Aggancio serie storiche ETFP-XPAR-XAMS

Trading Pattern Recon w Hurst v.3.1.6.py

@@ -23,6 +23,7 @@ import matplotlib.pyplot as plt
 from pathlib import Path
 import json
 import ssl
+import re
 from urllib.request import urlopen
 from urllib.error import URLError, HTTPError
 
@@ -96,19 +97,38 @@ def _apply_score(df_sum: pd.DataFrame) -> pd.DataFrame:
         df_sum["Score_mode"] = "degenerate_equal"
         return df_sum
 
-    res = calibrate_score_weights(
-        df_sum,
-        metrics_map=mm,
-        target_col=None
-    )
-    X_ranked = res["X_ranked"]
-    w = res["weights"]
-    df_sum["Score"] = (X_ranked[w.index] * w.values).sum(1)
-    df_sum["Score_mode"] = res["mode"]
-    print("Pesi stimati automaticamente (metriche usate):")
-    print("Disponibilita' metriche (righe non-NaN):",
-      {c: int(df_sum[c].notna().sum()) for c in df_sum.columns if c in ["Sharpe","CAGR_%","MaxDD_%eq","QualityScore","Confidence","OutcomeScore"]})
-    print(w)
+    # Se sono definiti pesi fissi in config, usali; altrimenti calibra automaticamente
+    use_fixed = False
+    if SCORE_WEIGHTS:
+        weights_raw = {k: float(v) for k, v in SCORE_WEIGHTS.items() if k in df_sum.columns}
+        weights_raw = {k: v for k, v in weights_raw.items() if df_sum[k].notna().sum() > 0}
+        if weights_raw:
+            use_fixed = True
+            w = pd.Series(weights_raw)
+            w = w / w.sum()
+            X_ranked = df_sum[w.index].rank(pct=True)
+            df_sum["Score"] = (X_ranked[w.index] * w.values).sum(1)
+            df_sum["Score_mode"] = "fixed_weights"
+            if SCORE_VERBOSE:
+                print("Pesi fissi (config):", w.to_dict())
+        else:
+            print("[WARN] score_weights in config non compatibili con le metriche disponibili. Uso calibrazione automatica.")
+
+    if not use_fixed:
+        res = calibrate_score_weights(
+            df_sum,
+            metrics_map=mm,
+            target_col=None
+        )
+        X_ranked = res["X_ranked"]
+        w = res["weights"]
+        df_sum["Score"] = (X_ranked[w.index] * w.values).sum(1)
+        df_sum["Score_mode"] = res["mode"]
+        if SCORE_VERBOSE:
+            print("Pesi stimati automaticamente (metriche usate):")
+            print("Disponibilita' metriche (righe non-NaN):",
+              {c: int(df_sum[c].notna().sum()) for c in df_sum.columns if c in ["Sharpe","CAGR_%","MaxDD_%eq","QualityScore","Confidence","OutcomeScore"]})
+            print(w)
     return df_sum
 
 # =============================
@@ -123,13 +143,141 @@ def _build_symbol_euronext(row: pd.Series) -> tuple[str, str]:
         return base, tok
     if isin and venue:
         return base, f"{isin}-{venue}"
+    if isin:
+        return base, f"{isin}-ETFP"  # fallback generico per endpoint history
     return base, isin
 
 def fetch_price_history(isins, universe: pd.DataFrame, start_date: str, end_date: str) -> pd.DataFrame:
     """
     Scarica la serie storica open/close per una lista di ISIN usando l'endpoint storico.
+    - API chiamata 1 ISIN alla volta: https://fin.scorer.app/finance/etf-inv/history/{ticker}?fromDate=YYYYMMDD&toDate=YYYYMMDD
+    - Caching locale su CSV per ridurre le richieste; se l'API fallisce, tenta di usare la cache.
+    - Fallback mercati: ETFP → XPAR → XAMS. Se si estende una serie con un altro mercato,
+      la giunta avviene solo se il prezzo all'ultimo punto del segmento precedente e al primo del successivo
+      differisce < 2% (per evitare salti di valuta/quotazione).
     Ritorna DataFrame con colonne: Date (datetime), ISIN, Open, Close.
     """
+    start_dt = pd.to_datetime(start_date).date()
+    end_dt = pd.to_datetime(end_date).date()
+
+    def _symbol_cache_path(symbol: str) -> Path:
+        safe = re.sub(r"[^A-Za-z0-9_-]+", "_", str(symbol))
+        return OPEN_CACHE_DIR / f"{safe}.csv"
+
+    def _load_cache(path: Path) -> pd.DataFrame | None:
+        try:
+            if path.exists():
+                dfc = pd.read_csv(path, parse_dates=["Date"])
+                dfc["ISIN"] = dfc["ISIN"].astype(str)
+                return dfc
+        except Exception as e:
+            print(f"[WARN] Cache prezzi corrotta {path}: {e}")
+        return None
+
+    def _normalize_payload_to_df(payload, isin):
+        # Il nuovo endpoint ritorna [{"ticker": "...", "data": [ {...}, ... ]}]
+        data_block = payload
+        if isinstance(payload, list) and payload:
+            if isinstance(payload[0], dict) and "data" in payload[0]:
+                data_block = payload[0].get("data", [])
+            else:
+                data_block = payload
+        if isinstance(payload, dict) and "data" in payload:
+            data_block = payload.get("data", [])
+        rows = []
+        for d in data_block or []:
+            dt_raw = d.get("date") or d.get("Date") or d.get("data") or d.get("timestamp")
+            if dt_raw is None:
+                continue
+            try:
+                if isinstance(dt_raw, (int, float)):
+                    dt_parsed = pd.to_datetime(int(dt_raw), unit="ms").tz_localize(None)
+                else:
+                    dt_parsed = pd.to_datetime(dt_raw).tz_localize(None)
+            except Exception:
+                continue
+            rows.append({
+                "Date": dt_parsed,
+                "ISIN": str(isin),
+                "Open": _to_float_safe(d.get("open")),
+                "Close": _to_float_safe(d.get("close") or d.get("last"))
+            })
+        return pd.DataFrame(rows) if rows else pd.DataFrame(columns=["Date","ISIN","Open","Close"])
+
+    def _fetch_symbol(symbol: str, isin: str):
+        url = f"{OPEN_PRICE_BASE_URL}/{symbol}?fromDate={start_dt.strftime('%Y%m%d')}&toDate={end_dt.strftime('%Y%m%d')}"
+        cache_path = _symbol_cache_path(symbol)
+        cache_df = _load_cache(cache_path)
+
+        df_api = pd.DataFrame()
+        ok = False
+        for attempt in range(1, OPEN_MAX_RETRY + 1):
+            try:
+                with urlopen(url, timeout=OPEN_TIMEOUT, context=ssl.create_default_context()) as resp:
+                    payload = json.loads(resp.read().decode("utf-8"))
+                df_api = _normalize_payload_to_df(payload, isin)
+                if df_api.empty:
+                    print(f"[WARN] Nessun dato per {symbol}")
+                ok = True
+                break
+            except (HTTPError, URLError, ssl.SSLError, json.JSONDecodeError) as e:
+                if attempt < OPEN_MAX_RETRY:
+                    print(f"[WARN] Download {symbol} tentativo {attempt}/{OPEN_MAX_RETRY} fallito: {e}. Retry in {OPEN_SLEEP_SEC}s")
+                    time.sleep(OPEN_SLEEP_SEC)
+                else:
+                    print(f"[ERROR] Download {symbol} fallito: {e}")
+
+        df_use = pd.DataFrame()
+        if ok and not df_api.empty:
+            df_api = df_api.sort_values("Date")
+            if cache_df is not None and not cache_df.empty:
+                df_use = (
+                    pd.concat([cache_df, df_api], ignore_index=True)
+                      .drop_duplicates(subset=["Date"])
+                      .sort_values("Date")
+                )
+            else:
+                df_use = df_api
+            try:
+                OPEN_CACHE_DIR.mkdir(parents=True, exist_ok=True)
+                df_use.to_csv(cache_path, index=False)
+            except Exception as e:
+                print(f"[WARN] Salvataggio cache prezzi fallito ({cache_path}): {e}")
+        elif cache_df is not None and not cache_df.empty:
+            df_use = cache_df
+            print(f"[INFO] Uso cache prezzi per {symbol} (API indisponibile).")
+        return df_use
+
+    def _merge_with_check(df_base: pd.DataFrame, df_add: pd.DataFrame, label_prev: str, label_next: str):
+        """
+        Estende df_base aggiungendo il tratto df_add antecedente al primo punto di df_base.
+        Controlla il salto di prezzo all'incrocio: se > 2%, non fonde e avvisa.
+        """
+        if df_base is None or df_base.empty:
+            return df_add, False
+        if df_add is None or df_add.empty:
+            return df_base, False
+        cutoff = df_base["Date"].min()
+        prev_part = df_add[df_add["Date"] < cutoff]
+        if prev_part.empty:
+            return df_base, False
+        merged = pd.concat([prev_part, df_base], ignore_index=True)
+        merged = merged.sort_values("Date").drop_duplicates(subset=["Date"], keep="last")
+        # controllo salto: ultimo prezzo del segmento precedente vs primo del successivo
+        prev_last = prev_part.sort_values("Date").iloc[-1]
+        next_first = df_base[df_base["Date"] >= cutoff].sort_values("Date").iloc[0]
+        def _price(row):
+            return _to_float_safe(row.get("Close")) if pd.notna(row.get("Close")) else _to_float_safe(row.get("Open"))
+        p_prev = _price(prev_last)
+        p_next = _price(next_first)
+        if p_prev is None or p_next is None or not np.isfinite(p_prev) or not np.isfinite(p_next) or p_next == 0:
+            return merged, True
+        gap = abs(p_prev - p_next) / abs(p_next)
+        if gap > 0.02:
+            print(f"[WARN] Salto prezzo >2% tra {label_prev} e {label_next} su {prev_last['Date'].date()}→{next_first['Date'].date()} (gap {gap:.2%}). Fallback non applicato.")
+            return df_base, False
+        return merged, True
+
     records = []
     for i, isin in enumerate(isins, 1):
         try:
@@ -138,43 +286,60 @@ def fetch_price_history(isins, universe: pd.DataFrame, start_date: str, end_date
             print(f"[WARN] ISIN {isin} non trovato nell'universo.")
             continue
         base, symbol = _build_symbol_euronext(row)
-        url = f"{base}/{symbol}?fromDate={start_date}&toDate={end_date}"
-        ok = False
-        for attempt in range(1, OPEN_MAX_RETRY + 1):
-            try:
-                with urlopen(url, timeout=OPEN_TIMEOUT, context=ssl.create_default_context()) as resp:
-                    data = json.loads(resp.read().decode("utf-8"))
-                if not isinstance(data, list) or not data:
-                    print(f"[WARN] Nessun dato per {symbol}")
-                    break
-                for d in data:
-                    dt_str = d.get("date") or d.get("Date") or d.get("data")
-                    if not dt_str:
-                        continue
-                    try:
-                        dt_parsed = pd.to_datetime(dt_str).tz_localize(None)
-                    except Exception:
-                        continue
-                    records.append({
-                        "Date": dt_parsed,
-                        "ISIN": str(isin),
-                        "Open": _to_float_safe(d.get("open")),
-                        "Close": _to_float_safe(d.get("close"))
-                    })
-                ok = True
-                break
-            except (HTTPError, URLError, ssl.SSLError) as e:
-                if attempt < OPEN_MAX_RETRY:
-                    print(f"[WARN] Download {symbol} tentativo {attempt}/{OPEN_MAX_RETRY} fallito: {e}. Retry in {OPEN_SLEEP_SEC}s")
-                    time.sleep(OPEN_SLEEP_SEC)
+        df_primary = _fetch_symbol(symbol, isin)
+
+        # Fallback mercati aggiuntivi (XPAR, poi XAMS) per estendere indietro la serie
+        fallback_symbols = []
+        if "-" in symbol:
+            root = symbol.rsplit("-", 1)[0]
+            fallback_symbols.append(f"{root}-XPAR")
+            fallback_symbols.append(f"{root}-XAMS")
+        else:
+            fallback_symbols.append(f"{symbol}-XPAR")
+            fallback_symbols.append(f"{symbol}-XAMS")
+
+        df_use = df_primary
+        applied_any = False
+        for fb_sym in fallback_symbols:
+            # servono solo se la serie non parte da start_dt
+            need_fb = df_use.empty or (df_use["Date"].min().date() > start_dt)
+            if not need_fb:
+                continue
+            df_fb = _fetch_symbol(fb_sym, isin)
+            if df_fb.empty:
+                print(f"[WARN] Fallback {fb_sym} assente per {isin}")
+                continue
+            if df_use.empty:
+                df_use = df_fb
+                applied_any = True
+                print(f"[INFO] Uso fallback {fb_sym} per tutto il periodo.")
+            else:
+                merged, merged_ok = _merge_with_check(df_use, df_fb, fb_sym, symbol)
+                if merged_ok:
+                    df_use = merged
+                    applied_any = True
+                    cutoff = df_use["Date"].min()
+                    print(f"[INFO] Serie estesa con {fb_sym} fino a {cutoff.date()} per {isin}")
                 else:
-                    print(f"[ERROR] Download {symbol} fallito: {e}")
-        if not ok:
+                    print(f"[WARN] Fallback {fb_sym} scartato per gap >2% su {isin}")
+
+        if df_use.empty:
             print(f"[WARN] Serie open/close non disponibile per {isin}")
+            continue
+
+        # Filtro range richiesto
+        df_use["Date"] = pd.to_datetime(df_use["Date"])
+        mask = (df_use["Date"].dt.date >= start_dt) & (df_use["Date"].dt.date <= end_dt)
+        df_use = df_use.loc[mask]
+        if df_use.empty:
+            print(f"[WARN] Nessun dato nel range richiesto per {symbol}")
+            continue
+        records.append(df_use)
+
     if not records:
         return pd.DataFrame(columns=["Date","ISIN","Open","Close"])
-    df_px = pd.DataFrame(records)
-    df_px = df_px.sort_values(["ISIN","Date"])
+    df_px = pd.concat(records, ignore_index=True)
+    df_px = df_px.sort_values(["ISIN","Date"]).reset_index(drop=True)
     return df_px
 
 def _to_float_safe(x):
@@ -223,10 +388,12 @@ N_BARS = int(require_value(DB_CONFIG, "n_bars", "db"))
 PTF_CURR = str(require_value(DB_CONFIG, "ptf_curr", "db"))
 RANKING_WINDOW_BARS = int(RANKING_CONFIG.get("rolling_window_bars", N_BARS))
 RP_LOOKBACK = int(SIGNALS_CONFIG.get("risk_parity_lookback", 60))
-OPEN_PRICE_BASE_URL = str(PRICES_CONFIG.get("base_url", "https://fin.scorer.app/finance/euronext/price"))
+OPEN_PRICE_BASE_URL = str(PRICES_CONFIG.get("base_url", "https://fin.scorer.app/finance/etf-inv/history"))
 OPEN_MAX_RETRY = int(PRICES_CONFIG.get("max_retry", 3))
 OPEN_SLEEP_SEC = float(PRICES_CONFIG.get("sleep_sec", 0.1))
 OPEN_TIMEOUT = float(PRICES_CONFIG.get("timeout", 10))
+OPEN_CACHE_DIR = Path(PRICES_CONFIG.get("cache_dir", OUTPUT_DIR / "price_cache"))
+RECOMPUTE_PORTF_FROM_OPEN = bool(PRICES_CONFIG.get("recompute_portfolio_open", False))
 
 # Pattern-matching (iper-parametri)
 WP = int(require_value(PATTERN_CONFIG, "wp", "pattern"))          # lunghezza finestra pattern (barre)
@@ -250,6 +417,8 @@ if RP_MAX_WEIGHT is None:
     RP_MAX_WEIGHT = 2 / max(TOP_N_MAX, 1)
 else:
     RP_MAX_WEIGHT = float(RP_MAX_WEIGHT)
+SCORE_VERBOSE = bool(RANKING_CONFIG.get("score_verbose", False))
+SCORE_WEIGHTS = RANKING_CONFIG.get("score_weights")
 HURST_MIN_LENGTH = int(HURST_CONFIG.get("min_length", 200))
 HURST_WIN_GRID = HURST_CONFIG.get("win_grid")
 HURST_MIN_SEGMENTS = int(HURST_CONFIG.get("min_segments", 1))
@@ -277,6 +446,26 @@ def format_eta(seconds):
         return f"{hours}h {minutes:02d}m {secs:02d}s"
     return f"{minutes}m {secs:02d}s"
 
+# Timer helper per fasi post-backtest
+_post_timer = {"t0": None, "tprev": None, "total": None, "done": 0}
+def start_post_timer(total_steps: int):
+    _post_timer["t0"] = time.perf_counter()
+    _post_timer["tprev"] = _post_timer["t0"]
+    _post_timer["total"] = total_steps
+    _post_timer["done"] = 0
+
+def checkpoint_post_timer(label: str):
+    if _post_timer["t0"] is None or _post_timer["total"] is None:
+        return
+    _post_timer["done"] += 1
+    now = time.perf_counter()
+    step_dt = now - _post_timer["tprev"]
+    total_dt = now - _post_timer["t0"]
+    avg = total_dt / max(_post_timer["done"], 1)
+    eta = avg * max(_post_timer["total"] - _post_timer["done"], 0)
+    print(f"[TIMER] post { _post_timer['done']}/{_post_timer['total']} {label} — step {step_dt:.2f}s, total {total_dt:.2f}s, ETA {format_eta(eta)}")
+    _post_timer["tprev"] = now
+
 # ================= HURST (sui RENDIMENTI) =================
 def hurst_rs_returns(r, win_grid=None, min_seg=None):
     r = pd.Series(r).dropna().astype("float64").values
@@ -746,13 +935,14 @@ def drawdown_stats_simple(ret_series: pd.Series):
     }
 
 def knn_forward_backtest_one_asset(df_isin: pd.DataFrame, col_date: str, col_ret: str,
-                                   Wp: int, Ha: int, k: int,
-                                   theta_entry: float,
-                                   fee_bps: float = 10,
-                                   # --- EXIT PARAMS (tutte opzionali) ---
-                                   sl_bps: float | None = 300.0,     # Stop loss assoluto (bps sul PnL cumulato del trade)
-                                   tp_bps: float | None = 800.0,     # Take profit assoluto (bps)
-                                   trail_bps: float | None = 300.0,  # Trailing stop (drawdown dal picco, bps)
+                                    Wp: int, Ha: int, k: int,
+                                    theta_entry: float,
+                                    exec_ret: pd.Series | None = None,
+                                    fee_bps: float = 10,
+                                    # --- EXIT PARAMS (tutte opzionali) ---
+                                    sl_bps: float | None = 300.0,     # Stop loss assoluto (bps sul PnL cumulato del trade)
+                                    tp_bps: float | None = 800.0,     # Take profit assoluto (bps)
+                                    trail_bps: float | None = 300.0,  # Trailing stop (drawdown dal picco, bps)
                                    time_stop_bars: int | None = 20,  # Massimo holding
                                    theta_exit: float | None = 0.0,   # esci se est_out <= theta_exit (se None, ignora)
                                    weak_days_exit: int | None = None # esci se per N giorni est_out <= theta_exit
@@ -763,8 +953,17 @@ def knn_forward_backtest_one_asset(df_isin: pd.DataFrame, col_date: str, col_ret
     Nota: usa solo dati daily → le soglie sono valutate a fine giornata,
     l'uscita avviene sulla barra successiva (modello prudente).
     """
-    r = pd.to_numeric(df_isin[col_ret], errors="coerce").astype(float) / 100.0   # rendimenti in decimali
+    r = pd.to_numeric(df_isin[col_ret], errors="coerce").astype(float) / 100.0   # rendimenti in decimali (close/close)
     idx = df_isin[col_date] if col_date in df_isin.columns else pd.RangeIndex(len(r))
+    if exec_ret is not None:
+        r_exec = pd.to_numeric(exec_ret, errors="coerce").astype(float)
+        if not r_exec.index.equals(idx):
+            r_exec = r_exec.reindex(idx)
+        if len(r_exec) != len(r):
+            # riallinea sullo stesso index; se mancano date, restano NaN
+            r_exec = pd.Series(r_exec.values, index=idx).reindex(idx)
+    else:
+        r_exec = r
     fee = fee_bps / 10000.0
 
     # helper per costruire libreria solo passato
@@ -794,7 +993,7 @@ def knn_forward_backtest_one_asset(df_isin: pd.DataFrame, col_date: str, col_ret
         if past.dropna().shape[0] < (Wp + Ha):
             sig_out, est_out, avg_dist = 0, np.nan, np.nan
             # PnL a t+1 sempre riportato in colonna Ret+1
-            rows.append((idx.iloc[t], sig_out, est_out, avg_dist, r.iloc[t+1]))
+            rows.append((idx.iloc[t], sig_out, est_out, avg_dist, r_exec.iloc[t+1] if t+1 < len(r_exec) else np.nan))
             continue
 
         win_last = r.iloc[t-Wp:t].values
@@ -818,7 +1017,7 @@ def knn_forward_backtest_one_asset(df_isin: pd.DataFrame, col_date: str, col_ret
             # 1) aggiorna PnL del trade con il rendimento della barra che verrà *incassato* domani:
             #    Per coerenza EOD, PnL di oggi (da riportare) è su r[t+1] quando Signal(t)=1.
             #    Per controlli di stop a fine giornata, stimiamo la "pnl se restassi" accumulando r[t+1] ex-ante.
-            next_ret = r.iloc[t+1]  # rendimento che si applicherà se resto in posizione
+            next_ret = r_exec.iloc[t+1] if t+1 < len(r_exec) else np.nan  # rendimento che si applicherà se resto in posizione
             pnl_if_stay = (1.0 + trade_pnl) * (1.0 + next_ret) - 1.0
 
             # 2) aggiorna trailing peak ipotetico
@@ -871,7 +1070,7 @@ def knn_forward_backtest_one_asset(df_isin: pd.DataFrame, col_date: str, col_ret
                 trade_peak = peak_if_stay
 
         # Registra la riga odierna; il PnL riportato è sempre il r[t+1]
-        rows.append((idx.iloc[t], sig_out, est_out, avg_dist, r.iloc[t+1]))
+        rows.append((idx.iloc[t], sig_out, est_out, avg_dist, r_exec.iloc[t+1] if t+1 < len(r_exec) else np.nan))
 
     sig_df = pd.DataFrame(rows, columns=["Date", "Signal", "EstOutcome", "AvgDist", "Ret+1"])
 
@@ -938,6 +1137,29 @@ for i, isin in enumerate(isins, 1):
             errors.append({"ISIN": isin, "Errore": f"Serie troppo corta (BT) ({df_isin[col_ret].dropna().shape[0]} punti)"})
             continue
 
+        # --- Fetch open/close per calcolare rendimenti di esecuzione (open->open) ---
+        try:
+            date_min = df_isin[col_date].min().date() if col_date else None
+            date_max = df_isin[col_date].max().date() if col_date else None
+            if date_min and date_max:
+                px_hist_one = fetch_price_history(
+                    isins=[isin],
+                    universe=meta_df if 'meta_df' in globals() else pd.DataFrame(),
+                    start_date=date_min.isoformat(),
+                    end_date=date_max.isoformat()
+                )
+                px_hist_one = px_hist_one.sort_values("Date")
+                open_series = px_hist_one[["Date","Open"]].dropna()
+                open_series = open_series.drop_duplicates(subset=["Date"]).set_index("Date")["Open"]
+                open_ret = open_series.pct_change()
+                # riallinea sulla stessa sequenza di date del df_isin
+                exec_ret = open_ret.reindex(pd.to_datetime(df_isin[col_date]))
+            else:
+                exec_ret = None
+        except Exception as e:
+            print(f"[WARN] Fetch open/close fallito per {isin}: {e}")
+            exec_ret = None
+
         # ============================
         # THETA = HURST IN PERCENTUALE
         # H = 0.50 -> theta_entry = 0.005 (0.5%)
@@ -957,6 +1179,7 @@ for i, isin in enumerate(isins, 1):
             Ha=HA,
             k=KNN_K,
             theta_entry=theta_entry,
+            exec_ret=exec_ret,
             fee_bps=10,
         )
 
@@ -1015,6 +1238,9 @@ if errors:
     pd.DataFrame(errors).to_csv(ERROR_LOG_CSV, index=False)
     print(f"ℹ️ Log errori aggiornato: {ERROR_LOG_CSV} (tot: {len(errors)})")
 
+# Timer per fasi post-backtest (sezione 5 in poi)
+start_post_timer(total_steps=4)
+
 # ======================================================================
 # 5) STRATEGIE PORTAFOGLIO DINAMICHE + EQUITY + HEATMAP + TRADE REPORT
 # ======================================================================
@@ -1085,13 +1311,14 @@ def plot_heatmap_monthly(r: pd.Series, title: str, save_path: str = None):
     plt.tight_layout()
     if save_path:
         savefig_safe(save_path, dpi=150)
+    plt.close(fig)
     # Non mostrare il plot durante l'esecuzione
 
 def inverse_vol_weights(df, window=60, max_weight=None):
     vol = df.rolling(window).std()
     inv = 1 / vol.replace(0, np.nan)
     w = inv.div(inv.sum(axis=1), axis=0)
-    w = w.fillna(method="ffill").fillna(1 / max(1, df.shape[1]))
+    w = w.ffill().fillna(1 / max(1, df.shape[1]))
 
     if max_weight is not None:
         w = w.clip(upper=max_weight)
@@ -1385,32 +1612,34 @@ wide_sig = (
       .astype(int)
 )
 
-# Sostituisce i PnL close->close con rendimenti open->open usando l'API storica
-try:
-    date_min = (bt["Date"].min() - pd.Timedelta(days=5)).date()
-    date_max = (bt["Date"].max() + pd.Timedelta(days=5)).date()
-    px_hist = fetch_price_history(
-        isins=bt["ISIN"].unique(),
-        universe=meta_df if 'meta_df' in globals() else pd.DataFrame(),
-        start_date=date_min.isoformat(),
-        end_date=date_max.isoformat()
-    )
-    open_pivot = (
-        px_hist.pivot(index="Date", columns="ISIN", values="Open")
-              .sort_index()
-    )
-    open_ret = open_pivot.pct_change()
-    wide_pnl = open_ret.shift(-1)  # segnale su giorno t, esecuzione a open t+1
-    common_idx = wide_sig.index.intersection(wide_pnl.index)
-    # forza DatetimeIndex per sicurezza
-    common_idx = pd.to_datetime(common_idx)
-    wide_sig = wide_sig.reindex(common_idx).fillna(0).astype(int)
-    wide_pnl = wide_pnl.reindex(common_idx).fillna(0.0)
-    wide_sig.index = pd.to_datetime(wide_sig.index)
-    wide_pnl.index = pd.to_datetime(wide_pnl.index)
-    print(f"[INFO] PnL ricostruito su open->open per {len(open_pivot.columns)} ISIN.")
-except Exception as e:
-    print(f"[WARN] Ricostruzione PnL open->open fallita, uso PnL originale: {e}")
+# (Opzionale) ricostruzione PnL portafoglio con open->open: disattivata di default perché il PnL
+# viene già calcolato a livello di singolo asset usando gli open.
+if globals().get("RECOMPUTE_PORTF_FROM_OPEN", False):
+    try:
+        date_min = (bt["Date"].min() - pd.Timedelta(days=5)).date()
+        date_max = (bt["Date"].max() + pd.Timedelta(days=5)).date()
+        px_hist = fetch_price_history(
+            isins=bt["ISIN"].unique(),
+            universe=meta_df if 'meta_df' in globals() else pd.DataFrame(),
+            start_date=date_min.isoformat(),
+            end_date=date_max.isoformat()
+        )
+        open_pivot = (
+            px_hist.pivot(index="Date", columns="ISIN", values="Open")
+                  .sort_index()
+        )
+        open_ret = open_pivot.pct_change()
+        # segnale su giorno t, esecuzione a open t+1
+        wide_pnl = wide_sig * open_ret.shift(-1)
+        common_idx = wide_sig.index.intersection(wide_pnl.index)
+        common_idx = pd.to_datetime(common_idx)
+        wide_sig = wide_sig.reindex(common_idx).fillna(0).astype(int)
+        wide_pnl = wide_pnl.reindex(common_idx).fillna(0.0)
+        wide_sig.index = pd.to_datetime(wide_sig.index)
+        wide_pnl.index = pd.to_datetime(wide_pnl.index)
+        print(f"[INFO] PnL ricostruito su open->open per {len(open_pivot.columns)} ISIN.")
+    except Exception as e:
+        print(f"[WARN] Ricostruzione PnL open->open fallita, uso PnL originale: {e}")
 
 # I portafogli verranno costruiti piu' sotto con ranking rolling (vedi _build_dynamic_portfolio_returns).
 
@@ -1664,6 +1893,7 @@ w_rp_act = _main_port["w_rp_act"]
 selection_by_date = _main_port["selection"]
 weights_rp = w_rp.copy()
 print(f"[INFO] Portafoglio rolling calcolato (TopN={TOP_N}, finestra={RANKING_WINDOW_BARS} barre, rp_lookback={RP_LOOKBACK}).")
+checkpoint_post_timer("Portafoglio rolling")
 
 # -----------------------------
 # 5.4 Equity line + Heatmap (salva PNG)
@@ -1759,6 +1989,7 @@ def plot_portfolio_composition_fixed(weights: pd.DataFrame,
     ax.set_ylim(0, max(1.0, ymax))
     ax.grid(True, alpha=0.3)
     ax.set_ylabel("Peso")
+    ax.set_yticks(ax.get_yticks())
     ax.set_yticklabels([f"{y*100:.0f}%" for y in ax.get_yticks()])
 
     ncol = 2 if len(ordered) > 10 else 1
@@ -1811,6 +2042,7 @@ w_agg_act = make_active_weights(w_agg, wide_sig, renorm_to_1=False, add_cash=Tru
 # --- 3) Plot + salvataggio ---
 plot_portfolio_composition_fixed(w_eq_act,  "Equal Weight (attivi + Cash)",        str(PLOT_DIR / "composition_equal_weight_active.png"))
 plot_portfolio_composition_fixed(w_rp_act,  "Risk Parity (attivi + Cash)",         str(PLOT_DIR / "composition_risk_parity_active.png"))
+checkpoint_post_timer("Pesi/plot portafogli")
 
 
 # -----------------------------
@@ -1836,7 +2068,7 @@ def make_trades_report(sig: pd.DataFrame, pnl: pd.DataFrame, weights: pd.DataFra
 
     # Sanitizza
     sig = sig.fillna(0).astype(int).clip(lower=0)  # solo long
-    pnl = pnl.apply(pd.to_numeric, errors="coerce").fillna(0.0)
+    pnl = pnl.apply(pd.to_numeric, errors="coerce")  # mantieni NaN per buchi open
 
     rows = []
 
@@ -1845,7 +2077,7 @@ def make_trades_report(sig: pd.DataFrame, pnl: pd.DataFrame, weights: pd.DataFra
         s = sig[isin].fillna(0).astype(int).shift(1).fillna(0).astype(int)
 
         # 2) PnL allineato al giorno di esposizione (EOD): usa pnl.shift(-1)
-        r = pnl[isin].shift(1).fillna(0.0)
+        r = pnl[isin].shift(1)
 
         # 3) Pesi (se disponibili)
         w = (weights[isin].fillna(0.0) if (isin in weights.columns) else pd.Series(0.0, index=s.index))
@@ -1857,17 +2089,20 @@ def make_trades_report(sig: pd.DataFrame, pnl: pd.DataFrame, weights: pd.DataFra
 
             # CHIUSURA: primo 0 dopo un periodo in posizione → chiudi oggi (dt)
             if in_pos and (sig_t == 0):
-                pnl_val = np.prod([1.0 + x for x in acc]) - 1.0 if acc else 0.0
-                rows.append(dict(
-                    Strategy=name,
-                    ISIN=isin,
-                    OpenDate=start,
-                    CloseDate=dt,
-                    Direction="long",
-                    Size=float(w.get(start, 0.0)),
-                    Duration_bars=len(acc),
-                    **{"PnL_%": pnl_val * 100.0}
-                ))
+                if any(pd.isna(acc)):
+                    print(f"[WARN] Trade derubricato {name} {isin}: open/close price mancante nel range {start.date()}-{dt.date()}")
+                else:
+                    pnl_val = np.prod([1.0 + x for x in acc]) - 1.0 if acc else 0.0
+                    rows.append(dict(
+                        Strategy=name,
+                        ISIN=isin,
+                        OpenDate=start,
+                        CloseDate=dt,
+                        Direction="long",
+                        Size=float(w.get(start, 0.0)),
+                        Duration_bars=len(acc),
+                        **{"PnL_%": pnl_val * 100.0}
+                    ))
                 in_pos, start, acc = False, None, []
 
             # APERTURA: primo 1 (laggato) quando non in posizione
@@ -1876,22 +2111,25 @@ def make_trades_report(sig: pd.DataFrame, pnl: pd.DataFrame, weights: pd.DataFra
 
             # ACCUMULO: PnL del giorno di esposizione
             if in_pos:
-                acc.append(float(r.at[dt]))
+                acc.append(r.at[dt])
 
         # CHIUSURA A FINE SERIE → prossimo business day
         if in_pos:
-            pnl_val = np.prod([1.0 + x for x in acc]) - 1.0 if acc else 0.0
             close_dt = s.index[-1] + BDay(1)
-            rows.append(dict(
-                Strategy=name,
-                ISIN=isin,
-                OpenDate=start,
-                CloseDate=close_dt,
-                Direction="long",
-                Size=float(w.get(start, 0.0)),
-                Duration_bars=len(acc),
-                **{"PnL_%": pnl_val * 100.0}
-            ))
+            if any(pd.isna(acc)):
+                print(f"[WARN] Trade derubricato {name} {isin}: open/close price mancante nel range {start.date()}-{close_dt.date()}")
+            else:
+                pnl_val = np.prod([1.0 + x for x in acc]) - 1.0 if acc else 0.0
+                rows.append(dict(
+                    Strategy=name,
+                    ISIN=isin,
+                    OpenDate=start,
+                    CloseDate=close_dt,
+                    Direction="long",
+                    Size=float(w.get(start, 0.0)),
+                    Duration_bars=len(acc),
+                    **{"PnL_%": pnl_val * 100.0}
+                ))
 
     # Ordina colonne
     cols = ["Strategy","ISIN","OpenDate","CloseDate","Direction","Size","Duration_bars","PnL_%"]
@@ -1914,6 +2152,7 @@ rep_rp  = make_trades_report(wide_sig[[c for c in asset_cols if c in wide_sig.co
 with pd.ExcelWriter(TRADES_REPORT_XLSX) as xw:
     rep_eq.to_excel(xw, "Equal_Weight", index=False)
     rep_rp.to_excel(xw, "Risk_Parity", index=False)
+checkpoint_post_timer("Report trades")
 
 # Performance attribution per ISIN
 def _build_performance_attribution(trades_df: pd.DataFrame, meta_df: pd.DataFrame | None) -> pd.DataFrame:
@@ -2068,6 +2307,8 @@ if not daily_from_trades.empty:
 else:
     print("[INFO] daily_from_trades risulta vuoto: nessun plot/CSV generato.")
 
+checkpoint_post_timer("Ricostruzione daily/plot")
+
 
 # ============================================================
 # METRICS UTILS (guard) — richieste da _calc_all_metrics_...

config/pattern_knn_config.json

@@ -18,7 +18,13 @@
   },
   "ranking": {
     "top_n_max": 15,
-    "rp_max_weight": 0.1333333333
+    "rp_max_weight": 0.1333333333,
+    "score_verbose": false,
+    "score_weights": {
+      "Sharpe": 0.4,
+      "CAGR_%": 0.4,
+      "MaxDD_%eq": 0.2
+    }
   },
   "signals": {
     "sl_bps": 300.0,
@@ -66,10 +72,12 @@
     "min_segments": 2
   },
   "prices": {
-    "base_url": "https://fin.scorer.app/finance/euronext/price",
+    "base_url": "https://fin.scorer.app/finance/etf-inv/history",
     "max_retry": 3,
     "sleep_sec": 0.1,
-    "timeout": 10
+    "timeout": 10,
+    "cache_dir": "output/price_cache",
+    "recompute_portfolio_open": false
   },
   "run": {
     "business_days_only": true,