Technical Analysis#

This library provides technical analysis functions for the Polars DataFrame API.

Financial Disclaimer#

The indicators and outputs generated by this library do NOT constitute financial, investment, trading, or professional advice. They are for educational and informational purposes only.

Users should perform independent analysis, validate results, and consult a licensed financial professional before making investment decisions.

The authors assume no liability for losses or damages arising from the use of this software.

class polars_extensions.technical_analysis.TechnicalAnalysisNamespace(df: DataFrame)[source]#

Bases: object

Methods

`ao`(high, low[, short, long])	Awesome Oscillator (AO).
`atr`(high, low, close[, window])	Average True Range (ATR).
`bollinger`(col[, window, num_std])	Bollinger Bands (mid, upper, lower).
`bop`(open, high, low, close)	Balance of Power (BOP).
`cci`(high, low, close[, window])	Commodity Channel Index (CCI).
`chop`(high, low, close[, window])	Choppiness Index (CHOP).
`compare`(col, value[, op])	Compare column values to a scalar with operators: le, ge, lt, gt, eq, ne.
`coppock`(col[, long_period, short_period, ...])	Coppock Curve indicator.
`cti`(col[, window])	Correlation Trend Indicator (CTI).
`delta`(col[, periods])	Difference between current value and value N periods ago.
`dma`(col[, short_window, long_window])	Difference of Moving Averages (DMA): short SMA minus long SMA.
`dmi`(high, low, close[, window])	Directional Movement Index (DMI) with +DI, -DI, ADX, ADXR.
`ema`(col, span)	Exponential Moving Average (EMA).
`eri`(high, low, close[, window])	Elder-Ray Index (ERI): Bull Power and Bear Power.
`fisher_transform`(col[, window])	Gaussian Fisher Transform (FTR).
`ichimoku`(high, low, close[, tenkan, kijun, ...])	Ichimoku Cloud indicator: Tenkan-sen, Kijun-sen, Senkou Span A/B, Chikou Span.
`kama`(col[, window, fast, slow])	Kaufman Adaptive Moving Average (KAMA).
`kdj`(high, low, close[, window, k_smooth, ...])	KDJ Stochastic Oscillator.
`log_return`(col[, periods])	Log return: ln(current / previous N-period value)
`lrma`(col[, window])	Linear Regression Moving Average (LRMA).
`macd`(col[, fast, slow, signal])	MACD (Moving Average Convergence Divergence).
`mad`(col, window)	Mean Absolute Deviation over rolling window.
`mstd`(col, window)	Moving Standard Deviation.
`mvar`(col, window)	Moving Variance.
`ppo`(col[, fast, slow, signal])	Percentage Price Oscillator (PPO).
`qqe`(rsi_col[, window, smooth])	Quantitative Qualitative Estimation (QQE) indicator.
`roc`(col[, periods])	Rate of Change (ROC) momentum indicator.
`rsi`(col[, window])	Relative Strength Index (RSI).
`sma`(col, window)	Simple Moving Average (SMA).
`stoch_rsi`(col[, window])	Stochastic RSI.
`supertrend`(high, low, close[, period, ...])	Supertrend indicator with Upper Band and Lower Band.
`tema`(col[, span])	Triple Exponential Moving Average (TEMA).
`tr`(high, low, close)	True Range (TR).
`trix`(col[, window])	Triple Exponential Moving Average (TRIX).
`wr`(high, low, close[, window])	Williams %R Overbought/Oversold indicator.
`zscore`(col, window)	Z-score normalized column over rolling window.

ao( high: str, low: str, short: int = 5, long: int = 34, ) → DataFrame[source]#

Awesome Oscillator (AO).

Parameters:

highstr: High price column.
lowstr: Low price column.
shortint, default 5: Short-term SMA.
longint, default 34: Long-term SMA.

Returns:

DataFrame with column: ao_<short>_<long>

atr( high: str, low: str, close: str, window: int = 14, ) → DataFrame[source]#: Average True Range (ATR).

bollinger( col: str, window: int = 20, num_std: float = 2.0, ) → DataFrame[source]#: Bollinger Bands (mid, upper, lower).

bop( open: str, high: str, low: str, close: str, ) → DataFrame[source]#

Balance of Power (BOP).

Parameters:

open_str: Open price column.
highstr: High price column.
lowstr: Low price column.
closestr: Close price column.

Returns:

DataFrame with column: bop

cci( high: str, low: str, close: str, window: int = 20, ) → DataFrame[source]#

Commodity Channel Index (CCI).

Parameters:

highstr: High price column.
lowstr: Low price column.
closestr: Close price column.
windowint, default 20: Rolling window for calculation.

Returns:

DataFrame with column: <col>_cci_<window>

chop( high: str, low: str, close: str, window: int = 14, ) → DataFrame[source]#: Choppiness Index (CHOP).

compare( col: str, value: float, op: str = 'ge', ) → DataFrame[source]#

Compare column values to a scalar with operators: le, ge, lt, gt, eq, ne.

Returns <col>_<op>_<value>

coppock( col: str, long_period: int = 14, short_period: int = 11, wma_period: int = 10, ) → DataFrame[source]#

Coppock Curve indicator.

Parameters:

colstr: Column to compute Coppock curve.
long_periodint, default 14: Long-term ROC period.
short_periodint, default 11: Short-term ROC period.
wma_periodint, default 10: Smoothing period for WMA.

Returns:

DataFrame with column: coppock

cti( col: str, window: int = 14, ) → DataFrame[source]#

Correlation Trend Indicator (CTI).

Parameters:

colstr: Column to compute CTI on.
windowint, default 14: Rolling window.

Returns:

DataFrame with column: <col>_cti_<window>

delta( col: str, periods: int = 1, ) → DataFrame[source]#

Difference between current value and value N periods ago.

Returns <col>_delta_<periods>

dma( col: str, short_window: int = 10, long_window: int = 50, ) → DataFrame[source]#

Difference of Moving Averages (DMA): short SMA minus long SMA.

Parameters:

colstr: Column to compute DMA on.
short_windowint, default 10: Short period moving average.
long_windowint, default 50: Long period moving average.

Returns:

DataFrame with new column: <col>_dma_<short>_<long>

dmi( high: str, low: str, close: str, window: int = 14, ) → DataFrame[source]#

Directional Movement Index (DMI) with +DI, -DI, ADX, ADXR.

Parameters:

highstr: High price column.
lowstr: Low price column.
closestr: Close price column.
windowint, default 14: Rolling window for smoothing.

Returns:

DataFrame with columns: +DI, -DI, ADX, ADXR

ema(col: str, span: int) → DataFrame[source]#: Exponential Moving Average (EMA).

eri( high: str, low: str, close: str, window: int = 13, ) → DataFrame[source]#

Elder-Ray Index (ERI): Bull Power and Bear Power.

Parameters:

highstr: High price column.
lowstr: Low price column.
closestr: Close price column.
windowint, default 13: EMA period.

Returns:

DataFrame with columns: bull_power, bear_power

fisher_transform( col: str, window: int = 10, ) → DataFrame[source]#

Gaussian Fisher Transform (FTR).

Parameters:

colstr: Column to transform.
windowint, default 10: Lookback period.

Returns:

DataFrame with column: ftr_<window>

ichimoku( high: str, low: str, close: str, tenkan: int = 9, kijun: int = 26, senkou: int = 52, ) → DataFrame[source]#

Ichimoku Cloud indicator: Tenkan-sen, Kijun-sen, Senkou Span A/B, Chikou Span.

Parameters:

highstr: High price column.
lowstr: Low price column.
closestr: Close price column.
tenkanint, default 9: Conversion line period.
kijunint, default 26: Base line period.
senkouint, default 52: Leading span B period.

Returns:

DataFrame with columns: tenkan_sen, kijun_sen, senkou_a, senkou_b, chikou_span

kama( col: str, window: int = 10, fast: int = 2, slow: int = 30, ) → DataFrame[source]#

Kaufman Adaptive Moving Average (KAMA).

Parameters:

colstr: Column to compute KAMA on.
windowint, default 10: Efficiency ratio window.
fastint, default 2: Fast EMA period.
slowint, default 30: Slow EMA period.

Returns:

DataFrame with new column: <col>_kama_<window>

kdj( high: str, low: str, close: str, window: int = 14, k_smooth: int = 3, d_smooth: int = 3, ) → DataFrame[source]#: KDJ Stochastic Oscillator.

log_return( col: str, periods: int = 1, ) → DataFrame[source]#

Log return: ln(current / previous N-period value)

Returns <col>_log_return_<periods>

lrma( col: str, window: int = 14, ) → DataFrame[source]#

Linear Regression Moving Average (LRMA).

Parameters:

colstr: Column to compute LRMA on.
windowint, default 14: Rolling window.

Returns:

DataFrame with column: <col>_lrma_<window>

macd( col: str, fast: int = 12, slow: int = 26, signal: int = 9, ) → DataFrame[source]#: MACD (Moving Average Convergence Divergence).

mad( col: str, window: int, ) → DataFrame[source]#: Mean Absolute Deviation over rolling window.

mstd( col: str, window: int, ) → DataFrame[source]#: Moving Standard Deviation.

mvar( col: str, window: int, ) → DataFrame[source]#: Moving Variance.

ppo( col: str, fast: int = 12, slow: int = 26, signal: int = 9, ) → DataFrame[source]#

Percentage Price Oscillator (PPO).

Parameters:

colstr: Column to calculate PPO.
fastint, default 12: Fast EMA period.
slowint, default 26: Slow EMA period.
signalint, default 9: Signal line EMA period.

Returns:

DataFrame with columns: <col>_ppo, <col>_ppo_signal, <col>_ppo_hist

qqe( rsi_col: str, window: int = 14, smooth: int = 5, ) → DataFrame[source]#

Quantitative Qualitative Estimation (QQE) indicator.

Parameters:

rsi_colstr: Column containing RSI values.
windowint, default 14: Smoothing period.
smoothint, default 5: Signal smoothing period.

Returns:

DataFrame with columns: qqe_rsi, qqe_signal

roc( col: str, periods: int = 1, ) → DataFrame[source]#

Rate of Change (ROC) momentum indicator.

Parameters:

colstr: Column to calculate ROC.
periodsint, default 1: Number of periods for difference.

Returns:

DataFrame with column: <col>_roc_<periods>

rsi( col: str, window: int = 14, ) → DataFrame[source]#: Relative Strength Index (RSI).

sma( col: str, window: int, ) → DataFrame[source]#: Simple Moving Average (SMA).

stoch_rsi( col: str, window: int = 14, ) → DataFrame[source]#: Stochastic RSI.

supertrend( high: str, low: str, close: str, period: int = 10, multiplier: float = 3.0, ) → DataFrame[source]#

Supertrend indicator with Upper Band and Lower Band.

Parameters:

highstr: High price column.
lowstr: Low price column.
closestr: Close price column.
periodint, default 10: ATR period.
multiplierfloat, default 3.0: ATR multiplier.

Returns:

DataFrame with columns: supertrend, upper_band, lower_band

tema( col: str, span: int = 15, ) → DataFrame[source]#

Triple Exponential Moving Average (TEMA).

Parameters:

colstr: Column to compute TEMA on.
spanint, default 15: EMA span.

Returns:

DataFrame with new column: <col>_tema_<span>

tr( high: str, low: str, close: str, ) → DataFrame[source]#: True Range (TR).

trix( col: str, window: int = 15, ) → DataFrame[source]#

Triple Exponential Moving Average (TRIX).

Parameters:

colstr: Column to compute TRIX on.
windowint, default 15: EMA span.

Returns:

DataFrame with new column: <col>_trix_<window>

wr( high: str, low: str, close: str, window: int = 14, ) → DataFrame[source]#

Williams %R Overbought/Oversold indicator.

Parameters:

highstr: High price column.
lowstr: Low price column.
closestr: Close price column.
windowint, default 14: Rolling window for calculation.

Returns:

DataFrame with column: wr_<window>

zscore( col: str, window: int, ) → DataFrame[source]#: Z-score normalized column over rolling window.