""" Safety net tests for technical indicators module. These tests ensure that the core functionality of the indicators module remains intact during refactoring. """ import pytest from datetime import datetime, timezone, timedelta from decimal import Decimal import pandas as pd import numpy as np from data.common.indicators import ( TechnicalIndicators, IndicatorResult, create_default_indicators_config, validate_indicator_config ) from data.common.data_types import OHLCVCandle class TestTechnicalIndicatorsSafety: """Safety net test suite for TechnicalIndicators class.""" @pytest.fixture def sample_candles(self): """Create sample OHLCV candles for testing.""" candles = [] base_time = datetime(2024, 1, 1, 9, 0, 0, tzinfo=timezone.utc) # Create 30 candles with realistic price movement prices = [100.0, 101.0, 102.5, 101.8, 103.0, 104.2, 103.8, 105.0, 104.5, 106.0, 107.5, 108.0, 107.2, 109.0, 108.5, 110.0, 109.8, 111.0, 110.5, 112.0, 111.8, 113.0, 112.5, 114.0, 113.2, 115.0, 114.8, 116.0, 115.5, 117.0] for i, price in enumerate(prices): candle = OHLCVCandle( symbol='BTC-USDT', timeframe='1m', start_time=base_time + timedelta(minutes=i), end_time=base_time + timedelta(minutes=i+1), open=Decimal(str(price - 0.2)), high=Decimal(str(price + 0.5)), low=Decimal(str(price - 0.5)), close=Decimal(str(price)), volume=Decimal('1000'), trade_count=10, exchange='test', is_complete=True ) candles.append(candle) return candles @pytest.fixture def sparse_candles(self): """Create sample OHLCV candles with time gaps for testing.""" candles = [] base_time = datetime(2024, 1, 1, 9, 0, 0, tzinfo=timezone.utc) # Create 15 candles with gaps (every other minute) prices = [100.0, 102.5, 104.2, 105.0, 106.0, 108.0, 109.0, 110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0] for i, price in enumerate(prices): # Create 2-minute gaps between candles candle = OHLCVCandle( symbol='BTC-USDT', timeframe='1m', start_time=base_time + timedelta(minutes=i*2), end_time=base_time + timedelta(minutes=(i*2)+1), open=Decimal(str(price - 0.2)), high=Decimal(str(price + 0.5)), low=Decimal(str(price - 0.5)), close=Decimal(str(price)), volume=Decimal('1000'), trade_count=10, exchange='test', is_complete=True ) candles.append(candle) return candles @pytest.fixture def indicators(self): """Create TechnicalIndicators instance.""" return TechnicalIndicators() def test_initialization(self, indicators): """Test indicator calculator initialization.""" assert isinstance(indicators, TechnicalIndicators) def test_prepare_dataframe_from_list(self, indicators, sample_candles): """Test DataFrame preparation from OHLCV candles.""" df = indicators._prepare_dataframe_from_list(sample_candles) assert isinstance(df, pd.DataFrame) assert not df.empty assert len(df) == len(sample_candles) assert 'close' in df.columns assert 'timestamp' in df.index.names def test_prepare_dataframe_empty(self, indicators): """Test DataFrame preparation with empty candles list.""" df = indicators._prepare_dataframe_from_list([]) assert isinstance(df, pd.DataFrame) assert df.empty def test_sma_calculation(self, indicators, sample_candles): """Test Simple Moving Average calculation.""" period = 5 df = indicators._prepare_dataframe_from_list(sample_candles) results = indicators.sma(df, period) assert len(results) > 0 assert isinstance(results[0], IndicatorResult) assert 'sma' in results[0].values assert results[0].metadata['period'] == period def test_sma_insufficient_data(self, indicators, sample_candles): """Test SMA with insufficient data.""" period = 50 # More than available candles df = indicators._prepare_dataframe_from_list(sample_candles) results = indicators.sma(df, period) assert len(results) == 0 def test_ema_calculation(self, indicators, sample_candles): """Test Exponential Moving Average calculation.""" period = 10 df = indicators._prepare_dataframe_from_list(sample_candles) results = indicators.ema(df, period) assert len(results) > 0 assert isinstance(results[0], IndicatorResult) assert 'ema' in results[0].values assert results[0].metadata['period'] == period def test_rsi_calculation(self, indicators, sample_candles): """Test Relative Strength Index calculation.""" period = 14 df = indicators._prepare_dataframe_from_list(sample_candles) results = indicators.rsi(df, period) assert len(results) > 0 assert isinstance(results[0], IndicatorResult) assert 'rsi' in results[0].values assert results[0].metadata['period'] == period assert 0 <= results[0].values['rsi'] <= 100 def test_macd_calculation(self, indicators, sample_candles): """Test MACD calculation.""" fast_period = 12 slow_period = 26 signal_period = 9 df = indicators._prepare_dataframe_from_list(sample_candles) results = indicators.macd(df, fast_period, slow_period, signal_period) # MACD should start producing results after slow_period periods assert len(results) > 0 if results: # Only test if we have results first_result = results[0] assert isinstance(first_result, IndicatorResult) assert 'macd' in first_result.values assert 'signal' in first_result.values assert 'histogram' in first_result.values # Histogram should equal MACD - Signal expected_histogram = first_result.values['macd'] - first_result.values['signal'] assert abs(first_result.values['histogram'] - expected_histogram) < 0.001 def test_bollinger_bands_calculation(self, indicators, sample_candles): """Test Bollinger Bands calculation.""" period = 20 std_dev = 2.0 df = indicators._prepare_dataframe_from_list(sample_candles) results = indicators.bollinger_bands(df, period, std_dev) assert len(results) > 0 assert isinstance(results[0], IndicatorResult) assert 'upper_band' in results[0].values assert 'middle_band' in results[0].values assert 'lower_band' in results[0].values assert results[0].metadata['period'] == period assert results[0].metadata['std_dev'] == std_dev def test_sparse_data_handling(self, indicators, sparse_candles): """Test indicators with sparse data (time gaps).""" period = 5 df = indicators._prepare_dataframe_from_list(sparse_candles) sma_df = indicators.sma(df, period) assert not sma_df.empty timestamps = sma_df.index.to_list() for i in range(1, len(timestamps)): time_diff = timestamps[i] - timestamps[i-1] assert time_diff >= timedelta(minutes=1) def test_calculate_multiple_indicators(self, indicators, sample_candles): """Test calculating multiple indicators at once.""" config = { 'sma_10': {'type': 'sma', 'period': 10}, 'ema_12': {'type': 'ema', 'period': 12}, 'rsi_14': {'type': 'rsi', 'period': 14}, 'macd': {'type': 'macd'}, 'bb_20': {'type': 'bollinger_bands', 'period': 20} } df = indicators._prepare_dataframe_from_list(sample_candles) results = indicators.calculate_multiple_indicators(df, config) assert len(results) == len(config) assert 'sma_10' in results assert 'ema_12' in results assert 'rsi_14' in results assert 'macd' in results assert 'bb_20' in results # Check that each indicator has appropriate results assert len(results['sma_10']) > 0 assert len(results['ema_12']) > 0 assert len(results['rsi_14']) > 0 assert len(results['macd']) > 0 assert len(results['bb_20']) > 0 def test_different_price_columns(self, indicators, sample_candles): """Test indicators with different price columns.""" df = indicators._prepare_dataframe_from_list(sample_candles) # Test SMA with 'high' price column sma_high = indicators.sma(df, 5, price_column='high') assert len(sma_high) > 0 # Test SMA with 'low' price column sma_low = indicators.sma(df, 5, price_column='low') assert len(sma_low) > 0 # Values should be different assert sma_high[0].values['sma'] != sma_low[0].values['sma'] class TestIndicatorHelperFunctions: """Test suite for indicator helper functions.""" def test_create_default_indicators_config(self): """Test default indicator configuration creation.""" config = create_default_indicators_config() assert isinstance(config, dict) assert len(config) > 0 assert 'sma_20' in config assert 'ema_12' in config assert 'rsi_14' in config assert 'macd_default' in config assert 'bollinger_bands_20' in config def test_validate_indicator_config_valid(self): """Test indicator configuration validation with valid config.""" valid_configs = [ {'type': 'sma', 'period': 20}, {'type': 'ema', 'period': 12}, {'type': 'rsi', 'period': 14}, {'type': 'macd'}, {'type': 'bollinger_bands', 'period': 20, 'std_dev': 2.0} ] for config in valid_configs: assert validate_indicator_config(config) def test_validate_indicator_config_invalid(self): """Test indicator configuration validation with invalid config.""" invalid_configs = [ {}, # Empty config {'type': 'unknown'}, # Invalid type {'type': 'sma', 'period': -1}, # Invalid period {'type': 'bollinger_bands', 'std_dev': -1}, # Invalid std_dev {'type': 'sma', 'period': 'not_a_number'} # Wrong type for period ] for config in invalid_configs: assert not validate_indicator_config(config) class TestIndicatorResultDataClass: """Test suite for IndicatorResult dataclass.""" def test_indicator_result_creation(self): """Test IndicatorResult creation with all fields.""" timestamp = datetime.now(timezone.utc) values = {'sma': 100.0} metadata = {'period': 20} result = IndicatorResult( timestamp=timestamp, symbol='BTC-USDT', timeframe='1m', values=values, metadata=metadata ) assert result.timestamp == timestamp assert result.symbol == 'BTC-USDT' assert result.timeframe == '1m' assert result.values == values assert result.metadata == metadata def test_indicator_result_without_metadata(self): """Test IndicatorResult creation without optional metadata.""" timestamp = datetime.now(timezone.utc) values = {'sma': 100.0} result = IndicatorResult( timestamp=timestamp, symbol='BTC-USDT', timeframe='1m', values=values ) assert result.timestamp == timestamp assert result.symbol == 'BTC-USDT' assert result.timeframe == '1m' assert result.values == values assert result.metadata is None