Adriano Dal Pastro
5ac4e16af8
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>
132 lines
4.1 KiB
Python
132 lines
4.1 KiB
Python
"""MRV02 — BB reversion in calm regime (1d, discrete signals).
|
|
|
|
HYPOTHESIS: Buy lower BB(20,2) ONLY when realized vol is in low expanding-percentile
|
|
(calm regime). Exit at mid-BB. The gate is the alpha: filter out high-vol / volatile
|
|
periods; only trade the gentle reversions.
|
|
|
|
Style: al.study_signals (discrete entry/exit, 1d only)
|
|
Gate: RV <= expanding percentile of RV (calm = low expanding percentile threshold)
|
|
Entry: close <= lower BB(20,2)
|
|
TP: mid-BB (dynamic, recomputed each bar in the trade management)
|
|
SL: 2 * ATR below entry
|
|
Max bars: 20 days
|
|
"""
|
|
import sys
|
|
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
|
|
import altlib as al
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
|
|
def make_entries(df: pd.DataFrame, bb_win: int = 20, bb_k: float = 2.0,
|
|
rv_win_days: int = 20, rv_pct_thresh: float = 30.0,
|
|
atr_win: int = 14, max_bars: int = 20):
|
|
"""
|
|
Causal entry logic for MRV02.
|
|
|
|
Entry conditions at close[i]:
|
|
1. close[i] <= lower_BB(20,2) — price touched/crossed lower band
|
|
2. rv_percentile(i) <= rv_pct_thresh — calm regime (low expanding RV percentile)
|
|
|
|
TP: mid_BB at entry time (static target for the trade)
|
|
SL: entry - 2*ATR (static)
|
|
max_bars: 20 days
|
|
"""
|
|
c = df["close"].values.astype(float)
|
|
n = len(c)
|
|
bpd = al.bars_per_day(df)
|
|
bpy = bpd * 365.25
|
|
|
|
# Bollinger Bands (causal: value at i uses data <= i)
|
|
upper_bb, mid_bb, lower_bb = al.bbands(c, win=bb_win, k=bb_k)
|
|
|
|
# Realized vol (annualized), window = rv_win_days bars
|
|
rv_win = max(2, rv_win_days * bpd)
|
|
r = al.simple_returns(c)
|
|
rv = al.realized_vol(r, rv_win, bpy)
|
|
|
|
# Expanding percentile of RV (causal: percentile of all RV values seen up to i)
|
|
rv_series = pd.Series(rv)
|
|
rv_pct = rv_series.expanding().rank(pct=True) * 100.0 # 0-100 percentile
|
|
rv_pct = rv_pct.values
|
|
|
|
# ATR for SL
|
|
atr_vals = al.atr(df, win=atr_win)
|
|
|
|
entries = [None] * n
|
|
warmup = max(bb_win, rv_win, atr_win) + 1
|
|
|
|
for i in range(warmup, n):
|
|
# Gate: RV must be in calm regime
|
|
if not np.isfinite(rv_pct[i]) or rv_pct[i] > rv_pct_thresh:
|
|
continue
|
|
# Gate: lower BB must be defined
|
|
if not np.isfinite(lower_bb[i]) or not np.isfinite(mid_bb[i]):
|
|
continue
|
|
# Entry: close touches or crosses lower BB
|
|
if c[i] > lower_bb[i]:
|
|
continue
|
|
# ATR must be defined
|
|
if not np.isfinite(atr_vals[i]) or atr_vals[i] <= 0:
|
|
continue
|
|
|
|
tp_price = mid_bb[i] # exit at mid-band (static target)
|
|
sl_price = c[i] - 2.0 * atr_vals[i] # SL: 2 ATR below entry
|
|
|
|
# Only take trade if TP > entry price (there's room to profit)
|
|
if tp_price <= c[i]:
|
|
continue
|
|
|
|
entries[i] = {
|
|
"dir": +1,
|
|
"tp": tp_price,
|
|
"sl": sl_price,
|
|
"max_bars": max_bars,
|
|
}
|
|
|
|
return entries
|
|
|
|
|
|
# ----------------------------------------------------------------
|
|
# Small parameter grid: bb_win x rv_pct_thresh (4 combos max)
|
|
# ----------------------------------------------------------------
|
|
GRID = [
|
|
# (bb_win, rv_pct_thresh)
|
|
(20, 30), # canonical
|
|
(20, 40), # slightly more permissive gate
|
|
(30, 30), # wider bands
|
|
(30, 40), # wider bands + more permissive gate
|
|
]
|
|
|
|
print("MRV02 — BB reversion in calm regime")
|
|
print(f"Grid: {GRID}")
|
|
print()
|
|
|
|
best_rep = None
|
|
best_score = -999.0
|
|
|
|
for bb_win, rv_pct_thresh in GRID:
|
|
label = f"MRV02[BB{bb_win},RVp{rv_pct_thresh}]"
|
|
print(f"--- Testing {label} ---")
|
|
|
|
def make_fn(bw=bb_win, rp=rv_pct_thresh):
|
|
def entries_fn(df):
|
|
return make_entries(df, bb_win=bw, rv_pct_thresh=rp)
|
|
return entries_fn
|
|
|
|
rep = al.study_signals(label, make_fn(), tfs=("1d",))
|
|
print(al.fmt(rep))
|
|
print()
|
|
|
|
v = rep["verdict"]
|
|
score = v.get("best_holdout_sharpe", -999.0) or -999.0
|
|
if score > best_score:
|
|
best_score = score
|
|
best_rep = rep
|
|
best_rep["_config"] = dict(bb_win=bb_win, rv_pct_thresh=rv_pct_thresh)
|
|
|
|
print("\n=== BEST CONFIG ===")
|
|
print(al.fmt(best_rep))
|
|
print()
|
|
print("JSON:", al.as_json(best_rep))
|