Enhance backtesting framework with static task processing and progress management. Introduced static task processing for parallel execution, improved error handling, and added a progress manager for better task tracking. Updated BacktestRunner to support progress callbacks and optimized worker allocation based on system resources. Added new configuration files for flexible backtesting setups.

backtest_runner.py

@@ -5,9 +5,85 @@ from typing import List, Tuple, Dict, Any, Optional
 
 from cycles.utils.storage import Storage
 from cycles.utils.system import SystemUtils
+from cycles.utils.progress_manager import ProgressManager
 from result_processor import ResultProcessor
 
 
+def _process_single_task_static(task: Tuple[str, str, pd.DataFrame, float, float], progress_callback=None) -> Tuple[List[Dict], List[Dict]]:
+    """
+    Static version of _process_single_task for use with ProcessPoolExecutor
+    
+    Args:
+        task: Tuple of (task_id, timeframe, data_1min, stop_loss_pct, initial_usd)
+        progress_callback: Optional progress callback function
+        
+    Returns:
+        Tuple of (results, trades)
+    """
+    task_id, timeframe, data_1min, stop_loss_pct, initial_usd = task
+
+    try:
+        if timeframe == "1T" or timeframe == "1min":
+            df = data_1min.copy()
+        else:
+            df = _resample_data_static(data_1min, timeframe)
+
+        # Create required components for processing
+        from cycles.utils.storage import Storage
+        from result_processor import ResultProcessor
+        
+        # Create storage with default paths (for subprocess)
+        storage = Storage()
+        result_processor = ResultProcessor(storage)
+
+        results, trades = result_processor.process_timeframe_results(
+            data_1min, 
+            df, 
+            [stop_loss_pct], 
+            timeframe, 
+            initial_usd,
+            progress_callback=progress_callback
+        )
+
+        return results, trades
+
+    except Exception as e:
+        error_msg = f"Failed to process {timeframe} with stop loss {stop_loss_pct}: {e}"
+        raise RuntimeError(error_msg) from e
+
+
+def _resample_data_static(data_1min: pd.DataFrame, timeframe: str) -> pd.DataFrame:
+    """
+    Static function to resample 1-minute data to specified timeframe
+    
+    Args:
+        data_1min: 1-minute data DataFrame
+        timeframe: Target timeframe string
+        
+    Returns:
+        Resampled DataFrame
+    """
+    try:
+        agg_dict = {
+            'open': 'first',
+            'high': 'max',
+            'low': 'min',
+            'close': 'last',
+            'volume': 'sum'
+        }
+        
+        if 'predicted_close_price' in data_1min.columns:
+            agg_dict['predicted_close_price'] = 'last'
+        
+        resampled = data_1min.resample(timeframe).agg(agg_dict).dropna()
+        
+        return resampled.reset_index()
+        
+    except Exception as e:
+        error_msg = f"Failed to resample data to {timeframe}: {e}"
+        raise ValueError(error_msg) from e
+
+
 class BacktestRunner:
     """Handles the execution of backtests across multiple timeframes and parameters"""
     
@@ -16,7 +92,8 @@ class BacktestRunner:
         storage: Storage, 
         system_utils: SystemUtils, 
         result_processor: ResultProcessor,
-        logging_instance: Optional[logging.Logger] = None
+        logging_instance: Optional[logging.Logger] = None,
+        show_progress: bool = True
     ):
         """
         Initialize backtest runner
@@ -26,11 +103,14 @@ class BacktestRunner:
             system_utils: System utilities for resource management
             result_processor: Result processor for handling outputs
             logging_instance: Optional logging instance
+            show_progress: Whether to show visual progress bars
         """
         self.storage = storage
         self.system_utils = system_utils
         self.result_processor = result_processor
         self.logging = logging_instance
+        self.show_progress = show_progress
+        self.progress_manager = ProgressManager() if show_progress else None
 
     def run_backtests(
         self, 
@@ -56,10 +136,13 @@ class BacktestRunner:
         # Create tasks for all combinations
         tasks = self._create_tasks(timeframes, stop_loss_pcts, data_1min, initial_usd)
         
+        if self.logging:
+            self.logging.info(f"Starting {len(tasks)} backtest tasks")
+        
         if debug:
-            return self._run_sequential(tasks, debug)
+            return self._run_sequential(tasks)
         else:
-            return self._run_parallel(tasks, debug)
+            return self._run_parallel(tasks)
 
     def _create_tasks(
         self, 
@@ -72,50 +155,92 @@ class BacktestRunner:
         tasks = []
         for timeframe in timeframes:
             for stop_loss_pct in stop_loss_pcts:
-                task = (timeframe, data_1min, stop_loss_pct, initial_usd)
+                task_id = f"{timeframe}_{stop_loss_pct}"
+                task = (task_id, timeframe, data_1min, stop_loss_pct, initial_usd)
                 tasks.append(task)
         return tasks
+    
 
-    def _run_sequential(self, tasks: List[Tuple], debug: bool) -> Tuple[List[Dict], List[Dict]]:
+
+    def _run_sequential(self, tasks: List[Tuple]) -> Tuple[List[Dict], List[Dict]]:
         """Run tasks sequentially (for debug mode)"""
+        # Initialize progress tracking if enabled
+        if self.progress_manager:
+            for task in tasks:
+                task_id, timeframe, data_1min, stop_loss_pct, initial_usd = task
+                
+                # Calculate actual DataFrame size that will be processed
+                if timeframe == "1T" or timeframe == "1min":
+                    actual_df_size = len(data_1min)
+                else:
+                    # Get the actual resampled DataFrame size
+                    temp_df = self._resample_data(data_1min, timeframe)
+                    actual_df_size = len(temp_df)
+                
+                task_name = f"{timeframe} SL:{stop_loss_pct:.0%}"
+                self.progress_manager.start_task(task_id, task_name, actual_df_size)
+            
+            self.progress_manager.start_display()
+
         all_results = []
         all_trades = []
 
-        for task in tasks:
-            try:
-                results, trades = self._process_single_task(task, debug)
-                if results:
-                    all_results.extend(results)
-                if trades:
-                    all_trades.extend(trades)
+        try:
+            for task in tasks:
+                try:
+                    # Get progress callback for this task if available
+                    progress_callback = None
+                    if self.progress_manager:
+                        progress_callback = self.progress_manager.get_task_progress_callback(task[0])
                     
-            except Exception as e:
-                error_msg = f"Error processing task {task[0]} with stop loss {task[2]}: {e}"
-                if self.logging:
-                    self.logging.error(error_msg)
-                raise RuntimeError(error_msg) from e
+                    results, trades = self._process_single_task(task, progress_callback)
+                    
+                    if results:
+                        all_results.extend(results)
+                    if trades:
+                        all_trades.extend(trades)
+                    
+                    # Mark task as completed
+                    if self.progress_manager:
+                        self.progress_manager.complete_task(task[0])
+                        
+                except Exception as e:
+                    error_msg = f"Error processing task {task[1]} with stop loss {task[3]}: {e}"
+                    
+                    if self.logging:
+                        self.logging.error(error_msg)
+                    
+                    raise RuntimeError(error_msg) from e
+        finally:
+            # Stop progress display
+            if self.progress_manager:
+                self.progress_manager.stop_display()
 
         return all_results, all_trades
 
-    def _run_parallel(self, tasks: List[Tuple], debug: bool) -> Tuple[List[Dict], List[Dict]]:
+    def _run_parallel(self, tasks: List[Tuple]) -> Tuple[List[Dict], List[Dict]]:
         """Run tasks in parallel using ProcessPoolExecutor"""
         workers = self.system_utils.get_optimal_workers()
         
         if self.logging:
             self.logging.info(f"Running {len(tasks)} tasks with {workers} workers")
 
+        # OPTIMIZATION: Disable progress manager for parallel execution to reduce overhead
+        # Progress tracking adds significant overhead in multiprocessing
+        if self.progress_manager and self.logging:
+            self.logging.info("Progress tracking disabled for parallel execution (performance optimization)")
+
         all_results = []
         all_trades = []
+        completed_tasks = 0
 
         try:
             with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as executor:
-                # Submit all tasks
                 future_to_task = {
-                    executor.submit(self._process_single_task, task, debug): task 
+                    executor.submit(_process_single_task_static, task): task 
                     for task in tasks
                 }
 
-                # Collect results as they complete
                 for future in concurrent.futures.as_completed(future_to_task):
                     task = future_to_task[future]
                     try:
@@ -124,9 +249,14 @@ class BacktestRunner:
                             all_results.extend(results)
                         if trades:
                             all_trades.extend(trades)
+                        
+                        completed_tasks += 1
+                        
+                        if self.logging:
+                            self.logging.info(f"Completed task {task[0]} ({completed_tasks}/{len(tasks)})")
                             
                     except Exception as e:
-                        error_msg = f"Task {task[0]} with stop loss {task[2]} failed: {e}"
+                        error_msg = f"Task {task[1]} with stop loss {task[3]} failed: {e}"
                         if self.logging:
                             self.logging.error(error_msg)
                         raise RuntimeError(error_msg) from e
@@ -136,46 +266,56 @@ class BacktestRunner:
             if self.logging:
                 self.logging.error(error_msg)
             raise RuntimeError(error_msg) from e
+        
+        finally:
+            # Stop progress display
+            if self.progress_manager:
+                self.progress_manager.stop_display()
+
+        if self.logging:
+            self.logging.info(f"All {len(tasks)} tasks completed successfully")
 
         return all_results, all_trades
 
     def _process_single_task(
         self, 
-        task: Tuple[str, pd.DataFrame, float, float], 
-        debug: bool = False
+        task: Tuple[str, str, pd.DataFrame, float, float], 
+        progress_callback=None
     ) -> Tuple[List[Dict], List[Dict]]:
         """
         Process a single backtest task
         
         Args:
-            task: Tuple of (timeframe, data_1min, stop_loss_pct, initial_usd)
-            debug: Whether to enable debug output
+            task: Tuple of (task_id, timeframe, data_1min, stop_loss_pct, initial_usd)
+            progress_callback: Optional progress callback function
             
         Returns:
             Tuple of (results, trades)
         """
-        timeframe, data_1min, stop_loss_pct, initial_usd = task
+        task_id, timeframe, data_1min, stop_loss_pct, initial_usd = task
 
         try:
-            # Resample data if needed
             if timeframe == "1T" or timeframe == "1min":
                 df = data_1min.copy()
             else:
                 df = self._resample_data(data_1min, timeframe)
 
-            # Process timeframe results
             results, trades = self.result_processor.process_timeframe_results(
                 data_1min, 
                 df, 
                 [stop_loss_pct], 
                 timeframe, 
-                initial_usd, 
-                debug
+                initial_usd,
+                progress_callback=progress_callback
             )
 
-            # Save individual trade files if trades exist
-            if trades:
-                self.result_processor.save_trade_file(trades, timeframe, stop_loss_pct)
+            # OPTIMIZATION: Skip individual trade file saving during parallel execution
+            # Trade files will be saved in batch at the end
+            # if trades:
+            #     self.result_processor.save_trade_file(trades, timeframe, stop_loss_pct)
+
+            if self.logging:
+                self.logging.info(f"Completed task {task_id}: {len(results)} results, {len(trades)} trades")
 
             return results, trades
 
@@ -197,13 +337,18 @@ class BacktestRunner:
             Resampled DataFrame
         """
         try:
-            resampled = data_1min.resample(timeframe).agg({
+            agg_dict = {
                 'open': 'first',
                 'high': 'max',
                 'low': 'min',
                 'close': 'last',
                 'volume': 'sum'
-            }).dropna()
+            }
+            
+            if 'predicted_close_price' in data_1min.columns:
+                agg_dict['predicted_close_price'] = 'last'
+            
+            resampled = data_1min.resample(timeframe).agg(agg_dict).dropna()
             
             return resampled.reset_index()
             
@@ -213,6 +358,34 @@ class BacktestRunner:
                 self.logging.error(error_msg)
             raise ValueError(error_msg) from e
 
+    def _get_timeframe_factor(self, timeframe: str) -> int:
+        """
+        Get the factor by which data is reduced when resampling to timeframe
+        
+        Args:
+            timeframe: Target timeframe string (e.g., '1h', '4h', '1D')
+            
+        Returns:
+            Factor for estimating data size after resampling
+        """
+        timeframe_factors = {
+            '1T': 1, '1min': 1,
+            '5T': 5, '5min': 5,
+            '15T': 15, '15min': 15,
+            '30T': 30, '30min': 30,
+            '1h': 60, '1H': 60,
+            '2h': 120, '2H': 120,
+            '4h': 240, '4H': 240,
+            '6h': 360, '6H': 360,
+            '8h': 480, '8H': 480,
+            '12h': 720, '12H': 720,
+            '1D': 1440, '1d': 1440,
+            '2D': 2880, '2d': 2880,
+            '3D': 4320, '3d': 4320,
+            '1W': 10080, '1w': 10080
+        }
+        return timeframe_factors.get(timeframe, 60)  # Default to 1 hour if unknown
+
     def load_data(self, filename: str, start_date: str, stop_date: str) -> pd.DataFrame:
         """
         Load and validate data for backtesting
@@ -234,8 +407,11 @@ class BacktestRunner:
             if data.empty:
                 raise ValueError(f"No data loaded for period {start_date} to {stop_date}")
                 
-            # Validate required columns
             required_columns = ['open', 'high', 'low', 'close', 'volume']
+            
+            if 'predicted_close_price' in data.columns:
+                required_columns.append('predicted_close_price')
+            
             missing_columns = [col for col in required_columns if col not in data.columns]
             
             if missing_columns:
@@ -269,11 +445,9 @@ class BacktestRunner:
         Raises:
             ValueError: If any input is invalid
         """
-        # Validate timeframes
         if not timeframes:
             raise ValueError("At least one timeframe must be specified")
             
-        # Validate stop loss percentages
         if not stop_loss_pcts:
             raise ValueError("At least one stop loss percentage must be specified")
             
@@ -281,7 +455,6 @@ class BacktestRunner:
             if not 0 < pct < 1:
                 raise ValueError(f"Stop loss percentage must be between 0 and 1, got: {pct}")
                 
-        # Validate initial USD
         if initial_usd <= 0:
             raise ValueError("Initial USD must be positive")
             

config_manager.py

@@ -14,7 +14,7 @@ class ConfigManager:
         "initial_usd": 10000,
         "timeframes": ["1D", "6h", "3h", "1h", "30m", "15m", "5m", "1m"],
         "stop_loss_pcts": [0.01, 0.02, 0.03, 0.05],
-        "data_dir": "data",
+        "data_dir": "../data",
         "results_dir": "results"
     }
 

configs/flat_2021_2024_config.json

@@ -0,0 +1,10 @@
+{
+    "start_date": "2021-11-01",
+    "stop_date": "2024-04-01",
+    "initial_usd": 10000,
+    "timeframes": ["1min", "2min", "3min", "4min", "5min", "10min", "15min", "30min", "1h", "2h", "4h", "6h", "8h", "12h", "1d"],
+    "stop_loss_pcts": [0.01, 0.02, 0.03, 0.04, 0.05, 0.1],
+    "data_dir": "../data",
+    "results_dir": "../results",
+    "debug": 0
+} 

configs/full_config.json

@@ -0,0 +1,10 @@
+{
+    "start_date": "2020-01-01",
+    "stop_date": "2025-07-08",
+    "initial_usd": 10000,
+    "timeframes": ["1h", "4h", "15ME", "5ME", "1ME"],
+    "stop_loss_pcts": [0.01, 0.02, 0.03, 0.05],
+    "data_dir": "../data",
+    "results_dir": "../results",
+    "debug": 1
+} 

configs/sample_config.json

@@ -2,8 +2,9 @@
     "start_date": "2023-01-01",
     "stop_date": "2025-01-15",
     "initial_usd": 10000,
-    "timeframes": ["1h", "4h"],
-    "stop_loss_pcts": [0.02, 0.05],
+    "timeframes": ["4h"],
+    "stop_loss_pcts": [0.05],
     "data_dir": "../data",
-    "results_dir": "../results"
+    "results_dir": "../results",
+    "debug": 0
 } 

cycles/backtest.py

@@ -7,21 +7,32 @@ from cycles.market_fees import MarketFees
 
 class Backtest:
     @staticmethod
-    def run(min1_df, df, initial_usd, stop_loss_pct, debug=False):
+    def run(min1_df, df, initial_usd, stop_loss_pct, progress_callback=None, verbose=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)
+        - df: pandas DataFrame, main timeframe data for signals
         - 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
+        - progress_callback: callable, optional callback function to report progress (current_step)
+        - verbose: bool, enable debug logging for stop loss checks
         """
-        _df = df.copy().reset_index(drop=True)
+        _df = df.copy().reset_index()
+        
+        # Ensure we have a timestamp column regardless of original index name
+        if 'timestamp' not in _df.columns:
+            # If reset_index() created a column with the original index name, rename it
+            if len(_df.columns) > 0 and _df.columns[0] not in ['open', 'high', 'low', 'close', 'volume', 'predicted_close_price']:
+                _df = _df.rename(columns={_df.columns[0]: 'timestamp'})
+            else:
+                raise ValueError("Unable to identify timestamp column in DataFrame")
+        
         _df['timestamp'] = pd.to_datetime(_df['timestamp'])
         
-        supertrends = Supertrends(_df, verbose=False)
+        supertrends = Supertrends(_df, verbose=False, close_column='predicted_close_price')
 
         supertrend_results_list = supertrends.calculate_supertrend_indicators()
         trends = [st['results']['trend'] for st in supertrend_results_list]
@@ -41,18 +52,21 @@ class Backtest:
         drawdowns = []
         trades = []
         entry_time = None
-        current_trade_min1_start_idx = None
+        stop_loss_count = 0  # Track number of stop losses
 
-        min1_df.index = pd.to_datetime(min1_df.index)
-        min1_timestamps = min1_df.index.values
+        # Ensure min1_df has proper DatetimeIndex
+        if min1_df is not None and not min1_df.empty:
+            min1_df.index = pd.to_datetime(min1_df.index)
 
-        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
+            # Report progress if callback is provided
+            if progress_callback:
+                # Update more frequently for better responsiveness
+                update_frequency = max(1, len(_df) // 50)  # Update every 2% of dataset (50 updates total)
+                if i % update_frequency == 0 or i == len(_df) - 1:  # Always update on last iteration
+                    if verbose:  # Only print in verbose mode to avoid spam
+                        print(f"DEBUG: Progress callback called with i={i}, total={len(_df)-1}")
+                    progress_callback(i)
 
             price_open = _df['open'].iloc[i]
             price_close = _df['close'].iloc[i]
@@ -69,16 +83,13 @@ class Backtest:
                     entry_price,
                     stop_loss_pct,
                     coin,
-                    usd,
-                    debug,
-                    current_trade_min1_start_idx
+                    verbose=verbose
                 )
                 if stop_loss_result is not None:
-                    trade_log_entry, current_trade_min1_start_idx, position, coin, entry_price = stop_loss_result
+                    trade_log_entry, position, coin, entry_price, usd = stop_loss_result
                     trade_log.append(trade_log_entry)
+                    stop_loss_count += 1
                     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:
@@ -99,7 +110,9 @@ class Backtest:
             drawdown = (max_balance - balance) / max_balance
             drawdowns.append(drawdown)
 
-        print("\rProgress: 100%\r\n", end="", flush=True)
+        # Report completion if callback is provided
+        if progress_callback:
+            progress_callback(len(_df) - 1)
 
         # If still in position at end, sell at last close
         if position == 1:
@@ -122,22 +135,37 @@ class Backtest:
                 profit_pct = (trade['exit'] - trade['entry']) / trade['entry']
             else:
                 profit_pct = 0.0
+            
+            # Validate fee_usd field
+            if 'fee_usd' not in trade:
+                raise ValueError(f"Trade missing required field 'fee_usd': {trade}")
+            fee_usd = trade['fee_usd']
+            if fee_usd is None:
+                raise ValueError(f"Trade fee_usd is None: {trade}")
+                
+            # Validate trade type field
+            if 'type' not in trade:
+                raise ValueError(f"Trade missing required field 'type': {trade}")
+            trade_type = trade['type']
+            if trade_type is None:
+                raise ValueError(f"Trade type is None: {trade}")
+                
             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')
+                'type': trade_type,
+                'fee_usd': 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,
+            "n_stop_loss": stop_loss_count,  # Add stop loss count
             "win_rate": win_rate,
             "max_drawdown": max_drawdown,
             "avg_trade": avg_trade,
@@ -157,38 +185,112 @@ class Backtest:
         return results
 
     @staticmethod
-    def check_stop_loss(min1_df, entry_time, date, entry_price, stop_loss_pct, coin, usd, debug, current_trade_min1_start_idx):
+    def check_stop_loss(min1_df, entry_time, current_time, entry_price, stop_loss_pct, coin, verbose=False):
+        """
+        Check if stop loss should be triggered based on 1-minute data
+        
+        Args:
+            min1_df: 1-minute DataFrame with DatetimeIndex
+            entry_time: Entry timestamp
+            current_time: Current timestamp  
+            entry_price: Entry price
+            stop_loss_pct: Stop loss percentage (e.g. 0.05 for 5%)
+            coin: Current coin position
+            verbose: Enable debug logging
+            
+        Returns:
+            Tuple of (trade_log_entry, position, coin, entry_price, usd) if stop loss triggered, None otherwise
+        """
+        if min1_df is None or min1_df.empty:
+            if verbose:
+                print("Warning: No 1-minute data available for stop loss checking")
+            return None
+            
         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
+        try:
+            # Ensure min1_df has a DatetimeIndex
+            if not isinstance(min1_df.index, pd.DatetimeIndex):
+                if verbose:
+                    print("Warning: min1_df does not have DatetimeIndex")
+                return None
+                
+            # Convert entry_time and current_time to pandas Timestamps for comparison
+            entry_ts = pd.to_datetime(entry_time)
+            current_ts = pd.to_datetime(current_time)
+            
+            if verbose:
+                print(f"Checking stop loss from {entry_ts} to {current_ts}, stop_price: {stop_price:.2f}")
+            
+            # Handle edge case where entry and current time are the same (1-minute timeframe)
+            if entry_ts == current_ts:
+                if verbose:
+                    print("Entry and current time are the same, no range to check")
+                return None
+            
+            # Find the range of 1-minute data to check (exclusive of entry time, inclusive of current time)
+            # We start from the candle AFTER entry to avoid checking the entry candle itself
+            start_check_time = entry_ts + pd.Timedelta(minutes=1)
+            
+            # Get the slice of data to check for stop loss
+            mask = (min1_df.index > entry_ts) & (min1_df.index <= current_ts)
+            min1_slice = min1_df.loc[mask]
+            
+            if len(min1_slice) == 0:
+                if verbose:
+                    print(f"No 1-minute data found between {start_check_time} and {current_ts}")
+                return None
+                
+            if verbose:
+                print(f"Checking {len(min1_slice)} candles for stop loss")
+                
+            # Check if any low price in the slice hits the stop loss
+            stop_triggered = (min1_slice['low'] <= stop_price).any()
+            
+            if stop_triggered:
+                # Find the exact candle where stop loss was triggered
+                stop_candle = min1_slice[min1_slice['low'] <= stop_price].iloc[0]
+                
+                if verbose:
+                    print(f"Stop loss triggered at {stop_candle.name}, low: {stop_candle['low']:.2f}")
+                
+                # More realistic fill: if open < stop, fill at open, else at stop
+                if stop_candle['open'] < stop_price:
+                    sell_price = stop_candle['open']
+                    if verbose:
+                        print(f"Filled at open price: {sell_price:.2f}")
+                else:
+                    sell_price = stop_price
+                    if verbose:
+                        print(f"Filled at stop price: {sell_price:.2f}")
+
+                btc_to_sell = coin
+                usd_gross = btc_to_sell * sell_price
+                exit_fee = MarketFees.calculate_okx_taker_maker_fee(usd_gross, is_maker=False)
+                usd_after_stop = usd_gross - exit_fee
+                
+                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 USD balance
+                return trade_log_entry, 0, 0, 0, usd_after_stop
+            elif verbose:
+                print(f"No stop loss triggered, min low in range: {min1_slice['low'].min():.2f}")
+                
+        except Exception as e:
+            # In case of any error, don't trigger stop loss but log the issue
+            error_msg = f"Warning: Stop loss check failed: {e}"
+            print(error_msg)
+            if verbose:
+                import traceback
+                print(traceback.format_exc())
+            return None
+            
         return None
 
     @staticmethod

cycles/supertrend.py

@@ -65,30 +65,57 @@ def cached_supertrend_calculation(period, multiplier, data_tuple):
         'lower_band': final_lower
     }
 
-def calculate_supertrend_external(data, period, multiplier):
+def calculate_supertrend_external(data, period, multiplier, close_column='close'):
+    """
+    External function to calculate SuperTrend with configurable close column
+    
+    Parameters:
+    - data: DataFrame with OHLC data
+    - period: int, period for ATR calculation
+    - multiplier: float, multiplier for ATR
+    - close_column: str, name of the column to use as close price (default: 'close')
+    """
     high_tuple = tuple(data['high'])
     low_tuple = tuple(data['low'])
-    close_tuple = tuple(data['close'])
+    close_tuple = tuple(data[close_column])
     return cached_supertrend_calculation(period, multiplier, (high_tuple, low_tuple, close_tuple))
 
 class Supertrends:
-    def __init__(self, data, verbose=False, display=False):
+    def __init__(self, data, close_column='close', verbose=False, display=False):
+        """
+        Initialize Supertrends calculator
+        
+        Parameters:
+        - data: pandas DataFrame with OHLC data or list of prices
+        - close_column: str, name of the column to use as close price (default: 'close')
+        - verbose: bool, enable verbose logging
+        - display: bool, display mode (currently unused)
+        """
+        self.close_column = close_column
         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})
+                self.data = pd.DataFrame({self.close_column: self.data})
             else:
                 raise ValueError("Data must be a pandas DataFrame or a list")
+        
+        # Validate that required columns exist
+        required_columns = ['high', 'low', self.close_column]
+        missing_columns = [col for col in required_columns if col not in self.data.columns]
+        if missing_columns:
+            raise ValueError(f"Missing required columns: {missing_columns}")
 
     def calculate_tr(self):
+        """Calculate True Range using the configured close column"""
         df = self.data.copy()
         high = df['high'].values
         low = df['low'].values
-        close = df['close'].values
+        close = df[self.close_column].values
         tr = np.zeros_like(close)
         tr[0] = high[0] - low[0]
         for i in range(1, len(close)):
@@ -99,6 +126,7 @@ class Supertrends:
         return tr
 
     def calculate_atr(self, period=14):
+        """Calculate Average True Range"""
         tr = self.calculate_tr()
         atr = np.zeros_like(tr)
         atr[0] = tr[0]
@@ -109,18 +137,20 @@ class Supertrends:
 
     def calculate_supertrend(self, period=10, multiplier=3.0):
         """
-        Calculate SuperTrend indicator for the price data.
+        Calculate SuperTrend indicator for the price data using the configured close column.
         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
+        close = df[self.close_column].values
         atr = self.calculate_atr(period)
         upper_band = np.zeros_like(close)
         lower_band = np.zeros_like(close)

cycles/utils/progress_manager.py

@@ -0,0 +1,233 @@
+#!/usr/bin/env python3
+"""
+Progress Manager for tracking multiple parallel backtest tasks
+"""
+
+import threading
+import time
+import sys
+from typing import Dict, Optional, Callable
+from dataclasses import dataclass
+
+
+@dataclass
+class TaskProgress:
+    """Represents progress information for a single task"""
+    task_id: str
+    name: str
+    current: int
+    total: int
+    start_time: float
+    last_update: float
+    
+    @property
+    def percentage(self) -> float:
+        """Calculate completion percentage"""
+        if self.total == 0:
+            return 0.0
+        return (self.current / self.total) * 100
+    
+    @property
+    def elapsed_time(self) -> float:
+        """Calculate elapsed time in seconds"""
+        return time.time() - self.start_time
+    
+    @property
+    def eta(self) -> Optional[float]:
+        """Estimate time to completion in seconds"""
+        if self.current == 0 or self.percentage >= 100:
+            return None
+        
+        elapsed = self.elapsed_time
+        rate = self.current / elapsed
+        remaining = self.total - self.current
+        return remaining / rate if rate > 0 else None
+
+
+class ProgressManager:
+    """Manages progress tracking for multiple parallel tasks"""
+    
+    def __init__(self, update_interval: float = 1.0, display_width: int = 50):
+        """
+        Initialize progress manager
+        
+        Args:
+            update_interval: How often to update display (seconds)
+            display_width: Width of progress bar in characters
+        """
+        self.tasks: Dict[str, TaskProgress] = {}
+        self.update_interval = update_interval
+        self.display_width = display_width
+        self.lock = threading.Lock()
+        self.display_thread: Optional[threading.Thread] = None
+        self.running = False
+        self.last_display_height = 0
+        
+    def start_task(self, task_id: str, name: str, total: int) -> None:
+        """
+        Start tracking a new task
+        
+        Args:
+            task_id: Unique identifier for the task
+            name: Human-readable name for the task
+            total: Total number of steps in the task
+        """
+        with self.lock:
+            self.tasks[task_id] = TaskProgress(
+                task_id=task_id,
+                name=name,
+                current=0,
+                total=total,
+                start_time=time.time(),
+                last_update=time.time()
+            )
+    
+    def update_progress(self, task_id: str, current: int) -> None:
+        """
+        Update progress for a specific task
+        
+        Args:
+            task_id: Task identifier
+            current: Current progress value
+        """
+        with self.lock:
+            if task_id in self.tasks:
+                self.tasks[task_id].current = current
+                self.tasks[task_id].last_update = time.time()
+    
+    def complete_task(self, task_id: str) -> None:
+        """
+        Mark a task as completed
+        
+        Args:
+            task_id: Task identifier
+        """
+        with self.lock:
+            if task_id in self.tasks:
+                task = self.tasks[task_id]
+                task.current = task.total
+                task.last_update = time.time()
+    
+    def start_display(self) -> None:
+        """Start the progress display thread"""
+        if not self.running:
+            self.running = True
+            self.display_thread = threading.Thread(target=self._display_loop, daemon=True)
+            self.display_thread.start()
+    
+    def stop_display(self) -> None:
+        """Stop the progress display thread"""
+        self.running = False
+        if self.display_thread:
+            self.display_thread.join(timeout=1.0)
+        self._clear_display()
+    
+    def _display_loop(self) -> None:
+        """Main loop for updating the progress display"""
+        while self.running:
+            self._update_display()
+            time.sleep(self.update_interval)
+    
+    def _update_display(self) -> None:
+        """Update the console display with current progress"""
+        with self.lock:
+            if not self.tasks:
+                return
+            
+            # Clear previous display
+            self._clear_display()
+            
+            # Build display lines
+            lines = []
+            for task in sorted(self.tasks.values(), key=lambda t: t.task_id):
+                line = self._format_progress_line(task)
+                lines.append(line)
+            
+            # Print all lines
+            for line in lines:
+                print(line, flush=True)
+            
+            self.last_display_height = len(lines)
+    
+    def _clear_display(self) -> None:
+        """Clear the previous progress display"""
+        if self.last_display_height > 0:
+            # Move cursor up and clear lines
+            for _ in range(self.last_display_height):
+                sys.stdout.write('\033[F')  # Move cursor up one line
+                sys.stdout.write('\033[K')  # Clear line
+            sys.stdout.flush()
+    
+    def _format_progress_line(self, task: TaskProgress) -> str:
+        """
+        Format a single progress line for display
+        
+        Args:
+            task: TaskProgress instance
+            
+        Returns:
+            Formatted progress string
+        """
+        # Progress bar
+        filled_width = int(task.percentage / 100 * self.display_width)
+        bar = '█' * filled_width + '░' * (self.display_width - filled_width)
+        
+        # Time information
+        elapsed_str = self._format_time(task.elapsed_time)
+        eta_str = self._format_time(task.eta) if task.eta else "N/A"
+        
+        # Format line
+        line = (f"{task.name:<25} │{bar}│ "
+                f"{task.percentage:5.1f}% "
+                f"({task.current:,}/{task.total:,}) "
+                f"⏱ {elapsed_str} ETA: {eta_str}")
+        
+        return line
+    
+    def _format_time(self, seconds: float) -> str:
+        """
+        Format time duration for display
+        
+        Args:
+            seconds: Time in seconds
+            
+        Returns:
+            Formatted time string
+        """
+        if seconds < 60:
+            return f"{seconds:.0f}s"
+        elif seconds < 3600:
+            minutes = seconds / 60
+            return f"{minutes:.1f}m"
+        else:
+            hours = seconds / 3600
+            return f"{hours:.1f}h"
+    
+    def get_task_progress_callback(self, task_id: str) -> Callable[[int], None]:
+        """
+        Get a progress callback function for a specific task
+        
+        Args:
+            task_id: Task identifier
+            
+        Returns:
+            Callback function that updates progress for this task
+        """
+        def callback(current: int) -> None:
+            self.update_progress(task_id, current)
+        
+        return callback
+    
+    def all_tasks_completed(self) -> bool:
+        """Check if all tasks are completed"""
+        with self.lock:
+            return all(task.current >= task.total for task in self.tasks.values())
+    
+    def get_summary(self) -> str:
+        """Get a summary of all tasks"""
+        with self.lock:
+            total_tasks = len(self.tasks)
+            completed_tasks = sum(1 for task in self.tasks.values() 
+                                if task.current >= task.total)
+            
+            return f"Tasks: {completed_tasks}/{total_tasks} completed" 

cycles/utils/system.py

@@ -10,10 +10,12 @@ class SystemUtils:
         """Determine optimal number of worker processes based on system resources"""
         cpu_count = os.cpu_count() or 4
         memory_gb = psutil.virtual_memory().total / (1024**3)
-        # Heuristic: Use 75% of cores, but cap based on available memory
-        # Assume each worker needs ~2GB for large datasets
-        workers_by_memory = max(1, int(memory_gb / 2))
-        workers_by_cpu = max(1, int(cpu_count * 0.75))
+        
+        # OPTIMIZATION: More aggressive worker allocation for better performance
+        workers_by_memory = max(1, int(memory_gb / 2))  # 2GB per worker
+        workers_by_cpu = max(1, int(cpu_count * 0.8))  # Use 80% of CPU cores
+        optimal_workers = min(workers_by_cpu, workers_by_memory, 8)  # Cap at 8 workers
+        
         if self.logging is not None:
-            self.logging.info(f"Using {min(workers_by_cpu, workers_by_memory)} workers for processing")
-        return min(workers_by_cpu, workers_by_memory)
+            self.logging.info(f"Using {optimal_workers} workers for processing (CPU-based: {workers_by_cpu}, Memory-based: {workers_by_memory})")
+        return optimal_workers

main.py

@@ -79,7 +79,23 @@ def main():
         )
         system_utils = SystemUtils(logging=logger)
         result_processor = ResultProcessor(storage, logging_instance=logger)
-        runner = BacktestRunner(storage, system_utils, result_processor, logging_instance=logger)
+        
+        # OPTIMIZATION: Disable progress for parallel execution to improve performance
+        show_progress = config.get('show_progress', True)
+        debug_mode = config.get('debug', 0) == 1
+        
+        # Only show progress in debug (sequential) mode
+        if not debug_mode:
+            show_progress = False
+            logger.info("Progress tracking disabled for parallel execution (performance optimization)")
+        
+        runner = BacktestRunner(
+            storage, 
+            system_utils, 
+            result_processor, 
+            logging_instance=logger,
+            show_progress=show_progress
+        )
         
         # Validate inputs
         logger.info("Validating inputs...")
@@ -91,7 +107,8 @@ def main():
         
         # Load data
         logger.info("Loading market data...")
-        data_filename = 'btcusd_1-min_data.csv'
+        # data_filename = 'btcusd_1-min_data.csv'
+        data_filename = 'btcusd_1-min_data_with_price_predictions.csv'
         data_1min = runner.load_data(
             data_filename, 
             config['start_date'], 
@@ -100,7 +117,6 @@ def main():
         
         # Run backtests
         logger.info("Starting backtest execution...")
-        debug_mode = True  # Can be moved to config
         
         all_results, all_trades = runner.run_backtests(
             data_1min,
@@ -114,6 +130,11 @@ def main():
         logger.info("Processing and saving results...")
         timestamp = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M")
         
+        # OPTIMIZATION: Save trade files in batch after parallel execution
+        if all_trades and not debug_mode:
+            logger.info("Saving trade files in batch...")
+            result_processor.save_all_trade_files(all_trades)
+        
         # Create metadata
         metadata_lines = create_metadata_lines(config, data_1min, result_processor)
         

result_processor.py

@@ -29,8 +29,8 @@ class ResultProcessor:
         df: pd.DataFrame, 
         stop_loss_pcts: List[float], 
         timeframe_name: str, 
-        initial_usd: float, 
-        debug: bool = False
+        initial_usd: float,
+        progress_callback=None
     ) -> Tuple[List[Dict], List[Dict]]:
         """
         Process results for a single timeframe with multiple stop loss values
@@ -41,7 +41,7 @@ class ResultProcessor:
             stop_loss_pcts: List of stop loss percentages to test
             timeframe_name: Name of the timeframe (e.g., '1D', '6h')
             initial_usd: Initial USD amount
-            debug: Whether to enable debug output
+            progress_callback: Optional progress callback function
             
         Returns:
             Tuple of (results_rows, trade_rows)
@@ -59,7 +59,8 @@ class ResultProcessor:
                     df,
                     initial_usd=initial_usd,
                     stop_loss_pct=stop_loss_pct,
-                    debug=debug
+                    progress_callback=progress_callback,
+                    verbose=False  # Default to False for production runs
                 )
                 
                 # Calculate metrics
@@ -67,15 +68,14 @@ class ResultProcessor:
                 results_rows.append(metrics)
                 
                 # Process trades
-                trades = self._process_trades(results.get('trades', []), timeframe_name, stop_loss_pct)
+                if 'trades' not in results:
+                    raise ValueError(f"Backtest results missing 'trades' field for {timeframe_name} with {stop_loss_pct} stop loss")
+                trades = self._process_trades(results['trades'], timeframe_name, stop_loss_pct)
                 trade_rows.extend(trades)
                 
                 if self.logging:
                     self.logging.info(f"Timeframe: {timeframe_name}, Stop Loss: {stop_loss_pct}, Trades: {results['n_trades']}")
                 
-                if debug:
-                    self._debug_output(results)
-                    
             except Exception as e:
                 error_msg = f"Error processing {timeframe_name} with stop loss {stop_loss_pct}: {e}"
                 if self.logging:
@@ -92,36 +92,56 @@ class ResultProcessor:
         timeframe_name: str
     ) -> Dict[str, Any]:
         """Calculate performance metrics from backtest results"""
-        trades = results.get('trades', [])
+        if 'trades' not in results:
+            raise ValueError(f"Backtest results missing 'trades' field for {timeframe_name} with {stop_loss_pct} stop loss")
+        trades = results['trades']
         n_trades = results["n_trades"]
         
-        # Calculate win metrics
-        winning_trades = [t for t in trades if t.get('exit') is not None and t['exit'] > t['entry']]
+        # Validate that all required fields are present
+        required_fields = ['final_usd', 'max_drawdown', 'total_fees_usd', 'n_trades', 'n_stop_loss', 'win_rate', 'avg_trade']
+        missing_fields = [field for field in required_fields if field not in results]
+        if missing_fields:
+            raise ValueError(f"Backtest results missing required fields: {missing_fields}")
+        
+        # Calculate win metrics - validate trade fields
+        winning_trades = []
+        for t in trades:
+            if 'exit' not in t:
+                raise ValueError(f"Trade missing 'exit' field: {t}")
+            if 'entry' not in t:
+                raise ValueError(f"Trade missing 'entry' field: {t}")
+            if t['exit'] is not None and t['exit'] > t['entry']:
+                winning_trades.append(t)
         n_winning_trades = len(winning_trades)
         win_rate = n_winning_trades / n_trades if n_trades > 0 else 0
         
         # Calculate profit metrics
-        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)
-        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')
+        total_profit = sum(trade['profit_pct'] for trade in trades if trade['profit_pct'] > 0)
+        total_loss = abs(sum(trade['profit_pct'] for trade in trades if trade['profit_pct'] < 0))
+        avg_trade = sum(trade['profit_pct'] for trade in trades) / n_trades if n_trades > 0 else 0
+        profit_ratio = total_profit / total_loss if total_loss > 0 else (float('inf') if total_profit > 0 else 0)
         
-        # Calculate drawdown
-        max_drawdown = self._calculate_max_drawdown(trades)
+        # Get values directly from backtest results (no defaults)
+        max_drawdown = results['max_drawdown']
+        final_usd = results['final_usd'] 
+        total_fees_usd = results['total_fees_usd']
+        n_stop_loss = results['n_stop_loss']  # Get stop loss count directly from backtest
         
-        # Calculate final USD
-        final_usd = initial_usd
-        for trade in trades:
-            final_usd *= (1 + trade['profit_pct'])
-            
-        # Calculate fees
-        total_fees_usd = sum(trade.get('fee_usd', 0) for trade in trades)
+        # Validate no None values
+        if max_drawdown is None:
+            raise ValueError(f"max_drawdown is None for {timeframe_name} with {stop_loss_pct} stop loss")
+        if final_usd is None:
+            raise ValueError(f"final_usd is None for {timeframe_name} with {stop_loss_pct} stop loss")
+        if total_fees_usd is None:
+            raise ValueError(f"total_fees_usd is None for {timeframe_name} with {stop_loss_pct} stop loss")
+        if n_stop_loss is None:
+            raise ValueError(f"n_stop_loss is None for {timeframe_name} with {stop_loss_pct} stop loss")
         
         return {
             "timeframe": timeframe_name,
             "stop_loss_pct": stop_loss_pct,
             "n_trades": n_trades,
-            "n_stop_loss": sum(1 for trade in trades if trade.get('type') == 'STOP'),
+            "n_stop_loss": n_stop_loss,
             "win_rate": win_rate,
             "max_drawdown": max_drawdown,
             "avg_trade": avg_trade,
@@ -159,16 +179,22 @@ class ResultProcessor:
         processed_trades = []
         
         for trade in trades:
+            # Validate all required trade fields
+            required_fields = ["entry_time", "exit_time", "entry", "exit", "profit_pct", "type", "fee_usd"]
+            missing_fields = [field for field in required_fields if field not in trade]
+            if missing_fields:
+                raise ValueError(f"Trade missing required fields: {missing_fields} in trade: {trade}")
+            
             processed_trade = {
                 "timeframe": timeframe_name,
                 "stop_loss_pct": stop_loss_pct,
-                "entry_time": trade.get("entry_time"),
-                "exit_time": trade.get("exit_time"),
-                "entry_price": trade.get("entry"),
-                "exit_price": trade.get("exit"),
-                "profit_pct": trade.get("profit_pct"),
-                "type": trade.get("type"),
-                "fee_usd": trade.get("fee_usd"),
+                "entry_time": trade["entry_time"],
+                "exit_time": trade["exit_time"],
+                "entry_price": trade["entry"],
+                "exit_price": trade["exit"],
+                "profit_pct": trade["profit_pct"],
+                "type": trade["type"],
+                "fee_usd": trade["fee_usd"],
             }
             processed_trades.append(processed_trade)
             
@@ -176,17 +202,31 @@ class ResultProcessor:
 
     def _debug_output(self, results: Dict[str, Any]) -> None:
         """Output debug information for backtest results"""
-        trades = results.get('trades', [])
+        if 'trades' not in results:
+            raise ValueError("Backtest results missing 'trades' field for debug output")
+        trades = results['trades']
         
         # Print stop loss trades
-        stop_loss_trades = [t for t in trades if t.get('type') == 'STOP']
+        stop_loss_trades = []
+        for t in trades:
+            if 'type' not in t:
+                raise ValueError(f"Trade missing 'type' field: {t}")
+            if t['type'] == 'STOP':
+                stop_loss_trades.append(t)
+        
         if stop_loss_trades:
             print("Stop Loss Trades:")
             for trade in stop_loss_trades:
                 print(trade)
         
         # Print large loss trades
-        large_loss_trades = [t for t in trades if t.get('profit_pct', 0) < -0.09]
+        large_loss_trades = []
+        for t in trades:
+            if 'profit_pct' not in t:
+                raise ValueError(f"Trade missing 'profit_pct' field: {t}")
+            if t['profit_pct'] < -0.09:
+                large_loss_trades.append(t)
+        
         if large_loss_trades:
             print("Large Loss Trades:")
             for trade in large_loss_trades:
@@ -216,19 +256,32 @@ class ResultProcessor:
 
     def _aggregate_group(self, rows: List[Dict], timeframe: str, stop_loss_pct: float) -> Dict:
         """Aggregate a group of rows with the same timeframe and stop loss"""
+        if not rows:
+            raise ValueError(f"No rows to aggregate for {timeframe} with {stop_loss_pct} stop loss")
+        
+        # Validate all rows have required fields
+        required_fields = ['n_trades', 'n_stop_loss', 'win_rate', 'max_drawdown', 'avg_trade', 'profit_ratio', 'final_usd', 'total_fees_usd', 'initial_usd']
+        for i, row in enumerate(rows):
+            missing_fields = [field for field in required_fields if field not in row]
+            if missing_fields:
+                raise ValueError(f"Row {i} missing required fields: {missing_fields}")
+        
         total_trades = sum(r['n_trades'] for r in rows)
         total_stop_loss = sum(r['n_stop_loss'] for r in rows)
         
-        # Calculate averages
+        # Calculate averages (no defaults, expect all values to be present)
         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])
         
-        # Calculate final USD and fees
-        final_usd = np.mean([r.get('final_usd', r.get('initial_usd', 0)) for r in rows])
-        total_fees_usd = np.mean([r.get('total_fees_usd', 0) for r in rows])
-        initial_usd = rows[0].get('initial_usd', 0) if rows else 0
+        # Handle infinite profit ratios properly
+        finite_profit_ratios = [r['profit_ratio'] for r in rows if not np.isinf(r['profit_ratio'])]
+        avg_profit_ratio = np.mean(finite_profit_ratios) if finite_profit_ratios else 0
+        
+        # Calculate final USD and fees (no defaults)
+        final_usd = np.mean([r['final_usd'] for r in rows])
+        total_fees_usd = np.mean([r['total_fees_usd'] for r in rows])
+        initial_usd = rows[0]['initial_usd']
 
         return {
             "timeframe": timeframe,
@@ -278,7 +331,11 @@ class ResultProcessor:
                 writer = csv.DictWriter(f, fieldnames=trades_fieldnames)
                 writer.writeheader()
                 for trade in trades:
-                    writer.writerow({k: trade.get(k, "") for k in trades_fieldnames})
+                    # Validate all required fields are present
+                    missing_fields = [k for k in trades_fieldnames if k not in trade]
+                    if missing_fields:
+                        raise ValueError(f"Trade missing required fields for CSV: {missing_fields} in trade: {trade}")
+                    writer.writerow({k: trade[k] for k in trades_fieldnames})
                     
             if self.logging:
                 self.logging.info(f"Trades saved to {trades_filename}")
@@ -351,4 +408,39 @@ class ResultProcessor:
         except Exception as e:
             if self.logging:
                 self.logging.warning(f"Could not get price info for {date}: {e}")
-            return None, None 
+            return None, None 
+
+    def save_all_trade_files(self, all_trades: List[Dict]) -> None:
+        """
+        Save all trade files in batch after parallel execution completes
+        
+        Args:
+            all_trades: List of all trades from all tasks
+        """
+        if not all_trades:
+            return
+            
+        try:
+            # Group trades by timeframe and stop loss
+            trade_groups = {}
+            for trade in all_trades:
+                timeframe = trade.get('timeframe')
+                stop_loss_pct = trade.get('stop_loss_pct')
+                if timeframe and stop_loss_pct is not None:
+                    key = (timeframe, stop_loss_pct)
+                    if key not in trade_groups:
+                        trade_groups[key] = []
+                    trade_groups[key].append(trade)
+            
+            # Save each group
+            for (timeframe, stop_loss_pct), trades in trade_groups.items():
+                self.save_trade_file(trades, timeframe, stop_loss_pct)
+                
+            if self.logging:
+                self.logging.info(f"Saved {len(trade_groups)} trade files in batch")
+                
+        except Exception as e:
+            error_msg = f"Failed to save trade files in batch: {e}"
+            if self.logging:
+                self.logging.error(error_msg)
+            raise RuntimeError(error_msg) from e