from __future__ import annotations

import json
from datetime import timedelta

from crypto_spot_bot.models import Signal, utc_now
from crypto_spot_bot.storage import MAX_SIGNAL_DIAGNOSTICS_BYTES, PRUNE_BATCH_SIZE, Storage


def test_hold_sampling_is_independent_for_each_reason_and_diagnostics_are_bounded(tmp_path) -> None:
    storage = Storage(tmp_path / "tradebot.sqlite3")
    diagnostics = {
        "strategy_mode": "torch_forecast",
        "checks": {"model_fresh_ok": False},
        "forecast": {
            "model": "torch_lstm",
            "expected_return_percent": 0.42,
            "model_fresh": False,
            "feature_snapshot": [
                {"name": f"feature-{index}", "interpretation": "x" * 1000}
                for index in range(100)
            ],
        },
    }

    first = Signal("BTCUSDT", "HOLD", 0.2, "entry blocked", diagnostics)
    second = Signal("BTCUSDT", "HOLD", 0.2, "position held", diagnostics)

    assert storage.insert_signal(first, hold_sample_seconds=60) is True
    assert storage.insert_signal(second, hold_sample_seconds=60) is True
    assert storage.insert_signal(first, hold_sample_seconds=60) is False

    rows = storage.recent_signals(10)
    assert len(rows) == 2
    stored = json.loads(rows[0]["diagnostics_json"])
    assert len(rows[0]["diagnostics_json"].encode("utf-8")) <= MAX_SIGNAL_DIAGNOSTICS_BYTES
    assert stored["forecast"]["model"] == "torch_lstm"
    assert "feature_snapshot" not in stored["forecast"]


def test_prune_deletes_only_one_bounded_batch_per_table(tmp_path) -> None:
    storage = Storage(tmp_path / "tradebot.sqlite3")
    old_timestamp = (utc_now() - timedelta(days=90)).isoformat()
    rows = [
        ("BTCUSDT", "HOLD", 0.0, "old", "{}", old_timestamp)
        for _ in range(PRUNE_BATCH_SIZE + 5)
    ]
    with storage.connect() as conn:
        conn.executemany(
            """
            INSERT INTO signals (symbol, action, confidence, reason, diagnostics_json, created_at)
            VALUES (?, ?, ?, ?, ?, ?)
            """,
            rows,
        )

    deleted = storage.prune(30)

    assert deleted["signals"] == PRUNE_BATCH_SIZE
    assert len(storage.recent_signals(PRUNE_BATCH_SIZE + 10)) == 5