# screamer function index (name, short description)

# Functors
AD, Chaikin Accumulation/Distribution Line.
ADOSC, Difference of fast and slow EMA of the Accumulation/Distribution line.
ADX, Wilder's ADX with +DI and -DI (3 inputs -> 3 outputs).
ATR, Wilder-smoothed average of TrueRange.
Abs, Absolute value of each element.
Acos, Inverse cosine of each element (radians, input in [-1, 1]).
Add, Elementwise sum of two aligned streams (x + y).
And, Returns 1.0 if both inputs are nonzero, else 0.0. NaN in either input yields NaN.
Asin, Inverse sine of each element (radians, input in [-1, 1]).
Atan, Inverse tangent of each element (radians).
Atan2, Signed angle of (x, y) from the positive x-axis (numpy.arctan2 order).
BOP, (close - open) / (high - low) per bar. No smoothing.
BollingerBands, Mean +/- num_std rolling standard deviations.
Butter, General-order IIR Butterworth low-pass filter.
ButterBandpass, General-order IIR Butterworth band-pass filter.
ButterBandstop, General-order IIR Butterworth band-stop (notch) filter.
ButterHighpass, General-order IIR Butterworth high-pass filter (rejects low frequencies).
CCI, Commodity Channel Index over typical price.
Cart2Polar, Convert (x, y) to (r, theta).
Ceil, Round each element toward positive infinity.
Clip, Bound each element below and/or above.
CombineLatest, As-of latest-value join of N streams: one row per distinct index (same-index events coalesce).
Cos, Cosine of each element (radians).
Cube, x cubed (faster than Power(3)).
CumMax, Running maximum from t=0.
CumMin, Running minimum from t=0.
CumProd, Running product from t=0.
CumSum, Running sum from t=0.
DEMA, Mulloy's Double EMA: 2*EMA - EMA(EMA).
Detrend, x[t] minus its rolling mean.
Diff, x[t] - x[t-k] (first difference at lag k).
Diff2, Second-order finite difference (discrete second derivative).
Div, Elementwise quotient of two aligned streams (x / y).
DonchianChannels, Trend-following envelope: rolling max(high), rolling min(low), and midline.
Drawdown, Running drawdown from the cumulative peak.
Dropna, Drop events whose value is NaN.
Elu, Exponential linear unit.
Equal, Returns 1.0 if a == b, else 0.0. NaN in either input yields NaN.
Erf, Gauss error function.
Erfc, Complementary error function (1 - erf).
EwBeta, EW CAPM beta: cov(target, regressor) / var(regressor).
EwCorr, EW Pearson correlation of two parallel streams.
EwCov, EW covariance of two parallel streams.
EwGarmanKlassVar, Var form of the Garman-Klass range-based volatility estimator (OHLC).
EwGarmanKlassVol, Vol form of the Garman-Klass range-based volatility estimator (OHLC).
EwKurt, EW excess kurtosis.
EwMean, Exponentially-weighted moving average (pandas adjust=True).
EwParkinsonVar, Var form of the Parkinson range-based volatility estimator (uses high & low).
EwParkinsonVol, Vol form of the Parkinson range-based volatility estimator (uses high & low).
EwRms, EW root-mean-square.
EwRogersSatchellVar, Var form of the Rogers-Satchell drift-robust range-based estimator.
EwRogersSatchellVol, Vol form of the Rogers-Satchell drift-robust range-based estimator.
EwSkew, EW skewness.
EwStd, EW standard deviation.
EwVar, EW variance (pandas adjust=True bias-corrected).
EwZscore, Latest sample standardised by EW mean and std.
Exp, e to the power of each element.
ExpandingKurt, Running bias-corrected excess kurtosis (Fisher) over the whole history.
ExpandingMax, Running maximum from t=0.
ExpandingMean, Running mean over the whole history since the last reset.
ExpandingMin, Running minimum from t=0.
ExpandingProd, Running product from t=0.
ExpandingSkew, Running bias-corrected sample skewness (G1) over the whole history.
ExpandingSlope, Running OLS slope of the series against time over the whole history.
ExpandingStd, Running sample standard deviation (ddof=1) over the whole history.
ExpandingSum, Running sum from t=0.
ExpandingVar, Running sample variance (ddof=1) over the whole history.
Ffill, Replace NaN with the most recent finite value.
FillNa, Replace NaN with a user-specified scalar.
Filter, 2-input mask gate - keep each data value whose aligned mask is nonzero.
First, Latch the first finite value seen since reset.
Floor, Round each element toward negative infinity.
GreaterEqual, Returns 1.0 if a >= b, else 0.0. NaN in either input yields NaN.
GreaterThan, Returns 1.0 if a > b, else 0.0. NaN in either input yields NaN.
Hampel, Robust Hampel despiker, replace samples far from the window median (in MAD units).
HullMA, Hull's responsive MA: WMA(2*WMA(n/2) - WMA(n), sqrt(n)).
Hypot, Euclidean distance sqrt(x^2 + y^2), numerically stable.
Identity, Pass-through (y = x).
ImpulseClip, Causal impulse remover, detects spikes on the trend-free first difference.
Input, Create a source Node - a named placeholder for a timed stream.
IsFinite, Returns 1.0 for finite values, 0.0 for NaN or inf. Does not propagate NaN.
IsNan, Returns 1.0 if the input is NaN, else 0.0. Does not propagate NaN.
KAMA, Adaptive MA whose smoothing constant responds to the efficiency ratio.
KalmanFilter, Scalar 1-D Kalman filter for a noisy random-walk model.
KeltnerChannels, Volatility-adapted envelope: EMA midline plus/minus a multiple of ATR.
Lag, Output is the input delayed by k samples.
Last, Return the most recent finite value seen since reset.
LessEqual, Returns 1.0 if a <= b, else 0.0. NaN in either input yields NaN.
LessThan, Returns 1.0 if a < b, else 0.0. NaN in either input yields NaN.
Linear, Affine transform: scale * x + shift.
Linear2, Two-input affine combination: a*x + b*y + c.
Log, Natural logarithm of each element.
LogReturn, log(x[t] / x[t-k]) - log return at lag k.
MACD, MACD line, signal line, and histogram (3 outputs).
MFI, Volume-weighted analogue of RSI on the typical price.
MaxDrawdown, Worst drawdown experienced so far (since reset).
Merge, Merge N value streams into one index-sorted (values, sources, index).
Momentum, x[t] - x[t-k], TA-Lib's MOM. Mathematically identical to Diff(k).
MovingAverage, Finite-impulse-response filter with user-supplied taps.
Mul, Elementwise product of two aligned streams (x * y).
NATR, ATR scaled to a percentage of the current close.
NegPart, Negative part of x: max(-x, 0).
Node, An immutable handle for a stream in a computation graph.
Not, Returns 0.0 if input is nonzero, 1.0 if zero. NaN propagates.
NotEqual, Returns 1.0 if a != b, else 0.0. NaN in either input yields NaN.
OBV, On-Balance Volume: signed cumulative volume by close-direction (Granville, 1963).
Or, Returns 1.0 if either input is nonzero, else 0.0. NaN in either input yields NaN.
Pipeline, A reusable N-in / M-out function you define once and call on stored or live data.
Polar2Cart, Convert (r, theta) to (x, y).
PosPart, Positive part of x: max(x, 0).
Power, x raised to a fixed exponent p.
ROC, 100 * (x[t] / x[t-k] - 1) - TA-Lib's ROC.
ROCP, x[t] / x[t-k] - 1 - TA-Lib's ROCP. Identical to Return.
ROCR, x[t] / x[t-k] - TA-Lib's ROCR.
Relu, Rectified linear unit: max(0, x).
Resample, Causal windowed downsample of a 1-D or multi-column value stream.
Return, (x[t] - x[t-k]) / x[t-k] - the simple percentage return at lag k.
RollingAlpha, Rolling OLS intercept of target on regressor (companion to RollingBeta).
RollingArgmax, Window-offset of the trailing-window maximum (TA-Lib MAXINDEX).
RollingArgmin, Window-offset of the trailing-window minimum (TA-Lib MININDEX).
RollingBeta, cov(x, y) / var(y) - regression slope of x on y.
RollingCalmar, Annualised return divided by the worst rolling drawdown.
RollingCorr, Rolling Pearson correlation of two parallel streams.
RollingCov, Rolling sample covariance of two parallel streams.
RollingGarmanKlassVar, Var form of the Garman-Klass range-based volatility estimator (OHLC).
RollingGarmanKlassVol, Vol form of the Garman-Klass range-based volatility estimator (OHLC).
RollingHitRate, Fraction of strictly-positive samples in a trailing window.
RollingHurst, Rolling-window Hurst exponent via Anis-Lloyd corrected rescaled-range analysis.
RollingInfoRatio, Annualised information ratio: Sharpe of active returns against a benchmark.
RollingIqr, Q3 minus Q1 over the trailing window.
RollingKurt, Trailing-window excess kurtosis.
RollingLinearRegression, Full OLS fit returning (slope, intercept, r_squared, stderr).
RollingMad, Trailing-window mean absolute deviation from the rolling mean.
RollingMax, Trailing-window maximum (monotonic deque).
RollingMaxDrawdown, Worst peak-to-trough drawdown inside a trailing window.
RollingMean, Trailing-window arithmetic mean (simple moving average).
RollingMedian, Trailing-window median.
RollingMedianAD, Rolling median absolute deviation, median(|x - median|), a robust scale estimate.
RollingMin, Trailing-window minimum (monotonic deque).
RollingMinMax, Trailing-window (min, max) returned as a 2-tuple per step.
RollingOU, Rolling MLE fit of a mean-reverting Ornstein-Uhlenbeck process.
RollingParkinsonVar, Var form of the Parkinson range-based volatility estimator (uses high & low).
RollingParkinsonVol, Vol form of the Parkinson range-based volatility estimator (uses high & low).
RollingPercentile, Percentile (rank/window) of the current value in the trailing window.
RollingPoly1, OLS fit y = a + b*t over a trailing window. derivative_order selects value/slope/zero.
RollingPoly2, OLS fit y = a + b*t + c*t^2 over a trailing window.
RollingQuantile, Trailing-window quantile (e.g. 0.25 = lower quartile).
RollingRSI, Relative Strength Index. Wilder's smoothing by default; Cutler's via method='cutler'.
RollingRange, Trailing-window max minus min.
RollingRank, Rank of the current value within the trailing window (1-based, average tie rule).
RollingResidualStd, Standard deviation of the rolling-hedge-adjusted residual y - beta*x.
RollingRms, Trailing-window root-mean-square.
RollingRogersSatchellVar, Var form of the Rogers-Satchell drift-robust range-based estimator.
RollingRogersSatchellVol, Vol form of the Rogers-Satchell drift-robust range-based estimator.
RollingSharpe, Annualised Sharpe ratio over a trailing window of returns.
RollingSigmaClip, Replace samples outside [mean - lower*std, mean + upper*std] with NaN or the clipped bound.
RollingSkew, Trailing-window skewness.
RollingSortino, Annualised Sortino ratio: Sharpe with downside-only deviation.
RollingSpread, x - beta(x,y) * y - hedge-adjusted residual.
RollingStd, Trailing-window sample standard deviation (ddof=1).
RollingSum, Trailing-window sum.
RollingTSF, Linear regression of y on time, projected one step ahead. TA-Lib's TSF.
RollingVWAP, Rolling volume-weighted average price (typical-price weighted).
RollingVar, Trailing-window sample variance (ddof=1).
RollingYangZhangVar, Var form of the Yang-Zhang estimator (drift + gap robust).
RollingYangZhangVol, Vol form of the Yang-Zhang estimator (drift + gap robust).
RollingZscore, Latest sample standardised by trailing-window mean and std.
Round, Round each element to the nearest integer (half-to-even).
SchmittTrigger, Hysteresis comparator. Latches 1.0 above the upper threshold, 0.0 below the lower threshold, and retains its previous value in between.
Select, Pick column(s) from a wide (M, N) value stream.
Selu, Scaled exponential linear unit (self-normalizing networks).
Sigmoid, Logistic sigmoid: 1 / (1 + exp(-x)).
Sign, Sign of each element: -1, 0, or +1.
Sin, Sine of each element (radians).
Softsign, Softsign: x / (1 + |x|).
Sqrt, Square root of each element.
Square, x squared (faster than Power(2)).
Stoch, Stochastic oscillator %K and %D (3 inputs -> 2 outputs).
StochRSI, Stochastic applied to RSI (1 input -> 2 outputs).
Sub, Elementwise difference of two aligned streams (x - y).
TEMA, Mulloy's Triple EMA: 3*EMA - 3*EMA(EMA) + EMA(EMA(EMA)).
TRIMA, Triangular MA: SMA of an SMA. Heavier center-weighting than WMA.
TRIX, ROC of a triple-smoothed EMA.
Tanh, Hyperbolic tangent.
TrueRange, Per-bar true range accounting for overnight gaps (Wilder, 1978).
UltimateOscillator, Three-period weighted oscillator (Williams, 1976).
WMA, Linearly-weighted moving average. O(1) per step.
Where, Returns a if mask is nonzero, b otherwise. NaN mask yields NaN.
WilliamsR, Inverse stochastic oscillator in [-100, 0].
from_pandas, Convert a pandas Series or DataFrame to a (values, index) tuple.
split, Partition a merged tagged stream back into per-source streams.
stream-tuple-convention, The (values, index) tuple at every operator boundary.
to_pandas, Convert a (values, index) tuple to a pandas Series or DataFrame.

# Stream operators
Merge, Index-sorted N-way merge.
CombineLatest, As-of latest-value join.
Dropna, Drop NaN events.
Filter, 2-input mask gate: keep each data value whose aligned mask is nonzero (zero or NaN drops).
Select, Pick columns from a wide stream.
split, Partition a merged tagged stream back into per-source streams.
Resample, Causal windowed downsample.

# Pipeline
Input, Create a source Node - a named placeholder for a timed stream.
Pipeline, A positional N-in / M-out callable that evaluates a computation graph.
Node, An immutable handle for a stream in a computation graph.
