From 919e375e80e5ad969a2f707c773a8847d47d097a Mon Sep 17 00:00:00 2001 From: fredmaloggia Date: Mon, 9 Mar 2026 15:31:53 +0100 Subject: [PATCH] Fixato bug su generazione plot --- equity_from_log.py | 72 ++- signals_daily_kNN_prod_v.2.py | 861 ++++++++++++++++++++++++++++++++++ 2 files changed, 918 insertions(+), 15 deletions(-) create mode 100644 signals_daily_kNN_prod_v.2.py diff --git a/equity_from_log.py b/equity_from_log.py index 6cc8887..72b1d68 100644 --- a/equity_from_log.py +++ b/equity_from_log.py @@ -19,7 +19,6 @@ from pathlib import Path import pandas as pd import numpy as np import shutil -import io from shared_utils import ( detect_column, @@ -99,6 +98,21 @@ if raw_whitelist: # AUDIT LOG LOADER (FORMAT CHECKS) # ============================================================================= REQUIRED_AUDIT_COLS = ["Strategy", "ISIN", "Action", "TradeDate"] +CANONICAL_AUDIT_COLS = [ + "Strategy", + "ISIN", + "Action", + "TradeDate", + "EntryIndex", + "EntryAmount", + "SizeWeight", + "Price", + "PnL_%", + "ExitReason", + "LinkedOpenDate", + "Duration_bars", + "Notes", +] NUMERIC_COLS = [ "EntryIndex", "EntryAmount", @@ -146,6 +160,15 @@ def _clean_numeric_series(s: pd.Series) -> pd.Series: cleaned = txt.map(_fix_one) return pd.to_numeric(cleaned, errors="coerce") +def _parse_mixed_dates(series: pd.Series) -> pd.Series: + s = series.astype(str).str.strip() + s = s.replace({"": np.nan, "nan": np.nan, "None": np.nan}) + dt_iso = pd.to_datetime(s, format="%Y-%m-%d", errors="coerce") + dt_iso_ts = pd.to_datetime(s, format="%Y-%m-%d %H:%M:%S", errors="coerce") + dt_dmy = pd.to_datetime(s, format="%d/%m/%Y", errors="coerce") + dt_dmy_ts = pd.to_datetime(s, format="%d/%m/%Y %H:%M:%S", errors="coerce") + return dt_iso.fillna(dt_iso_ts).fillna(dt_dmy).fillna(dt_dmy_ts) + def load_audit_log(path: Path) -> pd.DataFrame: if not path.exists(): @@ -155,20 +178,39 @@ def load_audit_log(path: Path) -> pd.DataFrame: if not raw.strip(): raise SystemExit("Audit log vuoto.") - first_line = raw.splitlines()[0] - semi = first_line.count(";") - comma = first_line.count(",") - - # Fix duplicated header (semicolon header + comma header in same line) - if semi > 0 and comma > 0 and ",Strategy," in first_line and "Strategy;" in first_line: - fixed = first_line.split(",", 1)[0] - print("[WARN] Header duplicato rilevato. Uso solo la parte prima della virgola.") - raw = "\n".join([fixed] + raw.splitlines()[1:]) - sep = ";" # force semicolon when duplicated header detected + lines = raw.splitlines() + first_line = lines[0] + if ";" in first_line: + header = [c.strip() for c in first_line.split(",", 1)[0].split(";")] else: - sep = ";" if semi >= comma else "," + header = [c.strip() for c in first_line.split(",")] + if header and header[0] == "": + header = header[1:] + if not header or "TradeDate" not in header: + header = CANONICAL_AUDIT_COLS.copy() - df = pd.read_csv(io.StringIO(raw), sep=sep, dtype=str) + rows = [] + mixed_rows = 0 + for line in lines[1:]: + if not line or not line.strip(): + continue + if ";" in line and line.count(";") >= 5: + parts = line.split(";") + else: + parts = line.split(",") + if parts and parts[0] == "": + parts = parts[1:] + mixed_rows += 1 + + if len(parts) > len(header): + parts = parts[: len(header) - 1] + [",".join(parts[len(header) - 1 :])] + elif len(parts) < len(header): + parts = parts + [""] * (len(header) - len(parts)) + rows.append(parts) + + df = pd.DataFrame(rows, columns=header) + if mixed_rows > 0: + print(f"[WARN] Audit log con {mixed_rows} righe in formato legacy/misto: normalizzate in lettura.") missing = [c for c in REQUIRED_AUDIT_COLS if c not in df.columns] if missing: @@ -183,9 +225,9 @@ def load_audit_log(path: Path) -> pd.DataFrame: df["ISIN"] = df["ISIN"].astype(str).str.strip() # Dates - df["TradeDate"] = pd.to_datetime(df["TradeDate"], errors="coerce", dayfirst=True) + df["TradeDate"] = _parse_mixed_dates(df["TradeDate"]) if "LinkedOpenDate" in df.columns: - df["LinkedOpenDate"] = pd.to_datetime(df["LinkedOpenDate"], errors="coerce", dayfirst=True) + df["LinkedOpenDate"] = _parse_mixed_dates(df["LinkedOpenDate"]) # Drop rows with invalid dates before = len(df) diff --git a/signals_daily_kNN_prod_v.2.py b/signals_daily_kNN_prod_v.2.py new file mode 100644 index 0000000..3215244 --- /dev/null +++ b/signals_daily_kNN_prod_v.2.py @@ -0,0 +1,861 @@ +# -*- coding: utf-8 -*- +""" +Daily Signals Generator (kNN) – PRODUCTION (coerente al backtest v3.1.5) + +Novità principali: +- Cap giornaliero MAX_OPEN=15: ranking unico dei buy e revisione per differenza su ogni strategia +- Risk Parity con cap per singolo asset (RP_MAX_WEIGHT) allineato al backtest v3.1.5 +- Fetch OPEN una sola volta per ISIN coinvolti in OPEN/CLOSE (cache condivisa tra strategie) +- Audit log per TUTTE le strategie operative (Equal_Weight, Risk_Parity) + +Pipeline (giorno D, EOD -> t+1 OPEN): +1) Carica universo e serie rendimenti dal DB (stored procedure) +2) Pattern kNN (WP=60, HA=10, K=25), Signal=1 se EstOutcome > THETA (decimali) +3) Ranking unico dei buy e selezione Top-N (MAX_OPEN) come target giornaliero +4) Revisione per differenza: chiudi aperture fuori top, apri nuove entrate nel top (+ risk exits) +5) Sizing (Equal Weight / Risk Parity con cap) +6) Fetch OPEN prices UNA VOLTA per ISIN interessati (OPEN/CLOSE) e popolamento ordini +7) Log ordini e snapshot Excel con fogli: Open_Equal_Weight / Open_Risk_Parity +""" + +from __future__ import annotations + +import os +import ssl +import json +import time +import shutil +import warnings +import datetime as dt +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, List, Optional, Tuple, Iterable, Set + +import numpy as np +import pandas as pd +from urllib.request import urlopen +from urllib.error import URLError, HTTPError + +# DB +import sqlalchemy as sa +from sqlalchemy import text as sql_text + +from shared_utils import ( + build_hurst_map, + build_pattern_library, + characterize_window, + detect_column, + load_config, + predict_from_library, + read_connection_txt, + require_section, + require_value, + z_norm, +) + +# ========================= +# CONFIG +# ========================= +CONFIG = load_config() +DB_CONFIG = require_section(CONFIG, "db") +PATTERN_CONFIG = require_section(CONFIG, "pattern") +TAGGING_CONFIG = require_section(CONFIG, "tagging") +RANKING_CONFIG = require_section(CONFIG, "ranking") +SIGNALS_CONFIG = require_section(CONFIG, "signals") +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(".") +OUTPUT_DIR = BASE_DIR / "output" +# Universe now expected inside Input folder +UNIVERSO_XLSX = BASE_DIR / "Input" / "Universo per Trading System.xlsx" +CONNECTION_TXT = BASE_DIR / "connection.txt" +AUDIT_LOG_CSV = OUTPUT_DIR / "trades_audit_log.csv" +OPEN_TRADES_DIR = BASE_DIR / "open_trades" +DROPBOX_EXPORT_DIR = Path(r"C:\Users\Admin\Dropbox\Condivisa Lavoro\Segnali di trading su ETF") +AUDIT_COLUMNS = [ + "Strategy", + "ISIN", + "Action", + "TradeDate", + "EntryIndex", + "EntryAmount", + "SizeWeight", + "Price", + "PnL_%", + "ExitReason", + "LinkedOpenDate", + "Duration_bars", + "Notes", +] + +def _dated_signals_filename() -> Path: + date_prefix = pd.Timestamp.today().strftime("%Y%m%d") + return OUTPUT_DIR / f"{date_prefix}_signals.xlsx" + +# Stored procedure / parametri DB +SP_NAME_DEFAULT = str(require_value(DB_CONFIG, "stored_proc", "db")) +SP_N_DEFAULT = int(require_value(DB_CONFIG, "n_bars", "db")) +PTF_CURR_DEFAULT = str(require_value(DB_CONFIG, "ptf_curr", "db")) + +# Pattern recognition (come backtest) +WP = int(require_value(PATTERN_CONFIG, "wp", "pattern")) +HA = int(require_value(PATTERN_CONFIG, "ha", "pattern")) +KNN_K = int(require_value(PATTERN_CONFIG, "knn_k", "pattern")) +THETA = float(require_value(PATTERN_CONFIG, "theta", "pattern")) # 0,005% in decimali (identico al backtest) +Z_REV = float(require_value(TAGGING_CONFIG, "z_rev", "tagging")) +Z_VOL = float(require_value(TAGGING_CONFIG, "z_vol", "tagging")) +STD_COMP_PCT = float(require_value(TAGGING_CONFIG, "std_comp_pct", "tagging")) + +# Exit rules (identiche al backtest) +SL_BPS = float(require_value(SIGNALS_CONFIG, "sl_bps", "signals")) +TP_BPS = float(require_value(SIGNALS_CONFIG, "tp_bps", "signals")) +TRAIL_BPS = float(require_value(SIGNALS_CONFIG, "trail_bps", "signals")) +TIME_STOP_BARS = int(require_value(SIGNALS_CONFIG, "time_stop_bars", "signals")) +THETA_EXIT = float(require_value(SIGNALS_CONFIG, "theta_exit", "signals")) # soglia debolezza +WEAK_DAYS_EXIT = require_value(SIGNALS_CONFIG, "weak_days_exit", "signals") # uscita IMMEDIATA in caso di debolezza (come backtest) + +# Ranking e selezione Top-N per APERTURE +MAX_OPEN = int(require_value(SIGNALS_CONFIG, "max_open", "signals")) # cap strumenti aperti oggi (come backtest) + +# Allineamento al backtest v3.1.5 per il cap del Risk Parity +TOP_N_MAX = int(require_value(RANKING_CONFIG, "top_n_max", "ranking")) +RP_MAX_WEIGHT = require_value(RANKING_CONFIG, "rp_max_weight", "ranking") # ≈ 0.1333 = 13,33% per singolo asset +if RP_MAX_WEIGHT is None: + RP_MAX_WEIGHT = 2 / max(TOP_N_MAX, 1) +else: + RP_MAX_WEIGHT = float(RP_MAX_WEIGHT) + +# Sizing +BASE_CAPITAL_PER_STRATEGY = float(require_value(SIGNALS_CONFIG, "base_capital_per_strategy", "signals")) +MIN_TRADE_NOTIONAL = float(require_value(SIGNALS_CONFIG, "min_trade_notional", "signals")) +RISK_PARITY_LOOKBACK = int(require_value(SIGNALS_CONFIG, "risk_parity_lookback", "signals")) +SP_NAME_DEFAULT = DB_CONFIG.get("stored_proc", "opt_RendimentoGiornaliero1_ALL") +SP_N_DEFAULT = DB_CONFIG.get("n_bars", 1305) +PTF_CURR_DEFAULT = DB_CONFIG.get("ptf_curr", "EUR") + +# Pattern recognition (come backtest) +WP = PATTERN_CONFIG.get("wp", 60) +HA = PATTERN_CONFIG.get("ha", 10) +KNN_K = PATTERN_CONFIG.get("knn_k", 25) +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 = SIGNALS_CONFIG.get("sl_bps", 300.0) +TP_BPS = SIGNALS_CONFIG.get("tp_bps", 800.0) +TRAIL_BPS = SIGNALS_CONFIG.get("trail_bps", 300.0) +TIME_STOP_BARS = SIGNALS_CONFIG.get("time_stop_bars", 20) +THETA_EXIT = SIGNALS_CONFIG.get("theta_exit", 0.0) # soglia debolezza +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 = 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 = RANKING_CONFIG.get("top_n_max", MAX_OPEN) +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 = SIGNALS_CONFIG.get("base_capital_per_strategy", 100.0) +MIN_TRADE_NOTIONAL = SIGNALS_CONFIG.get("min_trade_notional", 0.01) +RISK_PARITY_LOOKBACK = SIGNALS_CONFIG.get("risk_parity_lookback", 60) + +# Calendario +BUSINESS_DAYS_ONLY = True +SEED = 42 + +warnings.filterwarnings("ignore") +np.random.seed(SEED) + +# ========================= +# UTILS +# ========================= +def ensure_dir(p: Path): + p.mkdir(parents=True, exist_ok=True) + +def copy_to_dropbox(src: Path, dst_dir: Path = DROPBOX_EXPORT_DIR): + if not src or not dst_dir: + return + if not src.exists(): + return + try: + ensure_dir(dst_dir) + dst = dst_dir / src.name + shutil.copy2(src, dst) + except Exception as exc: + print(f"[WARN] impossibile copiare {src} su {dst_dir}: {exc}") + +def next_business_day(d: dt.date) -> dt.date: + nd = d + dt.timedelta(days=1) + if not BUSINESS_DAYS_ONLY: + return nd + while nd.weekday() >= 5: # 5=Sat, 6=Sun + nd += dt.timedelta(days=1) + return nd + +def _safe_to_float(x) -> Optional[float]: + try: + return float(x) + except Exception: + return None + +def _db_fetch_returns(conn_str: str, + isins: List[str], + sp_name: Optional[str] = None, + n_bars: Optional[int] = None, + ptf_curr: Optional[str] = None) -> pd.DataFrame: + engine = sa.create_engine(conn_str, fast_executemany=True) + sp = sp_name or os.environ.get("SP_NAME", SP_NAME_DEFAULT) + n_val = n_bars if n_bars is not None else int(os.environ.get("SP_N", SP_N_DEFAULT)) + ptf = (ptf_curr or os.environ.get("PTF_CURR", PTF_CURR_DEFAULT) or "").strip() or PTF_CURR_DEFAULT + + sql_sp = sql_text(f"EXEC {sp} @ISIN = :isin, @n = :n, @PtfCurr = :ptf") + frames: List[pd.DataFrame] = [] + + 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) + try: + df = pd.read_sql_query(sql_sp, conn, params={"isin": str(isin), "n": int(n_val), "ptf": ptf}) + except Exception as e: + print(f"[ERROR] SP {sp} fallita per {isin}: {e}") + continue + + if df.empty: + print(f"[WARN] Nessun dato per {isin}") + continue + + col_date = detect_column(df, ["Date", "Data", "Datetime", "Timestamp", "Time"]) + 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 + + out = df[[col_date, col_ret]].copy() + out.columns = ["Date", "Ret"] + out["Date"] = pd.to_datetime(out["Date"], errors="coerce").dt.tz_localize(None) + out["ISIN"] = str(isin) + + med = pd.to_numeric(out["Ret"], errors="coerce").abs().median() + if pd.notnull(med) and med > 1.5: + out["Ret"] = out["Ret"].astype(float) / 100.0 + + print(f" ↳ righe scaricate: {len(out)}") + frames.append(out[["Date", "ISIN", "Ret"]]) + + if not frames: + return pd.DataFrame(columns=["Date", "ISIN", "Ret"]) + + out_all = pd.concat(frames, ignore_index=True) + out_all = out_all.dropna(subset=["Date"]).sort_values(["ISIN", "Date"]).reset_index(drop=True) + return out_all[["Date", "ISIN", "Ret"]] + +# ========================= +# UNIVERSO + OPEN PRICE API (schema checker) +# ========================= +OPEN_MAX_RETRY = 3 +OPEN_SLEEP_SEC = 0.1 +OPEN_TIMEOUT = 10 + +def load_universe(path: Path) -> pd.DataFrame: + df = pd.read_excel(path) + if "ISIN" not in df.columns: + raise KeyError("Nel file Universo manca la colonna 'ISIN'") + df["ISIN"] = df["ISIN"].astype(str).str.strip() + for col in ["Asset Class", "Mercato", "TickerOpen"]: + if col not in df.columns: + df[col] = "" + df[col] = df[col].astype(str).str.strip() + return df + +def _build_symbol_euronext(row: pd.Series) -> Tuple[str, str]: + isin = str(row.get("ISIN", "")).strip() + venue = str(row.get("Mercato", "")).strip() + tok = str(row.get("TickerOpen", "") or "").strip() + base = "https://fin.scorer.app/finance/euronext/price" + if tok and "-" in tok and tok.split("-")[0].upper() == isin.upper(): + return base, tok + if isin and venue: + return base, f"{isin}-{venue}" + return base, isin + +def get_open_price(isin: str, universe: pd.DataFrame) -> Optional[float]: + """ + Endpoint euronext/price/ISIN-Mercato con retry/backoff. + Log dettagliato stile checker: [TRY]/[RETRY]/[OK]/[FAIL]. + """ + try: + row = universe.loc[universe["ISIN"] == str(isin)].iloc[0] + except Exception: + print(f"[WARN] ISIN {isin} non trovato nell’universo.") + return None + + base, symbol = _build_symbol_euronext(row) + url = f"{base}/{symbol}" + print(f"[DOWNLOAD] {symbol:<30s} -> [TRY 1/{OPEN_MAX_RETRY}]", flush=True) + + for attempt in range(1, OPEN_MAX_RETRY + 1): + if attempt > 1: + print(f"[DOWNLOAD] {symbol:<30s} -> [TRY {attempt}/{OPEN_MAX_RETRY}]", flush=True) + 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(" ↳ NO DATA") + continue + + d = (data[0] or {}).get("data") or {} + px = d.get("open") if d.get("open") is not None else d.get("prevClose") + if px is None: + print(" ↳ WARN: 'open' e 'prevClose' assenti") + continue + + px = float(px) + print(f" ↳ OK open={d.get('open')} close={d.get('close')} (ritorno prezzo={px})") + return px + + except (HTTPError, URLError, ssl.SSLError) as e: + if attempt < OPEN_MAX_RETRY: + print(f" ↳ ERR {e}\nritento tra {OPEN_SLEEP_SEC}s") + time.sleep(OPEN_SLEEP_SEC) + else: + print(f" ↳ ERR {e}") + + print(f"[ERROR] nessun prezzo per {symbol} dopo {OPEN_MAX_RETRY} tentativi") + return None + +# ========================= +# HURST ESTIMATOR & MAP +# ========================= +# ========================= +# GENERAZIONE SEGNALI (EOD su D) +# ========================= +def generate_signals_today(universe: pd.DataFrame, + returns_long: pd.DataFrame, + today: dt.date, + hurst_map: Optional[Dict[str, float]] = None) -> pd.DataFrame: + ret_wide = returns_long.pivot(index="Date", columns="ISIN", values="Ret").sort_index() + decision_date = ret_wide.index[-1].date() + + rows = [] + for isin in ret_wide.columns: + r = ret_wide[isin].dropna().astype(float) + # THETA = HURST IN PERCENTUALE (H = 0.50 -> theta_entry = 0.005 = 0.5%) + theta_entry = THETA # fallback globale + H_val = None + if hurst_map is not None: + H_val = hurst_map.get(str(isin)) + if H_val is not None and not pd.isna(H_val): + theta_entry = float(H_val) / 100.0 + + if len(r) < max(200, WP + HA + 10): + rows.append({"Date": decision_date, "ISIN": isin, + "Signal": 0, "EstOutcome": np.nan, "AvgDist": np.nan, + "PatternType": None, "Confidence": np.nan}) + continue + + 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, 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, 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, z_rev=Z_REV, z_vol=Z_VOL, std_comp_pct=STD_COMP_PCT) + + 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","AssetName","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 + if "AssetName" not in df.columns: + df["AssetName"] = "" + 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 _read_audit_log_mixed(path: Path) -> pd.DataFrame: + if not path.exists(): + return pd.DataFrame(columns=AUDIT_COLUMNS) + + raw = path.read_text(encoding="utf-8", errors="ignore") + lines = raw.splitlines() + if not lines: + return pd.DataFrame(columns=AUDIT_COLUMNS) + + rows: List[Dict[str, str]] = [] + for line in lines[1:]: + if not line or not line.strip(): + continue + if ";" in line and line.count(";") >= 5: + parts = line.split(";") + else: + parts = line.split(",") + if parts and parts[0] == "": + parts = parts[1:] + + if len(parts) > len(AUDIT_COLUMNS): + parts = parts[: len(AUDIT_COLUMNS) - 1] + [",".join(parts[len(AUDIT_COLUMNS) - 1 :])] + elif len(parts) < len(AUDIT_COLUMNS): + parts = parts + [""] * (len(AUDIT_COLUMNS) - len(parts)) + + rows.append(dict(zip(AUDIT_COLUMNS, parts))) + + df = pd.DataFrame(rows, columns=AUDIT_COLUMNS) + if df.empty: + return df + + # Normalize string keys and force expected action values. + for col in ["Strategy", "ISIN", "Action", "ExitReason", "Notes"]: + if col in df.columns: + df[col] = df[col].astype(str).str.strip() + df["Action"] = df["Action"].str.upper() + return df + +def append_audit_rows(rows: List[Dict]): + if not rows: + return + ensure_dir(AUDIT_LOG_CSV.parent) + new_rows = pd.DataFrame(rows) + for c in AUDIT_COLUMNS: + if c not in new_rows.columns: + new_rows[c] = np.nan + new_rows = new_rows[AUDIT_COLUMNS] + + old = _read_audit_log_mixed(AUDIT_LOG_CSV) if AUDIT_LOG_CSV.exists() else pd.DataFrame(columns=AUDIT_COLUMNS) + if AUDIT_LOG_CSV.exists(): + log = pd.concat([old, new_rows], ignore_index=True) + else: + log = new_rows + log.to_csv(AUDIT_LOG_CSV, index=False, sep=";", encoding="utf-8") + +# 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, + asset_name_map: Optional[pd.Series] = 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) + + if asset_name_map is not None: + df_open["AssetName"] = df_open["ISIN"].astype(str).map(asset_name_map).fillna("") + else: + if "AssetName" not in df_open.columns: + df_open["AssetName"] = "" + if "AssetName" in df_open.columns: + cols = list(df_open.columns) + if "ISIN" in cols and "AssetName" in cols: + cols.insert(cols.index("ISIN") + 1, cols.pop(cols.index("AssetName"))) + df_open = df_open[cols] + + 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() + ensure_dir(OUTPUT_DIR) + + # 1) Universo + universe = load_universe(UNIVERSO_XLSX) + asset_name_col = detect_column(universe, [ + "Nome", "Name", "Asset", "Asset Name", "Descrizione", "Description" + ]) + if not asset_name_col: + print("[WARN] Colonna con il nome dell'asset non trovata nell'universo.") + asset_name_map: Optional[pd.Series] = None + if asset_name_col: + asset_name_map = ( + universe[["ISIN", asset_name_col]] + .dropna(subset=["ISIN"]) + .assign(ISIN=lambda df: df["ISIN"].astype(str).str.strip()) + ) + asset_name_map = asset_name_map.set_index("ISIN")[asset_name_col].astype(str).str.strip() + + # 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=1260) + + # 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, + asset_name_map=asset_name_map, + ) + + # 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() + signals_sheet = sig_df.reset_index() + if asset_name_map is not None: + signals_sheet["AssetName"] = signals_sheet["ISIN"].astype(str).map(asset_name_map).fillna("") + else: + signals_sheet["AssetName"] = "" + + # inserisci la colonna subito dopo l'ISIN + if "AssetName" in signals_sheet.columns: + cols = list(signals_sheet.columns) + cols.insert(cols.index("ISIN") + 1, cols.pop(cols.index("AssetName"))) + signals_sheet = signals_sheet[cols] + + with pd.ExcelWriter(signals_path) as xw: + signals_sheet.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) + + copy_to_dropbox(signals_path) + for strat in ["Equal_Weight", "Risk_Parity"]: + csv_path = open_trades_path(strat) + if csv_path.exists(): + copy_to_dropbox(csv_path) + + 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()