momentum-system/tests/unit/experiments/test_orderflow_interventions.py

"""Order-flow shelf interventions (docs/research/orderflow_notes.md H1/H2).

H1 trigger_erasure: exit on the first post-fill 1-min close back below the
trigger within a window — the 5-book transitory-impact/recross signature —
else the standing exit stack (baseline). H2 flow_persistence_gate: keep-or-
zero on pre-trigger volume-weighted bar-sign persistence (ongoing-metaorder
signature); fail-open on thin history. Both mirror the regime/dilution
keep-or-zero discipline: no-data can only KEEP, never zero."""

from datetime import date, datetime

import pandas as pd

from research.experiments.replay import iv_flow_persistence, iv_trigger_erasure, Fire

D = date(2026, 6, 10)


def _fire(entry=10.0, stop=9.0, trigger=10.0):
    return Fire(fire_id="f1", symbol="AAA", session_date=D,
                fill_ts=datetime(2026, 6, 10, 13, 36), entry_price=entry,
                stop_price=stop, exit_price=None, baseline_r=None,
                trigger_price=trigger, sim_baseline=True)


def _bars(rows):
    return pd.DataFrame(rows, columns=["open", "high", "low", "close", "volume"])


FID = {"atr_frac": 0.25, "spread_mult": 3, "adverse_ticks": 1}


# ---------------------------------------------------------------- H1: erasure
def test_erasure_exits_at_first_close_below_trigger():
    bars_after = _bars([(10.1, 10.2, 9.85, 9.90, 1000),   # close 9.90 < trigger 10 -> exit here
                        (9.9, 10.5, 9.8, 10.4, 1000)])
    v, d = iv_trigger_erasure(_fire(), bars_after, baseline=0.5,
                              params={"window_bars": 5}, fidelity=FID)
    # exit fill 9.90 - 0.01 adverse; entry fill 10.00 + 0.01; risk 1.0
    assert abs(v - ((9.89 - 10.01) / 1.0)) < 1e-9
    assert d["kind"] == "trigger_erasure" and d["erased"] is True and d["erasure_bar"] == 0


def test_no_erasure_keeps_baseline():
    bars_after = _bars([(10.1, 10.3, 10.05, 10.2, 1000)] * 6)
    v, d = iv_trigger_erasure(_fire(), bars_after, baseline=0.5,
                              params={"window_bars": 5}, fidelity=FID)
    assert v == 0.5 and d["erased"] is False


def test_erasure_outside_window_keeps_baseline():
    rows = [(10.1, 10.3, 10.05, 10.2, 1000)] * 5 + [(10.2, 10.2, 9.5, 9.6, 1000)]
    v, d = iv_trigger_erasure(_fire(), _bars(rows), baseline=0.5,
                              params={"window_bars": 5}, fidelity=FID)
    assert v == 0.5 and d["erased"] is False


# ------------------------------------------------------- H2: flow persistence
def _pre_bars(signs, vol=1000):
    rows = []
    px = 10.0
    for s in signs:
        o = px
        c = px + 0.05 * s
        rows.append((o, max(o, c) + 0.01, min(o, c) - 0.01, c, vol))
        px = c
    return _bars(rows)


def test_persistent_one_sided_flow_keeps_baseline():
    bars = _pre_bars([1] * 15)          # all up bars -> persistence 1.0
    v, d = iv_flow_persistence(bars, entry_idx=15, baseline=0.4,
                               params={"lookback_bars": 15, "min_persistence": 0.30})
    assert v == 0.4 and d["flagged"] is False and d["persistence"] > 0.9


def test_choppy_flow_is_zeroed():
    bars = _pre_bars([1, -1] * 8)       # alternating -> persistence ~0
    v, d = iv_flow_persistence(bars, entry_idx=16, baseline=0.4,
                               params={"lookback_bars": 15, "min_persistence": 0.30})
    assert v == 0.0 and d["flagged"] is True


def test_thin_history_fails_open():
    bars = _pre_bars([1, -1, 1])        # only 3 pre-trigger bars -> no-data KEEP
    v, d = iv_flow_persistence(bars, entry_idx=3, baseline=0.4,
                               params={"lookback_bars": 15, "min_persistence": 0.30})
    assert v == 0.4 and d["flagged"] is False and d.get("no_data") is True


def test_lookback_excludes_entry_bar():
    # 15 up bars then a huge DOWN entry bar at idx 15: excluded -> still persistent
    bars = pd.concat([_pre_bars([1] * 15), _bars([(10.0, 10.0, 8.0, 8.0, 99999)])],
                     ignore_index=True)
    v, d = iv_flow_persistence(bars, entry_idx=15, baseline=0.4,
                               params={"lookback_bars": 15, "min_persistence": 0.30})
    assert d["flagged"] is False and d["persistence"] > 0.9


# --------------------------------------------------------- registration JSONs
def test_orderflow_experiments_declared():
    import json, pathlib
    root = pathlib.Path(__file__).resolve().parents[3] / "experiments"
    h1 = json.loads((root / "orderflow_h1_erasure_v1.json").read_text())
    h2 = json.loads((root / "orderflow_h2_persistence_v1.json").read_text())
    assert h1["intervention"] == {"kind": "trigger_erasure", "window_bars": 5,
                                  "condition": "close_below"}
    assert h2["intervention"] == {"kind": "flow_persistence_gate",
                                  "lookback_bars": 15, "min_persistence": 0.30}
    for j in (h1, h2):
        assert j["bars"] == {"expR_delta_min": 0.05, "min_n": 300}
        assert j["fidelity"] == {"atr_frac": 0.25, "spread_mult": 3, "adverse_ticks": 1}
        assert j["population"] == {"min_entry_gap_pct": 0.10, "min_day_volume": 300000,
                                   "price_min": 2.0, "price_max": 20.0}


# ------------------------------------------------- H4: volume concentration
def test_isolated_spike_trigger_is_zeroed():
    from research.experiments.replay import iv_volume_spike_gate
    rows = [(10, 10.1, 9.9, 10, 100)] * 5 + [(10, 10.5, 10, 10.4, 5000)]  # spike bar
    v, d = iv_volume_spike_gate(_bars(rows), entry_idx=5, baseline=0.4,
                                params={"lookback_bars": 5, "max_concentration": 1.0})
    assert v == 0.0 and d["flagged"] is True and d["concentration"] == 10.0


def test_volume_ramp_trigger_is_kept():
    from research.experiments.replay import iv_volume_spike_gate
    rows = [(10, 10.1, 9.9, 10, v) for v in (500, 800, 1200, 1800, 2500)] + [(10, 10.5, 10, 10.4, 2000)]
    v, d = iv_volume_spike_gate(_bars(rows), entry_idx=5, baseline=0.4,
                                params={"lookback_bars": 5, "max_concentration": 1.0})
    assert v == 0.4 and d["flagged"] is False


def test_spike_gate_thin_history_fails_open():
    from research.experiments.replay import iv_volume_spike_gate
    rows = [(10, 10.1, 9.9, 10, 100), (10, 10.5, 10, 10.4, 5000)]
    v, d = iv_volume_spike_gate(_bars(rows), entry_idx=1, baseline=0.4,
                                params={"lookback_bars": 5, "max_concentration": 1.0})
    assert v == 0.4 and d.get("no_data") is True


# ------------------------------------------------- H3: spread expansion
def _seed_quotes(store, symbol, rows):
    store.con.execute(
        "CREATE TABLE IF NOT EXISTS quote_snapshots (symbol VARCHAR, ts TIMESTAMPTZ DEFAULT now(), "
        "bid_price DOUBLE PRECISION, bid_size BIGINT, ask_price DOUBLE PRECISION, ask_size BIGINT)")
    for ts, bid, ask in rows:
        store.con.execute(
            "INSERT INTO quote_snapshots (symbol, ts, bid_price, bid_size, ask_price, ask_size) "
            "VALUES (?,?,?,?,?,?)", [symbol, ts, bid, 100, ask, 100])


def test_spread_blowout_at_trigger_is_zeroed():
    from datetime import timezone, timedelta
    from storage.event_store import EventStore
    from research.experiments.replay import iv_spread_expansion_gate
    store = EventStore(":memory:")
    fill_naive_et = datetime(2026, 6, 10, 9, 40)          # naive ET fill
    fill_utc = datetime(2026, 6, 10, 13, 40, tzinfo=timezone.utc)
    quotes = [(fill_utc - timedelta(seconds=15 * (i + 2)), 10.00, 10.02) for i in range(8)]
    quotes.append((fill_utc - timedelta(seconds=5), 9.95, 10.15))  # last: spread 10x baseline
    _seed_quotes(store, "AAA", quotes)
    f = _fire()
    f = type(f)(**{**f.__dict__, "fill_ts": fill_naive_et})
    v, d = iv_spread_expansion_gate(store.con, f, baseline=0.4,
                                    params={"baseline_minutes": 10, "max_expansion_ratio": 2.0})
    assert v == 0.0 and d["flagged"] is True and d["expansion_ratio"] > 2.0


def test_spread_normal_at_trigger_is_kept():
    from datetime import timezone, timedelta
    from storage.event_store import EventStore
    from research.experiments.replay import iv_spread_expansion_gate
    store = EventStore(":memory:")
    fill_utc = datetime(2026, 6, 10, 13, 40, tzinfo=timezone.utc)
    quotes = [(fill_utc - timedelta(seconds=15 * (i + 1)), 10.00, 10.02) for i in range(9)]
    _seed_quotes(store, "AAA", quotes)
    f = _fire()
    f = type(f)(**{**f.__dict__, "fill_ts": datetime(2026, 6, 10, 9, 40)})
    v, d = iv_spread_expansion_gate(store.con, f, baseline=0.4,
                                    params={"baseline_minutes": 10, "max_expansion_ratio": 2.0})
    assert v == 0.4 and d["flagged"] is False


def test_no_quote_coverage_is_skipped_not_kept():
    """The dilution-v1 lesson: fires WITHOUT feature coverage must be EXCLUDED
    from n (variant None -> evaluate() skips), never silently counted."""
    from storage.event_store import EventStore
    from research.experiments.replay import iv_spread_expansion_gate
    store = EventStore(":memory:")
    _seed_quotes(store, "OTHER", [])   # table exists, no rows for AAA
    f = _fire()
    f = type(f)(**{**f.__dict__, "fill_ts": datetime(2026, 6, 10, 9, 40)})
    v, d = iv_spread_expansion_gate(store.con, f, baseline=0.4,
                                    params={"baseline_minutes": 10, "max_expansion_ratio": 2.0})
    assert v is None and d.get("skipped") == "no_quote_coverage"