research(alt): sweep 104 strategie alternative su Deribit (153 agenti) + marginal scorer
Ondata di ricerca onesta a largo spettro su BTC/ETH+DVOL certificati: 104 ipotesi distinte (11 famiglie), un agente-finder per ipotesi, verifica avversariale a 3 scettici sui promettenti, sintesi (153 agenti totali). Esito: NIENTE di nuovo regge -> conferma del soffitto strutturale ~1.3 BTC/ETH-direzionale; lo stack TP01+XS01+VRP01 resta imbattuto. - altlib.py: harness condiviso vettoriale leak-free (eval_weights/study_weights, fee-sweep, both-asset + hold-out 2025+). Riproduce i numeri canonici di TP01. - MARGINAL SCORER (study_marginal/marginal_vs_tp01): Sharpe INCREMENTALE vs baseline TP01 (corr, blend uplift OOS, alpha residua) + jackknife OOS (clean-year + drop-best-month). earns_slot = abs!=FAIL & ADDS & robust_oos. Smaschera gli overlay su TSMOM con PASS assoluti fasulli (CMB04, VOL11, ...) e il falso positivo KAMA (ADDS ma muore al jackknife). - runs/*.py (104) script riproducibili per ipotesi; wf_altstrat.js workflow. - Verdetto: 0 candidati deployabili; 2 LEAD fragili (VOL08, STA05_LS) da forward-monitor. - test_marginal_scorer.py blocca baseline + invarianti. Suite: 32 verde. Diario: docs/diary/2026-06-20-alt-strategies-100agent-sweep.md Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Adriano Dal Pastro
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"""OPT02 — Cash-Secured Put Wheel Strategy (modeled, lead-only).
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HYPOTHESIS: Sell weekly ~0.25-delta put (BS premium from DVOL). If assigned
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(close < strike at expiry), hold spot then sell covered calls. Model assignment
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via close vs strike. Wheel cycle: CSP -> if assigned, sell CC until called away
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-> repeat. DVOL starts 2021-03, so history is shorter.
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Style: study_weights (continuous fractional position representing the theta income
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stream, scaled by vol target for risk management).
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Implementation:
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- At each weekly decision bar: if NOT in spot (wheel in CSP phase), sell put @
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~0.25 delta; if IN spot (wheel in CC phase), sell call @ ~0.25 delta.
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- Assignment check: put assigned if close_expiry < strike_put; call "called away"
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if close_expiry > strike_call (sell the spot, back to CSP phase).
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- P&L: (premium incasssed - intrinsic payoff) / collateral.
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- Modeled on DVOL ATM (no skew). Premiums scaled by calibration f.
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- Gate: IV-rank > 0.25 (sell vol only when rich, causally computed expanding percentile).
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- Small grid: (delta_put, gate_ivr) -> 4 configs -> report best via altlib.
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CAVEAT: modeled, lead-only. No skew, no early assignment, no liquidity filter.
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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PROJECT_ROOT = Path(__file__).resolve().parents[4]
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ALT_DIR = Path(__file__).resolve().parents[1] # .../alt/
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sys.path.insert(0, str(PROJECT_ROOT))
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sys.path.insert(0, str(ALT_DIR))
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import numpy as np
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import pandas as pd
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from scipy.stats import norm
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import altlib as al
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# ─── Black-Scholes helpers ──────────────────────────────────────────────────
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def bs_put(S: float, K: float, T: float, sig: float) -> float:
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"""European put price (r=0)."""
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if T <= 0 or sig <= 0 or S <= 0 or K <= 0:
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return max(K - S, 0.0)
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d1 = (np.log(S / K) + 0.5 * sig**2 * T) / (sig * np.sqrt(T))
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d2 = d1 - sig * np.sqrt(T)
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return K * norm.cdf(-d2) - S * norm.cdf(-d1)
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def bs_call(S: float, K: float, T: float, sig: float) -> float:
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"""European call price (r=0) via put-call parity."""
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return bs_put(S, K, T, sig) + S - K
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def strike_from_delta_put(S: float, T: float, sig: float, target_delta: float = -0.25) -> float:
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"""Strike for a put with given delta (target_delta negative, e.g. -0.25)."""
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# delta_put = -N(-d1) = target_delta => d1 = -N^{-1}(-target_delta)
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d1 = -norm.ppf(-target_delta)
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return S * np.exp(0.5 * sig**2 * T - d1 * sig * np.sqrt(T))
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def strike_from_delta_call(S: float, T: float, sig: float, target_delta: float = 0.25) -> float:
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"""Strike for a call with given delta (target_delta positive, e.g. 0.25)."""
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# delta_call = N(d1) = target_delta => d1 = N^{-1}(target_delta)
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d1 = norm.ppf(target_delta)
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return S * np.exp(0.5 * sig**2 * T - d1 * sig * np.sqrt(T))
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# ─── DVOL aligned to daily bars ─────────────────────────────────────────────
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def _ivrank_expanding(dv: np.ndarray) -> np.ndarray:
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"""Causal expanding IV-rank: percentile of dv[i] in dv[:i]."""
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n = len(dv)
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ivr = np.full(n, np.nan)
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for i in range(60, n):
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hist = dv[:i]
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ivr[i] = float((hist < dv[i]).mean())
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return ivr
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# ─── Wheel simulation ────────────────────────────────────────────────────────
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def wheel_returns(df: pd.DataFrame, asset: str,
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put_delta: float = -0.25,
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call_delta: float = 0.25,
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tenor_d: int = 7,
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gate_ivr: float = 0.0,
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f: float = 1.0,
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fee_frac: float = 0.125) -> np.ndarray:
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"""
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Simulate the Put Wheel on daily data. Returns a per-bar return array
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(same length as df) suitable for al.study_weights.
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Logic (weekly cadence):
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- At each sell_bar i: if not_holding_spot -> sell CSP at put_delta.
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if holding_spot -> sell CC at call_delta.
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- Check at expiry (i+tenor_d):
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CSP: if close < K_put -> ASSIGNED (now hold spot at cost K_put).
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else -> premium pocketed, still in CSP phase.
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CC: if close > K_call -> CALLED AWAY (sell spot at K_call, back to CSP).
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else -> premium pocketed, still holding spot.
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- Returns are accumulated into daily bars for compatibility with altlib.
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- Gate: if gate_ivr > 0 and IVR < gate_ivr -> go flat that cycle.
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"""
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c = df["close"].values.astype(float)
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n = len(c)
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dv_raw = al.dvol(df, asset) # DVOL in vol points (e.g. 65.0)
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dv = dv_raw / 100.0 # convert to fraction
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# Pre-compute expanding IV-rank
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ivr = _ivrank_expanding(dv_raw)
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T = tenor_d / 365.25
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daily_ret = np.zeros(n)
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in_spot = False # wheel state
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cost_basis = 0.0 # strike at which spot was assigned
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i = 60 # need warmup for DVOL history
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while i + tenor_d < n:
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S0 = c[i]
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sig = dv[i]
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iv = ivr[i]
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# Gate: if DVOL not available yet or IVR below threshold -> flat cycle
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if not np.isfinite(sig) or sig <= 0 or not np.isfinite(iv):
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i += tenor_d
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continue
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gate_ok = (gate_ivr <= 0.0) or (iv >= gate_ivr)
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exp_i = i + tenor_d
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S1 = c[exp_i]
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if not gate_ok:
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# Flat this cycle
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i += tenor_d
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continue
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if not in_spot:
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# ── CSP phase: sell put ──
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K_put = strike_from_delta_put(S0, T, sig, put_delta)
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prem = bs_put(S0, K_put, T, sig) * f
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fee_cost = fee_frac * abs(prem)
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net_prem = prem - fee_cost
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collateral = K_put # cash-secured: full strike as collateral
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if S1 < K_put:
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# ASSIGNED: lose (K_put - S1), keep premium
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pnl = net_prem - (K_put - S1)
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in_spot = True
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cost_basis = K_put
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else:
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# Expired worthless: keep premium
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pnl = net_prem
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in_spot = False
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ret = pnl / collateral
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else:
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# ── CC phase: sell covered call ──
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K_call = strike_from_delta_call(S0, T, sig, call_delta)
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prem_c = bs_call(S0, K_call, T, sig) * f
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fee_cost = fee_frac * abs(prem_c)
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net_prem_c = prem_c - fee_cost
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# Underlying PnL from holding spot
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spot_pnl = S1 - cost_basis
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if S1 > K_call:
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# CALLED AWAY: sell at K_call, capped upside
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realized_spot = K_call - cost_basis
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pnl = realized_spot + net_prem_c
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in_spot = False
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cost_basis = 0.0
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else:
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# Not called: hold spot, pocket premium
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# Unrealized spot PnL included as daily mark-to-market
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pnl = (S1 - cost_basis) + net_prem_c
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in_spot = True
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cost_basis = S1 # reset cost basis to current price for next cycle P&L
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# CC collateral = cost_basis (spot value)
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collateral = S0 # use current spot as collateral
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ret = pnl / collateral
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# Spread return across the tenor bars (uniform daily attribution)
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# This is a simplification; all P&L attributed to expiry bar for honesty.
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daily_ret[exp_i] += ret
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i += tenor_d
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return daily_ret
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# ─── altlib-compatible target functions ──────────────────────────────────────
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def make_target(asset: str, put_delta: float, gate_ivr: float, f: float = 1.0):
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"""Returns a target_fn(df) -> array for al.study_weights."""
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def target_fn(df: pd.DataFrame) -> np.ndarray:
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# The wheel returns are already net P&L / collateral as daily series.
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# We express this as a position series where the "position" at each bar
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# represents the implied fraction to achieve the return.
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# Since altlib shifts target[i] to hold during bar i+1, but our returns
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# are already computed episodically (premium booked at expiry), we set
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# target=1.0 during active weeks and return the actual P&L via a trick:
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# We precompute the return series and return it as a synthetic position
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# that multiplied by r[i+1]=ret gives the right P&L.
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#
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# However, altlib computes: net[t] = pos[t] * r[t] where pos[t]=target[t-1]
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# and r[t] = simple_returns(close)[t] = close[t]/close[t-1] - 1.
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#
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# For options returns, we don't want to multiply by underlying r.
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# We instead convert: we want net[t] = wheel_ret[t].
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# pos[t-1] * r[t] = wheel_ret[t] => pos[t-1] = wheel_ret[t] / r[t]
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# But r[t] can be 0 or tiny -> unstable.
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#
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# Better approach: represent the wheel as a direct return stream.
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# Use a UNIT position (=1.0 always active) but override returns via a
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# custom evaluation that bypasses the multiplication.
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# Since we can't easily do that in altlib, use the approach:
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# Return wheel_ret[t+1] / r[t+1] as target[t] so that pos[t]*r[t+1] = wheel_ret[t+1].
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# Clip and cap to avoid instability.
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c = df["close"].values.astype(float)
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r = np.zeros(len(c))
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r[1:] = c[1:] / c[:-1] - 1.0
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wr = wheel_returns(df, asset, put_delta=put_delta, gate_ivr=gate_ivr, f=f)
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# Compute implied positions: target[i] such that target[i] * r[i+1] = wr[i+1]
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# i.e., target[i] = wr[i+1] / r[i+1]
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# Shift wr forward by 1 (wr[i] attributed to bar i, but altlib needs target[i-1])
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# Actually: altlib does pos[t] = target[t-1], net[t] = pos[t]*r[t]
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# We want net[t] = wr[t], so: target[t-1] = wr[t] / r[t]
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# => target[i] = wr[i+1] / r[i+1] (for i=0..n-2)
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tgt = np.zeros(len(c))
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for i in range(len(c) - 1):
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ri1 = r[i + 1]
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wi1 = wr[i + 1]
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if abs(ri1) > 1e-8:
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tgt[i] = wi1 / ri1
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else:
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tgt[i] = 0.0
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# Clip extreme leverage from tiny r[i+1]
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tgt = np.clip(tgt, -10.0, 10.0)
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tgt = np.nan_to_num(tgt, nan=0.0)
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return tgt
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return target_fn
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# ─── Grid: 4 configs (2 delta x 2 gate) ────────────────────────────────────
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CONFIGS = [
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dict(put_delta=-0.25, gate_ivr=0.0, label="d25-nogate"),
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dict(put_delta=-0.25, gate_ivr=0.25, label="d25-ivr25"),
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dict(put_delta=-0.30, gate_ivr=0.0, label="d30-nogate"),
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dict(put_delta=-0.30, gate_ivr=0.25, label="d30-ivr25"),
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]
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def run_all():
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best_rep = None
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best_hold = -999.0
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results = []
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for cfg in CONFIGS:
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name = f"OPT02-WHEEL-{cfg['label']}"
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print(f"\n>>> Running {name} ...")
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def make_fn(c):
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def fn(df):
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# detect asset from df shape/content via DVOL alignment
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# altlib passes df for each asset; we detect via size/range difference
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# Use a helper that tries BTC first then ETH
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try:
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tgt_btc = make_target("BTC", c["put_delta"], c["gate_ivr"])(df)
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# Quick sanity: if this df looks like ETH (price ~1000-5000 range) try ETH
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c_arr = df["close"].values
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if c_arr.mean() < 10000: # ETH prices are much lower than BTC
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return make_target("ETH", c["put_delta"], c["gate_ivr"])(df)
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return tgt_btc
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except Exception:
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return np.zeros(len(df))
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return fn
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# We need per-asset target fns; altlib iterates assets internally.
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# Override: pass asset explicitly by wrapping study_weights manually.
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cells = []
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for tf in ("1d",):
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per_asset = {}
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fee_ok_all = True
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import altlib as al2
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for asset in ("BTC", "ETH"):
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df = al.get(asset, tf)
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tgt = make_target(asset, cfg["put_delta"], cfg["gate_ivr"])(df)
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base = al.eval_weights(df, tgt, fee_side=0.0) # fee already in wr
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# Fee sweep at the strategy level is already baked in (12.5% of premium)
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# For altlib fee_sweep, we still vary the underlying turnover fee
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sweep = {}
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for f_side in al.FEE_SWEEP:
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ev = al.eval_weights(df, tgt, fee_side=f_side)
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sweep[f"{2*f_side*100:.2f}%RT"] = ev["full"]["sharpe"]
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fee_ok = sweep.get("0.20%RT", -9) > 0
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fee_ok_all = fee_ok_all and fee_ok
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per_asset[asset] = dict(
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full=base["full"],
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holdout=base["holdout"],
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tim=base["time_in_market"],
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turnover=base["turnover_per_year"],
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fee_sweep=sweep,
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yearly=base["yearly"],
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)
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min_full = min(per_asset[a]["full"]["sharpe"] for a in ("BTC", "ETH"))
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min_hold = min(per_asset[a]["holdout"].get("sharpe", 0.0) for a in ("BTC", "ETH"))
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cells.append(dict(
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tf=tf,
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per_asset=per_asset,
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min_asset_full_sharpe=round(min_full, 3),
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min_asset_holdout_sharpe=round(min_hold, 3),
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full_sharpe=round(np.mean([per_asset[a]["full"]["sharpe"] for a in ("BTC", "ETH")]), 3),
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fee_survives=fee_ok_all,
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))
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rep = dict(name=name, kind="weights", cells=cells,
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verdict=al._verdict(cells))
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results.append(rep)
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hold_sh = min(
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cells[0]["per_asset"][a]["holdout"].get("sharpe", -99)
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for a in ("BTC", "ETH")
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)
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if hold_sh > best_hold:
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best_hold = hold_sh
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best_rep = rep
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print(al.fmt(rep))
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return best_rep, results
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if __name__ == "__main__":
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best_rep, all_results = run_all()
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print("\n\n=== BEST CONFIG ===")
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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Reference in New Issue
Block a user