"""
Test cases for technical analysis module.
Tests edge cases for technical indicators.
"""

import unittest
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from src.technical_analysis import (
    calculate_sma,
    calculate_ema,
    calculate_rsi,
    calculate_macd,
    calculate_bollinger_bands
)


class TestTechnicalAnalysis(unittest.TestCase):
    """Test cases for technical analysis functions."""
    
    def setUp(self):
        """Set up test data."""
        # Create sample price data
        dates = pd.date_range(start='2024-01-01', periods=100, freq='D')
        # Generate realistic price data with trend
        base_price = 100
        trend = np.linspace(0, 20, 100)
        noise = np.random.normal(0, 2, 100)
        prices = base_price + trend + noise
        self.prices = pd.Series(prices, index=dates)
    
    # ========================================================================
    # SMA TESTS
    # ========================================================================
    
    def test_calculate_sma_normal(self):
        """Test SMA calculation with normal data."""
        sma = calculate_sma(self.prices, 20)
        self.assertEqual(len(sma), len(self.prices))
        self.assertFalse(sma.iloc[:19].notna().any())  # First 19 should be NaN
        self.assertTrue(sma.iloc[19:].notna().all())  # Rest should have values
    
    def test_calculate_sma_short_data(self):
        """Test SMA with insufficient data."""
        short_prices = self.prices[:10]
        sma = calculate_sma(short_prices, 20)
        self.assertTrue(sma.isna().all())  # All should be NaN
    
    def test_calculate_sma_single_value(self):
        """Test SMA with single price point."""
        single_price = pd.Series([100])
        sma = calculate_sma(single_price, 1)
        self.assertEqual(sma.iloc[0], 100)
    
    def test_calculate_sma_empty(self):
        """Test SMA with empty series."""
        empty_prices = pd.Series([], dtype=float)
        sma = calculate_sma(empty_prices, 20)
        self.assertEqual(len(sma), 0)
    
    # ========================================================================
    # EMA TESTS
    # ========================================================================
    
    def test_calculate_ema_normal(self):
        """Test EMA calculation with normal data."""
        ema = calculate_ema(self.prices, 12)
        self.assertEqual(len(ema), len(self.prices))
        self.assertTrue(ema.notna().any())  # Should have some values
    
    def test_calculate_ema_empty(self):
        """Test EMA with empty series."""
        empty_prices = pd.Series([], dtype=float)
        ema = calculate_ema(empty_prices, 12)
        self.assertEqual(len(ema), 0)
    
    # ========================================================================
    # RSI TESTS
    # ========================================================================
    
    def test_calculate_rsi_normal(self):
        """Test RSI calculation with normal data."""
        rsi = calculate_rsi(self.prices, 14)
        self.assertIsInstance(rsi, float)
        self.assertGreaterEqual(rsi, 0)
        self.assertLessEqual(rsi, 100)
    
    def test_calculate_rsi_insufficient_data(self):
        """Test RSI with insufficient data."""
        short_prices = self.prices[:10]
        rsi = calculate_rsi(short_prices, 14)
        self.assertEqual(rsi, 50.0)  # Should return neutral
    
    def test_calculate_rsi_all_gains(self):
        """Test RSI with all positive changes (should be high)."""
        increasing_prices = pd.Series([100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115])
        rsi = calculate_rsi(increasing_prices, 14)
        self.assertGreater(rsi, 50)  # Should be above neutral
    
    def test_calculate_rsi_all_losses(self):
        """Test RSI with all negative changes (should be low)."""
        decreasing_prices = pd.Series([115, 114, 113, 112, 111, 110, 109, 108, 107, 106, 105, 104, 103, 102, 101, 100])
        rsi = calculate_rsi(decreasing_prices, 14)
        self.assertLess(rsi, 50)  # Should be below neutral
    
    def test_calculate_rsi_empty(self):
        """Test RSI with empty series."""
        empty_prices = pd.Series([], dtype=float)
        rsi = calculate_rsi(empty_prices, 14)
        self.assertEqual(rsi, 50.0)
    
    # ========================================================================
    # MACD TESTS
    # ========================================================================
    
    def test_calculate_macd_normal(self):
        """Test MACD calculation with normal data."""
        macd = calculate_macd(self.prices)
        self.assertIn("macd", macd)
        self.assertIn("signal", macd)
        self.assertIn("histogram", macd)
        self.assertIn("trend", macd)
        self.assertIn(macd["trend"], ["bullish", "bearish"])
    
    def test_calculate_macd_insufficient_data(self):
        """Test MACD with insufficient data."""
        short_prices = self.prices[:20]  # Need at least 26+9=35 for full MACD
        macd = calculate_macd(short_prices)
        self.assertEqual(macd["trend"], "neutral")
        self.assertEqual(macd["macd"], 0.0)
    
    def test_calculate_macd_empty(self):
        """Test MACD with empty series."""
        empty_prices = pd.Series([], dtype=float)
        macd = calculate_macd(empty_prices)
        self.assertEqual(macd["trend"], "neutral")
    
    # ========================================================================
    # BOLLINGER BANDS TESTS
    # ========================================================================
    
    def test_calculate_bollinger_bands_normal(self):
        """Test Bollinger Bands calculation with normal data."""
        bands = calculate_bollinger_bands(self.prices, 20)
        self.assertIn("upper", bands)
        self.assertIn("middle", bands)
        self.assertIn("lower", bands)
        self.assertIn("current_price", bands)
        self.assertIn("position", bands)
        self.assertGreater(bands["upper"], bands["middle"])
        self.assertLess(bands["lower"], bands["middle"])
    
    def test_calculate_bollinger_bands_insufficient_data(self):
        """Test Bollinger Bands with insufficient data."""
        short_prices = self.prices[:10]
        bands = calculate_bollinger_bands(short_prices, 20)
        self.assertEqual(bands["upper"], bands["middle"])
        self.assertEqual(bands["lower"], bands["middle"])
        self.assertEqual(bands["position"], "neutral")
    
    def test_calculate_bollinger_bands_empty(self):
        """Test Bollinger Bands with empty series."""
        empty_prices = pd.Series([], dtype=float)
        bands = calculate_bollinger_bands(empty_prices, 20)
        self.assertEqual(bands["position"], "neutral")
        self.assertEqual(bands["current_price"], 0)
    
    def test_calculate_bollinger_bands_overbought(self):
        """Test Bollinger Bands when price is above upper band."""
        # Create prices that trend upward significantly
        high_prices = pd.Series([100 + i*2 for i in range(30)])
        bands = calculate_bollinger_bands(high_prices, 20)
        # Price should be above upper band (overbought)
        if bands["current_price"] > bands["upper"]:
            self.assertEqual(bands["position"], "overbought")
    
    def test_calculate_bollinger_bands_oversold(self):
        """Test Bollinger Bands when price is below lower band."""
        # Create prices that trend downward significantly
        low_prices = pd.Series([100 - i*2 for i in range(30)])
        bands = calculate_bollinger_bands(low_prices, 20)
        # Price should be below lower band (oversold)
        if bands["current_price"] < bands["lower"]:
            self.assertEqual(bands["position"], "oversold")


if __name__ == '__main__':
    unittest.main()