TCPDashboard/tests/test_indicators.py

"""
Unit tests for technical indicators module.

Tests verify that all technical indicators work correctly with sparse OHLCV data
and handle edge cases appropriately.
"""

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 TestTechnicalIndicators:
    """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 sparse OHLCV candles (with gaps) for testing."""
        candles = []
        base_time = datetime(2024, 1, 1, 9, 0, 0, tzinfo=timezone.utc)
        
        # Create candles with time gaps (sparse data)
        gap_minutes = [0, 1, 3, 5, 8, 10, 15, 18, 22, 25]
        prices = [100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0]
        
        for i, (gap, price) in enumerate(zip(gap_minutes, prices)):
            candle = OHLCVCandle(
                symbol='BTC-USDT',
                timeframe='1m',
                start_time=base_time + timedelta(minutes=gap),
                end_time=base_time + timedelta(minutes=gap+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 TechnicalIndicators initialization."""
        assert indicators is not None
        assert indicators.logger is None
    
    def test_prepare_dataframe(self, indicators, sample_candles):
        """Test DataFrame preparation from OHLCV candles."""
        df = indicators.prepare_dataframe(sample_candles)
        
        assert not df.empty
        assert len(df) == len(sample_candles)
        assert list(df.columns) == ['symbol', 'timeframe', 'open', 'high', 'low', 'close', 'volume', 'trade_count']
        assert df.index.name == 'timestamp'
        
        # Check that timestamps are sorted
        assert df.index.is_monotonic_increasing
    
    def test_prepare_dataframe_empty(self, indicators):
        """Test DataFrame preparation with empty candles list."""
        df = indicators.prepare_dataframe([])
        assert df.empty
    
    def test_sma_calculation(self, indicators, sample_candles):
        """Test Simple Moving Average calculation."""
        period = 5
        results = indicators.sma(sample_candles, period)
        
        # Should have results starting from period 5
        assert len(results) == len(sample_candles) - period + 1
        
        # Check first result
        first_result = results[0]
        assert isinstance(first_result, IndicatorResult)
        assert first_result.symbol == 'BTC-USDT'
        assert first_result.timeframe == '1m'
        assert 'sma' in first_result.values
        assert first_result.metadata['period'] == period
        
        # Verify SMA calculation manually for first result
        first_5_closes = [float(candle.close) for candle in sample_candles[:5]]
        expected_sma = sum(first_5_closes) / len(first_5_closes)
        assert abs(first_result.values['sma'] - expected_sma) < 0.001
    
    def test_sma_insufficient_data(self, indicators, sample_candles):
        """Test SMA with insufficient data."""
        period = 50  # More than available candles
        results = indicators.sma(sample_candles, period)
        assert len(results) == 0
    
    def test_ema_calculation(self, indicators, sample_candles):
        """Test Exponential Moving Average calculation."""
        period = 10
        results = indicators.ema(sample_candles, period)
        
        # Should have results starting from period 10
        assert len(results) == len(sample_candles) - period + 1
        
        # Check first result
        first_result = results[0]
        assert isinstance(first_result, IndicatorResult)
        assert 'ema' in first_result.values
        assert first_result.metadata['period'] == period
        
        # EMA should be between the range of input prices
        min_price = min(float(c.close) for c in sample_candles[:period])
        max_price = max(float(c.close) for c in sample_candles[:period])
        assert min_price <= first_result.values['ema'] <= max_price
    
    def test_rsi_calculation(self, indicators, sample_candles):
        """Test Relative Strength Index calculation."""
        period = 14
        results = indicators.rsi(sample_candles, period)
        
        # Should have results starting from period 15 (period + 1 for price change calculation)
        assert len(results) == len(sample_candles) - period
        
        # Check first result
        first_result = results[0]
        assert isinstance(first_result, IndicatorResult)
        assert 'rsi' in first_result.values
        assert 0 <= first_result.values['rsi'] <= 100  # RSI should be between 0 and 100
        assert first_result.metadata['period'] == period
    
    def test_macd_calculation(self, indicators, sample_candles):
        """Test MACD calculation."""
        fast_period = 12
        slow_period = 26
        signal_period = 9
        results = indicators.macd(sample_candles, fast_period, slow_period, signal_period)
        
        # MACD needs slow_period + signal_period data points
        expected_count = len(sample_candles) - slow_period - signal_period + 1
        assert len(results) == max(0, expected_count)
        
        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
        results = indicators.bollinger_bands(sample_candles, period, std_dev)
        
        # Should have results starting from period 20
        assert len(results) == len(sample_candles) - period + 1
        
        # Check first result
        first_result = results[0]
        assert isinstance(first_result, IndicatorResult)
        assert 'upper_band' in first_result.values
        assert 'middle_band' in first_result.values
        assert 'lower_band' in first_result.values
        assert 'bandwidth' in first_result.values
        assert 'percent_b' in first_result.values
        
        # Upper band should be greater than middle band, which should be greater than lower band
        assert first_result.values['upper_band'] > first_result.values['middle_band']
        assert first_result.values['middle_band'] > first_result.values['lower_band']
    
    def test_sparse_data_handling(self, indicators, sparse_candles):
        """Test indicators with sparse data (time gaps)."""
        period = 5
        sma_results = indicators.sma(sparse_candles, period)
        
        # Should handle sparse data without issues
        assert len(sma_results) > 0
        
        # Check that timestamps are preserved correctly
        for result in sma_results:
            assert result.timestamp is not None
            assert isinstance(result.timestamp, datetime)
    
    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}
        }
        
        results = indicators.calculate_multiple_indicators(sample_candles, 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
    
    def test_invalid_indicator_config(self, indicators, sample_candles):
        """Test handling of invalid indicator configuration."""
        config = {
            'invalid_indicator': {'type': 'unknown_type', 'period': 10}
        }
        
        results = indicators.calculate_multiple_indicators(sample_candles, config)
        
        assert 'invalid_indicator' in results
        assert len(results['invalid_indicator']) == 0  # Should return empty list
    
    def test_different_price_columns(self, indicators, sample_candles):
        """Test indicators with different price columns."""
        # Test SMA with 'high' price column
        sma_high = indicators.sma(sample_candles, 5, price_column='high')
        sma_close = indicators.sma(sample_candles, 5, price_column='close')
        
        assert len(sma_high) == len(sma_close)
        # High prices should generally give higher SMA values
        assert sma_high[0].values['sma'] >= sma_close[0].values['sma']


class TestIndicatorHelperFunctions:
    """Test helper functions for indicators."""
    
    def test_create_default_indicators_config(self):
        """Test default indicators configuration creation."""
        config = create_default_indicators_config()
        
        assert isinstance(config, dict)
        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
        
        # Check structure of configurations
        assert config['sma_20']['type'] == 'sma'
        assert config['sma_20']['period'] == 20
        assert config['macd_default']['type'] == 'macd'
    
    def test_validate_indicator_config_valid(self):
        """Test validation of valid indicator configurations."""
        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) == True
    
    def test_validate_indicator_config_invalid(self):
        """Test validation of invalid indicator configurations."""
        invalid_configs = [
            {},  # Missing type
            {'type': 'unknown'},  # Invalid type
            {'type': 'sma', 'period': -5},  # Invalid period
            {'type': 'sma', 'period': 'not_a_number'},  # Invalid period type
            {'type': 'bollinger_bands', 'std_dev': -1.0},  # Invalid std_dev
        ]
        
        for config in invalid_configs:
            assert validate_indicator_config(config) == False


class TestIndicatorResultDataClass:
    """Test IndicatorResult dataclass."""
    
    def test_indicator_result_creation(self):
        """Test IndicatorResult creation and attributes."""
        timestamp = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
        values = {'sma': 100.5, 'ema': 101.2}
        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 metadata."""
        timestamp = datetime(2024, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
        values = {'rsi': 65.5}
        
        result = IndicatorResult(
            timestamp=timestamp,
            symbol='ETH-USDT',
            timeframe='5m',
            values=values
        )
        
        assert result.metadata is None


if __name__ == '__main__':
    pytest.main([__file__])