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PythagorasGoal/scripts/research/r0822_hl_exec.py
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"""HL-EXEC (2026-08-22) — AUDIT DI FATTO: le regole VERE di Hyperliquid contro le soglie ASSUNTE.
DOMANDA. Il progetto tiene due edge cross-sectional fuori dal libro **per taglia**:
XS01 "serve ~$20.000" (origine: diario 2026-06-19-hyperliquid-xsec, "rumore arrotondamento" —
una STIMA A OCCHIO, mai calcolata)
XSR01 "diventa reale a ~$5.000" + gate pre-registrato 2026-10-23 con soglia
"haircut di eseguibilita' a $5.000 <= 40%, altrimenti RITIRO"
Entrambe sono calcolate col pavimento **min_order $5**, che e' il minimo di **DERIBIT**.
XS01/XSR01 si eseguirebbero su **HYPERLIQUID**, che ha regole sue. Qui le regole si LEGGONO dal
venue invece di assumerle, e si dice quali conclusioni del progetto cambiano.
NON manda ordini, non tocca il conto: sole letture PUBBLICHE (nessuna chiave, nessuna firma).
GRADO DELLE FONTI (dichiarato per ogni numero, come chiede il brief):
[A] letto dall'API pubblica del venue api.hyperliquid.xyz/info (meta, metaAndAssetCtxs,
userFees, l2Book)
[B] derivato da [A] + verificato empiricamente sui book live
[C] documentazione ufficiale (hyperliquid.gitbook.io)
[D] assunto / ereditato dal progetto
La verifica MAINNET non e' un atto di fede: i mark price dell'API si incrociano con l'ultima
chiusura del feed CERTIFICATO su disco (data/raw/hl_*_1d.parquet). Il testnet non puo' superarlo.
(Il progetto ha gia' pagato il prezzo di un feed testnet creduto vero: e' la causa del reset v2.0.0.)
nice -n 19 timeout 900 uv run python scripts/research/r0822_hl_exec.py
... --no-net usa solo la cache su disco (nessuna chiamata di rete)
... --snaps N quanti snapshot del book L2 (default 3, ~45s di distanza)
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
import urllib.request
from pathlib import Path
import numpy as np
import pandas as pd
PROJECT_ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(PROJECT_ROOT))
sys.path.insert(0, str(PROJECT_ROOT / "scripts" / "research"))
RAW = PROJECT_ROOT / "data" / "raw"
CACHE = Path(os.environ.get("HLEXEC_CACHE", "/tmp/claude-1001/-opt-docker-PythagorasGoal/"
"b6cc75e7-14f8-4c32-bd07-ab8a0d2aaee6/scratchpad/hlexec"))
INFO_URL = "https://api.hyperliquid.xyz/info" # MAINNET (il testnet e' api.hyperliquid-testnet.xyz)
# --- parametri sotto esame ----------------------------------------------------------------------
MIN_ORDER_ASSUNTO = 5.0 # [D] pavimento DERIBIT usato da eval_weights_smallcap e da paper_xsr
MIN_ORDER_HL = 10.0 # [C] "Order must have minimum value of $10." (docs/error-responses)
FEE_LEG_MODELLO = 0.0005 # [D] 0.05%/gamba, config CONGELATA di XSR01
XSR_SOGLIA_HAIRCUT = 0.40 # [D] soglia pre-registrata del gate 2026-10-23
CAPITALI = (600.0, 1000.0, 1500.0, 2000.0, 3000.0, 4000.0, 5000.0, 7500.0,
10000.0, 20000.0, 50000.0)
TICKETS = (50.0, 100.0, 300.0)
ANN = np.sqrt(365.0)
# ==================================================================================================
# 0. RETE — letture pubbliche, con cache su disco (una corsa non deve ri-martellare il venue)
# ==================================================================================================
def _post(payload: dict, timeout: int = 30) -> object:
req = urllib.request.Request(INFO_URL, data=json.dumps(payload).encode(),
headers={"Content-Type": "application/json"})
with urllib.request.urlopen(req, timeout=timeout) as r:
return json.loads(r.read().decode())
def cached(name: str, payload: dict, no_net: bool, ttl_s: float = 6 * 3600) -> object:
CACHE.mkdir(parents=True, exist_ok=True)
f = CACHE / f"{name}.json"
if f.exists() and (no_net or (time.time() - f.stat().st_mtime) < ttl_s):
return json.loads(f.read_text())
if no_net:
raise FileNotFoundError(f"cache assente per {name} e --no-net attivo")
d = _post(payload)
f.write_text(json.dumps(d))
return d
def universo_certificato() -> list[str]:
"""I 51 alt+BTC gia' CERTIFICATI su disco. Non si inventa un universo: si legge quello vero."""
return sorted(p.stem.replace("hl_", "").replace("_1d", "").upper()
for p in RAW.glob("hl_*_1d.parquet"))
# ==================================================================================================
# 1. REGOLE DEL VENUE
# ==================================================================================================
def sig_figs_ok(px_str: str, sz_dec: int, max_dec: int = 6) -> bool:
"""[C] regola tick perp: <=5 cifre significative E <= (MAX_DECIMALS - szDecimals) decimali;
i prezzi INTERI sono sempre ammessi. Qui serve per VERIFICARLA sui book veri -> [B]."""
s = px_str.lstrip("-")
dec = len(s.split(".")[1]) if "." in s else 0
if dec == 0:
return True
digits = s.replace(".", "").lstrip("0")
return len(digits.rstrip("0")) <= 5 and dec <= (max_dec - sz_dec)
def leggi_regole(no_net: bool) -> tuple[pd.DataFrame, dict]:
meta_ctx = cached("metaAndAssetCtxs", {"type": "metaAndAssetCtxs"}, no_net)
fees = cached("userFees", {"type": "userFees",
"user": "0x0000000000000000000000000000000000000001"}, no_net)
uni, ctxs = meta_ctx[0]["universe"], meta_ctx[1]
rows = []
for a, c in zip(uni, ctxs):
mark = float(c["markPx"]) if c.get("markPx") else np.nan
rows.append(dict(sym=a["name"], szDecimals=int(a["szDecimals"]),
maxLeverage=int(a["maxLeverage"]),
delisted=bool(a.get("isDelisted", False)),
markPx=mark, dayNtlVlm=float(c.get("dayNtlVlm") or np.nan),
openInterest=float(c.get("openInterest") or np.nan)))
df = pd.DataFrame(rows).set_index("sym")
df["lot_units"] = 10.0 ** (-df["szDecimals"]) # [A] passo di size in unita'
df["lot_usd"] = df["lot_units"] * df["markPx"] # [B] passo di size in dollari
df["px_decimals_max"] = (6 - df["szDecimals"]).clip(lower=0) # [C]
return df, fees["feeSchedule"]
def verifica_mainnet(df: pd.DataFrame, syms: list[str]) -> pd.DataFrame:
"""Incrocia i mark dell'API con l'ULTIMA chiusura del feed certificato su disco.
Un feed testnet non puo' superare questo controllo: i suoi prezzi sono fantasia."""
out = []
for s in syms:
p = RAW / f"hl_{s.lower()}_1d.parquet"
if not p.exists() or s not in df.index:
continue
d = pd.read_parquet(p, columns=["timestamp", "close"])
last_close = float(d["close"].iloc[-1])
last_ts = pd.Timestamp(int(d["timestamp"].iloc[-1]), unit="ms", tz="UTC")
mk = float(df.loc[s, "markPx"])
out.append(dict(sym=s, disco=last_close, api=mk,
dev_pct=100.0 * (mk - last_close) / last_close,
eta_h=(pd.Timestamp.now("UTC") - last_ts) / pd.Timedelta("1h")))
return pd.DataFrame(out).set_index("sym")
# ==================================================================================================
# 2. BOOK L2 -> spread, profondita', costo di un ticket
# ==================================================================================================
def scarica_book(syms: list[str], snaps: int, no_net: bool, pause: float = 0.25,
gap_s: float = 45.0) -> dict[str, list[dict]]:
books: dict[str, list[dict]] = {s: [] for s in syms}
for k in range(snaps):
if k and not no_net:
time.sleep(gap_s)
for s in syms:
try:
b = cached(f"l2_{s}_{k}", {"type": "l2Book", "coin": s}, no_net)
except Exception as e: # un book mancante e' un dato, non un crash
print(f" [book] {s} snap{k}: {type(e).__name__}")
continue
books[s].append(b)
if not no_net:
time.sleep(pause) # pacing: il rate limit e' PER-IP e condiviso
return books
def costo_ticket(levels: list[dict], mid: float, notional: float, side: str) -> tuple[float, bool]:
"""VWAP di un ordine marketable da `notional` dollari contro il book, in bps DA MID.
Ritorna (bps, book_esaurito). Per ticket piccoli converge al MEZZO SPREAD: e' giusto cosi',
a $14 non si 'cammina' il book, si paga il livello top."""
resid, cost, filled = notional, 0.0, 0.0
for lv in levels:
px, sz = float(lv["px"]), float(lv["sz"])
cap = px * sz
take = min(resid, cap)
cost += take
filled += take / px
resid -= take
if resid <= 1e-9:
break
if filled <= 0:
return float("nan"), True
vwap = cost / filled
bps = (vwap - mid) / mid * 1e4 * (1.0 if side == "buy" else -1.0)
return bps, resid > 1e-6
def misura_liquidita(books: dict[str, list[dict]], meta: pd.DataFrame) -> pd.DataFrame:
rows = []
for s, snaps in books.items():
if not snaps:
continue
rec: dict[str, list[float]] = {"spread": [], "d10": [], "viol": []}
for t in TICKETS:
rec[f"t{int(t)}"] = []
for b in snaps:
bids, asks = b["levels"][0], b["levels"][1]
if not bids or not asks:
continue
bb, ba = float(bids[0]["px"]), float(asks[0]["px"])
mid = 0.5 * (bb + ba)
rec["spread"].append((ba - bb) / mid * 1e4)
near = sum(float(l["px"]) * float(l["sz"]) for l in asks
if (float(l["px"]) - mid) / mid <= 0.0010)
near += sum(float(l["px"]) * float(l["sz"]) for l in bids
if (mid - float(l["px"])) / mid <= 0.0010)
rec["d10"].append(near)
sd = int(meta.loc[s, "szDecimals"]) if s in meta.index else 0
rec["viol"].append(sum(0 if sig_figs_ok(l["px"], sd) else 1 for l in bids + asks))
for t in TICKETS:
bb_, _ = costo_ticket(asks, mid, t, "buy")
sb_, _ = costo_ticket(bids, mid, t, "sell")
if np.isfinite(bb_) and np.isfinite(sb_):
rec[f"t{int(t)}"].append(0.5 * (bb_ + sb_))
if not rec["spread"]:
continue
row = dict(sym=s, n_snap=len(rec["spread"]),
spread_bps=float(np.median(rec["spread"])),
half_spread_bps=float(np.median(rec["spread"])) / 2.0,
depth10bps_usd=float(np.median(rec["d10"])),
tick_viol=int(sum(rec["viol"])))
for t in TICKETS:
v = rec[f"t{int(t)}"]
row[f"slip{int(t)}_bps"] = float(np.median(v)) if v else np.nan
rows.append(row)
return pd.DataFrame(rows).set_index("sym").sort_values("spread_bps")
def min_notional_osservato(books: dict[str, list[dict]]) -> dict:
"""Controllo EMPIRICO del pavimento $10: i livelli con n==1 sono UN ordine solo.
Un ordine singolo sotto $10 non falsifica la regola (i fill parziali erodono un resto),
ma il PAVIMENTO della distribuzione dice dove il venue taglia."""
vals = []
for snaps in books.values():
for b in snaps:
for side in b["levels"]:
for l in side:
if int(l.get("n", 0)) == 1:
vals.append(float(l["px"]) * float(l["sz"]))
v = np.array(vals, float)
if not len(v):
return {}
return dict(n=len(v), minimo=float(v.min()), p01=float(np.percentile(v, 1)),
p05=float(np.percentile(v, 5)), mediana=float(np.median(v)),
sotto10_pct=100.0 * float((v < 10.0).mean()),
sotto5_pct=100.0 * float((v < 5.0).mean()))
# ==================================================================================================
# 3. SIMULATORE DI LIBRO A N GAMBE (la contabilita' e' quella di paper_xsr._step, generalizzata)
# ==================================================================================================
def sim_libro(Weff: np.ndarray, R: np.ndarray, r_hedge: np.ndarray | None, cap0: float,
min_order: float | None, lot_usd: np.ndarray | None, px: np.ndarray | None,
fee_leg: float | np.ndarray) -> dict:
"""Un passo per barra. Una gamba il cui |dw|*capitale sta sotto min_order NON si muove
(stessa convenzione di altlib.eval_weights_smallcap e di scripts/live/paper_xsr._step).
In piu': la size si arrotonda al LOTTO del venue -> se arrotonda a zero, non si esegue.
`fee_leg` puo' essere uno scalare o un vettore per-gamba (costo asset-specifico)."""
n, k = Weff.shape
w = np.zeros(k)
cap = cap0
eq = np.empty(n)
nets = np.zeros(n)
n_fill = n_skip = n_lot0 = 0
fee = np.broadcast_to(np.asarray(fee_leg, float).ravel(), (k,)) if np.ndim(fee_leg) else None
for i in range(n):
ret = float(np.dot(w, R[i]))
if r_hedge is not None:
ret -= float(w.sum()) * r_hedge[i]
w_t = Weff[i]
if min_order is None:
w_new = w_t.copy()
else:
move = np.abs(w_t - w) * cap >= min_order
if lot_usd is not None and px is not None:
# arrotondamento al lotto: la size eseguita e' un multiplo di lot_units
dn = np.abs(w_t - w) * cap
lots = np.floor(dn / np.maximum(lot_usd, 1e-12))
zero = move & (lots < 1)
n_lot0 += int(zero.sum())
move = move & (lots >= 1)
need = np.abs(w_t - w) > 1e-12 # una gamba gia' a posto NON e' un ordine saltato
w_new = np.where(move, w_t, w)
n_fill += int((move & need).sum())
n_skip += int(((~move) & need).sum())
d = np.abs(w_new - w)
c = float((d * fee).sum()) if fee is not None else float(fee_leg) * float(d.sum())
if r_hedge is not None: # la gamba di copertura paga anch'essa
dh = abs(float(w_new.sum()) - float(w.sum()))
c += (float(fee.mean()) if fee is not None else float(fee_leg)) * dh
net = ret - c
nets[i] = net
cap *= (1.0 + max(net, -0.99))
eq[i] = cap
w = w_new
tot = n_fill + n_skip
return dict(net=nets, eq=eq, n_fill=n_fill, n_skip=n_skip, n_lot0=n_lot0,
pct_eseguite=(100.0 * n_fill / tot) if tot else np.nan)
def metriche(net: np.ndarray, idx: pd.DatetimeIndex) -> dict:
x = np.asarray(net, float)
sd = x.std(ddof=1)
sh = float(x.mean() / sd * ANN) if sd > 0 else 0.0
eq = np.cumprod(1.0 + np.clip(x, -0.99, None))
dd = float((1.0 - eq / np.maximum.accumulate(eq)).max())
yrs = max((idx[-1] - idx[0]) / pd.Timedelta("365.25D"), 1e-9)
cagr = float(eq[-1] ** (1.0 / yrs) - 1.0)
return dict(sharpe=round(sh, 3), maxdd=round(100 * dd, 1), cagr=round(100 * cagr, 1))
# ==================================================================================================
# 4. XS01 — ricostruzione dei PESI (il sleeve espone solo i ritorni) + prova d'identita'
# ==================================================================================================
def xs01_pesi():
from src.portfolio.sleeves import XS_CFG, XS_UNIVERSE, _xsec_returns
cols = {}
for sym in XS_UNIVERSE:
p = RAW / f"hl_{sym.lower()}_1d.parquet"
if not p.exists():
continue
d = pd.read_parquet(p)
cols[sym] = pd.Series(d["close"].values.astype(float),
index=pd.to_datetime(d["timestamp"], unit="ms", utc=True))
C = pd.concat(cols, axis=1, join="inner").sort_index().dropna()
px = C.values
n, A = px.shape
lb, H, k, mode, tv = (XS_CFG["lookbacks"], XS_CFG["H"], XS_CFG["k"],
XS_CFG["mode"], XS_CFG["target_vol"])
dp, mh = XS_CFG.get("disp_pct", 0), XS_CFG.get("disp_minhist", 20)
mlb = max(lb)
dret = np.vstack([np.zeros(A), px[1:] / px[:-1] - 1.0])
W = np.zeros((n, A))
w = np.zeros(A)
hist: list[float] = []
for i in range(n):
if i >= mlb and i % H == 0:
rLs = [px[i] / px[i - L] - 1.0 for L in lb]
di = float(np.mean([r.std() for r in rLs]))
thr = np.percentile(hist, dp) if (dp > 0 and len(hist) >= mh) else -np.inf
if di >= thr:
sc = np.zeros(A); cnt = 0
for rL in rLs:
sd = rL.std()
if sd > 0:
sc += (rL - rL.mean()) / sd; cnt += 1
if cnt:
sc /= cnt
o = np.argsort(sc); w = np.zeros(A); lo, hi = o[:k], o[-k:]
if mode == "mom": w[hi] = 0.5 / k; w[lo] = -0.5 / k
else: w[lo] = 0.5 / k; w[hi] = -0.5 / k
else:
w = np.zeros(A)
hist.append(di)
W[i] = w
gross = np.zeros(n); gross[1:] = np.sum(W[:-1] * dret[1:], axis=1)
turn = np.zeros(n); turn[0] = np.abs(W[0]).sum()
turn[1:] = np.abs(np.diff(W, axis=0)).sum(axis=1)
net = gross - turn * (0.001 / 2.0)
s = pd.Series(net, index=C.index)
rv = s.rolling(30, min_periods=15).std().shift(1) * np.sqrt(365.25)
scale = np.clip(np.nan_to_num(tv / rv.replace(0, np.nan).values, nan=0.0), 0, 3.0)
# prova d'identita' col sleeve UFFICIALE (max|diff| deve essere 0.0)
off = _xsec_returns()
dif = float(np.abs(pd.Series(s.values * scale, index=C.index).reindex(off.index).values
- off.values).max())
# posizione TENUTA durante la barra i = W[i-1]*scale[i] => posta a fine barra i: W[i]*scale[i+1]
pos_scale = np.concatenate([scale[1:], scale[-1:]])
return W * pos_scale[:, None], dret, C, list(C.columns), dif
# ==================================================================================================
# MAIN
# ==================================================================================================
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--no-net", action="store_true")
ap.add_argument("--snaps", type=int, default=3)
a = ap.parse_args()
pd.set_option("display.width", 200)
print("=" * 104)
print(" HL-EXEC — le regole VERE di Hyperliquid contro le soglie ASSUNTE di XS01 / XSR01")
print("=" * 104)
# ---------------------------------------------------------------- 1. regole
meta, fee_sched = leggi_regole(a.no_net)
syms = universo_certificato()
print(f"\n[1] REGOLE DEL VENUE (fonte: {INFO_URL} = MAINNET)")
print(f" universo perp quotato: {len(meta)} strumenti; certificati da noi: {len(syms)}")
mancanti = [s for s in syms if s not in meta.index]
delist = [s for s in syms if s in meta.index and bool(meta.loc[s, "delisted"])]
print(f" certificati assenti dal venue: {mancanti or 'nessuno'} delistati: {delist or 'nessuno'}")
ck = verifica_mainnet(meta, syms)
print(f"\n [A] verifica MAINNET (mark API vs ultima chiusura del feed certificato, "
f"eta' feed {ck['eta_h'].median():.0f}h):")
print(f" deviazione |%| mediana {ck['dev_pct'].abs().median():.2f}% "
f"max {ck['dev_pct'].abs().max():.2f}% ({ck['dev_pct'].abs().idxmax()}) "
f"asset controllati {len(ck)}")
print(" -> un feed TESTNET non supera questo controllo: i suoi prezzi sono scollegati.")
tk = fee_sched
print(f"\n [A] FEE perp lette dal venue (tier BASE, nessuna chiave):")
print(f" taker (cross) {float(tk['cross'])*1e4:.2f} bps/lato "
f"maker (add) {float(tk['add'])*1e4:.2f} bps/lato")
print(f" primo scaglione VIP a ${float(tk['tiers']['vip'][0]['ntlCutoff']):,.0f} di "
f"volume 14g -> a $600-20k si sta SEMPRE al tier base.")
print(f" [C] la doc (gitbook/trading/fees) dichiara 0.045% / 0.015%: **le due fonti "
f"coincidono**.")
print(f" [C] pavimento d'ordine perp: \"Order must have minimum value of $10.\" "
f"(docs/for-developers/api/error-responses); nessuna esenzione documentata per reduce-only.")
print(f" [C] tick: <=5 cifre significative E <= (6 - szDecimals) decimali; size arrotondata "
f"a szDecimals.")
sub = meta.loc[[s for s in syms if s in meta.index]].copy()
print(f"\n [A/B] LOTTO per asset (passo di size) — i 6 piu' grossolani e i 6 piu' fini:")
o = sub.sort_values("lot_usd", ascending=False)
for who, part in (("piu' grossolano", o.head(6)), ("piu' fine", o.tail(6))):
for s, r in part.iterrows():
print(f" {s:<6} szDec={int(r.szDecimals)} lotto {r.lot_units:>10.5f} unita' = "
f"${r.lot_usd:>8.4f} mark ${r.markPx:<12.6g} ({who})")
print(f" lotto in $: mediana ${sub['lot_usd'].median():.4f}, max "
f"${sub['lot_usd'].max():.2f} ({sub['lot_usd'].idxmax()})")
print(f" -> il LOTTO non e' mai il vincolo: max ${sub['lot_usd'].max():.2f} << $10 "
f"di pavimento. Il vincolo e' il MIN NOTIONAL.")
print(f" [A] leva massima: BTC {int(meta.loc['BTC','maxLeverage'])}x, "
f"ETH {int(meta.loc['ETH','maxLeverage'])}x, mediana alt certificati "
f"{int(sub['maxLeverage'].median())}x, minimo {int(sub['maxLeverage'].min())}x")
# ---------------------------------------------------------------- 2. book
print(f"\n[2] BOOK L2 — spread, profondita', costo di un ticket ({a.snaps} snapshot per asset)")
books = scarica_book(syms, a.snaps, a.no_net)
liq = misura_liquidita(books, meta)
mn = min_notional_osservato(books)
if mn:
print(f" [B] controllo empirico del pavimento $10 su {mn['n']} livelli a UN SOLO ordine "
f"(n==1):")
print(f" minimo ${mn['minimo']:.2f} p01 ${mn['p01']:.2f} p05 ${mn['p05']:.2f} "
f"mediana ${mn['mediana']:.0f} sotto $10: {mn['sotto10_pct']:.1f}% "
f"sotto $5: {mn['sotto5_pct']:.1f}%")
print(f" (un residuo sotto soglia NON falsifica la regola — i fill parziali erodono "
f"un ordine gia' piazzato — ma il pavimento della distribuzione dice dove taglia.)")
viol = int(liq["tick_viol"].sum())
print(f" [B] regola tick verificata sui book veri: {viol} violazioni su "
f"{int(liq['n_snap'].sum())*40} livelli letti -> la regola [C] e' CONFERMATA dal venue.")
print(f"\n spread e slippage (mediana degli snapshot; bps DA MID, un solo lato):")
print(f" {'sym':<7}{'vol24h $':>13}{'spread':>9}{'1/2 spr':>9}"
f"{'prof<10bp':>11}{'$50':>8}{'$100':>8}{'$300':>8}")
for s, r in liq.iterrows():
print(f" {s:<7}{r.dayNtlVlm if 'dayNtlVlm' in r else meta.loc[s,'dayNtlVlm']:>13,.0f}"
f"{r.spread_bps:>8.1f}{r.half_spread_bps:>9.1f}{r.depth10bps_usd:>11,.0f}"
f"{r.slip50_bps:>8.1f}{r.slip100_bps:>8.1f}{r.slip300_bps:>8.1f}")
XS19 = ["BTC", "ETH", "SOL", "BNB", "XRP", "DOGE", "AVAX", "LINK", "LTC", "ADA",
"ARB", "OP", "SUI", "APT", "INJ", "TIA", "SEI", "NEAR", "AAVE"]
maj = liq.reindex([s for s in XS19 if s in liq.index])
coda = liq.drop(index=maj.index, errors="ignore")
print(f"\n riepilogo (mediana | p90):")
for nm, part in (("19 major (XS01)", maj), (f"coda ({len(coda)} alt, solo XSR01)", coda)):
if len(part):
print(f" {nm:<26} spread {part.spread_bps.median():>5.1f} | "
f"{part.spread_bps.quantile(.9):>5.1f} bps "
f"slip$100 {part.slip100_bps.median():>5.1f} | "
f"{part.slip100_bps.quantile(.9):>5.1f} "
f"slip$300 {part.slip300_bps.median():>5.1f} | "
f"{part.slip300_bps.quantile(.9):>5.1f}")
# ---------------------------------------------------------------- 3. XS01
print(f"\n[3] XS01 — soglia PUBBLICATA ~$20.000 (origine: stima a occhio, 'rumore arrotondamento')")
Wxs, dret, C, cols, dif = xs01_pesi()
print(f" prova d'identita' col sleeve ufficiale `_xsec_returns()`: max|diff| = {dif:.3e}")
gross_xs = np.abs(Wxs).sum(axis=1)
att = gross_xs > 1e-9
dW = np.abs(np.diff(Wxs, axis=0, prepend=np.zeros((1, Wxs.shape[1]))))
tk_nz = dW[dW > 1e-9]
print(f" gambe simultanee: {int((np.abs(Wxs) > 1e-9).sum(axis=1)[att].max())} "
f"(k=5 long + 5 short); lordo mediano {np.median(gross_xs[att]):.2f}x il capitale "
f"(vol-target, cap 3x)")
print(f" peso mediano mosso per gamba per ribilanciamento: {np.median(tk_nz)*100:.2f}% "
f"del capitale; 10° pctl {np.percentile(tk_nz,10)*100:.2f}%")
print(f"\n ticket per gamba (mediano) e % di gambe ESEGUIBILI, per capitale allocato a XS01:")
print(f" {'cap XS01':>10}{'ticket med':>12}{'ticket p10':>12}"
f"{'>=$5 [D]':>10}{'>=$10 [C]':>11}{'lotto ko':>10}")
R_xs = dret
idx = C.index
base = None
for cap in CAPITALI:
med = np.median(tk_nz) * cap
p10 = np.percentile(tk_nz, 10) * cap
pct5 = 100.0 * float((tk_nz * cap >= MIN_ORDER_ASSUNTO).mean())
pct10 = 100.0 * float((tk_nz * cap >= MIN_ORDER_HL).mean())
lot = np.array([meta.loc[s, "lot_usd"] if s in meta.index else 0.0 for s in cols])
lotko = 100.0 * float((tk_nz * cap < np.median(lot)).mean())
print(f" {cap:>10,.0f}{med:>12.2f}{p10:>12.2f}{pct5:>9.0f}%{pct10:>10.0f}%{lotko:>9.1f}%")
lot_xs = np.array([float(meta.loc[s, "lot_usd"]) if s in meta.index else 0.0 for s in cols])
px_xs = np.array([float(meta.loc[s, "markPx"]) if s in meta.index else np.nan for s in cols])
fee_xs_mod = 0.001 / 2.0 # [D] cio' che il sleeve modella: 5 bps/lato
print(f"\n haircut di eseguibilita' (Sharpe modellato - Sharpe realistico), fee {fee_xs_mod*1e4:.1f} bps/lato:")
print(f" {'cap XS01':>10}{'Sh mod':>9}{'Sh $5 [D]':>11}{'Sh $10 [C]':>12}"
f"{'haircut $10':>13}{'esegui%':>9}")
for cap in CAPITALI:
m0 = sim_libro(Wxs, R_xs, None, cap, None, None, None, fee_xs_mod)
m5 = sim_libro(Wxs, R_xs, None, cap, MIN_ORDER_ASSUNTO, None, None, fee_xs_mod)
m10 = sim_libro(Wxs, R_xs, None, cap, MIN_ORDER_HL, lot_xs, px_xs, fee_xs_mod)
a0, a5, a10 = (metriche(m["net"], idx) for m in (m0, m5, m10))
hc = (a0["sharpe"] - a10["sharpe"]) / abs(a0["sharpe"]) * 100 if a0["sharpe"] else np.nan
print(f" {cap:>10,.0f}{a0['sharpe']:>9.2f}{a5['sharpe']:>11.2f}{a10['sharpe']:>12.2f}"
f"{hc:>12.0f}%{m10['pct_eseguite']:>9.0f}%")
# ---------------------------------------------------------------- 4. XSR01
print(f"\n[4] XSR01 — soglia PUBBLICATA ~$5.000, gate 2026-10-23 con haircut a $5.000 <= 40%")
from scripts.live.paper_xsr import build_panel # stessa costruzione del monitor in produzione
ts, dtx, Wx, Rx, rbx, sx = build_panel()
idxx = pd.DatetimeIndex(dtx)
lot_x = np.array([float(meta.loc[s, "lot_usd"]) if s in meta.index else 0.0 for s in sx])
px_x = np.array([float(meta.loc[s, "markPx"]) if s in meta.index else np.nan for s in sx])
dWx = np.abs(np.diff(Wx, axis=0, prepend=np.zeros((1, Wx.shape[1]))))
tkx = dWx[dWx > 1e-9]
print(f" gambe {len(sx)} lordo mediano {np.median(np.abs(Wx).sum(axis=1)):.2f}x "
f"peso mosso mediano/gamba {np.median(tkx)*100:.3f}% p10 {np.percentile(tkx,10)*100:.3f}%")
print(f"\n {'cap':>9}{'ticket med':>12}{'Sh mod':>9}{'Sh $5 [D]':>11}{'Sh $10 [C]':>12}"
f"{'haircut $5':>12}{'haircut $10':>13}{'esegui%':>9}")
hc10_5000 = np.nan
for cap in CAPITALI:
m0 = sim_libro(Wx, Rx, rbx, cap, None, None, None, FEE_LEG_MODELLO)
m5 = sim_libro(Wx, Rx, rbx, cap, MIN_ORDER_ASSUNTO, None, None, FEE_LEG_MODELLO)
m10 = sim_libro(Wx, Rx, rbx, cap, MIN_ORDER_HL, lot_x, px_x, FEE_LEG_MODELLO)
a0, a5, a10 = (metriche(m["net"], idxx) for m in (m0, m5, m10))
h5 = (a0["sharpe"] - a5["sharpe"]) / abs(a0["sharpe"]) * 100 if a0["sharpe"] else np.nan
h10 = (a0["sharpe"] - a10["sharpe"]) / abs(a0["sharpe"]) * 100 if a0["sharpe"] else np.nan
if abs(cap - 5000.0) < 1e-9:
hc10_5000 = h10
print(f" {cap:>9,.0f}{np.median(tkx)*cap:>12.2f}{a0['sharpe']:>9.2f}"
f"{a5['sharpe']:>11.2f}{a10['sharpe']:>12.2f}{h5:>11.0f}%{h10:>12.0f}%"
f"{m10['pct_eseguite']:>9.0f}%")
print(f" -> haircut a $5.000 col pavimento VERO ($10): {hc10_5000:.0f}% "
f"(soglia pre-registrata {XSR_SOGLIA_HAIRCUT*100:.0f}%)")
print(f" NB: e' la lettura del PARAMETRO sul backtest, NON il gate del 23/10 — "
f"quello si decide sulla finestra FORWARD e anticiparlo sarebbe selezione.")
# -------- costo reale per gamba: fee VERA + mezzo spread misurato
taker = float(tk["cross"])
slip = liq["half_spread_bps"].reindex(sx).astype(float) / 1e4
slip_med = float(np.nanmedian(slip.values))
fee_real = taker + np.nan_to_num(slip.values, nan=slip_med)
print(f"\n [B] COSTO REALE PER GAMBA = taker {taker*1e4:.2f} bps + mezzo spread misurato:")
print(f" mediana {np.median(fee_real)*1e4:.1f} bps p90 {np.percentile(fee_real,90)*1e4:.1f} bps"
f" max {fee_real.max()*1e4:.1f} bps ({sx[int(np.argmax(fee_real))]})")
print(f" contro i {FEE_LEG_MODELLO*1e4:.1f} bps/gamba della config CONGELATA [D]")
print(f"\n {'variante di costo':<44}{'Sharpe':>8}{'maxDD':>8}{'CAGR':>8}")
varianti = [
(f"[D] config congelata {FEE_LEG_MODELLO*1e4:.1f} bps piatti", FEE_LEG_MODELLO),
(f"[A] solo taker vero {taker*1e4:.2f} bps piatti", taker),
(f"[B] taker + 1/2 spread PER-ASSET (mediana {np.median(fee_real)*1e4:.1f})", fee_real),
(f"[B] taker + spread PIENO per-asset (pessimista)", taker + 2 * np.nan_to_num(slip.values, nan=slip_med)),
]
for nm, f in varianti:
m = sim_libro(Wx, Rx, rbx, 5000.0, None, None, None, f)
a_ = metriche(m["net"], idxx)
print(f" {nm:<44}{a_['sharpe']:>8.2f}{a_['maxdd']:>7.1f}%{a_['cagr']:>7.1f}%")
print(f" (a capitale infinito = nessun vincolo di min-order: isola il COSTO dal PAVIMENTO)")
print("\n" + "=" * 104)
print(" LIMITI DICHIARATI: gli spread sono UNO/POCHI snapshot di OGGI su un venue la cui")
print(" liquidita' e' cresciuta -> sono la stima piu' FAVOREVOLE per i 2.6 anni di backtest.")
print(" Il pavimento $10 e' [C] (doc + messaggio d'errore), non provato da un ordine: provarlo")
print(" richiederebbe di mandarne uno, e questo audit non tocca il conto.")
print("=" * 104)
if __name__ == "__main__":
main()
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