# -*- coding: utf-8 -*- """ Ottimizzatore ITA """ # ========================= # IMPORT & PARAMETRI # ========================= import sys import os import numpy as np import pandas as pd import matplotlib.pyplot as plt import yaml import logging from datetime import datetime from sqlalchemy import create_engine, text from sqlalchemy.exc import SQLAlchemyError from pypfopt import risk_models from pypfopt.efficient_frontier import EfficientFrontier from pypfopt.exceptions import OptimizationError # --------------------------------------------------- # Patch PyPortfolioOpt: usa cp.psd_wrap sulla covarianza # --------------------------------------------------- import cvxpy as cp from pypfopt import objective_functions as _obj def portfolio_variance_psdwrap(w, cov_matrix): """ Versione patchata di portfolio_variance: usa cp.psd_wrap(cov_matrix) per evitare che CVXPY faccia il controllo spettrale con ARPACK. Comportamento identico all'originale, ma più robusto. """ variance = cp.quad_form(w, cp.psd_wrap(cov_matrix)) return _obj._objective_value(w, variance) # Monkey patch globale: da qui in poi EfficientFrontier usa questa versione _obj.portfolio_variance = portfolio_variance_psdwrap # Cartelle di input/output/plot OUTPUT_DIR = "Output" INPUT_DIR = "Input" PLOT_DIR = "Plot" CONFIG_FILE = "config.yaml" os.makedirs(OUTPUT_DIR, exist_ok=True) os.makedirs(INPUT_DIR, exist_ok=True) os.makedirs(PLOT_DIR, exist_ok=True) logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") logger = logging.getLogger(__name__) def excel_path(filename: str) -> str: """Costruisce il percorso completo per un file Excel nella cartella di output.""" return os.path.join(OUTPUT_DIR, filename) def plot_path(filename: str) -> str: """Costruisce il percorso completo per un file PNG nella cartella Plot.""" return os.path.join(PLOT_DIR, filename) def load_targets_and_limits(config_file: str): """Legge target di volatilità e limiti asset class dal file di configurazione (nessun fallback).""" try: with open(config_file, "r", encoding="utf-8") as f: cfg = yaml.safe_load(f) or {} except FileNotFoundError: logger.error("File di configurazione mancante: %s", config_file) sys.exit(1) vt_cfg = cfg.get("volatility_targets", {}) vt_list = [] if isinstance(vt_cfg, dict): vt_list = vt_cfg.get("default") or [] elif isinstance(vt_cfg, list): vt_list = vt_cfg if not vt_list: logger.error("Sezione 'volatility_targets' mancante o vuota nel file di configurazione.") sys.exit(1) volatility_targets_local = { (int(item["years"]), float(item["target_vol"])): item["name"] for item in vt_list if "years" in item and "target_vol" in item and "name" in item } if not volatility_targets_local: logger.error("Nessun target di volatilita valido trovato in configurazione.") sys.exit(1) asset_limits_cfg = cfg.get("asset_class_limits") or {} if not asset_limits_cfg: logger.error("Sezione 'asset_class_limits' mancante o vuota nel file di configurazione.") sys.exit(1) asset_class_limits_local = {k: float(v) for k, v in asset_limits_cfg.items()} return volatility_targets_local, asset_class_limits_local def validate_universe(df_universe: pd.DataFrame): """Verifica colonne obbligatorie e duplicati ISIN nel file universo.""" required_cols = ['ISIN', 'Nome', 'Categoria', 'Asset Class'] missing_cols = [c for c in required_cols if c not in df_universe.columns] if missing_cols: print(f"[warn] Colonne mancanti nel file universo: {', '.join(missing_cols)}") dup_isin = df_universe['ISIN'][df_universe['ISIN'].duplicated()].unique().tolist() if dup_isin: print(f"[warn] ISIN duplicati nel file universo: {dup_isin}") empty_isin = df_universe['ISIN'].isna().sum() if empty_isin: print(f"[warn] Righe con ISIN mancante nel file universo: {int(empty_isin)}") def validate_returns_frame(df_returns: pd.DataFrame, threshold: float = 0.2): """Avvisa se i rendimenti hanno molte celle NaN prima del riempimento.""" if df_returns.empty: print("[errore] Nessun dato di rendimento recuperato: final_df vuoto.") sys.exit(1) na_ratio = df_returns.isna().mean() high_na = na_ratio[na_ratio > threshold] if not high_na.empty: cols = ", ".join([f"{c} ({v:.0%})" for c, v in high_na.items()]) print(f"[warn] Colonne con >{threshold:.0%} di NaN prima del fill: {cols}") # --------------------------------- # Utility per R^2 sull’equity line # --------------------------------- def r2_equity_line(returns: pd.Series) -> float: """R^2 della regressione OLS di log(equity) sul tempo (con intercetta).""" s = returns.dropna() if s.size < 3: return np.nan equity = (1.0 + s).cumprod() equity = equity.replace([0, np.inf, -np.inf], np.nan).dropna() if equity.size < 3: return np.nan y = np.log(equity.values) if np.allclose(y.var(ddof=1), 0.0): return 0.0 x = np.arange(y.size, dtype=float) X = np.column_stack([np.ones_like(x), x]) beta, *_ = np.linalg.lstsq(X, y, rcond=None) y_hat = X @ beta ss_res = np.sum((y - y_hat) ** 2) ss_tot = np.sum((y - y.mean()) ** 2) r2 = 1.0 - (ss_res / ss_tot) if ss_tot > 0 else np.nan if np.isnan(r2): return np.nan return float(np.clip(r2, 0.0, 1.0)) # --------------------------------- # Utility per metriche di drawdown # --------------------------------- def drawdown_metrics(returns: pd.Series, sentinel_ttr: int = 1250): """ Calcola: - max_dd: profondità massima del drawdown (negativa o zero) - max_dd_duration: durata massima (in giorni) di qualsiasi drawdown - ttr_from_mdd: giorni dal minimo del Max DD al pieno recupero del picco precedente (sentinel se non recupera) """ s = returns.fillna(0.0).astype(float) if s.size == 0: return np.nan, np.nan, np.nan equity = (1.0 + s).cumprod() if equity.size == 0: return np.nan, np.nan, np.nan run_max = equity.cummax() dd = equity / run_max - 1.0 # Max Drawdown (valore più negativo) max_dd = float(dd.min()) if dd.size else np.nan # Durata massima di drawdown (giorni consecutivi sotto zero drawdown) under_water = dd < 0 if under_water.any(): max_dd_duration = 0 current = 0 for flag in under_water.values: if flag: current += 1 if current > max_dd_duration: max_dd_duration = current else: current = 0 else: max_dd_duration = 0 # Time-to-Recovery dal Max DD if dd.size: trough_idx = int(np.argmin(dd.values)) if trough_idx > 0: peak_idx = int(np.argmax(equity.values[:trough_idx+1])) peak_level = float(equity.values[peak_idx]) rec_idx = None for t in range(trough_idx + 1, equity.size): if equity.values[t] >= peak_level: rec_idx = t break if rec_idx is None: ttr_from_mdd = sentinel_ttr # non recuperato else: ttr_from_mdd = rec_idx - trough_idx else: ttr_from_mdd = np.nan else: ttr_from_mdd = np.nan return max_dd, int(max_dd_duration), (int(ttr_from_mdd) if not np.isnan(ttr_from_mdd) else np.nan) # --------------------------------- # Utility per AAW, AUW e Heal Index # --------------------------------- def heal_index_metrics(returns: pd.Series): """ Calcola: - AAW: area sopra acqua (run-up vs minimo cumulato) - AUW: area sotto acqua (drawdown vs massimo cumulato) - Heal Index: (AAW - AUW) / AUW """ s = returns.fillna(0.0).astype(float) if s.size == 0: return np.nan, np.nan, np.nan equity = (1.0 + s).cumprod() if equity.size == 0: return np.nan, np.nan, np.nan run_max = equity.cummax() dd = equity / run_max - 1.0 AUW = float((-dd[dd < 0]).sum()) if dd.size else np.nan run_min = equity.cummin() ru = equity / run_min - 1.0 AAW = float((ru[ru > 0]).sum()) if ru.size else np.nan heal = ((AAW - AUW) / AUW) if (AUW is not None and np.isfinite(AUW) and AUW > 0) else np.nan return AAW, AUW, heal # --------------------------------- # Utility per H_min (100% finestre positive) # --------------------------------- def h_min_100(returns: pd.Series, month_len: int = 21): """ Orizzonte minimo h_days tale che TUTTE le finestre rolling di ampiezza h_days hanno rendimento cumulato >= 0. Restituisce (h_days, ceil(h_days/21)). """ s = returns.dropna().astype(float) n = s.size if n == 0: return np.nan, np.nan log1p = np.log1p(s.values) csum = np.cumsum(log1p) def rolling_sum_k(k: int): if k > n: return np.array([]) head = csum[k - 1:] tail = np.concatenate(([0.0], csum[:-k])) return head - tail for k in range(1, n + 1): rs = rolling_sum_k(k) if rs.size == 0: break roll_ret = np.exp(rs) - 1.0 if np.all(roll_ret >= 0): h_days = k h_months = int(np.ceil(h_days / month_len)) return h_days, h_months return np.nan, np.nan # --------------------------------- # Utility per rendere PSD/robusta la covarianza # --------------------------------- def regularize_covariance(cov_df: pd.DataFrame, ridge_factor: float = 1e-6) -> pd.DataFrame: """ Rende la matrice di covarianza numericamente piu' robusta: - la simmetrizza - aggiunge un piccolo termine di ridge sulla diagonale Ritorna un nuovo DataFrame con stessa index/columns. """ if cov_df.empty: return cov_df Sigma = cov_df.values.astype(float) # simmetrizza (elimina piccole asimmetrie numeriche) Sigma = 0.5 * (Sigma + Sigma.T) # piccolo ridge proporzionato alla scala media delle varianze n = Sigma.shape[0] trace = np.trace(Sigma) if np.isfinite(trace) and n > 0: eps = ridge_factor * (trace / n) else: eps = ridge_factor Sigma_reg = Sigma + eps * np.eye(n) return pd.DataFrame(Sigma_reg, index=cov_df.index, columns=cov_df.columns) # --- Lettura parametri dal file connection.txt --- params = {} with open("connection.txt", "r") as f: for line in f: line = line.strip() if line and not line.startswith("#"): key, value = line.split("=", 1) params[key.strip()] = value.strip() username = params.get("username") password = params.get("password") host = params.get("host") port = params.get("port", "1433") database = params.get("database") connection_string = ( f"mssql+pyodbc://{username}:{password}@{host}:{port}/{database}" "?driver=ODBC+Driver+17+for+SQL+Server" ) print("Connection string letta correttamente") # ========================= # CONNESSIONE AL DB # ========================= try: engine = create_engine(connection_string) with engine.connect() as connection: _ = connection.execute(text("SELECT 1")) print("Connessione al database riuscita.") except SQLAlchemyError as e: print("Errore durante la connessione al database:", e) sys.exit() # ========================= # INPUT / TEMPLATE # ========================= template_path = os.path.join(INPUT_DIR, 'Template_Guardian.xls') template_df = pd.read_excel(template_path) file_path = os.path.join(INPUT_DIR, 'Universo per ottimizzatore.xlsx') df = pd.read_excel( file_path, usecols=['ISIN', 'Nome', 'Categoria', 'Asset Class', 'PesoMax', 'PesoFisso', 'Codice Titolo'], dtype={'Codice Titolo': str} ) validate_universe(df) # ========================= # SERIE STORICHE RENDIMENTI # ========================= end_date = pd.Timestamp.now().normalize() - pd.Timedelta(days=1) start_date = end_date - pd.DateOffset(years=5) all_dates = pd.date_range(start=start_date, end=end_date, freq='B').normalize() final_df = pd.DataFrame(index=all_dates) isin_from_db = set() for isin in df['ISIN'].unique(): print(f"Working on ISIN: {isin}") procedure_call = f"EXEC opt_RendimentoGiornaliero1_ALL @ISIN = '{isin}', @n = 1305, @PtfCurr = EUR" try: temp_df = pd.read_sql_query(procedure_call, engine) if temp_df.empty: print(f"Nessun dato recuperato per {isin}, skipping...") continue temp_df['Px_Date'] = pd.to_datetime(temp_df['Px_Date'], format='%Y-%m-%d', errors='coerce').dt.normalize() temp_df = temp_df.dropna(subset=['Px_Date']) temp_df.set_index('Px_Date', inplace=True) temp_df['RendimentoGiornaliero'] = temp_df['RendimentoGiornaliero'] / 100 final_df[isin] = temp_df['RendimentoGiornaliero'].reindex(all_dates) isin_from_db.add(isin) print(f"Dati recuperati per {isin}: {final_df[isin].count()} righe di dati non-null prelevate.") except SQLAlchemyError as e: print(f"Errore durante l'esecuzione della stored procedure per {isin}:", e) validate_returns_frame(final_df) final_df.fillna(0, inplace=True) # -------- H_min sempre su 5 anni (21 gg = 1 mese) -------- five_year_df = final_df.loc[end_date - pd.DateOffset(years=5): end_date] # ========================= # CONFIGURAZIONE OBIETTIVI # ========================= volatility_targets, asset_class_limits = load_targets_and_limits(CONFIG_FILE) days_per_year = 252 riskfree_rate = 0.02 # ========================= # LOOP OTTIMIZZAZIONI (PH1 tradizionale) # ========================= optimized_weights = pd.DataFrame() per_asset_metrics = {} export_rows = [] for (years, target_vol), name in volatility_targets.items(): period_start_date = end_date - pd.DateOffset(years=years) period_df = final_df.loc[period_start_date:end_date] daily_returns_mean = period_df.mean() annual_returns_mean = daily_returns_mean * days_per_year annual_covariance_matrix = risk_models.sample_cov(period_df, returns_data=True) # --- Regularizzazione covarianza per l'ottimizzatore --- annual_covariance_matrix = regularize_covariance(annual_covariance_matrix) # ---------- PER-ASSET METRICS ---------- n_days = int(period_df.shape[0]) years_elapsed = n_days / days_per_year if n_days > 0 else np.nan asset_ann_return = daily_returns_mean * days_per_year asset_ann_vol = period_df.std(ddof=1) * np.sqrt(days_per_year) gross = (1.0 + period_df).prod(skipna=True) asset_cagr = gross.pow(1.0 / years_elapsed) - 1.0 if years_elapsed and years_elapsed > 0 else pd.Series(np.nan, index=period_df.columns) asset_r2 = pd.Series({col: r2_equity_line(period_df[col]) for col in period_df.columns}, index=period_df.columns) maxdd_dict, dddur_dict, ttr_dict = {}, {}, {} aaw_dict, auw_dict, heal_dict = {}, {}, {} hmin_5y_months_dict = {} for col in period_df.columns: mdd, dddur, ttr = drawdown_metrics(period_df[col], sentinel_ttr=1250) maxdd_dict[col], dddur_dict[col], ttr_dict[col] = mdd, dddur, ttr aaw, auw, heal = heal_index_metrics(period_df[col]) aaw_dict[col], auw_dict[col], heal_dict[col] = aaw, auw, heal if col in five_year_df.columns: _, h_months_5y = h_min_100(five_year_df[col], month_len=21) else: h_months_5y = np.nan hmin_5y_months_dict[col] = h_months_5y asset_metrics_df = ( pd.DataFrame({ 'ISIN': period_df.columns, 'Rendimento_Ann': asset_ann_return.reindex(period_df.columns).values, 'Volatilita_Ann': asset_ann_vol.reindex(period_df.columns).values, 'CAGR': asset_cagr.reindex(period_df.columns).values, 'R2_Equity': asset_r2.reindex(period_df.columns).values, 'MaxDD': pd.Series(maxdd_dict).reindex(period_df.columns).values, 'DD_Duration_Max': pd.Series(dddur_dict).reindex(period_df.columns).values, 'TTR_from_MDD': pd.Series(ttr_dict).reindex(period_df.columns).values, 'AAW': pd.Series(aaw_dict).reindex(period_df.columns).values, 'AUW': pd.Series(auw_dict).reindex(period_df.columns).values, 'Heal_Index': pd.Series(heal_dict).reindex(period_df.columns).values, 'H_min_100m_5Y': pd.Series(hmin_5y_months_dict).reindex(period_df.columns).values }) .merge(df[['ISIN', 'Nome', 'Categoria', 'Asset Class']], on='ISIN', how='left') [['ISIN', 'Nome', 'Categoria', 'Asset Class', 'Rendimento_Ann', 'Volatilita_Ann', 'CAGR', 'R2_Equity', 'MaxDD', 'DD_Duration_Max', 'TTR_from_MDD', 'AAW', 'AUW', 'Heal_Index', 'H_min_100m_5Y']] .sort_values('ISIN', kind='stable') .reset_index(drop=True) ) per_asset_metrics[name] = asset_metrics_df # ---------- OTTIMIZZAZIONE ---------- ef = EfficientFrontier(annual_returns_mean, annual_covariance_matrix) # Vincoli PesoFisso / PesoMax for _, row in df.iterrows(): isin_i = row['ISIN'] if isin_i in period_df.columns: idx = period_df.columns.get_loc(isin_i) pf = row.get('PesoFisso') pm = row.get('PesoMax') if pd.notna(pf): ef.add_constraint(lambda w, idx=idx, val=pf: w[idx] == val) elif pd.notna(pm): ef.add_constraint(lambda w, idx=idx, maxw=pm: w[idx] <= maxw) # Vincoli per Categoria categories_limits = df.groupby('Categoria')['PesoMax'].max().to_dict() for cat, maxw in categories_limits.items(): isin_list = df[df['Categoria'] == cat]['ISIN'].tolist() idxs = [period_df.columns.get_loc(isin) for isin in isin_list if isin in period_df.columns] if idxs: ef.add_constraint(lambda w, idxs=idxs, maxw=maxw: sum(w[i] for i in idxs) <= maxw) # Vincoli per Asset Class for ac, maxw in asset_class_limits.items(): isin_list = df[df['Asset Class'] == ac]['ISIN'].tolist() idxs = [period_df.columns.get_loc(isin) for isin in isin_list if isin in period_df.columns] if idxs: ef.add_constraint(lambda w, idxs=idxs, maxw=maxw: sum(w[i] for i in idxs) <= maxw) # ---------- Risoluzione ---------- try: ef.efficient_risk(target_volatility=target_vol) weights = ef.clean_weights() optimized_weights[name] = pd.Series(weights) exp_ret, exp_vol, sharpe = ef.portfolio_performance(verbose=False, risk_free_rate=riskfree_rate) print(f"=== Ottimizzazione: {name} (anni={years}, target_vol={target_vol}) ===") print(f"Expected annual return: {exp_ret:.2%}") print(f"Annual volatility: {exp_vol:.2%}") print(f"Sharpe Ratio: {sharpe:.2f}") # --- Beneficio di diversificazione --- w_vec_tmp = np.array([weights.get(isin, 0) for isin in period_df.columns]) indiv_ann_vols = np.sqrt(np.diag(annual_covariance_matrix.loc[period_df.columns, period_df.columns].values)) weighted_avg_vol = float(np.dot(w_vec_tmp, indiv_ann_vols)) diversification_benefit = (exp_vol / weighted_avg_vol) - 1 if (weighted_avg_vol > 0 and exp_vol > 0) else np.nan print(f"Beneficio di diversificazione: {diversification_benefit:.2%}") # --- File Excel per import gestionale (uno per portafoglio) --- template_cols = list(template_df.columns) results_rows = [] for isin, weight in weights.items(): if weight > 0: r_sel = df.loc[df['ISIN'] == isin] codice_titolo = r_sel['Codice Titolo'].iloc[0] if not r_sel.empty else "" nome = r_sel['Nome'].iloc[0] if not r_sel.empty else "" row = {col: "" for col in template_cols} row['cod_por'] = f'PTFOPT{name}' row['cod_tit'] = codice_titolo row['des_tit'] = nome row['peso'] = float(weight * 99) results_rows.append(row) results_full_df = pd.DataFrame(results_rows, columns=template_cols) if results_full_df.empty: output_df = template_df.iloc[0:0].copy() else: output_df = results_full_df.reindex(columns=template_cols) export_rows.append(output_df) # --- Pie chart asset allocation: salva in Output senza mostrare --- asset_allocations = {asset: 0 for asset in asset_class_limits} for isin, weight in weights.items(): r_sel = df.loc[df['ISIN'] == isin] if r_sel.empty: continue asset_class = r_sel['Asset Class'].iloc[0] asset_allocations.setdefault(asset_class, 0) asset_allocations[asset_class] += weight total_alloc = sum(asset_allocations.values()) if total_alloc > 0: plt.figure(figsize=(8, 6)) plt.pie(asset_allocations.values(), labels=asset_allocations.keys(), autopct='%1.1f%%') plt.title(f'Asset Allocation for {name}') pie_path = plot_path(f'Asset_Allocation_{name}.png') plt.savefig(pie_path, dpi=150, bbox_inches='tight') plt.close() except OptimizationError as e: print(f"Optimization failed for {name}: {e}") optimized_weights[name] = pd.Series([0] * len(annual_returns_mean)) # ========================= # RIEPILOGO METRICHE (PORTAFOGLI PH1) # ========================= summary_data = [] for (years, target_vol), name in volatility_targets.items(): if name in optimized_weights.columns: period_start_date = end_date - pd.DateOffset(years=years) period_df = final_df.loc[period_start_date:end_date] daily_returns_mean = period_df.mean() annual_returns_mean = daily_returns_mean * days_per_year annual_covariance_matrix = risk_models.sample_cov(period_df, returns_data=True) # --- Regularizzazione covarianza per l'ottimizzatore --- annual_covariance_matrix = regularize_covariance(annual_covariance_matrix) w_series = optimized_weights[name].reindex(period_df.columns).fillna(0.0) w_vec = w_series.values port_returns = (period_df[period_df.columns] * w_series).sum(axis=1) n_days = int(port_returns.shape[0]) years_elapsed = n_days / days_per_year if n_days > 0 else np.nan port_ann_return = float(port_returns.mean() * days_per_year) if n_days > 0 else np.nan port_ann_vol = float(port_returns.std(ddof=1) * np.sqrt(days_per_year)) if n_days > 1 else np.nan gross = float((1.0 + port_returns).prod()) if n_days > 0 else np.nan port_cagr = (gross**(1.0 / years_elapsed) - 1.0) if (years_elapsed and years_elapsed > 0 and gross and gross > 0) else np.nan port_r2 = r2_equity_line(port_returns) port_maxdd, port_dddur, port_ttr = drawdown_metrics(port_returns, sentinel_ttr=1250) port_aaw, port_auw, port_heal = heal_index_metrics(port_returns) common_cols = [c for c in w_series.index if c in five_year_df.columns] if len(common_cols) > 0: w_5y = w_series.reindex(common_cols).fillna(0.0) port_returns_5y = (five_year_df[common_cols] * w_5y).sum(axis=1) _, port_hmin_5y_months = h_min_100(port_returns_5y, month_len=21) else: port_hmin_5y_months = np.nan exp_ret = float(np.dot(w_vec, annual_returns_mean.loc[period_df.columns].values)) cov_mat = annual_covariance_matrix.loc[period_df.columns, period_df.columns].values exp_vol = float(np.sqrt(np.dot(w_vec, np.dot(cov_mat, w_vec)))) sharpe = (exp_ret - riskfree_rate) / exp_vol if exp_vol > 0 else np.nan indiv_ann_vols = np.sqrt(np.diag(cov_mat)) weighted_avg_vol = float(np.dot(w_vec, indiv_ann_vols)) diversification_benefit = (exp_vol / weighted_avg_vol) - 1 if (weighted_avg_vol > 0 and exp_vol > 0) else np.nan diversification_ratio = weighted_avg_vol / exp_vol if (weighted_avg_vol > 0 and exp_vol > 0) else np.nan print(f"=== Riepilogo: {name} (anni={years}, target_vol={target_vol}) ===") print(f"Expected annual return: {exp_ret:.2%}") print(f"Annual volatility: {exp_vol:.2%}") print(f"Sharpe Ratio: {sharpe:.2f}") print(f"Diversification Ratio: {diversification_ratio:.3f}" if not np.isnan(diversification_ratio) else "Diversification Ratio: NaN") print(f"Beneficio di diversificazione: {diversification_benefit:.2%}") summary_data.append({ "Portafoglio": name, "Years": years, "Target Vol": f"{target_vol:.2%}", "Expected annual return": f"{exp_ret:.2%}", "Annual volatility": f"{exp_vol:.2%}", "Sharpe Ratio": f"{sharpe:.2f}", "Beneficio di diversificazione": f"{diversification_benefit:.2%}" if not np.isnan(diversification_benefit) else "", "Rendimento_Ann": f"{port_ann_return:.2%}" if pd.notna(port_ann_return) else "", "Volatilita_Ann": f"{port_ann_vol:.2%}" if pd.notna(port_ann_vol) else "", "CAGR": f"{port_cagr:.2%}" if pd.notna(port_cagr) else "", "R2_Equity": round(port_r2, 3) if pd.notna(port_r2) else np.nan, "MaxDD": f"{port_maxdd:.2%}" if pd.notna(port_maxdd) else "", "DD_Duration_Max": int(port_dddur) if pd.notna(port_dddur) else "", "TTR_from_MDD": int(port_ttr) if pd.notna(port_ttr) else "", "AAW": float(port_aaw) if pd.notna(port_aaw) else np.nan, "AUW": float(port_auw) if pd.notna(port_auw) else np.nan, "Heal_Index": float(port_heal) if pd.notna(port_heal) else np.nan, "H_min_100m_5Y": int(port_hmin_5y_months) if pd.notna(port_hmin_5y_months) else "" }) # ========================= # EXPORT — (1) ASSET METRICS SOLO # ========================= asset_metrics_path = excel_path('Asset metrics ITA.xlsx') with pd.ExcelWriter(asset_metrics_path, engine='openpyxl', mode='w') as writer: for name, metrics_df in per_asset_metrics.items(): metrics_df.to_excel(writer, sheet_name=f'Metriche_{name}', index=False) consolidated = [] for name, metrics_df in per_asset_metrics.items(): tmp = metrics_df.copy() tmp.insert(0, 'Periodo', name) consolidated.append(tmp) consolidated_df = pd.concat(consolidated, ignore_index=True) if consolidated else pd.DataFrame() if not consolidated_df.empty: consolidated_df.to_excel(writer, sheet_name='Metriche_Consolidate', index=False) print(f"File '{asset_metrics_path}' creato con soli fogli Metriche_* e Metriche_Consolidate.") # ========================= # COSTRUZIONE "WITH NAMES" # ========================= optimized_weights_with_names = optimized_weights.copy() optimized_weights_with_names['Nome ETF'] = [ df.loc[df['ISIN'] == isin, 'Nome'].values[0] if (df['ISIN'] == isin).any() else "" for isin in optimized_weights.index ] # ========================= # EXPORT — (2) RIEPILOGO PESI (SOLO PH1) # ========================= summary_df = pd.DataFrame(summary_data) summary_path = excel_path('Riepilogo pesi ITA.xlsx') with pd.ExcelWriter(summary_path, engine='openpyxl', mode='w') as writer: optimized_weights_with_names.to_excel(writer, sheet_name='Pesi Ottimizzati', index=True) summary_df.to_excel(writer, sheet_name='Riepilogo', index=False) print(f"File '{summary_path}' creato con 'Pesi Ottimizzati' e 'Riepilogo'.") # ========================= # EXPORT UNICO PESI OTTIMIZZATI # ========================= date_tag = datetime.now().strftime("%Y%m%d") combined_path = excel_path(f"{date_tag} Pesi ottimizzati ITA.xlsx") with pd.ExcelWriter(combined_path, engine='openpyxl', mode='w') as writer: if export_rows: combined_df = pd.concat([template_df] + export_rows, ignore_index=True) else: combined_df = template_df.copy() combined_df.to_excel(writer, sheet_name='Pesi Ottimizzati', index=False) print(f"Pesi ottimizzati salvati in un unico file/sheet: '{combined_path}'.")