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_data_for_test)
        .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_model(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.
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.
price_range_position	price_range_position	Rolling range position over a wider window.
range_pct	range_pct	Current bar range as a percentage.
trend_strength	trend_strength	Fast-versus-slow trend strength summary.
volume_regime	volume_regime	Volume context over a lookback window.
vwap	vwap	Requires a datetime-like datetime column because VWAP resets by trading day.

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.
ema_breakout	breakout_ema	Requires target_col; flags price displacement from EMA beyond breakout_delta.
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.
compute_quantile_cutoff	scalar cutoff value	Utility helper, not a DataFrame transform.
quantile_flag	quantile_flag	Commonly used in targets after computing the cutoff on train only.

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 compute_quantile_cutoff and quantile_flag when the target boundary itself is part of the split-safe training logic.
• 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