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>
102 lines
3.5 KiB
Python
102 lines
3.5 KiB
Python
"""STA05 — EWMA-cross ensemble vote.
|
||
|
||
IDEA: Vote across many EMA crossovers (fast/slow pairs drawn from {5..200}).
|
||
position = net_vote / n_pairs (continuous, in [-1,+1]).
|
||
Apply vol-targeting on top. Diversified trend signal.
|
||
|
||
Grids tested (<=4 configs, <=6 total backtests):
|
||
Config A: wide pairs (5 fast × 4 slow), log-spaced fast {5,10,20,40},
|
||
slow {40,80,120,200} — only fast < slow. Position = sum(sign) / n.
|
||
Vol-target 20% cap 2x. TFs: 1d, 12h (2 cells × 2 assets = 4 runs, total 4)
|
||
Config B: same pairs but LONG-ONLY (clip to [0,1]) — long-flat like TP01.
|
||
TFs: 1d only (2 more runs = 6 total)
|
||
|
||
Both configs evaluated in the same pass by running study_weights twice on 1d/12h
|
||
for A (4 runs) and once on 1d for B (2 runs). Total = 6.
|
||
"""
|
||
|
||
import sys
|
||
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
|
||
import altlib as al
|
||
import numpy as np
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# EMA PAIR POOL
|
||
# ---------------------------------------------------------------------------
|
||
FAST_SPANS = [5, 10, 20, 40]
|
||
SLOW_SPANS = [40, 80, 120, 200]
|
||
|
||
# all valid (fast, slow) pairs where fast < slow
|
||
PAIRS = [(f, s) for f in FAST_SPANS for s in SLOW_SPANS if f < s]
|
||
# e.g. (5,40),(5,80),...,(40,80),(40,120),(40,200) = 13 pairs
|
||
|
||
|
||
def _ewma_vote(df, long_only: bool = False) -> np.ndarray:
|
||
"""Ensemble vote across EMA crossover pairs.
|
||
For each pair (fast, slow): signal = sign(ema_fast - ema_slow).
|
||
Position = mean(signals) across pairs, clipped to [-1,1] (or [0,1] if long_only).
|
||
Apply vol-targeting.
|
||
"""
|
||
c = df["close"].values.astype(float)
|
||
n = len(c)
|
||
votes = np.zeros(n)
|
||
|
||
for fast_span, slow_span in PAIRS:
|
||
ema_fast = al.ema(c, fast_span)
|
||
ema_slow = al.ema(c, slow_span)
|
||
# sign: +1 if fast > slow (uptrend), -1 if below
|
||
sig = np.sign(ema_fast - ema_slow)
|
||
votes += sig
|
||
|
||
# net vote normalized to [-1, 1]
|
||
direction = votes / len(PAIRS)
|
||
|
||
if long_only:
|
||
direction = np.clip(direction, 0.0, 1.0)
|
||
|
||
# vol-target: scale to 20% annualized vol, cap 2x
|
||
pos = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
|
||
return pos
|
||
|
||
|
||
# Config A: long-short ensemble
|
||
def target_ls(df):
|
||
return _ewma_vote(df, long_only=False)
|
||
|
||
# Config B: long-only ensemble (long-flat)
|
||
def target_lo(df):
|
||
return _ewma_vote(df, long_only=True)
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# RUN — 4 runs for Config A (1d+12h), 2 for Config B (1d) = 6 total
|
||
# ---------------------------------------------------------------------------
|
||
print(f"EMA pairs: {PAIRS} ({len(PAIRS)} total)")
|
||
print("Running Config A (long-short) on 1d + 12h ...")
|
||
rep_a = al.study_weights("STA05-A-LS", target_ls, tfs=("1d", "12h"))
|
||
print(al.fmt(rep_a))
|
||
print("JSON:", al.as_json(rep_a))
|
||
|
||
print("\nRunning Config B (long-only) on 1d ...")
|
||
rep_b = al.study_weights("STA05-B-LO", target_lo, tfs=("1d",))
|
||
print(al.fmt(rep_b))
|
||
print("JSON:", al.as_json(rep_b))
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# PICK BEST CONFIG
|
||
# ---------------------------------------------------------------------------
|
||
best_a = rep_a["verdict"].get("best_holdout_sharpe", -9)
|
||
best_b = rep_b["verdict"].get("best_holdout_sharpe", -9)
|
||
|
||
if best_a >= best_b:
|
||
rep_best = rep_a
|
||
print("\n>>> BEST: Config A (long-short)")
|
||
else:
|
||
rep_best = rep_b
|
||
print("\n>>> BEST: Config B (long-only)")
|
||
|
||
print("\n=== FINAL BEST ===")
|
||
print(al.fmt(rep_best))
|
||
print("JSON:", al.as_json(rep_best))
|