stat_pairs_backtest/tests/test_spbt_day.py

import sqlite3

import pandas as pd
import pytest

from scripts.spbt_day import (
    calculate_pair_theo_ret,
    calculate_ranked_pairs_theo_ret,
    load_selector_pair_rankings,
    load_trading_instructions,
    pair_assets_and_quote,
    parse_mr_score_final,
    rank_selector_pairs,
)


@pytest.mark.parametrize(
    ("raw_score", "expected_score", "expected_status"),
    [
        ('{"final":"0.75"}', 0.75, "ok"),
        ('{"final":0.5}', 0.5, "ok"),
        (None, None, "missing_mr_score"),
        ("not-json", None, "malformed_json"),
        ("[]", None, "unexpected_json_type"),
        ('{"other": "0.1"}', None, "missing_final"),
        ('{"final": ""}', None, "missing_final"),
        ('{"final": true}', None, "non_numeric_final"),
        ('{"final": "abc"}', None, "non_numeric_final"),
        ('{"final": "NaN"}', None, "non_finite_final"),
    ],
)
def test_parse_mr_score_final(raw_score, expected_score, expected_status):
    assert parse_mr_score_final(raw_score) == (expected_score, expected_status)


def test_rank_selector_pairs_uses_dense_descending_rank_and_preserves_bad_rows():
    selector_pairs = pd.DataFrame(
        {
            "pair_name": ["PAIR_C", "PAIR_A", "PAIR_B", "PAIR_BAD"],
            "mr_score": [
                '{"final":"0.7"}',
                '{"final":"0.9"}',
                '{"final":"0.7"}',
                '{"final":"bad"}',
            ],
        }
    )

    ranked = rank_selector_pairs(selector_pairs)

    assert ranked["pair_name"].tolist() == ["PAIR_A", "PAIR_B", "PAIR_C", "PAIR_BAD"]
    assert ranked["pair_rank"].iloc[:3].tolist() == [1, 2, 2]
    assert pd.isna(ranked["pair_rank"].iloc[3])
    assert ranked["mr_score_parse_status"].tolist() == [
        "ok",
        "ok",
        "ok",
        "non_numeric_final",
    ]


def test_load_selector_pair_rankings_validates_required_table():
    conn = sqlite3.connect(":memory:")

    with pytest.raises(ValueError, match="missing required table: selector_pairs"):
        load_selector_pair_rankings(conn)


def test_load_selector_pair_rankings_reads_sqlite_table():
    conn = sqlite3.connect(":memory:")
    conn.execute("CREATE TABLE selector_pairs (pair_name TEXT, mr_score TEXT)")
    conn.executemany(
        "INSERT INTO selector_pairs (pair_name, mr_score) VALUES (?, ?)",
        [
            ("PAIR_A", '{"final":"0.1"}'),
            ("PAIR_B", '{"final":"0.2"}'),
        ],
    )

    ranked = load_selector_pair_rankings(conn)

    assert ranked[["pair_rank", "pair_name", "mr_score_final"]].to_dict("records") == [
        {"pair_rank": 1, "pair_name": "PAIR_B", "mr_score_final": 0.2},
        {"pair_rank": 2, "pair_name": "PAIR_A", "mr_score_final": 0.1},
    ]


def test_pair_assets_and_quote_parses_two_leg_pair():
    assert pair_assets_and_quote("ADA:USD-BTC:USD") == (("ADA", "BTC"), "USD")


def test_calculate_pair_theo_ret_replaces_targets_and_closes_open_position():
    trd_inst_df = pd.DataFrame(
        {
            "time_ns": [1, 2, 3],
            "tstamp": ["t1", "t2", "t3"],
            "data": [
                (
                    '{"action":"TARGET","quote_asset":"USD","assets":'
                    '{"AAA":{"reference_price":"100","strength":"0.01"},'
                    '"BBB":{"reference_price":"50","strength":"-0.02"}}}'
                ),
                (
                    '{"action":"TARGET","quote_asset":"USD","assets":'
                    '{"AAA":{"reference_price":"120","strength":"0.01"},'
                    '"BBB":{"reference_price":"60","strength":"-0.02"}}}'
                ),
                (
                    '{"action":"CLOSE","quote_asset":"USD","assets":'
                    '{"AAA":{"reference_price":"132"},'
                    '"BBB":{"reference_price":"54"}}}'
                ),
            ],
        }
    )

    theo_ret = calculate_pair_theo_ret("AAA:USD-BBB:USD", trd_inst_df)

    assert theo_ret == {
        "pair_name": "AAA:USD-BBB:USD",
        "realized_pnl": pytest.approx(24.0),
        "unrealized_pnl": 0.0,
    }


def test_calculate_pair_theo_ret_ignores_unmatched_quote_and_close_without_target():
    trd_inst_df = pd.DataFrame(
        {
            "time_ns": [1, 2],
            "data": [
                (
                    '{"action":"TARGET","quote_asset":"EUR","assets":'
                    '{"AAA":{"reference_price":"100","strength":"0.01"},'
                    '"BBB":{"reference_price":"50","strength":"-0.02"}}}'
                ),
                (
                    '{"action":"CLOSE","quote_asset":"USD","assets":'
                    '{"AAA":{"reference_price":"110"},'
                    '"BBB":{"reference_price":"45"}}}'
                ),
            ],
        }
    )

    assert calculate_pair_theo_ret("AAA:USD-BBB:USD", trd_inst_df) == {
        "pair_name": "AAA:USD-BBB:USD",
        "realized_pnl": 0.0,
        "unrealized_pnl": 0.0,
    }


def test_calculate_ranked_pairs_theo_ret_preserves_pairs_without_instructions():
    rankings = pd.DataFrame(
        {
            "pair_name": ["AAA:USD-BBB:USD", "CCC:USD-DDD:USD"],
            "pair_rank": pd.Series([1, 2], dtype="Int64"),
        }
    )
    trd_inst_df = pd.DataFrame(
        {
            "time_ns": [1, 2],
            "data": [
                (
                    '{"action":"TARGET","quote_asset":"USD","assets":'
                    '{"AAA":{"reference_price":"100","strength":"0.01"},'
                    '"BBB":{"reference_price":"50","strength":"-0.02"}}}'
                ),
                (
                    '{"action":"CLOSE","quote_asset":"USD","assets":'
                    '{"AAA":{"reference_price":"110"},'
                    '"BBB":{"reference_price":"45"}}}'
                ),
            ],
        }
    )

    result = calculate_ranked_pairs_theo_ret(rankings, trd_inst_df)

    assert result.to_dict("records") == [
        {
            "pair_name": "AAA:USD-BBB:USD",
            "mr_ranking": 1,
            "realized_pnl": 20.0,
            "unrealized_pnl": 0.0,
        },
        {
            "pair_name": "CCC:USD-DDD:USD",
            "mr_ranking": 2,
            "realized_pnl": 0.0,
            "unrealized_pnl": 0.0,
        },
    ]


def test_load_trading_instructions_validates_required_table():
    conn = sqlite3.connect(":memory:")

    with pytest.raises(ValueError, match="missing required table: trading_instructions"):
        load_trading_instructions(conn)