research(blind): 52 agenti ciechi su curve anonime BTC/ETH — orchestratore valuta PnL/maxDD, niente di nuovo regge

Flotta di 52 subagenti "esperti di segnali" su storico BTC/ETH ANONIMIZZATO (Series A/B
rebased a 100, calendario sintetico, split 70/30) — non sanno cosa siano. Ognuno scrive un
signal(df)->position causale (script o ML), tunato solo sul train. Orchestratore valuta su
PnL e maxDD nel test held-out.

Harness cieco leak-free (riusabile):
- make_blind.py: export anonimo + overlay; blindlib.py: evaluator con shift della posizione +
  GUARDIA DI CAUSALITA' online (squalifica ogni look-ahead, ML incluso); blind_eval.py CLI;
  score_all.py giudice OOS; verify_top.py (corr-al-trend, fee-stress, jackknife).
- 52/52 passano la guardia (zero leak su tutta la flotta).

Esito OOS (benchmark buy&hold: -7% PnL, 68% DD):
- top = macd (+21%, DD 11%, Sh 0.84), accel, vol_of_vol, regime_switch, rf, obv — tutti
  trend/vol-regime. Sharpe OOS ~0.84 decade dal train ~1.4. Mean-rev e ML in fondo.
- 3 scettici indipendenti: REFUTED. regime-luck (top-5 bar = 67-102% del PnL); trend-redundancy
  (HAC alpha t=+0.9..+1.5, nessuno >1.96 — TSMOM travestito); overfit (accel/vov knife-edge).

Verdetto: ri-conferma CIECA e indipendente del soffitto direzionale ~1.3. macd = classe-TP01,
forward-monitor non deploy. Diario 2026-06-21-blind-signal-fleet.md.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/research/blind/agents/agent_26_stoch.py

@@ -0,0 +1,134 @@
+"""Agent 26 — Stochastic oscillator reversion + cross, trend-gated (family=osc, slug=stoch).
+
+The angle (assigned): a rolling Stochastic oscillator (%K / %D). %K = where the close sits
+in its rolling [min(low), max(high)] window (0..100); %D = a short SMA of %K (the signal
+line). Trade the REVERSION (%K leaving an oversold extreme) timed by the %K-vs-%D CROSS,
+GATED by a longer trend filter. Tune the windows.
+
+Reading the train curves first (both A and B, split='train'): they trend UP very hard
+(A 100->792, B 100->2400 over the window). UNLIKE RSI — which in these up-curves never
+dips below ~40 so textbook 30/70 is dead — the Stochastic %K is normalized against its
+OWN rolling high/low, so it sweeps the FULL 0..100 range even inside the bull: %K<20
+~12-14% of bars, %K>80 ~24-27% of bars (measured). That is exactly the structure a
+stochastic reversion rule needs, so the angle is genuinely playable here, but it still
+has to be REGIME-AWARE because the curves drift up:
+
+  * In an UPTREND (close above a long SMA) %K oversold (<LO) is a BUY-THE-DIP setup, and we
+    require %K to CROSS BACK UP through its signal line %D — the standard stochastic long
+    trigger — before going LONG. That waits for the dip to actually TURN (anticipating the
+    bounce) instead of knife-catching while %K is still falling. We HOLD the long
+    (hysteresis) until %K recovers into EXIT, then go flat. We do NOT short %K>80 in an
+    uptrend — overbought in a bull keeps running (that is momentum, not reversion).
+  * In a DOWNTREND (close below the long SMA) the symmetry returns: %K overbought (>80) with
+    a %K cross DOWN through %D is a reversion SHORT (rips fade). %K<LO we stand flat (don't
+    knife-catch long under a downtrend). The short side is down-weighted (SHORT_W) because
+    the drift is up; on train it is marginal (see HONEST NOTE).
+
+WHY THE CROSS MATTERS (the "anticipation" the angle asks for): entering the instant %K
+prints <LO is usually early — %K is still falling. Waiting for the %K/%D up-cross times the
+turn, which on train is the difference between a coin-flip dip rule and a positive one: with
+the cross the dip-long sits at ~9-12% DD with a clean positive Sharpe; without it the same
+thresholds bleed. The cross also cuts whipsaw turnover (~5-6 round-trips/yr, fee-cheap).
+
+The trend gate does two jobs: it picks WHICH side of the oscillator is reversion (buy dips
+in up-trend / sell rips in down-trend) and it suppresses the side that fights the drift.
+Sizing is smooth (deeper oversold -> bigger appetite, floored at BASE while holding) then
+VOL-TARGETED so the two curves are risk-comparable and exposure shrinks into vol spikes
+(crashes are vol spikes) — that is what bounds the drawdown. Note the leverage cap never
+binds here (post-vol-target appetite stays <=1), so the edge does NOT rely on leverage.
+
+HONEST NOTE (negative findings kept): (1) the downtrend short side is essentially free but
+adds nothing on train — SHORT_W=0.5 gives sharpe_min 0.51 vs 0.53 at SHORT_W=0; it is kept
+small to honor the bidirectional angle, not because it earns. (2) A continuous always-on
+oscillator weighting (no flat state) was tried and pushed time-in-market to ~99% and DD to
+0.20-0.37 — it degenerated into buy-and-hold; the hysteresis flat state is what keeps the
+DD at ~12%. (3) In a market that trends this hard, even a cross-gated dip-buy is PARTLY
+trend participation (the dips it buys recover and it rides them). The genuine reversion
+content is the oversold-entry / cross-timed turn / overbought-exit cycle plus the DD control
+from the trend gate + vol-target. Result: an honest, MODEST combined train Sharpe ~0.5 at
+~12% DD — a fraction of buy&hold's huge PnL but ~6x less drawdown (it anticipates the dip
+rather than just holding the asset through every crash).
+
+CAUSAL: %K uses trailing rolling max(high)/min(low) (<= i); %D is a trailing SMA of %K; the
+cross compares (%K-%D) at i vs i-1 (past only); the hold-state is a forward cumulative pass
+over PAST bars only; the SMA trend filter and vol_target use trailing data. No shift(-k), no
+centered windows, no global fit. Verified by causality_ok (max_diff 0.0).
+
+Tuning (train only, combined A&B; coarse->fine sweep + plateau check). The chosen cell sits
+on a broad plateau (K in [14..20], LO in [40..50], EXIT in [55..65], D in [3..5], TREND_WIN
+in [150..200] all hold sharpe_min ~0.37..0.53 at DD ~0.09..0.12 — a plateau, not a spike):
+  K_WIN=20, D_WIN=5, LO=50, EXIT=55, TREND_WIN=150
+  SHORT_W=0.5, BASE=0.7, TARGET_VOL=0.25, VOL_WIN_DAYS=35, LEV_CAP=1.5
+  -> train combined: pnl_mean ~0.17, maxdd_worst ~0.12, sharpe_min ~0.51
+"""
+import numpy as np
+import pandas as pd
+import blindlib as bl
+
+K_WIN = 20          # %K lookback (rolling high/low window). 20 > textbook 14 for these trends.
+D_WIN = 5           # %D = SMA(%K, D_WIN): the signal line the %K crosses.
+LO = 50.0           # oversold threshold below which a %K/%D up-cross is a dip-long entry.
+EXIT = 55.0         # dip-long HELD until %K recovers past EXIT (hysteresis entry/exit pair).
+TREND_WIN = 150     # long SMA: above = uptrend (buy dips), below = downtrend (sell rips).
+SHORT_W = 0.5       # weight on the downtrend reversion-short; marginal (see HONEST NOTE).
+BASE = 0.7          # base long size while holding a dip (scaled up if %K still oversold).
+TARGET_VOL = 0.25
+VOL_WIN_DAYS = 35
+LEV_CAP = 1.5
+
+
+def _stoch(df, k_win, d_win):
+    """Causal Stochastic oscillator. %K[i] uses high/low/close over the trailing
+    k_win bars (<= i); %D[i] = SMA(%K, d_win) (trailing). No look-ahead."""
+    h = df["high"].values.astype(float)
+    l = df["low"].values.astype(float)
+    c = df["close"].values.astype(float)
+    hh = pd.Series(h).rolling(k_win, min_periods=1).max().values
+    ll = pd.Series(l).rolling(k_win, min_periods=1).min().values
+    rng = hh - ll
+    k = np.where(rng > 1e-12, (c - ll) / rng * 100.0, 50.0)
+    d = bl.sma(k, d_win)
+    return k, d
+
+
+def signal(df):
+    c = df["close"].values.astype(float)
+    n = len(c)
+    k, d = _stoch(df, K_WIN, D_WIN)
+    trend_up = c > bl.sma(c, TREND_WIN)               # causal trailing SMA trend gate
+
+    # --- %K/%D crosses (past-only: compares i vs i-1) ---
+    kd = k - d
+    kd_prev = np.concatenate(([0.0], kd[:-1]))
+    cross_up = (kd > 0) & (kd_prev <= 0)              # %K turns up through its signal line
+    cross_dn = (kd < 0) & (kd_prev >= 0)              # %K turns down through its signal line
+
+    # --- smooth reversion appetite from %K (further past threshold -> bigger) ---
+    long_app = np.clip((LO - k) / LO, 0.0, 1.0)               # oversold depth -> long appetite
+    short_app = np.clip((k - 80.0) / 20.0, 0.0, 1.0)         # overbought depth -> short appetite
+
+    # --- trend-gated stochastic reversion with cross-triggered entry + hysteresis ---
+    # Forward pass is PURE PAST-ONLY: in_long at bar i depends only on bars <= i.
+    held = np.zeros(n)
+    in_long = False
+    for i in range(n):
+        if in_long:
+            # exit the held dip-long when trend breaks down OR %K has recovered past EXIT
+            if (not trend_up[i]) or (k[i] >= EXIT):
+                in_long = False
+        else:
+            # enter a dip-long in an uptrend when %K is oversold AND turns up through %D
+            if trend_up[i] and (k[i] < LO) and cross_up[i]:
+                in_long = True
+        if in_long:
+            held[i] = max(BASE, long_app[i])         # ride the recovery, bigger if still oversold
+        else:
+            # downtrend reversion-short: overbought AND %K turning down through %D
+            if (not trend_up[i]) and (k[i] > 80.0) and cross_dn[i]:
+                held[i] = -SHORT_W * short_app[i]
+            else:
+                held[i] = 0.0
+
+    pos = bl.vol_target(held, df, target_vol=TARGET_VOL,
+                        vol_win_days=VOL_WIN_DAYS, leverage_cap=LEV_CAP)
+    return np.clip(np.nan_to_num(pos, nan=0.0), -1.0, 1.0)