#!/usr/bin/env python3
"""
ForexBot2 — Comprehensive QA Test Suite
=========================================
Tests every function in the codebase without requiring live API connections.
"""

import sys
import os
import time
import traceback
from datetime import datetime, timedelta, timezone
from unittest.mock import patch, MagicMock
from io import StringIO

import numpy as np
import pandas as pd

sys.path.insert(0, os.path.dirname(__file__))

PASS = 0
FAIL = 0
ERRORS = []


def test(name):
    def decorator(func):
        global PASS, FAIL
        try:
            func()
            PASS += 1
            print(f"  ✓ {name}")
        except Exception as e:
            FAIL += 1
            tb = traceback.format_exc()
            ERRORS.append((name, str(e), tb))
            print(f"  ✗ {name}: {e}")
        return func
    return decorator


def make_ohlcv(n=300, start_price=1.1000, trend=0.0001, seed=42):
    """Generate realistic OHLCV data for testing."""
    np.random.seed(seed)
    dates = pd.date_range(
        end=pd.Timestamp.now(tz="UTC").floor("h"),
        periods=n, freq="h"
    )
    close = start_price + np.cumsum(np.random.randn(n) * 0.001 + trend)
    high = close + np.abs(np.random.randn(n) * 0.0005)
    low = close - np.abs(np.random.randn(n) * 0.0005)
    open_ = close + np.random.randn(n) * 0.0003
    volume = np.random.randint(100, 10000, n).astype(float)
    df = pd.DataFrame({
        "Open": open_, "High": high, "Low": low,
        "Close": close, "Volume": volume,
    }, index=dates)
    df.index.name = "Datetime"
    return df


def make_xau_ohlcv(n=300, start_price=2400.0, seed=42):
    """Generate XAU/USD OHLCV data."""
    np.random.seed(seed)
    dates = pd.date_range(
        end=pd.Timestamp.now(tz="UTC").floor("h"),
        periods=n, freq="h"
    )
    close = start_price + np.cumsum(np.random.randn(n) * 2.0 + 0.05)
    high = close + np.abs(np.random.randn(n) * 1.0)
    low = close - np.abs(np.random.randn(n) * 1.0)
    open_ = close + np.random.randn(n) * 0.5
    volume = np.random.randint(100, 10000, n).astype(float)
    df = pd.DataFrame({
        "Open": open_, "High": high, "Low": low,
        "Close": close, "Volume": volume,
    }, index=dates)
    df.index.name = "Datetime"
    return df


# ══════════════════════════════════════════════════════════════════════
#  MODULE 1: config.py
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 1: config.py ═══")

from config import (
    Timeframe, SignalStrength, MarketSession,
    PlatformConfig, TechnicalConfig, SignalConfig, NewsConfig,
    FilterConfig, XauStrategyConfig, EurUsdStrategyConfig,
    XAUUSD, EURUSD, PAIR_INFO,
)


@test("Timeframe enum values")
def _():
    assert Timeframe.M1.value == "1min"
    assert Timeframe.M5.value == "5min"
    assert Timeframe.M15.value == "15min"
    assert Timeframe.M30.value == "30min"
    assert Timeframe.H1.value == "1h"
    assert Timeframe.H4.value == "4h"
    assert Timeframe.D1.value == "1d"
    assert Timeframe.W1.value == "1wk"


@test("SignalStrength enum values and ordering")
def _():
    assert SignalStrength.STRONG_BUY.value == 2
    assert SignalStrength.BUY.value == 1
    assert SignalStrength.NEUTRAL.value == 0
    assert SignalStrength.SELL.value == -1
    assert SignalStrength.STRONG_SELL.value == -2
    assert SignalStrength.STRONG_BUY.value > SignalStrength.BUY.value
    assert SignalStrength.SELL.value > SignalStrength.STRONG_SELL.value


@test("MarketSession enum values")
def _():
    assert MarketSession.SYDNEY.value == "sydney"
    assert MarketSession.TOKYO.value == "tokyo"
    assert MarketSession.LONDON.value == "london"
    assert MarketSession.NEW_YORK.value == "new_york"
    assert MarketSession.OVERLAP_LONDON_NY.value == "london_ny_overlap"


@test("PAIR_INFO completeness and correctness")
def _():
    assert XAUUSD in PAIR_INFO
    for pair, info in PAIR_INFO.items():
        assert "display" in info, f"Missing 'display' for {pair}"
        assert "pip_value" in info, f"Missing 'pip_value' for {pair}"
        assert "is_jpy_pair" in info, f"Missing 'is_jpy_pair' for {pair}"
        assert info["pip_value"] > 0, f"pip_value must be > 0 for {pair}"
    assert PAIR_INFO[XAUUSD]["is_jpy_pair"] is False
    assert PAIR_INFO[XAUUSD]["pip_value"] == 0.10


@test("TechnicalConfig defaults")
def _():
    tc = TechnicalConfig()
    assert tc.ema_fast == 9
    assert tc.ema_medium == 21
    assert tc.ema_slow == 50
    assert tc.ema_trend == 200
    assert tc.rsi_period == 14
    assert tc.rsi_overbought == 70.0
    assert tc.rsi_oversold == 30.0
    assert tc.macd_fast == 12
    assert tc.macd_slow == 26
    assert tc.macd_signal == 9
    assert tc.bb_period == 20
    assert tc.bb_std == 2.0
    assert tc.atr_period == 14
    assert tc.adx_period == 14
    assert tc.ema_mode == "4ema"
    assert tc.rsi_mode == "classic"
    assert tc.macd_as_pillar is True
    assert tc.disabled_indicators == []
    assert tc.roc_period == 10
    assert tc.kc_period == 20
    assert tc.adx_slope_lookback == 5


@test("SignalConfig defaults")
def _():
    sc = SignalConfig()
    assert sc.min_confidence == 55.0
    assert sc.min_rr_ratio == 0.5
    assert sc.atr_sl_multiplier == 1.5
    assert sc.default_rr_ratio == 2.0
    assert sc.cooldown_bars == 5
    assert "ema_trend" in sc.weights
    assert "rsi" in sc.weights


@test("PlatformConfig defaults")
def _():
    pc = PlatformConfig()
    assert pc.symbol == XAUUSD
    assert pc.primary_timeframe == Timeframe.H1
    assert Timeframe.M15 in pc.confirmation_timeframes
    assert Timeframe.H4 in pc.confirmation_timeframes
    assert pc.data_lookback_days == 60
    assert pc.refresh_interval == 300
    assert XAUUSD in pc.scan_pairs


@test("XauStrategyConfig defaults")
def _():
    xc = XauStrategyConfig()
    assert xc.enabled is False
    assert xc.rsi_entry_threshold == 45.0
    assert xc.min_ema200_tfs == 2
    assert xc.allow_generic_trades is True
    assert xc.tp_atr_multiplier == 1.0
    assert xc.sl_atr_multiplier == 2.0
    assert isinstance(xc.blocked_hours_utc, list)


@test("FilterConfig defaults")
def _():
    fc = FilterConfig()
    assert fc.session_filter is True
    assert fc.regime_filter is False


@test("PlatformConfig dataclass mutable default independence")
def _():
    c1 = PlatformConfig()
    c2 = PlatformConfig()
    c1.scan_pairs.append("TEST")
    assert "TEST" not in c2.scan_pairs, "Mutable defaults should be independent"


# ══════════════════════════════════════════════════════════════════════
#  MODULE 2: data_provider.py
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 2: data_provider.py ═══")

from data_provider import ForexDataProvider, DataIntegrityError


@test("ForexDataProvider._to_td_symbol conversion")
def _():
    assert ForexDataProvider._to_td_symbol("XAU/USD") == "XAU/USD"


@test("ForexDataProvider TD_INTERVALS complete mapping")
def _():
    dp = ForexDataProvider(PlatformConfig())
    for tf in Timeframe:
        assert tf in dp.TD_INTERVALS, f"Missing TD_INTERVALS mapping for {tf}"


@test("ForexDataProvider BARS_PER_DAY mapping")
def _():
    dp = ForexDataProvider(PlatformConfig())
    for tf in Timeframe:
        assert tf in dp.BARS_PER_DAY, f"Missing BARS_PER_DAY for {tf}"
    assert dp.BARS_PER_DAY[Timeframe.H1] == 24
    assert dp.BARS_PER_DAY[Timeframe.M15] == 96
    assert dp.BARS_PER_DAY[Timeframe.D1] == 1


@test("ForexDataProvider CANDLE_SECONDS mapping")
def _():
    dp = ForexDataProvider(PlatformConfig())
    for tf in Timeframe:
        assert tf in dp.CANDLE_SECONDS, f"Missing CANDLE_SECONDS for {tf}"
    assert dp.CANDLE_SECONDS[Timeframe.M1] == 60
    assert dp.CANDLE_SECONDS[Timeframe.H1] == 3600
    assert dp.CANDLE_SECONDS[Timeframe.D1] == 86400


@test("ForexDataProvider._validate_and_clean basic valid data")
def _():
    dp = ForexDataProvider(PlatformConfig())
    df = make_ohlcv(300)
    result = dp._validate_and_clean(df, "XAU/USD", Timeframe.H1)
    assert len(result) > 0
    assert result.index.is_monotonic_increasing
    assert not result.index.has_duplicates


@test("ForexDataProvider._validate_and_clean empty df raises error")
def _():
    dp = ForexDataProvider(PlatformConfig())
    try:
        dp._validate_and_clean(pd.DataFrame(), "XAU/USD", Timeframe.H1)
        assert False, "Should have raised DataIntegrityError"
    except DataIntegrityError:
        pass


@test("ForexDataProvider._validate_and_clean None df raises error")
def _():
    dp = ForexDataProvider(PlatformConfig())
    try:
        dp._validate_and_clean(None, "XAU/USD", Timeframe.H1)
        assert False, "Should have raised DataIntegrityError"
    except DataIntegrityError:
        pass


@test("ForexDataProvider._validate_and_clean fixes OHLC inconsistencies")
def _():
    dp = ForexDataProvider(PlatformConfig())
    df = make_ohlcv(300)
    df.iloc[10, df.columns.get_loc("High")] = df.iloc[10]["Low"] - 1
    result = dp._validate_and_clean(df, "XAU/USD", Timeframe.H1)
    assert (result["High"] >= result["Low"]).all()


@test("ForexDataProvider._validate_and_clean drops future timestamps")
def _():
    dp = ForexDataProvider(PlatformConfig())
    df = make_ohlcv(300)
    future = pd.Timestamp.now(tz="UTC") + pd.Timedelta(hours=10)
    df.loc[future] = df.iloc[-1]
    result = dp._validate_and_clean(df, "XAU/USD", Timeframe.H1)
    assert (result.index <= pd.Timestamp.utcnow() + pd.Timedelta(minutes=5)).all()


@test("ForexDataProvider._validate_and_clean removes duplicates")
def _():
    dp = ForexDataProvider(PlatformConfig())
    df = make_ohlcv(300)
    dup = pd.concat([df, df.iloc[-5:]])
    assert dup.index.has_duplicates, "Setup: should have duplicates"
    result = dp._validate_and_clean(dup, "XAU/USD", Timeframe.H1)
    assert not result.index.has_duplicates, "Should remove duplicate indices"


@test("ForexDataProvider._validate_and_clean forward fills NaN (limit 3)")
def _():
    dp = ForexDataProvider(PlatformConfig())
    df = make_ohlcv(300)
    df.iloc[100, df.columns.get_loc("Close")] = np.nan
    df.iloc[101, df.columns.get_loc("Close")] = np.nan
    result = dp._validate_and_clean(df, "XAU/USD", Timeframe.H1)
    assert not result["Close"].isna().any()


@test("ForexDataProvider._is_data_fresh weekend override")
def _():
    dp = ForexDataProvider(PlatformConfig())
    # Create data that ends on a Friday, call _is_data_fresh on a Saturday
    friday = pd.Timestamp("2025-05-09 16:00:00", tz="UTC")
    old_dates = pd.date_range(end=friday, periods=300, freq="h")
    df = make_ohlcv(300)
    df.index = old_dates
    # Simulate calling on Saturday by patching pd.Timestamp.now
    saturday = pd.Timestamp("2025-05-10 12:00:00", tz="UTC")
    original_now = pd.Timestamp.now
    with patch.object(pd.Timestamp, "now", return_value=saturday):
        result = dp._is_data_fresh(df, "XAU/USD", Timeframe.H1)
        assert result is True, "Weekend data should always be considered fresh"


@test("ForexDataProvider._is_data_fresh returns False for empty df")
def _():
    dp = ForexDataProvider(PlatformConfig())
    assert dp._is_data_fresh(pd.DataFrame(), "XAU/USD", Timeframe.H1) is False
    assert dp._is_data_fresh(None, "XAU/USD", Timeframe.H1) is False


@test("ForexDataProvider.clear_cache clears all entries")
def _():
    dp = ForexDataProvider(PlatformConfig())
    dp._cache["test_key"] = make_ohlcv(10)
    dp._cache_timestamps["test_key"] = time.time()
    count = dp.clear_cache()
    assert count == 1
    assert len(dp._cache) == 0
    assert len(dp._cache_timestamps) == 0


@test("ForexDataProvider.get_pair_info returns correct info")
def _():
    dp = ForexDataProvider(PlatformConfig())
    info = dp.get_pair_info(XAUUSD)
    assert info["symbol"] == XAUUSD
    assert info["display"] == "XAU/USD"
    assert info["pip_value"] == 0.10

    info_unknown = dp.get_pair_info("UNKNOWN")
    assert info_unknown["display"] == "XAU/USD"


@test("ForexDataProvider._throttle rate limiting logic")
def _():
    dp = ForexDataProvider(PlatformConfig())
    dp._request_times.clear()
    for _ in range(10):
        dp._throttle(credits=1)
    assert len(dp._request_times) == 10


@test("ForexDataProvider caching - in-memory cache returns same data")
def _():
    dp = ForexDataProvider(PlatformConfig())
    df = make_ohlcv(300)
    dp._cache["XAU/USD_1h"] = df
    dp._cache_timestamps["XAU/USD_1h"] = time.time()
    cached = dp._cache.get("XAU/USD_1h")
    assert cached is df


# ══════════════════════════════════════════════════════════════════════
#  MODULE 3: engine.py
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 3: engine.py ═══")

from engine import TechnicalEngine, TechnicalAnalysis, IndicatorSignal


@test("TechnicalEngine.analyze returns TechnicalAnalysis with insufficient data")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(10)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert isinstance(result, TechnicalAnalysis)
    assert result.confidence == 0.0


@test("TechnicalEngine.analyze returns TechnicalAnalysis with None df")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    result = engine.analyze(None, "XAU/USD", "1h")
    assert isinstance(result, TechnicalAnalysis)
    assert result.confidence == 0.0


@test("TechnicalEngine.analyze returns valid result with good data")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert isinstance(result, TechnicalAnalysis)
    assert result.symbol == "XAU/USD"
    assert result.timeframe == "1h"
    assert result.composite_signal in list(SignalStrength)
    assert -100 <= result.composite_score <= 100
    assert 0 <= result.confidence <= 100


@test("TechnicalEngine EMA-4 mode computes all 4 EMAs")
def _():
    config = TechnicalConfig(ema_mode="4ema")
    engine = TechnicalEngine(config)
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "ema_trend" in result.indicators
    ema_ind = result.indicators["ema_trend"]
    assert isinstance(ema_ind, IndicatorSignal)
    assert ema_ind.name == "EMA Trend"
    assert ema_ind.signal in list(SignalStrength)
    assert "ema_fast" in result.key_levels
    assert "ema_medium" in result.key_levels
    assert "ema_slow" in result.key_levels


@test("TechnicalEngine EMA-2 mode computes EMA50/200")
def _():
    config = TechnicalConfig(ema_mode="2ema")
    engine = TechnicalEngine(config)
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "ema_trend" in result.indicators
    assert "ema_trend" in result.key_levels or "ema_slow" in result.key_levels


@test("TechnicalEngine RSI computation")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "rsi" in result.indicators
    rsi = result.indicators["rsi"]
    assert 0 <= rsi.value <= 100, f"RSI value {rsi.value} out of range"
    assert rsi.signal in list(SignalStrength)
    assert rsi.weight == 1.5


@test("TechnicalEngine RSI with NaN handling")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    df["Close"] = df["Close"].iloc[0]  # flat price = 0 delta
    result = engine.analyze(df, "XAU/USD", "1h")
    rsi = result.indicators.get("rsi")
    if rsi:
        assert not pd.isna(rsi.value), "RSI should handle NaN gracefully"


@test("TechnicalEngine Bollinger Bands computation")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "bollinger" in result.indicators
    bb = result.indicators["bollinger"]
    assert isinstance(bb.value, float)
    assert "bb_upper" in result.key_levels
    assert "bb_lower" in result.key_levels
    assert "bb_mid" in result.key_levels
    assert result.key_levels["bb_upper"] > result.key_levels["bb_lower"]


@test("TechnicalEngine ADX computation")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "adx" in result.indicators
    adx = result.indicators["adx"]
    assert adx.value >= 0, f"ADX value {adx.value} should be >= 0"
    assert "+DI" in adx.description
    assert "-DI" in adx.description


@test("TechnicalEngine ATR computation and volatility regime")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "atr" in result.indicators
    atr = result.indicators["atr"]
    assert atr.value > 0, "ATR should be positive"
    assert atr.weight == 0.0, "ATR weight should be 0 (not directional)"
    assert result.volatility_regime in ("low", "normal", "high", "extreme")
    assert "atr" in result.key_levels
    assert "atr_pct" in result.key_levels


@test("TechnicalEngine MACD as pillar (enabled by default)")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "macd" in result.indicators
    macd = result.indicators["macd"]
    assert macd.weight == 1.5

@test("TechnicalEngine MACD not as pillar when disabled")
def _():
    config = TechnicalConfig(macd_as_pillar=False)
    engine = TechnicalEngine(config)
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "macd" not in result.indicators
    assert hasattr(result, "_macd_histogram")


@test("TechnicalEngine disabled indicators are skipped")
def _():
    config = TechnicalConfig(disabled_indicators=["rsi", "bollinger", "divergence"])
    engine = TechnicalEngine(config)
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "rsi" not in result.indicators
    assert "bollinger" not in result.indicators


@test("TechnicalEngine candlestick pattern detection")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    # Create a hammer pattern: long lower wick, small body, no upper wick
    idx = -1
    close_val = df["Close"].iloc[idx]
    df.iloc[idx, df.columns.get_loc("Open")] = close_val - 0.0001
    df.iloc[idx, df.columns.get_loc("Close")] = close_val
    df.iloc[idx, df.columns.get_loc("High")] = close_val + 0.00005
    df.iloc[idx, df.columns.get_loc("Low")] = close_val - 0.001
    result = engine.analyze(df, "XAU/USD", "1h")
    # Pattern may or may not be detected depending on threshold, just ensure no crash


@test("TechnicalEngine swing point detection")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    series = pd.Series([10, 8, 6, 4, 6, 8, 10, 8, 6, 4, 6, 8, 10])
    lows = engine._find_swing_points(series, mode="low", window=2)
    highs = engine._find_swing_points(series, mode="high", window=2)
    assert len(lows) > 0, "Should find swing lows in V-pattern data"
    assert any(v == 4 for _, v in lows), "Should find value 4 as swing low"
    assert len(highs) > 0, "Should find swing highs"


@test("TechnicalEngine swing points with NaN values")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    series = pd.Series([10, 8, np.nan, 4, 6, 8, 10, 8, 6, 4, 6, 8, 10])
    lows = engine._find_swing_points(series, mode="low", window=2)
    # Should not crash


@test("TechnicalEngine market regime classification")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300, trend=0.001)  # strong uptrend
    result = engine.analyze(df, "XAU/USD", "1h")
    assert result.market_regime in ("trending_up", "trending_down", "trending", "ranging")


@test("TechnicalEngine composite score within bounds")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert -100 <= result.composite_score <= 100
    assert result.composite_signal in list(SignalStrength)


@test("TechnicalEngine regime-aware RSI reinterpretation")
def _():
    config = TechnicalConfig(rsi_mode="regime_aware")
    engine = TechnicalEngine(config)
    df = make_ohlcv(300, trend=0.001)
    result = engine.analyze(df, "XAU/USD", "1h")
    rsi = result.indicators.get("rsi")
    assert rsi is not None


@test("TechnicalEngine structure S/R levels detection")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    swing_keys = [k for k in result.key_levels if k.startswith("swing_")]
    assert len(swing_keys) >= 0  # may or may not have swings


@test("TechnicalEngine divergence detection (enabled by default)")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    # divergence may or may not be detected depending on data, just ensure no crash

@test("TechnicalEngine divergence detection (disabled)")
def _():
    config = TechnicalConfig(disabled_indicators=["divergence"])
    engine = TechnicalEngine(config)
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "divergence" not in result.indicators


@test("TechnicalEngine EMA-2 golden cross detection")
def _():
    config = TechnicalConfig(ema_mode="2ema")
    engine = TechnicalEngine(config)
    df = make_ohlcv(300, trend=0.002)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "ema_trend" in result.indicators


@test("TechnicalEngine with very short data")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(31)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert isinstance(result, TechnicalAnalysis)


@test("TechnicalEngine ROC computation")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300, trend=0.002)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "roc" in result.indicators
    roc = result.indicators["roc"]
    assert roc.weight == 1.5
    assert roc.signal in list(SignalStrength)


@test("TechnicalEngine Keltner Channels computation")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "keltner" in result.indicators
    kc = result.indicators["keltner"]
    assert kc.weight == 1.5
    assert "kc_upper" in result.key_levels
    assert "kc_lower" in result.key_levels
    assert "kc_mid" in result.key_levels
    assert result.key_levels["kc_upper"] > result.key_levels["kc_lower"]


@test("TechnicalEngine ADX slope computation")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300, trend=0.002)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "adx" in result.indicators
    assert hasattr(result, "_adx_slope")


@test("TechnicalEngine transitioning regime detection")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert result.market_regime in (
        "trending_up", "trending_down", "trending", "ranging", "transitioning"
    )


@test("TechnicalEngine gold-specific composite weights")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_xau_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert -100 <= result.composite_score <= 100
    assert result.composite_signal in list(SignalStrength)


@test("TechnicalEngine EMA ribbon compression detection")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300)
    result = engine.analyze(df, "XAU/USD", "1h")
    assert "ema_trend" in result.indicators


# ══════════════════════════════════════════════════════════════════════
#  MODULE 4: news_engine.py
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 4: news_engine.py ═══")

from news_engine import NewsEngine, FundamentalContext, EconomicEvent, NewsSentiment


@test("NewsEngine._parse_pair with slash")
def _():
    base, quote = NewsEngine._parse_pair("XAU/USD")
    assert base == "XAU"
    assert quote == "USD"


@test("NewsEngine._parse_pair with =X suffix")
def _():
    base, quote = NewsEngine._parse_pair("EURUSD=X")
    assert base == "EUR"
    assert quote == "USD"


@test("NewsEngine._parse_pair for XAU pair")
def _():
    base, quote = NewsEngine._parse_pair("XAU/USD")
    assert base == "XAU"
    assert quote == "USD"


@test("NewsEngine._analyze_headline_sentiment bullish")
def _():
    ne = NewsEngine(NewsConfig())
    score = ne._analyze_headline_sentiment("Fed hawkish rate hike boosts dollar rally")
    assert score > 0, f"Expected positive sentiment, got {score}"


@test("NewsEngine._analyze_headline_sentiment bearish")
def _():
    ne = NewsEngine(NewsConfig())
    score = ne._analyze_headline_sentiment("Recession fears drive dovish rate cut expectations")
    assert score < 0, f"Expected negative sentiment, got {score}"


@test("NewsEngine._analyze_headline_sentiment neutral")
def _():
    ne = NewsEngine(NewsConfig())
    score = ne._analyze_headline_sentiment("Markets remain quiet ahead of data release")
    assert score == 0.0


@test("NewsEngine._extract_currencies USD")
def _():
    ne = NewsEngine(NewsConfig())
    currencies = ne._extract_currencies("Federal Reserve raises interest rates, dollar soars")
    assert "USD" in currencies


@test("NewsEngine._extract_currencies EUR")
def _():
    ne = NewsEngine(NewsConfig())
    currencies = ne._extract_currencies("ECB meeting pushes euro higher")
    assert "EUR" in currencies


@test("NewsEngine._extract_currencies GBP")
def _():
    ne = NewsEngine(NewsConfig())
    currencies = ne._extract_currencies("Bank of England holds rates, pound steady")
    assert "GBP" in currencies


@test("NewsEngine._extract_currencies XAU")
def _():
    ne = NewsEngine(NewsConfig())
    currencies = ne._extract_currencies("Gold prices surge as safe haven demand rises")
    assert "XAU" in currencies


@test("NewsEngine._extract_currencies multiple")
def _():
    ne = NewsEngine(NewsConfig())
    currencies = ne._extract_currencies("Gold drops as Fed hawkish, dollar surges")
    assert "USD" in currencies
    assert "XAU" in currencies


@test("NewsEngine._extract_keywords")
def _():
    ne = NewsEngine(NewsConfig())
    kws = ne._extract_keywords("Hawkish Fed rate hike surprises with positive surprise")
    assert "rate hike" in kws
    assert "hawkish" in kws
    assert "positive surprise" in kws


@test("NewsEngine._parse_rss_date RFC822 format")
def _():
    dt = NewsEngine._parse_rss_date("Mon, 12 May 2025 14:30:00 +0000")
    assert dt is not None
    assert dt.year == 2025
    assert dt.month == 5
    assert dt.day == 12
    assert dt.tzinfo is not None


@test("NewsEngine._parse_rss_date ISO format")
def _():
    dt = NewsEngine._parse_rss_date("2025-05-12T14:30:00+00:00")
    assert dt is not None
    assert dt.year == 2025


@test("NewsEngine._parse_rss_date naive format assumes UTC")
def _():
    dt = NewsEngine._parse_rss_date("2025-05-12 14:30:00")
    assert dt is not None
    assert dt.tzinfo == timezone.utc


@test("NewsEngine._parse_rss_date None input")
def _():
    assert NewsEngine._parse_rss_date(None) is None


@test("NewsEngine._parse_rss_date garbage input")
def _():
    assert NewsEngine._parse_rss_date("not-a-date") is None


@test("NewsEngine._get_affected_pairs")
def _():
    pairs = NewsEngine._get_affected_pairs("USD")
    assert "XAU/USD" in pairs

    pairs_xau = NewsEngine._get_affected_pairs("XAU")
    assert "XAU/USD" in pairs_xau

    pairs_unknown = NewsEngine._get_affected_pairs("ARS")
    assert pairs_unknown == []


@test("EconomicEvent properties")
def _():
    future_time = datetime.now(timezone.utc) + timedelta(minutes=30)
    event = EconomicEvent(
        time=future_time, currency="USD",
        impact="high", event="NFP",
    )
    assert event.is_upcoming is True
    assert 25 < event.minutes_until < 35
    assert event.is_high_impact is True

    past_time = datetime.now(timezone.utc) - timedelta(minutes=10)
    past_event = EconomicEvent(
        time=past_time, currency="EUR",
        impact="low", event="CPI",
    )
    assert past_event.is_upcoming is False
    assert past_event.is_high_impact is False


@test("NewsEngine.get_event_risk_level danger zone")
def _():
    ne = NewsEngine(NewsConfig())
    context = FundamentalContext()
    context.events_upcoming_1h = [
        EconomicEvent(
            time=datetime.now(timezone.utc) + timedelta(minutes=15),
            currency="USD", impact="high", event="NFP",
        )
    ]
    risk, desc = ne.get_event_risk_level("XAU/USD", context)
    assert risk == "danger"
    assert "NFP" in desc


@test("NewsEngine.get_event_risk_level caution zone")
def _():
    ne = NewsEngine(NewsConfig())
    context = FundamentalContext()
    context.events_upcoming_1h = [
        EconomicEvent(
            time=datetime.now(timezone.utc) + timedelta(minutes=45),
            currency="USD", impact="high", event="GDP",
        )
    ]
    risk, desc = ne.get_event_risk_level("XAU/USD", context)
    assert risk == "caution"


@test("NewsEngine.get_event_risk_level clear when no events")
def _():
    ne = NewsEngine(NewsConfig())
    context = FundamentalContext()
    risk, desc = ne.get_event_risk_level("XAU/USD", context)
    assert risk == "clear"


@test("NewsEngine.get_event_risk_level ignores unrelated currency events")
def _():
    ne = NewsEngine(NewsConfig())
    context = FundamentalContext()
    context.events_upcoming_1h = [
        EconomicEvent(
            time=datetime.now(timezone.utc) + timedelta(minutes=15),
            currency="AUD", impact="high", event="RBA Rate",
        )
    ]
    risk, _ = ne.get_event_risk_level("XAU/USD", context)
    assert risk == "clear"


@test("NewsEngine.get_pair_sentiment with positive base sentiment")
def _():
    ne = NewsEngine(NewsConfig())
    context = FundamentalContext()
    context.news_sentiment = {"EUR": 0.5, "USD": -0.1}
    score, desc = ne.get_pair_sentiment("XAU/USD", context)
    assert score > 0, "EUR bullish + USD bearish should be net bullish for EUR/USD"
    assert "bullish" in desc.lower()


@test("NewsEngine.get_pair_sentiment with negative pair sentiment")
def _():
    ne = NewsEngine(NewsConfig())
    context = FundamentalContext()
    context.news_sentiment = {"EUR": -0.5, "USD": 0.3}
    score, desc = ne.get_pair_sentiment("XAU/USD", context)
    assert score < 0


@test("NewsEngine.get_pair_sentiment neutral")
def _():
    ne = NewsEngine(NewsConfig())
    context = FundamentalContext()
    context.news_sentiment = {"EUR": 0.0, "USD": 0.0}
    score, desc = ne.get_pair_sentiment("XAU/USD", context)
    assert score == 0.0
    assert "neutral" in desc.lower()


@test("FundamentalContext defaults")
def _():
    ctx = FundamentalContext()
    assert ctx.events_today == []
    assert ctx.events_upcoming_1h == []
    assert ctx.high_impact_warning is False
    assert ctx.calendar_available is False
    assert ctx.news_sentiment == {}
    assert ctx.news_headlines == []


# ══════════════════════════════════════════════════════════════════════
#  MODULE 5: signal_aggregator.py
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 5: signal_aggregator.py ═══")

from signal_aggregator import SignalAggregator, SignalReport, TradeSetup


@test("SignalReport defaults")
def _():
    r = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="now")
    assert r.final_signal == SignalStrength.NEUTRAL
    assert r.final_score == 0.0
    assert r.primary_signal == SignalStrength.NEUTRAL
    assert r.trade_setup is None
    assert r.mtf_agreement is False
    assert r.signal_quality == ""
    assert r.bullish_reasons == []
    assert r.bearish_reasons == []
    assert r.warnings == []


@test("TradeSetup dataclass creation")
def _():
    ts = TradeSetup(
        pair="XAU/USD", display_name="XAU/USD", direction="BUY",
        entry_price=1.1000, stop_loss=1.0950, take_profit_1=1.1100,
        take_profit_2=1.1200, risk_reward=2.0, risk_pips=50, reward_pips=100,
        pip_value=0.0001,
    )
    assert ts.direction == "BUY"
    assert ts.risk_reward == 2.0
    assert ts.entry_price > ts.stop_loss


@test("SignalAggregator._check_mtf_agreement with 2 bullish")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    report = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="now")
    report.mtf_signals = {
        "15min": SignalStrength.BUY,
        "1h": SignalStrength.STRONG_BUY,
        "4h": SignalStrength.NEUTRAL,
    }
    assert agg._check_mtf_agreement(report) is True


@test("SignalAggregator._check_mtf_agreement with mixed signals")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    report = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="now")
    report.mtf_signals = {
        "15min": SignalStrength.BUY,
        "1h": SignalStrength.SELL,
        "4h": SignalStrength.NEUTRAL,
    }
    assert agg._check_mtf_agreement(report) is False


@test("SignalAggregator._check_mtf_agreement with single signal")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    report = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="now")
    report.mtf_signals = {"1h": SignalStrength.BUY}
    assert agg._check_mtf_agreement(report) is False


@test("SignalAggregator._latest_adx computes valid value")
def _():
    df = make_ohlcv(300)
    adx = SignalAggregator._latest_adx(df)
    assert adx is not None
    assert adx >= 0


@test("SignalAggregator._latest_adx with insufficient data")
def _():
    df = make_ohlcv(10)
    adx = SignalAggregator._latest_adx(df)
    assert adx is None


@test("SignalAggregator.analyze_from_dataframes produces valid report")
def _():
    config = PlatformConfig()
    config.primary_timeframe = Timeframe.H1
    config.confirmation_timeframes = [Timeframe.M15, Timeframe.H4]
    agg = SignalAggregator(config)

    dfs = {
        Timeframe.H1: make_ohlcv(300, seed=42),
        Timeframe.M15: make_ohlcv(300, seed=43),
        Timeframe.H4: make_ohlcv(300, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    assert isinstance(report, SignalReport)
    assert report.symbol == "XAU/USD"
    assert report.display_name == "XAU/USD"
    assert report.final_signal in list(SignalStrength)
    assert report.signal_quality in ("A+", "A", "B", "C", "D", "F")
    assert report.current_price > 0


@test("SignalAggregator._compute_final_signal quality grades")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_ohlcv(300, seed=42),
        Timeframe.M15: make_ohlcv(300, seed=43),
        Timeframe.H4: make_ohlcv(300, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    assert report.signal_quality in ("A+", "A", "B", "C", "D", "F")
    assert report.action_recommendation != ""


@test("SignalAggregator._generate_trade_setup BUY direction")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_ohlcv(300, trend=0.003, seed=42),
        Timeframe.M15: make_ohlcv(300, trend=0.003, seed=43),
        Timeframe.H4: make_ohlcv(300, trend=0.003, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    if report.trade_setup:
        ts = report.trade_setup
        if ts.direction == "BUY":
            assert ts.stop_loss < ts.entry_price, "BUY SL should be below entry"
            assert ts.take_profit_1 > ts.entry_price, "BUY TP should be above entry"
            assert ts.risk_reward >= config.signals.min_rr_ratio


@test("SignalAggregator._collect_reasons populates reasons")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_ohlcv(300, seed=42),
        Timeframe.M15: make_ohlcv(300, seed=43),
        Timeframe.H4: make_ohlcv(300, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    total_reasons = len(report.bullish_reasons) + len(report.bearish_reasons)
    assert total_reasons > 0, "Should have at least one reason"


@test("SignalAggregator session filter blocks outside hours")
def _():
    config = PlatformConfig()
    config.backtest.session_filter = True
    config.xau_strategy.blocked_hours_utc = list(range(24))
    config.xau_strategy.enabled = False
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_xau_ohlcv(300, seed=42),
        Timeframe.M15: make_xau_ohlcv(300, seed=43),
        Timeframe.H4: make_xau_ohlcv(300, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    assert report.signal_quality == "F"


@test("SignalAggregator with missing primary timeframe")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.M15: make_ohlcv(300, seed=43),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    assert report.signal_quality == "F"
    assert any("unavailable" in w.lower() for w in report.warnings)


@test("SignalAggregator XAU strategy check (conditions not met)")
def _():
    config = PlatformConfig()
    config.xau_strategy.enabled = True
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_xau_ohlcv(300, seed=42),
        Timeframe.M15: make_xau_ohlcv(300, seed=43),
        Timeframe.H4: make_xau_ohlcv(300, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    assert isinstance(report, SignalReport)


@test("SignalAggregator event risk affects confidence")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    context = FundamentalContext()
    context.events_upcoming_1h = [
        EconomicEvent(
            time=datetime.now(timezone.utc) + timedelta(minutes=10),
            currency="USD", impact="high", event="NFP",
        )
    ]
    dfs = {
        Timeframe.H1: make_ohlcv(300, seed=42),
        Timeframe.M15: make_ohlcv(300, seed=43),
        Timeframe.H4: make_ohlcv(300, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, context=context, use_news=True)
    assert any("EVENT RISK" in w or "event" in w.lower() for w in report.warnings)


# ══════════════════════════════════════════════════════════════════════
#  MODULE 6: main.py
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 6: main.py ═══")

from main import (
    signal_color, quality_color, _compact_reason,
    report_to_dict, build_config, Colors,
)


@test("signal_color maps all SignalStrength values")
def _():
    for sig in SignalStrength:
        color = signal_color(sig)
        assert isinstance(color, str)


@test("quality_color maps all quality grades")
def _():
    for q in ("A+", "A", "B", "C", "D", "F"):
        color = quality_color(q)
        assert isinstance(color, str)


@test("_compact_reason strips STRONG prefix")
def _():
    result = _compact_reason("STRONG: EMA Trend: Full bullish alignment")
    assert not result.startswith("STRONG: ")


@test("_compact_reason strips indicator prefix")
def _():
    result = _compact_reason("RSI: RSI 45.3 — Neutral")
    assert not result.startswith("RSI: ")


@test("_compact_reason strips detail after pipe")
def _():
    result = _compact_reason("EMA alignment: 3/4 | Golden cross (fast/medium)")
    assert "|" not in result


@test("_compact_reason handles plain text")
def _():
    result = _compact_reason("Simple reason text")
    assert result == "Simple reason text"


@test("report_to_dict basic conversion")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_ohlcv(300, seed=42),
        Timeframe.M15: make_ohlcv(300, seed=43),
        Timeframe.H4: make_ohlcv(300, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    d = report_to_dict(report)
    assert "symbol" in d
    assert "signal" in d
    assert "score" in d
    assert "confidence" in d
    assert "quality" in d
    assert "action" in d
    assert "market_regime" in d
    assert "volatility_regime" in d
    assert "mtf_agreement" in d
    assert "mtf_signals" in d
    assert d["symbol"] == "XAU/USD"
    assert d["display_name"] == "XAU/USD"


@test("report_to_dict includes trade_setup when present")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_ohlcv(300, trend=0.003, seed=42),
        Timeframe.M15: make_ohlcv(300, trend=0.003, seed=43),
        Timeframe.H4: make_ohlcv(300, trend=0.003, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    d = report_to_dict(report)
    if report.trade_setup:
        assert "trade_setup" in d
        ts = d["trade_setup"]
        assert "direction" in ts
        assert "entry" in ts
        assert "stop_loss" in ts
        assert "take_profit_1" in ts
        assert "risk_reward" in ts


@test("report_to_dict includes indicators")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_ohlcv(300, seed=42),
        Timeframe.M15: make_ohlcv(300, seed=43),
        Timeframe.H4: make_ohlcv(300, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    d = report_to_dict(report)
    assert "indicators" in d
    assert "key_levels" in d
    for name, ind_dict in d["indicators"].items():
        assert "signal" in ind_dict
        assert "confidence" in ind_dict
        assert "description" in ind_dict


@test("build_config sets correct timeframe and confirmations")
def _():
    class FakeArgs:
        timeframe = "H4"
    config = build_config(FakeArgs())
    assert config.primary_timeframe == Timeframe.H4
    assert Timeframe.H1 in config.confirmation_timeframes
    assert Timeframe.D1 in config.confirmation_timeframes


@test("build_config all valid timeframes")
def _():
    for tf_str in ["M5", "M15", "M30", "H1", "H4", "D1"]:
        class FakeArgs:
            timeframe = tf_str
        config = build_config(FakeArgs())
        assert config.primary_timeframe is not None


# ══════════════════════════════════════════════════════════════════════
#  MODULE 7: testing_telegram_bot.py
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 7: testing_telegram_bot.py ═══")

from testing_telegram_bot import (
    _escape_md, _signal_label, _mtf_line,
    format_signal_message, _format_scan_summary,
    _seconds_until_boundary, _check_level_breach,
    _format_stats_dashboard,
    scan_keyboard, SCAN_BUTTONS, SCAN_LOOKUP,
    _update_level_cache, _level_cache, _level_cache_ts,
)


@test("_escape_md escapes markdown characters")
def _():
    assert _escape_md("hello *world*") == "hello \\*world\\*"
    assert _escape_md("test_value") == "test\\_value"
    assert _escape_md("`code`") == "\\`code\\`"
    assert _escape_md("[link]") == "\\[link\\]"


@test("_signal_label formatting")
def _():
    assert _signal_label(SignalStrength.STRONG_BUY) == "STRONG BUY"
    assert _signal_label(SignalStrength.STRONG_SELL) == "STRONG SELL"
    assert _signal_label(SignalStrength.NEUTRAL) == "NEUTRAL"
    assert _signal_label(SignalStrength.BUY) == "BUY"
    assert _signal_label(SignalStrength.SELL) == "SELL"


@test("_mtf_line formatting with signals")
def _():
    report = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="now")
    report.mtf_signals = {
        "15min": SignalStrength.BUY,
        "1h": SignalStrength.NEUTRAL,
    }
    report.mtf_scores = {"15min": 25.0, "1h": 5.0}
    line = _mtf_line(report)
    assert "15min" in line
    assert "1h" in line
    assert "BUY" in line


@test("_mtf_line empty when no signals")
def _():
    report = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="now")
    assert _mtf_line(report) == ""


@test("format_signal_message produces valid text")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_ohlcv(300, seed=42),
        Timeframe.M15: make_ohlcv(300, seed=43),
        Timeframe.H4: make_ohlcv(300, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    msg = format_signal_message(report)
    assert isinstance(msg, str)
    assert len(msg) > 0
    assert "XAU/USD" in msg
    assert "Signal Analysis" in msg
    assert "Price" in msg
    assert "Indicators" in msg


@test("_format_scan_summary produces valid text")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_ohlcv(300, seed=42),
        Timeframe.M15: make_ohlcv(300, seed=43),
        Timeframe.H4: make_ohlcv(300, seed=44),
    }
    reports = [agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)]
    summary = _format_scan_summary(reports, Timeframe.H1)
    assert "Auto-Scan" in summary
    assert "XAU/USD" in summary


@test("_seconds_until_boundary returns positive value")
def _():
    result = _seconds_until_boundary(15, buffer=10)
    assert result > 0
    assert result <= 15 * 60 + 10 + 1


@test("_seconds_until_boundary for M5 interval")
def _():
    result = _seconds_until_boundary(5, buffer=10)
    assert result > 0
    assert result <= 5 * 60 + 10 + 1


@test("_seconds_until_boundary for H1 interval")
def _():
    result = _seconds_until_boundary(60, buffer=10)
    assert result > 0
    assert result <= 60 * 60 + 10 + 1


@test("_seconds_until_boundary for H4 interval")
def _():
    result = _seconds_until_boundary(240, buffer=10)
    assert result > 0
    assert result <= 240 * 60 + 10 + 1


@test("_check_level_breach detects price near level")
def _():
    _level_cache["TEST_PAIR"] = {"swing_high_1": 1.1000}
    _level_cache_ts["TEST_PAIR"] = time.time()
    result = _check_level_breach("TEST_PAIR", 1.10005)
    assert result == "swing_high_1"
    del _level_cache["TEST_PAIR"]
    del _level_cache_ts["TEST_PAIR"]


@test("_check_level_breach detects price at exact level")
def _():
    _level_cache["TEST_PAIR"] = {"swing_low_1": 1.0900}
    _level_cache_ts["TEST_PAIR"] = time.time()
    result = _check_level_breach("TEST_PAIR", 1.0900)
    assert result == "swing_low_1"
    del _level_cache["TEST_PAIR"]
    del _level_cache_ts["TEST_PAIR"]


@test("_check_level_breach no breach when price is far from level")
def _():
    _level_cache["TEST_PAIR"] = {"swing_high_1": 1.1000}
    _level_cache_ts["TEST_PAIR"] = time.time()
    result = _check_level_breach("TEST_PAIR", 1.0950)
    assert result is None
    del _level_cache["TEST_PAIR"]
    del _level_cache_ts["TEST_PAIR"]


@test("_check_level_breach returns None for expired cache")
def _():
    _level_cache["TEST_PAIR"] = {"swing_high_1": 1.1000}
    _level_cache_ts["TEST_PAIR"] = time.time() - 7200  # 2h old
    result = _check_level_breach("TEST_PAIR", 1.1000)
    assert result is None
    del _level_cache["TEST_PAIR"]
    del _level_cache_ts["TEST_PAIR"]


@test("_check_level_breach returns None for empty cache")
def _():
    result = _check_level_breach("NONEXISTENT_PAIR", 1.1000)
    assert result is None


@test("SCAN_BUTTONS and SCAN_LOOKUP consistency")
def _():
    assert len(SCAN_BUTTONS) == 5  # 4 TFs + All for EUR/USD
    for btn in SCAN_BUTTONS:
        assert btn["cb"] in SCAN_LOOKUP
        assert "symbol" in btn
        assert "tf" in btn
        assert "label" in btn


@test("scan_keyboard returns InlineKeyboardMarkup")
def _():
    kb = scan_keyboard()
    assert kb is not None


@test("_update_level_cache populates cache from reports")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_ohlcv(300, seed=42),
        Timeframe.M15: make_ohlcv(300, seed=43),
        Timeframe.H4: make_ohlcv(300, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    _level_cache.clear()
    _level_cache_ts.clear()
    _update_level_cache([report])
    if report.primary_analysis and report.primary_analysis.key_levels:
        assert "XAU/USD" in _level_cache or len(report.primary_analysis.key_levels) == 0
    _level_cache.clear()
    _level_cache_ts.clear()


@test("_format_stats_dashboard returns waiting message when empty")
def _():
    import testing_telegram_bot
    old_stats = testing_telegram_bot._signal_stats.copy()
    testing_telegram_bot._signal_stats.clear()
    text = _format_stats_dashboard()
    assert "Waiting" in text or "waiting" in text.lower()
    testing_telegram_bot._signal_stats.update(old_stats)


# ══════════════════════════════════════════════════════════════════════
#  MODULE 8: Integration / Cross-module tests
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 8: Integration Tests ═══")


@test("Full pipeline: config → engine → aggregator → report → dict")
def _():
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    df = make_ohlcv(300, seed=99)
    analysis = engine.analyze(df, "XAU/USD", "1h")
    assert isinstance(analysis, TechnicalAnalysis)

    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_ohlcv(300, seed=99),
        Timeframe.M15: make_ohlcv(300, seed=100),
        Timeframe.H4: make_ohlcv(300, seed=101),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    d = report_to_dict(report)
    assert isinstance(d, dict)
    assert d["symbol"] == "XAU/USD"


@test("Full pipeline: XAU/USD analysis")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_xau_ohlcv(300, seed=42),
        Timeframe.M15: make_xau_ohlcv(300, seed=43),
        Timeframe.H4: make_xau_ohlcv(300, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    assert report.display_name == "XAU/USD"
    assert report.current_price > 1000  # XAU should be in the thousands


@test("Full pipeline: format Telegram message from report")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {
        Timeframe.H1: make_ohlcv(300, seed=42),
        Timeframe.M15: make_ohlcv(300, seed=43),
        Timeframe.H4: make_ohlcv(300, seed=44),
    }
    report = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    msg = format_signal_message(report)
    assert isinstance(msg, str)
    assert len(msg) > 100


@test("XAU/USD analysis with different seeds")
def _():
    config = PlatformConfig()
    for seed_base in [10, 20, 30]:
        agg = SignalAggregator(config)
        dfs = {
            Timeframe.H1: make_xau_ohlcv(300, seed=seed_base),
            Timeframe.M15: make_xau_ohlcv(300, seed=seed_base+1),
            Timeframe.H4: make_xau_ohlcv(300, seed=seed_base+2),
        }
        report = agg.analyze_from_dataframes(XAUUSD, dfs, use_news=False)
        assert report.symbol == XAUUSD
        assert report.current_price > 0
        assert report.signal_quality in ("A+", "A", "B", "C", "D", "F")


@test("Edge case: all EMAs at same price (flat market)")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    dates = pd.date_range(end=pd.Timestamp.now(tz="UTC").floor("h"), periods=300, freq="h")
    df = pd.DataFrame({
        "Open": 1.1, "High": 1.1001, "Low": 1.0999, "Close": 1.1, "Volume": 1000.0,
    }, index=dates)
    df.index.name = "Datetime"
    result = engine.analyze(df, "XAU/USD", "1h")
    assert result.composite_signal == SignalStrength.NEUTRAL


@test("Edge case: extreme volatility data")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    np.random.seed(77)
    dates = pd.date_range(end=pd.Timestamp.now(tz="UTC").floor("h"), periods=300, freq="h")
    close = 1.1 + np.cumsum(np.random.randn(300) * 0.01)
    high = close + np.abs(np.random.randn(300) * 0.005)
    low = close - np.abs(np.random.randn(300) * 0.005)
    df = pd.DataFrame({
        "Open": close + np.random.randn(300) * 0.003,
        "High": high, "Low": low, "Close": close,
        "Volume": np.random.randint(100, 50000, 300).astype(float),
    }, index=dates)
    df.index.name = "Datetime"
    result = engine.analyze(df, "XAU/USD", "1h")
    assert isinstance(result, TechnicalAnalysis)
    assert result.volatility_regime in ("low", "normal", "high", "extreme")


@test("Determinism: same data produces same results")
def _():
    engine = TechnicalEngine(TechnicalConfig())
    df = make_ohlcv(300, seed=42)
    r1 = engine.analyze(df.copy(), "XAU/USD", "1h")
    r2 = engine.analyze(df.copy(), "XAU/USD", "1h")
    assert r1.composite_score == r2.composite_score
    assert r1.composite_signal == r2.composite_signal
    assert r1.market_regime == r2.market_regime


# ══════════════════════════════════════════════════════════════════════
#  MODULE 9: EUR/USD pair support
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 9: EUR/USD pair support ═══")


@test("PAIR_INFO includes EUR/USD with correct pip_value")
def _():
    assert EURUSD in PAIR_INFO
    assert PAIR_INFO[EURUSD]["pip_value"] == 0.0001
    assert PAIR_INFO[EURUSD]["is_jpy_pair"] is False
    assert PAIR_INFO[EURUSD]["display"] == "EUR/USD"


@test("EurUsdStrategyConfig defaults are sensible")
def _():
    cfg = EurUsdStrategyConfig()
    assert cfg.enabled is True
    assert 0 in cfg.blocked_hours_utc
    assert 5 not in cfg.allowed_weekdays  # No Saturday
    assert 6 not in cfg.allowed_weekdays  # No Sunday
    assert cfg.min_atr_threshold == 0.0003
    assert cfg.sl_atr_floor == 1.2
    assert cfg.min_rr_ratio == 1.0
    assert cfg.mature_move_threshold == 35.0
    assert cfg.tp_quality_scale["A+"] == 1.0
    assert cfg.tp_quality_scale["A"] == 1.0
    assert cfg.tp_quality_scale["C"] == 1.0
    assert cfg.blocked_regimes == ["ranging", "transitioning"]
    assert "A" in cfg.min_quality_grades
    assert "B" in cfg.min_quality_grades


@test("PlatformConfig has eur_strategy field")
def _():
    config = PlatformConfig()
    assert hasattr(config, "eur_strategy")
    assert isinstance(config.eur_strategy, EurUsdStrategyConfig)


@test("EUR/USD config factory produces correct config")
def _():
    from pair_signals.eurusd import make_config
    config = make_config(Timeframe.H1)
    assert config.symbol == EURUSD
    assert config.display_name == "EUR/USD"
    assert config.pip_value == 0.0001
    assert config.xau_strategy.enabled is False
    assert config.technical.regime_weights_override
    assert "trending" in config.technical.regime_weights_override
    assert "ranging" in config.technical.regime_weights_override


@test("XAU/USD config factory unchanged")
def _():
    from pair_signals.xauusd import make_config
    config = make_config(Timeframe.H1)
    assert config.symbol == XAUUSD
    assert config.display_name == "XAU/USD"
    assert config.pip_value == 0.10
    assert config.xau_strategy.enabled is True


@test("pair_signals registry includes both pairs")
def _():
    from pair_signals import get_supported_pairs
    pairs = get_supported_pairs()
    assert XAUUSD in pairs
    assert EURUSD in pairs


@test("EUR/USD signal analysis produces valid report (mocked data)")
def _():
    from pair_signals.eurusd import make_config
    from signal_aggregator import SignalAggregator
    config = make_config(Timeframe.H1)
    config.backtest.session_filter = False
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, start_price=1.0800, trend=0.00005)
    dfs = {
        Timeframe.H1: df,
        Timeframe.M15: df,
        Timeframe.H4: df,
    }
    report = agg.analyze_from_dataframes(symbol=EURUSD, dfs=dfs, use_news=False)
    assert report.display_name == "EUR/USD"
    assert report.signal_quality in ("A+", "A", "B", "C", "D", "F")
    assert not any("SELL FILTER" in w for w in report.warnings)


@test("XAU sell gate does not apply to EUR/USD")
def _():
    from pair_signals.eurusd import make_config
    from signal_aggregator import SignalAggregator
    config = make_config(Timeframe.H1)
    config.backtest.session_filter = False
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, start_price=1.0800, trend=-0.0005, seed=99)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=EURUSD, dfs=dfs, use_news=False)
    sell_filter_warnings = [w for w in report.warnings if "SELL FILTER" in w]
    assert len(sell_filter_warnings) == 0


@test("NewsEngine._get_affected_pairs includes EUR/USD for EUR and USD events")
def _():
    from news_engine import NewsEngine
    assert "EUR/USD" in NewsEngine._get_affected_pairs("EUR")
    assert "EUR/USD" in NewsEngine._get_affected_pairs("USD")
    assert "XAU/USD" in NewsEngine._get_affected_pairs("USD")
    assert "EUR/USD" not in NewsEngine._get_affected_pairs("GBP")


@test("EUR/USD trade setup uses tighter RR and SL")
def _():
    from pair_signals.eurusd import make_config
    from signal_aggregator import SignalAggregator
    config = make_config(Timeframe.H1)
    config.backtest.session_filter = False
    config.eur_strategy.blocked_regimes = []
    config.eur_strategy.min_quality_grades = []
    assert config.eur_strategy.sl_atr_floor == 1.2
    assert config.eur_strategy.min_rr_ratio == 1.0
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, start_price=1.0800, trend=0.0003, seed=77)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=EURUSD, dfs=dfs, use_news=False)
    if report.trade_setup:
        assert report.trade_setup.risk_reward >= 1.0 - 0.01


# ══════════════════════════════════════════════════════════════════════
#  MODULE 10: Bug Fix Regressions & Comprehensive Edge Cases
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 10: Bug Fix Regressions & Edge Cases ═══")

# ── 10a. Swing Point Bug Fix (engine.py) ─────────────────────────────

@test("Swing points: all-NaN neighbors produces no false positives")
def _():
    series = pd.Series([np.nan, np.nan, np.nan, 5.0, np.nan, np.nan, np.nan])
    lows = TechnicalEngine._find_swing_points(series, mode="low", window=3)
    highs = TechnicalEngine._find_swing_points(series, mode="high", window=3)
    assert lows == [], f"Expected no lows with all-NaN neighbors, got {lows}"
    assert highs == [], f"Expected no highs with all-NaN neighbors, got {highs}"


@test("Swing points: partial NaN neighbors still detect valid swings")
def _():
    series = pd.Series([10.0, 8.0, np.nan, 4.0, np.nan, 8.0, 10.0])
    lows = TechnicalEngine._find_swing_points(series, mode="low", window=2)
    low_vals = [v for _, v in lows]
    assert 4.0 in low_vals, f"Expected 4.0 as swing low, got {low_vals}"


@test("Swing points: empty series returns empty list")
def _():
    series = pd.Series([], dtype=float)
    assert TechnicalEngine._find_swing_points(series, mode="low") == []
    assert TechnicalEngine._find_swing_points(series, mode="high") == []


@test("Swing points: series shorter than 2*window returns empty")
def _():
    series = pd.Series([1.0, 2.0, 3.0])
    assert TechnicalEngine._find_swing_points(series, mode="low", window=3) == []


@test("Swing points: monotonic increasing has no interior lows")
def _():
    series = pd.Series([float(i) for i in range(20)])
    lows = TechnicalEngine._find_swing_points(series, mode="low", window=2)
    assert len(lows) == 0, f"Monotonic increase should have no interior lows, got {lows}"


@test("Swing points: monotonic decreasing has no interior highs")
def _():
    series = pd.Series([float(20 - i) for i in range(20)])
    highs = TechnicalEngine._find_swing_points(series, mode="high", window=2)
    assert len(highs) == 0, f"Monotonic decrease should have no interior highs, got {highs}"


@test("Swing points: V-shape detects low at bottom")
def _():
    series = pd.Series([10.0, 8.0, 6.0, 4.0, 6.0, 8.0, 10.0])
    lows = TechnicalEngine._find_swing_points(series, mode="low", window=2)
    low_vals = [v for _, v in lows]
    assert 4.0 in low_vals, f"V-shape should detect 4.0 as low, got {low_vals}"


@test("Swing points: inverted V-shape detects high at top")
def _():
    series = pd.Series([4.0, 6.0, 8.0, 10.0, 8.0, 6.0, 4.0])
    highs = TechnicalEngine._find_swing_points(series, mode="high", window=2)
    high_vals = [v for _, v in highs]
    assert 10.0 in high_vals, f"Inverted V should detect 10.0 as high, got {high_vals}"


# ── 10b. ROC Bug Fix (engine.py) ─────────────────────────────────────

@test("ROC: zero close at lookback position does not produce inf")
def _():
    df = make_ohlcv(n=100, seed=42)
    df.iloc[-11, df.columns.get_loc("Close")] = 0.0
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    if "roc" in result.indicators:
        assert np.isfinite(result.indicators["roc"].value), "ROC should not be inf"


@test("ROC: normal computation produces finite values")
def _():
    df = make_ohlcv(n=100, seed=55)
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    if "roc" in result.indicators:
        assert np.isfinite(result.indicators["roc"].value)
        assert 0 <= result.indicators["roc"].confidence <= 100


# ── 10c. ADX Epsilon Fix (engine.py) ────────────────────────────────

@test("ADX: flat data produces finite ADX value")
def _():
    n = 100
    dates = pd.date_range(end=pd.Timestamp.now(tz="UTC"), periods=n, freq="h")
    df = pd.DataFrame({
        "Open": [100.0]*n, "High": [100.01]*n, "Low": [99.99]*n,
        "Close": [100.0]*n, "Volume": [1000.0]*n,
    }, index=dates)
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    if "adx" in result.indicators:
        assert np.isfinite(result.indicators["adx"].value), "ADX should be finite on flat data"


@test("ADX: strong uptrend shows high ADX")
def _():
    df = make_ohlcv(n=300, trend=0.001, seed=10)
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    assert "adx" in result.indicators
    assert result.indicators["adx"].value > 0


# ── 10d. RSI Boundaries ─────────────────────────────────────────────

@test("RSI: monotonic up approaches 100")
def _():
    n = 100
    dates = pd.date_range(end=pd.Timestamp.now(tz="UTC"), periods=n, freq="h")
    close = np.linspace(100, 150, n)
    df = pd.DataFrame({
        "Open": close - 0.1, "High": close + 0.5, "Low": close - 0.5,
        "Close": close, "Volume": [1000.0]*n,
    }, index=dates)
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    assert "rsi" in result.indicators
    assert result.indicators["rsi"].value > 80, f"RSI should be high on uptrend, got {result.indicators['rsi'].value}"


@test("RSI: monotonic down approaches 0")
def _():
    n = 100
    dates = pd.date_range(end=pd.Timestamp.now(tz="UTC"), periods=n, freq="h")
    close = np.linspace(150, 100, n)
    df = pd.DataFrame({
        "Open": close + 0.1, "High": close + 0.5, "Low": close - 0.5,
        "Close": close, "Volume": [1000.0]*n,
    }, index=dates)
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    assert "rsi" in result.indicators
    assert result.indicators["rsi"].value < 20, f"RSI should be low on downtrend, got {result.indicators['rsi'].value}"


@test("RSI: flat price produces RSI near 50")
def _():
    n = 100
    dates = pd.date_range(end=pd.Timestamp.now(tz="UTC"), periods=n, freq="h")
    np.random.seed(99)
    close = 100.0 + np.random.randn(n) * 0.001
    df = pd.DataFrame({
        "Open": close, "High": close + 0.01, "Low": close - 0.01,
        "Close": close, "Volume": [1000.0]*n,
    }, index=dates)
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    if "rsi" in result.indicators:
        rsi = result.indicators["rsi"].value
        assert 30 <= rsi <= 70, f"RSI on flat should be near 50, got {rsi}"


# ── 10e. MACD Edge Cases ────────────────────────────────────────────

@test("MACD: very short data (< 35 bars) skips cleanly")
def _():
    df = make_ohlcv(n=30, seed=42)
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    # Should not crash; macd may or may not be present


@test("MACD: strong uptrend produces non-SELL signal")
def _():
    df = make_ohlcv(n=300, trend=0.001, seed=20)
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    if "macd" in result.indicators:
        assert result.indicators["macd"].signal not in (SignalStrength.SELL, SignalStrength.STRONG_SELL), \
            f"MACD on uptrend should not be bearish, got {result.indicators['macd'].signal}"


# ── 10f. Bollinger Bands Edge Cases ─────────────────────────────────

@test("Bollinger: flat price collapses bands, position near 0.5")
def _():
    n = 100
    dates = pd.date_range(end=pd.Timestamp.now(tz="UTC"), periods=n, freq="h")
    df = pd.DataFrame({
        "Open": [100.0]*n, "High": [100.01]*n, "Low": [99.99]*n,
        "Close": [100.0]*n, "Volume": [1000.0]*n,
    }, index=dates)
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    if "bollinger" in result.indicators:
        pos = result.indicators["bollinger"].value
        assert 0.0 <= pos <= 1.0, f"BB position should be in [0,1], got {pos}"


# ── 10g. Keltner Channel Edge Cases ────────────────────────────────

@test("Keltner: BB disabled does not crash squeeze detection")
def _():
    df = make_ohlcv(n=200, seed=42)
    config = PlatformConfig()
    config.technical.disabled_indicators = ["bollinger"]
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    assert "keltner" in result.indicators, "Keltner should still work with BB disabled"


# ── 10h. Candlestick Pattern Edge Cases ─────────────────────────────

@test("Candlestick: zero-range bar (H==L) does not crash")
def _():
    df = make_ohlcv(n=100, seed=42)
    df.iloc[-1, df.columns.get_loc("High")] = df.iloc[-1]["Close"]
    df.iloc[-1, df.columns.get_loc("Low")] = df.iloc[-1]["Close"]
    df.iloc[-1, df.columns.get_loc("Open")] = df.iloc[-1]["Close"]
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    assert result is not None


@test("Candlestick: very short data (< 5 bars) does not crash")
def _():
    df = make_ohlcv(n=4, seed=42)
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    assert result is not None


# ── 10i. Regime Classification ──────────────────────────────────────

@test("Regime: strong uptrend produces trending_up or trending")
def _():
    df = make_ohlcv(n=300, trend=0.002, seed=15)
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    assert result.market_regime in ("trending_up", "trending", "transitioning"), \
        f"Strong uptrend should be trending, got {result.market_regime}"


@test("Regime: strong downtrend produces trending_down or trending")
def _():
    df = make_ohlcv(n=300, trend=-0.002, seed=16)
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    assert result.market_regime in ("trending_down", "trending", "transitioning"), \
        f"Strong downtrend should be trending, got {result.market_regime}"


@test("Regime: flat/choppy data produces ranging or transitioning")
def _():
    n = 300
    dates = pd.date_range(end=pd.Timestamp.now(tz="UTC"), periods=n, freq="h")
    np.random.seed(17)
    close = 100.0 + np.random.randn(n) * 0.001
    df = pd.DataFrame({
        "Open": close, "High": close + 0.002, "Low": close - 0.002,
        "Close": close, "Volume": [1000.0]*n,
    }, index=dates)
    config = PlatformConfig()
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    assert result.market_regime in ("ranging", "transitioning"), \
        f"Flat data should be ranging, got {result.market_regime}"


# ── 10j. Composite Score ────────────────────────────────────────────

@test("Composite score always clamped to [-100, 100]")
def _():
    for seed in [1, 2, 3, 10, 20, 30]:
        df = make_ohlcv(n=300, trend=0.005, seed=seed)
        config = PlatformConfig()
        engine = TechnicalEngine(config.technical)
        result = engine.analyze(df, "EUR/USD", Timeframe.H1)
        assert -100 <= result.composite_score <= 100, \
            f"Score {result.composite_score} out of bounds (seed={seed})"


@test("Composite: regime weights affect score magnitude")
def _():
    df = make_ohlcv(n=300, trend=0.001, seed=42)
    config1 = PlatformConfig()
    config2 = PlatformConfig()
    config2.technical.regime_weights_override = {
        "trending_up": {"ema_trend": 0.1, "rsi": 0.1, "macd": 0.1,
                        "bollinger": 0.1, "keltner": 0.1, "roc": 0.1,
                        "candlestick": 0.1, "divergence": 0.1, "adx": 0.1},
        "trending_down": {"ema_trend": 0.1, "rsi": 0.1, "macd": 0.1,
                          "bollinger": 0.1, "keltner": 0.1, "roc": 0.1,
                          "candlestick": 0.1, "divergence": 0.1, "adx": 0.1},
        "ranging": {"ema_trend": 0.1, "rsi": 0.1, "macd": 0.1,
                    "bollinger": 0.1, "keltner": 0.1, "roc": 0.1,
                    "candlestick": 0.1, "divergence": 0.1, "adx": 0.1},
        "transitioning": {"ema_trend": 0.1, "rsi": 0.1, "macd": 0.1,
                          "bollinger": 0.1, "keltner": 0.1, "roc": 0.1,
                          "candlestick": 0.1, "divergence": 0.1, "adx": 0.1},
    }
    engine1 = TechnicalEngine(config1.technical)
    engine2 = TechnicalEngine(config2.technical)
    r1 = engine1.analyze(df, "EUR/USD", Timeframe.H1)
    r2 = engine2.analyze(df, "EUR/USD", Timeframe.H1)
    assert r1.composite_score != r2.composite_score or r1.market_regime != r2.market_regime, \
        "Different regime weights should produce different results"


# ── 10k. Divergence Edge Cases ──────────────────────────────────────

@test("Divergence: short lookback finds no divergence")
def _():
    df = make_ohlcv(n=100, seed=42)
    config = PlatformConfig()
    config.technical.divergence_lookback = 5
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    # Should not crash, divergence may or may not appear


@test("Divergence: disabled divergence not in indicators")
def _():
    df = make_ohlcv(n=300, seed=42)
    config = PlatformConfig()
    config.technical.disabled_indicators = ["divergence"]
    engine = TechnicalEngine(config.technical)
    result = engine.analyze(df, "EUR/USD", Timeframe.H1)
    assert "divergence" not in result.indicators


# ══════════════════════════════════════════════════════════════════════
#  MODULE 11: News Engine Edge Cases
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 11: News Engine Edge Cases ═══")

from news_engine import NewsEngine

@test("Currency extraction: USDA does not false-match USD")
def _():
    ne = NewsEngine(PlatformConfig().news)
    result = ne._extract_currencies("USDA releases crop report")
    assert "USD" not in result, f"USDA should not match USD, got {result}"


@test("Currency extraction: custodian does not false-match USD")
def _():
    ne = NewsEngine(PlatformConfig().news)
    result = ne._extract_currencies("custodian reports quarterly earnings")
    assert "USD" not in result, f"custodian should not match USD, got {result}"


@test("Currency extraction: euro matches EUR correctly")
def _():
    ne = NewsEngine(PlatformConfig().news)
    result = ne._extract_currencies("euro strengthens against yen")
    assert "EUR" in result, f"euro should match EUR, got {result}"


@test("Currency extraction: Eurozone matches EUR")
def _():
    ne = NewsEngine(PlatformConfig().news)
    result = ne._extract_currencies("Eurozone GDP beats forecast")
    assert "EUR" in result, f"Eurozone should match EUR, got {result}"


@test("Currency extraction: empty headline returns empty list")
def _():
    ne = NewsEngine(PlatformConfig().news)
    result = ne._extract_currencies("")
    assert result == [], f"Empty headline should return empty list, got {result}"


@test("Currency extraction: headline with multiple currencies")
def _():
    ne = NewsEngine(PlatformConfig().news)
    result = ne._extract_currencies("EUR/USD pair rises as dollar weakens")
    assert "EUR" in result, f"Should match EUR, got {result}"
    assert "USD" in result, f"Should match USD, got {result}"


@test("Sentiment: both bullish and bearish keywords gives bounded score")
def _():
    ne = NewsEngine(PlatformConfig().news)
    score = ne._analyze_headline_sentiment("rate hike fears amid bullish recovery expectations")
    assert -1.0 <= score <= 1.0, f"Score should be bounded, got {score}"


@test("Sentiment: empty headline returns 0.0")
def _():
    ne = NewsEngine(PlatformConfig().news)
    assert ne._analyze_headline_sentiment("") == 0.0


@test("Sentiment: very long headline does not crash")
def _():
    ne = NewsEngine(PlatformConfig().news)
    long_headline = "market " * 500 + "bullish recovery"
    score = ne._analyze_headline_sentiment(long_headline)
    assert isinstance(score, float)


# ══════════════════════════════════════════════════════════════════════
#  MODULE 12: Signal Aggregator Edge Cases
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 12: Signal Aggregator Edge Cases ═══")

# ── 12a. Quality Grade Boundaries ────────────────────────────────────

@test("Quality grade: A+ requires score>=40, confidence>=65, MTF agreement")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, trend=0.003, seed=100)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    if report.signal_quality == "A+":
        assert abs(report.final_score) >= 40
        assert report.final_confidence >= 65
        assert report.mtf_agreement is True


@test("Quality grade: A requires score>=33, confidence>=55, MTF")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, trend=0.002, seed=200)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    if report.signal_quality == "A":
        assert abs(report.final_score) >= 33
        assert report.final_confidence >= 55
        assert report.mtf_agreement is True


@test("Quality grade: B requires score>=28, confidence>=50")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, seed=300)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    if report.signal_quality == "B":
        assert abs(report.final_score) >= 28
        assert report.final_confidence >= 50


@test("Quality grade: C requires score>=25, confidence>=45")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, seed=400)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    if report.signal_quality == "C":
        assert abs(report.final_score) >= 25
        assert report.final_confidence >= 45


@test("Quality grade: D requires score>=15")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, seed=500)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    if report.signal_quality == "D":
        assert abs(report.final_score) >= 15


@test("Quality grade: F has score<15 or zero confidence")
def _():
    n = 300
    dates = pd.date_range(end=pd.Timestamp.now(tz="UTC"), periods=n, freq="h")
    np.random.seed(600)
    close = 100.0 + np.random.randn(n) * 0.0001
    df = pd.DataFrame({
        "Open": close, "High": close + 0.001, "Low": close - 0.001,
        "Close": close, "Volume": [1000.0]*n,
    }, index=dates)
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    if report.signal_quality == "F":
        assert abs(report.final_score) < 15 or report.final_confidence < 45


@test("Quality grade: all grades belong to valid set")
def _():
    valid_grades = {"A+", "A", "B", "C", "D", "F"}
    for seed in range(42, 62):
        df = make_ohlcv(n=300, seed=seed, trend=0.001 * (seed % 5 - 2))
        config = PlatformConfig()
        agg = SignalAggregator(config)
        dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
        report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
        assert report.signal_quality in valid_grades, \
            f"Unexpected grade '{report.signal_quality}' (seed={seed})"


# ── 12b. EUR Filter Pipeline ────────────────────────────────────────

@test("EUR filter: blocked regime sets quality F")
def _():
    from pair_signals.eurusd import make_config as eur_make_config
    config = eur_make_config(Timeframe.H1)
    config.eur_strategy.blocked_regimes = ["ranging"]
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, seed=42)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=EURUSD, dfs=dfs, use_news=False)
    if report.market_regime == "ranging":
        assert report.signal_quality == "F", \
            f"Ranging should be blocked, got {report.signal_quality}"


@test("EUR filter: quality not in min_quality_grades sets F")
def _():
    from pair_signals.eurusd import make_config as eur_make_config
    config = eur_make_config(Timeframe.H1)
    config.eur_strategy.min_quality_grades = ["A+"]
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, seed=42, trend=0.001)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=EURUSD, dfs=dfs, use_news=False)
    if report.signal_quality not in ("A+", "F"):
        assert False, f"Non-A+ grade should be blocked, got {report.signal_quality}"


@test("EUR filter: score below min sets F")
def _():
    from pair_signals.eurusd import make_config as eur_make_config
    config = eur_make_config(Timeframe.H1)
    config.eur_strategy.min_entry_score = 99.0
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, seed=42, trend=0.001)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=EURUSD, dfs=dfs, use_news=False)
    assert report.signal_quality == "F", \
        f"Score 99 threshold should block all, got {report.signal_quality}"


@test("EUR filter: confidence below min sets F")
def _():
    from pair_signals.eurusd import make_config as eur_make_config
    config = eur_make_config(Timeframe.H1)
    config.eur_strategy.min_entry_confidence = 99.0
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, seed=42, trend=0.001)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=EURUSD, dfs=dfs, use_news=False)
    assert report.signal_quality == "F", \
        f"Confidence 99 threshold should block all, got {report.signal_quality}"


@test("EUR filter: RSI extended buy (RSI>70) blocked")
def _():
    from pair_signals.eurusd import make_config as eur_make_config
    config = eur_make_config(Timeframe.H1)
    config.eur_strategy.block_rsi_extended = True
    config.eur_strategy.min_entry_score = 0.0
    config.eur_strategy.min_entry_confidence = 0.0
    config.eur_strategy.min_quality_grades = ["A+", "A", "B", "C", "D"]
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, trend=0.005, seed=42)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=EURUSD, dfs=dfs, use_news=False)
    if report.primary_analysis and "rsi" in report.primary_analysis.indicators:
        rsi_v = report.primary_analysis.indicators["rsi"].value
        if report.final_score > 0 and rsi_v > 70:
            assert report.signal_quality == "F", \
                f"Buy with RSI {rsi_v:.0f} should be blocked"


# ── 12c. Trade Setup Edge Cases ─────────────────────────────────────

@test("Trade setup: ATR zero returns None")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, trend=0.002, seed=42)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    if report.primary_analysis:
        report.primary_analysis.key_levels["atr"] = 0.0
        setup = agg._generate_trade_setup(report, pip_value=config.pip_value)
        assert setup is None, "ATR=0 should produce no trade setup"


@test("Trade setup: pip_value zero returns None")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, trend=0.002, seed=42)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    if report.primary_analysis and report.final_signal != SignalStrength.NEUTRAL:
        setup = agg._generate_trade_setup(report, pip_value=0.0)
        assert setup is None, "pip_value=0 should produce no trade setup"


@test("Trade setup: BUY SL always below entry")
def _():
    for seed in [10, 20, 30, 40, 50]:
        df = make_ohlcv(n=300, trend=0.002, seed=seed)
        config = PlatformConfig()
        agg = SignalAggregator(config)
        dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
        report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
        if report.trade_setup and report.trade_setup.direction == "BUY":
            assert report.trade_setup.stop_loss < report.trade_setup.entry_price, \
                f"BUY SL should be below entry (seed={seed})"


@test("Trade setup: SELL SL always above entry")
def _():
    for seed in [10, 20, 30, 40, 50]:
        df = make_ohlcv(n=300, trend=-0.002, seed=seed)
        config = PlatformConfig()
        agg = SignalAggregator(config)
        dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
        report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
        if report.trade_setup and report.trade_setup.direction == "SELL":
            assert report.trade_setup.stop_loss > report.trade_setup.entry_price, \
                f"SELL SL should be above entry (seed={seed})"


@test("Trade setup: risk_reward is always positive when present")
def _():
    for seed in range(10, 30):
        df = make_ohlcv(n=300, trend=0.001 * (seed % 5 - 2), seed=seed)
        config = PlatformConfig()
        agg = SignalAggregator(config)
        dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
        report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
        if report.trade_setup:
            assert report.trade_setup.risk_reward > 0, \
                f"RR should be positive, got {report.trade_setup.risk_reward} (seed={seed})"


@test("Trade setup: EUR uses EUR-specific parameters")
def _():
    from pair_signals.eurusd import make_config as eur_make_config
    config = eur_make_config(Timeframe.H1)
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, trend=0.002, start_price=1.08, seed=77)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=EURUSD, dfs=dfs, use_news=False)
    if report.trade_setup:
        assert report.trade_setup.risk_reward >= config.eur_strategy.min_rr_ratio - 0.01


# ── 12d. MTF Agreement ──────────────────────────────────────────────

@test("MTF: all NEUTRAL returns False")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    report = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="now")
    report.mtf_signals = {
        "15min": SignalStrength.NEUTRAL,
        "1h": SignalStrength.NEUTRAL,
        "4h": SignalStrength.NEUTRAL,
    }
    assert agg._check_mtf_agreement(report) is False


@test("MTF: 2 bearish + 1 bullish returns True (bearish agreement)")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    report = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="now")
    report.mtf_signals = {
        "15min": SignalStrength.SELL,
        "1h": SignalStrength.STRONG_SELL,
        "4h": SignalStrength.BUY,
    }
    assert agg._check_mtf_agreement(report) is True


@test("MTF: all same direction returns True")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    report = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="now")
    report.mtf_signals = {
        "15min": SignalStrength.BUY,
        "1h": SignalStrength.BUY,
        "4h": SignalStrength.STRONG_BUY,
    }
    assert agg._check_mtf_agreement(report) is True


@test("MTF: empty signals returns False")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    report = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="now")
    report.mtf_signals = {}
    assert agg._check_mtf_agreement(report) is False


# ── 12e. Final Signal Computation ───────────────────────────────────

@test("Final signal: STRONG_BUY requires score>=35, confidence>=55")
def _():
    for seed in range(42, 62):
        df = make_ohlcv(n=300, trend=0.001 * (seed % 4), seed=seed)
        config = PlatformConfig()
        agg = SignalAggregator(config)
        dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
        report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
        if report.final_signal == SignalStrength.STRONG_BUY:
            assert report.final_score >= 35, f"STRONG_BUY needs score>=35, got {report.final_score}"
            assert report.final_confidence >= 55, f"STRONG_BUY needs conf>=55, got {report.final_confidence}"


@test("Final signal: STRONG_SELL requires score<=-35, confidence>=55")
def _():
    for seed in range(42, 62):
        df = make_ohlcv(n=300, trend=-0.001 * (seed % 4), seed=seed)
        config = PlatformConfig()
        agg = SignalAggregator(config)
        dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
        report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
        if report.final_signal == SignalStrength.STRONG_SELL:
            assert report.final_score <= -35, f"STRONG_SELL needs score<=-35, got {report.final_score}"
            assert report.final_confidence >= 55, f"STRONG_SELL needs conf>=55, got {report.final_confidence}"


@test("Final signal: NEUTRAL when score is low regardless of confidence")
def _():
    n = 300
    dates = pd.date_range(end=pd.Timestamp.now(tz="UTC"), periods=n, freq="h")
    np.random.seed(42)
    close = 100.0 + np.random.randn(n) * 0.0001
    df = pd.DataFrame({
        "Open": close, "High": close + 0.001, "Low": close - 0.001,
        "Close": close, "Volume": [1000.0]*n,
    }, index=dates)
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    if abs(report.final_score) < 25:
        assert report.final_signal == SignalStrength.NEUTRAL


# ── 12f. Penalty System ─────────────────────────────────────────────

@test("Penalty: confidence never exceeds 95 or goes below 0")
def _():
    for seed in range(42, 72):
        df = make_ohlcv(n=300, trend=0.001 * (seed % 6 - 3), seed=seed)
        config = PlatformConfig()
        agg = SignalAggregator(config)
        dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
        report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
        assert 0.0 <= report.final_confidence <= 95.0, \
            f"Confidence out of [0,95] range: {report.final_confidence} (seed={seed})"


# ── 12g. Weekend Filter ─────────────────────────────────────────────

@test("Weekend filter: Friday 22:01 UTC is weekend")
def _():
    from signal_aggregator import _is_forex_weekend
    dt = datetime(2026, 5, 15, 22, 1, tzinfo=timezone.utc)  # Friday
    assert dt.weekday() == 4
    assert _is_forex_weekend(dt) is True


@test("Weekend filter: Friday 21:59 UTC is NOT weekend")
def _():
    from signal_aggregator import _is_forex_weekend
    dt = datetime(2026, 5, 15, 21, 59, tzinfo=timezone.utc)  # Friday
    assert dt.weekday() == 4
    assert _is_forex_weekend(dt) is False


@test("Weekend filter: Saturday noon is weekend")
def _():
    from signal_aggregator import _is_forex_weekend
    dt = datetime(2026, 5, 16, 12, 0, tzinfo=timezone.utc)  # Saturday
    assert dt.weekday() == 5
    assert _is_forex_weekend(dt) is True


@test("Weekend filter: Sunday 21:59 UTC is weekend")
def _():
    from signal_aggregator import _is_forex_weekend
    dt = datetime(2026, 5, 17, 21, 59, tzinfo=timezone.utc)  # Sunday
    assert dt.weekday() == 6
    assert _is_forex_weekend(dt) is True


@test("Weekend filter: Sunday 22:00 UTC is NOT weekend")
def _():
    from signal_aggregator import _is_forex_weekend
    dt = datetime(2026, 5, 17, 22, 0, tzinfo=timezone.utc)  # Sunday
    assert dt.weekday() == 6
    assert _is_forex_weekend(dt) is False


@test("Weekend filter: Monday is not weekend")
def _():
    from signal_aggregator import _is_forex_weekend
    dt = datetime(2026, 5, 18, 10, 0, tzinfo=timezone.utc)  # Monday
    assert dt.weekday() == 0
    assert _is_forex_weekend(dt) is False


# ══════════════════════════════════════════════════════════════════════
#  MODULE 13: Integration Edge Cases
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 13: Integration Edge Cases ═══")


@test("Integration: all indicators disabled produces valid report")
def _():
    config = PlatformConfig()
    config.technical.disabled_indicators = [
        "ema_trend", "rsi", "bollinger", "macd", "adx",
        "keltner", "roc", "candlestick", "divergence",
    ]
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, seed=42)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    assert report is not None
    assert report.signal_quality in ("A+", "A", "B", "C", "D", "F", "")


@test("Integration: data with 50% NaN still produces valid report")
def _():
    df = make_ohlcv(n=300, seed=42)
    mask = np.random.RandomState(99).choice([True, False], size=len(df), p=[0.5, 0.5])
    df.loc[mask, "Close"] = np.nan
    df.loc[mask, "High"] = np.nan
    df.loc[mask, "Low"] = np.nan
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {Timeframe.H1: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    assert report is not None
    assert report.signal_quality in ("A+", "A", "B", "C", "D", "F", "")


@test("Integration: XAU and EUR produce independent results")
def _():
    from pair_signals.eurusd import make_config as eur_make_config
    df = make_ohlcv(n=300, seed=42, trend=0.001)

    xau_config = PlatformConfig()
    xau_agg = SignalAggregator(xau_config)
    xau_dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    xau_report = xau_agg.analyze_from_dataframes(symbol=XAUUSD, dfs=xau_dfs, use_news=False)

    eur_config = eur_make_config(Timeframe.H1)
    eur_agg = SignalAggregator(eur_config)
    eur_dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    eur_report = eur_agg.analyze_from_dataframes(symbol=EURUSD, dfs=eur_dfs, use_news=False)

    assert xau_report.display_name == "XAU/USD"
    assert eur_report.display_name == "EUR/USD"


@test("Integration: missing confirmation TFs still produces valid report")
def _():
    config = PlatformConfig()
    agg = SignalAggregator(config)
    df = make_ohlcv(n=300, seed=42)
    dfs = {Timeframe.H1: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    assert report is not None
    assert report.mtf_agreement is False


@test("Integration: extreme volatility data does not crash")
def _():
    n = 300
    dates = pd.date_range(end=pd.Timestamp.now(tz="UTC"), periods=n, freq="h")
    np.random.seed(42)
    close = 100.0 + np.cumsum(np.random.randn(n) * 10.0)
    close = np.maximum(close, 1.0)
    df = pd.DataFrame({
        "Open": close * 0.99, "High": close * 1.05,
        "Low": close * 0.95, "Close": close,
        "Volume": [5000.0]*n,
    }, index=dates)
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    assert report is not None
    assert -100 <= report.final_score <= 100


@test("Integration: very short data (50 bars) produces valid report")
def _():
    df = make_ohlcv(n=50, seed=42)
    config = PlatformConfig()
    agg = SignalAggregator(config)
    dfs = {Timeframe.H1: df}
    report = agg.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    assert report is not None


@test("Integration: multiple seeds produce deterministic results")
def _():
    df = make_ohlcv(n=300, seed=42)
    config = PlatformConfig()
    agg1 = SignalAggregator(config)
    agg2 = SignalAggregator(config)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    r1 = agg1.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    r2 = agg2.analyze_from_dataframes(symbol=XAUUSD, dfs=dfs, use_news=False)
    assert r1.final_score == r2.final_score
    assert r1.final_signal == r2.final_signal
    assert r1.signal_quality == r2.signal_quality


@test("Integration: session filter blocks outside hours correctly")
def _():
    from pair_signals.eurusd import make_config as eur_make_config
    config = eur_make_config(Timeframe.H1)
    config.eur_strategy.session_hours = [8, 9, 10, 11, 12, 13, 14, 15, 16]
    agg = SignalAggregator(config)
    n = 300
    end_time = pd.Timestamp("2026-05-18 03:00:00", tz="UTC")
    dates = pd.date_range(end=end_time, periods=n, freq="h")
    np.random.seed(42)
    close = 1.08 + np.cumsum(np.random.randn(n) * 0.001 + 0.0002)
    df = pd.DataFrame({
        "Open": close - 0.0001, "High": close + 0.0005,
        "Low": close - 0.0005, "Close": close, "Volume": [1000.0]*n,
    }, index=dates)
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    report = agg.analyze_from_dataframes(symbol=EURUSD, dfs=dfs, use_news=False)
    bar_hour = dates[-1].hour
    if bar_hour not in config.eur_strategy.session_hours and report.signal_quality != "":
        assert report.signal_quality == "F", \
            f"Hour {bar_hour} should be blocked, got {report.signal_quality}"


# ══════════════════════════════════════════════════════════════════════
#  SUMMARY
# ══════════════════════════════════════════════════════════════════════

print(f"\n{'═' * 60}")
print(f"  RESULTS: {PASS} passed, {FAIL} failed, {PASS + FAIL} total")
print(f"{'═' * 60}")

if ERRORS:
    print("\n  FAILURES:")
    for name, err, tb in ERRORS:
        print(f"\n  ✗ {name}")
        print(f"    Error: {err}")
        for line in tb.strip().split("\n")[-3:]:
            print(f"    {line}")

print()
sys.exit(1 if FAIL > 0 else 0)