Segnali giornalieri
This commit is contained in:
fredmaloggia
809
signals_daily_kNN_prod_v.2.py
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809
signals_daily_kNN_prod_v.2.py
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# -*- coding: utf-8 -*-
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"""
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Daily Signals Generator (kNN) – PRODUCTION (coerente al backtest v3.1.5)
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Novità principali:
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- Cap giornaliero MAX_OPEN=15: ranking unico dei buy e revisione per differenza su ogni strategia
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- Risk Parity con cap per singolo asset (RP_MAX_WEIGHT) allineato al backtest v3.1.5
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- Fetch OPEN una sola volta per ISIN coinvolti in OPEN/CLOSE (cache condivisa tra strategie)
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- Audit log per TUTTE le strategie operative (Equal_Weight, Risk_Parity)
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Pipeline (giorno D, EOD -> t+1 OPEN):
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1) Carica universo e serie rendimenti dal DB (stored procedure)
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2) Pattern kNN (WP=60, HA=10, K=25), Signal=1 se EstOutcome > THETA (decimali)
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3) Ranking unico dei buy e selezione Top-N (MAX_OPEN) come target giornaliero
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4) Revisione per differenza: chiudi aperture fuori top, apri nuove entrate nel top (+ risk exits)
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5) Sizing (Equal Weight / Risk Parity con cap)
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6) Fetch OPEN prices UNA VOLTA per ISIN interessati (OPEN/CLOSE) e popolamento ordini
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7) Log ordini e snapshot Excel con fogli: Open_Equal_Weight / Open_Risk_Parity
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"""
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from __future__ import annotations
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import os
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import ssl
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import json
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import time
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import warnings
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import datetime as dt
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple, Iterable, Set
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import numpy as np
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import pandas as pd
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from urllib.request import urlopen
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from urllib.error import URLError, HTTPError
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# DB
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import sqlalchemy as sa
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from sqlalchemy import text as sql_text
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import pyodbc
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# =========================
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# CONFIG
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# =========================
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BASE_DIR = Path(".")
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UNIVERSO_XLSX = BASE_DIR / "Universo per Trading System.xlsx"
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CONNECTION_TXT = BASE_DIR / "connection.txt"
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AUDIT_LOG_CSV = BASE_DIR / "trades_audit_log.csv"
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OPEN_TRADES_DIR = BASE_DIR / "open_trades"
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def _dated_signals_filename() -> Path:
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date_prefix = pd.Timestamp.today().strftime("%Y%m%d")
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return BASE_DIR / f"{date_prefix}_signals.xlsx"
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# Stored procedure / parametri DB
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SP_NAME_DEFAULT = "opt_RendimentoGiornaliero1_ALL"
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SP_N_DEFAULT = 1305
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PTF_CURR_DEFAULT = "EUR"
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# Pattern recognition (come backtest)
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WP = 60
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HA = 10
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KNN_K = 25
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THETA = 0.005 # 0,005% in decimali (identico al backtest)
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# Exit rules (identiche al backtest)
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SL_BPS = 300.0
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TP_BPS = 800.0
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TRAIL_BPS = 300.0
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TIME_STOP_BARS = 20
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THETA_EXIT = 0.0 # soglia debolezza
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WEAK_DAYS_EXIT = None # uscita IMMEDIATA in caso di debolezza (come backtest)
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# Ranking e selezione Top-N per APERTURE
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MAX_OPEN = 15 # cap strumenti aperti oggi (come backtest)
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# Allineamento al backtest v3.1.5 per il cap del Risk Parity
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TOP_N_MAX = MAX_OPEN
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RP_MAX_WEIGHT = 2 / TOP_N_MAX # ≈ 0.1333 = 13,33% per singolo asset
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# Sizing
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BASE_CAPITAL_PER_STRATEGY = 100.0
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MIN_TRADE_NOTIONAL = 0.01
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RISK_PARITY_LOOKBACK = 60
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# Calendario
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BUSINESS_DAYS_ONLY = True
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SEED = 42
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warnings.filterwarnings("ignore")
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np.random.seed(SEED)
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# =========================
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# UTILS
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# =========================
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def ensure_dir(p: Path):
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p.mkdir(parents=True, exist_ok=True)
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def next_business_day(d: dt.date) -> dt.date:
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nd = d + dt.timedelta(days=1)
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if not BUSINESS_DAYS_ONLY:
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return nd
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while nd.weekday() >= 5: # 5=Sat, 6=Sun
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nd += dt.timedelta(days=1)
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return nd
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def _safe_to_float(x) -> Optional[float]:
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try:
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return float(x)
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except Exception:
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return None
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# =========================
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# CONNESSIONE DB
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# =========================
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def read_connection_txt(path: Path) -> str:
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if not path.exists():
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raise FileNotFoundError(f"Missing connection.txt at {path}")
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params: Dict[str, str] = {}
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for line in path.read_text(encoding="utf-8").splitlines():
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line = line.strip()
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if not line or line.startswith("#") or "=" not in line:
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continue
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k, v = line.split("=", 1)
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params[k.strip().lower()] = v.strip()
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username = params.get("username")
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password = params.get("password")
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host = params.get("host")
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port = params.get("port", "1433")
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database = params.get("database")
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if not all([username, password, host, database]):
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raise ValueError("connection.txt incompleto: servono username/password/host/database.")
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installed = [d for d in pyodbc.drivers()]
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driver_q = "ODBC+Driver+18+for+SQL+Server" if "ODBC Driver 18 for SQL Server" in installed else "ODBC+Driver+17+for+SQL+Server"
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return f"mssql+pyodbc://{username}:{password}@{host}:{port}/{database}?driver={driver_q}"
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def _db_fetch_returns(conn_str: str,
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isins: List[str],
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sp_name: Optional[str] = None,
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n_bars: Optional[int] = None,
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ptf_curr: Optional[str] = None) -> pd.DataFrame:
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engine = sa.create_engine(conn_str, fast_executemany=True)
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sp = sp_name or os.environ.get("SP_NAME", SP_NAME_DEFAULT)
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n_val = n_bars if n_bars is not None else int(os.environ.get("SP_N", SP_N_DEFAULT))
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ptf = (ptf_curr or os.environ.get("PTF_CURR", PTF_CURR_DEFAULT) or "").strip() or PTF_CURR_DEFAULT
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sql_sp = sql_text(f"EXEC {sp} @ISIN = :isin, @n = :n, @PtfCurr = :ptf")
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frames: List[pd.DataFrame] = []
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def _pick(df: pd.DataFrame, candidates: List[str]) -> Optional[str]:
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low = {c.lower(): c for c in df.columns}
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for c in candidates:
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if c.lower() in low:
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return low[c.lower()]
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for c in df.columns:
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cl = c.lower()
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if any(tok in cl for tok in [x.lower() for x in candidates]):
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return c
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return None
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with engine.begin() as conn:
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for i, isin in enumerate(isins, start=1):
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print(f"[DB] ({i}/{len(isins)}) scarico serie storica per {isin} ...", flush=True)
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try:
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df = pd.read_sql_query(sql_sp, conn, params={"isin": str(isin), "n": int(n_val), "ptf": ptf})
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except Exception as e:
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print(f"[ERROR] SP {sp} fallita per {isin}: {e}")
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continue
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if df.empty:
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print(f"[WARN] Nessun dato per {isin}")
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continue
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col_date = _pick(df, ["Date", "Data", "Datetime", "Timestamp", "Time"])
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col_ret = _pick(df, ["Ret", "Return", "Rendimento", "Rend", "Ret_%", "RET"])
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if not col_date or not col_ret:
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print(f"[WARN] Colonne mancanti per {isin}")
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continue
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out = df[[col_date, col_ret]].copy()
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out.columns = ["Date", "Ret"]
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out["Date"] = pd.to_datetime(out["Date"], errors="coerce").dt.tz_localize(None)
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out["ISIN"] = str(isin)
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med = pd.to_numeric(out["Ret"], errors="coerce").abs().median()
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if pd.notnull(med) and med > 1.5:
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out["Ret"] = out["Ret"].astype(float) / 100.0
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print(f" ↳ righe scaricate: {len(out)}")
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frames.append(out[["Date", "ISIN", "Ret"]])
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if not frames:
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return pd.DataFrame(columns=["Date", "ISIN", "Ret"])
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out_all = pd.concat(frames, ignore_index=True)
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out_all = out_all.dropna(subset=["Date"]).sort_values(["ISIN", "Date"]).reset_index(drop=True)
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return out_all[["Date", "ISIN", "Ret"]]
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# =========================
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# UNIVERSO + OPEN PRICE API (schema checker)
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# =========================
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OPEN_MAX_RETRY = 3
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OPEN_SLEEP_SEC = 0.1
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OPEN_TIMEOUT = 10
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def load_universe(path: Path) -> pd.DataFrame:
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df = pd.read_excel(path)
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if "ISIN" not in df.columns:
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raise KeyError("Nel file Universo manca la colonna 'ISIN'")
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df["ISIN"] = df["ISIN"].astype(str).str.strip()
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for col in ["Asset Class", "Mercato", "TickerOpen"]:
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if col not in df.columns:
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df[col] = ""
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df[col] = df[col].astype(str).str.strip()
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return df
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def _build_symbol_euronext(row: pd.Series) -> Tuple[str, str]:
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isin = str(row.get("ISIN", "")).strip()
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venue = str(row.get("Mercato", "")).strip()
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tok = str(row.get("TickerOpen", "") or "").strip()
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base = "https://fin.scorer.app/finance/euronext/price"
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if tok and "-" in tok and tok.split("-")[0].upper() == isin.upper():
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return base, tok
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if isin and venue:
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return base, f"{isin}-{venue}"
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return base, isin
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def get_open_price(isin: str, universe: pd.DataFrame) -> Optional[float]:
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"""
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Endpoint euronext/price/ISIN-Mercato con retry/backoff.
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Log dettagliato stile checker: [TRY]/[RETRY]/[OK]/[FAIL].
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"""
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try:
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row = universe.loc[universe["ISIN"] == str(isin)].iloc[0]
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except Exception:
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print(f"[WARN] ISIN {isin} non trovato nell’universo.")
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return None
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base, symbol = _build_symbol_euronext(row)
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url = f"{base}/{symbol}"
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print(f"[DOWNLOAD] {symbol:<30s} -> [TRY 1/{OPEN_MAX_RETRY}]", flush=True)
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for attempt in range(1, OPEN_MAX_RETRY + 1):
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if attempt > 1:
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print(f"[DOWNLOAD] {symbol:<30s} -> [TRY {attempt}/{OPEN_MAX_RETRY}]", flush=True)
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try:
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with urlopen(url, timeout=OPEN_TIMEOUT, context=ssl.create_default_context()) as resp:
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data = json.loads(resp.read().decode("utf-8"))
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if not isinstance(data, list) or not data:
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print(" ↳ NO DATA")
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continue
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d = (data[0] or {}).get("data") or {}
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px = d.get("open") if d.get("open") is not None else d.get("prevClose")
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if px is None:
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print(" ↳ WARN: 'open' e 'prevClose' assenti")
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continue
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px = float(px)
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print(f" ↳ OK open={d.get('open')} close={d.get('close')} (ritorno prezzo={px})")
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return px
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except (HTTPError, URLError, ssl.SSLError) as e:
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if attempt < OPEN_MAX_RETRY:
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print(f" ↳ ERR {e}\nritento tra {OPEN_SLEEP_SEC}s")
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time.sleep(OPEN_SLEEP_SEC)
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else:
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print(f" ↳ ERR {e}")
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print(f"[ERROR] nessun prezzo per {symbol} dopo {OPEN_MAX_RETRY} tentativi")
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return None
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# =========================
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# HURST ESTIMATOR & MAP
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# =========================
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from typing import Optional # in cima al file c'è già Optional nei type hints, quindi ok
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def _hurst_rs(series: pd.Series) -> Optional[float]:
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"""
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Stima semplice del coefficiente di Hurst tramite Rescaled Range (R/S) su un'unica finestra.
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Ritorna NaN se la serie è troppo corta o degenerata.
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"""
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x = pd.to_numeric(series.dropna(), errors="coerce").astype(float).values
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n = len(x)
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if n < 100:
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return None
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x = x - x.mean()
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z = np.cumsum(x)
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R = z.max() - z.min()
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S = x.std(ddof=1)
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if S <= 0 or R <= 0:
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return None
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H = np.log(R / S) / np.log(n)
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if not np.isfinite(H):
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return None
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return float(H)
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def build_hurst_map(returns_long: pd.DataFrame,
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lookback: int = 252) -> Dict[str, float]:
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"""
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Costruisce una mappa ISIN -> Hurst usando gli ultimi `lookback` rendimenti.
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"""
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if returns_long.empty:
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return {}
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ret_wide = returns_long.pivot(index="Date", columns="ISIN", values="Ret").sort_index()
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hurst_map: Dict[str, float] = {}
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for isin in ret_wide.columns:
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s = ret_wide[isin].dropna().astype(float)
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if len(s) < max(lookback, 100):
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continue
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h = _hurst_rs(s.iloc[-lookback:])
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if h is None or not np.isfinite(h):
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continue
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hurst_map[str(isin)] = float(h)
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return hurst_map
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# =========================
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# PATTERN RECOGNITION (WP/HA)
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# =========================
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def z_norm(arr: np.ndarray) -> Optional[np.ndarray]:
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arr = np.asarray(arr, dtype=float)
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mu = arr.mean()
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sd = arr.std()
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if sd < 1e-12:
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return None
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return (arr - mu) / (sd + 1e-12)
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def build_pattern_library(ret_series: pd.Series, Wp: int, Ha: int) -> Tuple[Optional[np.ndarray], Optional[np.ndarray]]:
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x = ret_series.dropna().values
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N = len(x)
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if N < Wp + Ha + 10:
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return None, None
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wins, outs = [], []
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for t in range(0, N - Wp - Ha):
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win = x[t:t+Wp]
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winzn = z_norm(win)
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if winzn is None:
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continue
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outcome = np.sum(x[t+Wp : t+Wp+Ha]) # somma rendimenti futuri su Ha (decimali)
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wins.append(winzn); outs.append(outcome)
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if not wins:
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return None, None
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return np.array(wins), np.array(outs)
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def predict_from_library(curr_win: np.ndarray,
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lib_wins: np.ndarray,
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lib_out: np.ndarray,
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k: int = 25) -> Tuple[float, float, np.ndarray]:
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dists = np.linalg.norm(lib_wins - curr_win, axis=1)
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idx = np.argsort(dists)[:min(k, len(dists))]
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return float(np.median(lib_out[idx])), float(np.mean(dists[idx])), idx
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def characterize_window(ret_series: pd.Series, Wp: int) -> Tuple[Optional[str], float]:
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x = ret_series.dropna().values
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if len(x) < max(WP, 30):
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return None, 0.0
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win = x[-Wp:]
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mu, sd = win.mean(), win.std()
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if sd < 1e-12:
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return "compression", 0.5
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last3 = win[-3:] if len(win) >= 3 else win
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if np.sign(last3).sum() in (3, -3):
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return "momentum_burst", min(1.0, abs(last3.sum())/(sd+1e-12))
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return None, 0.0
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# =========================
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# GENERAZIONE SEGNALI (EOD su D)
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# =========================
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def generate_signals_today(universe: pd.DataFrame,
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returns_long: pd.DataFrame,
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today: dt.date,
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hurst_map: Optional[Dict[str, float]] = None) -> pd.DataFrame:
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ret_wide = returns_long.pivot(index="Date", columns="ISIN", values="Ret").sort_index()
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decision_date = ret_wide.index[-1].date()
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rows = []
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for isin in ret_wide.columns:
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r = ret_wide[isin].dropna().astype(float)
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# THETA = HURST IN PERCENTUALE (H = 0.50 -> theta_entry = 0.005 = 0.5%)
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theta_entry = THETA # fallback globale
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H_val = None
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if hurst_map is not None:
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H_val = hurst_map.get(str(isin))
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if H_val is not None and not pd.isna(H_val):
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theta_entry = float(H_val) / 100.0
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if len(r) < max(200, WP + HA + 10):
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rows.append({"Date": decision_date, "ISIN": isin,
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"Signal": 0, "EstOutcome": np.nan, "AvgDist": np.nan,
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"PatternType": None, "Confidence": np.nan})
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continue
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lib_wins, lib_out = build_pattern_library(r, WP, HA)
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if lib_wins is None or len(r) < WP + HA:
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est_out, avg_dist, sig = np.nan, np.nan, 0
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ptype, pconf = characterize_window(r, WP)
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else:
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curr = r.values[-WP:]
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curr_zn = z_norm(curr)
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if curr_zn is None:
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est_out, avg_dist, sig = np.nan, np.nan, 0
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ptype, pconf = characterize_window(r, WP)
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else:
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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)
|
||||
|
||||
rows.append({
|
||||
"Date": decision_date, "ISIN": isin,
|
||||
"Signal": int(sig),
|
||||
"EstOutcome": (None if pd.isna(est_out) else float(est_out)),
|
||||
"AvgDist": (None if pd.isna(avg_dist) else float(avg_dist)),
|
||||
"PatternType": ptype,
|
||||
"Confidence": (None if pconf is None else float(max(0.0, min(1.0, pconf)))),
|
||||
"Hurst": (None if H_val is None else float(H_val)),
|
||||
"Theta": float(theta_entry),
|
||||
})
|
||||
|
||||
sig_df = pd.DataFrame(rows).set_index(["Date","ISIN"]).sort_index()
|
||||
return sig_df
|
||||
|
||||
# =========================
|
||||
# TOP-N SELECTION & PRICE CACHE
|
||||
# =========================
|
||||
def _rank_buy(signals_today: pd.DataFrame, decision_date: dt.date) -> pd.DataFrame:
|
||||
"""
|
||||
Ritorna un DataFrame con i soli buy del giorno, ordinati per:
|
||||
EstOutcome desc, Confidence desc, AvgDist asc.
|
||||
Colonne: ['ISIN','EstOutcome','Confidence','AvgDist']
|
||||
"""
|
||||
if signals_today.empty:
|
||||
return pd.DataFrame(columns=["ISIN","EstOutcome","Confidence","AvgDist"])
|
||||
day_df = signals_today.reset_index().query("Date == @decision_date")
|
||||
if day_df.empty:
|
||||
return pd.DataFrame(columns=["ISIN","EstOutcome","Confidence","AvgDist"])
|
||||
|
||||
buy = (day_df[day_df["Signal"] == 1]
|
||||
.assign(
|
||||
EstOutcome=pd.to_numeric(day_df.loc[day_df["Signal"] == 1, "EstOutcome"], errors="coerce"),
|
||||
Confidence=pd.to_numeric(day_df.loc[day_df["Signal"] == 1, "Confidence"], errors="coerce"),
|
||||
AvgDist=pd.to_numeric(day_df.loc[day_df["Signal"] == 1, "AvgDist"], errors="coerce"),
|
||||
)
|
||||
.sort_values(by=["EstOutcome","Confidence","AvgDist"], ascending=[False,False,True], na_position="last"))
|
||||
return buy[["ISIN","EstOutcome","Confidence","AvgDist"]].reset_index(drop=True)
|
||||
|
||||
def _select_top_signals(buy_rank_df: pd.DataFrame, max_open: int) -> List[str]:
|
||||
if buy_rank_df.empty:
|
||||
return []
|
||||
return buy_rank_df["ISIN"].head(max_open).tolist()
|
||||
|
||||
def _fetch_open_prices_once(isins: Iterable[str], universe: pd.DataFrame) -> Dict[str, Optional[float]]:
|
||||
cache: Dict[str, Optional[float]] = {}
|
||||
for isin in sorted(set(isins)):
|
||||
cache[isin] = get_open_price(isin, universe)
|
||||
return cache
|
||||
|
||||
# =========================
|
||||
# POSIZIONI / ORDINI / LOG
|
||||
# =========================
|
||||
@dataclass
|
||||
class OpenPos:
|
||||
Strategy: str
|
||||
ISIN: str
|
||||
EntryDate: dt.date
|
||||
EntryIndex: int
|
||||
EntryAmount: float
|
||||
SizeWeight: float
|
||||
PeakPnL: float = 0.0
|
||||
Notes: str = ""
|
||||
|
||||
def open_trades_path(strategy: str) -> Path:
|
||||
ensure_dir(OPEN_TRADES_DIR)
|
||||
return OPEN_TRADES_DIR / f"open_{strategy}.csv"
|
||||
|
||||
def load_open_trades(strategy: str) -> pd.DataFrame:
|
||||
p = open_trades_path(strategy)
|
||||
if not p.exists():
|
||||
return pd.DataFrame(columns=[
|
||||
"Strategy","ISIN","EntryDate","EntryIndex","EntryAmount","SizeWeight","PeakPnL","WeakDays","Notes"
|
||||
])
|
||||
df = pd.read_csv(p)
|
||||
if "EntryDate" in df.columns:
|
||||
df["EntryDate"] = pd.to_datetime(df["EntryDate"], errors="coerce").dt.date
|
||||
if "WeakDays" not in df.columns:
|
||||
df["WeakDays"] = 0
|
||||
df["Strategy"] = strategy
|
||||
return df
|
||||
|
||||
def save_open_trades(strategy: str, df: pd.DataFrame):
|
||||
p = open_trades_path(strategy)
|
||||
df.to_csv(p, index=False)
|
||||
|
||||
def append_audit_rows(rows: List[Dict]):
|
||||
if not rows:
|
||||
return
|
||||
log = pd.DataFrame(rows)
|
||||
if AUDIT_LOG_CSV.exists():
|
||||
old = pd.read_csv(AUDIT_LOG_CSV)
|
||||
log = pd.concat([old, log], ignore_index=True)
|
||||
log.to_csv(AUDIT_LOG_CSV, index=False)
|
||||
|
||||
# sizing
|
||||
def compute_current_capital_from_log(strategy: str,
|
||||
returns_wide: pd.DataFrame,
|
||||
asof_date: dt.date) -> float:
|
||||
return BASE_CAPITAL_PER_STRATEGY
|
||||
|
||||
def size_equal_weight(candidates: List[str]) -> Dict[str, float]:
|
||||
if not candidates:
|
||||
return {}
|
||||
w = 1.0 / max(1, len(candidates))
|
||||
return {isin: w for isin in candidates}
|
||||
|
||||
def size_risk_parity(candidates: List[str],
|
||||
returns_wide: pd.DataFrame,
|
||||
asof_idx: int) -> Dict[str, float]:
|
||||
"""
|
||||
Risk Parity sui soli candidati, con cap per singolo asset (RP_MAX_WEIGHT),
|
||||
allineato alla logica usata nel backtest v3.1.5.
|
||||
Eventuale parte non allocata resta in cash (non rinormalizziamo dopo il cap).
|
||||
"""
|
||||
if not candidates:
|
||||
return {}
|
||||
L = RISK_PARITY_LOOKBACK
|
||||
start = max(0, asof_idx - L + 1)
|
||||
sub = returns_wide.iloc[start:asof_idx+1][candidates]
|
||||
vol = sub.std().replace(0, np.nan)
|
||||
inv_vol = 1.0 / vol
|
||||
inv_vol = inv_vol.replace([np.inf, -np.inf], np.nan).fillna(0.0)
|
||||
if inv_vol.sum() == 0:
|
||||
return size_equal_weight(candidates)
|
||||
w = (inv_vol / inv_vol.sum()).to_dict()
|
||||
# Cap per singolo peso, come RP_MAX_WEIGHT nel Pattern Recon
|
||||
if RP_MAX_WEIGHT is not None:
|
||||
w = {k: min(RP_MAX_WEIGHT, float(v)) for k, v in w.items()}
|
||||
return w
|
||||
|
||||
def _risk_exit_flags(isin: str,
|
||||
entry_date: Optional[dt.date],
|
||||
ret_wide: pd.DataFrame,
|
||||
decision_date: dt.date,
|
||||
est_map_today: pd.Series) -> List[str]:
|
||||
"""Calcola motivi di uscita (SL/TP/Trailing/Time/Weak) per una posizione."""
|
||||
reasons: List[str] = []
|
||||
pnl_if_stay = 0.0
|
||||
peak_dd = 0.0
|
||||
if entry_date is not None and isin in ret_wide.columns:
|
||||
sub = ret_wide[isin].loc[pd.Timestamp(entry_date):pd.Timestamp(decision_date)]
|
||||
if not sub.empty and sub.index[0].date() == entry_date:
|
||||
sub = sub.iloc[1:]
|
||||
if not sub.empty:
|
||||
eq_path = (1.0 + sub.fillna(0.0)).cumprod()
|
||||
pnl_if_stay = float(eq_path.iloc[-1] - 1.0)
|
||||
peak_curve = eq_path.cummax()
|
||||
dd_series = eq_path / peak_curve - 1.0
|
||||
peak_dd = float(-dd_series.min())
|
||||
|
||||
if SL_BPS is not None and pnl_if_stay <= -SL_BPS/10000.0:
|
||||
reasons.append("SL")
|
||||
if TP_BPS is not None and pnl_if_stay >= TP_BPS/10000.0:
|
||||
reasons.append("TP")
|
||||
if TRAIL_BPS is not None and peak_dd >= TRAIL_BPS/10000.0:
|
||||
reasons.append("TRAIL")
|
||||
|
||||
if TIME_STOP_BARS is not None and entry_date is not None and isin in ret_wide.columns:
|
||||
sub_all = ret_wide[isin].loc[pd.Timestamp(entry_date):pd.Timestamp(decision_date)]
|
||||
bars = len(sub_all) - (1 if (not sub_all.empty and sub_all.index[0].date() == entry_date) else 0)
|
||||
if bars >= int(TIME_STOP_BARS):
|
||||
reasons.append("TIME")
|
||||
|
||||
if THETA_EXIT is not None and est_map_today is not None and isin in est_map_today.index:
|
||||
est_out_today = est_map_today.loc[isin]
|
||||
if pd.notna(est_out_today) and float(est_out_today) <= float(THETA_EXIT):
|
||||
reasons.append("WEAK")
|
||||
return reasons
|
||||
|
||||
def update_positions_and_build_orders(universe: pd.DataFrame,
|
||||
returns_long: pd.DataFrame,
|
||||
signals_today: pd.DataFrame,
|
||||
today: dt.date,
|
||||
buy_rank_df: Optional[pd.DataFrame],
|
||||
allowed_open_isins: Optional[List[str]] = None) -> Tuple[pd.DataFrame, List[Dict]]:
|
||||
"""
|
||||
- decision_date = ultima data disponibile (EOD)
|
||||
- target giornaliero = primi MAX_OPEN del ranking buy (uguale per tutte le strategie)
|
||||
- per ogni strategia: revisione per differenza vs posizioni già aperte
|
||||
- CLOSE anche per risk exits (SL/TP/Trailing/Time/Weak)
|
||||
- Esecuzione a t+1 open; fetch prezzi UNA VOLTA per tutti gli ISIN con ordini
|
||||
|
||||
NB: strategia Aggressiva/Crypto rimossa. Restano ONLY:
|
||||
- Equal_Weight
|
||||
- Risk_Parity (con cap per singolo asset)
|
||||
"""
|
||||
strategies = ["Equal_Weight", "Risk_Parity"]
|
||||
|
||||
ret_wide = returns_long.pivot(index="Date", columns="ISIN", values="Ret").sort_index()
|
||||
decision_date: dt.date = ret_wide.index[-1].date()
|
||||
asof_idx = len(ret_wide.index) - 1
|
||||
next_open_date = next_business_day(decision_date)
|
||||
|
||||
# Ranking e maps di oggi
|
||||
est_map_all = None
|
||||
if buy_rank_df is not None and not buy_rank_df.empty:
|
||||
est_map_all = buy_rank_df.set_index("ISIN")["EstOutcome"]
|
||||
else:
|
||||
# fallback: usa tutti i segnali di oggi
|
||||
day_df = signals_today.reset_index().query("Date == @decision_date")
|
||||
if not day_df.empty:
|
||||
est_map_all = day_df.set_index("ISIN")["EstOutcome"]
|
||||
|
||||
target_isins: List[str] = allowed_open_isins or []
|
||||
target_set: Set[str] = set(target_isins)
|
||||
|
||||
# Sizing per strategia basato sul target
|
||||
size_eq = size_equal_weight(target_isins)
|
||||
size_rp = size_risk_parity(target_isins, ret_wide, asof_idx)
|
||||
sizing_by_strategy = {"Equal_Weight": size_eq, "Risk_Parity": size_rp}
|
||||
|
||||
open_concat: List[pd.DataFrame] = []
|
||||
audit_rows_all: List[Dict] = []
|
||||
isins_for_open_fetch: Set[str] = set()
|
||||
isins_for_close_fetch: Set[str] = set()
|
||||
|
||||
for strat in strategies:
|
||||
df_open = load_open_trades(strat)
|
||||
current_set: Set[str] = set(df_open["ISIN"].astype(str).tolist())
|
||||
|
||||
# --- risk exits su posizioni correnti ---
|
||||
closers: List[Tuple[str, str]] = []
|
||||
if not df_open.empty:
|
||||
if "WeakDays" not in df_open.columns:
|
||||
df_open["WeakDays"] = 0
|
||||
|
||||
for _, row_open in df_open.iterrows():
|
||||
isin = str(row_open["ISIN"])
|
||||
entry_date = pd.to_datetime(row_open["EntryDate"]).date() if pd.notna(row_open["EntryDate"]) else None
|
||||
reasons = _risk_exit_flags(isin, entry_date, ret_wide, decision_date, est_map_all if est_map_all is not None else pd.Series(dtype=float))
|
||||
if reasons:
|
||||
closers.append((isin, "+".join(sorted(set(reasons)))))
|
||||
|
||||
# --- chiusure per ranking (scivolati fuori dal top-N) ---
|
||||
drop_by_rank = list(current_set - target_set)
|
||||
for isin in drop_by_rank:
|
||||
closers.append((isin, "RANK"))
|
||||
|
||||
# --- applica chiusure (accumula ordini) ---
|
||||
for isin, reason in closers:
|
||||
row_open = df_open.loc[df_open["ISIN"] == isin]
|
||||
linked_date = row_open["EntryDate"].iloc[0] if not row_open.empty else None
|
||||
|
||||
isins_for_close_fetch.add(isin)
|
||||
audit_rows_all.append({
|
||||
"Strategy": strat,
|
||||
"ISIN": isin,
|
||||
"Action": "CLOSE",
|
||||
"TradeDate": next_open_date,
|
||||
"EntryIndex": np.nan,
|
||||
"EntryAmount": np.nan,
|
||||
"SizeWeight": np.nan,
|
||||
"Price": None, # riempito dopo fetch
|
||||
"PnL_%": np.nan,
|
||||
"ExitReason": reason,
|
||||
"LinkedOpenDate": linked_date,
|
||||
"Duration_bars": np.nan,
|
||||
"Notes": f"DecisionDate={decision_date}"
|
||||
})
|
||||
df_open = df_open[df_open["ISIN"] != isin]
|
||||
current_set.discard(isin)
|
||||
|
||||
# --- aperture per differenza verso target ---
|
||||
add_list = [isin for isin in target_isins if isin not in current_set] # mantiene l'ordine del ranking
|
||||
w_dict = sizing_by_strategy[strat]
|
||||
if add_list and w_dict:
|
||||
cap = compute_current_capital_from_log(strat, ret_wide, decision_date)
|
||||
for isin in add_list:
|
||||
w = float(w_dict.get(isin, 0.0))
|
||||
if w <= 0:
|
||||
continue
|
||||
notional = max(MIN_TRADE_NOTIONAL, cap * w)
|
||||
entry_idx = asof_idx + 1
|
||||
|
||||
isins_for_open_fetch.add(isin)
|
||||
audit_rows_all.append({
|
||||
"Strategy": strat,
|
||||
"ISIN": isin,
|
||||
"Action": "OPEN",
|
||||
"TradeDate": next_open_date,
|
||||
"EntryIndex": entry_idx,
|
||||
"EntryAmount": float(notional),
|
||||
"SizeWeight": float(w),
|
||||
"Price": None, # riempito dopo fetch
|
||||
"PnL_%": np.nan,
|
||||
"ExitReason": "",
|
||||
"LinkedOpenDate": "",
|
||||
"Duration_bars": 0,
|
||||
"Notes": f"DecisionDate={decision_date}"
|
||||
})
|
||||
df_open = pd.concat([df_open, pd.DataFrame([{
|
||||
"Strategy": strat,
|
||||
"ISIN": isin,
|
||||
"EntryDate": decision_date,
|
||||
"EntryIndex": entry_idx,
|
||||
"EntryAmount": float(notional),
|
||||
"SizeWeight": float(w),
|
||||
"PeakPnL": 0.0,
|
||||
"WeakDays": 0,
|
||||
"Notes": ""
|
||||
}])], ignore_index=True)
|
||||
current_set.add(isin)
|
||||
|
||||
save_open_trades(strat, df_open)
|
||||
df_open["Strategy"] = strat
|
||||
open_concat.append(df_open)
|
||||
|
||||
# ---- FETCH UNA VOLTA (OPEN + CLOSE) ----
|
||||
fetch_isins = sorted(isins_for_open_fetch.union(isins_for_close_fetch))
|
||||
if fetch_isins:
|
||||
print(f"[PRICE] fetch open per {len(fetch_isins)} ISIN (open={len(isins_for_open_fetch)}, close={len(isins_for_close_fetch)})", flush=True)
|
||||
px_cache = _fetch_open_prices_once(fetch_isins, universe)
|
||||
for r in audit_rows_all:
|
||||
if r.get("Price") is None:
|
||||
r["Price"] = _safe_to_float(px_cache.get(r["ISIN"]))
|
||||
|
||||
# ---- SUMMARY ----
|
||||
n_open = sum(1 for r in audit_rows_all if r.get("Action") == "OPEN")
|
||||
n_close = sum(1 for r in audit_rows_all if r.get("Action") == "CLOSE")
|
||||
print(f"[SUMMARY] decision_date={decision_date} opens={n_open} closes={n_close} target={len(target_isins)} (cap={MAX_OPEN})", flush=True)
|
||||
|
||||
open_all = pd.concat(open_concat, ignore_index=True) if open_concat else pd.DataFrame()
|
||||
return open_all, audit_rows_all
|
||||
|
||||
# =========================
|
||||
# MAIN RUN
|
||||
# =========================
|
||||
def main_run(run_date: Optional[dt.date] = None):
|
||||
today = run_date or dt.date.today()
|
||||
|
||||
# 1) Universo
|
||||
universe = load_universe(UNIVERSO_XLSX)
|
||||
|
||||
# 2) Ritorni (DB)
|
||||
conn_str = read_connection_txt(CONNECTION_TXT)
|
||||
isins = universe["ISIN"].tolist()
|
||||
returns_long = _db_fetch_returns(
|
||||
conn_str,
|
||||
isins,
|
||||
sp_name=SP_NAME_DEFAULT,
|
||||
n_bars=SP_N_DEFAULT,
|
||||
ptf_curr=PTF_CURR_DEFAULT,
|
||||
)
|
||||
if returns_long.empty:
|
||||
raise RuntimeError("Nessun rendimento disponibile dal DB (SP vuota?).")
|
||||
|
||||
# 2b) Hurst map per ISIN (stessa logica concettuale del backtest)
|
||||
hurst_map = build_hurst_map(returns_long, lookback=252)
|
||||
|
||||
# 3) Segnali EOD su D con THETA = Hurst/100 per ISIN
|
||||
sig_df = generate_signals_today(universe, returns_long, today, hurst_map=hurst_map)
|
||||
|
||||
# 3b) Ranking unico e selezione Top-N
|
||||
decision_date = returns_long["Date"].max().date()
|
||||
buy_rank_df = _rank_buy(sig_df, decision_date) # tutti i buy ordinati
|
||||
allowed_open = _select_top_signals(buy_rank_df, MAX_OPEN) # top-N ISIN
|
||||
|
||||
# 4) Posizioni + audit (OPEN/CLOSE) con target Top-N
|
||||
open_df, audit_rows = update_positions_and_build_orders(
|
||||
universe, returns_long, sig_df, today,
|
||||
buy_rank_df=buy_rank_df,
|
||||
allowed_open_isins=allowed_open
|
||||
)
|
||||
|
||||
# 5) Append audit log (TUTTE le strategie operative)
|
||||
if audit_rows:
|
||||
append_audit_rows(audit_rows)
|
||||
|
||||
# 6) Snapshot Excel datato — fogli con nomi completi
|
||||
ensure_dir(OPEN_TRADES_DIR)
|
||||
signals_path = _dated_signals_filename()
|
||||
with pd.ExcelWriter(signals_path) as xw:
|
||||
sig_df.reset_index().to_excel(xw, sheet_name="Signals", index=False)
|
||||
if not open_df.empty:
|
||||
for strat, g in open_df.groupby("Strategy"):
|
||||
sheet_name_map = {
|
||||
"Equal_Weight": "Open_Equal_Weight",
|
||||
"Risk_Parity": "Open_Risk_Parity",
|
||||
}
|
||||
sheet_name = sheet_name_map.get(strat, f"Open_{strat}")[:31]
|
||||
g.to_excel(xw, sheet_name=sheet_name, index=False)
|
||||
|
||||
print(f"✅ Signals generated for {today}. Saved to {signals_path}")
|
||||
print(f"Open trades saved in {OPEN_TRADES_DIR}")
|
||||
print(f"Audit log updated at {AUDIT_LOG_CSV}")
|
||||
|
||||
# =========================
|
||||
# ENTRY POINT
|
||||
# =========================
|
||||
if __name__ == "__main__":
|
||||
main_run()
|
||||
Reference in New Issue
Block a user