ForexBot2/test_all.py

#!/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 threading
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__))

# ── Hermetic app-config: the suite must not depend on the developer's
#    local config.json or TELEGRAM_*/TWELVE_DATA_API_KEY env vars. The
#    dummy TD key mirrors the pre-config.json behavior where a key was
#    always present (TDClient built, no network at construction).
import json
import app_config
for _k in ("TELEGRAM_TOKEN", "TELEGRAM_CHAT_ID", "TELEGRAM_CHANNEL_ID",
           "TWELVE_DATA_API_KEY"):
    os.environ.pop(_k, None)
_TEST_APP = app_config.AppConfig(
    provider_settings={"twelve_data": {"api_key": "test-key"}}
)
app_config.set_app_config(_TEST_APP)

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,
    PlatformConfig, TechnicalConfig, SignalConfig, NewsConfig,
    FilterConfig, XauStrategyConfig,
    XAUUSD, 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("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 info["pip_value"] > 0, f"pip_value must be > 0 for {pair}"
    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 == "regime_aware"
    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.mtf_agreement_threshold == 2
    assert sc.min_rr_ratio == 0.5
    assert sc.tp_tightening_factor == 1.0


@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 True
    assert xc.tp_atr_multiplier == 1.0
    assert xc.sl_atr_multiplier == 2.0
    assert isinstance(xc.blocked_hours_utc, list)
    assert xc.sell_blocked_hours_utc == [], \
        "sell hour gate must be off by default (per-TF factories opt in)"


@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 named zones carry their real offset")
def _():
    # EST is UTC−5: 12:00 EST == 17:00 UTC. The old "%Z" path accepted the
    # abbreviation but stamped UTC — a silent 5-hour shift.
    dt = NewsEngine._parse_rss_date("Mon, 20 Jul 2026 12:00:00 EST")
    assert dt is not None and dt.tzinfo is not None
    assert dt.astimezone(timezone.utc).hour == 17
    # GMT stays a no-op; without-seconds (MyFXBook) form still parses.
    dt2 = NewsEngine._parse_rss_date("Mon, 20 Jul 2026 12:00 GMT")
    assert dt2 is not None and dt2.astimezone(timezone.utc).hour == 12


@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 grades from the soonest event")
def _():
    # Far event listed FIRST: an unsorted feed must still yield danger
    # for the nearer event, not caution for the first match.
    ne = NewsEngine(NewsConfig())
    context = FundamentalContext()
    context.events_upcoming_1h = [
        EconomicEvent(
            time=datetime.now(timezone.utc) + timedelta(minutes=55),
            currency="USD", impact="high", event="GDP",
        ),
        EconomicEvent(
            time=datetime.now(timezone.utc) + timedelta(minutes=10),
            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 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 = {"XAU": 0.5, "USD": -0.1}
    score, desc = ne.get_pair_sentiment("XAU/USD", context)
    assert score > 0, "XAU bullish + USD bearish should be net bullish for XAU/USD"
    assert "bullish" in desc.lower()


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


@test("NewsEngine._extract_currencies no USD false positive from 'versus'")
def _():
    ne = NewsEngine(NewsConfig())
    currencies = ne._extract_currencies(
        "Canada building permits for May -1.7% versus 2.4% estimate"
    )
    assert "USD" not in currencies, f"'versus' must not tag USD, got {currencies}"
    assert "CAD" in currencies


@test("NewsEngine._extract_currencies no AUD false positive from 'audit'")
def _():
    ne = NewsEngine(NewsConfig())
    currencies = ne._extract_currencies("Treasury audit finds accounting errors")
    assert "AUD" not in currencies


@test("EconomicEvent.surprise from actual vs forecast")
def _():
    now = datetime.now(timezone.utc)
    beat = EconomicEvent(time=now, currency="USD", impact="high",
                         event="NFP", forecast="900K", actual="1.1M")
    assert beat.surprise == "positive"

    miss = EconomicEvent(time=now, currency="USD", impact="high",
                         event="CPI y/y", forecast="0.3%", actual="0.2%")
    assert miss.surprise == "negative"

    inline = EconomicEvent(time=now, currency="USD", impact="high",
                           event="CPI y/y", forecast="0.3%", actual="0.3%")
    assert inline.surprise == "inline"

    pending = EconomicEvent(time=now, currency="USD", impact="high",
                            event="CPI y/y", forecast="0.3%")
    assert pending.surprise is None

    negative = EconomicEvent(time=now, currency="USD", impact="high",
                             event="Permits m/m", forecast="2.4%", actual="-1.7%")
    assert negative.surprise == "negative"


@test("NewsEngine.get_event_risk_level surprise widens post-event window")
def _():
    ne = NewsEngine(NewsConfig())
    released_at = datetime.now(timezone.utc) - timedelta(minutes=30)

    # Surprise print 30 min ago: outside the 15-min inline buffer but
    # inside the 45-min surprise buffer → still caution.
    context = FundamentalContext()
    context.events_today = [EconomicEvent(
        time=released_at, currency="USD", impact="high",
        event="CPI m/m", forecast="0.3%", actual="0.5%",
    )]
    risk, desc = ne.get_event_risk_level("XAU/USD", context)
    assert risk == "caution", f"Expected caution after surprise, got {risk}"
    assert "surprise" in desc

    # Same timing but inline print → clear.
    context.events_today = [EconomicEvent(
        time=released_at, currency="USD", impact="high",
        event="CPI m/m", forecast="0.3%", actual="0.3%",
    )]
    risk, _ = ne.get_event_risk_level("XAU/USD", context)
    assert risk == "clear", f"Inline release 30min ago should be clear, got {risk}"


@test("NewsEngine gold-inverted sentiment for policy keywords")
def _():
    ne = NewsEngine(NewsConfig())
    headline = "Fed signals rate hike as hawkish stance hardens"
    # Currency-bullish for the dollar...
    assert ne._analyze_headline_sentiment(headline) > 0
    assert ne._analyze_headline_sentiment(headline, "USD") > 0
    # ...but bearish for gold.
    assert ne._analyze_headline_sentiment(headline, "XAU") < 0

    dovish = "Fed turns dovish, rate cut expected"
    assert ne._analyze_headline_sentiment(dovish) < 0
    assert ne._analyze_headline_sentiment(dovish, "XAU") > 0

    # Non-policy keywords keep their direction for gold.
    data = "Gold surges to record high"
    assert ne._analyze_headline_sentiment(data, "XAU") > 0


@test("NewsEngine calendar cache avoids refetch within TTL")
def _():
    NewsEngine._calendar_cache = (0.0, [])
    try:
        ne = NewsEngine(NewsConfig())
        calls = {"n": 0}

        def fake_fetch():
            calls["n"] += 1
            return [EconomicEvent(
                time=datetime.now(timezone.utc), currency="USD",
                impact="low", event="Test Event",
            )]

        ne._try_fetch_calendar_api = fake_fetch
        ne._try_fetch_myfxbook_rss = lambda: []

        first = ne._fetch_economic_calendar(["USD", "XAU"])
        second = ne._fetch_economic_calendar(["USD", "XAU"])
        assert calls["n"] == 1, f"Expected 1 fetch, got {calls['n']}"
        assert len(first) == 1 and len(second) == 1

        # A second engine instance (new scan) must also hit the cache.
        ne2 = NewsEngine(NewsConfig())
        ne2._try_fetch_calendar_api = fake_fetch
        ne2._try_fetch_myfxbook_rss = lambda: []
        third = ne2._fetch_economic_calendar(["USD", "XAU"])
        assert calls["n"] == 1, "New engine instance should reuse class-level cache"
        assert len(third) == 1
    finally:
        NewsEngine._calendar_cache = (0.0, [])


@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
    assert ts.trail_arm_r is None and ts.trail_dist_r is None  # unstamped default


@test("trade setups carry trail params from the producing config")
def _():
    config = PlatformConfig()
    config.signals.trail_arm_r = 0.70
    config.signals.trail_dist_r = 0.85
    agg = SignalAggregator(config)
    report = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="now")
    report.final_signal = SignalStrength.BUY
    report.final_score = 40.0
    report.signal_quality = "A+"
    report.current_price = 3300.0
    ts = agg._generate_xau_atr_trade_setup(report, 10.0)
    assert ts is not None, "A+ setup with ATR 10 should generate"
    assert ts.trail_arm_r == 0.70, "setup must carry the producing config's arm"
    assert ts.trail_dist_r == 0.85, "setup must carry the producing config's dist"


@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("Event-risk signal gating obeys news.event_risk_caps_signals")
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)

    def recompute(risk, caps):
        config.news.event_risk_caps_signals = caps
        report.event_risk = risk
        report.event_description = "USD Test Event in 10min"
        report.warnings = []
        agg._compute_final_signal(report)
        return report.final_confidence

    baseline = recompute("clear", True)
    # default XAU behavior (caps off): danger warns but does not gut
    conf_off = recompute("danger", False)
    assert conf_off == baseline, "flag off must not change confidence"
    assert any("EVENT RISK" in w for w in report.warnings), "warning must persist"
    # caps on: danger hard-caps confidence at 30
    conf_on = recompute("danger", True)
    assert conf_on <= 30, f"danger with caps on must cap conf at 30, got {conf_on}"
    # caution with caps off is also warning-only
    conf_caut_off = recompute("caution", False)
    assert conf_caut_off == baseline
    assert any("Upcoming event" in w for w in report.warnings)
    # PlatformConfig default is off (XAU-tuned)
    assert PlatformConfig().news.event_risk_caps_signals is False


@test("Sentiment signal gating obeys news.sentiment_affects_signals")
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)

    def recompute(sentiment, affects):
        config.news.sentiment_affects_signals = affects
        report.news_sentiment = sentiment
        report.warnings = []
        agg._compute_final_signal(report)
        return report.final_confidence

    score_sign = 1 if report.primary_score >= 0 else -1
    aligned, conflicting = 0.5 * score_sign, -0.5 * score_sign

    baseline = recompute(0.0, False)
    # default: neither aligned sentiment nor conflict moves confidence
    assert recompute(aligned, False) == baseline, "aligned sentiment must not add confidence"
    assert recompute(conflicting, False) == baseline, "conflicting sentiment must not penalize"
    assert any("CONFLICTS" in w for w in report.warnings), "conflict warning must persist"
    # flag on: aligned sentiment adds confidence again
    assert recompute(aligned, True) > baseline, "flag on must restore the alignment bonus"
    # PlatformConfig default is off (news is informational only)
    assert PlatformConfig().news.sentiment_affects_signals is False


@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:
        pair = "XAUUSD"
        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:
            pair = "XAUUSD"
            timeframe = tf_str
        config = build_config(FakeArgs())
        assert config.primary_timeframe is not None


# ══════════════════════════════════════════════════════════════════════
#  MODULE 7: telegram_bot.py
# ══════════════════════════════════════════════════════════════════════

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

from telegram_bot import (
    _escape_md, _signal_label, _mtf_line, _indicator_summary,
    format_signal_message, _format_scan_summary,
    _seconds_until_boundary, _check_level_breach,
    scan_keyboard, SCAN_BUTTONS, SCAN_LOOKUP,
    _update_level_cache, _last_prices, _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 "Score" in msg
    assert "Regime" in msg
    assert "Not financial advice" in msg


@test("format_signal_message renders stamped per-TF trail plan")
def _():
    def _setup(**extra):
        return TradeSetup(
            pair="XAU/USD", display_name="XAU/USD", direction="BUY",
            entry_price=3300.0, stop_loss=3290.0, take_profit_1=3315.0,
            take_profit_2=3325.0, risk_reward=1.5, risk_pips=100,
            reward_pips=150, pip_value=0.10, **extra,
        )
    report = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="now")
    report.final_signal = SignalStrength.BUY
    report.signal_quality = "A"
    # Stamped setup (M15-style overrides) must render the stamped values…
    report.trade_setup = _setup(trail_arm_r=0.70, trail_dist_r=0.85)
    msg = format_signal_message(report)
    assert "+0.7R" in msg, "must render the stamped arm, not the 0.6 default"
    assert "$8.50" in msg, "must render the stamped dist × risk, not the default"
    # …while a legacy unstamped setup falls back to platform defaults.
    report.trade_setup = _setup()
    msg = format_signal_message(report)
    assert "+0.6R" in msg
    assert "$7.50" in msg


@test("format_signal_message renders 100-pip informational TP")
def _():
    def _setup(direction="BUY", **extra):
        return TradeSetup(
            pair="XAU/USD", display_name="XAU/USD", direction=direction,
            entry_price=3300.0, stop_loss=3290.0, take_profit_1=3315.0,
            take_profit_2=3325.0, risk_reward=1.5, risk_pips=100,
            reward_pips=150, pip_value=0.10, **extra,
        )
    report = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="now")
    report.final_signal = SignalStrength.BUY
    report.signal_quality = "A"
    # BUY: 100 pips × $0.10 = $10 above entry; risk $10 → exactly 1.0R.
    report.trade_setup = _setup()
    msg = format_signal_message(report)
    assert "TP 100p" in msg
    assert "3310.00" in msg, "BUY 100p TP must be entry + $10.00"
    assert "≈1.0R" in msg
    # SELL mirror: $10 below entry.
    report.final_signal = SignalStrength.SELL
    report.trade_setup = _setup(direction="SELL")
    msg = format_signal_message(report)
    assert "TP 100p" in msg
    assert "3290.00" in msg, "SELL 100p TP must be entry - $10.00"
    # No setup → no informational TP line.
    report.trade_setup = None
    msg = format_signal_message(report)
    assert "TP 100p" not in msg


@test("_indicator_summary condenses indicators to one arrow line")
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)
    summary = _indicator_summary(report)
    assert "EMA" in summary
    assert "RSI" in summary
    assert summary.count("\n") == 0, "summary must be a single line"
    msg = format_signal_message(report)
    assert summary in msg
    assert "[Bullish]" not in msg and "[Bearish]" not in msg and "[Neutral]" not 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 upward cross")
def _():
    _level_cache["TEST_PAIR"] = {"swing_high_1": 1.1000}
    _level_cache_ts["TEST_PAIR"] = time.time()
    _last_prices["TEST_PAIR"] = 1.0999
    result = _check_level_breach("TEST_PAIR", 1.1001)
    assert result == "swing_high_1"
    # cleanup
    del _level_cache["TEST_PAIR"]
    del _level_cache_ts["TEST_PAIR"]
    del _last_prices["TEST_PAIR"]


@test("_check_level_breach detects downward cross")
def _():
    _level_cache["TEST_PAIR"] = {"swing_low_1": 1.0900}
    _level_cache_ts["TEST_PAIR"] = time.time()
    _last_prices["TEST_PAIR"] = 1.0901
    result = _check_level_breach("TEST_PAIR", 1.0899)
    assert result == "swing_low_1"
    del _level_cache["TEST_PAIR"]
    del _level_cache_ts["TEST_PAIR"]
    del _last_prices["TEST_PAIR"]


@test("_check_level_breach no breach when price stays same side")
def _():
    _level_cache["TEST_PAIR"] = {"swing_high_1": 1.1000}
    _level_cache_ts["TEST_PAIR"] = time.time()
    _last_prices["TEST_PAIR"] = 1.0990
    result = _check_level_breach("TEST_PAIR", 1.0995)
    assert result is None
    del _level_cache["TEST_PAIR"]
    del _level_cache_ts["TEST_PAIR"]
    del _last_prices["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
    _last_prices["TEST_PAIR"] = 1.0999
    result = _check_level_breach("TEST_PAIR", 1.1001)
    assert result is None
    del _level_cache["TEST_PAIR"]
    del _level_cache_ts["TEST_PAIR"]
    del _last_prices["TEST_PAIR"]


@test("_check_level_breach returns None when no previous price")
def _():
    _level_cache["TEST_PAIR"] = {"swing_high_1": 1.1000}
    _level_cache_ts["TEST_PAIR"] = time.time()
    if "TEST_PAIR" in _last_prices:
        del _last_prices["TEST_PAIR"]
    result = _check_level_breach("TEST_PAIR", 1.1001)
    assert result is None
    del _level_cache["TEST_PAIR"]
    del _level_cache_ts["TEST_PAIR"]


@test("SCAN_BUTTONS and SCAN_LOOKUP consistency")
def _():
    from telegram_bot import SCAN_PAIRS
    # 4 TF buttons + one "Scan All" per scanned pair
    assert len(SCAN_BUTTONS) == 5 * len(SCAN_PAIRS)
    for btn in SCAN_BUTTONS:
        assert btn["cb"] in SCAN_LOOKUP
        assert "symbol" in btn
        assert btn["symbol"] in SCAN_PAIRS
        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("streaming symbol mapping covers every scanned pair")
def _():
    # iter_ticks maps the provider's WS symbols (display names) back to
    # canonical pair symbols — the mapping must be invertible for every
    # scanned pair or ticks would be silently dropped.
    from telegram_bot import SCAN_PAIRS
    ws_to_pair = {PAIR_INFO[p]["display"]: p for p in SCAN_PAIRS}
    for p in SCAN_PAIRS:
        assert ws_to_pair[PAIR_INFO[p]["display"]] == p
    assert ws_to_pair["XAU/USD"] == XAUUSD


# ══════════════════════════════════════════════════════════════════════
#  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


print("\n═══ Module 9: pair_signals registry ═══")

@test("pair_signals registry includes XAU/USD only")
def _():
    import pair_signals
    pairs = pair_signals.get_supported_pairs()
    assert XAUUSD in pairs, f"XAU missing from registry: {pairs}"
    assert pairs == [XAUUSD], f"registry must be XAU-only: {pairs}"


@test("xauusd.make_config equals inline PlatformConfig construction")
def _():
    import dataclasses
    from pair_signals import PAIR_PRODUCERS
    for tf, ctfs in [
        (Timeframe.M5, [Timeframe.M15, Timeframe.H1]),
        (Timeframe.M15, [Timeframe.M5, Timeframe.H1]),
        (Timeframe.H1, [Timeframe.M15, Timeframe.H4]),
    ]:
        factory_cfg = PAIR_PRODUCERS[XAUUSD](tf, ctfs)
        inline_cfg = PlatformConfig(
            symbol=XAUUSD,
            display_name="XAU/USD",
            pip_value=0.10,
            primary_timeframe=tf,
            confirmation_timeframes=ctfs,
        )
        app_config.apply_pair_overrides(inline_cfg)
        if tf == Timeframe.M15:
            # Documented per-TF overrides (see xauusd.py docstring):
            # 1R minimum targets + mixed-"trending"-regime block +
            # SELL hour block (15/17 UTC), all validated for M15 only.
            inline_cfg.signals.tp_floor_rr = 1.0
            inline_cfg.xau_strategy.blocked_regimes = ["trending"]
            inline_cfg.xau_strategy.sell_blocked_hours_utc = [15, 17]
            inline_cfg.signals.trail_arm_r = 0.70
            inline_cfg.signals.trail_dist_r = 0.75
        if tf == Timeframe.M5:
            # Documented per-TF overrides (see xauusd.py docstring):
            # hour 9 UTC blocked + BUY-only chart patterns on M5 only.
            inline_cfg.xau_strategy.blocked_hours_utc = (
                inline_cfg.xau_strategy.blocked_hours_utc + [9]
            )
            inline_cfg.xau_strategy.cp_enabled = True
            inline_cfg.xau_strategy.cp_patterns = ["inv_hs", "double_bottom", "bull_flag"]
            inline_cfg.signals.trail_arm_r = 0.60
            inline_cfg.signals.trail_dist_r = 0.85
        assert dataclasses.asdict(factory_cfg) == dataclasses.asdict(inline_cfg), \
            f"XAU factory config differs from inline construction for {tf}"


@test("produce_signal raises for unregistered pair")
def _():
    from pair_signals import produce_signal
    try:
        produce_signal("GBP/JPY", Timeframe.H1, None)
        assert False, "Expected ValueError for unregistered pair"
    except ValueError as e:
        assert "GBP/JPY" in str(e)


print("\n═══ Module 9b: Engine Bug Fix Regressions ═══")

# ── 9a. Swing point NaN fix ──────────────────────────────────────────

@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: 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: 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}"


# ── 9b. ROC div-by-zero fix ──────────────────────────────────────────

@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
    engine = TechnicalEngine(PlatformConfig().technical)
    result = engine.analyze(df, "XAU/USD", Timeframe.H1)
    if "roc" in result.indicators:
        assert np.isfinite(result.indicators["roc"].value), "ROC should not be inf"


# ── 9c. ADX epsilon fix ──────────────────────────────────────────────

@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)
    engine = TechnicalEngine(PlatformConfig().technical)
    result = engine.analyze(df, "XAU/USD", Timeframe.H1)
    if "adx" in result.indicators:
        assert np.isfinite(result.indicators["adx"].value), "ADX should be finite on flat data"


# ══════════════════════════════════════════════════════════════════════
#  MODULE 11: 2026-07-16 audit-fix regressions
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 11: 2026-07-16 audit fixes ═══")

_BT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "backtest")
if _BT_DIR not in sys.path:
    sys.path.insert(0, _BT_DIR)


def make_xau_bars(n=400, end="2026-07-08 16:55", freq="5min", drift=-0.8, seed=7):
    """Seeded XAU-priced bars ending at a FIXED timestamp (deterministic)."""
    rng = np.random.default_rng(seed)
    idx = pd.date_range(end=pd.Timestamp(end, tz="UTC"), periods=n, freq=freq)
    close = 3400 + np.cumsum(drift + rng.normal(0, 0.5, n))
    open_ = np.roll(close, 1)
    open_[0] = close[0]
    high = np.maximum(open_, close) + rng.uniform(0.5, 2.0, n)
    low = np.minimum(open_, close) - rng.uniform(0.5, 2.0, n)
    df = pd.DataFrame({
        "Open": open_, "High": high, "Low": low,
        "Close": close, "Volume": rng.uniform(1e3, 5e3, n),
    }, index=idx)
    df.index.name = "Datetime"
    return df


@test("Gate 6e: session-blocked sell-direction report stays F (no F→D upgrade)")
def _():
    # Bar LABELED in a blocked hour (16:xx UTC, Wednesday) with a bearish
    # score: gate 6b sets F; before the fix, 6e re-graded it D, which
    # re-opened the M5 pullback path (entry check: quality != "F").
    from pair_signals.xauusd import make_config as xau_make_config
    primary = make_xau_bars(end="2026-07-08 16:55", freq="5min")
    m15 = make_xau_bars(end="2026-07-08 16:45", freq="15min")
    h1 = make_xau_bars(end="2026-07-08 16:00", freq="60min")
    agg = SignalAggregator(xau_make_config(Timeframe.M5, [Timeframe.M15, Timeframe.H1]))
    report = agg.analyze_from_dataframes(
        XAUUSD, {Timeframe.M5: primary, Timeframe.M15: m15, Timeframe.H1: h1},
        use_news=False,
    )
    assert any("SESSION FILTER" in w for w in report.warnings), \
        f"session filter did not fire: {report.warnings}"
    assert report.primary_score < 0, "test setup must produce a sell-direction score"
    assert report.signal_quality == "F", \
        f"blocked bar must stay F, got {report.signal_quality}"
    assert report.trade_setup is None
    assert not any("SELL FILTER: quality" in w for w in report.warnings), \
        "6e must not fire on non-actionable grades"


@test("GPU engine parity: composite + ema_trend identical to live (incl. ribbon)")
def _():
    from gpu_backtest import precompute_indicators, GPUTechnicalEngine
    from engine import TechnicalEngine
    cfg = PlatformConfig()
    # Two-phase series: flat chop (EMA ribbon compresses) then a strong
    # trend (ribbon expands) — guarantees the sweep crosses both ribbon
    # states, the exact logic the GPU engine was missing.
    rng = np.random.default_rng(11)
    n = 650
    drift = np.where(np.arange(n) < 450, 0.0, 1.2)
    idx = pd.date_range(end=pd.Timestamp("2026-06-30 12:00", tz="UTC"),
                        periods=n, freq="60min")
    close = 3400 + np.cumsum(drift + rng.normal(0, 0.5, n))
    open_ = np.roll(close, 1)
    open_[0] = close[0]
    high = np.maximum(open_, close) + rng.uniform(0.5, 2.0, n)
    low = np.minimum(open_, close) - rng.uniform(0.5, 2.0, n)
    df = pd.DataFrame({"Open": open_, "High": high, "Low": low, "Close": close,
                       "Volume": rng.uniform(1e3, 5e3, n)}, index=idx)
    df.index.name = "Datetime"
    cache = precompute_indicators(df, cfg.technical)
    gpu = GPUTechnicalEngine(cfg.technical, {"1h": cache})
    live = TechnicalEngine(cfg.technical)
    seen_compressed = seen_expanding = False
    for i in range(250, n + 1, 3):
        s = df.iloc[:i]
        r_live = live.analyze(s, "XAU/USD", "1h")
        r_gpu = gpu.analyze(s, "XAU/USD", "1h")
        assert r_live.composite_signal == r_gpu.composite_signal, f"signal flip @ {i}"
        assert abs(r_live.composite_score - r_gpu.composite_score) < 1e-9, f"score @ {i}"
        assert abs(r_live.confidence - r_gpu.confidence) < 1e-9, f"confidence @ {i}"
        el = r_live.indicators["ema_trend"]
        eg = r_gpu.indicators["ema_trend"]
        assert el.confidence == eg.confidence, f"ema_trend conf @ {i}"
        assert el.description == eg.description, f"ema_trend desc @ {i}"
        bl = r_live.indicators["bollinger"]
        bg = r_gpu.indicators["bollinger"]
        assert bl.signal == bg.signal and bl.description == bg.description, f"bollinger @ {i}"
        al = r_live.indicators["atr"]
        ag = r_gpu.indicators["atr"]
        assert abs(al.value - ag.value) < 1e-9, f"atr value @ {i}"
        assert al.description == ag.description, f"atr description @ {i}"
        assert r_live.volatility_regime == r_gpu.volatility_regime, f"vol regime @ {i}"
        assert abs(r_live.key_levels["atr"] - r_gpu.key_levels["atr"]) < 1e-9, f"key_levels atr @ {i}"
        assert abs(r_live.key_levels["atr_pct"] - r_gpu.key_levels["atr_pct"]) < 1e-9, f"key_levels atr_pct @ {i}"
        if "Ribbon COMPRESSED" in el.description:
            seen_compressed = True
        if "Ribbon EXPANDING" in el.description:
            seen_expanding = True
    assert seen_compressed, "sweep never saw a compressed ribbon — no teeth"
    assert seen_expanding, "sweep never saw a ribbon expansion (+10 conf path) — no teeth"


@test("Regime filter: live gate semantics + backtest calls the same method")
def _():
    from signal_aggregator import SignalAggregator
    from pair_signals import PAIR_PRODUCERS
    cfg = PAIR_PRODUCERS[XAUUSD](Timeframe.M15, [Timeframe.M5, Timeframe.H1])
    cfg.backtest.regime_filter = True
    agg = SignalAggregator(cfg)

    df = make_ohlcv(n=300, start_price=3400.0, trend=0.05, seed=3)
    flat = make_ohlcv(n=300, start_price=3400.0, trend=0.0, seed=7)
    adx = agg._latest_adx(flat, cfg.technical.adx_period)
    assert adx is not None and adx < cfg.backtest.regime_filter_min_adx, \
        f"fixture must be choppy (ADX {adx}) or the test has no teeth"

    # Low higher-TF ADX → signal downgraded to NEUTRAL/F, setup killed.
    report = agg.analyze_from_dataframes(XAUUSD, {Timeframe.M15: df},
                                         context=None, use_news=False)
    agg._apply_regime_filter_live(report, {Timeframe.H4: flat})
    assert report.final_signal == SignalStrength.NEUTRAL
    assert report.signal_quality == "F"
    assert report.trade_setup is None
    assert any("REGIME FILTER" in w for w in report.warnings)

    # Regime TF absent from dfs → warning only, report untouched.
    report2 = agg.analyze_from_dataframes(XAUUSD, {Timeframe.M15: df},
                                          context=None, use_news=False)
    quality_before = report2.signal_quality
    agg._apply_regime_filter_live(report2, {})
    assert report2.signal_quality == quality_before
    assert any("data unavailable" in w for w in report2.warnings)

    # Backtest wiring: the replay applies the SAME method under the SAME
    # guard as live analyze_pair (function-identity parity).
    import gpu_backtest as _gb
    src = open(_gb.__file__, encoding="utf-8").read()
    assert "_apply_regime_filter_live(report, slice_dfs)" in src
    assert "backtest.regime_filter" in src
    # …and loads the regime-filter TF like live analyze_pair does, or the
    # method silently no-ops for M5/M15 primaries (confirm maps lack H4).
    assert "regime_filter_timeframe not in timeframes" in src, \
        "replay must fetch/slice the regime-filter TF when the gate is on"


@test("disabled_indicators=['macd'] removes the pillar but keeps the histogram")
def _():
    from engine import TechnicalEngine
    from config import TechnicalConfig
    df = make_xau_ohlcv(n=300, seed=5)
    result = TechnicalEngine(TechnicalConfig(disabled_indicators=["macd"])).analyze(
        df, "XAU/USD", "1h")
    assert "macd" not in result.indicators, "disabled MACD still scored"
    assert getattr(result, "_macd_histogram", None) is not None, \
        "histogram must survive for divergence"
    baseline = TechnicalEngine(TechnicalConfig()).analyze(df, "XAU/USD", "1h")
    assert "macd" in baseline.indicators


@test("Outlier filter drops the spike bar but keeps the snap-back bar")
def _():
    provider = ForexDataProvider(PlatformConfig())
    df = make_ohlcv(n=300, seed=3)
    k = 150
    spike_ts, after_ts = df.index[k], df.index[k + 1]
    spike_val = df["Close"].iloc[k - 1] * 1.05  # ~+5% on a ~0.1%-σ series
    df.loc[spike_ts, ["Open", "High", "Low", "Close"]] = spike_val
    cleaned = provider._validate_and_clean(df, "XAU/USD", Timeframe.H1)
    assert spike_ts not in cleaned.index, "spike bar must be removed"
    assert after_ts in cleaned.index, \
        "snap-back bar after the spike must be kept (two-bar hole regression)"


@test("Cache: pre-boundary snapshot refetched; boundary-complete snapshot served")
def _():
    def bars_ending(last):
        idx = pd.date_range(end=last, periods=300, freq="15min", tz="UTC")
        v = np.linspace(3000.0, 3100.0, 300)
        return pd.DataFrame({"Open": v, "High": v + 1, "Low": v - 1,
                             "Close": v, "Volume": 1000.0}, index=idx)

    for _attempt in range(2):
        provider = ForexDataProvider(PlatformConfig())
        started = pd.Timestamp.now(tz="UTC").floor("15min")
        last_closed = started - pd.Timedelta(minutes=15)
        stale = bars_ending(last_closed - pd.Timedelta(minutes=15))
        fresh = bars_ending(last_closed)
        key = "XAU/USD_15min"
        with patch("providers.base._is_forex_weekend", return_value=False):
            # (a) snapshot cached mid-candle 5 min ago, missing the
            # just-closed bar: TTL says fresh, boundary check must refetch.
            provider._cache[key] = stale
            provider._cache_timestamps[key] = time.time() - 300
            with patch.object(provider, "_fetch_twelve_data",
                              return_value=fresh.copy()) as mock_fetch:
                served = provider.fetch_ohlcv("XAU/USD", Timeframe.M15)
            # (b) cache now boundary-complete: served without any fetch.
            with patch.object(provider, "_fetch_twelve_data",
                              side_effect=AssertionError("must not refetch")):
                served2 = provider.fetch_ohlcv("XAU/USD", Timeframe.M15)
        if pd.Timestamp.now(tz="UTC").floor("15min") != started:
            continue  # boundary crossed mid-test — retry once
        assert mock_fetch.called, "stale pre-boundary snapshot was served from cache"
        assert served.index[-1] == last_closed
        assert served2.index[-1] == last_closed
        break


@test("Fresh fetch missing the just-closed bar retries once, then warns")
def _():
    def bars_ending(last):
        idx = pd.date_range(end=last, periods=300, freq="15min", tz="UTC")
        v = np.linspace(3000.0, 3100.0, 300)
        return pd.DataFrame({"Open": v, "High": v + 1, "Low": v - 1,
                             "Close": v, "Volume": 1000.0}, index=idx)

    for _attempt in range(2):
        provider = ForexDataProvider(PlatformConfig())
        started = pd.Timestamp.now(tz="UTC").floor("15min")
        last_closed = started - pd.Timedelta(minutes=15)
        lagging = bars_ending(last_closed - pd.Timedelta(minutes=15))
        complete = bars_ending(last_closed)
        with patch("providers.base._is_forex_weekend", return_value=False), \
             patch.object(ForexDataProvider, "FRESH_FETCH_RETRY_SECONDS", 0.0):
            # (a) lagging first response → one retry returns the closed bar.
            with patch.object(provider, "_fetch_twelve_data",
                              side_effect=[lagging.copy(), complete.copy()]) as mf:
                served = provider.fetch_ohlcv("XAU/USD", Timeframe.M15)
            # (b) complete first response → no retry at all.
            provider._cache.clear()
            provider._cache_timestamps.clear()
            with patch.object(provider, "_fetch_twelve_data",
                              return_value=complete.copy()) as mf2:
                provider.fetch_ohlcv("XAU/USD", Timeframe.M15)
        if pd.Timestamp.now(tz="UTC").floor("15min") != started:
            continue  # boundary crossed mid-test — retry once
        assert mf.call_count == 2, "lagging response must trigger exactly one retry"
        assert served.index[-1] == last_closed, "retry result must be served"
        assert mf2.call_count == 1, "boundary-complete response must not retry"
        break


@test("Alert dedup: drift dedups, materially-moved setup re-alerts, no early commit")
def _():
    import telegram_bot as tb
    from signal_aggregator import SignalReport, TradeSetup

    def rpt(entry, sl):
        r = SignalReport(symbol="XAU/USD", display_name="XAU/USD", timestamp="t")
        r.final_signal = SignalStrength.BUY
        r.signal_quality = "B"
        r.trade_setup = TradeSetup(
            pair="XAU/USD", display_name="XAU/USD", direction="BUY",
            entry_price=entry, stop_loss=sl, take_profit_1=entry + 12,
            take_profit_2=entry + 20, risk_reward=1.5,
            risk_pips=80.0, reward_pips=120.0, pip_value=0.10,
        )
        return r

    key = "TEST_ALERT_FP"
    tb._last_alert_fp.pop(key, None)
    tb._last_alert_ts.pop(key, None)
    tb._last_alert_setup.pop(key, None)
    r1 = rpt(3400.0, 3392.0)          # risk $8 → re-alert threshold $4
    assert tb._should_alert(key, r1)
    assert tb._should_alert(key, r1), "_should_alert must not commit state itself"
    tb._commit_alert(key, r1)
    assert not tb._should_alert(key, r1), "identical signal must dedup"
    assert not tb._should_alert(key, rpt(3400.5, 3392.5)), \
        "sub-threshold drift must dedup"
    assert not tb._should_alert(key, rpt(3401.0, 3392.6)), \
        "SL-distance drift (ATR/anchor wobble) must not re-fingerprint"
    assert tb._should_alert(key, rpt(3412.0, 3404.0)), \
        "setup moved 1.5x risk — must re-alert"
    assert tb._should_alert(key, rpt(3395.5, 3387.5)), \
        "setup moved half risk AGAINST the entry — must re-alert too"
    tb._last_alert_fp.pop(key, None)
    tb._last_alert_ts.pop(key, None)
    tb._last_alert_setup.pop(key, None)


@test("Backtester --confirm-tfs defaults to None (factory's live map)")
def _():
    from gpu_backtest import _parse_args
    args = _parse_args(["--pair", "XAUUSD"])
    assert args.confirm_tfs is None


@test("Backtest entries re-anchor to the first RAW bar after the signal bar")
def _():
    from gpu_backtest import Trade, _reanchor_entries_to_raw
    idx = pd.date_range("2026-01-05 10:00", periods=6, freq="5min", tz="UTC")
    opens = np.array([100.0, 101.0, 110.0, 102.0, 103.0, 104.0])
    sim = pd.DataFrame({"Open": opens, "High": opens + 1,
                        "Low": opens - 1, "Close": opens}, index=idx)

    def mk_trade(entry_time, entry_price):
        return Trade(pair="XAU/USD", entry_time=entry_time, entry_price=entry_price,
                     sl=entry_price - 8, tp1=entry_price + 12, tp2=entry_price + 20,
                     direction="BUY", pip_value=0.10, risk_pips=80.0,
                     reward_pips=120.0, rr_planned=1.5, quality="B",
                     final_score=30.0, final_confidence=60.0,
                     market_regime="trending_up")

    # Cleaned frame lacked the spike at idx[2], so the replay entered at idx[3].
    t = mk_trade(idx[3], 102.0)
    _reanchor_entries_to_raw([t], [idx[1]], sim)
    assert t.entry_time == idx[2] and t.entry_price == 110.0
    assert t.sl == 102.0 and t.tp1 == 122.0 and t.tp2 == 130.0, \
        "SL/TP distances must be preserved relative to the corrected entry"

    # Entry already the first raw bar after the signal → untouched.
    t2 = mk_trade(idx[2], 110.0)
    _reanchor_entries_to_raw([t2], [idx[1]], sim)
    assert t2.entry_time == idx[2] and t2.entry_price == 110.0 and t2.sl == 102.0


@test("Max drawdown counts the decline from flat starting equity")
def _():
    from gpu_backtest import Trade, compute_metrics

    def mk(r):
        t = Trade(pair="XAU/USD",
                  entry_time=pd.Timestamp("2026-01-05 10:00", tz="UTC"),
                  entry_price=3300.0, sl=3292.0, tp1=3312.0, tp2=3320.0,
                  direction="BUY", pip_value=0.10, risk_pips=80.0,
                  reward_pips=120.0, rr_planned=1.5, quality="B",
                  final_score=30.0, final_confidence=60.0,
                  market_regime="trending_up")
        t.exit_reason = "SL" if r < 0 else "TP1"
        t.r_multiple = r
        return t

    # Opening losing streak: equity 0 → −3R with no prior peak. The old
    # high-water started at the first trade's cum (−1) and reported 2R.
    m = compute_metrics([mk(-1.0), mk(-1.0), mk(-1.0)])
    assert m.max_drawdown_r == 3.0, f"expected 3.0, got {m.max_drawdown_r}"
    # Unchanged when a peak precedes the streak.
    m2 = compute_metrics([mk(2.0), mk(-1.0), mk(-1.0), mk(-1.0)])
    assert m2.max_drawdown_r == 3.0, f"expected 3.0, got {m2.max_drawdown_r}"


# ══════════════════════════════════════════════════════════════════════
#  MODULE 12: Chart-pattern entry (XAU_CP)
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 12: chart patterns (XAU_CP) ═══")

from chart_patterns import detect_chart_pattern
from config import XauStrategyConfig


def make_cp_df(closes, end="2026-07-08 14:00", freq="1h", wick=1.5):
    """Deterministic OHLCV from an explicit close path (fixed end time:
    Wed, label hour 14, close hour 15 — clear of every session block)."""
    idx = pd.date_range(end=end, periods=len(closes), freq=freq, tz="UTC")
    c = np.asarray(closes, dtype=float)
    o = np.concatenate([[c[0]], c[:-1]])
    return pd.DataFrame({"Open": o, "High": c + wick, "Low": c - wick,
                         "Close": c, "Volume": 1000.0}, index=idx)


def mirror_cp_df(df, pivot=6000.0):
    """Price-mirror a fixture: bullish pattern → exact bearish twin."""
    return pd.DataFrame({
        "Open": pivot - df["Open"], "High": pivot - df["Low"],
        "Low": pivot - df["High"], "Close": pivot - df["Close"],
        "Volume": df["Volume"]}, index=df.index)


def double_bottom_closes():
    """L1@30 (2984) and L2@65 (2984.5), neckline peak 3000 @45,
    below-neckline drift, breakout close 3004 on the last bar."""
    return (list(np.linspace(2988, 3013, 15))
            + list(np.linspace(3011, 2984, 16))
            + list(np.linspace(2986, 3000, 15))
            + list(np.linspace(2999, 2984.5, 20))
            + list(np.linspace(2986, 2998, 13))
            + [3004.0])


def hs_top_closes():
    """S1@20 (3000), head@38 (3010), S2@62 (2999), neckline ~2989.5,
    breakdown close 2988 on the last bar."""
    return (list(np.linspace(2982, 3000, 21))
            + list(np.linspace(2998.5, 2990, 7))
            + list(np.linspace(2992, 3010, 11))
            + list(np.linspace(3008, 2991, 8))
            + list(np.linspace(2992, 2999, 16))
            + list(np.linspace(2998.5, 2991, 16))
            + [2988.0])


def bull_flag_closes():
    """Pole 60→69 (3000→3025), flag drift 70→78, breakout close 3028.5."""
    return (list(np.linspace(3000, 2996, 60))
            + list(np.linspace(2999, 3025, 10))
            + list(np.linspace(3023, 3018, 9))
            + [3028.5])


_CP_CFG = XauStrategyConfig()


@test("CP: double bottom detected on breakout bar with correct geometry")
def _():
    df = make_cp_df(double_bottom_closes())
    hit = detect_chart_pattern(df, atr=5.0, cfg=_CP_CFG)
    assert hit is not None, "double bottom not detected"
    assert hit["pattern"] == "double_bottom" and hit["direction"] == "BUY"
    assert abs(hit["anchor"] - 2982.5) < 1e-9, f"anchor {hit['anchor']}"
    assert abs(hit["trigger"] - 3001.5) < 1e-9, f"trigger {hit['trigger']}"
    assert abs(hit["height"] - 19.0) < 1e-9, f"height {hit['height']}"


@test("CP: double top detected on mirrored fixture")
def _():
    df = mirror_cp_df(make_cp_df(double_bottom_closes()))
    hit = detect_chart_pattern(df, atr=5.0, cfg=_CP_CFG)
    assert hit is not None and hit["pattern"] == "double_top"
    assert hit["direction"] == "SELL"
    assert abs(hit["anchor"] - (6000 - 2982.5)) < 1e-9
    assert abs(hit["trigger"] - (6000 - 3001.5)) < 1e-9


@test("CP: trigger discipline — no signal before breakout close")
def _():
    df = make_cp_df(double_bottom_closes()).iloc[:-1]
    assert detect_chart_pattern(df, atr=5.0, cfg=_CP_CFG) is None


@test("CP: trigger discipline — no re-fire on the bar after breakout")
def _():
    df = make_cp_df(double_bottom_closes() + [3006.0])
    assert detect_chart_pattern(df, atr=5.0, cfg=_CP_CFG) is None


@test("CP: double bottom rejected when lows differ beyond tolerance")
def _():
    closes = double_bottom_closes()
    closes[46:66] = list(np.linspace(2999, 2979, 20))  # L2 5.5 below L1
    df = make_cp_df(closes)
    assert detect_chart_pattern(df, atr=5.0, cfg=_CP_CFG) is None


@test("CP: double bottom invalidated by a lower low after L2")
def _():
    closes = double_bottom_closes()
    closes[77] = 2978.0  # undercuts both bottoms before the breakout
    df = make_cp_df(closes)
    assert detect_chart_pattern(df, atr=5.0, cfg=_CP_CFG) is None


@test("CP: head & shoulders top detected (SELL, right-shoulder anchor)")
def _():
    df = make_cp_df(hs_top_closes())
    hit = detect_chart_pattern(df, atr=5.0, cfg=_CP_CFG)
    assert hit is not None, "H&S top not detected"
    assert hit["pattern"] == "hs_top" and hit["direction"] == "SELL"
    assert abs(hit["anchor"] - 3000.5) < 1e-9, f"anchor {hit['anchor']}"
    assert abs(hit["trigger"] - 2989.5) < 1e-9, f"trigger {hit['trigger']}"


@test("CP: inverse head & shoulders detected on mirrored fixture")
def _():
    df = mirror_cp_df(make_cp_df(hs_top_closes()))
    hit = detect_chart_pattern(df, atr=5.0, cfg=_CP_CFG)
    assert hit is not None and hit["pattern"] == "inv_hs"
    assert hit["direction"] == "BUY"


@test("CP: bull flag detected (pole, shallow retrace, new-high close)")
def _():
    df = make_cp_df(bull_flag_closes())
    hit = detect_chart_pattern(df, atr=5.0, cfg=_CP_CFG)
    assert hit is not None, "bull flag not detected"
    assert hit["pattern"] == "bull_flag" and hit["direction"] == "BUY"
    assert abs(hit["trigger"] - 3026.5) < 1e-9
    assert hit["height"] > 15.0  # pole ≥ cp_flag_pole_atr × atr


@test("CP: bear flag detected on mirrored fixture")
def _():
    df = mirror_cp_df(make_cp_df(bull_flag_closes()))
    hit = detect_chart_pattern(df, atr=5.0, cfg=_CP_CFG)
    assert hit is not None and hit["pattern"] == "bear_flag"
    assert hit["direction"] == "SELL"


@test("CP: cp_patterns subset filter excludes disabled detectors")
def _():
    from dataclasses import replace
    df = make_cp_df(double_bottom_closes())
    cfg = XauStrategyConfig(cp_patterns=["bull_flag"])
    assert detect_chart_pattern(df, atr=5.0, cfg=cfg) is None


def _cp_report(df, config, vol_regime="normal", mtf_signals=None):
    """SignalReport shell around a real engine analysis of `df`."""
    from engine import TechnicalEngine
    eng = TechnicalEngine(config.technical)
    pa = eng.analyze(df, "XAU/USD", config.primary_timeframe.value)
    rep = SignalReport(symbol="XAU/USD", display_name="XAU/USD",
                       timestamp=str(df.index[-1]))
    rep.primary_analysis = pa
    rep.current_price = float(df["Close"].iloc[-1])
    rep.volatility_regime = vol_regime
    rep.mtf_signals = mtf_signals or {}
    rep.signal_quality = "D"
    return rep


def _cp_config(**xau_overrides):
    cfg = PlatformConfig()
    cfg.primary_timeframe = Timeframe.H1
    cfg.xau_strategy.cp_enabled = True
    for k, v in xau_overrides.items():
        setattr(cfg.xau_strategy, k, v)
    return cfg


@test("CP gate: BUY pattern builds an XAU_CP setup with measured-move TP")
def _():
    cfg = _cp_config()
    df = make_cp_df(double_bottom_closes())
    rep = _cp_report(df, cfg)
    SignalAggregator(cfg)._try_chart_pattern(rep, df, 0.10)
    ts = rep.trade_setup
    assert ts is not None, "pattern setup not generated"
    assert ts.strategy == "XAU_CP" and ts.direction == "BUY"
    assert ts.stop_loss < ts.entry_price
    assert abs((ts.take_profit_1 - ts.entry_price) - 19.0) < 1e-6, \
        "TP1 must be the measured move"
    assert rep.signal_quality == "B"
    assert rep.bullish_reasons[0].startswith("XAU CHART PATTERN"), \
        "tag marker must sit at reasons[0]"


@test("CP gate: MTF opposition blocks the pattern trade")
def _():
    cfg = _cp_config()
    df = make_cp_df(double_bottom_closes())
    rep = _cp_report(df, cfg, mtf_signals={
        "15min": SignalStrength.SELL, "4h": SignalStrength.STRONG_SELL})
    SignalAggregator(cfg)._try_chart_pattern(rep, df, 0.10)
    assert rep.trade_setup is None, "BUY pattern must not fire against consensus"


@test("CP gate: SELL pattern blocked in quiet volatility, allowed in high")
def _():
    cfg = _cp_config()
    df = mirror_cp_df(make_cp_df(double_bottom_closes()))
    rep = _cp_report(df, cfg, vol_regime="normal")
    SignalAggregator(cfg)._try_chart_pattern(rep, df, 0.10)
    assert rep.trade_setup is None, "quiet-vol SELL must be blocked"
    rep2 = _cp_report(df, cfg, vol_regime="high")
    SignalAggregator(cfg)._try_chart_pattern(rep2, df, 0.10)
    assert rep2.trade_setup is not None and rep2.trade_setup.direction == "SELL"


@test("CP gate: SELL blocked-hour (bar close) stops pattern shorts only")
def _():
    cfg = _cp_config(sell_blocked_hours_utc=[15])  # fixture closes 15:00
    df = mirror_cp_df(make_cp_df(double_bottom_closes()))
    rep = _cp_report(df, cfg, vol_regime="high")
    SignalAggregator(cfg)._try_chart_pattern(rep, df, 0.10)
    assert rep.trade_setup is None, "SELL at blocked close hour must not fire"
    # BUY side unaffected at the same hour.
    df_buy = make_cp_df(double_bottom_closes())
    rep_buy = _cp_report(df_buy, cfg)
    SignalAggregator(cfg)._try_chart_pattern(rep_buy, df_buy, 0.10)
    assert rep_buy.trade_setup is not None


@test("CP gate: M5 chop-transition volatility blocks both directions")
def _():
    cfg = _cp_config()
    cfg.primary_timeframe = Timeframe.M5
    df = make_cp_df(double_bottom_closes())
    rep = _cp_report(df, cfg, vol_regime="high")  # 'high' blocked on M5
    SignalAggregator(cfg)._try_chart_pattern(rep, df, 0.10)
    assert rep.trade_setup is None


@test("CP gate: spread gate rejects patterns smaller than TP minimum")
def _():
    cfg = _cp_config()
    cfg.signals.min_tp_cost_multiple = 1000.0  # $300 minimum target
    df = make_cp_df(double_bottom_closes())
    rep = _cp_report(df, cfg)
    SignalAggregator(cfg)._try_chart_pattern(rep, df, 0.10)
    assert rep.trade_setup is None


@test("CP pipeline: fires end-to-end when core generators pass")
def _():
    cfg = _cp_config()
    cfg.confirmation_timeframes = [Timeframe.M15, Timeframe.H4]
    df = make_cp_df(double_bottom_closes())
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    agg = SignalAggregator(cfg)
    with patch.object(SignalAggregator, "_generate_trade_setup",
                      return_value=None), \
         patch.object(SignalAggregator, "_generate_xau_atr_trade_setup",
                      return_value=None):
        rep = agg.analyze_from_dataframes("XAU/USD", dfs, use_news=False)
    assert rep.trade_setup is not None, \
        f"pipeline did not produce a setup (warnings: {rep.warnings})"
    assert rep.trade_setup.strategy == "XAU_CP"


@test("CP pipeline: disabled by default except M5 BUY-only")
def _():
    assert XauStrategyConfig().cp_enabled is False
    assert PlatformConfig().xau_strategy.cp_enabled is False
    from pair_signals import PAIR_PRODUCERS
    for tf in (Timeframe.M15, Timeframe.H1, Timeframe.H4):
        assert PAIR_PRODUCERS[XAUUSD](tf, None).xau_strategy.cp_enabled is False
    m5_cfg = PAIR_PRODUCERS[XAUUSD](Timeframe.M5, None)
    assert m5_cfg.xau_strategy.cp_enabled is True
    assert m5_cfg.xau_strategy.cp_patterns == ["inv_hs", "double_bottom", "bull_flag"]
    cfg = PlatformConfig()
    cfg.primary_timeframe = Timeframe.H1
    cfg.confirmation_timeframes = [Timeframe.M15, Timeframe.H4]
    df = make_cp_df(double_bottom_closes())
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    with patch.object(SignalAggregator, "_generate_trade_setup",
                      return_value=None), \
         patch.object(SignalAggregator, "_generate_xau_atr_trade_setup",
                      return_value=None):
        rep = SignalAggregator(cfg).analyze_from_dataframes(
            "XAU/USD", dfs, use_news=False)
    assert rep.trade_setup is None


@test("CP pipeline: session-blocked bar never hosts a pattern trade")
def _():
    cfg = _cp_config()
    cfg.confirmation_timeframes = [Timeframe.M15, Timeframe.H4]
    # Label hour 13 is in blocked_hours_utc → 6b sets F + SESSION FILTER.
    df = make_cp_df(double_bottom_closes(), end="2026-07-08 13:00")
    dfs = {Timeframe.H1: df, Timeframe.M15: df, Timeframe.H4: df}
    with patch.object(SignalAggregator, "_generate_trade_setup",
                      return_value=None), \
         patch.object(SignalAggregator, "_generate_xau_atr_trade_setup",
                      return_value=None):
        rep = SignalAggregator(cfg).analyze_from_dataframes(
            "XAU/USD", dfs, use_news=False)
    assert rep.trade_setup is None
    assert rep.signal_quality == "F"


@test("CP backtester: --cp flag enables patterns, tag filter knows XAU_CP")
def _():
    from gpu_backtest import _parse_args
    args = _parse_args(["--pair", "XAUUSD", "--cp",
                        "--cp-patterns", "double_bottom,bull_flag"])
    assert args.cp is True
    assert args.cp_patterns == "double_bottom,bull_flag"
    args2 = _parse_args(["--pair", "XAUUSD"])
    assert args2.cp is False


@test("Backtester: --trail-arm/--trail-dist research knobs parse, default None")
def _():
    from gpu_backtest import _parse_args
    args = _parse_args(["--pair", "XAUUSD",
                        "--trail-arm", "0.45", "--trail-dist", "0.75"])
    assert args.trail_arm == 0.45 and args.trail_dist == 0.75
    args2 = _parse_args(["--pair", "XAUUSD"])
    assert args2.trail_arm is None and args2.trail_dist is None


# ══════════════════════════════════════════════════════════════════════
#  MODULE 13: app_config.py (config.json overlay)
# ══════════════════════════════════════════════════════════════════════
print("\n═══ Module 13: app_config.py ═══")

import tempfile
from app_config import (
    AppConfig, AppConfigError, load_app_config, set_app_config,
    telegram_token, telegram_chat_id, telegram_channel_id,
    provider_api_key, apply_pair_overrides,
)


def _write_json(payload) -> str:
    fh = tempfile.NamedTemporaryFile(
        "w", suffix=".json", delete=False, encoding="utf-8")
    if isinstance(payload, str):
        fh.write(payload)
    else:
        json.dump(payload, fh)
    fh.close()
    return fh.name


_FULL_CFG = {
    "telegram": {"token": "t0k", "chat_id": "111", "channel_id": "-100222",
                 "alert_min_quality": "B"},
    "data_providers": {"active": "twelve_data",
                       "twelve_data": {"api_key": "json-key"}},
    "scanning": {"pairs": ["XAU/USD"], "timeframes": ["M15", "H1"]},
    "pairs": {"XAU/USD": {"blocked_hours_utc": [3, 4]}},
    "log_level": "WARNING",
}


@test("app_config: valid config.json maps every field")
def _():
    app = load_app_config(_write_json(_FULL_CFG))
    assert app.telegram_token == "t0k"
    assert app.telegram_chat_id == "111"
    assert app.telegram_channel_id == "-100222"
    assert app.alert_min_quality == "B"
    assert app.active_provider == "twelve_data"
    assert app.provider_settings["twelve_data"]["api_key"] == "json-key"
    assert app.scan_pairs == [XAUUSD]
    assert app.scan_timeframes == [Timeframe.M15, Timeframe.H1]
    assert app.pair_overrides[XAUUSD]["blocked_hours_utc"] == [3, 4]
    assert app.log_level == "WARNING"


@test("app_config: missing file returns pure defaults")
def _():
    app = load_app_config("/nonexistent/config.json")
    assert app == AppConfig(), "absent config.json must yield pure defaults"


@test("app_config: malformed JSON raises AppConfigError")
def _():
    path = _write_json("{not json")
    try:
        load_app_config(path)
        assert False, "should have raised"
    except AppConfigError as e:
        assert "not valid JSON" in str(e)


@test("app_config: unknown keys raise AppConfigError")
def _():
    for payload in (
        {"telgram": {}},                                   # top-level typo
        {"telegram": {"tokn": "x"}},                       # section typo
        {"pairs": {"XAU/USD": {"blocked_hours": [1]}}},    # pair-block typo
        {"scanning": {"pair": ["XAU/USD"]}},               # scanning typo
    ):
        try:
            load_app_config(_write_json(payload))
            assert False, f"should have raised for {payload}"
        except AppConfigError as e:
            assert "unknown key" in str(e).lower()


@test("app_config: invalid values raise AppConfigError")
def _():
    bad_payloads = [
        {"pairs": {"XAU/USD": {"blocked_hours_utc": [24]}}},      # hour range
        {"pairs": {"XAU/USD": {"blocked_hours_utc": "3"}}},       # not a list
        {"pairs": {"EUR/USD": {"blocked_hours_utc": [3]}}},       # unknown pair
        {"scanning": {"pairs": ["EUR/USD"]}},                     # unknown pair
        {"scanning": {"timeframes": ["M7"]}},                     # bad TF name
        {"scanning": {"timeframes": ["W1"]}},                     # unscannable TF
        {"scanning": {"pairs": 5}},                               # pairs not a list
        {"scanning": {"timeframes": 5}},                          # timeframes not a list
        {"data_providers": {"active": "polygon",
                            "twelve_data": {"api_key": "k"}}},    # no block
        {"telegram": {"alert_min_quality": "Z"}},                 # bad quality
        {"log_level": "CHATTY"},                                  # bad level
    ]
    for payload in bad_payloads:
        try:
            load_app_config(_write_json(payload))
            assert False, f"should have raised for {payload}"
        except AppConfigError:
            pass


@test("app_config: env precedence env → config.json → default")
def _():
    app = load_app_config(_write_json(_FULL_CFG))
    with patch.dict(os.environ, {}, clear=True):
        assert telegram_token(app) == "t0k"
        assert telegram_chat_id(app) == "111"
        assert telegram_channel_id(app) == "-100222"
        assert provider_api_key(app) == "json-key"
    with patch.dict(os.environ, {"TELEGRAM_TOKEN": "env-tok",
                                 "TELEGRAM_CHAT_ID": "999",
                                 "TWELVE_DATA_API_KEY": "env-key"}):
        assert telegram_token(app) == "env-tok"
        assert telegram_chat_id(app) == "999"
        assert provider_api_key(app) == "env-key"
    # channel: env set-but-empty means "disable feed" and must beat JSON
    with patch.dict(os.environ, {"TELEGRAM_CHANNEL_ID": ""}):
        assert telegram_channel_id(app) == ""


@test("app_config: non-object sections raise AppConfigError")
def _():
    for payload in (
        {"telegram": None},
        {"telegram": "oops"},
        {"scanning": 5},
        {"pairs": ["oops"]},
        {"pairs": {"XAU/USD": ["blocked_hours_utc"]}},
    ):
        try:
            load_app_config(_write_json(payload))
            assert False, f"should have raised for {payload}"
        except AppConfigError:
            pass


@test("app_config: apply_pair_overrides stamps both blocked-hours mirrors")
def _():
    app = AppConfig(pair_overrides={XAUUSD: {"blocked_hours_utc": [3, 4]}},
                    scan_pairs=[XAUUSD],
                    scan_timeframes=[Timeframe.M15])
    cfg = apply_pair_overrides(PlatformConfig(), app)
    assert cfg.xau_strategy.blocked_hours_utc == [3, 4]
    assert cfg.backtest.blocked_hours_utc == [3, 4]
    assert cfg.scan_timeframes == [Timeframe.M15]
    # the two mirrors must be independent lists, not aliases
    cfg.xau_strategy.blocked_hours_utc.append(5)
    assert cfg.backtest.blocked_hours_utc == [3, 4]


@test("app_config: M5 factory appends hour 9 on top of a JSON base")
def _():
    from pair_signals import PAIR_PRODUCERS
    app = AppConfig(pair_overrides={XAUUSD: {"blocked_hours_utc": [3, 4]}},
                    provider_settings={"twelve_data": {"api_key": "test-key"}})
    set_app_config(app)
    try:
        cfg = PAIR_PRODUCERS[XAUUSD](Timeframe.M5, None)
    finally:
        set_app_config(_TEST_APP)
    assert cfg.xau_strategy.blocked_hours_utc == [3, 4, 9], \
        "validated M5 hour-9 delta must land on top of the JSON base"
    assert cfg.backtest.blocked_hours_utc == [3, 4], \
        "mirror carries the base only (per-TF delta is XAU-strategy-side)"


@test("app_config: config.json.example is valid and behavior-neutral")
def _():
    import dataclasses
    from pair_signals import PAIR_PRODUCERS
    example = load_app_config(
        os.path.join(os.path.dirname(__file__), "config.json.example"))
    for tf in (Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4):
        baseline = PAIR_PRODUCERS[XAUUSD](tf, None)   # under _TEST_APP
        set_app_config(example)
        try:
            with_example = PAIR_PRODUCERS[XAUUSD](tf, None)
        finally:
            set_app_config(_TEST_APP)
        assert dataclasses.asdict(baseline) == dataclasses.asdict(with_example), \
            f"example config.json must be byte-identical to defaults on {tf}"


@test("data_provider: api key resolves env → config.json (no code default)")
def _():
    from data_provider import ForexDataProvider
    app = AppConfig(provider_settings={"twelve_data": {"api_key": "json-key"}})
    set_app_config(app)
    try:
        with patch.dict(os.environ, {}, clear=True):
            dp = ForexDataProvider(PlatformConfig())
            assert dp._api_key == "json-key"
        with patch.dict(os.environ, {"TWELVE_DATA_API_KEY": "env-key"}):
            dp = ForexDataProvider(PlatformConfig())
            assert dp._api_key == "env-key"
        set_app_config(AppConfig())        # no key anywhere
        with patch.dict(os.environ, {}, clear=True):
            dp = ForexDataProvider(PlatformConfig())
            assert dp._api_key is None and dp._td is None
    finally:
        set_app_config(_TEST_APP)


@test("data_provider: make_data_provider dispatches on active provider")
def _():
    from data_provider import ForexDataProvider, make_data_provider
    assert isinstance(make_data_provider(PlatformConfig()), ForexDataProvider)
    set_app_config(AppConfig(active_provider="polygon",
                             provider_settings={"polygon": {"api_key": "k"}}))
    try:
        make_data_provider(PlatformConfig())
        assert False, "unregistered provider must raise"
    except AppConfigError as e:
        assert "polygon" in str(e)
    finally:
        set_app_config(_TEST_APP)


@test("config.py: hardcoded twelve_data_api_key field is gone")
def _():
    assert not hasattr(PlatformConfig(), "twelve_data_api_key"), \
        "credentials must not live in committed source"


@test("telegram_bot: credentials, quality, pairs, loops from app_config")
def _():
    import importlib
    import telegram_bot
    app = AppConfig(telegram_token="json-tok", telegram_chat_id="42",
                    telegram_channel_id="", alert_min_quality="B",
                    scan_timeframes=[Timeframe.M15, Timeframe.H1],
                    provider_settings={"twelve_data": {"api_key": "k"}})
    set_app_config(app)
    try:
        with patch.dict(os.environ, {}, clear=True):
            tb = importlib.reload(telegram_bot)
            assert tb.TOKEN == "json-tok"
            assert tb.OWNER_ID == 42
            assert tb.CHANNEL_ID is None, '"" channel id must disable the feed'
            assert tb._alert_min_quality == "B"
            assert [tf for tf, _ in tb._AUTOSCAN_LOOPS] == \
                [Timeframe.M15, Timeframe.H1]
            assert tb._AUTOSCAN_LOOPS == [(Timeframe.M15, 15),
                                          (Timeframe.H1, 60)]
    finally:
        set_app_config(_TEST_APP)
        importlib.reload(telegram_bot)


@test("telegram_bot: main() refuses to start without credentials")
def _():
    import telegram_bot
    if telegram_bot.TOKEN:      # only meaningful in the hermetic no-cred env
        return
    try:
        telegram_bot.main()
        assert False, "main() must refuse to start without token/chat_id"
    except SystemExit as e:
        assert "credentials" in str(e)


@test("app_config: absolute parity — default overlay leaves tuned values untouched")
def _():
    from pair_signals import PAIR_PRODUCERS
    cfg = PAIR_PRODUCERS[XAUUSD](Timeframe.H1, None)
    assert cfg.xau_strategy.blocked_hours_utc == [0, 1, 2, 10, 11, 13, 16, 21]
    assert cfg.backtest.blocked_hours_utc == [0, 1, 2, 10, 11, 13, 16, 21]
    assert cfg.scan_pairs == [XAUUSD]
    assert cfg.scan_timeframes == [Timeframe.M5, Timeframe.M15,
                                   Timeframe.H1, Timeframe.H4]


# ══════════════════════════════════════════════════════════════════════
#  MODULE 14: .env loader + telegram_bot resilience
# ══════════════════════════════════════════════════════════════════════
print("\n═══ Module 14: .env loader + telegram_bot resilience ═══")

from app_config import load_dotenv


@test("app_config: load_dotenv parses lines, never overrides real env")
def _():
    fh = tempfile.NamedTemporaryFile(
        "w", suffix=".env", delete=False, encoding="utf-8")
    fh.write("# comment\n\nPLAIN=abc\nexport EXPORTED=def\n"
             'QUOTED="with spaces"\nSINGLE=\'sq\'\nEXISTING=from-dotenv\n'
             "NOT A KV LINE\n"
             "COMMENTED=abc123 # prod key\n"
             'QUOTED_HASH="keep #this"\n'
             "HASH_IN_VALUE=abc#123\n")
    fh.close()
    with patch.dict(os.environ, {"EXISTING": "from-shell"}, clear=True):
        load_dotenv(fh.name)
        assert os.environ["PLAIN"] == "abc"
        assert os.environ["EXPORTED"] == "def"
        assert os.environ["QUOTED"] == "with spaces"
        assert os.environ["SINGLE"] == "sq"
        assert os.environ["EXISTING"] == "from-shell", \
            "a real shell export must beat .env"
        assert os.environ["COMMENTED"] == "abc123", \
            "inline comment must be stripped, not absorbed into the value"
        assert os.environ["QUOTED_HASH"] == "keep #this", \
            "quoted values keep their hashes"
        assert os.environ["HASH_IN_VALUE"] == "abc#123", \
            "a hash with no preceding whitespace is part of the value"
    load_dotenv("/nonexistent/.env")   # missing file is a silent no-op


@test("telegram_bot: log rotation is 3-hourly with 30-day retention")
def _():
    import logging.handlers
    import telegram_bot as tb
    h = tb._file_handler
    assert isinstance(h, logging.handlers.TimedRotatingFileHandler)
    assert h.when.upper() == "H" and h.interval == 3 * 3600, \
        "rotation must fire every 3 hours"
    assert h.backupCount == 240, "240 × 3h = 30-day retention"


@test("telegram_bot: candle loop survives a failing scan cycle")
def _():
    import asyncio
    import telegram_bot as tb
    calls = {"n": 0}

    async def boom(bot, tf, confirm):
        calls["n"] += 1
        if calls["n"] == 1:
            raise RuntimeError("scan blew up")
        tb._autoscan_chats.clear()   # let the loop exit after call 2

    tb._autoscan_chats.add(1)
    try:
        with patch.object(tb, "_scan_timeframe", boom), \
             patch.object(tb, "_seconds_until_boundary",
                          lambda interval, buffer=10: 0.0):
            asyncio.run(tb._candle_loop(None, Timeframe.M15, 15))
        assert calls["n"] == 2, \
            "loop must keep firing after an exception, not die on it"
    finally:
        tb._autoscan_chats.clear()


# ══════════════════════════════════════════════════════════════════════
#  MODULE 15: Oanda provider / executor / daemon
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 15: Oanda provider / executor / daemon ═══")

import oanda_provider
import oanda_executor
import oanda_daemon


@test("oanda_provider: _is_forex_weekend matches data_provider's")
def _():
    from data_provider import _is_forex_weekend as dp_weekend
    from oanda_provider import _is_forex_weekend as oa_weekend
    import pandas as pd
    for ts_str in [
        "2026-07-17 12:00",   # Friday noon
        "2026-07-17 22:30",   # Friday after close
        "2026-07-18 15:00",   # Saturday
        "2026-07-19 12:00",   # Sunday before open
        "2026-07-19 22:30",   # Sunday after open
        "2026-07-20 08:00",   # Monday
    ]:
        ts = pd.Timestamp(ts_str, tz="UTC")
        assert dp_weekend(ts) == oa_weekend(ts), \
            f"Weekend check mismatch at {ts_str}"


@test("oanda_provider: OandaDataProvider parses Oanda candle JSON into correct DataFrame")
def _():
    sample_candles = [
        {
            "complete": True,
            "time": "2026-07-20T10:00:00.000000000Z",
            "mid": {"o": "2400.00", "h": "2405.00", "l": "2398.00", "c": "2403.00"},
            "bid": {"o": "2399.85", "h": "2404.85", "l": "2397.85", "c": "2402.85"},
            "ask": {"o": "2400.15", "h": "2405.15", "l": "2398.15", "c": "2403.15"},
            "volume": 1234,
        },
        {
            "complete": True,
            "time": "2026-07-20T11:00:00.000000000Z",
            "mid": {"o": "2403.00", "h": "2408.00", "l": "2401.00", "c": "2406.00"},
            "bid": {"o": "2402.85", "h": "2407.85", "l": "2400.85", "c": "2405.85"},
            "ask": {"o": "2403.15", "h": "2408.15", "l": "2401.15", "c": "2406.15"},
            "volume": 5678,
        },
        {
            "complete": False,
            "time": "2026-07-20T12:00:00.000000000Z",
            "mid": {"o": "2406.00", "h": "2406.50", "l": "2405.50", "c": "2406.20"},
            "bid": {"o": "2405.85", "h": "2406.35", "l": "2405.35", "c": "2406.05"},
            "ask": {"o": "2406.15", "h": "2406.65", "l": "2405.65", "c": "2406.35"},
            "volume": 100,
        },
    ]

    mock_resp = type("MockResp", (), {
        "status_code": 200,
        "json": lambda self: {"candles": sample_candles, "instrument": "XAU_USD"},
        "raise_for_status": lambda self: None,
    })()

    provider2 = oanda_provider.OandaDataProvider.__new__(
        oanda_provider.OandaDataProvider)
    provider2.config = PlatformConfig()
    provider2._base_url = "https://test"
    provider2._cache = {}
    provider2._cache_timestamps = {}
    provider2._cache_lock = threading.Lock()
    provider2._spread_cache = {}
    provider2._session = type("S", (), {
        "get": lambda self, *a, **k: mock_resp,
        "headers": {},
    })()

    df = provider2._fetch_oanda_candles("XAU/USD", Timeframe.H1, 300)

    assert len(df) == 2, f"Expected 2 complete bars, got {len(df)}"
    assert list(df.columns) == ["Open", "High", "Low", "Close", "Volume"], \
        f"Wrong columns: {list(df.columns)}"
    assert df.index.name == "Datetime"
    assert df.index.tz is not None, "Index must be tz-aware"
    assert df.iloc[0]["Open"] == 2400.0
    assert df.iloc[1]["Close"] == 2406.0
    assert df.iloc[0]["Volume"] == 1234

    spread = provider2._spread_cache.get("XAU/USD")
    assert spread is not None, "Spread should be cached"
    assert abs(spread - 0.30) < 0.01, f"Expected spread ~0.30, got {spread}"


@test("oanda_provider: instrument name mapping")
def _():
    provider = oanda_provider.OandaDataProvider.__new__(
        oanda_provider.OandaDataProvider)
    assert provider._to_oanda_instrument("XAU/USD") == "XAU_USD"
    assert provider._to_oanda_instrument("EUR/USD") == "EUR_USD"


@test("oanda_provider: granularity mapping covers all scan timeframes")
def _():
    for tf in [Timeframe.M5, Timeframe.M15, Timeframe.H1, Timeframe.H4]:
        assert tf in oanda_provider.OandaDataProvider.OANDA_GRANULARITY, \
            f"{tf} missing from OANDA_GRANULARITY"


@test("oanda_provider: get_spread returns default when no cache")
def _():
    provider = oanda_provider.OandaDataProvider.__new__(
        oanda_provider.OandaDataProvider)
    provider._spread_cache = {}
    spread = provider.get_spread("XAU/USD")
    assert spread == 0.30, f"Expected default spread 0.30, got {spread}"


@test("oanda_executor: market order body construction")
def _():
    executor = oanda_executor.OandaExecutor.__new__(
        oanda_executor.OandaExecutor)
    executor._account_id = "test-account"
    executor._base_url = "https://test"
    executor._lot_size = 1
    executor._price_precision = 2
    executor._session = None

    captured = {}

    def mock_request(self, method, url, **kwargs):
        captured["method"] = method
        captured["url"] = url
        captured["json"] = kwargs.get("json")
        return type("R", (), {
            "status_code": 201,
            "json": lambda self: {
                "orderFillTransaction": {
                    "price": "2400.50",
                    "tradeOpened": {"tradeID": "123"},
                }
            },
        })()

    executor._session = type("S", (), {
        "request": mock_request,
        "headers": {},
    })()

    result = executor.place_market_order(
        instrument="XAU_USD",
        direction="BUY",
        stop_loss=2392.50,
        take_profit=2420.00,
    )

    body = captured["json"]
    order = body["order"]
    assert order["type"] == "MARKET"
    assert order["instrument"] == "XAU_USD"
    assert order["units"] == "1"
    # XAU price precision is 2 — Oanda rejects excess decimals.
    assert order["stopLossOnFill"]["price"] == "2392.50"
    assert order["takeProfitOnFill"]["price"] == "2420.00"

    fill = result.get("orderFillTransaction")
    assert fill is not None
    assert fill["tradeOpened"]["tradeID"] == "123"


@test("oanda_executor: SELL order uses negative units")
def _():
    executor = oanda_executor.OandaExecutor.__new__(
        oanda_executor.OandaExecutor)
    executor._account_id = "test-account"
    executor._base_url = "https://test"
    executor._lot_size = 1
    executor._price_precision = 2

    captured = {}

    def mock_request(self, method, url, **kwargs):
        captured["json"] = kwargs.get("json")
        return type("R", (), {
            "status_code": 201,
            "json": lambda self: {
                "orderFillTransaction": {
                    "price": "2400.50",
                    "tradeOpened": {"tradeID": "456"},
                }
            },
        })()

    executor._session = type("S", (), {
        "request": mock_request,
        "headers": {},
    })()

    executor.place_market_order(
        instrument="XAU_USD", direction="SELL", stop_loss=2410.00)

    order = captured["json"]["order"]
    assert order["units"] == "-1", f"SELL should be -1, got {order['units']}"
    assert "takeProfitOnFill" not in order, "No TP should be set when None"


@test("oanda_executor: 4xx error raises immediately (no retry)")
def _():
    executor = oanda_executor.OandaExecutor.__new__(
        oanda_executor.OandaExecutor)
    executor._account_id = "test"
    executor._base_url = "https://test"
    executor._lot_size = 1
    call_count = {"n": 0}

    def mock_request(self, method, url, **kwargs):
        call_count["n"] += 1
        return type("R", (), {
            "status_code": 400,
            "text": "INVALID_ORDER",
        })()

    executor._session = type("S", (), {
        "request": mock_request,
        "headers": {},
    })()

    try:
        executor._request_with_retry("POST", "https://test/orders")
        assert False, "Should have raised OandaAPIError"
    except oanda_executor.OandaAPIError as e:
        assert e.status_code == 400
    assert call_count["n"] == 1, "4xx should not retry"


@test("oanda_daemon: trailing stop ratchet matches backtester logic")
def _():
    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)

    info_buy = {
        "direction": "BUY",
        "entry": 2400.0,
        "sl_initial": 2384.0,
        "current_sl": 2384.0,
        "risk": 16.0,
        "peak": 2400.0,
        "trail_armed": False,
        "trail_arm_r": 0.6,
        "trail_dist_r": 0.75,
    }

    # Bar 1: price moves up but not enough to arm (< 0.6R = $9.60)
    new_sl = daemon._compute_trail(info_buy, bar_high=2408.0, bar_low=2399.0)
    assert new_sl is None, "Should not arm yet (excursion 0.5R)"
    assert info_buy["peak"] == 2408.0

    # Bar 2: excursion reaches 0.625R = $10 — arms, trail = 2408 - 0.75*16 = 2396
    new_sl = daemon._compute_trail(info_buy, bar_high=2410.0, bar_low=2403.0)
    assert new_sl is not None, "Should arm at 0.625R"
    assert abs(new_sl - 2398.0) < 0.01, f"Expected trail ~2398, got {new_sl}"
    assert info_buy["trail_armed"] is True

    # Bar 3: price goes higher, trail ratchets up
    info_buy["current_sl"] = new_sl
    new_sl2 = daemon._compute_trail(info_buy, bar_high=2420.0, bar_low=2410.0)
    assert new_sl2 is not None
    assert new_sl2 > new_sl, "Trail must ratchet up"
    expected = 2420.0 - 0.75 * 16.0  # 2408.0
    assert abs(new_sl2 - expected) < 0.01, f"Expected {expected}, got {new_sl2}"

    # Bar 4: price pulls back — trail must NOT ratchet down
    info_buy["current_sl"] = new_sl2
    new_sl3 = daemon._compute_trail(info_buy, bar_high=2412.0, bar_low=2405.0)
    assert new_sl3 is None, "Trail must not ratchet backwards"


@test("oanda_daemon: trailing stop for SELL direction")
def _():
    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)

    info_sell = {
        "direction": "SELL",
        "entry": 2400.0,
        "sl_initial": 2416.0,
        "current_sl": 2416.0,
        "risk": 16.0,
        "peak": 2400.0,
        "trail_armed": False,
        "trail_arm_r": 0.6,
        "trail_dist_r": 0.75,
    }

    # Not enough excursion
    new_sl = daemon._compute_trail(info_sell, bar_high=2401.0, bar_low=2393.0)
    assert new_sl is None, "Should not arm yet"
    assert info_sell["peak"] == 2393.0

    # Arms: peak at 2390, excursion = 10/16 = 0.625R
    new_sl = daemon._compute_trail(info_sell, bar_high=2395.0, bar_low=2390.0)
    assert new_sl is not None
    expected = 2390.0 + 0.75 * 16.0  # 2402.0
    assert abs(new_sl - expected) < 0.01, f"Expected {expected}, got {new_sl}"
    assert info_sell["trail_armed"] is True

    # Ratchets down
    info_sell["current_sl"] = new_sl
    new_sl2 = daemon._compute_trail(info_sell, bar_high=2392.0, bar_low=2380.0)
    assert new_sl2 is not None
    assert new_sl2 < new_sl, "SELL trail must ratchet down"


@test("oanda_daemon: _should_execute respects quality gate")
def _():
    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
    daemon._quality_gate = "B"
    daemon._state = {"trades": {}, "last_entries": {}}
    daemon._state_lock = threading.Lock()

    from signal_aggregator import TradeSetup
    setup = TradeSetup(
        pair="XAU/USD", display_name="XAU/USD", direction="BUY",
        entry_price=2400.0, stop_loss=2384.0,
        take_profit_1=2416.0, take_profit_2=2432.0,
        risk_reward=1.0, risk_pips=160.0, reward_pips=160.0,
        pip_value=0.10)

    report_c = SignalReport(
        symbol="XAU/USD", display_name="XAU/USD", timestamp="2026-07-20")
    report_c.final_signal = SignalStrength.BUY
    report_c.signal_quality = "C"
    report_c.trade_setup = setup

    assert not daemon._should_execute(report_c, Timeframe.H1), \
        "C should be blocked by gate=B"

    report_a = SignalReport(
        symbol="XAU/USD", display_name="XAU/USD", timestamp="2026-07-20")
    report_a.final_signal = SignalStrength.BUY
    report_a.signal_quality = "A"
    report_a.trade_setup = setup

    assert daemon._should_execute(report_a, Timeframe.H1), \
        "A should pass gate=B"


@test("oanda_daemon: _should_execute blocks when position exists")
def _():
    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
    daemon._quality_gate = "C"
    daemon._state = {"trades": {"12345": {"direction": "BUY"}},
                     "last_entries": {}}
    daemon._state_lock = threading.Lock()

    from signal_aggregator import TradeSetup
    setup = TradeSetup(
        pair="XAU/USD", display_name="XAU/USD", direction="BUY",
        entry_price=2400.0, stop_loss=2384.0,
        take_profit_1=2416.0, take_profit_2=2432.0,
        risk_reward=1.0, risk_pips=160.0, reward_pips=160.0,
        pip_value=0.10)

    report = SignalReport(
        symbol="XAU/USD", display_name="XAU/USD", timestamp="2026-07-20")
    report.final_signal = SignalStrength.BUY
    report.signal_quality = "A"
    report.trade_setup = setup

    assert not daemon._should_execute(report, Timeframe.H1), \
        "Should block when a position already exists"


@test("oanda_daemon: NEUTRAL signal is never executed")
def _():
    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
    daemon._quality_gate = "C"
    daemon._state = {"trades": {}, "last_entries": {}}
    daemon._state_lock = threading.Lock()

    report = SignalReport(
        symbol="XAU/USD", display_name="XAU/USD", timestamp="2026-07-20")
    report.final_signal = SignalStrength.NEUTRAL
    report.signal_quality = "F"
    report.trade_setup = None

    assert not daemon._should_execute(report, Timeframe.H1)


def _make_daemon_report(quality="A", direction="BUY"):
    from signal_aggregator import TradeSetup
    setup = TradeSetup(
        pair="XAU/USD", display_name="XAU/USD", direction=direction,
        entry_price=2400.0, stop_loss=2384.0,
        take_profit_1=2416.0, take_profit_2=2432.0,
        risk_reward=1.0, risk_pips=160.0, reward_pips=160.0,
        pip_value=0.10, trail_arm_r=0.6, trail_dist_r=0.85)
    report = SignalReport(
        symbol="XAU/USD", display_name="XAU/USD",
        timestamp="2026-07-20 10:00:00+00:00")
    report.final_signal = (SignalStrength.BUY if direction == "BUY"
                           else SignalStrength.SELL)
    report.signal_quality = quality
    report.trade_setup = setup
    return report


@test("oanda_daemon: 5-bar entry cooldown blocks re-entry (backtest parity)")
def _():
    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
    daemon._quality_gate = "C"
    daemon._state = {"trades": {}, "last_entries": {}}
    daemon._state_lock = threading.Lock()

    report = _make_daemon_report()

    # Entry 2 minutes ago on M5 → inside 5×5min cooldown → blocked
    recent = (datetime.now(timezone.utc) - timedelta(minutes=2)).isoformat()
    daemon._state["last_entries"]["5min"] = recent
    assert not daemon._should_execute(report, Timeframe.M5), \
        "Entry 2min ago must block M5 (cooldown 25min)"

    # Entry 30 minutes ago on M5 → past 5×5min cooldown → allowed
    old = (datetime.now(timezone.utc) - timedelta(minutes=30)).isoformat()
    daemon._state["last_entries"]["5min"] = old
    assert daemon._should_execute(report, Timeframe.M5), \
        "Entry 30min ago must clear the 25min M5 cooldown"

    # M5 cooldown must not block H1
    daemon._state["last_entries"] = {"5min": recent}
    assert daemon._should_execute(report, Timeframe.H1), \
        "Cooldown is per-timeframe"


@test("oanda_daemon: orders carry NO take-profit (trailing book parity)")
def _():
    import tempfile
    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
    daemon._state = {"trades": {}, "last_entries": {}}
    daemon._state_lock = threading.Lock()
    daemon._entry_lock = threading.Lock()

    captured = {}

    class MockExecutor:
        def place_market_order(self, instrument, direction, stop_loss,
                               take_profit=None, units=None):
            captured["take_profit"] = take_profit
            captured["stop_loss"] = stop_loss
            return {
                "orderFillTransaction": {
                    "price": "2400.60",
                    "tradeOpened": {"tradeID": "777"},
                }
            }

    daemon._executor = MockExecutor()

    with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as f:
        tmp_path = f.name
    orig_state_file = oanda_daemon.STATE_FILE
    try:
        oanda_daemon.STATE_FILE = tmp_path
        daemon._execute_trade(_make_daemon_report(), Timeframe.M15)
    finally:
        oanda_daemon.STATE_FILE = orig_state_file
        os.unlink(tmp_path)

    assert captured.get("take_profit") is None, \
        "Backtested trailing model has no hard TP — order must not carry one"
    assert captured["stop_loss"] == 2384.0
    trade = daemon._state["trades"]["777"]
    assert trade["entry"] == 2400.60, "Risk must be computed from actual fill"
    assert abs(trade["risk"] - 16.60) < 0.01
    assert trade["trail_arm_r"] == 0.6 and trade["trail_dist_r"] == 0.85, \
        "Trail params must come from the setup stamp (per-TF factory values)"
    assert daemon._state["last_entries"]["15min"], \
        "Entry must be recorded for the cooldown"


@test("oanda_daemon: entry-lock recheck prevents double entry from concurrent scans")
def _():
    import tempfile
    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
    # A concurrent TF loop already opened a trade after our _should_execute
    daemon._state = {"trades": {"111": {"direction": "BUY"}},
                     "last_entries": {}}
    daemon._state_lock = threading.Lock()
    daemon._entry_lock = threading.Lock()

    placed = {"n": 0}

    class MockExecutor:
        def place_market_order(self, *a, **k):
            placed["n"] += 1
            return {}

    daemon._executor = MockExecutor()

    with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as f:
        tmp_path = f.name
    orig_state_file = oanda_daemon.STATE_FILE
    try:
        oanda_daemon.STATE_FILE = tmp_path
        daemon._execute_trade(_make_daemon_report(), Timeframe.M5)
    finally:
        oanda_daemon.STATE_FILE = orig_state_file
        os.unlink(tmp_path)

    assert placed["n"] == 0, \
        "Recheck under the entry lock must skip when a position exists"


@test("oanda_daemon: same closed bar is never processed twice")
def _():
    import tempfile
    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
    daemon._max_hold_bars = 300
    daemon._price_precision = 2
    daemon._state_lock = threading.Lock()

    bar_time = pd.Timestamp("2026-07-20 10:00:00", tz="UTC")
    df = pd.DataFrame(
        {"Open": [2400.0], "High": [2405.0], "Low": [2398.0],
         "Close": [2403.0], "Volume": [100.0]},
        index=pd.DatetimeIndex([bar_time], name="Datetime"))

    class MockProvider:
        def fetch_ohlcv(self, symbol, tf):
            return df

    daemon._provider = MockProvider()
    daemon._executor = None  # trail must not arm on this bar

    daemon._state = {
        "trades": {
            "555": {
                "tf": "5min", "direction": "BUY", "entry": 2400.0,
                "sl_initial": 2384.0, "current_sl": 2384.0, "risk": 16.0,
                "peak": 2400.0, "trail_armed": False,
                "trail_arm_r": 0.6, "trail_dist_r": 0.85,
                "bars_held": 0, "last_bar_ts": None, "signal_bar_ts": None,
            }
        },
        "last_entries": {},
    }

    with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as f:
        tmp_path = f.name
    orig_state_file = oanda_daemon.STATE_FILE
    try:
        oanda_daemon.STATE_FILE = tmp_path
        daemon._manage_trailing(Timeframe.M5)
        daemon._manage_trailing(Timeframe.M5)   # same bar re-served (weekend/lag)
        daemon._manage_trailing(Timeframe.M5)
    finally:
        oanda_daemon.STATE_FILE = orig_state_file
        os.unlink(tmp_path)

    assert daemon._state["trades"]["555"]["bars_held"] == 1, \
        "A re-served bar must not inflate bars_held (weekend timeout bug)"


@test("oanda_daemon: failed timeout-close keeps the trade tracked")
def _():
    import tempfile
    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
    daemon._max_hold_bars = 1
    daemon._price_precision = 2
    daemon._state_lock = threading.Lock()

    bar_time = pd.Timestamp("2026-07-20 10:00:00", tz="UTC")
    df = pd.DataFrame(
        {"Open": [2400.0], "High": [2405.0], "Low": [2398.0],
         "Close": [2403.0], "Volume": [100.0]},
        index=pd.DatetimeIndex([bar_time], name="Datetime"))

    class MockProvider:
        def fetch_ohlcv(self, symbol, tf):
            return df

    class MockExecutor:
        def close_trade(self, trade_id):
            raise oanda_executor.OandaAPIError("market halted", 503)

    daemon._provider = MockProvider()
    daemon._executor = MockExecutor()

    daemon._state = {
        "trades": {
            "666": {
                "tf": "5min", "direction": "BUY", "entry": 2400.0,
                "sl_initial": 2384.0, "current_sl": 2384.0, "risk": 16.0,
                "peak": 2400.0, "trail_armed": False,
                "trail_arm_r": 0.6, "trail_dist_r": 0.85,
                "bars_held": 0, "last_bar_ts": None, "signal_bar_ts": None,
            }
        },
        "last_entries": {},
    }

    with tempfile.NamedTemporaryFile(suffix=".json", delete=False) as f:
        tmp_path = f.name
    orig_state_file = oanda_daemon.STATE_FILE
    try:
        oanda_daemon.STATE_FILE = tmp_path
        daemon._manage_trailing(Timeframe.M5)
    finally:
        oanda_daemon.STATE_FILE = orig_state_file
        os.unlink(tmp_path)

    assert "666" in daemon._state["trades"], \
        "A failed broker close must keep the trade tracked for retry"


@test("app_config: invalid oanda section values raise AppConfigError")
def _():
    import tempfile
    for bad in (
        {"oanda": {"quality_gate": "Z"}},
        {"oanda": {"lot_size": 0}},
        {"oanda": {"max_hold_bars": -5}},
        {"oanda": {"unknown_key": 1}},
    ):
        with tempfile.NamedTemporaryFile(
                mode="w", suffix=".json", delete=False) as f:
            json.dump(bad, f)
            tmp_path = f.name
        try:
            app_config.load_app_config(tmp_path)
            assert False, f"Should have raised for {bad}"
        except app_config.AppConfigError:
            pass
        finally:
            os.unlink(tmp_path)


@test("oanda_daemon: boundary timing matches telegram_bot")
def _():
    from telegram_bot import _seconds_until_boundary as tb_boundary
    from oanda_daemon import _seconds_until_boundary as od_boundary
    for interval in (5, 15, 60, 240):
        tb_val = tb_boundary(interval, buffer=10)
        od_val = od_boundary(interval, buffer=10)
        assert abs(tb_val - od_val) < 0.01, \
            f"Boundary timing mismatch for {interval}min: {tb_val} vs {od_val}"


@test("oanda_daemon: state file round-trip")
def _():
    import tempfile
    state = {
        "trades": {
            "999": {
                "tf": "M15",
                "direction": "BUY",
                "entry": 2400.0,
                "sl_initial": 2384.0,
                "current_sl": 2392.0,
                "risk": 16.0,
                "peak": 2412.0,
                "trail_armed": True,
                "trail_arm_r": 0.6,
                "trail_dist_r": 0.75,
                "bars_held": 25,
            }
        }
    }
    with tempfile.NamedTemporaryFile(
            mode="w", suffix=".json", delete=False) as f:
        tmp_path = f.name

    orig_state_file = oanda_daemon.STATE_FILE
    try:
        oanda_daemon.STATE_FILE = tmp_path
        oanda_daemon._save_state(state)
        loaded = oanda_daemon._load_state()
        assert loaded["trades"]["999"]["entry"] == 2400.0
        assert loaded["trades"]["999"]["trail_armed"] is True
        assert loaded["trades"]["999"]["bars_held"] == 25
    finally:
        oanda_daemon.STATE_FILE = orig_state_file
        os.unlink(tmp_path)


@test("app_config: oanda in _KNOWN_TOP (no unknown-key error)")
def _():
    assert "oanda" in app_config._KNOWN_TOP, \
        "'oanda' must be in _KNOWN_TOP to avoid config.json validation errors"


@test("app_config: oanda resolver helpers return defaults without config")
def _():
    url = app_config.oanda_base_url()
    assert "oanda.com" in url, f"Expected Oanda URL, got {url}"


@test("oanda_provider: HTTP error surfaces status code and body")
def _():
    mock_resp = type("MockResp", (), {
        "status_code": 401,
        "text": '{"errorMessage":"Insufficient authorization to perform request"}',
    })()

    provider = oanda_provider.OandaDataProvider.__new__(
        oanda_provider.OandaDataProvider)
    provider._base_url = "https://test"
    provider._spread_cache = {}
    provider._session = type("S", (), {
        "get": lambda self, *a, **k: mock_resp,
        "headers": {},
    })()

    try:
        provider._fetch_oanda_candles("XAU/USD", Timeframe.H1, 300)
        assert False, "Should have raised OandaDataError"
    except oanda_provider.OandaDataError as e:
        msg = str(e)
        assert "401" in msg, f"Status code missing from error: {msg}"
        assert "Insufficient authorization" in msg, \
            f"Body detail missing from error: {msg}"


@test("oanda_provider: boundary-retry failure keeps original data")
def _():
    provider = oanda_provider.OandaDataProvider.__new__(
        oanda_provider.OandaDataProvider)
    provider.config = PlatformConfig()
    provider._cache = {}
    provider._cache_timestamps = {}
    provider._cache_lock = threading.Lock()
    provider._spread_cache = {}
    provider.FRESH_FETCH_RETRY_SECONDS = 0  # instance override — no sleep

    call_count = {"n": 0}
    idx = pd.date_range("2026-07-01", periods=250, freq="5min", tz="UTC")
    original_df = pd.DataFrame({
        "Open": 2400.0, "High": 2401.0, "Low": 2399.0,
        "Close": 2400.5, "Volume": 100.0,
    }, index=idx)

    def _fetch_oanda_candles(symbol, timeframe, count):
        call_count["n"] += 1
        if call_count["n"] == 1:
            return original_df
        raise oanda_provider.OandaDataError("boom")

    def _validate_and_clean(df, symbol, timeframe):
        return df

    def _has_latest_closed_bar(df, timeframe):
        return False   # forces the retry path every call

    def _is_data_fresh(df, symbol, timeframe):
        return True

    # Plain functions assigned to the instance bypass the descriptor
    # protocol — they run unbound, so they take exactly the params
    # fetch_ohlcv passes (no self).
    provider._fetch_oanda_candles = _fetch_oanda_candles
    provider._validate_and_clean = _validate_and_clean
    provider._has_latest_closed_bar = _has_latest_closed_bar
    provider._is_data_fresh = _is_data_fresh

    result = provider.fetch_ohlcv("XAU/USD", Timeframe.M5)

    assert call_count["n"] == 2, \
        f"Expected original fetch + one retry attempt, got {call_count['n']} calls"
    assert len(result) == 250, \
        f"Retry failure must keep the original 250-bar df, got {len(result)}"


@test("oanda_executor: non-JSON response raises OandaAPIError with status")
def _():
    def _bad_json(self):
        raise ValueError("Expecting value: line 1 column 1 (char 0)")

    mock_resp = type("MockResp", (), {
        "status_code": 200,
        "text": "<html>gateway error</html>",
        "json": _bad_json,
    })()

    executor = oanda_executor.OandaExecutor.__new__(
        oanda_executor.OandaExecutor)
    executor._account_id = "test-account"
    executor._base_url = "https://test"
    executor._session = type("S", (), {
        "request": lambda self, method, url, **kwargs: mock_resp,
        "headers": {},
    })()

    try:
        executor.get_open_trades()
        assert False, "Should have raised OandaAPIError"
    except oanda_executor.OandaAPIError as e:
        assert e.status_code == 200, f"Expected status 200, got {e.status_code}"
        assert "non-JSON" in str(e), f"Expected non-JSON in message: {e}"


@test("oanda_daemon: boot fails fast on rejected credentials (4xx)")
def _():
    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)

    class MockExecutor401:
        def get_account_summary(self):
            raise oanda_executor.OandaAPIError("unauthorized", 401)

    daemon._executor = MockExecutor401()
    try:
        daemon._verify_account_access()
        assert False, "A 4xx credential rejection must raise SystemExit"
    except SystemExit:
        pass

    class MockExecutorConnFail:
        def get_account_summary(self):
            raise oanda_executor.OandaAPIError("conn refused", 0)

    daemon._executor = MockExecutorConnFail()
    daemon._verify_account_access()   # transient — must NOT raise


@test("oanda_daemon: heartbeat never raises and logs on account failure")
def _():
    daemon = oanda_daemon.TradingDaemon.__new__(oanda_daemon.TradingDaemon)
    daemon._state = {"trades": {}, "last_entries": {}}
    daemon._state_lock = threading.Lock()

    class MockExecutorDown:
        def get_account_summary(self):
            raise oanda_executor.OandaAPIError("down", 503)

    daemon._executor = MockExecutorDown()
    daemon._heartbeat_once()   # must not raise

    class MockExecutorUp:
        def get_account_summary(self):
            return {"balance": "1000"}

    daemon._executor = MockExecutorUp()
    daemon._heartbeat_once()   # must not raise


# ══════════════════════════════════════════════════════════════════════
#  MODULE 16: Provider abstraction (providers/ package)
# ══════════════════════════════════════════════════════════════════════

print("\n═══ Module 16: Provider abstraction ═══")

from providers import (
    BaseDataProvider, PROVIDER_REGISTRY, TwelveDataProvider,
    make_data_provider,
)
from providers.oanda import OandaDataProvider as _POanda
from providers.tiingo import TiingoDataError, TiingoDataProvider


@test("providers: registry holds all providers, active default builds TD")
def _():
    assert PROVIDER_REGISTRY["twelve_data"] is TwelveDataProvider
    assert PROVIDER_REGISTRY["oanda"] is _POanda
    assert PROVIDER_REGISTRY["tiingo"] is TiingoDataProvider
    dp = make_data_provider(PlatformConfig())
    assert isinstance(dp, TwelveDataProvider)
    assert isinstance(dp, BaseDataProvider)


@test("providers: single knob — data_providers.active selects the class")
def _():
    from app_config import AppConfig
    for name, cls in PROVIDER_REGISTRY.items():
        fake = AppConfig()
        fake.active_provider = name
        fake.provider_settings = {name: {"api_key": ""}}
        with patch("providers.get_app_config", return_value=fake):
            dp = make_data_provider(PlatformConfig())
        assert type(dp) is cls, f"active={name} built {type(dp).__name__}"
    # An unknown name must fail loudly, not fall back silently.
    from app_config import AppConfigError
    fake = AppConfig()
    fake.active_provider = "nonsense"
    with patch("providers.get_app_config", return_value=fake):
        try:
            make_data_provider(PlatformConfig())
            assert False, "unknown provider must raise"
        except AppConfigError as e:
            assert "nonsense" in str(e)


@test("providers: shared behavior is defined ONCE on the base class")
def _():
    # The pre-refactor bug this guards against: Oanda's provider was a
    # copy-paste of the TD one, so shared-logic fixes had to be made
    # twice. Every shared method must now BE the base implementation.
    for meth in ("fetch_ohlcv", "fetch_range", "_validate_and_clean",
                 "_is_data_fresh", "_has_latest_closed_bar",
                 "_drop_forming_candle", "clear_cache", "get_pair_info"):
        for cls in PROVIDER_REGISTRY.values():
            assert getattr(cls, meth) is getattr(BaseDataProvider, meth), \
                f"{cls.__name__} overrides shared method {meth}"
    assert TwelveDataProvider.CANDLE_SECONDS is BaseDataProvider.CANDLE_SECONDS
    assert _POanda.BARS_PER_DAY is BaseDataProvider.BARS_PER_DAY
    assert TiingoDataProvider.CANDLE_SECONDS is BaseDataProvider.CANDLE_SECONDS


@test("providers: back-compat shims re-export the real classes")
def _():
    import data_provider as dp_shim
    import oanda_provider as oa_shim
    assert dp_shim.ForexDataProvider is TwelveDataProvider
    assert dp_shim.make_data_provider is make_data_provider
    assert oa_shim.OandaDataProvider is _POanda
    from providers.base import DataIntegrityError as base_die
    assert dp_shim.DataIntegrityError is base_die
    # Provider-specific errors are catchable as the shared type.
    assert issubclass(oa_shim.OandaDataError, base_die)


@test("providers: fetch_range paginates, dedupes, and trims to the window")
def _():
    class FakeProvider(BaseDataProvider):
        name = "fake"

        def __init__(self, config):
            super().__init__(config)
            idx = pd.date_range("2026-06-01", "2026-06-30 23:00",
                                freq="1h", tz="UTC")
            v = np.linspace(3000.0, 3100.0, len(idx))
            self.series = pd.DataFrame(
                {"Open": v, "High": v + 1, "Low": v - 1,
                 "Close": v, "Volume": 100.0}, index=idx)
            self.series.index.name = "Datetime"
            self.calls = 0

        def _fetch_raw(self, symbol, timeframe, outputsize,
                       start=None, end=None):
            self.calls += 1
            df = self.series
            if start is not None:
                df = df[df.index >= pd.Timestamp(start)]
            if end is not None:
                df = df[df.index <= pd.Timestamp(end)]
            # Overlap by a bar to prove concat dedupes.
            return df.tail(min(len(df), 200))

        # Keep the test date-stable: nothing here is "forming".
        def _drop_forming_candle(self, df, timeframe):
            return df

    fp = FakeProvider(PlatformConfig())
    start = datetime(2026, 6, 5, tzinfo=timezone.utc)
    end = datetime(2026, 6, 25, tzinfo=timezone.utc)
    df = fp.fetch_range("XAU/USD", Timeframe.H1, start, end)

    assert fp.calls > 1, "a 20-day H1 window must take multiple chunks"
    assert df.index.is_monotonic_increasing
    assert not df.index.duplicated().any()
    assert df.index[0] >= pd.Timestamp(start)
    assert df.index[-1] <= pd.Timestamp(end)
    expected = fp.series[(fp.series.index >= pd.Timestamp(start))
                         & (fp.series.index <= pd.Timestamp(end))]
    assert len(df) == len(expected), \
        f"window must be gapless: {len(df)} vs {len(expected)}"


@test("providers: streaming capability flags and base refusal")
def _():
    assert TwelveDataProvider.supports_streaming is True
    assert _POanda.supports_streaming is False
    assert TiingoDataProvider.supports_streaming is False

    class Minimal(BaseDataProvider):
        name = "minimal"

        def _fetch_raw(self, symbol, timeframe, outputsize,
                       start=None, end=None):
            raise NotImplementedError

    m = Minimal(PlatformConfig())
    assert m.supports_streaming is False
    try:
        m.iter_ticks(["XAU/USD"])
        assert False, "base iter_ticks must refuse"
    except NotImplementedError:
        pass


@test("providers: TD WS price-message parsing")
def _():
    parse = TwelveDataProvider._parse_ws_price
    assert parse('{"event":"price","symbol":"XAU/USD","price":"3345.5"}') \
        == ("XAU/USD", 3345.5)
    assert parse('{"event":"subscribe-status","fails":[]}') is None
    assert parse('{"event":"heartbeat"}') is None
    assert parse('not json{{') is None
    assert parse('{"event":"price","symbol":"XAU/USD"}') is None  # no price
    assert parse('{"event":"price","symbol":"XAU/USD","price":"abc"}') is None


@test("providers: Oanda date-window fetch omits count and sends from/to")
def _():
    captured = {}

    def mock_get(self, url, params=None, timeout=None):
        captured["params"] = params
        return type("R", (), {
            "status_code": 200,
            "json": lambda self: {"candles": [{
                "complete": True,
                "time": "2026-06-02T10:00:00.000000000Z",
                "mid": {"o": "3000", "h": "3001", "l": "2999", "c": "3000.5"},
                "bid": {"c": "3000.2"}, "ask": {"c": "3000.8"},
                "volume": 10,
            }]},
        })()

    provider = _POanda.__new__(_POanda)
    provider.config = PlatformConfig()
    provider._base_url = "https://test"
    provider._spread_cache = {}
    provider._session = type("S", (), {"get": mock_get})()

    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
    end = datetime(2026, 6, 3, tzinfo=timezone.utc)
    df = provider._fetch_raw("XAU/USD", Timeframe.H1, 5000, start, end)
    assert "count" not in captured["params"], \
        "v20 rejects count combined with from/to"
    assert captured["params"]["from"].startswith("2026-06-01")
    assert captured["params"]["to"].startswith("2026-06-03")
    assert len(df) == 1

    # Live path (no window) keeps sending count only.
    provider._fetch_raw("XAU/USD", Timeframe.H1, 300)
    assert captured["params"]["count"] == 300
    assert "from" not in captured["params"]


def _tiingo_provider(response):
    """TiingoDataProvider with a canned session response (no network)."""
    provider = TiingoDataProvider.__new__(TiingoDataProvider)
    provider.config = PlatformConfig()
    provider._api_key = "test-key"
    provider._spread_cache = {}
    captured = {}

    def mock_get(self, url, params=None, timeout=None):
        captured["url"] = url
        captured["params"] = params
        return response

    provider._session = type("S", (), {"get": mock_get})()
    return provider, captured


def _tiingo_resp(status_code, payload):
    return type("R", (), {
        "status_code": status_code,
        "json": lambda self: payload,
        "text": json.dumps(payload) if not isinstance(payload, str) else payload,
    })()


@test("providers: Tiingo parses FX JSON into the normalized frame")
def _():
    payload = [
        {"date": "2026-07-20T11:00:00.000Z", "ticker": "xauusd",
         "open": 2403.0, "high": 2408.0, "low": 2401.0, "close": 2406.0},
        # Deliberately out of order — provider must sort oldest-first.
        {"date": "2026-07-20T10:00:00.000Z", "ticker": "xauusd",
         "open": 2400.0, "high": 2405.0, "low": 2398.0, "close": 2403.0},
    ]
    provider, captured = _tiingo_provider(_tiingo_resp(200, payload))
    df = provider._fetch_raw("XAU/USD", Timeframe.H1, 300)

    assert list(df.columns) == ["Open", "High", "Low", "Close", "Volume"]
    assert df.index.name == "Datetime"
    assert df.index.tz is not None, "Index must be tz-aware UTC"
    assert df.index.is_monotonic_increasing, "must be oldest-first"
    assert df.iloc[0]["Open"] == 2400.0 and df.iloc[1]["Close"] == 2406.0
    assert (df["Volume"] == 0.0).all(), "FX endpoint has no volume"
    assert captured["url"].endswith("/tiingo/fx/xauusd/prices")
    assert captured["params"]["resampleFreq"] == "1hour"
    assert "startDate" in captured["params"], \
        "live fetch must derive a start window from the bar count"
    assert "token" not in captured["params"], \
        "key must travel in the auth header, never the URL"


@test("providers: Tiingo date-window fetch sends startDate AND endDate")
def _():
    payload = [{"date": "2026-06-02T10:00:00.000Z",
                "open": 1, "high": 2, "low": 0, "close": 1}]
    provider, captured = _tiingo_provider(_tiingo_resp(200, payload))
    start = datetime(2026, 6, 1, tzinfo=timezone.utc)
    end = datetime(2026, 6, 3, tzinfo=timezone.utc)
    provider._fetch_raw("XAU/USD", Timeframe.H1, 5000, start, end)
    assert captured["params"]["startDate"].startswith("2026-06-01")
    assert captured["params"]["endDate"].startswith("2026-06-03")


@test("providers: Tiingo fails LOUDLY on every bad-response shape")
def _():
    cases = [
        (_tiingo_resp(429, "quota"), "rate limit"),
        (_tiingo_resp(401, "denied"), "auth failed"),
        (_tiingo_resp(500, "boom"), "HTTP 500"),
        (_tiingo_resp(200, []), "Empty candle response"),
        (_tiingo_resp(200, [{"open": 1}]), "missing 'date'"),
        (_tiingo_resp(200, [{"date": "2026-07-20T10:00:00Z",
                             "close": "n/a"}]), "no numeric closes"),
    ]
    for resp, needle in cases:
        provider, _cap = _tiingo_provider(resp)
        try:
            provider._fetch_raw("XAU/USD", Timeframe.H1, 300)
            assert False, f"must raise for: {needle}"
        except TiingoDataError as e:
            assert needle.lower() in str(e).lower(), \
                f"error must name the cause ({needle}): {e}"

    # Missing key and unsupported timeframe are named errors too.
    provider, _cap = _tiingo_provider(_tiingo_resp(200, []))
    provider._api_key = None
    try:
        provider._fetch_raw("XAU/USD", Timeframe.H1, 300)
        assert False, "missing key must raise"
    except TiingoDataError as e:
        assert "TIINGO_API_KEY" in str(e)
    provider._api_key = "k"
    try:
        provider._fetch_raw("XAU/USD", Timeframe.W1, 300)
        assert False, "unsupported TF must raise"
    except TiingoDataError as e:
        assert "Unsupported timeframe" in str(e)
    # And everything above is catchable as the shared base error.
    assert issubclass(TiingoDataError, DataIntegrityError)


@test("providers: per-provider env keys (TIINGO_API_KEY vs TWELVE_DATA_API_KEY)")
def _():
    from app_config import AppConfig, provider_api_key
    app = AppConfig()
    app.active_provider = "tiingo"
    app.provider_settings = {"tiingo": {"api_key": "json-tiingo"},
                             "twelve_data": {"api_key": "json-td"}}
    with patch.dict(os.environ, {"TIINGO_API_KEY": "env-tiingo",
                                 "TWELVE_DATA_API_KEY": "env-td"}):
        assert provider_api_key(app) == "env-tiingo"
        assert provider_api_key(app, provider="twelve_data") == "env-td"
    with patch.dict(os.environ, {}, clear=False):
        os.environ.pop("TIINGO_API_KEY", None)
        assert provider_api_key(app) == "json-tiingo"
        # The TD env var must never leak into another provider's key.
        with patch.dict(os.environ, {"TWELVE_DATA_API_KEY": "env-td"}):
            assert provider_api_key(app, provider="tiingo") == "json-tiingo"


@test("providers: aggregator bar-close clock uses the base-class table")
def _():
    # signal_aggregator computes bar-CLOSE hours (session filter, SELL
    # hour gate) from BaseDataProvider.CANDLE_SECONDS — a provider swap
    # must never change the session clock.
    import signal_aggregator as sa
    assert sa.BaseDataProvider.CANDLE_SECONDS[Timeframe.M15] == 900
    assert not hasattr(sa, "ForexDataProvider"), \
        "aggregator must not depend on a concrete provider class"


# ══════════════════════════════════════════════════════════════════════
#  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)