from __future__ import annotations import polars as pl import pytest from microstructure.config import ExecutionConfig from microstructure.execution import simulate_predictions def execution_config(**overrides: object) -> ExecutionConfig: values: dict[str, object] = { "decision_latency_events": 0, "order_latency_events": 0, "maker_fee_bps": 10.0, "taker_fee_bps": 10.0, "half_spread_bps": 1.0, "slippage_bps_per_unit": 0.0, "signal_threshold": 0.6, "max_position_units": 10.0, "order_size_units": 1.0, "limit_fill_base_probability": 1.0, "queue_ahead_units": 0.0, "limit_max_age_events": 10, "cancel_latency_events": 1, "liquidate_at_end": False, "capacity_multipliers": (1.0,), } values.update(overrides) return ExecutionConfig(**values) # type: ignore[arg-type] def event_frame(rows: list[dict[str, object]]) -> pl.DataFrame: defaults: dict[str, object] = { "symbol": "BTCUSDT", "bid_depth_1": 100.0, "ask_depth_1": 100.0, "trade_side": 0, "trade_quantity": 0.0, "trade_price": 101.0, } return pl.DataFrame([{**defaults, **row} for row in rows]) def test_market_round_trip_accounts_for_taker_fees() -> None: events = event_frame( [ {"sample_id": 0, "event_ts_ns": 0, "best_bid": 100.0, "best_ask": 102.0}, {"sample_id": 1, "event_ts_ns": 1, "best_bid": 103.0, "best_ask": 105.0}, ] ) predictions = pl.DataFrame( { "sample_id": [0, 1], "symbol": ["BTCUSDT", "BTCUSDT"], "probability_up": [0.9, 0.1], "is_oos": [True, True], "split": ["test", "test"], } ) result = simulate_predictions(events, predictions, execution_config()) assert result.metrics["gross_pnl"] == pytest.approx(1.0) assert result.metrics["total_fees"] == pytest.approx(0.205) assert result.metrics["net_pnl"] == pytest.approx(0.795) assert result.metrics["turnover_notional"] == pytest.approx(205.0) assert result.metrics["maximum_drawdown"] == pytest.approx(1.102) assert "net_equity" in result.positions.columns def test_maximum_drawdown_marks_inventory_on_intervening_events_without_fills() -> None: events = event_frame( [ {"sample_id": 0, "event_ts_ns": 0, "best_bid": 99.0, "best_ask": 101.0}, {"sample_id": 1, "event_ts_ns": 1, "best_bid": 49.0, "best_ask": 51.0}, {"sample_id": 2, "event_ts_ns": 2, "best_bid": 100.0, "best_ask": 102.0}, ] ) predictions = pl.DataFrame( { "sample_id": [0, 2], "symbol": ["BTCUSDT", "BTCUSDT"], "probability_up": [0.9, 0.1], "is_oos": [True, True], "split": ["test", "test"], } ) result = simulate_predictions(events, predictions, execution_config()) assert result.metrics["maximum_drawdown"] == pytest.approx(51.101) def test_market_order_uses_arrival_state_and_top_depth_only() -> None: events = event_frame( [ {"sample_id": 0, "event_ts_ns": 0, "best_bid": 99.0, "best_ask": 101.0}, { "sample_id": 1, "event_ts_ns": 1, "best_bid": 109.0, "best_ask": 111.0, "ask_depth_1": 1.5, }, ] ) predictions = pl.DataFrame( { "sample_id": [0], "symbol": ["BTCUSDT"], "probability_up": [0.9], "is_oos": [True], "split": ["test"], } ) config = execution_config(order_latency_events=1, order_size_units=2.0) result = simulate_predictions(events, predictions, config) assert result.fills["price"].to_list() == [111.0] assert result.fills["quantity"].to_list() == [1.5] assert result.orders["status"].to_list() == ["partially_filled_canceled"] def test_limit_queue_proxy_produces_partial_then_complete_fill() -> None: events = event_frame( [ { "sample_id": 0, "event_ts_ns": 0, "best_bid": 100.0, "best_ask": 102.0, "trade_price": 101.0, }, { "sample_id": 1, "event_ts_ns": 1, "best_bid": 100.0, "best_ask": 102.0, "trade_side": -1, "trade_quantity": 3.0, "trade_price": 100.0, }, { "sample_id": 2, "event_ts_ns": 2, "best_bid": 100.0, "best_ask": 102.0, "trade_side": -1, "trade_quantity": 4.0, "trade_price": 100.0, }, { "sample_id": 3, "event_ts_ns": 3, "best_bid": 100.0, "best_ask": 102.0, "trade_side": -1, "trade_quantity": 1.0, "trade_price": 100.0, }, ] ) predictions = pl.DataFrame( { "sample_id": [0], "symbol": ["BTCUSDT"], "probability_up": [0.9], "is_oos": [True], "split": ["test"], } ) config = execution_config(order_size_units=3.0, queue_ahead_units=5.0) result = simulate_predictions(events, predictions, config, order_type="limit", seed=7) assert result.fills["quantity"].to_list() == [2.0, 1.0] assert result.fills["event_id"].to_list() == [2, 3] assert result.orders["status"].to_list() == ["filled"] def test_equal_position_trade_occurs_before_limit_arrival() -> None: events = event_frame( [ {"sample_id": 0, "event_ts_ns": 0, "best_bid": 100.0, "best_ask": 102.0}, { "sample_id": 1, "event_ts_ns": 1, "best_bid": 100.0, "best_ask": 102.0, "trade_side": -1, "trade_quantity": 10.0, "trade_price": 100.0, }, ] ) predictions = pl.DataFrame( { "sample_id": [0], "symbol": ["BTCUSDT"], "probability_up": [0.9], "is_oos": [True], "split": ["test"], } ) config = execution_config(order_latency_events=1) result = simulate_predictions(events, predictions, config, order_type="limit") assert result.fills.is_empty() assert result.orders["status"].to_list() == ["end_of_data"] def test_simulator_rejects_in_sample_predictions() -> None: events = event_frame([{"sample_id": 0, "event_ts_ns": 0, "best_bid": 100.0, "best_ask": 102.0}]) predictions = pl.DataFrame( { "sample_id": [0], "symbol": ["BTCUSDT"], "probability_up": [0.9], "is_oos": [False], "split": ["test"], } ) with pytest.raises(ValueError, match="non-OOS"): simulate_predictions(events, predictions, execution_config()) def test_inventory_cap_and_partial_end_liquidation_are_explicit() -> None: events = event_frame( [ {"sample_id": 0, "event_ts_ns": 0, "best_bid": 99.0, "best_ask": 101.0}, {"sample_id": 1, "event_ts_ns": 1, "best_bid": 99.0, "best_ask": 101.0}, { "sample_id": 2, "event_ts_ns": 2, "best_bid": 100.0, "best_ask": 102.0, "bid_depth_1": 1.0, }, ] ) predictions = pl.DataFrame( { "sample_id": [0, 1, 2], "symbol": ["BTCUSDT"] * 3, "probability_up": [0.9, 0.9, 0.9], "is_oos": [True] * 3, "split": ["test"] * 3, } ) config = execution_config(max_position_units=2.0, liquidate_at_end=True) result = simulate_predictions(events, predictions, config) assert result.orders.filter(pl.col("status") == "rejected").height == 1 assert result.metrics["maximum_absolute_inventory"] == pytest.approx(2.0) assert result.metrics["forced_liquidation_quantity"] == pytest.approx(1.0) assert result.metrics["unliquidated_quantity"] == pytest.approx(1.0) assert result.metrics["gross_pnl"] == pytest.approx(-1.0) def test_execution_requires_explicit_held_out_provenance() -> None: events = event_frame([{"sample_id": 0, "event_ts_ns": 0, "best_bid": 100.0, "best_ask": 102.0}]) missing_oos = pl.DataFrame( { "sample_id": [0], "symbol": ["BTCUSDT"], "probability_up": [0.9], "split": ["test"], } ) validation = missing_oos.with_columns( pl.lit(True).alias("is_oos"), pl.lit("validation").alias("split") ) with pytest.raises(ValueError, match="explicit OOS"): simulate_predictions(events, missing_oos, execution_config()) with pytest.raises(ValueError, match="held-out test"): simulate_predictions(events, validation, execution_config()) def test_invalid_probability_and_negative_latency_or_markout_fail_closed() -> None: events = event_frame([{"sample_id": 0, "event_ts_ns": 0, "best_bid": 100.0, "best_ask": 102.0}]) predictions = pl.DataFrame( { "sample_id": [0], "symbol": ["BTCUSDT"], "probability_up": [float("nan")], "is_oos": [True], "split": ["test"], } ) with pytest.raises(ValueError, match="finite"): simulate_predictions(events, predictions, execution_config()) valid = predictions.with_columns(pl.lit(0.9).alias("probability_up")) with pytest.raises(ValueError, match="latency"): simulate_predictions(events, valid, execution_config(order_latency_events=-1)) with pytest.raises(ValueError, match="markout"): simulate_predictions(events, valid, execution_config(), markout_events=-1) with pytest.raises(ValueError, match="queue_ahead_units"): simulate_predictions( events, valid, execution_config(queue_ahead_units=-1.0), order_type="limit", ) def test_one_print_cannot_fill_multiple_passive_orders_beyond_its_volume() -> None: events = event_frame( [ {"sample_id": 0, "event_ts_ns": 0, "best_bid": 100.0, "best_ask": 102.0}, { "sample_id": 1, "event_ts_ns": 1, "best_bid": 100.0, "best_ask": 102.0, "trade_side": -1, "trade_quantity": 1.0, "trade_price": 100.0, }, ] ) predictions = pl.DataFrame( { "sample_id": [0, 0], "symbol": ["BTCUSDT", "BTCUSDT"], "probability_up": [0.9, 0.9], "is_oos": [True, True], "split": ["test", "test"], } ) result = simulate_predictions( events, predictions, execution_config(), order_type="limit", seed=5 ) assert result.fills["quantity"].sum() == pytest.approx(1.0) assert result.metrics["filled_quantity"] == pytest.approx(1.0) def test_orders_and_markouts_do_not_cross_continuity_gaps() -> None: events = event_frame( [ { "sample_id": 0, "event_ts_ns": 0, "continuity_id": "A", "best_bid": 100.0, "best_ask": 102.0, }, { "sample_id": 1, "event_ts_ns": 1, "continuity_id": "B", "best_bid": 101.0, "best_ask": 103.0, "trade_side": -1, "trade_quantity": 10.0, "trade_price": 100.0, }, ] ) predictions = pl.DataFrame( { "sample_id": [0], "symbol": ["BTCUSDT"], "probability_up": [0.9], "is_oos": [True], "split": ["test"], } ) delayed = simulate_predictions( events, predictions, execution_config(order_latency_events=1), order_type="limit", ) immediate = simulate_predictions( events, predictions, execution_config(), order_type="market", markout_events=1 ) assert delayed.fills.is_empty() assert delayed.orders["status"].to_list() == ["canceled_continuity_gap"] assert immediate.fills["markout_available"].to_list() == [False] assert immediate.fills["post_fill_markout_bps"].to_list() == [None]