CATEGORY

Statistics nodes

11 Statistics building blocks in the Algovex node library. Each is a node you can drop on the visual canvas, connect into a strategy, backtest and export. No code required.

  • Correlation

    Pearson correlation between two configurable data series over a rolling window. Outputs correlation coefficient, R-squared, and strength classification.

  • Linear Regression

    Least-squares linear regression with forecast line, standard-error channel, normalized slope, R-squared goodness of fit, and trend classification.

  • Momentum Phase

    Detects Wyckoff-style market phases using price momentum and volume patterns. Accumulation: flat price + rising volume. Markup: rising price + rising volume. Distribution: flat price + falling volume. Markdown: falling price + falling volume.

  • Percentile Rank

    Computes the percentile rank of current value within a lookback window (0-100). Classifies conditions as overbought, oversold, or normal.

  • Rolling Quantile

    Computes upper and lower quantile values within a rolling window to form a dynamic channel. Outputs channel width percentage and price position.

  • Spread Z-Score

    Pairs trading via OLS hedge ratio and spread z-score. Set secondarySymbol for real two-asset pairs, or leave empty for SMA proxy.

  • Trend/Range Regime

    Classifies market state as trending, ranging, or volatile using ADX and ATR. Trending: ADX > threshold. Ranging: ADX < threshold and low ATR. Volatile: low ADX but high ATR. Use as a filter to select appropriate strategy type.

  • Variance Ratio

    Tests the random walk hypothesis by comparing variance of multi-period returns to scaled single-period variance. Configurable trending/reverting thresholds.

  • Variance (Returns)

    Rolling sample variance and standard deviation of percentage returns with variance-ratio regime classification and optional annualized volatility.

  • Volatility State

    Classifies volatility regime as low, medium, or high by computing ATR percentile over a lookback window. Low volatility often precedes breakouts. High volatility often precedes mean reversion. Use as filter.

  • Z-Score

    Measures how many standard deviations price is from the rolling mean. Outputs z-score value, Bollinger-style bands, and condition classification.

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