"""
Unit tests for KJB signal generator.
"""
import pandas as pd
from datetime import date, timedelta

from app.services.strategy.kjb import KJBSignalGenerator


def _make_price_df(closes, volumes=None, start_date=date(2024, 1, 2)):
    dates = pd.bdate_range(start=start_date, periods=len(closes))
    if volumes is None:
        volumes = [1000000] * len(closes)
    highs = [c * 1.01 for c in closes]
    lows = [c * 0.99 for c in closes]
    opens = closes.copy()
    return pd.DataFrame({
        "open": opens,
        "high": highs,
        "low": lows,
        "close": closes,
        "volume": volumes,
    }, index=dates)


def _make_kospi_df(closes, start_date=date(2024, 1, 2)):
    dates = pd.bdate_range(start=start_date, periods=len(closes))
    return pd.DataFrame({"close": closes}, index=dates)


def test_relative_strength_above_market():
    gen = KJBSignalGenerator()
    stock_closes = [100 + i for i in range(25)]
    kospi_closes = [100 + i * 0.5 for i in range(25)]
    stock_df = _make_price_df(stock_closes)
    kospi_df = _make_kospi_df(kospi_closes)
    rs = gen.calculate_relative_strength(stock_df, kospi_df, lookback=10)
    assert rs.dropna().iloc[-1] > 100


def test_relative_strength_below_market():
    gen = KJBSignalGenerator()
    stock_closes = [100 + i * 0.3 for i in range(25)]
    kospi_closes = [100 + i for i in range(25)]
    stock_df = _make_price_df(stock_closes)
    kospi_df = _make_kospi_df(kospi_closes)
    rs = gen.calculate_relative_strength(stock_df, kospi_df, lookback=10)
    assert rs.dropna().iloc[-1] < 100


def test_detect_breakout():
    gen = KJBSignalGenerator()
    closes = [100.0] * 20 + [105.0]
    stock_df = _make_price_df(closes)
    breakouts = gen.detect_breakout(stock_df, lookback=20)
    assert breakouts.iloc[-1] == True
    assert breakouts.iloc[-2] == False


def test_detect_large_candle():
    gen = KJBSignalGenerator()
    closes = [100.0] * 21 + [106.0]
    volumes = [1000000] * 21 + [3000000]
    stock_df = _make_price_df(closes, volumes)
    large = gen.detect_large_candle(stock_df, pct_threshold=0.05, vol_multiplier=1.5)
    assert large.iloc[-1] == True
    assert large.iloc[-2] == False


def test_no_large_candle_low_volume():
    gen = KJBSignalGenerator()
    closes = [100.0] * 21 + [106.0]
    volumes = [1000000] * 22
    stock_df = _make_price_df(closes, volumes)
    large = gen.detect_large_candle(stock_df)
    assert large.iloc[-1] == False


def test_generate_buy_signal():
    gen = KJBSignalGenerator()
    closes = [100.0] * 20 + [106.0]
    volumes = [1000000] * 20 + [3000000]
    kospi_closes = [100.0 + i * 0.1 for i in range(21)]

    stock_df = _make_price_df(closes, volumes)
    kospi_df = _make_kospi_df(kospi_closes)

    signals = gen.generate_signals(stock_df, kospi_df)
    assert signals["buy"].iloc[-1] == True


def test_no_signal_weak_stock():
    gen = KJBSignalGenerator()
    # Stock underperforms market
    closes = [100.0 + i * 0.1 for i in range(25)]
    kospi_closes = [100 + i for i in range(25)]
    stock_df = _make_price_df(closes)
    kospi_df = _make_kospi_df(kospi_closes)
    signals = gen.generate_signals(stock_df, kospi_df)
    assert signals["buy"].iloc[-1] == False