Analytics helpers

quantalytics.analytics bundles every descriptive and performance-centric helper into a consistent namespace so you can explore returns with minimal boilerplate.

The module covers basic stats (skewness, kurtosis, total_return, cagr, volatility) and also exposes specialties you just added, such as:

• Risk balance – payoff_ratio, profit_ratio, win_loss_ratio, profit_factor, risk_of_ruin, and gain_to_pain_ratio help you understand how wins compare to losses and what it would take to stay solvent.
• Streaks & consistency – max_consecutive_wins/losses, avg_win, avg_loss, and win_rate make it easy to quantify the cadence of profitable periods.
• Advanced ratios – omega, tail_ratio, common_sense_ratio, information_ratio, and r_squared highlight distributional skew, tail leverage, benchmark tracking, and explained variance.

Every helper works with pandas Series (or any iterable) and returns float results with consistent NaN/inf semantics. Use them alongside qa.metrics and qa.charts so you can summarize, visualize, and report on performance from a single code path.