wehub-resource-sync
250 lines
8.9 KiB
Python
250 lines
8.9 KiB
Python
from __future__ import annotations
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from datetime import datetime
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import polars as pl
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from case_studies.utils.backtest_loaders import get_backtest_config, load_backtest_prices
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from case_studies.utils.backtest_presets import (
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cost_view,
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ensure_backtest_spec,
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is_backtest_spec,
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load_backtest_preset,
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strategy_view,
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)
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from case_studies.utils.backtest_runner import normalize_prediction_columns
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from case_studies.utils.registry.specs import backtest_hash_from_parts
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from case_studies.utils.registry.store import _infer_stage
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def test_etf_backtest_base_preset_exists() -> None:
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preset = load_backtest_preset("etfs")
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assert preset["calendar"]["calendar"] == "NYSE"
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assert preset["commission"]["rate"] == 0.0006
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assert preset["slippage"]["rate"] == 0.0004
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def test_ensure_backtest_spec_builds_composite_spec() -> None:
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bt = get_backtest_config("etfs")
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prices = load_backtest_prices("etfs", max_symbols=2)
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legacy_spec = {
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"chapter": "ch18",
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"signal": {"method": "equal_weight_top_k", "top_k": 10, "long_short": False},
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"execution": {
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"mode": "engine",
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"engine_preset": "realistic",
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"cadence": bt.cadence,
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"fill_timing": bt.execution_delay.upper(),
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},
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"costs": {"commission_bps": 6.0, "slippage_bps": 4.0},
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"allocation": {"method": "risk_parity", "top_k": 10},
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}
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spec = ensure_backtest_spec(
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"etfs",
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bt,
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legacy_spec,
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prices=prices,
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prediction_hash="pred123",
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initial_cash=1_000_000.0,
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)
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assert is_backtest_spec(spec)
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assert spec["strategy"]["signal"]["method"] == "equal_weight_top_k"
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assert spec["strategy"]["allocation"]["method"] == "risk_parity"
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assert spec["strategy"]["rebalance"]["cadence"] == bt.cadence
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assert spec["backtest_config"]["commission"]["rate"] == 0.0006
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assert spec["backtest_config"]["slippage"]["rate"] == 0.0004
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assert spec["backtest_config"]["metadata"]["prediction_hash"] == "pred123"
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assert cost_view(spec) == {"commission_bps": 6.0, "slippage_bps": 4.0}
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def test_backtest_hash_changes_with_resolved_config() -> None:
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bt = get_backtest_config("etfs")
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prices = load_backtest_prices("etfs", max_symbols=2)
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base = {
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"signal": {"method": "equal_weight_top_k", "top_k": 10, "long_short": False},
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"execution": {
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"mode": "engine",
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"engine_preset": "realistic",
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"cadence": bt.cadence,
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"fill_timing": bt.execution_delay.upper(),
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},
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"costs": {"commission_bps": 6.0, "slippage_bps": 4.0},
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}
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cheap = ensure_backtest_spec(
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"etfs",
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bt,
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base,
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prices=prices,
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prediction_hash="pred123",
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initial_cash=1_000_000.0,
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)
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expensive = ensure_backtest_spec(
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"etfs",
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bt,
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{**base, "costs": {"commission_bps": 10.0, "slippage_bps": 4.0}},
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prices=prices,
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prediction_hash="pred123",
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initial_cash=1_000_000.0,
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)
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assert backtest_hash_from_parts("pred123", cheap) != backtest_hash_from_parts(
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"pred123", expensive
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)
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def test_stage_inference_supports_v2_specs() -> None:
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spec = {
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"version": 2,
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"chapter": "ch19",
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"strategy": {
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"signal": {"method": "equal_weight_top_k", "top_k": 10},
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"rebalance": {"mode": "engine", "cadence": "monthly_month_end"},
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"risk": {"name": "trailing", "position_rules": [{"type": "trailing_stop"}]},
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},
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"backtest_config": {"commission": {"rate": 0.0006}, "slippage": {"rate": 0.0004}},
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}
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assert _infer_stage(spec) == "risk_overlay"
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assert strategy_view(spec)["risk"]["name"] == "trailing"
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def test_microstructure_preset_auto_loads_quote_columns() -> None:
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prices = load_backtest_prices("nasdaq100_microstructure", max_symbols=2)
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assert "bid_open" in prices.columns
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assert "ask_open" in prices.columns
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def test_sp500_options_short_only_engine_spec_preserves_explicit_feed() -> None:
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bt = get_backtest_config("sp500_options")
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prices = load_backtest_prices("sp500_options", max_symbols=2)
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legacy_spec = {
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"chapter": "ch16",
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"signal": {
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"method": "score_weighted_top_k",
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"top_k": 10,
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"long_short": False,
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"direction": "short_only",
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},
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"execution": {
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"mode": "engine",
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"engine_preset": "realistic",
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"cadence": bt.cadence,
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"fill_timing": bt.execution_delay.upper(),
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},
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"costs": {"commission_bps": bt.commission_bps, "slippage_bps": bt.slippage_bps},
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}
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spec = ensure_backtest_spec(
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"sp500_options",
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bt,
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legacy_spec,
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prices=prices,
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prediction_hash="pred123",
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initial_cash=1_000_000.0,
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)
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cfg = spec["backtest_config"]
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assert spec["strategy"]["rebalance"]["mode"] == "engine"
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assert cfg["account"]["allow_short_selling"] is True
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assert cfg["execution"]["execution_price"] == "quote_side"
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assert cfg["execution"]["mark_price"] == "price"
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assert cfg["feed"]["price_col"] == "instr_mid"
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assert cfg["feed"]["bid_col"] == "instr_bid"
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assert cfg["feed"]["ask_col"] == "instr_ask"
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def test_ensure_backtest_spec_pins_enforce_sessions_for_cme_calendar() -> None:
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"""CME-calendar specs must set enforce_sessions=True at construction time.
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Regression: without this, ensure_backtest_spec emits a runtime with
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enforce_sessions=False, but _run_engine later mutates it to True, so the
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plan-time and post-engine hashes diverge (verify fires "0/N in_registry").
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BacktestConfig.to_dict() does NOT serialize enforce_sessions; the hash
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picks it up through ``_runtime_backtest_config`` in the spec (canonical_json
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uses default=str on the dataclass repr), so the runtime is the load-bearing
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surface and what we pin here.
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Also pins that the NYSE projection branch does NOT trip the re-serialize
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path — its original ``backtest_config`` dict and metadata are preserved.
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"""
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bt_cme = get_backtest_config("cme_futures")
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prices_cme = load_backtest_prices("cme_futures", max_symbols=2)
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canonical_cme = {
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"version": 2,
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"chapter": "ch18",
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"strategy": {
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"signal": {"method": "equal_weight_top_k", "top_k": 5, "long_short": True},
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"rebalance": {"mode": "engine", "cadence": bt_cme.cadence},
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},
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"backtest_config": {
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"commission": {"rate": 0.0},
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"slippage": {"rate": 0.0},
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"metadata": {"chapter": "ch18", "extra": "preserved"},
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},
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}
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spec_cme = ensure_backtest_spec(
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"cme_futures",
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bt_cme,
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canonical_cme,
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prices=prices_cme,
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prediction_hash="pred_cme",
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initial_cash=1_000_000.0,
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)
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assert spec_cme["_runtime_backtest_config"].enforce_sessions is True
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# Caller-supplied metadata keys must survive the re-serialization.
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assert spec_cme["backtest_config"]["metadata"]["extra"] == "preserved"
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assert spec_cme["backtest_config"]["metadata"]["prediction_hash"] == "pred_cme"
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# NYSE specs (etfs) must NOT trip the projection re-serialize path —
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# the original backtest_config dict should be preserved untouched.
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bt_etf = get_backtest_config("etfs")
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prices_etf = load_backtest_prices("etfs", max_symbols=2)
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canonical_etf = {
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"version": 2,
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"chapter": "ch18",
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"strategy": {
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"signal": {"method": "equal_weight_top_k", "top_k": 10, "long_short": False},
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"rebalance": {"mode": "engine", "cadence": bt_etf.cadence},
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},
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"backtest_config": {
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"commission": {"rate": 0.0006},
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"slippage": {"rate": 0.0004},
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"metadata": {"chapter": "ch18", "extra": "preserved"},
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},
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}
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spec_etf = ensure_backtest_spec(
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"etfs",
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bt_etf,
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canonical_etf,
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prices=prices_etf,
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prediction_hash="pred_etf",
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initial_cash=1_000_000.0,
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)
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assert spec_etf["_runtime_backtest_config"].enforce_sessions is False
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# Original dict preserved (we did not re-serialize) — keys the caller
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# passed in are exactly the keys present.
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assert set(spec_etf["backtest_config"].keys()) == {"commission", "slippage", "metadata"}
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assert spec_etf["backtest_config"]["metadata"]["extra"] == "preserved"
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assert spec_etf["backtest_config"]["metadata"]["prediction_hash"] == "pred_etf"
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def test_normalize_prediction_columns_maps_causal_and_legacy_fields() -> None:
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df = pl.DataFrame(
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{
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"timestamp": [datetime(2024, 1, 2)],
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"symbol": ["AAPL"],
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"fold": [0],
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"actual": [0.01],
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"prediction": [0.02],
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}
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)
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normalized = normalize_prediction_columns(df)
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assert "y_score" in normalized.columns
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assert "y_true" in normalized.columns
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assert "fold_id" in normalized.columns
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assert normalized["y_score"].to_list() == [0.02]
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assert normalized["y_true"].to_list() == [0.01]
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