feat(shape): SH01 Shape-ML validato come diversificatore + doc
Validazione dura del solo edge sopravvissuto alla ricerca shape (ML walk-forward LogisticRegression sulle feature di forma). SH01 config W24 H12 th0.58: - BTC robusto ovunque (expanding +219%/OOS+42% Sharpe2.72 8-9anni; rolling2y +166%/+96%; stress leva2x+slippage OK), ETH/ADA solo expanding, LTC/SOL/XRP no. - Griglia 5/27 robuste su cresta W24/H8-12 -> overfit moderato, config conservativa. - Free-lunch: corr +0.08 col MASTER, aggiungerlo migliora OOS (Sharpe 4.33->5.10, DD 4.7->4.2%). Diversificatore, non motore standalone. Regge fee 0.20% RT. SH01 come Strategy (in MODULE_MAP) + run() riproducibile. shape_ml_research esteso con walk-forward rolling (train_window). Live richiede worker con retraining. Diario 2026-05-29-shape.md, CLAUDE.md famiglia SHAPE-ML. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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"""Validazione DURA del solo edge sopravvissuto alla ricerca shape: ML walk-forward
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(LogisticRegression) sulle feature di FORMA. Tutto il resto della famiglia shape e' rumore.
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Candidato: BTC logit W24H12 th0.58 (FULL +219% / OOS +42% / Sharpe 2.72 / 8-9 anni+,
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regge fee 0.20% RT). Prima di promuoverlo a strategia serve (metodologia obbligatoria):
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1. ROBUSTEZZA MULTI-ASSET: stessa config su BTC/ETH/LTC/SOL/ADA/XRP 1h.
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2. WALK-FORWARD ROLLING (train fisso 2y) oltre all'expanding -> niente "memoria infinita".
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3. STRESS leva 2x + slippage doppio (fee 0.20% RT) -> regge in condizioni realistiche?
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4. ROBUSTEZZA SU GRIGLIA (th, W, H) -> plateau, non picco.
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5. CORRELAZIONE col MASTER + integrazione -> e' un diversificatore (free-lunch)?
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Tutto netto-fee, OOS = ultimo 30%. Conta il PnL netto, non l'accuracy (lezione squeeze).
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Run: uv run python scripts/analysis/shape_ml_validate.py
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"""
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from __future__ import annotations
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import sys
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import time
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import warnings
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from pathlib import Path
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import numpy as np
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import pandas as pd
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PROJECT_ROOT = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(PROJECT_ROOT))
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warnings.filterwarnings("ignore")
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from scripts.analysis.explore_lab import get_df, evaluate, robust, FEE_RT, LEV, POS, OOS_FRAC
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from scripts.analysis.shape_ml_research import ml_wf_entries, acc_oos
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ASSETS = ["BTC", "ETH", "LTC", "SOL", "ADA", "XRP"]
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TWO_YEARS_1H = 24 * 365 * 2 # ~17520 barre = finestra rolling 2 anni
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# ---------------------------------------------------------------------------
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def line(name, ents, df, fees=(0.0, 0.001, 0.002)):
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"""Riga evaluate + accuracy OOS, ritorna (res, robusto?)."""
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res = evaluate(name, ents, df)
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ac = acc_oos(ents, df)
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rb = (res["full"]["ret"] > 0 and res["oos"]["ret"] > 0
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and res["sweep"][0.002] > 0 and res["sweep_oos"][0.002] > 0)
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print(f" ^ accOOS={ac:4.1f}% {'[ROBUST fee0.2%]' if rb else ''}")
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return res, rb
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# ---------------------------------------------------------------------------
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def sec_multi_asset(W=24, H=12, th=0.58):
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print("\n[1] MULTI-ASSET — logit W%dH%d th%.2f, walk-forward EXPANDING (1h):" % (W, H, th))
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ok = []
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dfs = {}
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for a in ASSETS:
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df = get_df(a, "1h"); dfs[a] = df
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ents = ml_wf_entries(df, W=W, H=H, model="logit", thresh=th)
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_, rb = line(f"{a} exp", ents, df)
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if rb:
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ok.append(a)
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print(f" -> EXPANDING robusti (fee0.2%): {ok if ok else 'NESSUNO'}")
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return dfs, ok
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def sec_rolling(dfs, W=24, H=12, th=0.58, tw=TWO_YEARS_1H):
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print("\n[2] WALK-FORWARD ROLLING — train fisso ~2 anni (%d barre), stessa config:" % tw)
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ok = []
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for a in ASSETS:
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ents = ml_wf_entries(dfs[a], W=W, H=H, model="logit", thresh=th, train_window=tw)
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_, rb = line(f"{a} roll2y", ents, dfs[a])
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if rb:
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ok.append(a)
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print(f" -> ROLLING robusti (fee0.2%): {ok if ok else 'NESSUNO'}")
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return ok
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def sec_stress(dfs, W=24, H=12, th=0.58):
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print("\n[3] STRESS — leva 2x + slippage doppio (fee 0.20% RT) su BTC/ETH:")
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print(" (la config nominale e' leva 3x fee 0.10%; qui peggioro entrambe)")
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from scripts.analysis.explore_lab import simulate
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for a in ["BTC", "ETH"]:
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ents = ml_wf_entries(dfs[a], W=W, H=H, model="logit", thresh=th)
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df = dfs[a]
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split = int(len(df) * (1 - OOS_FRAC))
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base = simulate(ents, df, fee_rt=0.001, lev=3.0)
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stress_f = simulate(ents, df, fee_rt=0.002, lev=2.0)
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stress_o = simulate(ents, df, fee_rt=0.002, lev=2.0, split=split)
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print(f" {a}: base(3x,0.1%) FULL={base['ret']:+.0f}% Shrp={base['sharpe']:.2f} | "
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f"STRESS(2x,0.2%) FULL={stress_f['ret']:+.0f}% OOS={stress_o['ret']:+.0f}% "
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f"DD={stress_f['dd']:.0f}% Shrp={stress_f['sharpe']:.2f} "
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f"{'OK' if stress_f['ret'] > 0 and stress_o['ret'] > 0 else 'KO'}")
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def sec_grid(dfs, asset="BTC"):
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print(f"\n[4] ROBUSTEZZA GRIGLIA su {asset} (plateau, non picco):")
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rob = tot = 0
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for W in (16, 24, 32):
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for H in (8, 12, 16):
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for th in (0.56, 0.58, 0.60):
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ents = ml_wf_entries(dfs[asset], W=W, H=H, model="logit", thresh=th)
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_, rb = line(f"{asset} W{W}H{H}th{th}", ents, dfs[asset])
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tot += 1; rob += rb
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print(f" -> {asset}: {rob}/{tot} celle robuste a fee 0.2% (plateau se alta frazione)")
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# ---------------------------------------------------------------------------
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def shape_daily_equity(asset, IDX, W=24, H=12, th=0.58):
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"""Equity giornaliera dello sleeve shape-ML (time-exit a H, non-overlap, pos 0.15,
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leva 3x, fee 0.10% RT), normalizzata sull'indice comune dei portafogli."""
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from src.data.downloader import load_data
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df = get_df(asset, "1h")
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c = df["close"].values
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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ents = ml_wf_entries(df, W=W, H=H, model="logit", thresh=th)
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n = len(c); eq = np.full(n, 1000.0); cap = 1000.0; last_exit = -1
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fee = FEE_RT * LEV
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for e in sorted(ents, key=lambda x: x["i"]):
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i, d, mb = e["i"], e["d"], e["max_bars"]
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if i <= last_exit or i + mb >= n:
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continue
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j = i + mb
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ret = (c[j] - c[i]) / c[i] * d * LEV - fee
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cap = max(cap + cap * POS * ret, 10.0)
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eq[j:] = cap; last_exit = j
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s = pd.Series(eq, index=ts).resample("1D").last().reindex(IDX).ffill().bfill()
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return s / s.iloc[0]
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def sec_master_integration():
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print("\n[5] CORRELAZIONE + INTEGRAZIONE COL MASTER:")
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from scripts.analysis.combine_portfolio import (
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build_all_sleeves, port_returns, metrics, IDX, SPLIT,
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)
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sleeves = build_all_sleeves()
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# sleeve shape: BTC + ETH (i due con piu' storia/edge)
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shape = {}
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for a in ("BTC", "ETH"):
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shape[f"SH_{a}"] = shape_daily_equity(a, IDX)
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dr_master = port_returns(sleeves) # MASTER equal-weight attuale
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dr_shape = port_returns(shape)
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corr = float(dr_master.corr(dr_shape))
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print(f" correlazione daily MASTER vs sleeve-shape: {corr:+.3f}")
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# correlazione media shape vs ogni sleeve esistente
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cs = {k: float(port_returns({k: v}).corr(dr_shape)) for k, v in sleeves.items()}
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print(" corr shape vs singole sleeve: " + ", ".join(f"{k}={v:+.2f}" for k, v in cs.items()))
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base = {**sleeves}
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ext = {**sleeves, **shape}
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fb, ob = metrics(port_returns(base)), metrics(port_returns(base), lo=SPLIT)
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fe, oe = metrics(port_returns(ext)), metrics(port_returns(ext), lo=SPLIT)
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print(" %-22s %9s %6s %6s %6s | %9s %6s %6s %6s" %
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("portafoglio", "FULLret", "CAGR", "DD", "Shrp", "OOSret", "CAGR", "DD", "Shrp"))
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print(" %-22s %+9.0f %6.0f %6.1f %6.2f | %+9.0f %6.0f %6.1f %6.2f" %
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("MASTER (9 sleeve)", fb["ret"], fb["cagr"], fb["dd"], fb["sharpe"],
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ob["ret"], ob["cagr"], ob["dd"], ob["sharpe"]))
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print(" %-22s %+9.0f %6.0f %6.1f %6.2f | %+9.0f %6.0f %6.1f %6.2f" %
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("MASTER + shape", fe["ret"], fe["cagr"], fe["dd"], fe["sharpe"],
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oe["ret"], oe["cagr"], oe["dd"], oe["sharpe"]))
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better = oe["sharpe"] > ob["sharpe"] and oe["dd"] <= ob["dd"] + 1
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print(f" -> aggiungere shape MIGLIORA il MASTER OOS (Sharpe up, DD ~stabile)? "
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f"{'SI' if better else 'NO'}")
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def run():
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t0 = time.time()
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print("=" * 100)
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print(" VALIDAZIONE DURA — shape-ML (LogisticRegression walk-forward sulle feature di forma)")
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print("=" * 100)
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dfs, _ = sec_multi_asset()
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sec_rolling(dfs)
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sec_stress(dfs)
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sec_grid(dfs, "BTC")
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sec_master_integration()
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print(f"\n tempo totale: {time.time() - t0:.0f}s")
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print("=" * 100)
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if __name__ == "__main__":
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run()
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