Getting Started

Feature engineering is a crucial step in quantitative trading. The Quantreo library provides several optimized functions to extract meaningful insights from raw market data.

To help you understand how to use each function, we provide detailed explanations and Jupyter Notebook examples in the tutorials folder.

📦 Installation & Import

Be sure you have installed the quantreo library before running any of these codes. To install it, just run pip install quantreo in your terminal.

PS: to import the Features Library Package, you just need to run

import quantreo.features_engineering as fe

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Data Format

Quantreo is designed to work with OHLCV (Open, High, Low, Close, Volume) data, which is the standard format in financial markets.

🔗 OHLCV focus

Not all features require all columns. So, if you compute a features needing only the high and the low prices, you can put a dataframe containing only these two variables.

Let's create really a very simple dataframe to show you the right format.

import pandas as pd

# Creating a sample OHLCV DataFrame
data = {
    "open": [100, 102, 101, 103, 105],
    "high": [105, 107, 106, 108, 110],
    "low": [98, 100, 99, 101, 102],
    "close": [102, 104, 103, 105, 107],
    "volume": [10000, 10050, 9950, 10000, 11500]
}
df = pd.DataFrame(data)

print(df)

Open	High	Low	Close	Volume
100	105	98	102	10000
102	107	100	104	10050
101	106	99	103	9950
103	108	101	105	10000
105	110	102	107	11500

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Features Available

Category	Function Name	Quick Explanation
Candle Information	candle_information	Returns the candle’s color, its fill state, and its range.
Candle Information	compute_spread	Computes the difference between the high and the low (spread).
Candle Information	price_distribution	Percentage of closes falling between two dynamic bounds within a rolling window.
Candle Information	internal_bar_strength	Normalized position of the close price within the candle's high-low range.
Market Regime	kama_market_regime	Detects market phases (bull / bear) using two KAMA curves.
Math	derivatives	Computes speed (1st derivative) and acceleration (2nd derivative).
Math	log_pct	Logarithmic return over a rolling window.
Math	auto_corr	Rolling autocorrelation on a given column.
Math	hurst	Computes the Hurst exponent over a rolling window.
Math	skewness	Rolling skewness: detects asymmetry.
Math	kurtosis	Rolling kurtosis: detects tail heaviness.
Math	bimodality_coefficient	Detects unimodal vs. bimodal/multimodal regimes using skewness and kurtosis.
Math	adf_test	Rolling Augmented Dickey-Fuller test to detect unit roots (non-stationarity).
Math	arch_test	Rolling Engle ARCH test to detect conditional heteroskedasticity (vol clustering).
Math	sample_entropy	Measures local signal unpredictability; higher = more irregular behavior.
Math	spectral_entropy	Frequency-domain entropy; higher = flatter spectrum, more randomness.
Math	permutation_entropy	Entropy based on ordinal patterns in data; robust to noise and nonlinearity.
Math	detrended_fluctuation	Detects fractal memory and persistence in time series via DFA exponent.
Math	petrosian_fd	Estimates structural complexity using directional changes in the signal.
Math	tail_index	Estimates the tail index (α̂) to characterize the heaviness of distribution tails.
Math	shapiro_wilk	Rolling Shapiro-Wilk test for local normality detection.
Transformation	fisher_transform	Transforms normalized price data into a Gaussian-like signal for detecting extremes.
Transformation	savgol_filter	Causal smoothing filter preserving local structure using polynomial regression.
Transformation	mma	Median moving average, ideal to smooth a curve
Transformation	fourier_reconstructor	Rolling Fourier transform for cycle extraction or noise reduction.
Transformation	wavelet_reconstructor	Rolling Wavelet transform for low/high-frequency signal reconstruction.
Transformation	logit_transform	Applies the logit transformation to features bounded in (0, 1).
Transformation	neg_log_transform	Applies the negative logarithm to values in (0, 1], highlighting small values.
Trend	sma	Simple moving average.
Trend	kama	Kaufman Adaptive Moving Average (noise-adaptive).
Trend	linear_slope	Slope of a linear regression over a rolling window.
Volatility	close_to_close_volatility	Volatility based on the standard deviation of log returns.
Volatility	parkinson_volatility	Volatility based on high/low prices only.
Volatility	rogers_satchell_volatility	Volatility that accounts for drift and intraday prices.
Volatility	yang_zhang_volatility	Gap-robust volatility combining multiple measures.