hgraph-analytics

C++-first numerical analytics for hgraph. The package owns the numerical analytical family migrated from core—diff, count, clip, ewma, and pct_change—plus quantile, shaped-array array_std, shaped-array rolling_window, window_values, array_get_item, cumulative_sum, correlation, generic std/var, trailing rolling_mean, scheduled resample, and the EWMA parameter conversion helpers.

For installation, semantics, and Python/C++ usage patterns, see the analytics user guide. Runnable examples covering scalar pipelines, rolling windows, and shaped-array analytics are in python/examples.

import hgraph as hg
import hgraph_analytics as hga

change = hga.pct_change(
    value,
    period=12,
    divide_by_zero=hg.DivideByZero.NAN,
)

bounded = hga.clip(change, -0.25, 0.25)
smoothed = hga.ewma(bounded, alpha=0.2)
median = hga.quantile(observations, 0.5)
volatility = hga.array_std(observations, ddof=1)
recent = hga.rolling_window(value, period=20, min_window_period=5)
window = hga.window_values(hg.to_window(value, 20))
running = hga.cumulative_sum(observations, axis=0)
correlation = hga.correlation(observations, rowvar=False)
sample_volatility = hga.std(hg.to_window(change, 20, 20), ddof=1)
moving_average = hga.rolling_mean(value, period=20)

The result is fractional: 0.05 denotes five percent. period counts valid source observations and must be positive. The operator does not infer dataframe ordering, elapsed-time sampling, market sessions, or financial price adjustment.

Native consumers link hgraph::analytics, call hgraph::analytics::register_analytics_operators(), and wire the markers in hgraph::analytics, including diff, count, clip, ewma, pct_change, window_values, array_get_item, cumulative_sum, correlation, quantile, array_std, rolling_window, std_, var_, rolling_mean, and resample.

See the hgraph user-guide migration note for the complete Python and C++ name mapping from the former core API.

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