from __future__ import annotations

from crypto_spot_bot.learning import TradeLearner
from crypto_spot_bot.models import Trade, utc_now
from crypto_spot_bot.storage import Storage


def test_trade_learner_penalizes_losing_symbol_pattern(make_settings, tmp_path) -> None:
    settings = make_settings(tmp_path, learning_min_samples=2)
    storage = Storage(settings.database_path)
    for value in (-0.4, -0.2):
        storage.insert_trade(
            Trade(
                id=None,
                symbol="BTCUSDT",
                side="SELL",
                qty=1,
                entry_price=100,
                exit_price=99,
                net_pnl=value,
                reason="test",
                entry_pattern="пробой вниз",
                entry_confidence=0.7,
                opened_at=utc_now(),
                closed_at=utc_now(),
            )
        )

    learner = TradeLearner(settings, storage)
    learner.refresh()
    adjustment = learner.adjustment_for("BTCUSDT", "пробой вниз")

    assert adjustment.sample_size >= 4
    assert adjustment.confidence_adjustment < 0
    assert "убыточными" in adjustment.reason


def test_trade_learner_builds_adaptive_rules_for_losing_ema_exit(make_settings, tmp_path) -> None:
    settings = make_settings(tmp_path, learning_min_samples=3)
    storage = Storage(settings.database_path)
    for value in (-0.05, -0.04, -0.03):
        storage.insert_trade(
            Trade(
                id=None,
                symbol="BTCUSDT",
                side="SELL",
                qty=1,
                entry_price=100,
                exit_price=99.9,
                net_pnl=value,
                reason="краткосрочный тренд ослаб ниже EMA50",
                entry_pattern="нейтрально",
                entry_confidence=0.7,
                opened_at=utc_now(),
                closed_at=utc_now(),
            )
        )

    learner = TradeLearner(settings, storage)
    state = learner.refresh()
    rules = learner.rules_for("BTCUSDT", "нейтрально")

    assert state.adaptive_rules["risk_mode"] == "defensive"
    assert rules["ema_exit_mode"] == "profit_only"
    assert rules["effective_entry_threshold_adjustment"] > 0
    assert rules["min_hold_seconds"] > settings.min_hold_seconds


def test_trade_learner_enters_capital_protection_and_validates_rules(make_settings, tmp_path) -> None:
    settings = make_settings(tmp_path, learning_min_samples=3, starting_balance_usdt=100, max_total_exposure_usdt=80)
    storage = Storage(settings.database_path)
    for value in (-0.12, -0.10, -0.08):
        storage.insert_trade(
            Trade(
                id=None,
                symbol="ETHUSDT",
                side="SELL",
                qty=1,
                entry_price=100,
                exit_price=99,
                net_pnl=value,
                reason="краткосрочный тренд ослаб ниже EMA50",
                entry_pattern="нейтрально",
                entry_confidence=0.7,
                opened_at=utc_now(),
                closed_at=utc_now(),
            )
        )

    learner = TradeLearner(settings, storage)
    state = learner.refresh()
    rules = state.adaptive_rules

    assert rules["trade_permission"] == "capital_protection"
    assert rules["reduce_exposure"] is True
    assert rules["bad_market_entry_block"] is True
    assert rules["target_total_exposure_usdt"] == 35.0
    assert rules["validation"]["status"] == "accepted"
    assert rules["validation"]["avoided_loss_usdt"] > 0