signalflow-ta 0.7.0

Technical analysis extension for signalflow-trading.

signalflow-ta

Technical analysis extension for SignalFlow — 290+ indicators + 24 signal detectors

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Part of the SignalFlow ecosystem.

290+ technical analysis indicators organized into 11 modules, 24 signal detectors with configurable filters, and AutoFeatureNormalizer for automatic normalization.

Installation

pip install signalflow-ta

Requires: Python ≥ 3.12, signalflow-trading ≥ 0.5.0, pandas-ta ≥ 0.4.67b0

Usage

import signalflow.ta as ta

from signalflow.ta.momentum import RsiMom, MacdMom
from signalflow.ta.volatility import BollingerVol
from signalflow.ta.signals import BollingerBandDetector1, RsiZscoreFilter

# Use as features
rsi = RsiMom(period=14)
macd = MacdMom(fast=12, slow=26, signal=9)
bb = BollingerVol(period=20, std_dev=2.0)

# Use as detector with filters
detector = BollingerBandDetector1(
    period=20, std=2.0, rsi_period=14,
    direction="long",
    filters=[RsiZscoreFilter(threshold=-1.5)]
)
signals = detector.run(raw_data_view)

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Indicators

Momentum (18)

Class	SF Name	Description	Parameters
RsiMom	momentum/rsi	Relative Strength Index	period, normalized
RocMom	momentum/roc	Rate of Change	period, normalized, norm_period
MomMom	momentum/mom	Momentum (price difference)	period, normalized, norm_period
CmoMom	momentum/cmo	Chande Momentum Oscillator	period, normalized
StochMom	momentum/stoch	Stochastic Oscillator	k_period, d_period, smooth_k
StochRsiMom	momentum/stochrsi	Stochastic RSI	rsi_period, stoch_period, k_period, d_period
WillrMom	momentum/willr	Williams %R	period
CciMom	momentum/cci	Commodity Channel Index	period, constant
UoMom	momentum/uo	Ultimate Oscillator	fast, medium, slow
AoMom	momentum/ao	Awesome Oscillator	fast, slow
MacdMom	momentum/macd	MACD	fast, slow, signal
PpoMom	momentum/ppo	Percentage Price Oscillator	fast, slow, signal
TsiMom	momentum/tsi	True Strength Index	fast, slow, signal
TrixMom	momentum/trix	Triple Exponential Average ROC	period, signal
AccelerationMom	momentum/acceleration	Price Acceleration (2nd derivative)	lag, smooth
JerkMom	momentum/jerk	Price Jerk (3rd derivative)	lag, smooth
AngularMomentumMom	momentum/angular_momentum	Angular Momentum	period, ma_period
TorqueMom	momentum/torque	Torque (dL/dt)	period, ma_period, torque_lag

Overlap / Smoothing (27)

Class	SF Name	Description
SmaSmooth	smooth/sma	Simple Moving Average
EmaSmooth	smooth/ema	Exponential Moving Average
WmaSmooth	smooth/wma	Weighted Moving Average
RmaSmooth	smooth/rma	Wilder's Smoothed MA
DemaSmooth	smooth/dema	Double EMA
TemaSmooth	smooth/tema	Triple EMA
HmaSmooth	smooth/hma	Hull Moving Average
TrimaSmooth	smooth/trima	Triangular MA
SwmaSmooth	smooth/swma	Symmetric Weighted MA
SsfSmooth	smooth/ssf	Ehlers Super Smoother
KamaSmooth	smooth/kama	Kaufman Adaptive MA
AlmaSmooth	smooth/alma	Arnaud Legoux MA
JmaSmooth	smooth/jma	Jurik MA
VidyaSmooth	smooth/vidya	Variable Index Dynamic Average
T3Smooth	smooth/t3	Tillson T3
ZlmaSmooth	smooth/zlma	Zero Lag MA
McGinleySmooth	smooth/mcginley	McGinley Dynamic
FramaSmooth	smooth/frama	Fractal Adaptive MA
KalmanSmooth	smooth/kalman	Adaptive Kalman Filter
LinRegPriceDiff	trend/linreg_price_diff	Price diff from regression
Hl2Price	price/hl2	High-Low Midpoint
Hlc3Price	price/hlc3	Typical Price
Ohlc4Price	price/ohlc4	OHLC Average
WcpPrice	price/wcp	Weighted Close Price
TypicalPrice	price/typical	Configurable Weighted Price
MidpointPrice	price/midpoint	Rolling Midpoint
MidpricePrice	price/midprice	Donchian Channel Midline

Volatility (19)

Class	SF Name	Description
TrueRangeVol	volatility/true_range	True Range
AtrVol	volatility/atr	Average True Range
NatrVol	volatility/natr	Normalized ATR (% of price)
BollingerVol	volatility/bollinger	Bollinger Bands
KeltnerVol	volatility/keltner	Keltner Channels
DonchianVol	volatility/donchian	Donchian Channels
AccBandsVol	volatility/accbands	Acceleration Bands
MassIndexVol	volatility/mass_index	Mass Index
UlcerIndexVol	volatility/ulcer_index	Ulcer Index
RviVol	volatility/rvi	Relative Volatility Index
GapVol	volatility/gaps	Gap Volatility
KineticEnergyVol	volatility/kinetic_energy	Kinetic Energy (½v²)
PotentialEnergyVol	volatility/potential_energy	Potential Energy
TotalEnergyVol	volatility/total_energy	Total Mechanical Energy
EnergyFlowVol	volatility/energy_flow	Energy Flow Rate (dE/dt)
ElasticStrainVol	volatility/elastic_strain	Elastic Strain
TemperatureVol	volatility/temperature	Market Temperature
HeatCapacityVol	volatility/heat_capacity	Heat Capacity
FreeEnergyVol	volatility/free_energy	Helmholtz Free Energy

Volume (16)

Class	SF Name	Description
ObvVolume	volume/obv	On Balance Volume
AdVolume	volume/ad	Accumulation/Distribution
PvtVolume	volume/pvt	Price Volume Trend
NviVolume	volume/nvi	Negative Volume Index
PviVolume	volume/pvi	Positive Volume Index
MfiVolume	volume/mfi	Money Flow Index
CmfVolume	volume/cmf	Chaikin Money Flow
EfiVolume	volume/efi	Elder Force Index
EomVolume	volume/eom	Ease of Movement
KvoVolume	volume/kvo	Klinger Volume Oscillator
MarketForceVolume	volume/market_force	Market Force (F = ma)
ImpulseVolume	volume/impulse	Market Impulse (∑F·dt)
MarketMomentumVolume	volume/market_momentum	Market Momentum (p = mv)
MarketPowerVolume	volume/market_power	Market Power (P = Fv)
MarketCapacitanceVolume	volume/market_capacitance	Market Capacitance
GravitationalPullVolume	volume/gravitational_pull	Gravitational Pull

Trend (28)

Class	SF Name	Description
AdxTrend	trend/adx	Average Directional Index
AroonTrend	trend/aroon	Aroon Indicator
VortexTrend	trend/vortex	Vortex Indicator
VhfTrend	trend/vhf	Vertical Horizontal Filter
ChopTrend	trend/chop	Choppiness Index
ViscosityTrend	trend/viscosity	Viscosity
ReynoldsTrend	trend/reynolds	Reynolds Number
RotationalInertiaTrend	trend/rotational_inertia	Rotational Inertia
MarketImpedanceTrend	trend/market_impedance	Market Impedance
RCTimeConstantTrend	trend/rc_time_constant	RC Time Constant
SNRTrend	trend/snr	Signal-to-Noise Ratio
OrderParameterTrend	trend/order_parameter	Order Parameter
SusceptibilityTrend	trend/susceptibility	Susceptibility
PsarTrend	trend/psar	Parabolic SAR
SupertrendTrend	trend/supertrend	Supertrend
ChandelierTrend	trend/chandelier	Chandelier Stop
HiloTrend	trend/hilo	HiLo Channel
CkspTrend	trend/cksp	Coppock Curve
IchimokuTrend	trend/ichimoku	Ichimoku Kinko Hyo
DpoTrend	trend/dpo	Detrended Price Oscillator
QstickTrend	trend/qstick	Qstick
TtmTrend	trend/ttm	TTM Trend
WilliamsAlligatorRegime	trend/alligator_regime	Williams Alligator
TwoMaRegime	trend/two_ma_regime	Two MA Crossover Regime
SmaDirection	trend/sma_direction	SMA Direction
SmaDiffDirection	trend/sma_diff_direction	SMA Difference Direction
LinRegDirection	trend/linreg_direction	Linear Regression Direction
LinRegDiffDirection	trend/linreg_diff_direction	LinReg Difference Direction

Statistics (75+)

Dispersion: Variance, Std, MAD, Z-Score, CV, Range, IQR, AAD, Robust Z-Score

Distribution: Median, Quantile, Percentile Rank, Min-Max, Skewness, Kurtosis, Entropy, Jarque-Bera, Mode Distance, Above Mean Ratio, Entropy Rate

Memory: Hurst Exponent, Autocorrelation, Variance Ratio, Diffusion Coefficient, Anomalous Diffusion, MSD Ratio, Spring Constant, Damping Ratio, Natural Frequency, Plastic Strain, Escape Velocity, Correlation Length

Cycle: Inst. Amplitude, Phase, Frequency (Hilbert), Phase Acceleration, Constructive Interference, Beat Frequency, Standing Wave Ratio, Spectral Centroid, Spectral Entropy

Regression: Correlation, Beta, R-Squared, LinReg Slope/Intercept/Residual

Realized Vol: Realized, Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang

Complexity: Permutation Entropy, Sample Entropy, Lempel-Ziv, Fisher Information, DFA

Information Theory: KL Divergence, JS Divergence, Rényi Entropy, Auto Mutual Info, Relative Info Gain

DSP: Spectral Bandwidth, Slope, Kurtosis, Contrast, MFCC Band Energy

Structure: Reverse Points, Time Since Spike, Volatility Spike, Volume Spike, Rolling Min/Max

Performance (2) & Divergence (2)

Class	SF Name	Description
LogReturn	perf/log_ret	Logarithmic Returns
PctReturn	perf/pct_ret	Percentage Returns
RsiDivergence	divergence/rsi	RSI Divergence (regular & hidden)
MacdDivergence	divergence/macd	MACD Divergence (regular & hidden)

Microstructure (14) — NEW

Candle geometry features added from sf-profit feature research. Strongest single-axis tested — BodyToRangeRatio and WickToBodyRatio rank in the top-10 of walk-forward IV pool.

Class	Module	Description
BodyToRangeRatio	microstructure/candles	|body| / range rolling mean (0=doji, 1=marubozu)
ClosePositionInBar	microstructure/candles	(close-low) / (high-low) ∈ [0,1]
HiLoMedianGap	microstructure/candles	(high+low)/2 - close rolling
LowerWickPersistence	microstructure/candles	fraction of bars with lower_wick > |body|
UpperWickPersistence	microstructure/candles	fraction of bars with upper_wick > |body|
WickAsymmetry	microstructure/candles	(upper-lower) / (upper+lower) ∈ [-1,+1]
WickToBodyLower	microstructure/candles	lower_wick / |body| rolling mean
WickToBodyRatio	microstructure/candles	(upper+lower) / |body| rolling mean
WickToBodyUpper	microstructure/candles	upper_wick / |body| rolling mean
HighLowImbalance	microstructure/ranges	signed position in recent (max-min) range
RangeExpansion	microstructure/ranges	(high-low) / SMA(high-low) - 1
RangeFragmentation	microstructure/ranges	sum_abs_returns / (max-min) over window
RangeNormalizedReturn	microstructure/ranges	return / prev_bar_range, rolling mean
SignedRollingRange	microstructure/ranges	signed position by ATR

Path-shape (15) — NEW

Geometric properties of price path: roughness, efficiency, streaks, entropy.

Class	Module	Description
PathRoughness	path_shape/shape	std(|return|) / mean(|return|) — CV of move size
PathEfficiency	path_shape/shape	Kaufman ER: net move / total path
PathTortuosity	path_shape/shape	total path / (max - min)
PathSimplicity	path_shape/shape	|net_return| / sum_abs_returns ∈ [0,1]
MaxConsecutiveGainRun	path_shape/streaks	longest streak of up-bars in window
MaxConsecutiveLossRun	path_shape/streaks	longest streak of down-bars in window
LongestStreak	path_shape/streaks	max(gain_run, loss_run) per window
ReversalCount	path_shape/streaks	count of local extrema
ZeroCrossingRate	path_shape/streaks	return sign flip rate
ReturnSignEntropy	path_shape/entropy	Shannon entropy of return signs
DirectionalEntropy	path_shape/entropy	entropy of (sign × magnitude_quintile) joint
VolumeEntropy	path_shape/entropy	entropy of volume distribution (10 quantized bins)
ReturnAutocorrShort	path_shape/autocorr	rolling autocorr(returns, lag)
VolatilityClusterScore	path_shape/autocorr	autocorr(|returns|) — GARCH proxy
ErrorAutoCorrelation	path_shape/autocorr	rolling autocorr of close - SMA(period)

Filters (7) — NEW

close - filter(close) residuals across various filters. Useful in mean-reversion and PID-style control strategies.

Class	Module	Description
AdaptiveEMAError	filters/smoother_errors	close - EMA(period). PID-P proxy
DEMAError	filters/smoother_errors	close - DEMA. Less lag than EMA
TEMAError	filters/smoother_errors	close - TEMA. Even less lag
HMAError	filters/smoother_errors	close - HMA. Hull-style smoother residual
KalmanResidual	filters/kalman	1D Kalman filter innovation (predictive residual)
PIDIntegralTerm	filters/pid	Rolling sum of (close - SMA). PID-I
PIDDerivativeTerm	filters/pid	Derivative of (close - SMA). PID-D

Cross-sectional (15) — NEW

Operate on full multi-pair DataFrames. Rank-based variants work robustly on 3+ pairs; distributional variants (skew, breadth) benefit from 5+ pairs.

Class	Description
CrossSectionalReturnRank	Rank of pair's return across pairs
CrossSectionalAtrRank	Rank of pair's ATR across pairs
CrossSectionalAdxRank	Rank of pair's |returns|-sum (ADX proxy)
CrossSectionalRangeRank	Rank of pair's (high-low) range
CrossSectionalRsiRank	Rank of pair's RSI proxy
CrossSectionalVolRank	Rank of pair's realized vol
CrossSectionalReturnAccelRank	Rank of pair's return acceleration
CrossSectionalBeta	Rolling β to market median
AvgPairwiseCorrMarket	Rolling corr(pair, market_median)
PairLeadLagCorr	Rolling corr(pair_t, market_{t-lag}) — leads/lags
CrossSectionalDispersion	Mean |pair_ret - market_mean| across pairs
CrossSectionalRetSkew	Skewness of returns across pairs (needs 5+)
MarketBreadth	Fraction of pairs with positive return ∈ [0,1]
RelativeStrengthVsMarket	z-score of pair vs cross-section
PairExcessReturn	pair_ret − market_mean_ret
DivergenceFromMarketMedian	pair_ret − market_median_ret

Stat / Volatility / Volume / Momentum / Trend extensions

Each existing module gained new classes from sf-profit feature research. Selected highlights:

Regression (sf-profit iter-15/18/20) — in stat: LinRegSlopeWindow, LinRegR2, LinRegResidualStd, LinRegSlopeChange, LinRegSlopeAcceleration, Poly2ResidualStd, LinRegResidualSkew/Kurtosis, LinRegSlopeRatio, LinRegSlopeNormalized, LinRegInterceptNormalized, LinRegFitQuality.

Distribution (sf-profit iter-20) — in stat: ReturnSkewWindow, ReturnKurtosisWindow, ReturnTailRatio.

Volatility (sf-profit iter-3.1/15/16/20) — in volatility: NatrRatio, NatrPctRank, VolOfVol, RealizedVolPctRank, RealizedVolRatio, ParkinsonZScore, ParkinsonAccel, ParkinsonVolRatio, AltVolDeviation, GarmanKlassRatio, GarmanKlassPctRank, PriceZAtr.

Volume×price coupling (sf-profit iter-15/16/18/20) — in volume: PriceImpactPerUnit, VWAPDeviation, SignedVolumeAccumulation, AbsReturnVolumeCorr, PriceVolumeCorrelation, VolumePerRange, VolumeImbalance, VolumeWeightedReturn, VolumeSpike, VolumeAcceleration, VolumeZScore, VolumeMomentumRatio, VolPctRankSignedTrend.

Momentum (sf-profit feature_research_lib + iter-15/16/18/20) — in momentum: MomPosNeg, RocSignedLog, MacdNorm, RsiSpread, PriceMomentumConfirmation, VolPriceConfirmation, TrendPersistence, PriceAcceleration, MomentumOfMomentum.

Trend (sf-profit feature_research_lib + iter-15/16/18) — in trend: DiBalance, NatrXDiBalance, UpDownEntropyAsymmetry, EntropyRatio, RsiDivPolarity, HilbertAmplitudeSlope.

Provenance. All ~100 new classes were extracted from the sf-profit feature research pipeline (iter-3 → iter-21). They were validated through walk-forward IV scoring on 6 monthly folds and Spearman-correlation dedup at |corr| ≥ 0.85. 370 features survived to the final pool. See sf-profit's docs/feature_catalog.md for per-feature scores and walk-forward stability.

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Signal Detectors (24)

All detectors support configurable direction ("long", "short", "both") and optional filters.

Category	Detectors
Momentum	RSI Anomaly, CCI Anomaly, Stochastic ×2
Volume	MFI Oversold/Overbought, MFI Z-Score
Trend	Aroon Cross, ADX Regime ×2
Volatility	Bollinger Breakout, Keltner + RSI, Keltner + MACD
Divergence	Price/RSI, Price/MACD
Filter-based	Hampel ×2, Adaptive Kalman
Market Condition	Volatility Regime, Cross-Sectional, Market Breadth
ML-based	Isolation Forest (Returns, RSI, Cross-Sectional)
Cross-pair	Correlation + Bollinger

Signal Filters (12)

PriceUptrendFilter, PriceDowntrendFilter, LowVolatilityFilter, HighVolatilityFilter, MeanReversionFilter, MeanExtensionFilter, RsiZscoreFilter, BelowBBLowerFilter, AboveBBUpperFilter, CciZscoreFilter, MacdBelowSignalFilter, MacdAboveSignalFilter

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Normalization

Most indicators support normalized=True:

• Bounded (RSI, Stochastic, Williams %R) → scaled to [0, 1] or [-1, 1]
• Unbounded (MACD, ROC, smoothers) → rolling z-score
• Normalized columns receive a _norm suffix

AutoFeatureNormalizer

from signalflow.ta.auto_norm import AutoFeatureNormalizer

norm = AutoFeatureNormalizer(window=256, warmup=256)
df_normalized = norm.fit_transform(df)

Methods: rolling_robust, rolling_z, rolling_rank, rolling_winsor, signed_log1p

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License: MIT  ·  Part of SignalFlow