import datetime, math
from puga.history import empirical_touch, _trailing_zscores


def _series(jump_idx, n=20, jump_price=220.0, base_price=100.0):
    base = datetime.date(2026, 1, 1)
    return [(base + datetime.timedelta(days=i), jump_price if i >= jump_idx else base_price) for i in range(n)]


def test_hit_factor_has_no_cliff_at_the_50pct_touched_threshold():
    """median_days flips from a number to None the instant touched_frac crosses 50% - that's
    fine as a reported diagnostic, but ranking by it (as sell.py's --tiers used to) creates a
    fake cliff: a tier touched 47% of the time would be valued at exactly 0, one touched 53% of
    the time at near-full value, even though hit_factor (the real E[e^-rho*tau]) moves smoothly
    between them. This is what tools/sell.py's --tiers ranks by instead."""
    y = math.log(2.0)
    below = empirical_touch(_series(jump_idx=9), y, rate=0.05, condition_on_state=False)
    above = empirical_touch(_series(jump_idx=10), y, rate=0.05, condition_on_state=False)
    assert below["median_days"] is None and above["median_days"] is not None
    assert below["touched_frac"] < 0.5 <= above["touched_frac"]
    # hit_factor must not have fallen off a cliff just because median_days did
    assert below["hit_factor"] > 0.5 * above["hit_factor"]


def test_empirical_touch_trivial_for_zero_move():
    r = empirical_touch(_series(jump_idx=5), 0.0, rate=0.05)
    assert r == {"hit_factor": 1.0, "touched_frac": 1.0, "median_days": 0.0, "n": 20}


def test_conditioning_on_state_uses_only_similar_regime_days():
    """Mean reversion means the answer should depend on whether today looks like the ticker's
    readily-rising early regime or its since-flattened plateau - not blend both in unconditionally.
    Series: 20 flat days, then a steady climb, then 10 more flat days at the new high (today is
    in the plateau, not the climb)."""
    base = datetime.date(2026, 1, 1)
    prices = [100] * 20 + [100 + i * 5 for i in range(1, 11)] + [150] * 10
    series = [(base + datetime.timedelta(days=i), float(p)) for i, p in enumerate(prices)]
    y = math.log(1.1)
    uncond = empirical_touch(series, y, rate=0.05, condition_on_state=False)
    cond = empirical_touch(series, y, rate=0.05, condition_on_state=True)
    assert uncond["touched_frac"] > 0.5          # blends in the climb regime, looks like it moves readily
    assert cond["touched_frac"] < uncond["touched_frac"]   # today's plateau regime moves less


def test_trailing_zscores_flat_window_is_zero_not_none():
    logp = [math.log(100.0)] * 15
    z = _trailing_zscores(logp, trail=60, min_window=10)
    assert z[:9] == [None] * 9        # not enough window yet
    assert z[9:] == [0.0] * 6         # flat price is exactly at its own mean - a real, defined state


def test_horizon_removes_recency_bias_from_right_censoring():
    """Without a fixed horizon, start days near the end of history get an unfairly short (or
    long) remaining window to register a touch, purely from running out of data. A flat series
    with a late jump looks like it "touches" readily only because the jump happens to sit right
    next to the end-of-history start days; a fixed horizon gives every sample the same window."""
    base = datetime.date(2026, 1, 1)
    prices = [100.0] * 30 + [220.0] * 5
    series = [(base + datetime.timedelta(days=i), p) for i, p in enumerate(prices)]
    y = math.log(2.0)
    uncapped = empirical_touch(series, y, rate=0.05, condition_on_state=False)
    capped = empirical_touch(series, y, rate=0.05, condition_on_state=False, horizon=5)
    assert capped["touched_frac"] < uncapped["touched_frac"]
    assert capped["n"] < uncapped["n"]   # the last `horizon` days of start days are dropped


def test_empirical_touch_never_reached_is_zero_not_excluded():
    # a move far beyond anything the series ever does should look like "basically never"
    r = empirical_touch(_series(jump_idx=19, jump_price=101.0), math.log(50.0), rate=0.05)
    assert r["touched_frac"] == 0.0
    assert r["hit_factor"] == 0.0
    assert r["median_days"] is None