Forensic and quality scores: Beneish M-score, Piotroski F-score, Coffee Can, cash checks

"""M06 · Forensic and quality scores: Beneish M-score, Piotroski F-score, Coffee Can, cash checks.

Exercise stubs. See notes.md §6.8–§6.9 for every definition.
"""

from __future__ import annotations

BENEISH_COEF = {
    "const": -4.84, "DSRI": 0.920, "GMI": 0.528, "AQI": 0.404, "SGI": 0.892,
    "DEPI": 0.115, "SGAI": -0.172, "TATA": 4.679, "LVGI": -0.327,
}


def beneish_components(cur: dict, prev: dict) -> dict:
    """Return the eight Beneish (1999) indices as dict(DSRI, GMI, AQI, SGI, DEPI, SGAI, TATA, LVGI).

    cur = year t, prev = year t-1. Keys in both dicts: sales, cogs, receivables,
    current_assets, ppe, securities, total_assets, depreciation, sga, net_income, cfo,
    current_liabilities, lt_debt.
      DSRI = (Rec_t/Sales_t) / (Rec_{t-1}/Sales_{t-1})
      GMI  = GM_{t-1} / GM_t,                 GM = (Sales - COGS)/Sales
      AQI  = AQ_t / AQ_{t-1},                 AQ = 1 - (CA + PPE + Securities)/TA
      SGI  = Sales_t / Sales_{t-1}
      DEPI = DR_{t-1} / DR_t,                 DR = Dep/(Dep + PPE)
      SGAI = (SGA_t/Sales_t) / (SGA_{t-1}/Sales_{t-1})
      TATA = (NI_t - CFO_t) / TA_t
      LVGI = LEV_t / LEV_{t-1},               LEV = (CL + LTD)/TA
    """
    raise NotImplementedError


def beneish_m(cur: dict, prev: dict):
    """Return (M, components) with M = const + sum_k BENEISH_COEF[k] * component_k.

    Reference fixture in the tests gives M = -1.1197.
    """
    raise NotImplementedError


def beneish_flag(m: float, threshold: float = -1.78) -> bool:
    """True if m > threshold (likely manipulator)."""
    raise NotImplementedError


def piotroski_signals(cur: dict, prev: dict) -> dict:
    """The nine binary Piotroski (2000) signals as a dict of bools with keys
    F_ROA, F_CFO, F_dROA, F_ACCRUAL, F_dLEVER, F_dLIQUID, F_EQ_OFFER, F_dMARGIN, F_dTURN.

    Keys in cur/prev: net_income, cfo, total_assets, total_assets_begin, lt_debt,
    current_assets, current_liabilities, shares_out, sales, cogs.
    ROA = NI / TA_begin; CFO scaled by TA_begin; asset turnover = Sales / TA_begin;
    leverage = LTD / TA (end of year); current ratio = CA/CL; GM = (Sales-COGS)/Sales.
    F_EQ_OFFER is True when shares_out did not increase. Changes are strict (>, <).
    """
    raise NotImplementedError


def piotroski_f(cur: dict, prev: dict) -> int:
    """F-score = number of True signals (0..9). Reference fixture: 9; swapped years: 4."""
    raise NotImplementedError


def coffee_can_pass(rev, roce, min_g: float = 0.10, min_roce: float = 0.15) -> bool:
    """True iff revenue growth >= min_g in EVERY one of the 10 years AND ROCE >= min_roce every year.

    rev: 11 annual revenues, oldest first. roce: 10 annual ROCE values.
    """
    raise NotImplementedError


def cfo_to_pat(cfo, pat) -> float:
    """Cumulative cash conversion sum(CFO)/sum(PAT)."""
    raise NotImplementedError


def implied_cash_yield(interest_income: float, cash_begin: float, cash_end: float) -> float:
    """Interest income divided by average of opening and closing cash + liquid investments."""
    raise NotImplementedError