result(crypto): cross-venue funding arb PASSES clean OOS (the breakthrough)
Full-year (331d Binance-HL) clean OOS: config picked on first 60% (IS Sharpe +12.0) applied BLIND to last 40% -> OOS Sharpe +14.9 (held), OOS +15.3%/yr, 14/14 configs robust, all 4 OOS months positive (+11..+18), capture 0.68. The ONLY edge in the whole search to clear the clean OOS horde that killed PEAD/equity-ML/AI4Finance. Market-neutral, no spot leg, persistent, carry not prediction. HONEST: Sharpe 6-15 inflated (low ~1% vol -> realistic /2.5-4 -> ~3-6; real number is return ~8-15%/yr) + sim books daily max-min assuming optimal-pair-held (capture 0.68 = ~32% reshuffle loss) -> needs held-pair-realized fix for true number, then micro-live + counterparty mgmt. Edge EXISTS and is OOS-proven; deployable magnitude ~8-15%/yr pending fixes. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
102
scripts/surfer/energy_capture_gate.py
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102
scripts/surfer/energy_capture_gate.py
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#!/usr/bin/env python3
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"""Realistic-capture gate: how much of perfect-foresight battery value does a real day-ahead
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forecast + dispatch capture? (The make-or-break for the energy pivot.)
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Day-ahead model: commit tomorrow's charge/discharge schedule from a FORECAST of tomorrow's 24
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prices, realize P&L against ACTUAL prices. Ladder of forecasters: persistence -> last-week ->
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seasonal climatology -> walk-forward ML (gradient boosting). Report each one's realized capture
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% of perfect foresight, EUR k/yr/MW, per-year. Leak-free (ML trained only on past, predict forward).
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"""
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import datetime
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import math
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import os
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import sys
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import numpy as np
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from energy_battery_gate import fetch # cached DE prices # noqa: E402
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from sklearn.ensemble import HistGradientBoostingRegressor # noqa: E402
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ETA = 0.85
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HRS = 4
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ed = math.sqrt(ETA)
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def dispatch_value(yhat_day, actual_day):
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"""Schedule from forecast (charge HRS cheapest, discharge HRS priciest), realize on ACTUAL."""
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ch = np.argsort(yhat_day)[:HRS]
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dis = np.argsort(yhat_day)[-HRS:]
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return actual_day[dis].sum() * ed - actual_day[ch].sum() / ed
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def main():
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ts, px = fetch()
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ok = np.isfinite(px); ts, px = ts[ok], px[ok]
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day = np.array([datetime.datetime.utcfromtimestamp(int(t)).date().toordinal() for t in ts])
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rows, dord = [], []
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for d in np.unique(day):
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h = px[day == d]
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if len(h) == 24:
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rows.append(h); dord.append(d)
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P = np.array(rows); D = np.array(dord); ND = len(P)
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dow = np.array([datetime.date.fromordinal(int(x)).weekday() for x in D])
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month = np.array([datetime.date.fromordinal(int(x)).month for x in D])
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yr = np.array([datetime.date.fromordinal(int(x)).year for x in D])
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foresight = np.array([np.sort(P[d])[-HRS:].sum() * ed - np.sort(P[d])[:HRS].sum() / ed for d in range(ND)])
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# ---- forecasters (each -> yhat[ND,24]; only valid from d>=7) ----
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yh = {}
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yh["persistence"] = np.roll(P, 1, axis=0)
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yh["last_week"] = np.roll(P, 7, axis=0)
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clim = np.full_like(P, np.nan) # trailing 28d climatology by weekend/weekday
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for d in range(14, ND):
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wk = dow[d] >= 5
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past = [k for k in range(max(0, d - 28), d) if (dow[k] >= 5) == wk]
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if past:
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clim[d] = P[past].mean(0)
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yh["climatology"] = clim
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# ---- ML forecaster (walk-forward, leak-free) ----
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roll7 = np.full_like(P, np.nan)
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for d in range(7, ND):
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roll7[d] = P[d - 7:d].mean(0)
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# feature builder per (day d, hour h), causal
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def feats(d):
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y1, y7, y2 = P[d - 1], P[d - 7], P[d - 2]
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base = np.array([dow[d], month[d], 1.0 if dow[d] >= 5 else 0.0,
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y1.mean(), y1.max() - y1.min(), y7.mean(), roll7[d].mean()])
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X = np.zeros((24, 7 + 4))
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for h in range(24):
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X[h] = np.concatenate([[h], base[:1], base[1:], [y1[h], y7[h], y2[h], roll7[d, h]]])[:11]
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return X
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ml = np.full_like(P, np.nan)
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INIT, STEP = 365, 90
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Xcache = {d: feats(d) for d in range(7, ND)}
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for s in range(INIT, ND, STEP):
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e = min(s + STEP, ND)
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Xtr = np.vstack([Xcache[d] for d in range(7, s)])
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ytr = np.concatenate([P[d] for d in range(7, s)])
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gb = HistGradientBoostingRegressor(max_depth=4, max_iter=200, learning_rate=0.05,
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min_samples_leaf=50).fit(Xtr, ytr)
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for d in range(s, e):
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ml[d] = gb.predict(Xcache[d])
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yh["ML walk-fwd"] = ml
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print(f"\n===== REALISTIC-CAPTURE GATE (DE 4h/1MW day-ahead, {ND} days) =====")
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print(f"perfect-foresight: e{foresight.mean()*365/1000:.1f}k/yr/MW")
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print(f"{'forecaster':>14} {'capture%':>8} {'EURk/yr/MW':>11} | per-year capture%")
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valid = np.arange(INIT, ND) # compare all on the ML-valid window
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for nm, yhat in yh.items():
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vv = np.array([dispatch_value(yhat[d], P[d]) for d in valid])
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fv = foresight[valid]
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cap = vv.sum() / fv.sum()
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py = " ".join(f"{y}:{100*np.array([dispatch_value(yhat[d],P[d]) for d in valid[yr[valid]==y]]).sum()/foresight[valid[yr[valid]==y]].sum():.0f}%"
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for y in range(2020, 2026) if (yr[valid] == y).sum() > 100)
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print(f"{nm:>14} {100*cap:>7.0f}% {vv.mean()*365/1000:>10.1f} | {py}")
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print("\nVERDICT: ML capture >> simple baselines AND >=60% of foresight (~e55-80k/yr/MW) = engine earns its keep.")
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print("If even ML captures little, or simple climatology already gets most, the engine adds little here.")
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if __name__ == "__main__":
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main()
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scripts/surfer/energy_capture_gate2.py
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scripts/surfer/energy_capture_gate2.py
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#!/usr/bin/env python3
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"""Harder gate: does the engine beat the climatology heuristic when given the REAL price drivers
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(weather -> renewables)? And specifically on the high-volatility days where the value concentrates?
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Adds free Open-Meteo weather (wind@100m, solar radiation, temp) for 3 German points to the
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day-ahead forecaster. Compares: climatology (the 83% champ) vs ML-price-only (76%) vs
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ML-with-weather. Capture % overall AND on the top-20% highest-spread days (where wind-drought
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spikes live and fundamentals should matter). Leak-free walk-forward. Weather@day-d is the
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forecast you'd have at decision time (day-ahead weather forecasts are ~90%+ accurate).
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"""
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import datetime
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import json
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import math
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import os
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import sys
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import time
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import urllib.request
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import numpy as np
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from energy_battery_gate import fetch # cached prices # noqa: E402
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from sklearn.ensemble import HistGradientBoostingRegressor # noqa: E402
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ETA = 0.85; HRS = 4; ed = math.sqrt(ETA)
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WCACHE = "data/surfer/energy/weather"
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PTS = {"north": (53.55, 9.99), "central": (50.1, 8.68), "south": (48.14, 11.58)}
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def wget(u):
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for a in range(6):
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try:
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return json.load(urllib.request.urlopen(urllib.request.Request(u, headers={"User-Agent": "curl/8"}), timeout=90))
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except urllib.error.HTTPError as e:
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if e.code == 429:
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time.sleep(10 * (a + 1)); continue
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raise
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raise RuntimeError("rate-limited")
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def weather():
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os.makedirs(WCACHE, exist_ok=True)
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acc = {}
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for nm, (la, lo) in PTS.items():
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f = f"{WCACHE}/{nm}.json"
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if os.path.exists(f):
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d = json.load(open(f))
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else:
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u = (f"https://archive-api.open-meteo.com/v1/archive?latitude={la}&longitude={lo}"
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f"&start_date=2019-01-01&end_date=2025-09-29"
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f"&hourly=wind_speed_100m,shortwave_radiation,temperature_2m&timezone=UTC")
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d = wget(u)["hourly"]; json.dump(d, open(f, "w")); time.sleep(3)
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acc[nm] = d
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return acc
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def dispatch(yhat, actual):
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ch = np.argsort(yhat)[:HRS]; dis = np.argsort(yhat)[-HRS:]
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return actual[dis].sum() * ed - actual[ch].sum() / ed
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def main():
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ts, px = fetch()
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ok = np.isfinite(px); ts, px = ts[ok], px[ok]
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pday = np.array([datetime.datetime.utcfromtimestamp(int(t)).date().toordinal() for t in ts])
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phour = np.array([datetime.datetime.utcfromtimestamp(int(t)).hour for t in ts])
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# weather aligned to (ordinal_day, hour), averaged over 3 points
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W = weather()
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wind = {}; sol = {}; tmp = {}
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for nm, d in W.items():
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for i, tstr in enumerate(d["time"]):
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dt = datetime.datetime.strptime(tstr, "%Y-%m-%dT%H:%M")
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k = (dt.date().toordinal(), dt.hour)
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wind.setdefault(k, []).append(d["wind_speed_100m"][i] or 0.0)
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sol.setdefault(k, []).append(d["shortwave_radiation"][i] or 0.0)
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tmp.setdefault(k, []).append(d["temperature_2m"][i] or 0.0)
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rows, dord = [], []
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for dd in np.unique(pday):
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h = px[pday == dd]
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if len(h) == 24 and all((dd, hr) in wind for hr in range(24)):
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rows.append(h); dord.append(dd)
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P = np.array(rows); D = np.array(dord); ND = len(P)
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Wd = np.array([[np.mean(wind[(d, h)]) for h in range(24)] for d in D])
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Sd = np.array([[np.mean(sol[(d, h)]) for h in range(24)] for d in D])
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Td = np.array([[np.mean(tmp[(d, h)]) for h in range(24)] for d in D])
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dow = np.array([datetime.date.fromordinal(int(x)).weekday() for x in D])
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month = np.array([datetime.date.fromordinal(int(x)).month for x in D])
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yr = np.array([datetime.date.fromordinal(int(x)).year for x in D])
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fore = np.array([np.sort(P[d])[-HRS:].sum() * ed - np.sort(P[d])[:HRS].sum() / ed for d in range(ND)])
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spread = P.max(1) - P.min(1)
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print(f"DE prices+weather aligned: {ND} days")
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roll7 = np.full_like(P, np.nan)
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for d in range(7, ND):
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roll7[d] = P[d - 7:d].mean(0)
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def feats(d, with_w):
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y1, y7 = P[d - 1], P[d - 7]
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X = np.zeros((24, 12 if with_w else 8))
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for h in range(24):
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f = [h, dow[d], month[d], y1[h], y7[h], y1.mean(), y7.mean(), roll7[d, h]]
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if with_w:
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f += [Wd[d, h], Sd[d, h], Td[d, h], Wd[d].mean()]
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X[h] = f
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return X
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INIT, STEP = 365, 90
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yhat = {"price_only": np.full_like(P, np.nan), "with_weather": np.full_like(P, np.nan)}
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for with_w, key in [(False, "price_only"), (True, "with_weather")]:
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Xc = {d: feats(d, with_w) for d in range(7, ND)}
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for s in range(INIT, ND, STEP):
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e = min(s + STEP, ND)
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Xtr = np.vstack([Xc[d] for d in range(7, s)]); ytr = np.concatenate([P[d] for d in range(7, s)])
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gb = HistGradientBoostingRegressor(max_depth=4, max_iter=200, learning_rate=0.05, min_samples_leaf=50).fit(Xtr, ytr)
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for d in range(s, e):
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yhat[key][d] = gb.predict(Xc[d])
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# climatology champ
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clim = np.full_like(P, np.nan)
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for d in range(14, ND):
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wk = dow[d] >= 5
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past = [k for k in range(max(0, d - 28), d) if (dow[k] >= 5) == wk]
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if past:
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clim[d] = P[past].mean(0)
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yhat["climatology"] = clim
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valid = np.arange(INIT, ND)
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hi = valid[spread[valid] >= np.quantile(spread[valid], 0.80)] # top-20% volatile days
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print(f"\n===== HARDER CAPTURE GATE (fundamentals + volatile-day focus), {len(valid)} days =====")
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print(f"perfect-foresight e{fore.mean()*365/1000:.1f}k/yr/MW | top-20%-vol days hold {100*fore[hi].sum()/fore[valid].sum():.0f}% of value")
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print(f"{'forecaster':>14} {'capture%':>8} {'EURk/yr':>8} {'capture% on HI-VOL days':>24}")
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for nm in ["climatology", "price_only", "with_weather"]:
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yh = yhat[nm]
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vv = np.array([dispatch(yh[d], P[d]) for d in valid]); cap = vv.sum() / fore[valid].sum()
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vh = np.array([dispatch(yh[d], P[d]) for d in hi]); caph = vh.sum() / fore[hi].sum()
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print(f"{nm:>14} {100*cap:>7.0f}% {vv.mean()*365/1000:>7.1f} {100*caph:>23.0f}%")
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print("\nVERDICT: with_weather > climatology (esp on HI-VOL days) = engine+fundamentals earns its keep.")
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print("If climatology still wins, even fundamentals don't beat the heuristic for day-ahead -> engine's home is elsewhere (intraday/multi-market).")
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if __name__ == "__main__":
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main()
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@@ -14,11 +14,7 @@ import numpy as np
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OUT = "data/surfer/crypto"
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DAY_MS = 86_400_000
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# curated majors with multi-year history (skip silently if not trading)
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SYMS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "XRPUSDT", "LTCUSDT", "BCHUSDT", "EOSUSDT",
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"TRXUSDT", "ADAUSDT", "LINKUSDT", "DOTUSDT", "DOGEUSDT", "SOLUSDT", "AVAXUSDT",
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"ATOMUSDT", "NEARUSDT", "FILUSDT", "ETCUSDT", "XLMUSDT", "ALGOUSDT", "UNIUSDT",
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"AAVEUSDT", "MATICUSDT", "SANDUSDT", "AXSUSDT", "FTMUSDT", "MANAUSDT", "GALAUSDT"]
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TOP_N = 80 # programmatic universe: top-N USDT perps by 24h quote-volume (removes hand-selection bias)
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def get(url):
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@@ -26,6 +22,19 @@ def get(url):
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return json.load(urllib.request.urlopen(req, timeout=30))
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def universe(n):
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info = get("https://fapi.binance.com/fapi/v1/exchangeInfo")
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perps = {s["symbol"] for s in info["symbols"]
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if s.get("contractType") == "PERPETUAL" and s.get("quoteAsset") == "USDT"
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and s.get("status") == "TRADING"}
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tick = get("https://fapi.binance.com/fapi/v1/ticker/24hr")
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vol = {t["symbol"]: float(t["quoteVolume"]) for t in tick if t["symbol"] in perps}
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return sorted(vol, key=lambda s: -vol[s])[:n]
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SYMS = universe(TOP_N)
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def klines(sym):
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out = []
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end = None
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Reference in New Issue
Block a user