5c90c8f351
modulation.py: VioletSizeModulation wraps BLUE's canonical esof_size_mult_from_score + esof_score_from_payload (exact ESOF_* constants), applies the SC haircut step-for-step as _apply_sc_entry_size_multiplier (nautilus_event_trader.py:3307): mult clamped [0,1] HAIRCUT-ONLY (:3316), near-1 no-op (:3318), round(lev*mult,6)/ round(notional*mult,12). 8 tests pass. Empirical mult-recovery on recorded BLUE: median 1.000, EsoF haircut bands (0.65/0.8/0.9/0.3) visible. NOTE: 28% upward tail (recorded>base) = NEXT parity step (base mid-range param OR gold/gauge up-mult); EsoF is haircut-only by design. Not yet wired into decision_engine (needs EsoF HZ score plane + restart, held). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
98 lines
4.0 KiB
Python
98 lines
4.0 KiB
Python
"""V3.2: EsoF size-modulation — exact replication of BLUE's SC haircut (profuse)."""
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from __future__ import annotations
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import math
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import sys
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sys.path.insert(0, "/mnt/dolphinng5_predict")
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import pytest
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from hypothesis import given, settings, strategies as st
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from prod.clean_arch.violet.alpha_wrappers import SizeDecision, VioletBetSizer
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from prod.clean_arch.violet.modulation import VioletSizeModulation
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MOD = VioletSizeModulation()
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G = MOD._gate
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def _base(vel_div: float = -0.20) -> SizeDecision:
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return VioletBetSizer(base_fraction=0.20, min_leverage=0.5,
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max_leverage=9.0).calculate(capital=69_000.0, vel_div=vel_div)
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# ── band mapping reproduces BLUE exactly (drift-guarded against live constants) ─
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def test_band_values_match_blue_constants():
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assert MOD.mult_for(0.0) == pytest.approx(G.ESOF_NEUTRAL_CORE_MULT) # neutral core
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assert MOD.mult_for(-0.5) == pytest.approx(G.ESOF_UNFAVORABLE_CORE_MULT) # deepest cut
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assert MOD.mult_for(None) == pytest.approx(
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min(1.0, max(0.0, G.ESOF_STALE_FALLBACK_MULT))) # stale fallback
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assert MOD.mult_for(0.5) == pytest.approx(1.0) # full size
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assert MOD.mult_for(-0.15) == pytest.approx(1.0) # mild-neg full
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def test_mult_equals_wrapped_blue_fn_clamped():
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# mult_for == clamp(esof_size_mult_from_score, 0, 1) for a grid of scores.
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for sc in [0.3, 0.05, 0.0, -0.03, -0.07, -0.2, -0.25, -0.3, -1.0, None]:
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expect = max(0.0, min(1.0, float(G.esof_size_mult_from_score(sc))))
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assert MOD.mult_for(sc) == pytest.approx(expect)
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# ── haircut-only [0,1] clamp (BLUE :3316) ──────────────────────────────────────
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@given(score=st.one_of(st.none(),
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st.floats(min_value=-5.0, max_value=5.0, allow_nan=False,
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allow_infinity=False)))
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@settings(max_examples=120, deadline=None)
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def test_mult_always_in_unit_interval(score):
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assert 0.0 <= MOD.mult_for(score) <= 1.0
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def test_nonfinite_score_is_safe():
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for bad in (float("nan"), float("inf"), float("-inf")):
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assert 0.0 <= MOD.mult_for(bad) <= 1.0
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# ── apply: near-1 = no-op; haircut scales with exact rounding (BLUE :3318-3338) ─
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def test_near_one_mult_returns_base_unchanged():
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base = _base()
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mod, mult = MOD.apply(base, 0.5) # full-size band -> mult 1.0
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assert mult >= 0.999
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assert mod is base or mod.conviction_leverage == base.conviction_leverage
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def test_haircut_scales_with_blue_rounding():
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base = _base() # conviction 9.0
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mod, mult = MOD.apply(base, 0.0) # neutral core -> mult 0.8
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assert mult == pytest.approx(0.8)
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assert mod.conviction_leverage == round(base.conviction_leverage * mult, 6)
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assert mod.notional_fraction == round(base.notional_fraction * mult, 12)
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# haircut ⇒ strictly smaller size
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assert mod.conviction_leverage < base.conviction_leverage
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assert mod.notional_fraction < base.notional_fraction
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def test_apply_preserves_fraction_bucket_signal():
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base = _base()
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mod, _ = MOD.apply(base, -0.5) # deep haircut
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assert mod.fraction == base.fraction # only size/leverage scale, not fraction
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assert mod.bucket_idx == base.bucket_idx
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assert mod.signal_bucket == base.signal_bucket
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assert isinstance(mod, SizeDecision)
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# ── property: modulated size never exceeds base (haircut-only) and stays finite ─
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@given(vel_div=st.floats(min_value=-0.5, max_value=-0.021, allow_nan=False),
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score=st.one_of(st.none(), st.floats(min_value=-1.0, max_value=1.0, allow_nan=False)))
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@settings(max_examples=80, deadline=None)
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def test_modulated_never_exceeds_base(vel_div, score):
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base = _base(vel_div)
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mod, mult = MOD.apply(base, score)
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assert mod.conviction_leverage <= base.conviction_leverage + 1e-9
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assert mod.notional_fraction <= base.notional_fraction + 1e-9
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assert math.isfinite(mod.conviction_leverage) and mod.conviction_leverage >= 0.0
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