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README.md

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+# Pricer Python port (WIP)
+
+This folder hosts the Python conversion of the C# pricer.
+
+Notes:
+- Uses the same `appsettings.json` from the parent `Pricer` project.
+- Target: feature parity with the C# console app and LibraryPricer.
+
+Run (once implemented):
+  python app.py

VALIDATION.md

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+Validation checklist (C# vs Python)
+
+Goal: verify output parity for the same ISIN and inputs.
+
+1) Build inputs
+   - Use the same ISIN and the same prompts in both apps.
+   - Set the same number of simulations.
+   - Use the same dividend flag (s/n).
+   - Optional: set the same RNG seed in Python.
+
+2) Compare core outputs
+   - Correlation matrix and volatility values.
+   - Fair value output line (value and date).
+   - Percent "Sopra/Sotto" and average gain/loss.
+   - Valore atteso.
+
+3) Edge cases
+   - Ask = 0 and Bid > 0 (reference price changes to Bid).
+   - Ex-date warning when DaysToExDate > DaysToObs and last ExDate > last ObsDate.
+
+Notes
+- Monte Carlo output is stochastic; use a seed to reduce variance.
+- If parity differs, compare intermediate inputs printed in the tables.

app.py

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+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from pricer.cli import main
+
+if __name__ == "__main__":
+    main()

appsettings.json

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+{
+  "ConnectionStrings": {
+    "FirstSolutionDB": "Driver={ODBC Driver 17 for SQL Server};Server=26.69.45.60;Database=FirstSolutionDB;UID=sa;PWD=Skyline72;"
+  },
+  "exclude": [
+    "**/bin",
+    "**/bower_components",
+    "**/jspm_packages",
+    "**/node_modules",
+    "**/obj",
+    "**/platforms"
+  ]
+}
+

pricer/__init__.py

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+"""Pricer package."""

pricer/cli.py

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+import sys
+import time
+from typing import List
+
+from tabulate import tabulate
+from . import calc
+from .comparison import clone_context, compare_fair_values
+from .db import default_sql_query
+from .payoffs import PayoffContext
+
+
+def _prompt_with_default(prompt: str, default: str) -> str:
+    value = input(prompt)
+    return default if not value else value
+
+
+def _print_table(title: str, headers: List[str], rows: List[List[object]]) -> None:
+    print()
+    print(title)
+    print(tabulate(rows, headers=headers, tablefmt="github"))
+
+
+def get_caso_string(airbag: int, sigma: int, relief: int, twinwin: int, onestar: int) -> str:
+    componenti = []
+    if airbag == 1:
+        componenti.append("Airbag")
+    if sigma == 1:
+        componenti.append("Sigma")
+    if relief == 1:
+        componenti.append("Relief")
+    if twinwin == 1:
+        componenti.append("TwinWin")
+    if onestar == 1:
+        componenti.append("OneStar")
+
+    return " + ".join(componenti) if componenti else "Standard"
+
+
+def restart_or_exit():
+    print()
+    print("Premere Invio per finire o R per riavviare il programma...")
+    restart = input()
+    if restart and restart.upper() == "R":
+        main()
+    else:
+        sys.exit(0)
+
+
+def main():
+    version = "2.2 - [12/06/2025]"
+    print(f"Pricer v.{version}")
+
+    fs_db = default_sql_query()
+
+    isin = _prompt_with_default("Inserire ISIN (default IT0006767633): ", "IT0006767633")
+
+    check_isin = fs_db.check_isin(isin)
+    if check_isin is not None:
+        check_value = check_isin[0] if check_isin else None
+        if check_value == "KO":
+            print(f"ISIN {isin} non processabile!")
+            print("Certificati nel pricer devono avere le seguenti caratteristiche : ")
+            print("- Categoria: Coupon,StepUp,Bonus (con Barriera Discreta)")
+            print("- Status: In Quotazione")
+            print("- Direction: Long")
+            print("- Basket Type : Worst")
+            print("- Currency : EUR")
+            print("- CedolaVariabile : FALSE")
+            print("- Darwin : FALSE")
+            print("- Domino : FALSE")
+            print("- Magnet : FALSE")
+            restart_or_exit()
+
+    prezzi_eod = _prompt_with_default(
+        "Num. Prezzi EOD per calcolo matrice correlazione e volatilita (default 252): ", "252"
+    )
+    num_prezzi_eod = int(prezzi_eod)
+
+    corr_matrix = calc.correl_certificates(isin, num_prezzi_eod, True)
+    volatility = calc.volatility_certificates(isin, num_prezzi_eod, True)
+
+    is_dividend = _prompt_with_default("Considerare i dividendi ? s/n (default s): ", "s")
+
+    num_sims = int(_prompt_with_default("Inserire # simulazioni montecarlo (default = 10000) : ", "10000"))
+    t_montecarlo_choice = time.perf_counter()
+
+    details_ul_model = fs_db.get_details_ul(isin)
+
+    nomi_sottostanti = [r.sottostante for r in details_ul_model]
+    corr_rows = calc.array_to_table(corr_matrix, nomi_sottostanti)
+    _print_table(f"Matrix Correl {isin}", nomi_sottostanti, corr_rows)
+
+    details_ctf_model = fs_db.get_details_ctf(isin)
+    details_event_model = fs_db.get_details_event(isin)
+    details_event_exdate_model = fs_db.get_details_event_exdate(isin)
+
+    ctf = details_ctf_model[0]
+    tasso_interesse = ctf.tasso_interesse
+    days_to_maturity = ctf.days_to_maturity
+    pdi_style = ctf.pdi_style
+    pdi_strike = ctf.pdi_strike
+    pdi_barrier = round(ctf.pdi_barrier, 4)
+    capital_value = ctf.capital_value
+    nominal_amount = ctf.nominal_amount
+    bid = ctf.bid
+    ask = ctf.ask
+    last_date_price = ctf.last_date_price
+    coupon_in_memory = ctf.coupon_in_memory
+    prot_min_val = ctf.prot_min_val
+    airbag = ctf.air_bag
+    fattore_airbag = ctf.fattore_airbag
+    one_star = ctf.one_star
+    trigger_onestar = round(ctf.trigger_onestar, 4)
+    twin_win = ctf.twin_win
+    sigma = ctf.sigma
+    relief = ctf.relief
+    domino = ctf.domino
+    category = ctf.category
+    cap = ctf.cap
+    leva = ctf.leva
+
+    prices_ul = [r.spot_price_normalized for r in details_ul_model]
+    dividends = [r.dividend for r in details_ul_model]
+    if is_dividend != "s":
+        dividends = [0.0 for _ in dividends]
+    num_sottostanti = len(details_ul_model)
+
+    days_to_obs = [r.days_to_obs for r in details_event_model]
+    days_to_ex_date = [r.days_to_ex_date for r in details_event_exdate_model]
+    days_to_obs_y_fract = [r.days_to_obs_y_fract for r in details_event_model]
+    coupon_values = [r.coupon_value for r in details_event_model]
+    coupon_triggers = [r.coupon_trigger for r in details_event_model]
+    autocall_values = [r.autocall_value for r in details_event_model]
+    autocall_triggers = [r.autocall_trigger for r in details_event_model]
+    memory_flags = [r.memory for r in details_event_model]
+    num_eventi = len(details_event_model)
+
+    input_ul_rows = []
+    for i in range(num_sottostanti):
+        input_ul_rows.append(
+            [
+                nomi_sottostanti[i],
+                round(prices_ul[i], 4),
+                round(dividends[i], 4),
+                round(volatility[i], 4),
+            ]
+        )
+    _print_table(
+        f"Dati Input Sottostanti {isin}",
+        ["Sottostante", "Price", "Dividend", "Volatility"],
+        input_ul_rows,
+    )
+
+    input_ctf_rows = [
+        [
+            round(tasso_interesse, 4),
+            days_to_maturity,
+            pdi_style,
+            pdi_barrier,
+            round(capital_value * nominal_amount, 5),
+            round(coupon_in_memory, 5),
+            round(prot_min_val, 3),
+        ]
+    ]
+    _print_table(
+        f"Dati Input Certificato {isin}",
+        [
+            "TassoInteresse",
+            "DaysToMaturity",
+            "PDI Style",
+            "PDI Barrier",
+            "Capital Value",
+            "Coupon In Memoria",
+            "Prot Min Val %",
+        ],
+        input_ctf_rows,
+    )
+
+    input_flag_rows = [
+        [
+            airbag,
+            round(fattore_airbag, 3),
+            one_star,
+            trigger_onestar,
+            twin_win,
+            sigma,
+            relief,
+        ]
+    ]
+    _print_table(
+        f"Dati Input Flags {isin}",
+        [
+            "AirBag",
+            "Fattore Airbag",
+            "OneStar",
+            "TriggerOnestar",
+            "TwinWin",
+            "Sigma",
+            "Relief",
+        ],
+        input_flag_rows,
+    )
+
+    input_event_rows = []
+    for i in range(num_eventi):
+        input_event_rows.append(
+            [
+                days_to_obs[i],
+                round(days_to_obs_y_fract[i], 4),
+                round(coupon_values[i] * nominal_amount, 6),
+                round(coupon_triggers[i], 6),
+                round(autocall_values[i] * nominal_amount, 6),
+                round(autocall_triggers[i], 6),
+                memory_flags[i],
+            ]
+        )
+    _print_table(
+        f"Dati Input Eventi {isin}",
+        [
+            "DaysToObs.",
+            "DaysToObsYFract",
+            "Cpn. Value",
+            "Cpn. Trigger",
+            "Autoc. Value",
+            "Autoc. Trigger",
+            "Memory",
+        ],
+        input_event_rows,
+    )
+
+    print("Elaborazione Fair value in corso...")
+
+    context_original = PayoffContext(
+        days_to_maturity=days_to_maturity,
+        r=tasso_interesse,
+        capital_value=capital_value,
+        prot_min_val=prot_min_val,
+        pdi_barrier=pdi_barrier,
+        pdi_style=pdi_style,
+        coupon_in_memory=coupon_in_memory,
+        days_to_obs_y_fract=days_to_obs_y_fract,
+        coupon_triggers=coupon_triggers,
+        coupon_values=coupon_values,
+        autocall_triggers=autocall_triggers,
+        autocall_values=autocall_values,
+        memory_flags=memory_flags,
+        caso=get_caso_string(airbag, sigma, relief, twin_win, one_star),
+        pdi_strike=pdi_strike,
+        fattore_airbag=fattore_airbag,
+        trigger_one_star=trigger_onestar,
+        cap=cap,
+        days_to_obs=days_to_obs,
+        airbag=airbag,
+        sigma=sigma,
+        relief=relief,
+        twin_win=twin_win,
+        one_star=one_star,
+        leva=leva,
+        volatility=volatility,
+        prices_ul=prices_ul,
+        nominal_amount=nominal_amount,
+    )
+
+    context_for_compare = clone_context(context_original)
+
+    fvalues = calc.fair_value_array(
+        prices_ul,
+        corr_matrix,
+        num_sottostanti,
+        num_sims,
+        tasso_interesse,
+        days_to_maturity,
+        dividends,
+        volatility,
+        days_to_obs,
+        days_to_obs_y_fract,
+        coupon_values,
+        coupon_triggers,
+        autocall_values,
+        autocall_triggers,
+        memory_flags,
+        coupon_in_memory,
+        pdi_style,
+        pdi_strike,
+        pdi_barrier,
+        capital_value,
+        prot_min_val,
+        airbag,
+        sigma,
+        twin_win,
+        relief,
+        fattore_airbag,
+        one_star,
+        trigger_onestar,
+        cap,
+        leva,
+    ).fair_value_array
+
+    fv = [int(value * nominal_amount / 100) for value in fvalues]
+
+    t_engine_choice = time.perf_counter()
+    print(f"Tempo fino alla scelta del motore: {t_engine_choice - t_montecarlo_choice:.2f} sec")
+
+    compare_fair_values(
+        num_sims,
+        prices_ul,
+        corr_matrix,
+        num_sottostanti,
+        days_to_maturity,
+        tasso_interesse,
+        dividends,
+        context_for_compare,
+        nominal_amount,
+        isin,
+        ask,
+        bid,
+        last_date_price,
+        category,
+    )
+
+    t_expected_done = time.perf_counter()
+    print(f"Tempo fino al valore atteso: {t_expected_done - t_engine_choice:.2f} sec")
+
+    print("----------------------------------------")
+
+    if len(days_to_ex_date) > len(days_to_obs):
+        last_obs = days_to_obs[-1]
+        last_ex = days_to_ex_date[-1]
+
+        if last_obs < last_ex:
+            print("ATTENZIONE: POSSIBILE SOTTOSTIMA DEL FAIR VALUE")
+            print("\n Il calcolo del fair value presume che il coupon sia gia stato pagato,")
+            print("   ma in realta non e ancora maturato (la Ex-Date non e ancora stata raggiunta).")
+            print("   Il titolo incorpora ancora quel coupon nel suo prezzo, ma il pricer lo ha gia scontato.")
+            print("\n Inoltre, se erano presenti coupon in memoria, questi sono stati azzerati nel calcolo,")
+            print("   come se il pagamento fosse gia avvenuto.")
+            print("   In realta il diritto al pagamento non e ancora stato acquisito.")
+            print("   Questo comporta una POSSIBILE SOTTOSTIMA SIGNIFICATIVA del FAIR VALUE.")
+            print("\n SUGGERIMENTO: verificare manualmente l'ultimo evento osservato")
+            print("   e valutare l'impatto potenziale sul fair value atteso.")
+
+    restart_or_exit()

pricer/comparison.py

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+from typing import List
+
+from . import calc
+from .payoffs import PayoffContext
+from .report import generate_report
+
+
+def clone_context(original: PayoffContext) -> PayoffContext:
+    return PayoffContext(
+        days_to_maturity=original.days_to_maturity,
+        r=original.r,
+        capital_value=original.capital_value,
+        prot_min_val=original.prot_min_val,
+        pdi_barrier=original.pdi_barrier,
+        pdi_style=original.pdi_style,
+        coupon_in_memory=original.coupon_in_memory,
+        days_to_obs_y_fract=list(original.days_to_obs_y_fract),
+        coupon_triggers=list(original.coupon_triggers),
+        coupon_values=list(original.coupon_values),
+        autocall_triggers=list(original.autocall_triggers),
+        autocall_values=list(original.autocall_values),
+        memory_flags=list(original.memory_flags),
+        caso=original.caso,
+        pdi_strike=original.pdi_strike,
+        fattore_airbag=original.fattore_airbag,
+        trigger_one_star=original.trigger_one_star,
+        cap=original.cap,
+        days_to_obs=list(original.days_to_obs),
+        airbag=original.airbag,
+        sigma=original.sigma,
+        relief=original.relief,
+        twin_win=original.twin_win,
+        one_star=original.one_star,
+        leva=original.leva,
+        volatility=list(original.volatility),
+        prices_ul=list(original.prices_ul),
+        nominal_amount=original.nominal_amount,
+    )
+
+
+def compare_fair_values(
+    num_simulations: int,
+    prices_ul: List[float],
+    corr_matrix: List[List[float]],
+    num_assets: int,
+    days_to_maturity: int,
+    rate: float,
+    dividends: List[float],
+    context: PayoffContext,
+    nominal_amount: float,
+    isin: str,
+    ask: float,
+    bid: float,
+    last_date,
+    category: str,
+) -> None:
+    print(f"Motore GBM con payoff engine '{context.caso}'")
+
+    results = calc.simulate_payoffs(
+        num_simulations,
+        prices_ul,
+        corr_matrix,
+        days_to_maturity,
+        rate,
+        dividends,
+        context.volatility,
+        context.days_to_obs,
+        context.days_to_obs_y_fract,
+        context.coupon_values,
+        context.coupon_triggers,
+        context.autocall_values,
+        context.autocall_triggers,
+        context.memory_flags,
+        context.coupon_in_memory,
+        context.pdi_style,
+        context.pdi_strike,
+        context.pdi_barrier,
+        context.capital_value,
+        context.prot_min_val,
+        context.airbag,
+        context.sigma,
+        context.twin_win,
+        context.relief,
+        context.fattore_airbag,
+        context.one_star,
+        context.trigger_one_star,
+        context.cap,
+    )
+    results = [float(val) * 100.0 for val in results]
+
+    mean = sum(results) / num_simulations
+    variance = sum((val - mean) ** 2 for val in results) / num_simulations
+    stddev = variance ** 0.5
+
+    fair_value_scaled = mean * nominal_amount / 100.0
+    fair_values = [int(val * nominal_amount / 100) for val in results]
+
+    generate_report(
+        isin=isin,
+        category=category,
+        fair_value=fair_value_scaled,
+        fair_values=fair_values,
+        ask=ask,
+        bid=bid,
+        num_sims=num_simulations,
+        last_date_price=last_date,
+    )

pricer/config.py

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+import json
+from pathlib import Path
+
+
+def get_connection_string(name="FirstSolutionDB", settings_path=None):
+    """Load connection string from appsettings.json in the parent project."""
+    if settings_path is None:
+        local_settings = Path(__file__).resolve().parents[1] / "appsettings.json"
+        settings_path = local_settings if local_settings.exists() else Path(__file__).resolve().parents[2] / "appsettings.json"
+    else:
+        settings_path = Path(settings_path)
+
+    data = json.loads(settings_path.read_text(encoding="utf-8"))
+    conn_strings = data.get("ConnectionStrings", {})
+    if name not in conn_strings:
+        raise KeyError(f"Connection string '{name}' not found in {settings_path}")
+    return conn_strings[name]

pricer/db.py

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+from typing import Any, Dict, Iterable, List, Tuple
+import pyodbc
+from decimal import Decimal
+
+from .config import get_connection_string
+from . import models
+
+
+def _rows_to_dicts(cursor, rows):
+    columns = [col[0] for col in cursor.description]
+    return [dict(zip(columns, row)) for row in rows]
+
+
+def _normalize_keys(row: Dict[str, Any]) -> Dict[str, Any]:
+    normalized = {}
+    for k, v in row.items():
+        if isinstance(v, Decimal):
+            normalized[k.lower()] = float(v)
+        else:
+            normalized[k.lower()] = v
+    return normalized
+
+
+class SqlDataAccess:
+    def load_data(self, stored_proc: str, params: Dict[str, Any], connection_string: str):
+        placeholders = ", ".join([f"@{k}=?" for k in params.keys()])
+        sql = f"EXEC {stored_proc} {placeholders}" if placeholders else f"EXEC {stored_proc}"
+        values = list(params.values())
+
+        with pyodbc.connect(connection_string) as conn:
+            cur = conn.cursor()
+            cur.execute(sql, values)
+            rows = cur.fetchall()
+            return _rows_to_dicts(cur, rows)
+
+
+class SqlQuery:
+    def __init__(self, connection_string: str):
+        self._connection_string = connection_string
+        self._db = SqlDataAccess()
+
+    def get_hist_price_ul(self, isin: str, num_prezzi_eod: int):
+        rows = self._db.load_data(
+            "pricer_ULPrices1",
+            {"Isin": isin, "numPrezzi": num_prezzi_eod},
+            self._connection_string,
+        )
+        return [
+            models.HistPriceULModel(
+                id_underlyings=r["idunderlyings"],
+                sottostante=r["sottostante"],
+                px_close=r["px_close"],
+                px_date=r["px_date"],
+            )
+            for r in map(_normalize_keys, rows)
+        ]
+
+    def get_details_ul(self, isin: str):
+        rows = self._db.load_data(
+            "pricer_ULDetails",
+            {"Isin": isin},
+            self._connection_string,
+        )
+        return [
+            models.DetailsULModel(
+                id_underlyings=r["idunderlyings"],
+                sottostante=r["sottostante"],
+                maturity_date=r.get("maturitydate"),
+                last_price=r["lastprice"],
+                strike=r["strike"],
+                spot_price_normalized=r["spotpricenormalized"],
+                dividend=r["dividend"],
+                volatility=r["volatility"],
+                days_to_maturity=r.get("daystomaturity", 0),
+                tasso_interesse=r.get("tassointeresse", 0.0),
+            )
+            for r in map(_normalize_keys, rows)
+        ]
+
+    def get_details_event(self, isin: str):
+        rows = self._db.load_data(
+            "pricer_EventDetails",
+            {"Isin": isin},
+            self._connection_string,
+        )
+        return [
+            models.DetailsEventModel(
+                days_to_obs=r["daystoobs"],
+                days_to_ex_date=r.get("daystoexdate", 0),
+                days_to_obs_y_fract=r["daystoobsyfract"],
+                coupon_value=r["couponvalue"],
+                coupon_trigger=r["coupontrigger"],
+                autocall_value=r["autocallvalue"],
+                autocall_trigger=r["autocalltrigger"],
+                memory=r["memory"],
+            )
+            for r in map(_normalize_keys, rows)
+        ]
+
+    def get_details_event_exdate(self, isin: str):
+        rows = self._db.load_data(
+            "pricer_EventDetails_ExDate",
+            {"Isin": isin},
+            self._connection_string,
+        )
+        return [
+            models.DetailsEventModel(
+                days_to_obs=r.get("daystoobs", 0),
+                days_to_ex_date=r["daystoexdate"],
+                days_to_obs_y_fract=r.get("daystoobsyfract", 0.0),
+                coupon_value=r.get("couponvalue", 0.0),
+                coupon_trigger=r.get("coupontrigger", 0.0),
+                autocall_value=r.get("autocallvalue", 0.0),
+                autocall_trigger=r.get("autocalltrigger", 0.0),
+                memory=r.get("memory", 0),
+            )
+            for r in map(_normalize_keys, rows)
+        ]
+
+    def get_details_ctf(self, isin: str):
+        rows = self._db.load_data(
+            "pricer_CTFDetails",
+            {"Isin": isin},
+            self._connection_string,
+        )
+        return [
+            models.DetailsCTFModel(
+                tasso_interesse=r["tassointeresse"],
+                days_to_maturity=r["daystomaturity"],
+                pdi_style=r["pdi_style"],
+                pdi_strike=r["pdi_strike"],
+                pdi_barrier=r["pdi_barrier"],
+                capital_value=r["capitalvalue"],
+                nominal_amount=r["nominalamount"],
+                bid=r["bid"],
+                ask=r["ask"],
+                last_date_price=r["lastdateprice"],
+                coupon_in_memory=r["couponinmemory"],
+                prot_min_val=r["protminval"],
+                air_bag=r["airbag"],
+                fattore_airbag=r["fattoreairbag"],
+                one_star=r["onestar"],
+                trigger_onestar=r["triggeronestar"],
+                twin_win=r["twinwin"],
+                sigma=r["sigma"],
+                relief=r["relief"],
+                domino=r["domino"],
+                category=r["category"],
+                cap=r["cap"],
+                leva=r["leva"],
+            )
+            for r in map(_normalize_keys, rows)
+        ]
+
+    def get_isin_universe(self):
+        rows = self._db.load_data(
+            "pricer_LoadISINUniverse",
+            {},
+            self._connection_string,
+        )
+        values = []
+        for row in rows:
+            if isinstance(row, dict):
+                if len(row) == 1:
+                    values.append(next(iter(row.values())))
+                else:
+                    values.append(row)
+            else:
+                values.append(row)
+        return values
+
+    def check_isin(self, isin: str):
+        rows = self._db.load_data(
+            "pricer_CheckISIN1",
+            {"Isin": isin},
+            self._connection_string,
+        )
+        values = []
+        for row in rows:
+            if isinstance(row, dict):
+                if len(row) == 1:
+                    values.append(next(iter(row.values())))
+                else:
+                    values.append(row)
+            else:
+                values.append(row)
+        return values
+
+    def bulk_update_certificates(self, table_name: str, list_fair_values: List[models.FairValues]) -> None:
+        if not list_fair_values:
+            return
+
+        sql = f"UPDATE {table_name} SET FairValue = ? WHERE ISIN = ?"
+        data = [(fv.fair_value, fv.isin) for fv in list_fair_values]
+        with pyodbc.connect(self._connection_string) as conn:
+            cur = conn.cursor()
+            cur.executemany(sql, data)
+            conn.commit()
+
+
+def default_sql_query():
+    return SqlQuery(get_connection_string("FirstSolutionDB"))

pricer/models.py

@@ -0,0 +1,93 @@
+from dataclasses import dataclass
+from datetime import datetime
+from typing import List, Optional
+
+
+@dataclass
+class HistPriceULModel:
+    id_underlyings: int
+    sottostante: str
+    px_close: float
+    px_date: datetime
+
+
+@dataclass
+class DetailsULModel:
+    id_underlyings: int
+    sottostante: str
+    maturity_date: Optional[datetime]
+    last_price: float
+    strike: float
+    spot_price_normalized: float
+    dividend: float
+    volatility: float
+    days_to_maturity: int
+    tasso_interesse: float
+
+
+@dataclass
+class DetailsEventModel:
+    days_to_obs: int
+    days_to_ex_date: int
+    days_to_obs_y_fract: float
+    coupon_value: float
+    coupon_trigger: float
+    autocall_value: float
+    autocall_trigger: float
+    memory: int
+
+
+@dataclass
+class DetailsCTFModel:
+    tasso_interesse: float
+    days_to_maturity: int
+    pdi_style: str
+    pdi_strike: float
+    pdi_barrier: float
+    capital_value: float
+    nominal_amount: float
+    bid: float
+    ask: float
+    last_date_price: datetime
+    coupon_in_memory: float
+    prot_min_val: float
+    air_bag: int
+    fattore_airbag: float
+    one_star: int
+    trigger_onestar: float
+    twin_win: int
+    sigma: int
+    relief: int
+    domino: int
+    category: str
+    cap: float
+    leva: float
+
+
+@dataclass
+class FairValues:
+    isin: str
+    fair_value: float
+
+
+@dataclass
+class GarchParams:
+    omega: float
+    alpha: float
+    beta: float
+    sigma0: float
+
+
+@dataclass
+class UnderlyingStats:
+    nome: str
+    prezzi: List[float]
+    log_returns: List[float]
+    volatility: float
+
+
+@dataclass
+class PrezziSottostanti:
+    id_underlyings: int
+    sottostante: str
+    prezzi_close: List[float]

pricer/payoffs.py

@@ -0,0 +1,560 @@
+import math
+from dataclasses import dataclass
+from typing import List
+
+import numpy as np
+
+
+class PayoffEvaluator:
+    def evaluate(self, path: List[List[float]], context: "PayoffContext") -> float:
+        raise NotImplementedError
+
+
+@dataclass
+class PayoffContext:
+    days_to_maturity: int
+    r: float
+    capital_value: float
+    prot_min_val: float
+    pdi_barrier: float
+    pdi_style: str
+    coupon_in_memory: float
+    days_to_obs_y_fract: List[float]
+    coupon_triggers: List[float]
+    coupon_values: List[float]
+    autocall_triggers: List[float]
+    autocall_values: List[float]
+    memory_flags: List[int]
+    caso: str
+    pdi_strike: float
+    fattore_airbag: float
+    trigger_one_star: float
+    cap: float
+    days_to_obs: List[int]
+    volatility: List[float]
+    airbag: int
+    sigma: int
+    relief: int
+    twin_win: int
+    one_star: int
+    leva: float
+    nominal_amount: float
+    prices_ul: List[float]
+
+
+class PayoffFactory:
+    @staticmethod
+    def get_evaluator(caso: str) -> PayoffEvaluator:
+        mapping = {
+            "Standard": PayoffStandard(),
+            "Airbag": PayoffAirbag(),
+            "Sigma": PayoffSigma(),
+            "Relief": PayoffRelief(),
+            "TwinWin": PayoffTwinWin(),
+            "OneStar": PayoffOneStar(),
+            "Airbag + OneStar": PayoffAirbagOneStar(),
+            "Sigma + OneStar": PayoffSigmaOneStar(),
+            "Relief + OneStar": PayoffReliefOneStar(),
+            "TwinWin + OneStar": PayoffTwinWinOneStar(),
+            "Airbag + TwinWin": PayoffAirbagTwinWin(),
+        }
+        if caso not in mapping:
+            raise ValueError(f"Payoff '{caso}' non gestito.")
+        return mapping[caso]
+
+
+def _row_min(path, index: int) -> float:
+    if isinstance(path, np.ndarray):
+        return float(path[index].min())
+    return min(path[index])
+
+
+def _row_max(path, index: int) -> float:
+    if isinstance(path, np.ndarray):
+        return float(path[index].max())
+    return max(path[index])
+
+
+def _overall_min(path) -> float:
+    if isinstance(path, np.ndarray):
+        return float(path.min())
+    return _overall_min(path)
+
+class PayoffStandard(PayoffEvaluator):
+    def __init__(self):
+        self.last_label = ""
+
+    def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
+        memory = c.coupon_in_memory
+        coupons = 0.0
+        payoff = 0.0
+
+        for k in range(len(c.days_to_obs)):
+            min_val = _row_min(path, c.days_to_obs[k])
+
+            if min_val >= c.autocall_triggers[k]:
+                payoff = (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+                self.last_label = (
+                    f"Autocall ? Trigger={c.autocall_triggers[k]:.2f}, Min={min_val:.2f} ? Payoff={payoff:.2f}"
+                )
+                return payoff
+
+            if min_val >= c.coupon_triggers[k]:
+                coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                memory = 0.0
+            else:
+                memory += c.memory_flags[k] * c.coupon_values[k]
+
+            if k == len(c.days_to_obs) - 1:
+                min_mat = _row_min(path, c.days_to_maturity)
+                min_total = _overall_min(path)
+                barrier_check = min_mat if c.pdi_style == "European" else min_total
+
+                if barrier_check >= c.pdi_barrier:
+                    payoff = (c.capital_value + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+                    self.last_label = (
+                        f"RIMBORSO standard ? minMat={min_mat:.4f}, minTotal={min_total:.4f} ? Payoff={payoff:.2f}"
+                    )
+                else:
+                    payoff = max(c.prot_min_val, min_mat / 100.0) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+                    self.last_label = (
+                        f"PERDITA standard ? minMat={min_mat:.4f}, minTotal={min_total:.4f} ? Payoff={payoff:.2f}"
+                    )
+
+                return payoff
+
+        self.last_label = "UNKNOWN"
+        return 0.0
+
+
+class PayoffAirbag(PayoffEvaluator):
+    def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
+        memory = c.coupon_in_memory
+        coupons = 0.0
+
+        for k in range(len(c.days_to_obs)):
+            min_val = _row_min(path, c.days_to_obs[k])
+
+            if min_val >= c.autocall_triggers[k]:
+                return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+            if min_val >= c.coupon_triggers[k]:
+                coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                memory = 0.0
+            else:
+                memory += c.memory_flags[k] * c.coupon_values[k]
+
+            if k == len(c.days_to_obs) - 1:
+                min_mat = _row_min(path, c.days_to_maturity)
+                min_total = _overall_min(path)
+
+                sopra_barriera = (
+                    (c.pdi_style == "European" and min_mat >= c.pdi_barrier)
+                    or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
+                )
+
+                if sopra_barriera:
+                    payoff = c.capital_value + memory
+                else:
+                    payoff = max(c.prot_min_val, (min_mat * c.fattore_airbag / 100.0))
+
+                return payoff * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+        return 0.0
+
+
+class PayoffTwinWin(PayoffEvaluator):
+    def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
+        memory = c.coupon_in_memory
+        coupons = 0.0
+
+        for k in range(len(c.days_to_obs)):
+            min_val = _row_min(path, c.days_to_obs[k])
+
+            if min_val >= c.autocall_triggers[k]:
+                return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+            if min_val >= c.coupon_triggers[k]:
+                coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                memory = 0.0
+            else:
+                memory += c.memory_flags[k] * c.coupon_values[k]
+
+            if k == len(c.days_to_obs) - 1:
+                min_mat = _row_min(path, c.days_to_maturity)
+                min_total = _overall_min(path)
+
+                sopra_strike = min_mat >= c.pdi_strike and c.autocall_triggers[k] == 999
+
+                sopra_barriera = (
+                    (c.pdi_style == "European" and min_mat >= c.pdi_barrier)
+                    or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
+                )
+
+                if sopra_strike:
+                    payoff = memory + min(min_mat / 100.0, c.cap)
+                elif sopra_barriera:
+                    payoff = (200.0 - min_mat) * c.capital_value / 100.0 + memory
+                else:
+                    payoff = max(c.prot_min_val, min_mat / 100.0) + memory
+
+                return payoff * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+        return 0.0
+
+
+class PayoffOneStar(PayoffEvaluator):
+    def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
+        memory = c.coupon_in_memory
+        coupons = 0.0
+
+        for k in range(len(c.days_to_obs)):
+            min_val = _row_min(path, c.days_to_obs[k])
+
+            if min_val >= c.autocall_triggers[k]:
+                return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+            if min_val >= c.coupon_triggers[k]:
+                coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                memory = 0.0
+            else:
+                memory += c.memory_flags[k] * c.coupon_values[k]
+
+            if k == len(c.days_to_obs) - 1:
+                max_mat = _row_max(path, c.days_to_obs[k])
+                min_mat = _row_min(path, c.days_to_maturity)
+                min_total = _overall_min(path)
+
+                sopra_trigger = max_mat >= c.trigger_one_star
+
+                sopra_barriera = (
+                    (c.pdi_style == "European" and min_mat >= c.pdi_barrier)
+                    or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
+                )
+
+                capitale = c.capital_value if (sopra_trigger or sopra_barriera) else max(c.prot_min_val, min_mat / 100.0)
+
+                cedola_finale = 0.0
+                if min_mat >= c.coupon_triggers[k]:
+                    cedola_finale = memory + c.coupon_values[k]
+
+                return (
+                    capitale * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                    + cedola_finale * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                    + coupons
+                )
+
+        return 0.0
+
+
+class PayoffSigma(PayoffEvaluator):
+    def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
+        memory = c.coupon_in_memory
+        coupons = 0.0
+
+        for k in range(len(c.days_to_obs)):
+            min_val = _row_min(path, c.days_to_obs[k])
+
+            if min_val >= c.autocall_triggers[k]:
+                return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+            if min_val >= c.coupon_triggers[k]:
+                coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                memory = 0.0
+            else:
+                memory += c.memory_flags[k] * c.coupon_values[k]
+
+            if k == len(c.days_to_obs) - 1:
+                min_mat = _row_min(path, c.days_to_maturity)
+                min_total = _overall_min(path)
+
+                sopra_barriera = (
+                    (c.pdi_style == "European" and min_mat >= c.pdi_barrier)
+                    or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
+                )
+
+                if sopra_barriera:
+                    payoff = c.capital_value + memory
+                else:
+                    payoff = max(c.prot_min_val, (min_mat + c.pdi_strike - c.pdi_barrier) / 100.0)
+
+                return payoff * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+        return 0.0
+
+
+class PayoffRelief(PayoffEvaluator):
+    def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
+        memory = c.coupon_in_memory
+        coupons = 0.0
+
+        for k in range(len(c.days_to_obs)):
+            min_val = _row_min(path, c.days_to_obs[k])
+
+            if min_val >= c.autocall_triggers[k]:
+                return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+            if min_val >= c.coupon_triggers[k]:
+                coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                memory = 0.0
+            else:
+                memory += c.memory_flags[k] * c.coupon_values[k]
+
+            if k == len(c.days_to_obs) - 1:
+                prices_at_mat = path[c.days_to_maturity]
+                sorted_prices = sorted(prices_at_mat)
+                second_min = sorted_prices[1] if len(sorted_prices) >= 2 else sorted_prices[0]
+                min_mat = min(prices_at_mat)
+                min_total = _overall_min(path)
+
+                sopra_barriera = (
+                    (c.pdi_style == "European" and min_mat >= c.pdi_barrier)
+                    or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
+                )
+
+                if sopra_barriera:
+                    payoff = c.capital_value + memory
+                else:
+                    payoff = max(c.prot_min_val, second_min / 100.0)
+
+                return payoff * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+        return 0.0
+
+
+class PayoffAirbagOneStar(PayoffEvaluator):
+    def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
+        memory = c.coupon_in_memory
+        coupons = 0.0
+
+        for k in range(len(c.days_to_obs)):
+            min_val = _row_min(path, c.days_to_obs[k])
+
+            if min_val >= c.autocall_triggers[k]:
+                return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+            if min_val >= c.coupon_triggers[k]:
+                coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                memory = 0.0
+            else:
+                memory += c.memory_flags[k] * c.coupon_values[k]
+
+            if k == len(c.days_to_obs) - 1:
+                max_mat = _row_max(path, c.days_to_obs[k])
+                min_mat = _row_min(path, c.days_to_maturity)
+                min_total = _overall_min(path)
+
+                sopra_trigger = max_mat >= c.trigger_one_star
+
+                sopra_barriera = (
+                    (c.pdi_style == "European" and min_mat >= c.pdi_barrier)
+                    or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
+                )
+
+                if sopra_trigger or sopra_barriera:
+                    capitale = c.capital_value
+                else:
+                    perc = max(c.prot_min_val, min_mat / 100.0) * c.fattore_airbag
+                    capitale = min(c.capital_value, perc)
+
+                cedola_finale = 0.0
+                if min_mat >= c.coupon_triggers[k]:
+                    cedola_finale = memory + c.coupon_values[k]
+
+                return (
+                    capitale * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                    + cedola_finale * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                    + coupons
+                )
+
+        return 0.0
+
+
+class PayoffSigmaOneStar(PayoffEvaluator):
+    def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
+        memory = c.coupon_in_memory
+        coupons = 0.0
+
+        for k in range(len(c.days_to_obs)):
+            min_val = _row_min(path, c.days_to_obs[k])
+
+            if min_val >= c.autocall_triggers[k]:
+                return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+            if min_val >= c.coupon_triggers[k]:
+                coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                memory = 0.0
+            else:
+                memory += c.memory_flags[k] * c.coupon_values[k]
+
+            if k == len(c.days_to_obs) - 1:
+                max_mat = _row_max(path, c.days_to_obs[k])
+                min_mat = _row_min(path, c.days_to_maturity)
+                min_total = _overall_min(path)
+
+                sopra_trigger = max_mat >= c.trigger_one_star
+                sopra_barriera = (
+                    (c.pdi_style == "European" and min_mat >= c.pdi_barrier)
+                    or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
+                )
+
+                if sopra_trigger or sopra_barriera:
+                    capitale = c.capital_value
+                else:
+                    capitale = max(c.prot_min_val, (min_mat + c.pdi_strike - c.pdi_barrier) / 100.0)
+
+                cedola_finale = 0.0
+                if min_mat >= c.coupon_triggers[k]:
+                    cedola_finale = memory + c.coupon_values[k]
+
+                return (
+                    capitale * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                    + cedola_finale * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                    + coupons
+                )
+
+        return 0.0
+
+
+class PayoffReliefOneStar(PayoffEvaluator):
+    def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
+        memory = c.coupon_in_memory
+        coupons = 0.0
+
+        for k in range(len(c.days_to_obs)):
+            min_val = _row_min(path, c.days_to_obs[k])
+
+            if min_val >= c.autocall_triggers[k]:
+                return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+            if min_val >= c.coupon_triggers[k]:
+                coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                memory = 0.0
+            else:
+                memory += c.memory_flags[k] * c.coupon_values[k]
+
+            if k == len(c.days_to_obs) - 1:
+                prices_at_mat = path[c.days_to_maturity]
+                sorted_prices = sorted(prices_at_mat)
+                second_min = sorted_prices[1] if len(sorted_prices) >= 2 else sorted_prices[0]
+                min_mat = min(prices_at_mat)
+                max_mat = _row_max(path, c.days_to_obs[k])
+                min_total = _overall_min(path)
+
+                sopra_trigger = max_mat >= c.trigger_one_star
+                sopra_barriera = (
+                    (c.pdi_style == "European" and min_mat >= c.pdi_barrier)
+                    or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
+                )
+
+                if sopra_trigger or sopra_barriera:
+                    capitale = c.capital_value
+                else:
+                    capitale = max(c.prot_min_val, second_min / 100.0)
+
+                cedola_finale = 0.0
+                if min_mat >= c.coupon_triggers[k]:
+                    cedola_finale = memory + c.coupon_values[k]
+
+                return (
+                    capitale * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                    + cedola_finale * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                    + coupons
+                )
+
+        return 0.0
+
+
+class PayoffTwinWinOneStar(PayoffEvaluator):
+    def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
+        memory = c.coupon_in_memory
+        coupons = 0.0
+
+        for k in range(len(c.days_to_obs)):
+            min_val = _row_min(path, c.days_to_obs[k])
+
+            if min_val >= c.autocall_triggers[k]:
+                return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+            if min_val >= c.coupon_triggers[k]:
+                coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                memory = 0.0
+            else:
+                memory += c.memory_flags[k] * c.coupon_values[k]
+
+            if k == len(c.days_to_obs) - 1:
+                max_mat = _row_max(path, c.days_to_obs[k])
+                min_mat = _row_min(path, c.days_to_maturity)
+                min_total = _overall_min(path)
+
+                sopra_strike = min_mat >= c.pdi_strike
+                sopra_barriera = (
+                    (c.pdi_style == "European" and min_mat >= c.pdi_barrier)
+                    or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
+                )
+
+                if sopra_strike:
+                    capitale = min(min_mat / 100.0, c.cap)
+                elif sopra_barriera:
+                    capitale = (2 * c.capital_value) - (min_mat / 100.0)
+                else:
+                    if max_mat >= c.trigger_one_star:
+                        capitale = c.capital_value
+                    else:
+                        capitale = max(c.prot_min_val, min_mat / 100.0)
+
+                cedola_finale = 0.0
+                if min_mat >= c.coupon_triggers[k]:
+                    cedola_finale = memory + c.coupon_values[k]
+
+                return (
+                    capitale * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                    + cedola_finale * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                    + coupons
+                )
+
+        return 0.0
+
+
+class PayoffAirbagTwinWin(PayoffEvaluator):
+    def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
+        memory = c.coupon_in_memory
+        coupons = 0.0
+
+        for k in range(len(c.days_to_obs)):
+            min_val = _row_min(path, c.days_to_obs[k])
+
+            if min_val >= c.autocall_triggers[k]:
+                return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
+
+            if min_val >= c.coupon_triggers[k]:
+                coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                memory = 0.0
+            else:
+                memory += c.memory_flags[k] * c.coupon_values[k]
+
+            if k == len(c.days_to_obs) - 1:
+                min_mat = _row_min(path, c.days_to_maturity)
+
+                if min_mat >= c.pdi_barrier:
+                    if c.autocall_triggers[k] == 999 and min_mat >= c.pdi_strike:
+                        return (
+                            math.exp(-c.r * c.days_to_obs_y_fract[k])
+                            * (memory + min(min_mat / 100.0, c.cap))
+                            + coupons
+                        )
+                    return (
+                        math.exp(-c.r * c.days_to_obs_y_fract[k])
+                        * ((2 * c.capital_value) + memory - (min_mat / 100.0))
+                        + coupons
+                    )
+
+                return (
+                    max(c.prot_min_val, min_mat / 100.0)
+                    * math.exp(-c.r * c.days_to_obs_y_fract[k])
+                    * c.fattore_airbag
+                    + coupons
+                )
+
+        return 0.0
+

pricer/report.py

@@ -0,0 +1,71 @@
+from typing import List
+
+from .utils import round_to_multiple
+
+
+def generate_report(
+    isin: str,
+    category: str,
+    fair_value: float,
+    fair_values: List[int],
+    ask: float,
+    bid: float,
+    num_sims: int,
+    last_date_price,
+) -> None:
+    rif_prezzo = ask
+    riferimento = "Ask"
+
+    print()
+
+    if ask == 0 and bid > 0:
+        rif_prezzo = bid
+        riferimento = "Bid"
+        print(f"Ask = 0, preso come valore di riferimento il Bid: {bid:.2f}")
+
+    casi_over_barriera = []
+    casi_under_barriera = []
+    casi_over_ask = []
+    casi_under_ask = []
+
+    for value in fair_values:
+        if value < 100:
+            casi_under_barriera.append(value)
+        else:
+            casi_over_barriera.append(value)
+
+        if value >= rif_prezzo:
+            casi_over_ask.append(value)
+        else:
+            casi_under_ask.append(value)
+
+    perc_over_barriera = round_to_multiple(100 * len(casi_over_barriera) / len(fair_values), 0.01)
+    perc_over_ask = round_to_multiple(100 * len(casi_over_ask) / len(fair_values), 0.01)
+    perc_under_ask = 100 - perc_over_ask
+
+    print(f"Caso calcolo Fair value {isin} = {category}")
+    print(
+        f"Fair value {isin} ({num_sims} iter.) = {fair_value:.2f} (Bid = {bid}, Ask = {ask}, Last updated on {last_date_price})"
+    )
+    print(
+        f"Sopra {riferimento} nel {perc_over_ask:.2f}% dei casi, sotto {riferimento} nel {perc_under_ask:.2f}% dei casi"
+    )
+
+    media_over_ask = round_to_multiple(sum(casi_over_ask) / len(casi_over_ask) if casi_over_ask else 0.0, 0.01)
+    media_under_ask = round_to_multiple(sum(casi_under_ask) / len(casi_under_ask) if casi_under_ask else 0.0, 0.01)
+    print(f"Valore medio casi sopra {riferimento}: {media_over_ask:.2f}")
+    print(f"Valore medio casi sotto {riferimento}: {media_under_ask:.2f}")
+
+    gain_medio = round_to_multiple(media_over_ask - rif_prezzo, 0.01)
+    loss_medio = round_to_multiple(rif_prezzo - media_under_ask, 0.01)
+    print(f"Gain medio: {media_over_ask:.2f} - {rif_prezzo:.2f} = {gain_medio:.2f}")
+    print(f"Loss medio: {rif_prezzo:.2f} - {media_under_ask:.2f} = {loss_medio:.2f}")
+
+    perc_media_gain = round_to_multiple(100 * gain_medio / rif_prezzo, 0.01)
+    perc_media_loss = round_to_multiple(100 * loss_medio / rif_prezzo, 0.01)
+    print(f"Perc media Gain: {gain_medio:.2f} / {rif_prezzo:.2f} = {perc_media_gain:.2f}%")
+    print(f"Perc media Loss: {loss_medio:.2f} / {rif_prezzo:.2f} = {perc_media_loss:.2f}%")
+
+    valore_atteso = round_to_multiple((perc_over_ask / 100) * gain_medio - (perc_under_ask / 100) * loss_medio, 0.01)
+    perc_valore_atteso = round_to_multiple(100 * valore_atteso / rif_prezzo, 0.01)
+    print(f"Valore atteso: {valore_atteso:.2f} ({perc_valore_atteso:.2f}% su {riferimento})")

pricer/utils.py

@@ -0,0 +1,28 @@
+import math
+from typing import Iterable, List
+
+
+def round_to_multiple(value: float, multiple: float) -> float:
+    if multiple == 0:
+        return value
+    return round(value / multiple) * multiple
+
+
+def cumulative_sums(values: List[float]) -> List[float]:
+    if not values:
+        return []
+    results = [0.0] * len(values)
+    results[0] = values[0]
+    for i in range(1, len(values)):
+        results[i] = results[i - 1] + values[i]
+    return results
+
+
+def standard_deviation(values: Iterable[float]) -> float:
+    vals = list(values)
+    n = len(vals)
+    if n <= 1:
+        return 0.0
+    avg = sum(vals) / n
+    sum_sq = sum((v - avg) ** 2 for v in vals)
+    return math.sqrt(sum_sq / (n - 1))

requirements.txt

@@ -0,0 +1,4 @@
+numpy
+pyodbc
+tabulate
+numba