Features

Features sit one layer above indicators. They usually combine, re-express, lag, or contextualize price and volume information into model-ready signals or regime flags.

Use this page when you need to choose between feature helpers, understand their input requirements, or see what each helper adds to a frame. For lower-level signal primitives, see Indicators.

Conventions

• Feature helpers usually append columns and return the original frame with the extra outputs attached.
• Some helpers operate on plain kline data only. Others require trade-derived columns such as maker_ratio, no_of_trades, price, or quantity.
• Not every helper is intended as a final predictor. Some are utilities used to build targets or wider feature families.
• Several regime helpers output compact categorical-style columns such as regime_ma_slope or regime_price_band.
• A few helpers return scalar values instead of frames. The main example is find_min_d, which searches for a stationarity-preserving fractional-differentiation order.

Quick Example

from limen.data import HistoricalData
from limen.experiment import Manifest
from limen.features import kline_imbalance, vwap
from limen.indicators import atr, roc
from limen.sfd.reference_architecture import logreg_binary


def manifest():
    return (
        Manifest()
        .set_data_source(
            method=HistoricalData.get_spot_klines,
            params={'kline_size': 3600, 'start_date_limit': '2025-01-01'},
        )
        .set_test_data_source(
            method=HistoricalData.get_spot_klines,
            params={'kline_size': 7200, 'row_count_limit': 5000},
        )
        .set_split_config(8, 1, 2)
        .add_indicator(roc, period='roc_period')
        .add_indicator(atr, period=14)
        .add_feature(vwap)
        .add_feature(kline_imbalance, window='imbalance_window')
        .with_reference_architecture(logreg_binary)
    )

Kline-Derived Position And Volatility Features

These helpers work directly on bar data and are usually the easiest feature layer to add to a first manifest.

Function	Adds by default	Notes
atr_percent_sma	atr_percent_sma	ATR scaled by close price using SMA smoothing.
atr_sma	atr_sma	SMA-smoothed ATR variant.
close_position	close_position	Close location inside the current bar's high-low range. Accepts window; 1 preserves single-bar behavior.
close_position_rolling	close_position_rolling	Rolling mean of close location inside the high-low range.
close_ma_distance_atr	close_ma_distance_atr	Close-to-SMA distance normalized by SMA-smoothed true range.
distance_from_ma	distance_from_ma	Close distance from its rolling moving average.
distance_from_high	distance_from_high	Distance from a rolling high.
distance_from_low	distance_from_low	Distance from a rolling low.
gap_high	gap_high	Current high relative to the previous close.
kaufman_efficiency_ratio	kaufman_efficiency_ratio	Directional displacement divided by the rolling path length.
narrow_range	narrow_range	Current range divided by trailing maximum range.
price_range_position	price_range_position	Rolling range position over a wider window.
range_pct	range_pct	Current bar range as a percentage.
stochastic_k_abs	stochastic_k_abs	Absolute distance between stochastic %K and 0.5.
trend_strength	trend_strength	Fast-versus-slow trend strength summary.
volume_to_range	volume_to_range	Rolling mean volume per unit of high-low range.
volume_regime	volume_regime	Volume context over a lookback window.
vwap	vwap	Requires a datetime-like datetime column because VWAP resets by trading day.
wick_proportion	wick_proportion	Rolling mean wick share of full candle range.

Calendar And Cyclical Time Features

These helpers derive time-of-bar context from datetime without depending on earlier indicator columns.

Function	Adds by default	Notes
calendar_time_features	hour, minute, weekday, day_of_month, day_of_year, week_of_year, month, quarter, half_of_year, is_weekend	Adds discrete calendar fields for downstream splits, filters, and rules. weekday uses ISO numbering (Monday=1 to Sunday=7), and week_of_year uses ISO week numbering.
cyclical_time_features	hour_sin, hour_cos, minute_sin, minute_cos, weekday_sin, weekday_cos, day_of_month_sin, day_of_month_cos, day_of_year_sin, day_of_year_cos, week_of_year_sin, week_of_year_cos, month_sin, month_cos, quarter_sin, quarter_cos	Encodes cyclical calendar fields without introducing artificial ordinal jumps. Uses the same ISO conventions as calendar_time_features; weekday cycles are phase-aligned with weekday - 1 before applying sine/cosine.
is_funding_hour	is_funding_hour	Parameterized funding-cadence hour indicator; default hours are 0, 8, and 16.
is_us_open_hour	is_us_open_hour	Parameterized US open-hour indicator; default hour is 14.

Range-Based Volatility Features

These helpers estimate volatility directly from OHLC structure instead of relying only on close-to-close returns.

Function	Adds by default	Notes
parkinson_volatility	parkinson_volatility	High-low range estimator that ignores close-to-close drift.
parkinson_vol_of_vol	parkinson_vol_of_vol	Rolling standard deviation of Parkinson variance.
garman_klass_volatility	garman_klass_volatility	OHLC estimator using open, high, low, and close information.
rogers_satchell_volatility	rogers_satchell_volatility	Drift-robust OHLC volatility estimator.
volatility_ratio	volatility_ratio	Short rolling Parkinson variance divided by long rolling Parkinson variance.
volatility_spike	volatility_spike	Current Parkinson variance divided by its fixed-lag value.
yang_zhang_volatility	yang_zhang_volatility	Combines overnight, open-close, and range information into a higher-fidelity volatility estimate. Requires window > 1.

Liquidity And Impact Features

These helpers translate ordinary OHLCV bars into simple liquidity, impact, and slippage proxies.

Function	Adds by default	Notes
dollar_volume	dollar_volume	Price-times-volume activity proxy using close and volume.
amihud_illiquidity	amihud_illiquidity	Absolute return per dollar of volume, a compact price-impact proxy.
return_per_dollar_volume	return_per_dollar_volume	Signed return per dollar of volume, useful when direction matters as much as impact.
range_per_dollar_volume	range_per_dollar_volume	Bar range scaled by dollar volume.
illiquidity_shock	illiquidity_shock	Current Amihud-style illiquidity relative to its own trailing mean.
liquidity_drop	liquidity_drop	Current LOB liquidity divided by fixed-lag LOB liquidity.
liquidity_range	liquidity_range	Rolling mean high-liquidity to low-liquidity ratio.
maker_liquidity_share	maker_liquidity_share	Maker liquidity divided by total liquidity.
maker_volume_share	maker_volume_share	Maker volume divided by total volume.
maker_volume_ratio	maker_volume_ratio	Rolling mean maker-volume share.
taker_imbalance_ratio	taker_imbalance_ratio	Rolling mean absolute taker imbalance as a share of volume.
trade_density	trade_density	Rolling mean number of trades per unit of volume.
trade_imbalance	trade_imbalance	Rolling maker volume divided by rolling total volume.
trade_size_ratio	trade_size_ratio	Short average trade size divided by long average trade size.

Realized Risk And Tail Features

These helpers describe the quality of recent movement, not just its level.

Function	Adds by default	Notes
realized_semivariance	upside_semivariance, downside_semivariance	Splits rolling squared returns into upside and downside components.
downside_volatility_ratio	downside_volatility_ratio	Rolling downside squared-return share of total squared returns.
realized_skewness	realized_skewness	Rolling skewness of close-to-close returns.
realized_kurtosis	realized_kurtosis	Rolling kurtosis of close-to-close returns.
jump_variation_proxy	jump_variation_proxy	Positive gap between realized variance and bipower variation proxy.
tail_event_intensity	tail_event_intensity	Share of recent bars whose absolute return exceeds a configurable threshold.
volatility_of_volatility	volatility_of_volatility	Rolling variability of rolling close-to-close return volatility.
return_autocorrelation	return_autocorrelation	Rolling correlation between returns and one-bar lagged returns.
return_volatility_correlation	return_volatility_correlation	Rolling correlation between returns and Parkinson variance.
volume_volatility_correlation	volume_volatility_correlation	Rolling correlation between volume and Parkinson variance.

Seasonality-Normalized Features

These helpers compare current bar behavior to the trailing mean for the same hour of the week.

Function	Adds by default	Notes
relative_volume_seasonality	relative_volume_seasonality	Current volume relative to the trailing baseline for the same hour-of-week bucket.
relative_range_seasonality	relative_range_seasonality	Current range percentage relative to the trailing hour-of-week baseline.
relative_volatility_seasonality	relative_volatility_seasonality	Current absolute return magnitude relative to the trailing hour-of-week baseline.

Candle Structure And Auction Features

These helpers focus on how a bar moved internally, not just where it finished.

Function	Adds by default	Notes
body_to_range	body_to_range	Absolute candle body size divided by the full bar range.
wick_imbalance	wick_imbalance	Upper-wick minus lower-wick imbalance as a share of full range.
range_overlap	range_overlap	Overlap share between the current bar range and the previous bar range.
rejection_intensity	rejection_intensity	Wick-heavy rejection proxy based on total wick share and directional close location.
absorption_intensity	absorption_intensity	High-volume, small-body absorption proxy using a trailing shifted volume baseline.

Cross-Timescale Context Features

These helpers summarize whether multiple horizons agree or disagree about market state.

Function	Adds by default	Notes
trend_coherence	trend_coherence	Average sign agreement across short, medium, and long return horizons.
volatility_term_structure	volatility_term_structure	Average ratio between short, medium, and long rolling volatility estimates.

Breakout And Regime Features

These helpers are useful when you want state or structure, not just a continuous numeric series.

Function	Adds by default	Notes
breakout_features	many lagged breakout columns plus long_roll_mean, long_roll_std, short_roll_mean, short_roll_std, roc_long_12_1, roc_short_12_1	Designed to enrich pre-existing breakout flags.
breakout_percentile_regime	price_range_position, regime_breakout_pct	Uses percentile thresholds over price-range position.
hh_hl_structure_regime	regime_hh_hl	Captures higher-high and higher-low style structure.
ichimoku_cloud	tenkan, kijun, senkou_a, senkou_b, chikou	Full Ichimoku feature set.
ma_slope_regime	regime_ma_slope	Regime label based on moving-average slope.
price_vs_band_regime	regime_price_band	Uses price distance relative to a band definition.
sma_crossover	crossover, signal	Compact crossover-state helper.
window_return_regime	ret_24, regime_window_return	Return plus regime thresholding over a window.

Lag Helpers And Threshold Utilities

These helpers are mainly used to expand existing columns or define cutoffs for target construction.

Function	Adds or returns	Notes
lag_column	one lagged column such as close_lag_2	Requires col and lag.
lag_columns	one lag per listed column	Requires cols and lag.
lag_range	a lag range such as close_lag_1 through close_lag_3	Requires col, start, and end.
lag_range_cols	a lag range for each listed column	Requires cols, start, and end.
rolling_zscore	configurable *_zscore_* column	Applies identity, log1p, or abs before rolling z-score standardization.

Stationarity And Long-Memory Helpers

These helpers are useful when you want to reduce non-stationarity while preserving more long-memory structure than a simple first difference would.

Function	Adds or returns	Notes
fractional_diff	one *_fracdiff column per selected input column	Applies fixed-width fractional differentiation. Original columns are preserved.
find_min_d	scalar d value	Iterates over candidate orders and uses the Augmented Dickey-Fuller test to find the smallest stationary order.

Two practical details matter:

• fractional_diff needs cols=[...] and writes new columns such as close_fracdiff.
• if one split is too short to produce the same fractional-diff column as another split, Manifest now drops that extra column during split alignment so the final data_dict stays consistent.

Trade-Shape And Microstructure Features

These helpers need richer data than ordinary OHLCV bars.

Function	Adds by default	Notes
kline_imbalance	imbalance	Requires maker_ratio and no_of_trades. Useful when those were carried through data retrieval or bar formation.
conserved_flux_renormalization	synthetic OHLCV plus value_sum, vwap, flux_rel_std_mean, flux_rel_std_var, entropy_mean, entropy_var, Δflux_rms, Δentropy_rms	Works on trade-level datetime, price, and quantity, then rolls those into kline-aligned diagnostics.

Choosing Between Indicators And Features

• Use an indicator when you want a direct market calculation such as RSI, ATR, or MACD.
• Use a feature when you want structure around those signals, such as lags, regimes, relative position, or multi-step aggregation.
• Use the lag helpers when the main value is temporal context rather than a new market calculation.
• Use fractional_diff when stationarity itself is part of the design problem, not just a preprocessing afterthought.

Read Next

• Indicators for lower-level signal primitives
• Transforms for target shaping and post-model calibration helpers
• Experiment Manifest for how features plug into the split-first manifest pipeline
• Utilities for the exported adf_test() helper that find_min_d builds on