713424a89d
The multistrat ETF book is an UNDER-levered risk-parity book: it runs at ~5.7% vol vs its own 10% target because leverage is capped at 1x (MAXLEV=1.0), leaving return on the table. Add a return-defined observe-only shadow (cf. bybit_4edge_levered) that levers the SAME series to the 10% vol target minus the HONEST financing drag on the borrowed leg. - LeveredShadowStrategy (paper_strategies.py): L = min(max_lev, target_vol/vol), floored at 1x; levered_ret = L·r − (L−1)·financing/252. Never executed. - multistrat_levered_nav asset (deps=multistrat_nav) publishes its forward track + basis-matched backtest ref like the base; registered in the nightly job. - multistrat_levered registry entry + SIM_BOOKS (Backtest switcher). - financing_bps=530 GROUNDED in IBKR's real USD schedule (verified 2026-07-18: Fed funds 3.63% + Pro tiered spread → 5.83% <$100k / ~5.3% $100k-1M). - leverage_financing_sensitivity() + a cockpit BAND on the levered book's Backtest view: levered return/vol/Sharpe across 1..6.83% + the break-even (= the book's 7.7% return), so the verdict is never hostage to one rate. Honest finding it surfaces: at any realistic IBKR retail rate (~5-7%) levering this modest-return book LOWERS risk-adjusted return (Sharpe 1.35 -> ~0.9); it only pays at institutional financing (~1-3%). The financing drag is what makes the shadow honest (without it, free leverage would look like 13.5%/Sharpe 1.35). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
101 lines
5.2 KiB
Python
101 lines
5.2 KiB
Python
"""LeveredShadowStrategy — the observe-only levered shadow wrapper (multistrat_levered): levers a base
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recompute-replay strategy's daily returns to the fund vol target (capped, floored at 1x) and charges the
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honest financing drag on the borrowed leg. Return-defined; a pure transform of the base series."""
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from __future__ import annotations
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import math
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from typing import Any
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from fxhnt.application.paper_strategies import (
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LeveredShadowStrategy,
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leverage_financing_sensitivity,
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)
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class _Base:
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"""A stub base strategy returning a fixed (date, ret) series + passing `extra` through unchanged."""
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def __init__(self, rows: list[tuple[str, float]]) -> None:
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self._rows = rows
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def advance(self, last_date: str | None, extra: dict[str, Any]) -> tuple[list[tuple[str, float]], dict[str, Any]]:
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return list(self._rows), extra
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def _ann_vol(rets: list[float]) -> float:
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mean = sum(rets) / len(rets)
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return math.sqrt(sum((r - mean) ** 2 for r in rets) / (len(rets) - 1)) * math.sqrt(252)
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def test_levers_a_low_vol_book_to_target_minus_financing() -> None:
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# A low-vol base (well under the 10% target) LEVERS UP; each day = L·base − (L−1)·financing/252.
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base_rows = [(f"d{i}", r) for i, r in enumerate([0.004, -0.004, 0.006, -0.005, 0.003, -0.004] * 30)]
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s = LeveredShadowStrategy(_Base(base_rows), target_vol=0.10, financing_bps=550.0, max_leverage=2.0)
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rows, extra = s.advance(None, {"carry": 1})
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assert extra == {"carry": 1} # extra passes through untouched
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rets = [r for _, r in base_rows]
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lev = min(2.0, 0.10 / _ann_vol(rets))
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lev = max(1.0, lev)
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assert lev > 1.0 # a low-vol book levers up
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daily_fin = (550.0 / 1e4) / 252.0 * (lev - 1.0)
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for (d0, r0), (d1, r1) in zip(base_rows, rows):
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assert d0 == d1
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assert abs(r1 - (lev * r0 - daily_fin)) < 1e-12
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# financing makes the levered MEAN strictly below the naive L·mean (leverage is not free)
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assert sum(r for _, r in rows) / len(rows) < lev * (sum(rets) / len(rets))
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def test_high_vol_book_is_not_de_levered_below_1x() -> None:
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# A base ALREADY above the vol target: L is floored at 1.0 (a shadow only levers UP) -> no financing, the
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# series is returned UNCHANGED.
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base_rows = [(f"d{i}", r) for i, r in enumerate([0.05, -0.05] * 100)] # ann vol >> 10%
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s = LeveredShadowStrategy(_Base(base_rows), target_vol=0.10, financing_bps=550.0, max_leverage=2.0)
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rows, _ = s.advance(None, {})
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assert rows == base_rows
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def test_leverage_is_capped_at_max_leverage() -> None:
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# A near-zero-vol base would want huge leverage; it is capped at max_leverage.
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base_rows = [(f"d{i}", r) for i, r in enumerate([0.0001, -0.0001] * 100)]
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s = LeveredShadowStrategy(_Base(base_rows), target_vol=0.10, financing_bps=550.0, max_leverage=2.0)
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rows, _ = s.advance(None, {})
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daily_fin = (550.0 / 1e4) / 252.0 * (2.0 - 1.0) # capped L = 2.0
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assert abs(rows[0][1] - (2.0 * base_rows[0][1] - daily_fin)) < 1e-12
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def test_short_series_passes_through_unchanged() -> None:
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# < 2 rows: no vol to compute -> the base series is returned untouched (never a divide-by-zero).
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s = LeveredShadowStrategy(_Base([("d0", 0.01)]), target_vol=0.10, financing_bps=550.0, max_leverage=2.0)
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rows, _ = s.advance(None, {})
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assert rows == [("d0", 0.01)]
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def test_financing_sensitivity_band_break_even_and_current_flag() -> None:
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# A low-vol base: levers up; the band shows the levered outcome at each rate. Break-even = the base book's
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# own ann return (above it, leverage subtracts). The `current_bps` row is flagged and included.
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base = [0.004, -0.004, 0.006, -0.005, 0.003, -0.004] * 30
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b = leverage_financing_sensitivity(base, target_vol=0.10, max_leverage=2.0,
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rates_bps=(100.0, 300.0, 683.0), current_bps=530.0)
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assert b["leverage"] > 1.0
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# break-even equals the base ann return
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assert abs(b["break_even_pct"] - b["base_ann_return_pct"]) < 1e-9
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# current_bps (530) is present + flagged even though not in rates_bps
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cur = [r for r in b["rows"] if r["is_current"]]
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assert len(cur) == 1 and abs(cur[0]["rate_pct"] - 5.30) < 1e-9
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# higher financing -> lower levered return + Sharpe (monotone)
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rets = [r["ann_return_pct"] for r in b["rows"]]
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shs = [r["sharpe"] for r in b["rows"]]
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assert rets == sorted(rets, reverse=True) and shs == sorted(shs, reverse=True)
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# every row shares the SAME levered vol (= L * base vol) — only the financing drag differs
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assert len({round(r["ann_vol_pct"], 6) for r in b["rows"]}) == 1
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def test_financing_sensitivity_marks_rates_above_break_even_as_subtracting() -> None:
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# A base whose ann return is small: a financing rate above it makes leverage SUBTRACT return (adds=False).
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base = [0.0005, -0.0003] * 200 # tiny positive drift -> low break-even
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b = leverage_financing_sensitivity(base, target_vol=0.10, max_leverage=3.0,
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rates_bps=(50.0, 2000.0))
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lo, hi = b["rows"][0], b["rows"][-1]
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assert lo["adds"] is True # 0.5% financing < break-even
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assert hi["adds"] is False # 20% financing >> break-even -> subtracts
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