6 +144,7 @@ class Storage:
             "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):
@@ -113,15 +169,19 @@ class Storage:
                 filtered_row = {k: v for k, v in row.items() if k in fieldnames}
                 writer.writerow(filtered_row)
     
-    def write_results_combined(self, filename, fieldnames, rows):
+    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:

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

@@ -4,15 +4,13 @@ import logging
 import concurrent.futures
 import os
 import datetime
-import queue
+import argparse
+import json
 
-from cycles.trend_detector_simple import TrendDetectorSimple
-from cycles.taxes import Taxes
 from cycles.utils.storage import Storage
-from cycles.utils.gsheets import GSheetBatchPusher
 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",
@@ -22,19 +20,17 @@ logging.basicConfig(
     ]
 )
 
-# Global queue for batching Google Sheets updates
-results_queue = queue.Queue()
-
 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
@@ -51,6 +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:
@@ -58,9 +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,
@@ -74,8 +76,10 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
             "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,
@@ -85,9 +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':
@@ -95,12 +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 a single (timeframe, stop_loss_pct) combination (no monthly split)"""
+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":
+
+    if rule == "1T" or rule == "1min":
         df = data_1min.copy()
     else:
         df = data_1min.resample(rule).agg({
@@ -111,8 +122,33 @@ def process_timeframe(timeframe_info, debug=False):
             'volume': 'sum'
         }).dropna()
     df = df.reset_index()
-    # Only process one stop loss
+
     results_rows, all_trade_rows = process_timeframe_data(data_1min, df, [stop_loss_pct], rule, initial_usd, debug=debug)
+
+    if all_trade_rows:
+        trades_fieldnames = ["entry_time", "exit_time", "entry_price", "exit_price", "profit_pct", "type", "fee_usd"]
+        # Prepare header
+        summary_fields = ["timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate", "max_drawdown", "avg_trade", "profit_ratio", "final_usd"]
+        summary_row = results_rows[0]
+        header_line = "\t".join(summary_fields) + "\n"
+        value_line = "\t".join(str(summary_row.get(f, "")) for f in summary_fields) + "\n"
+        # File name
+        tf = summary_row["timeframe"]
+        sl = summary_row["stop_loss_pct"]
+        sl_percent = int(round(sl * 100))
+        trades_filename = os.path.join(storage.results_dir, f"trades_{tf}_ST{sl_percent}pct.csv")
+        # Write header
+        with open(trades_filename, "w") as f:
+            f.write(header_line)
+            f.write(value_line)
+        # Now write trades (append mode, skip header)
+        with open(trades_filename, "a", newline="") as f:
+            import csv
+            writer = csv.DictWriter(f, fieldnames=trades_fieldnames)
+            writer.writeheader()
+            for trade in all_trade_rows:
+                writer.writerow({k: trade.get(k, "") for k in trades_fieldnames})
+
     return results_rows, all_trade_rows
 
 def aggregate_results(all_rows):
@@ -126,7 +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])
@@ -136,6 +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,
@@ -148,6 +184,7 @@ 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
 
@@ -161,30 +198,69 @@ def get_nearest_price(df, target_date):
         return nearest_time, price
 
 if __name__ == "__main__":
-    # Configuration
-    # start_date = '2022-01-01'
-    # stop_date = '2023-01-01'
-    start_date = '2024-05-15'
-    stop_date = '2025-05-15'
-    initial_usd = 10000
     debug = False
 
-    timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M")
+    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()
+
+    # 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],
+    }
+
+    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)
 
-    timeframes = ["1D"]
-    stop_loss_pcts = [0.01, 0.02, 0.03]
-
-    # Load data once
     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)
 
-    logging.info(f"Price at start_date ({start_date}) [nearest timestamp: {nearest_start_time}]: {start_price}")
-    logging.info(f"Price at stop_date ({stop_date}) [nearest timestamp: {nearest_stop_time}]: {stop_price}")
+    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)
@@ -194,72 +270,33 @@ if __name__ == "__main__":
     
     workers = system_utils.get_optimal_workers()
 
-    # Start the background batch pusher
-    # spreadsheet_name = "GlimBit Backtest Results"
-    # batch_pusher = GSheetBatchPusher(results_queue, timestamp, spreadsheet_name, interval=65)
-    # batch_pusher.start()
-    
-    # Process tasks with optimized concurrency
-    with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as executor:
-        futures = {executor.submit(process_timeframe, task, debug): task for task in tasks}
+    if debug:
         all_results_rows = []
         all_trade_rows = []
-        for future in concurrent.futures.as_completed(futures):
-            results, trades = future.result()
+
+        for task in tasks:
+            results, trades = process(task, debug)
             if results or trades:
                 all_results_rows.extend(results)
                 all_trade_rows.extend(trades)
-                # results_queue.put((results, trades))  # Enqueue for batch update
+    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 = []
 
-    # After all tasks, flush any remaining updates
-    # batch_pusher.stop()
-    # batch_pusher.join()
+            for future in concurrent.futures.as_completed(futures):
+                results, trades = future.result()
 
-    # Ensure all batches are pushed, even after 429 errors
-    # while not results_queue.empty():
-    #     logging.info("Waiting for Google Sheets quota to reset. Retrying batch push in 60 seconds...")
-    #     time.sleep(65)
-    #     batch_pusher.push_all()
+                if results or trades:
+                    all_results_rows.extend(results)
+                    all_trade_rows.extend(trades)
 
-    # Write all results to a single CSV file
-    combined_filename = os.path.join(f"{timestamp}_backtest_combined.csv")
-    combined_fieldnames = [
+    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"
+        "max_drawdown", "avg_trade", "profit_ratio", "final_usd", "total_fees_usd"
     ]
-    storage.write_results_combined(combined_filename, combined_fieldnames, all_results_rows)
-
-    # --- Add taxes to combined results CSV ---
-    # taxes = Taxes()  # Default 20% tax rate
-    # taxed_filename = combined_filename.replace('.csv', '_taxed.csv')
-    # taxes.add_taxes_to_results_csv(combined_filename, taxed_filename, profit_col='total_profit')
-    # logging.info(f"Taxed results written to {taxed_filename}")
-
-    # --- Write trades to separate CSVs per timeframe and stop loss ---
-    # Collect all trades from each task (need to run tasks to collect trades)
-    # Since only all_results_rows is collected above, we need to also collect all trades.
-    # To do this, modify the above loop to collect all trades as well.
-    # But for now, let's assume you have a list all_trade_rows (list of dicts)
-    # If not, you need to collect it in the ProcessPoolExecutor loop above.
-
-    # --- BEGIN: Collect all trades from each task ---
-    # To do this, modify the ProcessPoolExecutor loop above:
-    # 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)
-    # --- END: Collect all trades from each task ---
-
-    # Now, group all_trade_rows by (timeframe, stop_loss_pct)
-
-
-    trades_fieldnames = [
-        "entry_time", "exit_time", "entry_price", "exit_price", "profit_pct", "type"
-    ]
-    storage.write_trades(all_trade_rows, trades_fieldnames)
+    storage.write_backtest_results(backtest_filename, backtest_fieldnames, all_results_rows, metadata_lines)
 
     

pyproject.toml

@@ -10,4 +10,5 @@ dependencies = [
     "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.")
+    

uv.lock

@@ -172,6 +172,7 @@ dependencies = [
     { name = "pandas" },
     { name = "psutil" },
     { name = "scipy" },
+    { name = "seaborn" },
 ]
 
 [package.metadata]
@@ -181,6 +182,7 @@ requires-dist = [
     { name = "pandas", specifier = ">=2.2.3" },
     { name = "psutil", specifier = ">=7.0.0" },
     { name = "scipy", specifier = ">=1.15.3" },
+    { name = "seaborn", specifier = ">=0.13.2" },
 ]
 
 [[package]]
@@ -779,6 +781,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/81/06/0a5e5349474e1cbc5757975b21bd4fad0e72ebf138c5592f191646154e06/scipy-1.15.3-cp313-cp313t-win_amd64.whl", hash = "sha256:76ad1fb5f8752eabf0fa02e4cc0336b4e8f021e2d5f061ed37d6d264db35e3ca", size = 40308097, upload-time = "2025-05-08T16:08:27.627Z" },
 ]
 
+[[package]]
+name = "seaborn"
+version = "0.13.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "pandas" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/86/59/a451d7420a77ab0b98f7affa3a1d78a313d2f7281a57afb1a34bae8ab412/seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7", size = 1457696, upload-time = "2024-01-25T13:21:52.551Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987", size = 294914, upload-time = "2024-01-25T13:21:49.598Z" },
+]
+
 [[package]]
 name = "six"
 version = "1.17.0"