dev → main: ruff auto-fix — remove 183 lint errors

alpha/AutoRegression/autoregression.py

@@ -1,8 +1,10 @@
+from pathlib import Path
+
+import numpy as np
 import torch
 from torch import nn
-from torch.utils.data import Dataset, DataLoader
-import numpy as np
-from pathlib import Path
+from torch.utils.data import DataLoader, Dataset
+
 
 class AR(nn.Module):
     def __init__(self, lag):

alpha/AutoRegression/data_processing.py

@@ -1,6 +1,7 @@
 import numpy as np
 import pandas as pd
 
+
 def load_data(data_path: str = "./binance/BTC_USDT-5m.feather",
               start_date: str = "2021-01-01"):
     df = pd.read_feather(data_path)

alpha/AutoregressionAlpha.py

@@ -1,8 +1,9 @@
 import numpy as np
 import torch
 from pandas import DataFrame
+
+from alpha.AutoRegression.autoregression import build_train, trained_ar_model
 from alpha.interface import IAlpha
-from alpha.AutoRegression.autoregression import trained_ar_model, build_train
 
 AR_MODEL_PATH = "./ar_model.pth"
 AR_LAG = 90

alpha/BollingerAlpha.py

@@ -6,9 +6,9 @@
 """
 
 import talib.abstract as ta
+from freqtrade.strategy.parameters import IntParameter
 
 from alpha.interface import IAlpha
-from freqtrade.strategy.parameters import IntParameter
 
 
 class BollingerAlpha(IAlpha):

alpha/EventLstmAlpha.py

@@ -1,8 +1,10 @@
-import torch
 import numpy as np
+import torch
+import torch.nn as nn
 from pandas import DataFrame
+
 from alpha.interface import IAlpha
-import torch.nn as nn
+
 
 class EventLstmModel(nn.Module):
     def __init__(self):

alpha/MacdAlpha.py

@@ -5,9 +5,9 @@
 """
 
 import talib.abstract as ta
+from freqtrade.strategy.parameters import IntParameter
 
 from alpha.interface import IAlpha
-from freqtrade.strategy.parameters import IntParameter
 
 
 class MacdAlpha(IAlpha):

alpha/RsiAlpha.py

@@ -5,9 +5,9 @@
 """
 
 import talib.abstract as ta
+from freqtrade.strategy.parameters import IntParameter
 
 from alpha.interface import IAlpha
-from freqtrade.strategy.parameters import IntParameter
 
 
 class RsiAlpha(IAlpha):

alpha/SimpleEmaFactors.py

@@ -1,7 +1,8 @@
 import talib.abstract as ta
+from freqtrade.strategy.parameters import IntParameter
 
 from alpha.interface import IAlpha
-from freqtrade.strategy.parameters import IntParameter
+
 
 class EmaAlpha(IAlpha):
     def __init__(self, dataframe, metadata):

alpha/interface.py

@@ -1,6 +1,8 @@
 from abc import ABC, abstractmethod
+
 from pandas import DataFrame
 
+
 class IAlpha(ABC):
     def __init__(self, dataframe: DataFrame, metadata: dict = None):
         self.dataframe=dataframe

benchmark.py

@@ -14,12 +14,11 @@
     python benchmark.py --export report.json   # also write JSON results
 """
 
+import argparse
+import json
 import os
 import sys
-import json
 import time
-import traceback
-import argparse
 from concurrent.futures import ProcessPoolExecutor, as_completed
 from datetime import datetime
 from typing import Any, Dict, List, Optional, Tuple
@@ -40,8 +39,8 @@
 # ---------------------------------------------------------------------------
 try:
     from freqtrade.commands.optimize_commands import setup_optimize_configuration
-    from freqtrade.optimize.backtesting import Backtesting
     from freqtrade.enums import RunMode
+    from freqtrade.optimize.backtesting import Backtesting
     FREQTRADE_AVAILABLE = True
 except ImportError as e:
     FREQTRADE_AVAILABLE = False
@@ -481,10 +480,11 @@ def run_alpha_smoke_test() -> Dict[str, Any]:
     t0 = time.time()
     try:
         import pandas as pd
-        from alpha.SimpleEmaFactors import EmaAlpha
-        from alpha.RsiAlpha import RsiAlpha
-        from alpha.MacdAlpha import MacdAlpha
+
         from alpha.BollingerAlpha import BollingerAlpha
+        from alpha.MacdAlpha import MacdAlpha
+        from alpha.RsiAlpha import RsiAlpha
+        from alpha.SimpleEmaFactors import EmaAlpha
 
         # Load one pair's data
         filepath = os.path.join(DATA_DIR, "BTC_USDT-1d.feather")
@@ -564,9 +564,10 @@ def run_data_integrity_check() -> Dict[str, Any]:
     }
     t0 = time.time()
     try:
-        import pandas as pd
         from pathlib import Path
 
+        import pandas as pd
+
         data_path = Path(DATA_DIR)
         feather_files = sorted(data_path.glob("*.feather"))
         total = len(feather_files)

benchmark_all.py

@@ -22,10 +22,9 @@
 import os
 import sys
 import time
-import traceback
 from concurrent.futures import ProcessPoolExecutor, as_completed
 from datetime import datetime
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Tuple
 
 # ---------------------------------------------------------------------------
 # Ensure project root is on sys.path
@@ -433,8 +432,8 @@ def _run_single_backtest(
 ) -> Dict[str, Any]:
     """Run a single freqtrade backtest programmatically and return results."""
     from freqtrade.commands.optimize_commands import setup_optimize_configuration
-    from freqtrade.optimize.backtesting import Backtesting
     from freqtrade.enums import RunMode
+    from freqtrade.optimize.backtesting import Backtesting
 
     config_path = os.path.join(PROJECT_ROOT, "user_data", "config.json")
 

examples/pipeline_examples.py

@@ -13,22 +13,22 @@
 
 import sys
 from pathlib import Path
-import pandas as pd
+
 import numpy as np
+import pandas as pd
 
 # Add project root to path
 PROJECT_ROOT = Path(__file__).parent.parent
 sys.path.insert(0, str(PROJECT_ROOT))
 
 from pipeline.config import PipelineConfig, PresetConfigs
-from pipeline.orchestrator import PortfolioPipeline, run_pipeline
 from pipeline.integrations import (
-    FreqtradeIntegration,
     BatchPipelineRunner,
-    PresetPipelineRunner,
+    FreqtradeIntegration,
     PipelineComparator,
+    PresetPipelineRunner,
 )
-
+from pipeline.orchestrator import run_pipeline
 
 # ============================================================================
 # HELPER: Generate synthetic test data
@@ -99,13 +99,13 @@ def example_2_custom_config():
 
     from pipeline.config import (
         AlphaConfig,
-        StrategyConfig,
-        PortfolioConfig,
+        AlphaType,
         BacktestConfig,
         DataConfig,
-        AlphaType,
-        StrategyType,
         PortfolioAlgorithm,
+        PortfolioConfig,
+        StrategyConfig,
+        StrategyType,
     )
 
     # Build configuration programmatically
@@ -212,7 +212,7 @@ def example_4_compare_pipelines():
 
     # Print summary
     summary = runner.get_summary()
-    print(f"\nBatch Summary:")
+    print("\nBatch Summary:")
     print(f"  Total runs: {summary['total_runs']}")
     print(f"  Successful: {summary['successful']}")
     print(f"  Failed: {summary['failed']}")

fix_utc_imports.py

@@ -3,9 +3,10 @@
 import os
 import re
 
+
 def fix_utc_imports(filepath):
     """Fix UTC imports in a Python file for Python 3.10 compatibility."""
-    with open(filepath, 'r', encoding='utf-8') as f:
+    with open(filepath, encoding='utf-8') as f:
         content = f.read()
 
     # Check if already fixed
@@ -92,7 +93,7 @@ def fix_utc_imports(filepath):
         if file.endswith('.py'):
             filepath = os.path.join(root, file)
             try:
-                with open(filepath, 'r', encoding='utf-8') as f:
+                with open(filepath, encoding='utf-8') as f:
                     content = f.read()
                     if 'from datetime import' in content and 'UTC' in content:
                         attempted += 1

pipeline/config.py

@@ -11,10 +11,10 @@
 
 import json
 import os
-from dataclasses import dataclass, field, asdict
+from dataclasses import asdict, dataclass, field
+from enum import Enum
 from pathlib import Path
 from typing import Any, Dict, List, Optional
-from enum import Enum
 
 
 class AlphaType(str, Enum):
@@ -210,7 +210,7 @@ def from_yaml(cls, filepath: str | Path) -> PipelineConfig:
             raise ImportError("PyYAML required for YAML config support. "
                             "Install with: pip install PyYAML")
 
-        with open(filepath, 'r') as f:
+        with open(filepath) as f:
             data = yaml.safe_load(f)
         return cls.from_dict(data)
 

pipeline/integrations.py

@@ -13,11 +13,12 @@
 from __future__ import annotations
 
 import json
+import logging
 import pickle
 from pathlib import Path
-from typing import Dict, List, Any, Optional
+from typing import Any, Dict, List
+
 import pandas as pd
-import logging
 
 logger = logging.getLogger(__name__)
 

pipeline/orchestrator.py

@@ -14,34 +14,34 @@
 
 from __future__ import annotations
 
+import logging
 import os
 import sys
 import time
-import logging
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple, Any
-import pandas as pd
+from typing import Dict, List
+
 import numpy as np
+import pandas as pd
 
 # Ensure project root is on path
 _PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 if _PROJECT_ROOT not in sys.path:
     sys.path.insert(0, _PROJECT_ROOT)
 
-from pipeline.config import PipelineConfig, AlphaType, StrategyType
-from pipeline.verification import PipelineVerification, ValidationResult
+from pipeline.config import AlphaType, PipelineConfig, StrategyType
 from pipeline.results import PipelineResult, StageOutput
-
+from pipeline.verification import PipelineVerification
 from portfolio.PortfolioManagement import (
-    load_pair_data,
     align_close_prices,
-    ema_cross_signals,
+    backtest_portfolio,
+    blend_strategy_weights,
     build_ema_position_series,
     calculate_ons_weights,
-    equal_weight_allocation,
-    blend_strategy_weights,
-    backtest_portfolio,
     compute_metrics,
+    ema_cross_signals,
+    equal_weight_allocation,
+    load_pair_data,
 )
 
 logger = logging.getLogger(__name__)
@@ -184,7 +184,7 @@ def run(self) -> PipelineResult:
                     data_summary=metrics
                 ))
 
-                logger.info(f"\nBacktest Results:")
+                logger.info("\nBacktest Results:")
                 logger.info(f"  Total return:        {metrics['total_return_pct']:.2f}%")
                 logger.info(f"  Annualized return:   {metrics['annualised_return_pct']:.2f}%")
                 logger.info(f"  Annualized Sharpe:   {metrics['annualised_sharpe']:.4f}")

pipeline/results.py

@@ -10,13 +10,11 @@
 
 import json
 import logging
-from dataclasses import dataclass, field, asdict
+from dataclasses import asdict, dataclass, field
 from pathlib import Path
-from typing import Dict, List, Any, Optional
-from datetime import datetime
-import pandas as pd
-import numpy as np
+from typing import Any, Dict, List, Optional
 
+import pandas as pd
 
 logger = logging.getLogger(__name__)
 
@@ -204,7 +202,7 @@ def to_html_report(self) -> str:
             "</head>",
             "<body>",
             "<div class='header'>",
-            f"<h1>PortfolioBench Pipeline Report</h1>",
+            "<h1>PortfolioBench Pipeline Report</h1>",
             f"<p>Pipeline: {summary['pipeline_name']}</p>",
             f"<p>Duration: {summary['duration_s']:.2f}s</p>",
             "</div>",
@@ -325,7 +323,7 @@ def print_summary(self) -> None:
         print(f"  [~] Skipped:     {stages['skipped']}")
 
         if summary['metrics']:
-            print(f"\nMetrics:")
+            print("\nMetrics:")
             for key, value in summary['metrics'].items():
                 if value is not None:
                     if isinstance(value, float):