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feat: add fundamentals weighting backtest research

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dennisthiessen
2026-07-23 15:49:15 +02:00
parent ddc88b130b
commit eae4d34c06
9 changed files with 1831 additions and 56 deletions
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tests/unit/test_fundamentals_derivation.py
+94 -30
View File
@@ -2,7 +2,7 @@
from __future__ import annotations
from dataclasses import dataclass
from dataclasses import dataclass, replace
from datetime import date, datetime, timezone
import pytest
@@ -38,7 +38,9 @@ _ENDS = { # period_end per (fy, quarter index 0..3)
}
def _year(fy, discretes: dict[str, list[float]], instants: dict[str, list] | None = None):
def _year(
fy, discretes: dict[str, list[float]], instants: dict[str, list] | None = None
):
"""Build 4 snapshot rows (Q1,Q2,Q3,FY) with YTD-cumulative flow fields from the
given per-quarter discrete values; instants set as-is per quarter."""
rows = []
@@ -55,22 +57,38 @@ def _year(fy, discretes: dict[str, list[float]], instants: dict[str, list] | Non
def _two_years():
rev25 = [100, 110, 120, 130]
rev26 = [110, 121, 132, 143] # +10% each quarter YoY
rows = _year(2025, {
"revenue": rev25,
"operating_income": [x * 0.2 for x in rev25],
"diluted_eps": [1.0, 1.1, 1.2, 1.3],
"cfo": [x * 0.25 for x in rev25],
"capex": [x * 0.05 for x in rev25],
"depreciation_amortization": [x * 0.05 for x in rev25],
}, instants={"shares_outstanding": [1000, 1000, 1000, 1000], "cash_and_st_investments": [40] * 4, "total_debt": [140] * 4})
rows += _year(2026, {
"revenue": rev26,
"operating_income": [x * 0.2 for x in rev26],
"diluted_eps": [1.1, 1.21, 1.32, 1.43],
"cfo": [x * 0.25 for x in rev26],
"capex": [x * 0.05 for x in rev26],
"depreciation_amortization": [x * 0.05 for x in rev26],
}, instants={"shares_outstanding": [900, 900, 900, 900], "cash_and_st_investments": [50] * 4, "total_debt": [150] * 4})
rows = _year(
2025,
{
"revenue": rev25,
"operating_income": [x * 0.2 for x in rev25],
"diluted_eps": [1.0, 1.1, 1.2, 1.3],
"cfo": [x * 0.25 for x in rev25],
"capex": [x * 0.05 for x in rev25],
"depreciation_amortization": [x * 0.05 for x in rev25],
},
instants={
"shares_outstanding": [1000, 1000, 1000, 1000],
"cash_and_st_investments": [40] * 4,
"total_debt": [140] * 4,
},
)
rows += _year(
2026,
{
"revenue": rev26,
"operating_income": [x * 0.2 for x in rev26],
"diluted_eps": [1.1, 1.21, 1.32, 1.43],
"cfo": [x * 0.25 for x in rev26],
"capex": [x * 0.05 for x in rev26],
"depreciation_amortization": [x * 0.05 for x in rev26],
},
instants={
"shares_outstanding": [900, 900, 900, 900],
"cash_and_st_investments": [50] * 4,
"total_debt": [150] * 4,
},
)
return rows
@@ -97,9 +115,11 @@ def test_net_debt_leverage_and_share_dilution():
# net debt = total_debt - cash = 150 - 50 = 100 (latest instant)
assert d.metrics["net_debt"].value == pytest.approx(100.0)
# EBITDA TTM = TTM operating_income + TTM D&A; net_debt/ebitda
op_ttm = 506 * 0.2 # 101.2
da_ttm = 506 * 0.05 # 25.3
assert d.metrics["net_debt_to_ebitda"].value == pytest.approx(100.0 / (op_ttm + da_ttm), rel=1e-6)
op_ttm = 506 * 0.2 # 101.2
da_ttm = 506 * 0.05 # 25.3
assert d.metrics["net_debt_to_ebitda"].value == pytest.approx(
100.0 / (op_ttm + da_ttm), rel=1e-6
)
# shares 900 vs 1000 a year earlier -> -10% (buyback)
assert d.metrics["share_count_change_yoy"].value == pytest.approx(-10.0, abs=1e-6)
@@ -130,7 +150,9 @@ def test_net_debt_requires_both_components():
r.total_debt = None
d = fd.derive(rows)
assert d.metrics["net_debt"].value is None
assert d.metrics["net_debt_to_ebitda"].value is None # net debt null -> leverage null
assert (
d.metrics["net_debt_to_ebitda"].value is None
) # net debt null -> leverage null
def test_leverage_null_when_ebitda_nonpositive():
@@ -140,15 +162,23 @@ def test_leverage_null_when_ebitda_nonpositive():
r.depreciation_amortization = 1
d = fd.derive(rows)
assert d.metrics["net_debt"].value == pytest.approx(100.0) # net debt still valid
assert d.metrics["net_debt_to_ebitda"].value is None # but leverage nulled
assert d.metrics["net_debt_to_ebitda"].value is None # but leverage nulled
def test_tape_stops_at_a_gap():
rows = [r for r in _two_years() if not (r.fiscal_year == 2026 and r.fiscal_period == "Q1")]
rows = [
r
for r in _two_years()
if not (r.fiscal_year == 2026 and r.fiscal_period == "Q1")
]
d = fd.derive(rows)
hist = d.metrics["operating_margin"].history
# consecutive suffix ending at FY2026: Q2, Q3, FY (not compressed across the Q1 gap)
assert [p.period_end for p in hist] == [date(2026, 3, 31), date(2026, 6, 30), date(2026, 9, 30)]
assert [p.period_end for p in hist] == [
date(2026, 3, 31),
date(2026, 6, 30),
date(2026, 9, 30),
]
def test_yoy_growth_null_when_prior_nonpositive():
@@ -160,14 +190,48 @@ def test_yoy_growth_null_when_prior_nonpositive():
assert d.metrics["eps_growth_yoy"].value is None # loss->profit is not a %
def test_derive_as_of_excludes_future_amendment():
rows = _two_years()
original = next(
row for row in rows if row.fiscal_year == 2026 and row.fiscal_period == "FY"
)
amendment = replace(
original,
accepted_at=datetime(2027, 1, 1, tzinfo=UTC),
revenue=999999,
)
before = fd.derive_as_of([*rows, amendment], datetime(2026, 12, 31, tzinfo=UTC))
after = fd.derive_as_of([*rows, amendment], datetime(2027, 1, 2, tzinfo=UTC))
assert before.metrics["revenue_growth_yoy"].value == pytest.approx(10.0)
assert after.metrics["revenue_growth_yoy"].value != pytest.approx(10.0)
def test_derive_as_of_treats_sqlite_naive_acceptance_as_utc():
rows = _two_years()
rows[0].accepted_at = datetime(2025, 1, 1)
result = fd.derive_as_of(rows, datetime(2027, 1, 1, tzinfo=UTC))
assert result.latest_period_end == date(2026, 9, 30)
def test_amendment_selection_newest_accepted_wins():
rows = _two_years()
# an amendment to FY2026 FY restates revenue YTD higher, accepted later
amended = Snap(2026, "FY", date(2026, 9, 30), date(2026, 11, 1),
datetime(2027, 1, 1, tzinfo=UTC), revenue=999999,
operating_income=100, diluted_eps=1.43, cfo=100, capex=10,
depreciation_amortization=25, shares_outstanding=900,
cash_and_st_investments=50, total_debt=150)
amended = Snap(
2026,
"FY",
date(2026, 9, 30),
date(2026, 11, 1),
datetime(2027, 1, 1, tzinfo=UTC),
revenue=999999,
operating_income=100,
diluted_eps=1.43,
cfo=100,
capex=10,
depreciation_amortization=25,
shares_outstanding=900,
cash_and_st_investments=50,
total_debt=150,
)
d = fd.derive(rows + [amended])
# Q4 revenue discrete now uses the amended YTD(FY)=999999 minus YTD(Q3)=363
# so TTM/growth reflects the amendment, proving newest accepted_at won.
tests/unit/test_fundamentals_research.py
+58
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@@ -0,0 +1,58 @@
from __future__ import annotations
import math
import pytest
from app.services import fundamentals_research as research
def test_favorable_percentiles_are_tie_aware():
ranks = research.favorable_percentiles(
{"a": 3, "b": 3, "c": 3, "d": 3, "e": 3},
higher_is_better=True,
)
assert set(ranks.values()) == {50.0}
def test_lower_is_better_flips_the_rank():
ranks = research.favorable_percentiles(
{"a": 1, "b": 2, "c": 3, "d": 4, "e": 5},
higher_is_better=False,
)
assert ranks["a"] == 100.0
assert ranks["e"] == 0.0
def test_invalid_and_thin_cross_sections_stay_null():
ranks = research.favorable_percentiles(
{"a": 1, "b": 2, "c": math.nan, "d": None, "e": 5},
higher_is_better=True,
)
assert all(value is None for value in ranks.values())
def test_composites_use_equal_subgroup_weighting():
features = {
str(index): {
"operating_margin": index,
"fcf_margin": index,
"net_debt_to_ebitda": 6 - index,
"share_count_change_yoy": 6 - index,
"revenue_growth_yoy": index,
"eps_growth_yoy": index,
}
for index in range(1, 6)
}
scores = research.cross_section_scores(features)
assert scores["5"]["quality"] == 100.0
assert scores["5"]["growth"] == 100.0
assert scores["5"]["balanced"] == 100.0
assert scores["3"]["balanced"] == 50.0
def test_overlay_uses_neutral_missing_score_and_validates_weight():
assert research.overlay_rank(90, None, 0.2) == 82.0
assert research.overlay_rank(90, 100, 0.2) == 92.0
with pytest.raises(ValueError, match="weight"):
research.overlay_rank(90, 50, 1.1)
tests/unit/test_fundamentals_research_runner.py
+81
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@@ -0,0 +1,81 @@
from __future__ import annotations
import zipfile
from scripts import run_fundamentals_research as runner
def _window(name, sharpe, drawdown):
return {
"window": name,
"sharpe": sharpe,
"sharpe_se": 0.2,
"dsr": 0.8,
"cagr_pct": 10.0,
"max_drawdown_pct": drawdown,
"calmar": 1.0,
"trades": 20,
}
def test_arm_matrix_is_bounded_and_pre_registered():
assert runner.N_TRIALS == 13
assert runner.ARMS[0]["id"] == "control_w00"
assert {arm["weight"] for arm in runner.ARMS[1:]} == {0.1, 0.2, 0.3, 0.4}
assert {arm["composite"] for arm in runner.ARMS[1:]} == {
"quality",
"growth",
"balanced",
}
def test_development_grade_does_not_read_test_window():
control = {
"windows": [
_window("train", 1.0, 10.0),
_window("validation", 1.0, 10.0),
_window("test", 9.0, 1.0),
]
}
arm = {
"windows": [
_window("train", 1.1, 10.0),
_window("validation", 1.2, 11.0),
_window("test", -9.0, 90.0),
]
}
assert runner._development_grade(control, arm)["pass"] is True
def test_output_bundle_is_self_contained(tmp_path):
report = {
"generated_at": "2026-07-23T00:00:00Z",
"splits": {"train_end": "2024-01-01", "test_start": "2025-01-01"},
"n_trials": 13,
"warnings": ["survivorship bias"],
"factor_ic": {"full": []},
"arms": [],
"development_selection": None,
"final_check": None,
}
output = tmp_path / "result.json"
runner._write_outputs(report, output, bundle=True)
with zipfile.ZipFile(output.with_suffix(".zip")) as archive:
names = set(archive.namelist())
assert {
"result.json",
"result.md",
"result-arms.csv",
"result-factor-ic.csv",
"result-trades.csv",
} <= names
def test_winner_concentration_exposes_top_five_dependence():
details = [
{"pnl": value, "r": value / 10} for value in (100, 90, 80, 70, 60, -10, -20)
]
result = runner._winner_concentration(details)
assert result["top5_pnl"] == 400
assert result["net_pnl_ex_top5"] == -30
assert result["avg_r_ex_top5"] == -1.5