84c9fcd146
The equal-weight cap+renormalise was algebraically a no-op — n=1 got 100% weight despite a 50% cap. Cap without renormalising: when 1/n > max_strategy_weight the book holds cash rather than over- concentrating; common case (1/n <= cap) is plain equal weight. + tests that exercise the binding cap. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
82 lines
4.6 KiB
Python
82 lines
4.6 KiB
Python
from fxhnt.application.allocation_engine import compute_allocation
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from fxhnt.application.allocation_policy import AllocationPolicy
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def _series(n, ret): # n days of a constant-ish return
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return {f"2026-{(6 + d // 30):02d}-{(d % 30) + 1:02d}": ret for d in range(n)}
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def test_excludes_short_history_and_empty_returns_empty():
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pol = AllocationPolicy(min_history_days=60, marginal_gate=False)
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assert compute_allocation({"short": _series(10, 0.001)}, pol) == {} # < 60 obs → excluded → empty
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def test_equal_weight_and_ex_ante_leverage():
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pol = AllocationPolicy(min_history_days=5, marginal_gate=False, max_leverage=1.5,
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kelly_fraction=0.5, target_vol=0.12, kill_dd=0.99, reenter_dd=0.98)
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# NOTE: a and b use the SAME alternating pattern (not exact complements) — two strategies that are
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# perfect complements make the equal-weight book flat (constant per-day average), i.e. zero full-history
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# vol, which degenerately forces K=0 and defeats the point of this test (a positive, bounded K).
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a = {f"2026-06-{d:02d}": (0.01 if d % 2 else -0.005) for d in range(1, 21)}
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b = {f"2026-06-{d:02d}": (0.01 if d % 3 == 0 else -0.005) for d in range(1, 21)}
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w = compute_allocation({"a": a, "b": b}, pol)
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assert set(w) == {"a", "b"}
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assert abs(w["a"] - w["b"]) < 1e-9 # equal base weights
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# K applied: weights sum to K (the leverage), each = K/2
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k = sum(w.values())
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assert 0 < k <= 1.5
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def test_recent_vol_spike_does_not_change_K_antireactive():
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# THE load-bearing invariant: ex-ante vol uses the FULL history, so appending a recent spike must not
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# change the leverage on the earlier window.
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pol = AllocationPolicy(min_history_days=5, marginal_gate=False, kill_dd=0.99, reenter_dd=0.98)
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base = {f"2026-06-{d:02d}": (0.01 if d % 2 else -0.01) for d in range(1, 61)}
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w_before = compute_allocation({"a": dict(base), "b": dict(base)}, pol)
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spiked = dict(base)
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spiked["2026-06-30"] = 0.5 # a single huge recent day
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w_after = compute_allocation({"a": spiked, "b": dict(base)}, pol)
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# the full-history vol of "a" changes (it must — vol IS a full-history stat), so K adapts SLOWLY over the
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# whole record, NOT reactively to the last window. Assert K moved only via the full-history vol, i.e. the
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# book vol recomputed over ALL 60 days (not a trailing subset):
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from fxhnt.application.allocation_engine import full_history_vol
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from fxhnt.application.promotion_corr import deploy_book_returns
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vol_all = full_history_vol(deploy_book_returns({"a": spiked, "b": dict(base)}))
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k_before, k_after = sum(w_before.values()), sum(w_after.values())
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assert abs(k_after - k_before) > 1e-9 # the spike DOES move full-history vol...
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assert abs(k_after - min(pol.max_leverage, pol.kelly_fraction * pol.target_vol / vol_all)) < 1e-9
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def test_killswitch_flattens():
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pol = AllocationPolicy(min_history_days=5, marginal_gate=False, kill_dd=0.20, reenter_dd=0.10)
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crash = {f"2026-06-{d:02d}": -0.04 for d in range(1, 11)} # deep DD → killswitch
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w = compute_allocation({"a": crash, "b": crash}, pol)
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assert all(v == 0.0 for v in w.values()) # flattened
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def test_per_strategy_cap_binds_and_holds_cash():
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# n=1 with the default 0.5 cap: the sole survivor must be capped at 0.5 (not 1.0) BEFORE leverage —
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# the concentration guardrail fires and the book holds cash.
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pol = AllocationPolicy(min_history_days=5, marginal_gate=False, max_strategy_weight=0.5,
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kill_dd=0.99, reenter_dd=0.98)
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a = {f"2026-06-{d:02d}": (0.01 if d % 2 else -0.005) for d in range(1, 21)}
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w = compute_allocation({"a": a}, pol)
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# base weight is capped at 0.5; target = K * 0.5, so w["a"] / K == 0.5 (cap fired, not 1.0)
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from fxhnt.application.allocation_engine import full_history_vol
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from fxhnt.application.promotion_corr import deploy_book_returns
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vol = full_history_vol(deploy_book_returns({"a": a}))
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k = min(pol.max_leverage, pol.kelly_fraction * pol.target_vol / vol)
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assert abs(w["a"] - k * 0.5) < 1e-9 # capped at 0.5, NOT k*1.0
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def test_cap_does_not_bind_when_many_strategies():
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pol = AllocationPolicy(min_history_days=5, marginal_gate=False, max_strategy_weight=0.5,
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kill_dd=0.99, reenter_dd=0.98)
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strat = {f"s{i}": {f"2026-06-{d:02d}": (0.01 if (d + i) % 2 else -0.005) for d in range(1, 21)}
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for i in range(3)}
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w = compute_allocation(strat, pol)
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# 1/3 < 0.5 → no cap → equal 1/3 base each (× K)
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k = sum(w.values())
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for v in w.values():
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assert abs(v - k / 3) < 1e-9
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