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
@@ -0,0 +1,145 @@
|
||||
"""RSK01 — Vol-target B&H + DD breaker.
|
||||
|
||||
Hypothesis: Long-only vol-targeted (no trend signal) with a circuit breaker:
|
||||
- Normally always long, scaled by vol-targeting (target 20%, cap 2x)
|
||||
- Goes FLAT when the strategy equity drawdown from peak exceeds `dd_thresh`
|
||||
- Re-enters when the MARKET (asset price) recovers by `recovery_frac` from its
|
||||
trough level at the time the breaker fired
|
||||
(NOTE: recovery on MARKET price, not strategy equity — otherwise the flat
|
||||
position freezes equity and the breaker never clears, a death spiral)
|
||||
- Does the breaker beat pure vol-targeted buy&hold?
|
||||
|
||||
Grid: 3 configs on 1d (= 6 asset-backtests, within the 6-backtest limit).
|
||||
"""
|
||||
|
||||
import sys
|
||||
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
|
||||
import altlib as al
|
||||
import numpy as np
|
||||
|
||||
|
||||
def rsk01_target(df, dd_thresh: float = 0.15, recovery_frac: float = 0.50) -> np.ndarray:
|
||||
"""
|
||||
Causal vol-targeted long-only position with equity-DD circuit breaker.
|
||||
|
||||
Breaker fires when strategy equity drawdown > dd_thresh.
|
||||
Recovery: re-enter when asset price has risen by recovery_frac * (asset price drop
|
||||
from the time breaker fired). This is observable from MARKET price, avoids death-spiral.
|
||||
|
||||
At each bar i:
|
||||
1. Base vol-targeted position (direction=+1) computed causally
|
||||
2. Simulated strategy equity updated by previous bar's held position
|
||||
3. If equity-DD > dd_thresh → BREAKER ON, record price_trough = close[i]
|
||||
4. BREAKER recovers when close[i] >= price_trough * (1 + recovery_frac * rel_drop)
|
||||
where rel_drop = (price_at_breaker_on - price_trough_at_bar_i) / price_at_breaker_on
|
||||
More simply: re-enter when close[i] >= price_trough * (1 + recovery_frac * dd_thresh)
|
||||
"""
|
||||
c = df["close"].values.astype(float)
|
||||
r = al.simple_returns(c)
|
||||
|
||||
# Base vol-targeted position (always long direction=+1)
|
||||
direction = np.ones(len(c))
|
||||
base_pos = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
|
||||
|
||||
n = len(c)
|
||||
final_pos = np.zeros(n)
|
||||
|
||||
# Strategy equity tracking (causal: equity at i reflects positions through i-1)
|
||||
eq = 1.0
|
||||
peak = 1.0
|
||||
breaker_on = False
|
||||
price_trough = np.nan # asset price when breaker fired
|
||||
recovery_target_price = np.nan # asset price target for re-entry
|
||||
|
||||
for i in range(n):
|
||||
# Update strategy equity from previous bar's position
|
||||
if i > 0:
|
||||
prev_pos = final_pos[i - 1]
|
||||
eq *= (1.0 + prev_pos * r[i])
|
||||
|
||||
# Update running equity peak
|
||||
if eq > peak:
|
||||
peak = eq
|
||||
|
||||
dd = (peak - eq) / peak if peak > 0 else 0.0
|
||||
price_now = c[i]
|
||||
|
||||
if not breaker_on:
|
||||
if dd > dd_thresh:
|
||||
breaker_on = True
|
||||
# Record asset price trough at breakout trigger
|
||||
price_trough = price_now
|
||||
# Recovery target: price rises by recovery_frac * dd_thresh above trough
|
||||
# (dd_thresh is a proxy for the % drop in the asset that caused the DD)
|
||||
recovery_target_price = price_trough * (1.0 + recovery_frac * dd_thresh)
|
||||
else:
|
||||
# Re-enter when asset recovers to recovery_target_price
|
||||
if price_now >= recovery_target_price:
|
||||
breaker_on = False
|
||||
price_trough = np.nan
|
||||
recovery_target_price = np.nan
|
||||
# Also reset the equity peak to current level to avoid immediate re-trigger
|
||||
peak = eq
|
||||
|
||||
final_pos[i] = 0.0 if breaker_on else base_pos[i]
|
||||
|
||||
return final_pos
|
||||
|
||||
|
||||
def make_target(dd_thresh: float, recovery_frac: float):
|
||||
"""Factory to create a target function with fixed params."""
|
||||
def _target(df):
|
||||
return rsk01_target(df, dd_thresh=dd_thresh, recovery_frac=recovery_frac)
|
||||
_target.__name__ = f"RSK01_dd{int(dd_thresh*100)}_rec{int(recovery_frac*100)}"
|
||||
return _target
|
||||
|
||||
|
||||
# Grid: 3 configs on 1d (= 6 asset-backtests, within the 6-backtest limit)
|
||||
CONFIGS_SCREEN = [
|
||||
(0.10, 0.50), # tight breaker, recover 50% of dd_thresh in price terms
|
||||
(0.15, 0.50), # moderate breaker
|
||||
(0.20, 0.50), # loose breaker
|
||||
]
|
||||
|
||||
print("=== RSK01: Vol-target B&H + DD circuit breaker ===")
|
||||
print("Recovery measured on MARKET PRICE (not frozen strategy equity)")
|
||||
print("Screening 3 configs on 1d (6 asset-backtests)...")
|
||||
print()
|
||||
|
||||
best_rep = None
|
||||
best_score = -999
|
||||
best_cfg = None
|
||||
|
||||
for dd_thresh, rec_frac in CONFIGS_SCREEN:
|
||||
name = f"RSK01_dd{int(dd_thresh*100)}_rec{int(rec_frac*100)}"
|
||||
target_fn = make_target(dd_thresh, rec_frac)
|
||||
rep = al.study_weights(name, target_fn, tfs=("1d",))
|
||||
|
||||
score = rep["verdict"].get("best_holdout_sharpe", -9)
|
||||
btc = rep["cells"][0]["per_asset"]["BTC"]
|
||||
eth = rep["cells"][0]["per_asset"]["ETH"]
|
||||
print(f" {name}:")
|
||||
print(f" BTC: full Sh={btc['full']['sharpe']:.2f} DD={btc['full']['maxdd']:.1%} "
|
||||
f"TIM={btc['tim']:.1%} hold Sh={btc['holdout']['sharpe']:.2f}")
|
||||
print(f" ETH: full Sh={eth['full']['sharpe']:.2f} DD={eth['full']['maxdd']:.1%} "
|
||||
f"TIM={eth['tim']:.1%} hold Sh={eth['holdout']['sharpe']:.2f}")
|
||||
print(f" grade={rep['verdict']['grade']} minFull={rep['verdict'].get('best_full_sharpe'):.2f} "
|
||||
f"minHold={score:.2f}")
|
||||
print()
|
||||
|
||||
if score > best_score:
|
||||
best_score = score
|
||||
best_rep = rep
|
||||
best_cfg = (dd_thresh, rec_frac)
|
||||
|
||||
print(f"Best config: dd_thresh={best_cfg[0]}, recovery_frac={best_cfg[1]}")
|
||||
print()
|
||||
|
||||
# Final clean report on best config
|
||||
dd_thresh, rec_frac = best_cfg
|
||||
name = f"RSK01_dd{int(dd_thresh*100)}_rec{int(rec_frac*100)}"
|
||||
target_fn = make_target(dd_thresh, rec_frac)
|
||||
final_rep = al.study_weights(name, target_fn, tfs=("1d",))
|
||||
|
||||
print(al.fmt(final_rep))
|
||||
print("JSON:", al.as_json(final_rep))
|
||||
Reference in New Issue
Block a user