finkritq

Deterministic portfolio quant core: risk, performance, optimization, and tax analytics over holdings you supply. Pure numpy and scipy, no agent or web dependency. This is the open core of the finkrit stack, published on its own for use as a standalone library.

Install

pip install finkritq              # core: numpy + scipy
pip install "finkritq[data]"      # adds the live yfinance market data provider

What it does

• Risk: volatility, variance, semivariance, downside deviation, drawdown and maximum drawdown, value at risk and conditional value at risk, beta, marginal and component contribution to risk, forward return range.
• Performance: total and annualized return, Sharpe, Sortino, Calmar, information ratio, Jensen's alpha, contribution to return, net of fees, time and money weighted return, Brinson attribution.
• Optimization: mean variance weights (minimum variance, maximum Sharpe, target return), long only and box constrained, Ledoit-Wolf shrinkage, rebalancing to a target or a policy.
• Tax: tax lots, tax-loss harvest candidates, tax-aware rebalancing.

Quickstart

from datetime import date
from decimal import Decimal

from finkritq.asset import Stock
from finkritq.datatype import Currency, Exchange
from finkritq.portfolio import Portfolio, Position, TaxLot
from finkritq.anal.risk import portfolio_volatility

stock = Stock(ticker="AAPL", currency=Currency.USD, exchange=Exchange.NASDAQ, company_name="Apple Inc")
lot = TaxLot(id="lot-1", quantity=Decimal("100"), cost_per_share=Decimal("150"), acquired=date(2022, 1, 3))
portfolio = Portfolio(id="p1", name="Demo", positions=[Position(id="pos-1", asset=stock, lots=(lot,))])

Feed it your own price history, or install the data extra to pull live daily closes through the bundled provider.

Runnable demo

python -m finkritq              # seeded, offline synthetic market
python -m finkritq real NVDA KO PG --benchmark SPY --years 3    # needs [data]

License

Apache-2.0. See LICENSE.