gitadmin/foxhunt
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

feat: 60/40 paper-forward harness (the ~0.7-Sharpe non-crypto baseline)

Browse Source 
Local CLI paper-forward test of 60% SPY / 40% IEF (ETF analog of the ES/ZN 60/40 backtest,
+0.72 Sharpe), mirroring the funding harness. Free Yahoo adjusted closes (dividends+coupons =
true total return). Subcommands snapshot/run/status. Books each real trading day once with
catch-up (handles weekends/missed runs); forward-start (no history backfill). Idempotent via
last_date. Daytime cron 12/15/18 UTC. Always-invested baseline -> confirms forward Sharpe
tracks the ~0.7 backtest, the clean no-phantom option available regardless of crypto regime.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-06-07 15:55:44 +02:00
parent 107bcc6648
commit f203998613
1 changed files with 151 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

151
scripts/surfer/sixtyforty_paper.py Normal file
View File

@@ -0,0 +1,151 @@
#!/usr/bin/env python3
"""sixtyforty — local CLI paper-forward test of a simple 60/40 ETF portfolio (the ~0.7-Sharpe
non-crypto baseline). No agents, no cloud, no capital. Free Yahoo daily adjusted closes.
60% SPY (S&P 500) + 40% IEF (7-10yr Treasuries) — the deployable ETF analog of the ES/ZN 60/40
backtest (+0.72 Sharpe). Always invested (no regime filter); the test just confirms the realized
forward Sharpe/return tracks the backtest. Books each real trading day exactly once (catch-up on
weekends / missed runs), using ADJUSTED closes (dividends + coupons = true total return).
python3 sixtyforty_paper.py snapshot latest prices + recent daily portfolio returns
python3 sixtyforty_paper.py run book new trading days, persist (cron-compatible)
python3 sixtyforty_paper.py status cumulative return + annualized Sharpe/vol/maxDD
(alias: 'paper' == 'run')
"""
import datetime
import json
import math
import os
import sys
import urllib.request
_REPO = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
STATE = os.path.join(_REPO, "data/surfer/sixtyforty_state.json")
INSTR = [("SPY", 0.6), ("IEF", 0.4)]
def get(url, tries=4):
import time
for a in range(tries):
try:
req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
return json.loads(urllib.request.urlopen(req, timeout=30).read())
except Exception:
if a == tries - 1:
raise
time.sleep(2 * (a + 1))
def daily_adjclose(sym):
"""{ 'YYYY-MM-DD': adjclose } for ~3 months of trading days (Yahoo, free)."""
u = f"https://query1.finance.yahoo.com/v8/finance/chart/{sym}?interval=1d&range=3mo"
res = get(u)["chart"]["result"][0]
ts = res["timestamp"]
ind = res["indicators"]
adj = ind.get("adjclose", [{}])[0].get("adjclose") or ind["quote"][0]["close"]
out = {}
for t, c in zip(ts, adj):
if c is not None:
d = datetime.datetime.utcfromtimestamp(t).strftime("%Y-%m-%d")
out[d] = float(c)
return out
def load_state():
if os.path.exists(STATE):
return json.load(open(STATE))
return {"last_date": "", "days": 0, "sum_r": 0.0, "sumsq_r": 0.0,
"equity": 1.0, "peak": 1.0, "max_dd": 0.0}
def metrics(st):
n = st["days"]
if n < 2:
return None
mean = st["sum_r"] / n
var = max(st["sumsq_r"] / n - mean * mean, 0.0)
std = math.sqrt(var)
apr = mean * 252
vol = std * math.sqrt(252)
return dict(apr=apr, vol=vol, sharpe=(apr / vol if vol > 0 else float("nan")),
total=st["equity"] - 1.0, maxdd=st["max_dd"])
def series():
data = {s: daily_adjclose(s) for s, _ in INSTR}
dates = sorted(set.intersection(*[set(d) for d in data.values()]))
return data, dates
def cmd_run():
st = load_state()
data, dates = series()
if len(dates) < 2:
print("not enough data"); return
os.makedirs(os.path.dirname(STATE), exist_ok=True)
if not st["last_date"]: # fresh: start forward from latest close, no backfill
st["last_date"] = dates[-1]
json.dump(st, open(STATE, "w"))
print(f"initialized forward tracking from {dates[-1]}; first booked return on the next trading day.")
return
booked = 0
for i in range(1, len(dates)):
d, dprev = dates[i], dates[i - 1]
if d <= st["last_date"]:
continue
r = sum(w * (data[s][d] / data[s][dprev] - 1.0) for s, w in INSTR) # daily-rebalanced port return
st["days"] += 1
st["sum_r"] += r
st["sumsq_r"] += r * r
st["equity"] *= (1 + r)
st["peak"] = max(st["peak"], st["equity"])
st["max_dd"] = min(st["max_dd"], st["equity"] / st["peak"] - 1.0)
st["last_date"] = d
booked += 1
os.makedirs(os.path.dirname(STATE), exist_ok=True)
json.dump(st, open(STATE, "w"))
m = metrics(st)
tail = f" | Sharpe {m['sharpe']:+.2f} APR {100*m['apr']:+.1f}% maxDD {100*m['maxdd']:+.1f}%" if m else ""
print(f"{datetime.date.today()} booked {booked} new trading day(s) -> day {st['days']} "
f"(through {st['last_date']}); total {100*(st['equity']-1):+.2f}%{tail}")
def cmd_status():
st = load_state()
m = metrics(st)
print(f"60/40 (SPY 60% / IEF 40%) paper — day {st['days']} (through {st['last_date'] or 'n/a'})")
print(f" total return: {100*(st['equity']-1):+.2f}%")
if m:
print(f" annualized: Sharpe {m['sharpe']:+.2f} return {100*m['apr']:+.1f}% vol {100*m['vol']:.1f}% maxDD {100*m['maxdd']:+.1f}%")
print(f" backtest reference: ~0.72 Sharpe (ES/ZN 60/40, 2010-2026)")
else:
print(" (need >=2 booked trading days for annualized stats)")
def cmd_snapshot():
data, dates = series()
last = dates[-1]
print(f"60/40 snapshot {datetime.date.today()} (latest close {last})")
for s, w in INSTR:
print(f" {s} ({int(w*100)}%): {data[s][last]:.2f}")
print(" recent daily portfolio returns:")
for i in range(max(1, len(dates) - 5), len(dates)):
d, dprev = dates[i], dates[i - 1]
r = sum(w * (data[s][d] / data[s][dprev] - 1.0) for s, w in INSTR)
print(f" {d}: {100*r:+.2f}%")
def main():
cmd = sys.argv[1] if len(sys.argv) > 1 else "status"
if cmd in ("run", "paper"):
cmd_run()
elif cmd == "status":
cmd_status()
elif cmd == "snapshot":
cmd_snapshot()
else:
print(__doc__)
if __name__ == "__main__":
main()