179 lines
7.5 KiB
Python
179 lines
7.5 KiB
Python
#!/usr/bin/env python3
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"""Regenerate data/model.srf from the Plan Disclosure Booklet glidepath table.
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The weights are transcribed by machine rather than by hand: 21 projected eras of
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five to eight funds each is far too much to retype reliably.
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Usage:
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curl -sLo .tmp/booklet.html \
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https://marcom.vestwell.com/program-description/oregon-college.html
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python3 tools/gen_model.py .tmp/booklet.html > data/model.srf
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"""
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import html
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import re
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import sys
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DASH = "[-\u2010\u2011\u2012\u2013\u2014\u2015\u2212]"
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FUNDS = ["VSMPX", "VTPSX", "VIPIX", "VBMPX", "VTIFX", "VBIPX", "VTSPX", "VUSXX"]
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def parse_glidepath(raw):
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txt = re.sub(r"\s+", " ", html.unescape(re.sub(r"<[^>]+>", " ", raw)))
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i = txt.find(" ".join(FUNDS))
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if i < 0:
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sys.exit("glidepath table header not found; booklet layout changed")
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rows = re.findall(
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r"(20\d\d)((?:\s+(?:[\d.]+%|" + DASH + r")){8})", txt[i : i + 2600]
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)
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table = {}
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for year, cells in rows:
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vals = [
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0.0 if re.fullmatch(DASH, c) else float(c.rstrip("%"))
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for c in re.split(r"\s+", cells.strip())
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]
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table[int(year)] = vals
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if 2042 not in table or len(table) < 20:
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sys.exit(f"glidepath table parsed but looks wrong: {sorted(table)}")
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for year, vals in table.items():
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if abs(sum(vals) - 100.0) > 0.005:
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sys.exit(f"enrollment-year {year} weights sum to {sum(vals)}, not 100")
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return table
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def num(v):
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return f"{v:g}"
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def rows_for(symbol, era_start, basis, pairs):
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return [
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f"symbol::{symbol},era_start::{era_start},basis::{basis},"
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f"ticker::{t},weight:num:{num(w)}"
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for t, w in pairs
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if w > 0
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]
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def main():
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table = parse_glidepath(open(sys.argv[1], encoding="utf-8", errors="replace").read())
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out = []
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out.append(HEADER.rstrip("\n"))
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out.append("")
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out.append(ORCBI_NOTE.rstrip("\n"))
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out += rows_for(
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"ORCBI", "2018-09-01", "fitted",
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[("VSMPX", 36), ("VTPSX", 24), ("VBMPX", 40)],
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)
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out.append("")
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out.append(ORC42_NOTE.rstrip("\n"))
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out += rows_for(
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"ORC42", "2023-07-01", "fitted",
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[("VSMPX", 54), ("VTPSX", 36), ("VIPIX", 1.67), ("VBMPX", 6.66), ("VTIFX", 1.67)],
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)
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out.append("")
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out.append(
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"# Era 2 is the booklet current 2042 row. Validated: the reconstruction\n"
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"# reproduces all nine ORC42 anchors to within 0.06% using it."
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)
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out += rows_for(
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"ORC42", "2026-01-01", "fitted",
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[("VSMPX", 52.8), ("VTPSX", 35.2), ("VIPIX", 2), ("VBMPX", 8), ("VTIFX", 2)],
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)
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# ORC42 in calendar year Y holds what enrollment-year row E = 4068 - Y holds
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# in the 2026-07-01 cross-section. Y=2026 -> E=2042 reproduces the fitted
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# current era, which is the one point where the mapping can be checked.
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for year in range(2027, 2048):
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enrollment = 4068 - year
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weights = table.get(enrollment)
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if weights is None:
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continue
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out.append("")
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out.append(
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f"# Projected: calendar {year} takes the booklet enrollment-year "
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f"{enrollment} row."
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)
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out += rows_for(
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"ORC42", f"{year}-01-01", "projected", list(zip(FUNDS, weights))
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)
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sys.stdout.write("\n".join(out) + "\n")
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HEADER = """\
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#!srfv1
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# Underlying-fund weights for each plan portfolio, by era.
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#
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# GENERATED by tools/gen_model.py from the Plan Disclosure Booklet. Regenerate
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# rather than hand-editing the projected rows; 21 eras is too many to retype
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# reliably. Hand edits to the `fitted` rows are fine, but keep them in sync.
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#
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# These portfolios are unitized fund-of-funds trusts, not mutual funds. The Plan
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# Disclosure Booklet is explicit that they "reflect changes in value from income
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# and gains and losses on the sale of the Underlying Funds solely by increasing
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# or decreasing their Unit Value" -- all income compounds into the unit value and
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# nothing is distributed. That is why the reconstruction uses each underlying
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# fund's dividend-adjusted (total-return) close, not its raw NAV.
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#
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# Source of the weights: the Plan Disclosure Booklet allocation tables at
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# https://marcom.vestwell.com/program-description/oregon-college.html
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# Corroborated for the current era by two live JSON endpoints that agree exactly:
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# https://vss-api.vestwell.com/plans/oregon-college/portfolios (array form)
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# https://vss-api.vestwell.com/plans/oregon-college (object form)
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#
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# `era_start` is inclusive: a weight row applies to every trading day >= its
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# era_start, until superseded by a later era for the same symbol.
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#
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# `basis` records how much a row is worth trusting:
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# fitted In effect now or in the past, and validated -- the reconstruction
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# reproduces every observed anchor to within 0.08% using it.
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# projected Not yet in effect. Read off the booklet forward table, so it is
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# the plan's published intent, NOT an observed fact. The booklet
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# reserves the right to "change the asset allocations ... and change
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# the selection of Underlying Funds", so confirm before relying on
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# one. `verify` flags any projected era that has become current.
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#
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# NOTE ON FEES: no fee or drag term appears here, and none is needed. The
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# reconstruction pins the series at both ends of every anchor gap and distributes
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# the residual geometrically, which absorbs the asset-based fee, cash drag,
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# trade-date lag and securities-lending income together. `verify` reports the
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# drag each gap implies, as a cross-check against the booklet's published cost
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# table ($23.40 per $10,000/yr = 0.234% for ORCBI, $23.91 = 0.239% for ORC42)."""
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ORCBI_NOTE = """\
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# ORCBI -- "Balanced Index", a STATIC portfolio. The booklet: "Static Portfolio
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# investments remain fixed, subject to periodic rebalancing". No glidepath, so
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# one era covers its whole life. These weights change only by Board action."""
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ORC42_NOTE = """\
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# ORC42 -- "College Enrollment Year 2042", a GLIDEPATH portfolio.
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#
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# The booklet publishes a full cross-section of every enrollment-year portfolio
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# as of 2026-07-01. Because all of them follow one glidepath keyed on
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# years-to-enrollment, that cross-section IS ORC42's forward path shifted in
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# time: ORC42 in calendar year Y holds what enrollment-year row E = 4068 - Y
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# holds today. Y=2026 gives E=2042, which matches ORC42's fitted current era, so
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# the mapping checks out against observed data at the one point where it can.
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#
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# Two approximations in the projected rows:
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# 1. The booklet table is annual, but the booklet also says allocations step
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# QUARTERLY. So each projected era is up to three quarters coarse, and the
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# intermediate quarterly steps are not published anywhere.
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# 2. Era boundaries sit on 1 January. The current boundary was fitted to
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# 2026-01-01 by grid search over quarter boundaries; every candidate from
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# 2025-10-01 to 2026-04-01 gives rms <= 0.041%, so the fit cannot resolve
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# it better than that.
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#
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# Neither approximation matters much. Future eras cannot affect a reconstruction
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# that stops at today, and once daily feed values are appended to anchors.srf
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# every gap is one session, where pinning makes the weights literally inert (see
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# the test "weights are inert when anchors are one session apart"). They are
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# prefilled so the model degrades gracefully instead of silently using 2026
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# weights in 2035 if forward recording ever lapses.
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#
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# Era 1 is the booklet's max-equity plateau (its 2043/2044/2045 rows). ORC42
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# launched 19 years out from enrollment, so it sat on the plateau from
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# inception."""
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if __name__ == "__main__":
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main()
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