test on original data
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Vasily.onl
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3e94387dcb
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@ -28,6 +28,7 @@ from cycles.strategies.default_strategy import DefaultStrategy
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from cycles.IncStrategies.indicators.supertrend import SupertrendState, SupertrendCollection
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from cycles.Analysis.supertrend import Supertrends
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from cycles.backtest import Backtest
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from cycles.utils.storage import Storage
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# Configure logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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@ -44,6 +45,7 @@ class MetaTrendComparisonTest:
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self.test_data = None
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self.original_results = None
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self.incremental_results = None
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self.storage = Storage(logging=logger)
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# Supertrend parameters from original implementation
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self.supertrend_params = [
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@ -52,34 +54,44 @@ class MetaTrendComparisonTest:
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{"period": 11, "multiplier": 2.0}
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]
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def load_test_data(self, symbol: str = "BTCUSDT", limit: int = 500) -> pd.DataFrame:
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def load_test_data(self, symbol: str = "BTCUSD", start_date: str = "2022-01-01", end_date: str = "2023-01-01", limit: int = None) -> pd.DataFrame:
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"""
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Load test data for comparison.
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Load test data for comparison using the Storage class.
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Args:
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symbol: Trading symbol to load
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limit: Number of data points to load
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symbol: Trading symbol to load (used for filename)
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start_date: Start date in YYYY-MM-DD format
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end_date: End date in YYYY-MM-DD format
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limit: Optional limit on number of data points (applied after date filtering)
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Returns:
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DataFrame with OHLCV data
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"""
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logger.info(f"Loading test data for {symbol} (limit: {limit})")
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logger.info(f"Loading test data for {symbol} from {start_date} to {end_date}")
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try:
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# Try to load from existing data files
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data_file = f"data/{symbol}_1m.csv"
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if os.path.exists(data_file):
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df = pd.read_csv(data_file)
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df['timestamp'] = pd.to_datetime(df['timestamp'])
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df.set_index('timestamp', inplace=True)
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df = df.tail(limit)
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logger.info(f"Loaded {len(df)} data points from {data_file}")
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else:
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# Generate synthetic data for testing if no real data available
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logger.warning(f"No data file found at {data_file}, generating synthetic data")
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df = self._generate_synthetic_data(limit)
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# Use the Storage class to load data with date filtering
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filename = "btcusd_1-min_data.csv"
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# Ensure required columns
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# Convert date strings to pandas datetime
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start_dt = pd.to_datetime(start_date)
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end_dt = pd.to_datetime(end_date)
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# Load data using Storage class
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df = self.storage.load_data(filename, start_dt, end_dt)
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if df.empty:
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raise ValueError(f"No data found for the specified date range: {start_date} to {end_date}")
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logger.info(f"Loaded {len(df)} data points from {start_date} to {end_date}")
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logger.info(f"Date range in data: {df.index.min()} to {df.index.max()}")
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# Apply limit if specified
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if limit is not None and len(df) > limit:
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df = df.tail(limit)
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logger.info(f"Limited data to last {limit} points")
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# Ensure required columns (Storage class should handle column name conversion)
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required_cols = ['open', 'high', 'low', 'close', 'volume']
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for col in required_cols:
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if col not in df.columns:
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@ -92,12 +104,20 @@ class MetaTrendComparisonTest:
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df_with_timestamp = df.reset_index()
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self.test_data = df_with_timestamp
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logger.info(f"Test data prepared: {len(df_with_timestamp)} rows")
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logger.info(f"Columns: {list(df_with_timestamp.columns)}")
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logger.info(f"Sample data:\n{df_with_timestamp.head()}")
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return df_with_timestamp
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except Exception as e:
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logger.error(f"Failed to load test data: {e}")
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# Fallback to synthetic data
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df = self._generate_synthetic_data(limit)
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import traceback
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traceback.print_exc()
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# Fallback to synthetic data if real data loading fails
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logger.warning("Falling back to synthetic data generation")
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df = self._generate_synthetic_data(limit or 1000)
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df_with_timestamp = df.reset_index()
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self.test_data = df_with_timestamp
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return df_with_timestamp
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@ -681,13 +701,15 @@ class MetaTrendComparisonTest:
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return timeline_df
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def run_full_test(self, symbol: str = "BTCUSDT", limit: int = 500) -> bool:
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def run_full_test(self, symbol: str = "BTCUSD", start_date: str = "2022-01-01", end_date: str = "2023-01-01", limit: int = None) -> bool:
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"""
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Run the complete comparison test.
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Args:
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symbol: Trading symbol to test
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limit: Number of data points to test
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start_date: Start date in YYYY-MM-DD format
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end_date: End date in YYYY-MM-DD format
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limit: Optional limit on number of data points (applied after date filtering)
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Returns:
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True if all tests pass, False otherwise
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@ -698,7 +720,7 @@ class MetaTrendComparisonTest:
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try:
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# Load test data
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self.load_test_data(symbol, limit)
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self.load_test_data(symbol, start_date, end_date, limit)
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logger.info(f"Test data loaded: {len(self.test_data)} points")
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# Test original strategy
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@ -760,35 +782,21 @@ def main():
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"""Run the MetaTrend comparison test."""
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test = MetaTrendComparisonTest()
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# Run test with different data sizes
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test_cases = [
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("BTCUSDT", 200), # Small test
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("BTCUSDT", 500), # Medium test
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("BTCUSDT", 1000), # Large test
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]
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# Run test with real BTCUSD data from 2022-01-01 to 2023-01-01
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logger.info(f"\n{'='*80}")
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logger.info(f"RUNNING METATREND COMPARISON TEST")
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logger.info(f"Using real BTCUSD data from 2022-01-01 to 2023-01-01")
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logger.info(f"{'='*80}")
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all_passed = True
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# Test with the full year of data (no limit)
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passed = test.run_full_test("BTCUSD", "2022-01-01", "2023-01-01", limit=None)
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for symbol, limit in test_cases:
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logger.info(f"\n{'='*80}")
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logger.info(f"RUNNING TEST CASE: {symbol} with {limit} data points")
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logger.info(f"{'='*80}")
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passed = test.run_full_test(symbol, limit)
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all_passed = all_passed and passed
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if not passed:
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logger.error(f"Test case {symbol}:{limit} FAILED")
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break
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else:
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logger.info(f"Test case {symbol}:{limit} PASSED")
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if all_passed:
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logger.info("\n🎉 ALL TEST CASES PASSED!")
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if passed:
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logger.info("\n🎉 TEST PASSED! Incremental indicators match original strategy.")
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else:
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logger.error("\n❌ SOME TEST CASES FAILED!")
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logger.error("\n❌ TEST FAILED! Incremental indicators do not match original strategy.")
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return all_passed
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return passed
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if __name__ == "__main__":
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