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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"""OPT05 — Delta-Hedged Short Straddle (Variance Premium Harvest)
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IDEA: Sell ATM straddle every N days, delta-hedge daily with ACTUAL price moves.
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Net P&L = IV-RV spread (the variance risk premium).
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HONEST APPROACH — Direct P&L Simulation (avoids BS gamma approximation errors):
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1. At roll date i0: sell ATM straddle. Receive premium P = 2*BSCall(S0,S0,T,IV).
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2. Compute initial delta hedge: delta_straddle = delta_call + delta_put = N(d1) - N(-d1) ≈ 0 ATM.
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Set delta_hedge_position h0 = -delta_straddle ≈ 0 at initiation.
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3. Each subsequent bar k: compute new delta at current S_k, T_remaining.
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Rebalance: dh = new_delta - old_delta. Hedge cost includes:
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(a) Slippage/market-impact on spot hedge: dh * S_k * fee_hedge (spot fee per side)
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(b) The actual mark-to-market P&L of the short straddle:
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delta_PnL = -(C(S_k, K, T_k) + P(S_k, K, T_k) - C(S_{k-1}, K, T_{k-1}) - P(S_{k-1}, K, T_{k-1}))
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plus hedge_PnL = h * (S_k - S_{k-1})
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4. At expiry: close position at intrinsic value.
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Total cycle P&L = option_premium - (intrinsic_at_expiry + sum_of_theta_adj + hedge_slippage)
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This simulation directly uses ACTUAL price moves, so:
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- Big moves (jumps) correctly cause large losses
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- Small/quiet periods correctly generate theta income
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- Discrete rebalancing frequency exactly matches daily bars
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KEY METRICS EXPECTED:
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- Crypto IV ≈ 60-80%, RV ≈ 40-65%: IV>RV on average → net positive
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- But crypto has fat tails: occasional -10%/-20% single-day moves devastate short gamma
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- Expected Sharpe: 0.3–0.8 if honestly modeled (not 4.0)
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GATE: Only enter when DVOL/RV_20d >= gate threshold (IV-rich condition).
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GRID: roll_days in {7, 14} x iv_rv_gate in {1.10, 1.20} → 4 configs, 1d TF only.
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CAVEAT:
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- MODELED on DVOL ATM. Skew not modeled (OTM puts have higher IV in practice).
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- Straddle sell assumes fills at mid; real execution has bid-ask spread.
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- Tail risk (e.g., BTC -30% day) not captured via DVOL history smoothing.
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- DVOL history starts 2021-03 → backtest from 2021-03 only.
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- Lead-only; not for deployment without real options data.
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Style: study_weights (continuous modeled position evaluated via standalone P&L series).
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"""
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import sys
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sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
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import altlib as al
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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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# ── Black-Scholes helpers ──────────────────────────────────────────────────────
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def bs_price(S: float, K: float, T: float, sigma: float, option_type: str = "call") -> float:
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"""Black-Scholes option price. r=0 (crypto/futures context)."""
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if T <= 0 or sigma <= 0 or S <= 0 or K <= 0:
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# Intrinsic value
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if option_type == "call":
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return max(0.0, S - K)
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else:
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return max(0.0, K - S)
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d1 = (np.log(S / K) + 0.5 * sigma**2 * T) / (sigma * np.sqrt(T))
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d2 = d1 - sigma * np.sqrt(T)
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if option_type == "call":
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return float(S * norm.cdf(d1) - K * norm.cdf(d2))
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else:
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return float(K * norm.cdf(-d2) - S * norm.cdf(-d1))
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def bs_delta(S: float, K: float, T: float, sigma: float, option_type: str = "call") -> float:
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"""Black-Scholes delta."""
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if T <= 0 or sigma <= 0 or S <= 0 or K <= 0:
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if option_type == "call":
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return 1.0 if S > K else 0.0
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else:
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return -1.0 if S < K else 0.0
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d1 = (np.log(S / K) + 0.5 * sigma**2 * T) / (sigma * np.sqrt(T))
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if option_type == "call":
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return float(norm.cdf(d1))
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else:
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return float(norm.cdf(d1) - 1.0)
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def straddle_value(S: float, K: float, T: float, sigma: float) -> float:
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"""ATM straddle value = call + put."""
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return bs_price(S, K, T, sigma, "call") + bs_price(S, K, T, sigma, "put")
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def straddle_delta(S: float, K: float, T: float, sigma: float) -> float:
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"""Net delta of short straddle: call_delta + put_delta."""
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return bs_delta(S, K, T, sigma, "call") + bs_delta(S, K, T, sigma, "put")
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def simulate_straddle_cycle(
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close: np.ndarray,
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sigma_iv: np.ndarray,
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i0: int,
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roll_bars: int,
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fee_hedge: float = 0.0005 # spot hedge rebalance cost (0.05% per side taker)
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) -> tuple[float, int]:
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"""
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Simulate ONE delta-hedged short straddle cycle starting at bar i0.
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Returns (net_pnl_fraction_of_K, i_expiry) where:
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- net_pnl is in fraction of strike K (= S0 at entry)
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- i_expiry is the bar at which the cycle ends
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P&L components (all as fraction of K):
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+ straddle_premium/K received at i0 (short straddle → receive premium)
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- mark-to-market change of straddle value (we're short)
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+ hedge P&L from spot hedge position
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- hedge rebalancing cost (fee per trade)
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"""
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n = len(close)
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S0 = close[i0]
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K = S0 # sell ATM
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T0 = roll_bars / 365.25 # time to expiry in years
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sig0 = sigma_iv[i0]
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if not (np.isfinite(sig0) and sig0 > 0.01):
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return 0.0, min(i0 + roll_bars, n - 1)
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# Sell straddle at i0: receive premium
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prem0 = straddle_value(S0, K, T0, sig0)
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# Position: short straddle (we want straddle to decrease in value)
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# Short straddle value at entry = prem0
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# Initial delta hedge (fractional units of underlying per unit K)
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delta0 = straddle_delta(S0, K, T0, sig0) # ≈ 0 at ATM
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# Hedge: buy delta0 units of spot to hedge (position in spot = delta0 * K)
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# But we're SHORT the straddle, so our delta is +delta_straddle, we need to sell spot
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# Short straddle delta = -(call_delta + put_delta)
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# We go long (-straddle_delta) in spot to be delta-neutral
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hedge_pos = -delta0 # units of S per unit of notional (S0)
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# Running P&L tracking
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total_pnl = prem0 # we received this upfront (in $ terms, / K at end)
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# straddle_prev_value = prem0 # track mark-to-market
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prev_S = S0
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prev_sig = sig0
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prev_hedge = hedge_pos
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i_expiry = min(i0 + roll_bars, n - 1)
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total_hedge_cost = 0.0
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for i in range(i0 + 1, i_expiry + 1):
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S_curr = close[i]
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bars_to_exp = i_expiry - i
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T_rem = max(0.0, bars_to_exp / 365.25)
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# Current IV (use entry IV as fallback if current is invalid)
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sig_curr = sigma_iv[i]
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if not (np.isfinite(sig_curr) and sig_curr > 0.01):
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sig_curr = prev_sig
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# Mark-to-market change of SHORT straddle:
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# new_straddle_value = straddle_value(S_curr, K, T_rem, sig_curr)
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# P&L from option position = -(new_val - prev_val) [we're short]
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# But the hedge also moves
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# Spot hedge P&L = hedge_pos * (S_curr - prev_S)
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# We track this explicitly via the straddle formula
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# At expiry: T_rem = 0 → straddle = intrinsic = max(S-K,0) + max(K-S,0) = |S-K|
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if i == i_expiry:
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straddle_final = abs(S_curr - K)
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# Settle: short straddle loses if straddle_final > some_threshold
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# Net P&L = prem0 - straddle_final + hedge_pnl
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# Hedge P&L from last rebalance to now:
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hedge_pnl_final = prev_hedge * (S_curr - prev_S)
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# Close hedge: pay fee on closing the spot position
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close_hedge_cost = abs(prev_hedge) * S_curr * fee_hedge / K
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total_pnl = prem0 - straddle_final + (
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# Sum of all intermediate hedge P&L is already implicitly in the
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# straddle mark-to-market (via put-call parity at each step).
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# Actually: just compute total_pnl directly:
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# P&L = premium_received - intrinsic_paid - sum(hedge_rebalance_costs)
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# The hedge P&L and straddle MTM cancel each other (that's the whole
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# point of delta hedging — the delta exposure is neutralized).
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# So the final net = premium_received - realized_variance_cost - intrinsic_settlement
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# where realized_variance_cost = sum of gamma * (dS)^2 / 2 per bar.
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# This is what we compute below.
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0 # placeholder
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)
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# ACTUALLY let's compute it cleanly: the total delta-hedged P&L is:
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# P&L = premium_received - straddle_final_value + cumulative_hedge_rebalance_PnL - costs
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# cumulative_hedge_rebalance_PnL = sum over all rebal: hedge_k * (S_{k+1} - S_k)
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# This is complex to track; instead use the gamma P&L theorem:
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# Total delta-hedged short straddle P&L = 0.5 * sum_k(gamma_k * S_k^2 * r_k^2) * (IV^2/RV^2 - 1)
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# NO — let's just do it directly step by step.
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break
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# Intermediate bar: compute hedge rebalancing P&L
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new_delta = straddle_delta(S_curr, K, T_rem, sig_curr)
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new_hedge = -new_delta
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# Spot hedge P&L for this bar
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hedge_pnl = prev_hedge * (S_curr - prev_S)
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total_pnl += hedge_pnl / K # add in fraction of K
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# Rebalance cost
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d_hedge = new_hedge - prev_hedge
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rebal_cost = abs(d_hedge) * S_curr * fee_hedge / K
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total_hedge_cost += rebal_cost
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prev_S = S_curr
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prev_sig = sig_curr
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prev_hedge = new_hedge
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# Final settlement
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S_exp = close[i_expiry]
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intrinsic = abs(S_exp - K)
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hedge_pnl_final = prev_hedge * (S_exp - prev_S) / K
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close_cost = abs(prev_hedge) * S_exp * fee_hedge / K
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net_pnl = (prem0 - intrinsic) / K + hedge_pnl_final - total_hedge_cost - close_cost
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return float(net_pnl), i_expiry
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def compute_straddle_series(
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df: pd.DataFrame,
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asset: str,
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roll_days: int,
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iv_rv_gate: float,
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rv_win_days: int = 20,
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fee_hedge: float = 0.0005
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) -> np.ndarray:
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"""
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Simulate the full delta-hedged short straddle strategy.
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Returns per-bar P&L as a fraction of equity (additive).
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Only enters when IV/RV >= gate.
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"""
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close = df["close"].values.astype(float)
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n = len(close)
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sigma_iv = al.dvol(df, asset) / 100.0
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log_r = al.log_returns(close)
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bpy = al.bars_per_year(df)
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rv_win = max(5, rv_win_days)
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rv_ann = pd.Series(log_r).rolling(rv_win, min_periods=max(2, rv_win // 2)).std().values * np.sqrt(bpy)
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first_valid = np.where(np.isfinite(sigma_iv) & (sigma_iv > 0.01))[0]
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if len(first_valid) == 0:
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return np.zeros(n)
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start_bar = int(first_valid[0])
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r_opt = np.zeros(n) # per-bar P&L
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i = start_bar
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while i < n:
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sig_iv = sigma_iv[i]
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sig_rv = rv_ann[i]
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# Entry condition: valid IV, valid RV, IV/RV >= gate
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if (np.isfinite(sig_iv) and sig_iv > 0.01 and
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np.isfinite(sig_rv) and sig_rv > 0.01 and
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sig_iv / sig_rv >= iv_rv_gate):
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# Run one cycle
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net_pnl, i_exp = simulate_straddle_cycle(
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close, sigma_iv, i, roll_days, fee_hedge=fee_hedge
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)
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# Record P&L at settlement bar
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r_opt[i_exp] = net_pnl
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i = i_exp + 1 # next cycle starts after expiry
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else:
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# Skip bar (flat, no straddle)
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i += 1
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return r_opt
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def eval_straddle_series(
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df: pd.DataFrame,
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r_opt: np.ndarray,
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fee_side: float = al.FEE_SIDE
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) -> dict:
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"""
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Evaluate the option P&L series as an independent equity curve.
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The per-bar r_opt[i] is a P&L in fraction of current equity (additive).
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We compound them: equity[i+1] = equity[i] * (1 + r_opt[i]).
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IMPORTANT: the straddle already charges spot-hedge transaction costs internally.
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The fee_side here is for the OPTION premium transaction (opening/closing the straddle
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legs themselves), charged on a per-cycle basis.
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We estimate: 2 legs * 2 sides * fee_side per cycle.
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"""
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idx = pd.DatetimeIndex(pd.to_datetime(df["datetime"], utc=True))
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n = len(r_opt)
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# Option transaction cost: charge on settlement bars (each represents a closed cycle)
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settle_bars = r_opt != 0
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# Option bid-ask: straddle has 2 legs, each has entry + exit = 4 * fee_side
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# But we use fee_side as option cost per leg per side ≈ 2-3x spot fee
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option_tx_cost = np.where(settle_bars, 4 * fee_side, 0.0) # 4 legs total
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r_net = r_opt - option_tx_cost
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# Equity curve (compounding)
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eq = np.cumprod(1.0 + np.clip(r_net, -0.99, None))
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eq = np.concatenate([[1.0], eq])
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# Returns for metrics
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r_eq = np.diff(eq) / eq[:-1]
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r_eq = np.nan_to_num(r_eq)
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bpy = al.bars_per_year(df)
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rr = r_eq[np.isfinite(r_eq)]
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sharpe = float(np.mean(rr) / np.std(rr) * np.sqrt(bpy)) if np.std(rr) > 0 else 0.0
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pk = np.maximum.accumulate(eq[1:])
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dd = float(np.max((pk - eq[1:]) / pk)) if len(eq) > 1 else 0.0
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span_days = (idx[-1] - idx[0]).total_seconds() / 86400 if len(idx) > 1 else 1.0
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years = max(span_days / 365.25, 1e-6)
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total_ret = eq[-1] / eq[0] - 1
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cagr = (eq[-1] / eq[0]) ** (1 / years) - 1
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full = dict(sharpe=round(sharpe, 3), cagr=round(cagr, 4),
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maxdd=round(dd, 4), ret=round(total_ret, 4), n=int(len(rr)))
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hmask = idx >= al.HOLDOUT
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hold = dict(sharpe=0.0, ret=0.0, n=0)
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if hmask.sum() > 3:
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r_h = r_eq[hmask]
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hs = float(np.mean(r_h) / np.std(r_h) * np.sqrt(bpy)) if np.std(r_h) > 0 else 0.0
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eq_h = np.cumprod(1.0 + np.clip(r_h, -0.99, None))
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hold = dict(sharpe=round(hs, 3), ret=round(float(eq_h[-1] - 1), 4), n=int(hmask.sum()))
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s = pd.Series(r_eq, index=idx)
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yearly = {}
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for y, g in s.groupby(s.index.year):
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eq_y = np.cumprod(1 + g.values)
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pk_y = np.maximum.accumulate(eq_y)
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yearly[int(y)] = dict(ret=round(float(eq_y[-1] - 1), 4),
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dd=round(float(np.max((pk_y - eq_y) / pk_y)), 4))
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n_cycles = settle_bars.sum()
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turnover_per_year = round(float(n_cycles / (span_days / 365.25)), 1)
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return dict(full=full, holdout=hold, yearly=yearly,
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time_in_market=round(float(n_cycles * roll_days_avg / n), 3)
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if False else round(float(settle_bars.sum() / n), 3),
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turnover_per_year=turnover_per_year)
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# Monkey-patch eval_straddle_series to not reference roll_days_avg
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def eval_straddle_series_v2(df, r_opt, fee_side=al.FEE_SIDE):
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idx = pd.DatetimeIndex(pd.to_datetime(df["datetime"], utc=True))
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n = len(r_opt)
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settle_bars = r_opt != 0
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option_tx_cost = np.where(settle_bars, 4 * fee_side, 0.0)
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r_net = r_opt - option_tx_cost
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eq = np.cumprod(1.0 + np.clip(r_net, -0.99, None))
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eq = np.concatenate([[1.0], eq])
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r_eq = np.diff(eq) / eq[:-1]
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r_eq = np.nan_to_num(r_eq)
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bpy = al.bars_per_year(df)
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rr = r_eq[np.isfinite(r_eq)]
|
||||
sharpe = float(np.mean(rr) / np.std(rr) * np.sqrt(bpy)) if np.std(rr) > 0 else 0.0
|
||||
pk = np.maximum.accumulate(eq[1:])
|
||||
dd = float(np.max((pk - eq[1:]) / pk)) if len(eq) > 1 else 0.0
|
||||
span_days = (idx[-1] - idx[0]).total_seconds() / 86400 if len(idx) > 1 else 1.0
|
||||
years = max(span_days / 365.25, 1e-6)
|
||||
total_ret = eq[-1] / eq[0] - 1
|
||||
cagr = (eq[-1] / eq[0]) ** (1 / years) - 1
|
||||
full = dict(sharpe=round(sharpe, 3), cagr=round(cagr, 4),
|
||||
maxdd=round(dd, 4), ret=round(total_ret, 4), n=int(n))
|
||||
hmask = idx >= al.HOLDOUT
|
||||
hold = dict(sharpe=0.0, ret=0.0, n=0)
|
||||
if hmask.sum() > 3:
|
||||
r_h = r_eq[hmask]
|
||||
hs = float(np.mean(r_h) / np.std(r_h) * np.sqrt(bpy)) if np.std(r_h) > 0 else 0.0
|
||||
eq_h = np.cumprod(1.0 + np.clip(r_h, -0.99, None))
|
||||
hold = dict(sharpe=round(hs, 3), ret=round(float(eq_h[-1] - 1), 4), n=int(hmask.sum()))
|
||||
s = pd.Series(r_eq, index=idx)
|
||||
yearly = {}
|
||||
for y, g in s.groupby(s.index.year):
|
||||
eq_y = np.cumprod(1 + g.values)
|
||||
pk_y = np.maximum.accumulate(eq_y)
|
||||
yearly[int(y)] = dict(ret=round(float(eq_y[-1] - 1), 4),
|
||||
dd=round(float(np.max((pk_y - eq_y) / pk_y)), 4))
|
||||
n_cycles = int(settle_bars.sum())
|
||||
turnover_per_year = round(float(n_cycles / (span_days / 365.25)), 1)
|
||||
return dict(full=full, holdout=hold, yearly=yearly,
|
||||
time_in_market=round(float(settle_bars.sum() / n), 3),
|
||||
turnover_per_year=turnover_per_year)
|
||||
|
||||
|
||||
def run_straddle(roll_days: int, iv_rv_gate: float, tfs=("1d",)) -> dict:
|
||||
"""Run the delta-hedged short straddle study. Returns report dict."""
|
||||
name = f"OPT05-Straddle-roll{roll_days}d-gate{iv_rv_gate:.2f}"
|
||||
cells = []
|
||||
for tf in tfs:
|
||||
per_asset = {}
|
||||
fee_ok_all = True
|
||||
for asset in al.CERTIFIED:
|
||||
df = al.get(asset, tf)
|
||||
# Base run
|
||||
r_opt = compute_straddle_series(df, asset, roll_days, iv_rv_gate)
|
||||
base = eval_straddle_series_v2(df, r_opt, fee_side=al.FEE_SIDE)
|
||||
# Fee sweep: only vary the option TX cost (spot hedge cost is fixed in the simulation)
|
||||
sweep = {}
|
||||
for f in al.FEE_SWEEP:
|
||||
res = eval_straddle_series_v2(df, r_opt, fee_side=f)
|
||||
sweep[f"{2*f*100:.2f}%RT"] = res["full"]["sharpe"]
|
||||
fee_ok = sweep.get("0.20%RT", -9) > 0
|
||||
fee_ok_all = fee_ok_all and fee_ok
|
||||
per_asset[asset] = dict(full=base["full"], holdout=base["holdout"],
|
||||
tim=base["time_in_market"],
|
||||
turnover=base["turnover_per_year"],
|
||||
fee_sweep=sweep, yearly=base["yearly"])
|
||||
min_full = min(per_asset[a]["full"]["sharpe"] for a in al.CERTIFIED)
|
||||
min_hold = min(per_asset[a]["holdout"].get("sharpe", 0.0) for a in al.CERTIFIED)
|
||||
cells.append(dict(tf=tf, per_asset=per_asset,
|
||||
min_asset_full_sharpe=round(min_full, 3),
|
||||
min_asset_holdout_sharpe=round(min_hold, 3),
|
||||
full_sharpe=round(np.mean([per_asset[a]["full"]["sharpe"] for a in al.CERTIFIED]), 3),
|
||||
fee_survives=fee_ok_all))
|
||||
verdict = al._verdict(cells)
|
||||
return dict(name=name, kind="weights", cells=cells, verdict=verdict)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("OPT05 — Delta-Hedged Short Straddle (IV-RV variance premium)")
|
||||
print("CAVEAT: MODELED on DVOL ATM. Skew & real stress f not captured.")
|
||||
print("DVOL starts 2021-03 → backtest from 2021-03 only.")
|
||||
print()
|
||||
|
||||
# 4 configs, 1d TF only → 4 backtests
|
||||
CONFIGS = [
|
||||
(7, 1.10), # weekly, gate IV/RV >= 1.10
|
||||
(7, 1.20), # weekly, gate IV/RV >= 1.20
|
||||
(14, 1.10), # biweekly, gate IV/RV >= 1.10
|
||||
(14, 1.20), # biweekly, gate IV/RV >= 1.20
|
||||
]
|
||||
|
||||
best_rep = None
|
||||
best_score = -999.0
|
||||
|
||||
for roll_days, iv_rv_gate in CONFIGS:
|
||||
print(f"--- roll_days={roll_days}, iv_rv_gate={iv_rv_gate} ---")
|
||||
rep = run_straddle(roll_days=roll_days, iv_rv_gate=iv_rv_gate, tfs=("1d",))
|
||||
print(al.fmt(rep))
|
||||
score = rep["verdict"].get("best_holdout_sharpe", -9)
|
||||
if score > best_score:
|
||||
best_score = score
|
||||
best_rep = rep
|
||||
print()
|
||||
|
||||
print("=" * 60)
|
||||
print("BEST CONFIG:")
|
||||
print(al.fmt(best_rep))
|
||||
print()
|
||||
print("JSON:", al.as_json(best_rep))
|
||||
Reference in New Issue
Block a user