v0.1.0

Added

• quackz.returns: the position and cost conventions the whole library is built on. The
  position at bar t is decided at that bar's close and earns bar t+1's return; costs are
  charged on |pos[t-1] - pos[t-2]|, including the trade into the first position and the
  trade out of the last.
• quackz.metrics: Sharpe, Sortino, Calmar, maximum drawdown, ulcer index, CVaR,
  annualized return and volatility, method-of-moments skewness and non-excess kurtosis,
  the probabilistic Sharpe ratio, the expected maximum Sharpe of a search, the deflated
  Sharpe ratio, minimum track record length, a Newey-West t-statistic and the lag-1
  autocorrelation. Normal cdf and inverse cdf come from the standard library, so scipy is
  not a runtime dependency.
• quackz.checks: reconciliation against a claimed return stream, latency sensitivity
  graded on the level at zero delay and on how much of that level survives one bar of
  delay against what the holding period implies, a cost sweep with a closed-form
  break-even, a stationary bootstrap with a null-imposed p-value and a studentized
  interval, the noise floor of a declared search, subperiod stability with a noise-aware
  verdict, and profit concentration.
• quackz.splits: WalkForward and EmbargoedKFold, both following the scikit-learn
  splitter protocol without importing scikit-learn.
• quackz.evaluate.evaluate: one call that runs every check against a single price and
  position series and returns a frozen Evaluation, including the deflated Sharpe as a
  function of the number of trials and the break-even trial count.
• quackz.report: text_report, markdown_report and json_report, all built from one
  set of verdict sentences so the three formats cannot disagree.
• quackz.cli: quackz report DATA.csv, with additive --json and --md output and
  exit codes 0 for pass or warn, 1 for a failing verdict, 2 for anything the command could
  not run at all, including a file it could not open or decode. The dispersion of the
  search is reachable from the command line as well as from Python: --trial-sharpes PATH
  reads the annualized Sharpe of every configuration the search touched, and
  --var-trial-sharpes takes that spread already summarised.
• Configurable grading: every cut-off is a named constant with its reasoning beside it,
  gathered into Thresholds and overridable through evaluate(thresholds=...).
• Examples: examples/overfit_demo.py and examples/momentum_demo.py, both seeded,
  offline and deterministic.