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"""

Data models for MF Portfolio Analysis Tool.

"""
from dataclasses import dataclass, field
from typing import Optional


@dataclass
class Fund:
    """Represents a single mutual fund scheme from the fund universe CSV."""
    name: str
    category: str
    benchmark: str

    # Cost
    ter: Optional[float] = None          # Total Expense Ratio (%)
    turnover: Optional[float] = None     # Portfolio Turnover (%)

    # Returns
    mean: Optional[float] = None
    cagr_1y: Optional[float] = None
    cagr_1y_cat: Optional[float] = None
    cagr_1y_bm: Optional[float] = None
    cagr_3y: Optional[float] = None
    cagr_3y_cat: Optional[float] = None
    cagr_3y_bm: Optional[float] = None
    cagr_5y: Optional[float] = None
    cagr_5y_cat: Optional[float] = None
    cagr_5y_bm: Optional[float] = None
    cagr_10y: Optional[float] = None
    cagr_10y_cat: Optional[float] = None
    cagr_10y_bm: Optional[float] = None
    cagr_inception: Optional[float] = None
    nav: Optional[float] = None

    # Valuation
    pe_ratio: Optional[float] = None
    pb_ratio: Optional[float] = None

    # Risk metrics
    alpha: Optional[float] = None
    beta: Optional[float] = None
    std_dev: Optional[float] = None
    sharpe: Optional[float] = None
    volatility: Optional[float] = None
    sortino: Optional[float] = None
    up_capture: Optional[float] = None
    down_capture: Optional[float] = None
    max_drawdown: Optional[float] = None
    r_squared: Optional[float] = None
    info_ratio: Optional[float] = None
    aum: Optional[float] = None
    fill_status: Optional[str] = None

    # Scoring (computed)
    score: Optional[float] = None
    rank_in_category: Optional[int] = None
    is_top_quartile: bool = False
    weightage: Optional[int] = None  # Number of periods beating benchmark
    order: int = 0  # Preserves original CSV insertion order for sort tiebreaker


@dataclass
class ClientHolding:
    """Represents a single mutual fund holding in a client's portfolio."""
    scheme_name: str
    current_value: float
    invested_amount: Optional[float] = None
    sip_amount: Optional[float] = None
    sip_frequency: Optional[str] = None   # Monthly / Quarterly etc.

    # Matched fund data
    fund: Optional[Fund] = None

    # Computed
    allocation_pct: float = 0.0
    xirr: Optional[float] = None
    is_underperforming: bool = False

    # Advisory
    suggested_fund: Optional[Fund] = None
    switch_reason: Optional[str] = None


@dataclass
class Client:
    """Client details."""
    name: str
    age: Optional[int] = None
    email: Optional[str] = None
    mobile: Optional[str] = None
    pan: Optional[str] = None


@dataclass
class Advisor:
    """Financial advisor details."""
    name: str = "RAVICHANDRAN"
    phone: str = "9281364703"
    email: str = "c4c.ravi@gmail.com"
    arn: str = "ARN-243354"
    location: str = "Chennai"


@dataclass
class PortfolioReport:
    """The complete portfolio analysis report for a client."""
    client: Client
    advisor: Advisor
    holdings: list = field(default_factory=list)

    # Portfolio-level metrics
    total_current_value: float = 0.0
    total_invested: float = 0.0
    unrealized_gain: float = 0.0
    portfolio_xirr: Optional[float] = None
    sharpe: Optional[float] = None
    alpha: Optional[float] = None
    beta: Optional[float] = None
    std_dev: Optional[float] = None

    # Exposure warnings
    amc_exposure: dict = field(default_factory=dict)      # AMC -> pct
    scheme_exposure: dict = field(default_factory=dict)   # scheme -> pct
    exposure_warnings: list = field(default_factory=list) # list of warning strings

    # Allocation
    market_cap_allocation: dict = field(default_factory=dict)  # Large/Mid/Small/Other -> pct
    sector_allocation: dict = field(default_factory=dict)      # sector -> pct

    # Wealth projection
    wealth_projection: dict = field(default_factory=dict)  # years -> projected value