LIMEN

Reference / Limen research

Indicators

The configuration constraints apply to direct indicator calls as well as feature helpers.

Contents

Indicators are Limen's lowest-level signal helpers. They take a kline-style pl.DataFrame, append one or more derived columns, and return the resulting frame for ordinary Python preparation or the declared Compiled pipeline.

This page covers indicator selection, naming conventions, and default added columns. For higher-level derived signals, see Features.

Conventions

  • Indicators append columns. They do not replace the input frame with a reduced schema.
  • Helpers assume the standard OHLCV names: open, high, low, close, volume.
  • Indicator calculations are native implementations. TA-Lib supplies optional comparison/reference tooling for functions following its numerical conventions.
  • Output column names encode key defaults, such as rsi_14 or apo_12_26_0.
  • Candlestick pattern helpers add a same-named integer column following TA-Lib's positive bullish, negative bearish, zero neutral convention.
  • Legacy compatibility helpers intentionally expose legacy behavior. sma adds both sma_{period} and {price_col}_sma_{period}.

Use an indicator in the projects

Python Research can compute a column directly or call a Limen indicator. The Python project owns this decimal momentum expression and uses it in fitting, validation and inference:

python · research.py 
def features(bars, window):    return bars.with_columns(        (pl.col("close") / pl.col("close").shift(window) - 1).alias("momentum")    )
Merged source PR · 87894eb8Executed with Limen 6.0.1 · validation, inputs and tests · complete source · project and recorded inputs · native research records

The Compiled project declares a one-period rate of change in its recorded pipeline. The indicator returns percent units; it is not the Python project's decimal return column.

yaml · selection.yaml 
    indicators:      - func: limen.indicators.roc        params:          period: 1          group: momentum
Merged source PR · ea0b4cc8Executed with Limen 6.0.1 · validation, inputs and tests · complete source · project and recorded inputs · native research records

Both excerpts belong to the complete selection programs. Their source footers lead to the inputs, current wheel execution and semantic assertions. Python can use shared indicators in caller-owned preparation; Compiled Research declares them in YAML and executes them through the CLI.

Rate-of-change signature

The function below is a current package signature. It describes an interface; it is not a standalone research program.

python · roc.py 
def roc(data: pl.DataFrame, price_col: str='close', period: int=10) -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

Family index

  • Price and moving averages: direct price transforms and smoothing helpers.
  • Momentum and oscillators: rate-of-change, oscillator, regression, and Hilbert-style cycle helpers.
  • Volatility, bands, and range: dispersion, band, and stop-style helpers.
  • Volume, breadth, and bar diagnostics: flow, bar-body, and return-style helpers.
  • Candlestick patterns: TA-Lib-style pattern flags over OHLC bars.

Price and moving averages

These helpers take either a single price_col or basic OHLC inputs and add smoothed or transformed price views.

Function Adds by default Notes
avgprice avgprice Average OHLC price.
medprice medprice Median of high and low.
midprice midprice_14 Rolling midpoint of high and low over period.
typprice typprice Mean of high, low, and close.
wclprice wclprice Weighted close price.
midpoint midpoint_14 Rolling midpoint of a single price_col.
sma sma_30, close_sma_30 Adds both the canonical and compatibility alias columns.
ema ema_30 Exponential moving average.
wma wma_30 Weighted moving average.
dema dema_30 Double EMA.
tema tema_30 Triple EMA.
trima trima_30 Triangular moving average.
t3 t3_5_0.7 T3 moving average with period and vfactor.
kama kama_30 Kaufman adaptive moving average.
mama mama, fama MESA adaptive moving average pair.
ma ma_30_0 Generic moving average with explicit ma_type.
ht_trendline ht_trendline Hilbert Transform instantaneous trendline.

Momentum and oscillators

These helpers focus on rate of change, directional persistence, oscillator state, or cycle structure.

Function Adds by default Notes
apo apo_12_26_0 Absolute Price Oscillator.
ppo ppo_12_26_0 Percentage Price Oscillator.
macd macd, macd_signal, macd_hist Standard MACD triple.
macdfix macdfix, macdfix_signal, macdfix_hist Fixed-fast/slow MACD variant.
macdext macdext, macdext_signal, macdext_hist MACD with explicit MA-type controls.
mom mom_10 Momentum over period.
roc roc_10 Rate of change in percent-style form.
rocp rocp_10 Rate of change percentage as decimal fraction.
rocr rocr_10 Rate of change ratio.
rocr100 rocr100_10 Ratio scaled by 100.
rsi rsi_14 Native Wilder-smoothed RSI following TA-Lib conventions.
rsi_sma rsi_sma_14 SMA-based RSI-style helper.
wilder_rsi wilder_rsi_14 Wilder-smoothed RSI variant.
cmo cmo_14 Chande Momentum Oscillator.
cci cci_14 Commodity Channel Index.
ultosc ultosc_7_14_28 Ultimate Oscillator over three windows.
willr willr_14 Williams %R.
stoch stoch_slowk, stoch_slowd Native stochastic oscillator following TA-Lib conventions.
stochf stochf_fastk, stochf_fastd Fast stochastic variant.
stochrsi stochrsi_fastk, stochrsi_fastd Stochastic RSI.
stochastic_oscillator stoch_k, stoch_d Native rolling-high/low implementation.
trix trix_30 Triple-smoothed momentum oscillator.
tsf tsf_14 Time series forecast.
linearreg linearreg_14 Rolling linear regression level.
linearreg_angle linearreg_angle_14 Regression angle.
linearreg_intercept linearreg_intercept_14 Regression intercept.
linearreg_slope linearreg_slope_14 Regression slope.
ht_dcperiod ht_dcperiod Dominant cycle period.
ht_dcphase ht_dcphase Dominant cycle phase.
ht_phasor ht_phasor_inphase, ht_phasor_quadrature Hilbert phasor pair.
ht_sine ht_sine, ht_sine_lead Hilbert sine pair.
ht_trendmode ht_trendmode Trend-versus-cycle mode flag.

Volatility, bands, and range

These helpers quantify range, dispersion, band structure, or stop placement.

Function Adds by default Notes
atr atr_14 Average True Range.
natr natr_14 Normalized ATR.
trange trange True range.
stddev stddev_5_1 Rolling standard deviation.
var var_5_1 Rolling variance.
bbands bbands_upper, bbands_middle, bbands_lower TA-Lib-style Bollinger Bands with configurable ma_type.
bollinger_bands bb_middle, bb_upper, bb_lower Native SMA-based Bollinger Bands helper.
bollinger_position bollinger_position Requires close, bb_upper, and bb_lower to already exist.
rolling_volatility close_volatility_12 Rolling volatility over a single column.
sma_deviation_std sma30_dev_std Deviation from SMA normalized by rolling dispersion.
sar sar Parabolic SAR.
sarext sarext Extended SAR with separate long and short parameters.

Volume, breadth, and bar diagnostics

These helpers focus on participation, bar structure, or direct return transforms.

Function Adds by default Notes
ad ad Chaikin Accumulation/Distribution line.
adosc adosc_3_10 Chaikin A/D oscillator.
obv obv On-Balance Volume.
mfi mfi_14 Money Flow Index.
bop bop Balance of Power.
price_change_pct price_change_pct_1 Point-to-point percent change.
returns returns Single-step returns helper.
body_pct body_pct Candle body size as a share of the full range.
window_return ret_24 Return over a wider rolling window.

Candlestick patterns

All candlestick pattern helpers:

  • expect OHLC columns
  • append one integer column named after the function
  • should be treated as event flags rather than smooth continuous features

Only three patterns expose an extra parameter today.

Function Adds by default Extra params
cdl2crows cdl2crows None
cdl3blackcrows cdl3blackcrows None
cdl3inside cdl3inside None
cdl3linestrike cdl3linestrike None
cdl3starsinsouth cdl3starsinsouth None
cdl3whitesoldiers cdl3whitesoldiers None
cdlabandonedbaby cdlabandonedbaby penetration=0.3
cdladvancedblock cdladvancedblock None
cdlbelthold cdlbelthold None
cdlclosingmarubozu cdlclosingmarubozu None
cdlconcealbabyswall cdlconcealbabyswall None
cdlcounterattack cdlcounterattack None
cdldarkcloudcover cdldarkcloudcover penetration=0.5
cdldoji cdldoji None
cdldragonflydoji cdldragonflydoji None
cdlengulfing cdlengulfing None
cdlgravestonedoji cdlgravestonedoji None
cdlhammer cdlhammer None
cdlhangingman cdlhangingman None
cdlharami cdlharami None
cdlharamicross cdlharamicross None
cdlhighwave cdlhighwave None
cdlhikkake cdlhikkake None
cdlhikkakemod cdlhikkakemod None
cdlhomingpigeon cdlhomingpigeon None
cdlidentical3crows cdlidentical3crows None
cdlinvertedhammer cdlinvertedhammer None
cdlladderbottom cdlladderbottom None
cdllongleggeddoji cdllongleggeddoji None
cdllongline cdllongline None
cdlmarubozu cdlmarubozu None
cdlmatchinglow cdlmatchinglow None
cdlmathold cdlmathold penetration=0.5
cdlonneck cdlonneck None
cdlpiercing cdlpiercing None
cdlrickshawman cdlrickshawman None
cdlrisefall3methods cdlrisefall3methods None
cdlseparatinglines cdlseparatinglines None
cdlshootingstar cdlshootingstar None
cdlshortline cdlshortline None
cdlspinningtop cdlspinningtop None
cdlstalledpattern cdlstalledpattern None
cdlsticksandwich cdlsticksandwich None
cdltakuri cdltakuri None
cdlthrusting cdlthrusting None
cdltristar cdltristar None
cdlunique3river cdlunique3river None

Choosing between similar helpers

  • Use bbands for TA-Lib parity and configurable moving-average type.
  • Use bollinger_bands for the native SMA-based band helper.
  • stoch and stochf follow the stated TA-Lib conventions. stochastic_oscillator uses its separately documented rolling definition.
  • Use sma for the canonical moving average helper used across Limen manifests. Use ma when ma_type itself is part of the search space.
  • Use returns, price_change_pct, and window_return for one-bar returns, configurable percent change, and wider-window return summaries respectively.

Calculation cost

The indicator performance appendix retains the measured calculation costs, environments, numerical-parity boundaries and reproduction commands. Those measurements describe computation on their recorded inputs, not the returns of a research strategy.

Callable contracts

These entries give the exact public signatures and the source-owned argument, return and failure contracts. Use the project examples above for the research context; a signature is reference evidence, not a claim that every option ran in the base experiment.

limen.indicators.ad

python · ad.py 
def ad(data: pl.DataFrame, high_col: str='high', low_col: str='low', close_col: str='close', volume_col: str='volume') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

Compute Chaikin Accumulation/Distribution (A/D) line.

Argument Contract
data (pl.DataFrame) Klines dataset with high/low/close/volume columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices
volume_col (str) Column name for volume

Returns: pl.DataFrame: The input data with a new column 'ad'

limen.indicators.adosc

python · adosc.py 
def adosc(data: pl.DataFrame, high_col: str='high', low_col: str='low', close_col: str='close', volume_col: str='volume', fast_period: int=3, slow_period: int=10) -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

Compute Chaikin A/D Oscillator (ADOSC).

Argument Contract
data (pl.DataFrame) Klines dataset with high/low/close/volume columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices
volume_col (str) Column name for volume
fast_period (int) Fast EMA period
slow_period (int) Slow EMA period

Returns: pl.DataFrame: The input data with a new column 'adosc_{fast_period}_{slow_period}'

limen.indicators.apo

python · apo.py 
def apo(data: pl.DataFrame, price_col: str='close', fast_period: int=12, slow_period: int=26, ma_type: int=0) -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

Compute Absolute Price Oscillator (APO).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
fast_period (int) Number of periods for fast MA (2..100000)
slow_period (int) Number of periods for slow MA (2..100000, must be greater than fast_period)
ma_type (int) TA-Lib MA type (0..8)

Returns: pl.DataFrame: The input data with a new column 'apo_{fast_period}{slow_period}{ma_type}'

limen.indicators.atr

python · atr.py 
def atr(data: pl.DataFrame, high_col: str='high', low_col: str='low', close_col: str='close', period: int=14) -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

Compute Average True Range (ATR).

Argument Contract
data (pl.DataFrame) Klines dataset with high/low/close columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices
period (int) Number of periods for ATR calculation

Returns: pl.DataFrame: The input data with a new column 'atr_{period}'

limen.indicators.avgprice

python · avgprice.py 
def avgprice(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

Compute Average Price.

Argument Contract
data (pl.DataFrame) Dataset with OHLC columns
open_col (str) Column name for open prices
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices

Returns: pl.DataFrame: The input data with a new column 'avgprice'

limen.indicators.bbands

python · bbands.py 
def bbands(data: pl.DataFrame, price_col: str='close', period: int=5, nb_dev_up: float=2.0, nb_dev_dn: float=2.0, ma_type: int=0) -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

Compute Bollinger Bands (upper/middle/lower).

Argument Contract
data (pl.DataFrame) Dataset with price column
price_col (str) Column name for input price
period (int) Rolling window length
nb_dev_up (float) Upper-band deviation multiplier
nb_dev_dn (float) Lower-band deviation multiplier
ma_type (int) TA-Lib MA type

Returns: pl.DataFrame: Input data with 'bbands_upper', 'bbands_middle', 'bbands_lower'

limen.indicators.body_pct

python · body_pct.py 
def body_pct(data: pl.DataFrame) -> pl.DataFrame:    ...
Merged source PR · 7c385782Package signature · Limen 6.0.1 · current package validation · package source

Compute the body percentage (candle body size relative to open).

Argument Contract
data (pl.DataFrame) Klines dataset with 'open' and 'close' columns

Returns: pl.DataFrame: The input data with a new column 'body_pct'

limen.indicators.bollinger_bands

python · bollinger_bands.py 
def bollinger_bands(df: pl.DataFrame, price_col: str='close', window: int=20, num_std: float=2.0) -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

Compute Bollinger Bands using Simple Moving Average (SMA).

Argument Contract
df (pl.DataFrame) Klines dataset with 'close' column
price_col (str) Column name used for Bollinger Band calculation
window (int) Number of periods for SMA and standard deviation calculation
num_std (float) Number of standard deviations for upper and lower bands

Returns: pl.DataFrame: The input data with three new columns: 'bb_middle', 'bb_upper', and 'bb_lower'

limen.indicators.bollinger_position

python · bollinger_position.py 
def bollinger_position(data: pl.DataFrame) -> pl.DataFrame:    ...
Merged source PR · 334af5d8Package signature · Limen 6.0.1 · current package validation · package source

Compute price position within Bollinger Bands as percentage.

Returns 0 when price is at lower band, 1 when at upper band, 0.5 at middle.

Argument Contract
data (pl.DataFrame) Klines dataset with 'close', 'bb_upper', 'bb_lower' columns

Returns: pl.DataFrame: The input data with a new column 'bollinger_position'

limen.indicators.bop

python · bop.py 
def bop(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

Compute Balance of Power (BOP).

Argument Contract
data (pl.DataFrame) Dataset with OHLC columns
open_col (str) Column name for open prices
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices

Returns: pl.DataFrame: The input data with a new column 'bop'

limen.indicators.cci

python · cci.py 
def cci(data: pl.DataFrame, high_col: str='high', low_col: str='low', close_col: str='close', period: int=14) -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

Append CCI with the original chronological mean/deviation sums (period 2..100000).

limen.indicators.cdl2crows

python · cdl2crows.py 
def cdl2crows(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdl3blackcrows

python · cdl3blackcrows.py 
def cdl3blackcrows(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdl3inside

python · cdl3inside.py 
def cdl3inside(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdl3linestrike

python · cdl3linestrike.py 
def cdl3linestrike(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdl3starsinsouth

python · cdl3starsinsouth.py 
def cdl3starsinsouth(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdl3whitesoldiers

python · cdl3whitesoldiers.py 
def cdl3whitesoldiers(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlabandonedbaby

python · cdlabandonedbaby.py 
def cdlabandonedbaby(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close', penetration: float=0.3) -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdladvancedblock

python · cdladvancedblock.py 
def cdladvancedblock(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlbelthold

python · cdlbelthold.py 
def cdlbelthold(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlclosingmarubozu

python · cdlclosingmarubozu.py 
def cdlclosingmarubozu(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlconcealbabyswall

python · cdlconcealbabyswall.py 
def cdlconcealbabyswall(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlcounterattack

python · cdlcounterattack.py 
def cdlcounterattack(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdldarkcloudcover

python · cdldarkcloudcover.py 
def cdldarkcloudcover(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close', penetration: float=0.5) -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdldoji

python · cdldoji.py 
def cdldoji(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdldragonflydoji

python · cdldragonflydoji.py 
def cdldragonflydoji(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlengulfing

python · cdlengulfing.py 
def cdlengulfing(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlgravestonedoji

python · cdlgravestonedoji.py 
def cdlgravestonedoji(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlhammer

python · cdlhammer.py 
def cdlhammer(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlhangingman

python · cdlhangingman.py 
def cdlhangingman(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlharami

python · cdlharami.py 
def cdlharami(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlharamicross

python · cdlharamicross.py 
def cdlharamicross(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlhighwave

python · cdlhighwave.py 
def cdlhighwave(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlhikkake

python · cdlhikkake.py 
def cdlhikkake(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlhikkakemod

python · cdlhikkakemod.py 
def cdlhikkakemod(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
Merged source PR · d6e324c0Package signature · Limen 6.0.1 · current package validation · package source

limen.indicators.cdlhomingpigeon

python · cdlhomingpigeon.py 
def cdlhomingpigeon(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlidentical3crows

python · cdlidentical3crows.py 
def cdlidentical3crows(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlinvertedhammer

python · cdlinvertedhammer.py 
def cdlinvertedhammer(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlladderbottom

python · cdlladderbottom.py 
def cdlladderbottom(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdllongleggeddoji

python · cdllongleggeddoji.py 
def cdllongleggeddoji(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdllongline

python · cdllongline.py 
def cdllongline(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlmarubozu

python · cdlmarubozu.py 
def cdlmarubozu(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlmatchinglow

python · cdlmatchinglow.py 
def cdlmatchinglow(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlmathold

python · cdlmathold.py 
def cdlmathold(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close', penetration: float=0.5) -> pl.DataFrame:    ...
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limen.indicators.cdlonneck

python · cdlonneck.py 
def cdlonneck(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlpiercing

python · cdlpiercing.py 
def cdlpiercing(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlrickshawman

python · cdlrickshawman.py 
def cdlrickshawman(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlrisefall3methods

python · cdlrisefall3methods.py 
def cdlrisefall3methods(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlseparatinglines

python · cdlseparatinglines.py 
def cdlseparatinglines(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlshootingstar

python · cdlshootingstar.py 
def cdlshootingstar(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlshortline

python · cdlshortline.py 
def cdlshortline(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlspinningtop

python · cdlspinningtop.py 
def cdlspinningtop(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlstalledpattern

python · cdlstalledpattern.py 
def cdlstalledpattern(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlsticksandwich

python · cdlsticksandwich.py 
def cdlsticksandwich(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdltakuri

python · cdltakuri.py 
def cdltakuri(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlthrusting

python · cdlthrusting.py 
def cdlthrusting(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdltristar

python · cdltristar.py 
def cdltristar(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cdlunique3river

python · cdlunique3river.py 
def cdlunique3river(data: pl.DataFrame, open_col: str='open', high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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limen.indicators.cmo

python · cmo.py 
def cmo(data: pl.DataFrame, price_col: str='close', period: int=14) -> pl.DataFrame:    ...
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Compute Chande Momentum Oscillator (CMO).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods (2..100000)

Returns: pl.DataFrame: The input data with a new column 'cmo_{period}'

limen.indicators.dema

python · dema.py 
def dema(data: pl.DataFrame, price_col: str='close', period: int=30) -> pl.DataFrame:    ...
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Compute Double Exponential Moving Average (DEMA).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods

Returns: pl.DataFrame: The input data with a new column 'dema_{period}'

limen.indicators.ema

python · ema.py 
def ema(data: pl.DataFrame, price_col: str='close', period: int=30) -> pl.DataFrame:    ...
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Compute Exponential Moving Average (EMA).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods

Returns: pl.DataFrame: The input data with a new column 'ema_{period}'

limen.indicators.ht_dcperiod

python · ht_dcperiod.py 
def ht_dcperiod(data: pl.DataFrame, price_col: str='close') -> pl.DataFrame:    ...
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Compute Hilbert Transform - Dominant Cycle Period.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price

Returns: pl.DataFrame: The input data with a new column 'ht_dcperiod'

limen.indicators.ht_dcphase

python · ht_dcphase.py 
def ht_dcphase(data: pl.DataFrame, price_col: str='close') -> pl.DataFrame:    ...
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Compute Hilbert Transform - Dominant Cycle Phase.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price

Returns: pl.DataFrame: The input data with a new column 'ht_dcphase'

limen.indicators.ht_phasor

python · ht_phasor.py 
def ht_phasor(data: pl.DataFrame, price_col: str='close') -> pl.DataFrame:    ...
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Compute Hilbert Transform - Phasor Components.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price

Returns: pl.DataFrame: The input data with new columns 'ht_phasor_inphase' and

'ht_phasor_quadrature'

limen.indicators.ht_sine

python · ht_sine.py 
def ht_sine(data: pl.DataFrame, price_col: str='close') -> pl.DataFrame:    ...
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Compute Hilbert Transform - SineWave.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price

Returns: pl.DataFrame: The input data with new columns 'ht_sine' and

'ht_sine_lead'

limen.indicators.ht_trendline

python · ht_trendline.py 
def ht_trendline(data: pl.DataFrame, price_col: str='close') -> pl.DataFrame:    ...
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Compute Hilbert Transform - Instantaneous Trendline.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price

Returns: pl.DataFrame: The input data with a new column 'ht_trendline'

limen.indicators.ht_trendmode

python · ht_trendmode.py 
def ht_trendmode(data: pl.DataFrame, price_col: str='close') -> pl.DataFrame:    ...
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Compute Hilbert Transform - Trend vs Cycle Mode.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price

Returns: pl.DataFrame: The input data with a new column 'ht_trendmode'

limen.indicators.kama

python · kama.py 
def kama(data: pl.DataFrame, price_col: str='close', period: int=30) -> pl.DataFrame:    ...
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Compute Kaufman Adaptive Moving Average (KAMA).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods

Returns: pl.DataFrame: The input data with a new column 'kama_{period}'

limen.indicators.linearreg

python · linearreg.py 
def linearreg(data: pl.DataFrame, price_col: str='close', period: int=14) -> pl.DataFrame:    ...
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Append the trailing least-squares line value with the original warmup.

limen.indicators.linearreg_angle

python · linearreg_angle.py 
def linearreg_angle(data: pl.DataFrame, price_col: str='close', period: int=14) -> pl.DataFrame:    ...
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Compute Linear Regression Angle.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods

Returns: pl.DataFrame: The input data with a new column 'linearreg_angle_{period}'

limen.indicators.linearreg_intercept

python · linearreg_intercept.py 
def linearreg_intercept(data: pl.DataFrame, price_col: str='close', period: int=14) -> pl.DataFrame:    ...
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Compute Linear Regression Intercept.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods

Returns: pl.DataFrame: The input data with a new column 'linearreg_intercept_{period}'

limen.indicators.linearreg_slope

python · linearreg_slope.py 
def linearreg_slope(data: pl.DataFrame, price_col: str='close', period: int=14) -> pl.DataFrame:    ...
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Compute Linear Regression Slope.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods

Returns: pl.DataFrame: The input data with a new column 'linearreg_slope_{period}'

limen.indicators.ma

python · ma.py 
def ma(data: pl.DataFrame, price_col: str='close', period: int=30, ma_type: int=0) -> pl.DataFrame:    ...
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Compute Moving Average with selectable MA type (TA-Lib MA).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods
ma_type (int) TA-Lib MA type

Returns: pl.DataFrame: The input data with a new column 'ma_{period}_{ma_type}'

limen.indicators.macd

python · macd.py 
def macd(data: pl.DataFrame, price_col: str='close', fast_period: int=12, slow_period: int=26, signal_period: int=9) -> pl.DataFrame:    ...
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Compute Moving Average Convergence/Divergence (MACD).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
fast_period (int) Number of periods for fast EMA (2..100000)
slow_period (int) Number of periods for slow EMA (2..100000)
signal_period (int) Number of periods for signal EMA (1..100000)

Returns: pl.DataFrame: The input data with columns 'macd', 'macd_signal', 'macd_hist'

limen.indicators.macdext

python · macdext.py 
def macdext(data: pl.DataFrame, price_col: str='close', fast_period: int=12, fast_ma_type: int=0, slow_period: int=26, slow_ma_type: int=0, signal_period: int=9, signal_ma_type: int=0) -> pl.DataFrame:    ...
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Compute MACD with controllable MA types (MACDEXT).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
fast_period (int) Number of periods for fast MA (2..100000)
fast_ma_type (int) TA-Lib MA type for fast MA (0..8)
slow_period (int) Number of periods for slow MA (2..100000)
slow_ma_type (int) TA-Lib MA type for slow MA (0..8)
signal_period (int) Number of periods for signal MA (1..100000)
signal_ma_type (int) TA-Lib MA type for signal MA (0..8)

Returns: pl.DataFrame: The input data with columns 'macdext', 'macdext_signal', 'macdext_hist'

limen.indicators.macdfix

python · macdfix.py 
def macdfix(data: pl.DataFrame, price_col: str='close', signal_period: int=9) -> pl.DataFrame:    ...
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Compute MACD Fix 12/26 (MACDFIX).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
signal_period (int) Number of periods for signal EMA (1..100000)

Returns: pl.DataFrame: The input data with columns 'macdfix', 'macdfix_signal', 'macdfix_hist'

limen.indicators.mama

python · mama.py 
def mama(data: pl.DataFrame, price_col: str='close', fast_limit: float=0.5, slow_limit: float=0.05) -> pl.DataFrame:    ...
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Compute MESA Adaptive Moving Average (MAMA) and Following Adaptive MA (FAMA).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
fast_limit (float) Upper adaptive limit
slow_limit (float) Lower adaptive limit

Returns: pl.DataFrame: The input data with new columns 'mama' and 'fama'

limen.indicators.medprice

python · medprice.py 
def medprice(data: pl.DataFrame, high_col: str='high', low_col: str='low') -> pl.DataFrame:    ...
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Compute Median Price.

Argument Contract
data (pl.DataFrame) Dataset with high and low columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices

Returns: pl.DataFrame: The input data with a new column 'medprice'

limen.indicators.mfi

python · mfi.py 
def mfi(data: pl.DataFrame, high_col: str='high', low_col: str='low', close_col: str='close', volume_col: str='volume', period: int=14) -> pl.DataFrame:    ...
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Compute Money Flow Index (MFI).

Argument Contract
data (pl.DataFrame) Klines dataset with high/low/close/volume columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices
volume_col (str) Column name for volume
period (int) MFI period

Returns: pl.DataFrame: The input data with a new column 'mfi_{period}'

limen.indicators.midpoint

python · midpoint.py 
def midpoint(data: pl.DataFrame, price_col: str='close', period: int=14) -> pl.DataFrame:    ...
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Compute MidPoint over period.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods (2..100000)

Returns: pl.DataFrame: The input data with a new column 'midpoint_{period}'

limen.indicators.midprice

python · midprice.py 
def midprice(data: pl.DataFrame, high_col: str='high', low_col: str='low', period: int=14) -> pl.DataFrame:    ...
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Compute the trailing high/low midpoint over period (2..100000).

limen.indicators.mom

python · mom.py 
def mom(data: pl.DataFrame, price_col: str='close', period: int=10) -> pl.DataFrame:    ...
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Compute Momentum (MOM).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods (1..100000)

Returns: pl.DataFrame: The input data with a new column 'mom_{period}'

limen.indicators.natr

python · natr.py 
def natr(data: pl.DataFrame, high_col: str='high', low_col: str='low', close_col: str='close', period: int=14) -> pl.DataFrame:    ...
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Compute Normalized Average True Range (NATR).

Argument Contract
data (pl.DataFrame) Klines dataset with high/low/close columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices
period (int) Number of periods for NATR calculation

Returns: pl.DataFrame: The input data with a new column 'natr_{period}'

limen.indicators.obv

python · obv.py 
def obv(data: pl.DataFrame, price_col: str='close', volume_col: str='volume') -> pl.DataFrame:    ...
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Compute On-Balance Volume (OBV).

Argument Contract
data (pl.DataFrame) Dataset with price and volume columns
price_col (str) Column name for price series
volume_col (str) Column name for volume series

Returns: pl.DataFrame: The input data with a new column 'obv'

limen.indicators.ppo

python · ppo.py 
def ppo(data: pl.DataFrame, price_col: str='close', fast_period: int=12, slow_period: int=26, ma_type: int=0) -> pl.DataFrame:    ...
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Compute Percentage Price Oscillator (PPO).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
fast_period (int) Number of periods for fast MA (2..100000)
slow_period (int) Number of periods for slow MA (2..100000, must be greater than fast_period)
ma_type (int) TA-Lib MA type (0..8)

Returns: pl.DataFrame: The input data with a new column 'ppo_{fast_period}{slow_period}{ma_type}'

limen.indicators.price_change_pct

python · price_change_pct.py 
def price_change_pct(data: pl.DataFrame, period: int=1) -> pl.DataFrame:    ...
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Compute price change percentage over a specific period.

Argument Contract
data (pl.DataFrame) Klines dataset with 'close' column
period (int) Number of periods to look back

Returns: pl.DataFrame: The input data with a new column 'price_change_pct_{period}'

limen.indicators.returns

python · returns.py 
def returns(data: pl.DataFrame) -> pl.DataFrame:    ...
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Compute period-over-period returns of close prices.

Argument Contract
data (pl.DataFrame) Klines dataset with 'close' column

Returns: pl.DataFrame: The input data with a new column 'returns'

limen.indicators.roc

python · roc.py 
def roc(data: pl.DataFrame, price_col: str='close', period: int=10) -> pl.DataFrame:    ...
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Compute Rate of Change (ROC): ((price / prev_price) - 1) * 100.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods (1..100000)

Returns: pl.DataFrame: The input data with a new column 'roc_{period}'

limen.indicators.rocp

python · rocp.py 
def rocp(data: pl.DataFrame, price_col: str='close', period: int=10) -> pl.DataFrame:    ...
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Compute Rate of Change Percentage (ROCP): (price - prev_price) / prev_price.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods (1..100000)

Returns: pl.DataFrame: The input data with a new column 'rocp_{period}'

limen.indicators.rocr

python · rocr.py 
def rocr(data: pl.DataFrame, price_col: str='close', period: int=10) -> pl.DataFrame:    ...
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Compute Rate of Change Ratio (ROCR): price / prev_price.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods (1..100000)

Returns: pl.DataFrame: The input data with a new column 'rocr_{period}'

limen.indicators.rocr100

python · rocr100.py 
def rocr100(data: pl.DataFrame, price_col: str='close', period: int=10) -> pl.DataFrame:    ...
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Compute Rate of Change Ratio 100 scale (ROCR100): (price / prev_price) * 100.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods (1..100000)

Returns: pl.DataFrame: The input data with a new column 'rocr100_{period}'

limen.indicators.rolling_volatility

python · rolling_volatility.py 
def rolling_volatility(data: pl.DataFrame, column: str='close', window: int=12) -> pl.DataFrame:    ...
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Compute rolling volatility (standard deviation) over a specified period.

Argument Contract
data (pl.DataFrame) Klines dataset with price/returns column
column (str) Column name to calculate volatility on (typically returns)
window (int) Number of periods for rolling window calculation

Returns: pl.DataFrame: The input data with a new column '{column}volatility{window}'

limen.indicators.rsi

python · rsi.py 
def rsi(data: pl.DataFrame, price_col: str='close', period: int=14) -> pl.DataFrame:    ...
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Compute Relative Strength Index (RSI) using Wilder smoothing.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods (2..100000)

Returns: pl.DataFrame: The input data with a new column 'rsi_{period}'

limen.indicators.rsi_sma

python · rsi_sma.py 
def rsi_sma(data: pl.DataFrame, period: int=14) -> pl.DataFrame:    ...
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Compute RSI using Simple Moving Average smoothing (not Wilder's method).

NOTE: Different from wilder_rsi which uses exponential smoothing.

Argument Contract
data (pl.DataFrame) Klines dataset with 'close' column
period (int) Number of periods for RSI calculation

Returns: pl.DataFrame: The input data with a new column 'rsi_sma_{period}'

limen.indicators.sar

python · sar.py 
def sar(data: pl.DataFrame, high_col: str='high', low_col: str='low', acceleration: float=0.02, maximum: float=0.2) -> pl.DataFrame:    ...
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Compute Parabolic SAR.

Argument Contract
data (pl.DataFrame) Dataset with high/low columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
acceleration (float) Acceleration factor
maximum (float) Maximum acceleration factor

Returns: pl.DataFrame: The input data with a new column 'sar'

limen.indicators.sarext

python · sarext.py 
def sarext(data: pl.DataFrame, high_col: str='high', low_col: str='low', start_value: float=0.0, offset_on_reverse: float=0.0, acceleration_init_long: float=0.02, acceleration_long: float=0.02, acceleration_max_long: float=0.2, acceleration_init_short: float=0.02, acceleration_short: float=0.02, acceleration_max_short: float=0.2) -> pl.DataFrame:    ...
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Compute Parabolic SAR - Extended (SAREXT).

Argument Contract
data (pl.DataFrame) Dataset with high/low columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
start_value (float) Start value and direction
offset_on_reverse (float) Percent offset on reversal
acceleration_init_long (float) Initial AF for long
acceleration_long (float) AF increment for long
acceleration_max_long (float) AF max for long
acceleration_init_short (float) Initial AF for short
acceleration_short (float) AF increment for short
acceleration_max_short (float) AF max for short

Returns: pl.DataFrame: The input data with a new column 'sarext'

limen.indicators.sma

python · sma.py 
def sma(data: pl.DataFrame, price_col: str='close', period: int=30, column: str | None=None) -> pl.DataFrame:    ...
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Compute Simple Moving Average (SMA).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods
column (str | None) Backward-compatible alias for price_col

Returns: pl.DataFrame: Input data with a new column 'sma_{period}'.

Also includes '{price_col}sma{period}' as a compatibility alias.

limen.indicators.sma_deviation_std

python · sma_deviation_std.py 
def sma_deviation_std(data: pl.DataFrame, period: int=30, window: int=30) -> pl.DataFrame:    ...
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Compute rolling standard deviation of the detrended price relative to SMA(period).

Argument Contract
data (pl.DataFrame) Klines dataset with 'close' column
period (int) Period for the SMA
window (int) Rolling window for std of (close - SMA(period))

Returns: pl.DataFrame: The input data with a new column named using the pattern 'sma{period}_dev_std'

limen.indicators.stddev

python · stddev.py 
def stddev(data: pl.DataFrame, price_col: str='close', period: int=5, nb_dev: float=1.0) -> pl.DataFrame:    ...
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Compute Standard Deviation.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods
nb_dev (float) Number of deviations to scale the output

Returns: pl.DataFrame: The input data with a new column 'stddev_{period}_{nb_dev:g}'

limen.indicators.stoch

python · stoch.py 
def stoch(data: pl.DataFrame, high_col: str='high', low_col: str='low', close_col: str='close', fastk_period: int=5, slowk_period: int=3, slowk_ma_type: int=0, slowd_period: int=3, slowd_ma_type: int=0) -> pl.DataFrame:    ...
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Compute Stochastic Oscillator (TA_STOCH): slow %K and slow %D.

Argument Contract
data (pl.DataFrame) Dataset with high/low/close columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices
fastk_period (int) Time period for Fast-K (1..100000)
slowk_period (int) Smoothing period for Slow-K (1..100000)
slowk_ma_type (int) MA type for Slow-K (0..8)
slowd_period (int) Smoothing period for Slow-D (1..100000)
slowd_ma_type (int) MA type for Slow-D (0..8)

Returns: pl.DataFrame: The input data with 'stoch_slowk' and 'stoch_slowd'

limen.indicators.stochastic_oscillator

python · stochastic_oscillator.py 
def stochastic_oscillator(df: pl.DataFrame, window_k: int=14, window_d: int=3) -> pl.DataFrame:    ...
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Compute Stochastic Oscillator (%K and %D) using rolling highs and lows.

Argument Contract
df (pl.DataFrame) Klines dataset with 'high', 'low', 'close' columns
window_k (int) Number of periods for %K calculation
window_d (int) Number of periods for %D smoothing (SMA of %K)

Returns: pl.DataFrame: The input data with two new columns 'stoch_k' and 'stoch_d'

limen.indicators.stochf

python · stochf.py 
def stochf(data: pl.DataFrame, high_col: str='high', low_col: str='low', close_col: str='close', fastk_period: int=5, fastd_period: int=3, fastd_ma_type: int=0) -> pl.DataFrame:    ...
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Compute Fast Stochastic Oscillator (TA_STOCHF): fast %K and fast %D.

Argument Contract
data (pl.DataFrame) Dataset with high/low/close columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices
fastk_period (int) Time period for Fast-K (1..100000)
fastd_period (int) Smoothing period for Fast-D (1..100000)
fastd_ma_type (int) MA type for Fast-D (0..8)

Returns: pl.DataFrame: The input data with 'stochf_fastk' and 'stochf_fastd'

limen.indicators.stochrsi

python · stochrsi.py 
def stochrsi(data: pl.DataFrame, price_col: str='close', period: int=14, fastk_period: int=5, fastd_period: int=3, fastd_ma_type: int=0) -> pl.DataFrame:    ...
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Compute Stochastic RSI (TA_STOCHRSI): fast %K and fast %D on RSI values.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) RSI period (2..100000)
fastk_period (int) Time period for Fast-K (1..100000)
fastd_period (int) Smoothing period for Fast-D (1..100000)
fastd_ma_type (int) MA type for Fast-D (0..8)

Returns: pl.DataFrame: The input data with 'stochrsi_fastk' and 'stochrsi_fastd'

limen.indicators.t3

python · t3.py 
def t3(data: pl.DataFrame, price_col: str='close', period: int=5, vfactor: float=0.7) -> pl.DataFrame:    ...
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Compute Triple Exponential Moving Average (T3).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods
vfactor (float) Volume factor

Returns: pl.DataFrame: The input data with a new column 't3_{period}_{vfactor}'

limen.indicators.tema

python · tema.py 
def tema(data: pl.DataFrame, price_col: str='close', period: int=30) -> pl.DataFrame:    ...
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Compute Triple Exponential Moving Average (TEMA).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods

Returns: pl.DataFrame: The input data with a new column 'tema_{period}'

limen.indicators.trange

python · trange.py 
def trange(data: pl.DataFrame, high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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Compute True Range (TRANGE).

Argument Contract
data (pl.DataFrame) Klines dataset with high/low/close columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices

Returns: pl.DataFrame: The input data with a new column 'trange'

limen.indicators.trima

python · trima.py 
def trima(data: pl.DataFrame, price_col: str='close', period: int=30) -> pl.DataFrame:    ...
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Compute Triangular Moving Average (TRIMA).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods

Returns: pl.DataFrame: The input data with a new column 'trima_{period}'

limen.indicators.trix

python · trix.py 
def trix(data: pl.DataFrame, price_col: str='close', period: int=30) -> pl.DataFrame:    ...
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Compute TRIX: 1-day ROC of a triple-smoothed EMA.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods (1..100000)

Returns: pl.DataFrame: The input data with a new column 'trix_{period}'

limen.indicators.tsf

python · tsf.py 
def tsf(data: pl.DataFrame, price_col: str='close', period: int=14) -> pl.DataFrame:    ...
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Compute Time Series Forecast (TSF).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods

Returns: pl.DataFrame: The input data with a new column 'tsf_{period}'

limen.indicators.typprice

python · typprice.py 
def typprice(data: pl.DataFrame, high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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Compute Typical Price.

Argument Contract
data (pl.DataFrame) Dataset with high, low, and close columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices

Returns: pl.DataFrame: The input data with a new column 'typprice'

limen.indicators.ultosc

python · ultosc.py 
def ultosc(data: pl.DataFrame, high_col: str='high', low_col: str='low', close_col: str='close', period1: int=7, period2: int=14, period3: int=28) -> pl.DataFrame:    ...
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Compute Ultimate Oscillator (ULTOSC).

Argument Contract
data (pl.DataFrame) Dataset with high/low/close columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices
period1 (int) Number of bars for 1st period (1..100000)
period2 (int) Number of bars for 2nd period (1..100000)
period3 (int) Number of bars for 3rd period (1..100000)

Returns: pl.DataFrame: The input data with a new column 'ultosc_{period1}{period2}{period3}'

limen.indicators.var

python · var.py 
def var(data: pl.DataFrame, price_col: str='close', period: int=5, nb_dev: float=1.0) -> pl.DataFrame:    ...
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Compute Variance.

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods
nb_dev (float) Kept for TA-Lib compatibility; does not affect VAR output

Returns: pl.DataFrame: The input data with a new column 'var_{period}_{nb_dev:g}'

limen.indicators.wclprice

python · wclprice.py 
def wclprice(data: pl.DataFrame, high_col: str='high', low_col: str='low', close_col: str='close') -> pl.DataFrame:    ...
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Compute Weighted Close Price.

Argument Contract
data (pl.DataFrame) Dataset with high, low, and close columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices

Returns: pl.DataFrame: The input data with a new column 'wclprice'

limen.indicators.wilder_rsi

python · wilder_rsi.py 
def wilder_rsi(data: pl.DataFrame, period: int=14) -> pl.DataFrame:    ...
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Compute Wilder's RSI using canonical Wilder smoothing.

Argument Contract
data (pl.DataFrame) Klines dataset with 'close' column
period (int) Number of periods for RSI calculation

Returns: pl.DataFrame: The input data with a new column 'wilder_rsi_{period}'

limen.indicators.willr

python · willr.py 
def willr(data: pl.DataFrame, high_col: str='high', low_col: str='low', close_col: str='close', period: int=14) -> pl.DataFrame:    ...
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Compute Williams' %R (WILLR).

Argument Contract
data (pl.DataFrame) Dataset with high/low/close columns
high_col (str) Column name for high prices
low_col (str) Column name for low prices
close_col (str) Column name for close prices
period (int) Number of periods (2..100000)

Returns: pl.DataFrame: The input data with a new column 'willr_{period}'

limen.indicators.window_return

python · window_return.py 
def window_return(data: pl.DataFrame, period: int=24) -> pl.DataFrame:    ...
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Compute windowed return close/close.shift(period) - 1 for a given period.

Argument Contract
data (pl.DataFrame) Klines dataset with 'close' column
period (int) Window length for the return

Returns: pl.DataFrame: The input data with a new column named using the pattern 'ret_{period}'

limen.indicators.wma

python · wma.py 
def wma(data: pl.DataFrame, price_col: str='close', period: int=30) -> pl.DataFrame:    ...
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Compute Weighted Moving Average (WMA).

Argument Contract
data (pl.DataFrame) Dataset with input price column
price_col (str) Column name for input price
period (int) Number of periods

Returns: pl.DataFrame: The input data with a new column 'wma_{period}'

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