Merge pull request #3 from fredmaloggia/codex/review-function-consolidation-across-files

Refactor shared logic and load runtime config

Trading Pattern Recon w Hurst v.3.1.6.py

@@ -19,8 +19,17 @@ import pandas as pd
 import numpy as np
 import sqlalchemy as sa
 from sqlalchemy import text
-import pyodbc
 import matplotlib.pyplot as plt
+
+from shared_utils import (
+    build_pattern_library,
+    characterize_window,
+    detect_column,
+    load_config,
+    predict_from_library,
+    read_connection_txt,
+    z_norm,
+)
 #from math import isfinite
 import time
 
@@ -59,6 +68,12 @@ def savefig_safe(path, **kwargs):
 # =========================================
 # PARAMETRI GLOBALI
 # =========================================
+CONFIG = load_config()
+DB_CONFIG = CONFIG.get("db", {})
+PATTERN_CONFIG = CONFIG.get("pattern", {})
+TAGGING_CONFIG = CONFIG.get("tagging", {})
+RANKING_CONFIG = CONFIG.get("ranking", {})
+
 UNIVERSO_XLSX = "Universo per Trading System.xlsx"
 
 # Export
@@ -67,70 +82,30 @@ OUTPUT_PATTERN_XLSX = "pattern_signals.xlsx"
 ERROR_LOG_CSV = "errori_isin.csv"
 
 # Stored Procedure & parametri
-STORED_PROC = "opt_RendimentoGiornaliero1_ALL"
+STORED_PROC = DB_CONFIG.get("stored_proc", "opt_RendimentoGiornaliero1_ALL")
-N_BARS = 1305
+N_BARS = DB_CONFIG.get("n_bars", 1305)
-PTF_CURR = "EUR"
+PTF_CURR = DB_CONFIG.get("ptf_curr", "EUR")
 
 # Pattern-matching (iper-parametri)
-WP = 60          # lunghezza finestra pattern (barre)
+WP = PATTERN_CONFIG.get("wp", 60)          # lunghezza finestra pattern (barre)
-HA = 10          # orizzonte outcome (barre)
+HA = PATTERN_CONFIG.get("ha", 10)          # orizzonte outcome (barre)
-KNN_K = 25       # numero di vicini
+KNN_K = PATTERN_CONFIG.get("knn_k", 25)       # numero di vicini
-THETA = 0.005  # soglia su outcome per generare segnale
+THETA = PATTERN_CONFIG.get("theta", 0.005)  # soglia su outcome per generare segnale
-EMBARGO = WP + HA
+EMBARGO = PATTERN_CONFIG.get("embargo", WP + HA)
 
 # Tagging rule-based (soglie)
-Z_REV = 2.0
+Z_REV = TAGGING_CONFIG.get("z_rev", 2.0)
-Z_VOL = 2.0
+Z_VOL = TAGGING_CONFIG.get("z_vol", 2.0)
-STD_COMP_PCT = 0.15
+STD_COMP_PCT = TAGGING_CONFIG.get("std_comp_pct", 0.15)
 
 DAYS_PER_YEAR = 252
 
-TOP_N_MAX = 15              # numero massimo di asset ammessi
+TOP_N_MAX = RANKING_CONFIG.get("top_n_max", 15)              # numero massimo di asset ammessi
-RP_MAX_WEIGHT = 2 / TOP_N_MAX   # 2 x 1/15 ≈ 0.1333 = 13,33%
+RP_MAX_WEIGHT = RANKING_CONFIG.get("rp_max_weight", 2 / max(TOP_N_MAX, 1))   # 2 x 1/15 ≈ 0.1333 = 13,33%
 
 # =========================================
 # UTILS GENERALI
 # =========================================
-def pick_first(df, candidates):
-    low = {c.lower(): c for c in df.columns}
-    for c in candidates:
-        if c.lower() in low:
-            return low[c.lower()]
-    for c in candidates:
-        matches = [low[k] for k in low if c.lower() in k]
-        if matches:
-            return matches[0]
-    return None
-
-def read_connection_txt(path="connection.txt"):
-    """
-    connection.txt con:
-      username=...
-      password=...
-      host=...
-      port=1433
-      database=...
-    """
-    params = {}
-    with open(path, "r", encoding="utf-8") as f:
-        for line in f:
-            line = line.strip()
-            if line and not line.startswith("#") and "=" in line:
-                k, v = line.split("=", 1)
-                params[k.strip().lower()] = v.strip()
-
-    username = params.get("username")
-    password = params.get("password")
-    host     = params.get("host")
-    port     = params.get("port", "1433")
-    database = params.get("database")
-    if not all([username, password, host, database]):
-        raise ValueError("connection.txt incompleto: username/password/host/database richiesti.")
-
-    installed = [d for d in pyodbc.drivers()]
-    driver_q = "ODBC+Driver+18+for+SQL+Server" if "ODBC Driver 18 for SQL Server" in installed else "ODBC+Driver+17+for+SQL+Server"
-    return f"mssql+pyodbc://{username}:{password}@{host}:{port}/{database}?driver={driver_q}"
-
 def clamp01(x):
     if not np.isfinite(x):
         return np.nan
@@ -229,81 +204,6 @@ def hurst_dfa_returns(r, win_grid=None):
     slope, _ = np.polyfit(np.log(sizes), np.log(F_vals), 1)
     return clamp01(slope)
 
-# =========== PATTERN MATCHING k-NN (su rendimenti) ===========
-def z_norm(arr):
-    arr = np.asarray(arr, dtype=float)
-    mu = arr.mean(); sd = arr.std()
-    if sd < 1e-12:
-        return None
-    return (arr - mu)/(sd+1e-12)
-
-def build_pattern_library(ret_series: pd.Series, Wp: int, Ha: int, embargo: int = None):
-    x = ret_series.dropna().values
-    N = len(x)
-    if N < Wp + Ha + 10:
-        return None, None
-    wins = []
-    outs = []
-    for t in range(0, N - Wp - Ha):
-        win = x[t:t+Wp]
-        winzn = z_norm(win)
-        if winzn is None:
-            continue
-        outcome = np.sum(x[t+Wp : t+Wp+Ha])  # outcome futuro su Ha barre
-        wins.append(winzn); outs.append(outcome)
-    if not wins:
-        return None, None
-    return np.array(wins), np.array(outs)
-
-def predict_from_library(curr_win: np.ndarray, lib_wins: np.ndarray, lib_out: np.ndarray, k: int = 25):
-    dists = np.linalg.norm(lib_wins - curr_win, axis=1)
-    idx = np.argsort(dists)[:min(k, len(dists))]
-    return float(np.median(lib_out[idx])), float(np.mean(dists[idx])), idx
-
-# =========== TAGGING RULE-BASED (4 categorie) ===========
-def characterize_window(ret_series: pd.Series, Wp: int,
-                        z_rev=2.0, z_vol=2.0, std_comp_pct=0.15):
-    x = ret_series.dropna().values
-    if len(x) < max(WP, 30):
-        return None, 0.0
-    win = x[-Wp:]
-    mu, sd = win.mean(), win.std()
-    if sd < 1e-12:
-        return "compression", 0.5
-
-    last = win[-1]
-    z_last = (last - mu)/(sd+1e-12)
-    abs_z_last = abs(z_last)
-    last3 = win[-3:] if len(win) >= 3 else win
-    sum3 = np.sum(last3)
-
-    if len(x) > 3*Wp:
-        roll_std = pd.Series(x).rolling(Wp).std().dropna().values
-        if len(roll_std) > 20:
-            pct = (roll_std < np.std(win)).mean()
-        else:
-            pct = 0.5
-    else:
-        pct = 0.5
-
-    if pct < std_comp_pct:
-        return "compression", float(1.0 - pct)
-
-    if abs(sum3) > 2*sd/np.sqrt(3) and np.sign(last3).sum() in (3, -3):
-        conf = min(1.0, abs(sum3)/(sd+1e-12))
-        return "momentum_burst", float(conf)
-
-    mean_prev = np.mean(win[:-1]) if len(win) > 1 else 0.0
-    if abs_z_last >= z_rev and np.sign(last) != np.sign(mean_prev):
-        conf = min(1.0, abs_z_last/3.0)
-        return "reversal_candidate", float(conf)
-
-    if abs_z_last >= z_vol:
-        conf = min(1.0, abs_z_last/3.0)
-        return "vol_spike", float(conf)
-
-    return None, 0.0
-
 # ---------------------------------
 # R^2 su equity line (log-equity vs tempo)
 # ---------------------------------
@@ -455,13 +355,13 @@ def h_min_100(returns: pd.Series, month_len: int = 21):
 # =========================================
 universo = pd.read_excel(UNIVERSO_XLSX)
 
-col_isin_uni = pick_first(universo, ["ISIN", "isin", "codice isin"])
+col_isin_uni = detect_column(universo, ["ISIN", "isin", "codice isin"])
 if col_isin_uni is None:
     raise ValueError("Nel file universo non trovo una colonna ISIN.")
 
-col_name_uni = pick_first(universo, ["Nome", "Name", "Descrizione", "Description", "Security Name", "Instrument Name"])
+col_name_uni = detect_column(universo, ["Nome", "Name", "Descrizione", "Description", "Security Name", "Instrument Name"])
-col_cat_uni  = pick_first(universo, ["Categoria", "Category", "Classe", "Linea", "Tipo"])
+col_cat_uni  = detect_column(universo, ["Categoria", "Category", "Classe", "Linea", "Tipo"])
-col_ac_uni   = pick_first(universo, ["Asset Class", "AssetClass", "Classe di Attivo", "Classe Attivo", "Class"])
+col_ac_uni   = detect_column(universo, ["Asset Class", "AssetClass", "Classe di Attivo", "Classe Attivo", "Class"])
 
 isins = (
     universo[col_isin_uni].astype(str).str.strip()
@@ -493,9 +393,9 @@ last_dates = []
 sql_sp = text(f"EXEC {STORED_PROC} @ISIN = :isin, @n = :n, @PtfCurr = :ptf")
 
 def detect_cols(df0):
-    col_date = pick_first(df0, ["Date", "Data", "Datetime", "Timestamp", "Time"])
+    col_date = detect_column(df0, ["Date", "Data", "Datetime", "Timestamp", "Time"])
-    col_ret  = pick_first(df0, ["Ret", "Return", "Rendimento", "Rend", "LogRet", "r_log", "r", "pct_chg"])
+    col_ret  = detect_column(df0, ["Ret", "Return", "Rendimento", "Rend", "LogRet", "r_log", "r", "pct_chg"])
-    col_px   = pick_first(df0, ["Close", "AdjClose", "Price", "Px", "Last", "Prezzo", "Chiusura"])
+    col_px   = detect_column(df0, ["Close", "AdjClose", "Price", "Px", "Last", "Prezzo", "Chiusura"])
     return col_date, col_ret, col_px
 
 ok_count = 0

config/pattern_knn_config.json

@@ -0,0 +1,35 @@
+{
+  "db": {
+    "stored_proc": "opt_RendimentoGiornaliero1_ALL",
+    "n_bars": 1305,
+    "ptf_curr": "EUR"
+  },
+  "pattern": {
+    "wp": 60,
+    "ha": 10,
+    "knn_k": 25,
+    "theta": 0.005,
+    "embargo": null
+  },
+  "tagging": {
+    "z_rev": 2.0,
+    "z_vol": 2.0,
+    "std_comp_pct": 0.15
+  },
+  "ranking": {
+    "top_n_max": 15,
+    "rp_max_weight": 0.1333333333
+  },
+  "signals": {
+    "sl_bps": 300.0,
+    "tp_bps": 800.0,
+    "trail_bps": 300.0,
+    "time_stop_bars": 20,
+    "theta_exit": 0.0,
+    "weak_days_exit": null,
+    "max_open": 15,
+    "base_capital_per_strategy": 100.0,
+    "min_trade_notional": 0.01,
+    "risk_parity_lookback": 60
+  }
+}

shared_utils.py

@@ -0,0 +1,221 @@
+"""Shared helpers for trading pattern scripts."""
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Dict, List, Optional, Sequence, Tuple
+
+import numpy as np
+import pandas as pd
+import pyodbc
+
+DEFAULT_CONFIG_PATH = Path("config/pattern_knn_config.json")
+
+
+def load_config(path: Optional[Path] = None) -> Dict:
+    """Load the JSON configuration that holds operational parameters."""
+    cfg_path = Path(path or DEFAULT_CONFIG_PATH)
+    if not cfg_path.exists():
+        raise FileNotFoundError(f"Missing configuration file: {cfg_path}")
+    with cfg_path.open("r", encoding="utf-8") as fh:
+        return json.load(fh)
+
+
+def detect_column(df: pd.DataFrame, candidates: Sequence[str]) -> Optional[str]:
+    """Return the first column whose name matches one of the candidates (case insensitive)."""
+    low = {c.lower(): c for c in df.columns}
+    for cand in candidates:
+        cl = cand.lower()
+        if cl in low:
+            return low[cl]
+    for cand in candidates:
+        cl = cand.lower()
+        for col in df.columns:
+            if cl in col.lower():
+                return col
+    return None
+
+
+def read_connection_txt(path: Path | str = "connection.txt") -> str:
+    params: Dict[str, str] = {}
+    path = Path(path)
+    if not path.exists():
+        raise FileNotFoundError(f"Missing connection.txt at {path}")
+    for line in path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line or line.startswith("#") or "=" not in line:
+            continue
+        k, v = line.split("=", 1)
+        params[k.strip().lower()] = v.strip()
+
+    username = params.get("username")
+    password = params.get("password")
+    host = params.get("host")
+    port = params.get("port", "1433")
+    database = params.get("database")
+
+    if not all([username, password, host, database]):
+        raise ValueError("connection.txt incompleto: servono username/password/host/database.")
+
+    installed = [d for d in pyodbc.drivers()]
+    driver_q = "ODBC+Driver+18+for+SQL+Server" if "ODBC Driver 18 for SQL Server" in installed else "ODBC+Driver+17+for+SQL+Server"
+    return f"mssql+pyodbc://{username}:{password}@{host}:{port}/{database}?driver={driver_q}"
+
+
+def z_norm(arr: np.ndarray) -> Optional[np.ndarray]:
+    arr = np.asarray(arr, dtype=float)
+    if arr.size == 0:
+        return None
+    mu = arr.mean()
+    sd = arr.std()
+    if sd < 1e-12:
+        return None
+    return (arr - mu) / (sd + 1e-12)
+
+
+def build_pattern_library(
+    ret_series: pd.Series,
+    wp: int,
+    ha: int,
+    embargo: Optional[int] = None,
+) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
+    """Create the normalized pattern windows and their realized outcomes.
+
+    Args:
+        ret_series: Series of returns (ordered oldest→latest).
+        wp: Window length for the pattern.
+        ha: Holding horizon used to compute the outcome.
+        embargo: Optional number of most-recent observations to exclude when
+            building the library (useful to avoid leakage when reusing the
+            same series for inference).
+    """
+    x = ret_series.dropna().values
+    n = len(x)
+    if n < wp + ha + 10:
+        return None, None
+    embargo = int(embargo or 0)
+    usable_n = n - max(0, embargo)
+    if usable_n <= wp + ha:
+        return None, None
+    wins: List[np.ndarray] = []
+    outs: List[float] = []
+    last_start = usable_n - wp - ha
+    if last_start <= 0:
+        return None, None
+    for t in range(0, last_start + 1):
+        win = x[t : t + wp]
+        winzn = z_norm(win)
+        if winzn is None:
+            continue
+        outcome = np.sum(x[t + wp : t + wp + ha])
+        wins.append(winzn)
+        outs.append(outcome)
+    if not wins:
+        return None, None
+    return np.array(wins), np.array(outs)
+
+
+def predict_from_library(
+    curr_win: np.ndarray,
+    lib_wins: np.ndarray,
+    lib_out: np.ndarray,
+    k: int = 25,
+) -> Tuple[float, float, np.ndarray]:
+    dists = np.linalg.norm(lib_wins - curr_win, axis=1)
+    idx = np.argsort(dists)[: min(k, len(dists))]
+    return float(np.median(lib_out[idx])), float(np.mean(dists[idx])), idx
+
+
+def characterize_window(
+    ret_series: pd.Series,
+    wp: int,
+    z_rev: float = 2.0,
+    z_vol: float = 2.0,
+    std_comp_pct: float = 0.15,
+) -> Tuple[Optional[str], float]:
+    x = ret_series.dropna().values
+    if len(x) < max(wp, 30):
+        return None, 0.0
+    win = x[-wp:]
+    mu, sd = win.mean(), win.std()
+    if sd < 1e-12:
+        return "compression", 0.5
+
+    last = win[-1]
+    z_last = (last - mu) / (sd + 1e-12)
+    abs_z_last = abs(z_last)
+    last3 = win[-3:] if len(win) >= 3 else win
+    sum3 = np.sum(last3)
+
+    if len(x) > 3 * wp:
+        roll_std = pd.Series(x).rolling(wp).std().dropna().values
+        if len(roll_std) > 20:
+            pct = (roll_std < np.std(win)).mean()
+        else:
+            pct = 0.5
+    else:
+        pct = 0.5
+
+    if pct < std_comp_pct:
+        return "compression", float(1.0 - pct)
+
+    if abs(sum3) > 2 * sd / np.sqrt(3) and np.sign(last3).sum() in (3, -3):
+        conf = min(1.0, abs(sum3) / (sd + 1e-12))
+        return "momentum_burst", float(conf)
+
+    mean_prev = np.mean(win[:-1]) if len(win) > 1 else 0.0
+    if abs_z_last >= z_rev and np.sign(last) != np.sign(mean_prev):
+        conf = min(1.0, abs_z_last / 3.0)
+        return "reversal_candidate", float(conf)
+
+    if abs_z_last >= z_vol:
+        conf = min(1.0, abs_z_last / 3.0)
+        return "vol_spike", float(conf)
+
+    return None, 0.0
+
+
+def hurst_rs(series: pd.Series) -> Optional[float]:
+    x = pd.to_numeric(series.dropna(), errors="coerce").astype(float).values
+    n = len(x)
+    if n < 100:
+        return None
+    x = x - x.mean()
+    z = np.cumsum(x)
+    r = z.max() - z.min()
+    s = x.std(ddof=1)
+    if s <= 0 or r <= 0:
+        return None
+    h = np.log(r / s) / np.log(n)
+    if not np.isfinite(h):
+        return None
+    return float(h)
+
+
+def build_hurst_map(returns_long: pd.DataFrame, lookback: int = 252) -> Dict[str, float]:
+    if returns_long.empty:
+        return {}
+    ret_wide = returns_long.pivot(index="Date", columns="ISIN", values="Ret").sort_index()
+    hurst_map: Dict[str, float] = {}
+    for isin in ret_wide.columns:
+        series = ret_wide[isin].dropna().astype(float)
+        if len(series) < max(lookback, 100):
+            continue
+        h_val = hurst_rs(series.iloc[-lookback:])
+        if h_val is None or not np.isfinite(h_val):
+            continue
+        hurst_map[str(isin)] = float(h_val)
+    return hurst_map
+
+
+__all__ = [
+    "build_hurst_map",
+    "build_pattern_library",
+    "characterize_window",
+    "detect_column",
+    "hurst_rs",
+    "load_config",
+    "predict_from_library",
+    "read_connection_txt",
+    "z_norm",
+]

signals_daily_kNN_prod_v.2.py

@@ -38,11 +38,28 @@ from urllib.error import URLError, HTTPError
 # DB
 import sqlalchemy as sa
 from sqlalchemy import text as sql_text
-import pyodbc
+
+from shared_utils import (
+    build_hurst_map,
+    build_pattern_library,
+    characterize_window,
+    detect_column,
+    load_config,
+    predict_from_library,
+    read_connection_txt,
+    z_norm,
+)
 
 # =========================
 # CONFIG
 # =========================
+CONFIG = load_config()
+DB_CONFIG = CONFIG.get("db", {})
+PATTERN_CONFIG = CONFIG.get("pattern", {})
+TAGGING_CONFIG = CONFIG.get("tagging", {})
+RANKING_CONFIG = CONFIG.get("ranking", {})
+SIGNALS_CONFIG = CONFIG.get("signals", {})
+
 BASE_DIR             = Path(".")
 UNIVERSO_XLSX        = BASE_DIR / "Universo per Trading System.xlsx"
 CONNECTION_TXT       = BASE_DIR / "connection.txt"
@@ -54,35 +71,38 @@ def _dated_signals_filename() -> Path:
     return BASE_DIR / f"{date_prefix}_signals.xlsx"
 
 # Stored procedure / parametri DB
-SP_NAME_DEFAULT      = "opt_RendimentoGiornaliero1_ALL"
+SP_NAME_DEFAULT      = DB_CONFIG.get("stored_proc", "opt_RendimentoGiornaliero1_ALL")
-SP_N_DEFAULT         = 1305
+SP_N_DEFAULT         = DB_CONFIG.get("n_bars", 1305)
-PTF_CURR_DEFAULT     = "EUR"
+PTF_CURR_DEFAULT     = DB_CONFIG.get("ptf_curr", "EUR")
 
 # Pattern recognition (come backtest)
-WP       = 60
+WP       = PATTERN_CONFIG.get("wp", 60)
-HA       = 10
+HA       = PATTERN_CONFIG.get("ha", 10)
-KNN_K    = 25
+KNN_K    = PATTERN_CONFIG.get("knn_k", 25)
-THETA    = 0.005     # 0,005% in decimali (identico al backtest)
+THETA    = PATTERN_CONFIG.get("theta", 0.005)     # 0,005% in decimali (identico al backtest)
+Z_REV    = TAGGING_CONFIG.get("z_rev", 2.0)
+Z_VOL    = TAGGING_CONFIG.get("z_vol", 2.0)
+STD_COMP_PCT = TAGGING_CONFIG.get("std_comp_pct", 0.15)
 
 # Exit rules (identiche al backtest)
-SL_BPS           = 300.0
+SL_BPS           = SIGNALS_CONFIG.get("sl_bps", 300.0)
-TP_BPS           = 800.0
+TP_BPS           = SIGNALS_CONFIG.get("tp_bps", 800.0)
-TRAIL_BPS        = 300.0
+TRAIL_BPS        = SIGNALS_CONFIG.get("trail_bps", 300.0)
-TIME_STOP_BARS   = 20
+TIME_STOP_BARS   = SIGNALS_CONFIG.get("time_stop_bars", 20)
-THETA_EXIT       = 0.0       # soglia debolezza
+THETA_EXIT       = SIGNALS_CONFIG.get("theta_exit", 0.0)       # soglia debolezza
-WEAK_DAYS_EXIT   = None      # uscita IMMEDIATA in caso di debolezza (come backtest)
+WEAK_DAYS_EXIT   = SIGNALS_CONFIG.get("weak_days_exit")      # uscita IMMEDIATA in caso di debolezza (come backtest)
 
 # Ranking e selezione Top-N per APERTURE
-MAX_OPEN         = 15        # cap strumenti aperti oggi (come backtest)
+MAX_OPEN         = SIGNALS_CONFIG.get("max_open", 15)        # cap strumenti aperti oggi (come backtest)
 
 # Allineamento al backtest v3.1.5 per il cap del Risk Parity
-TOP_N_MAX        = MAX_OPEN
+TOP_N_MAX        = RANKING_CONFIG.get("top_n_max", MAX_OPEN)
-RP_MAX_WEIGHT    = 2 / TOP_N_MAX   # ≈ 0.1333 = 13,33% per singolo asset
+RP_MAX_WEIGHT    = RANKING_CONFIG.get("rp_max_weight", 2 / max(TOP_N_MAX, 1))   # ≈ 0.1333 = 13,33% per singolo asset
 
 # Sizing
-BASE_CAPITAL_PER_STRATEGY = 100.0
+BASE_CAPITAL_PER_STRATEGY = SIGNALS_CONFIG.get("base_capital_per_strategy", 100.0)
-MIN_TRADE_NOTIONAL        = 0.01
+MIN_TRADE_NOTIONAL        = SIGNALS_CONFIG.get("min_trade_notional", 0.01)
-RISK_PARITY_LOOKBACK      = 60
+RISK_PARITY_LOOKBACK      = SIGNALS_CONFIG.get("risk_parity_lookback", 60)
 
 # Calendario
 BUSINESS_DAYS_ONLY   = True
@@ -111,34 +131,6 @@ def _safe_to_float(x) -> Optional[float]:
     except Exception:
         return None
 
-# =========================
-# CONNESSIONE DB
-# =========================
-def read_connection_txt(path: Path) -> str:
-    if not path.exists():
-        raise FileNotFoundError(f"Missing connection.txt at {path}")
-
-    params: Dict[str, str] = {}
-    for line in path.read_text(encoding="utf-8").splitlines():
-        line = line.strip()
-        if not line or line.startswith("#") or "=" not in line:
-            continue
-        k, v = line.split("=", 1)
-        params[k.strip().lower()] = v.strip()
-
-    username = params.get("username")
-    password = params.get("password")
-    host     = params.get("host")
-    port     = params.get("port", "1433")
-    database = params.get("database")
-
-    if not all([username, password, host, database]):
-        raise ValueError("connection.txt incompleto: servono username/password/host/database.")
-
-    installed = [d for d in pyodbc.drivers()]
-    driver_q = "ODBC+Driver+18+for+SQL+Server" if "ODBC Driver 18 for SQL Server" in installed else "ODBC+Driver+17+for+SQL+Server"
-    return f"mssql+pyodbc://{username}:{password}@{host}:{port}/{database}?driver={driver_q}"
-
 def _db_fetch_returns(conn_str: str,
                       isins: List[str],
                       sp_name: Optional[str] = None,
@@ -152,17 +144,6 @@ def _db_fetch_returns(conn_str: str,
     sql_sp = sql_text(f"EXEC {sp} @ISIN = :isin, @n = :n, @PtfCurr = :ptf")
     frames: List[pd.DataFrame] = []
 
-    def _pick(df: pd.DataFrame, candidates: List[str]) -> Optional[str]:
-        low = {c.lower(): c for c in df.columns}
-        for c in candidates:
-            if c.lower() in low:
-                return low[c.lower()]
-        for c in df.columns:
-            cl = c.lower()
-            if any(tok in cl for tok in [x.lower() for x in candidates]):
-                return c
-        return None
-
     with engine.begin() as conn:
         for i, isin in enumerate(isins, start=1):
             print(f"[DB] ({i}/{len(isins)}) scarico serie storica per {isin} ...", flush=True)
@@ -176,8 +157,8 @@ def _db_fetch_returns(conn_str: str,
                 print(f"[WARN] Nessun dato per {isin}")
                 continue
 
-            col_date = _pick(df, ["Date", "Data", "Datetime", "Timestamp", "Time"])
+            col_date = detect_column(df, ["Date", "Data", "Datetime", "Timestamp", "Time"])
-            col_ret  = _pick(df, ["Ret", "Return", "Rendimento", "Rend", "Ret_%", "RET"])
+            col_ret  = detect_column(df, ["Ret", "Return", "Rendimento", "Rend", "Ret_%", "RET"])
             if not col_date or not col_ret:
                 print(f"[WARN] Colonne mancanti per {isin}")
                 continue
@@ -278,102 +259,6 @@ def get_open_price(isin: str, universe: pd.DataFrame) -> Optional[float]:
 # =========================
 # HURST ESTIMATOR & MAP
 # =========================
-from typing import Optional  # in cima al file c'è già Optional nei type hints, quindi ok
-
-def _hurst_rs(series: pd.Series) -> Optional[float]:
-    """
-    Stima semplice del coefficiente di Hurst tramite Rescaled Range (R/S) su un'unica finestra.
-    Ritorna NaN se la serie è troppo corta o degenerata.
-    """
-    x = pd.to_numeric(series.dropna(), errors="coerce").astype(float).values
-    n = len(x)
-    if n < 100:
-        return None
-
-    x = x - x.mean()
-    z = np.cumsum(x)
-    R = z.max() - z.min()
-    S = x.std(ddof=1)
-    if S <= 0 or R <= 0:
-        return None
-
-    H = np.log(R / S) / np.log(n)
-    if not np.isfinite(H):
-        return None
-    return float(H)
-
-
-def build_hurst_map(returns_long: pd.DataFrame,
-                    lookback: int = 252) -> Dict[str, float]:
-    """
-    Costruisce una mappa ISIN -> Hurst usando gli ultimi `lookback` rendimenti.
-    """
-    if returns_long.empty:
-        return {}
-
-    ret_wide = returns_long.pivot(index="Date", columns="ISIN", values="Ret").sort_index()
-    hurst_map: Dict[str, float] = {}
-
-    for isin in ret_wide.columns:
-        s = ret_wide[isin].dropna().astype(float)
-        if len(s) < max(lookback, 100):
-            continue
-        h = _hurst_rs(s.iloc[-lookback:])
-        if h is None or not np.isfinite(h):
-            continue
-        hurst_map[str(isin)] = float(h)
-
-    return hurst_map
-
-# =========================
-# PATTERN RECOGNITION (WP/HA)
-# =========================
-def z_norm(arr: np.ndarray) -> Optional[np.ndarray]:
-    arr = np.asarray(arr, dtype=float)
-    mu = arr.mean()
-    sd = arr.std()
-    if sd < 1e-12:
-        return None
-    return (arr - mu) / (sd + 1e-12)
-
-def build_pattern_library(ret_series: pd.Series, Wp: int, Ha: int) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
-    x = ret_series.dropna().values
-    N = len(x)
-    if N < Wp + Ha + 10:
-        return None, None
-    wins, outs = [], []
-    for t in range(0, N - Wp - Ha):
-        win = x[t:t+Wp]
-        winzn = z_norm(win)
-        if winzn is None:
-            continue
-        outcome = np.sum(x[t+Wp : t+Wp+Ha])  # somma rendimenti futuri su Ha (decimali)
-        wins.append(winzn); outs.append(outcome)
-    if not wins:
-        return None, None
-    return np.array(wins), np.array(outs)
-
-def predict_from_library(curr_win: np.ndarray,
-                         lib_wins: np.ndarray,
-                         lib_out: np.ndarray,
-                         k: int = 25) -> Tuple[float, float, np.ndarray]:
-    dists = np.linalg.norm(lib_wins - curr_win, axis=1)
-    idx = np.argsort(dists)[:min(k, len(dists))]
-    return float(np.median(lib_out[idx])), float(np.mean(dists[idx])), idx
-
-def characterize_window(ret_series: pd.Series, Wp: int) -> Tuple[Optional[str], float]:
-    x = ret_series.dropna().values
-    if len(x) < max(WP, 30):
-        return None, 0.0
-    win = x[-Wp:]
-    mu, sd = win.mean(), win.std()
-    if sd < 1e-12:
-        return "compression", 0.5
-    last3 = win[-3:] if len(win) >= 3 else win
-    if np.sign(last3).sum() in (3, -3):
-        return "momentum_burst", min(1.0, abs(last3.sum())/(sd+1e-12))
-    return None, 0.0
-
 # =========================
 # GENERAZIONE SEGNALI (EOD su D)
 # =========================
@@ -404,17 +289,17 @@ def generate_signals_today(universe: pd.DataFrame,
         lib_wins, lib_out = build_pattern_library(r, WP, HA)
         if lib_wins is None or len(r) < WP + HA:
             est_out, avg_dist, sig = np.nan, np.nan, 0
-            ptype, pconf = characterize_window(r, WP)
+            ptype, pconf = characterize_window(r, WP, z_rev=Z_REV, z_vol=Z_VOL, std_comp_pct=STD_COMP_PCT)
         else:
             curr = r.values[-WP:]
             curr_zn = z_norm(curr)
             if curr_zn is None:
                 est_out, avg_dist, sig = np.nan, np.nan, 0
-                ptype, pconf = characterize_window(r, WP)
+                ptype, pconf = characterize_window(r, WP, z_rev=Z_REV, z_vol=Z_VOL, std_comp_pct=STD_COMP_PCT)
             else:
                 est_out, avg_dist, _ = predict_from_library(curr_zn, lib_wins, lib_out, k=KNN_K)
                 sig = 1 if (pd.notna(est_out) and float(est_out) > float(theta_entry)) else 0
-                ptype, pconf = characterize_window(r, WP)
+                ptype, pconf = characterize_window(r, WP, z_rev=Z_REV, z_vol=Z_VOL, std_comp_pct=STD_COMP_PCT)
 
         rows.append({
             "Date": decision_date, "ISIN": isin,