Data Aggregation - Bar Metrics¶

This notebook demonstrates how to compute and visualize custom bar-level metrics from raw tick data using the Quantreo library.

Let’s explore how to create and extend your own metrics — all computed directly from the tick data inside each bar.

In [1]:

# Import the Data Aggregation Package from Quantreo
import quantreo.data_aggregation as da

# Import the Data Aggregation Package from Quantreo import quantreo.data_aggregation as da

In [2]:

# Import a dataset to test the functions and create new ones easily
from quantreo.datasets import load_generated_ticks
df = load_generated_ticks()


# Show the data
df

# Import a dataset to test the functions and create new ones easily from quantreo.datasets import load_generated_ticks df = load_generated_ticks() # Show the data df

Out[2]:

	price	volume
datetime
2023-03-03 13:36:36	114.806983	3
2023-03-03 13:36:37	114.806983	1
2023-03-03 13:36:38	114.806983	1
2023-03-03 13:36:39	114.799521	1
2023-03-03 13:36:40	114.799521	1
...	...	...
2023-03-15 03:23:11	118.686705	1
2023-03-15 03:23:12	118.686705	1
2023-03-15 03:23:13	118.686705	3
2023-03-15 03:23:14	118.686705	1
2023-03-15 03:23:15	118.672084	1

1000000 rows × 2 columns

Apply Additional Metrics¶

The additional_metrics parameter lets you enrich any bar with custom columns.

It must be a list of tuples, where each tuple follows this exact structure:

(
    function,                                # A callable applied to the bar's internal data
    "price" | "volume" | "price_volume",     # Data source passed to the function
    ["col_name1", "col_name2", ...]          # Names of the output columns
)

🔍 Component Breakdown

• function: A Python function that takes a NumPy array (or a tuple of arrays if "price_volume") and returns either a float or a tuple of floats.

• "price" / "volume" / "price_volume": Specifies which internal tick data will be passed to the function (price, volume or both).

• ["output_col_name"]: The names of the column(s) added to the resulting DataFrame.

✅ These metrics are computed independently for each bar, using only the ticks that belong to that bar.

In [3]:

time_bars = da.bar_building.ticks_to_time_bars(df, resample_factor="4H", col_price="price", col_volume="volume", additional_metrics=[
    (da.bar_metrics.kurtosis, "price", ["kurtosis"]),
    (da.bar_metrics.max_traded_volume, "price_volume", ["max_vol", "price_max_vol"])
])
time_bars

time_bars = da.bar_building.ticks_to_time_bars(df, resample_factor="4H", col_price="price", col_volume="volume", additional_metrics=[ (da.bar_metrics.kurtosis, "price", ["kurtosis"]), (da.bar_metrics.max_traded_volume, "price_volume", ["max_vol", "price_max_vol"]) ]) time_bars

Out[3]:

	open	high	low	close	volume	number_ticks	high_time	low_time	skewness	kurtosis	poc	poc_distance	max_vol	price_max_vol
time
2023-03-03 12:00:00	114.806983	114.821643	114.519924	114.640622	15573.0	8604	2023-03-03 13:39:22	2023-03-03 15:42:52	0.079447	-1.114048	114.716041	0.65	1111.0	114.650636
2023-03-03 16:00:00	114.640622	115.063405	114.577370	114.681267	24813.0	14400	2023-03-03 19:10:23	2023-03-03 16:54:07	0.503873	-1.256384	114.650276	0.15	936.0	114.957178
2023-03-03 20:00:00	114.681267	114.896731	114.411137	114.859736	24455.0	14400	2023-03-03 23:58:56	2023-03-03 20:54:14	-0.029107	-0.319391	114.629654	0.45	478.0	114.702053
2023-03-04 00:00:00	114.859736	115.313589	114.747424	115.149874	26909.0	14400	2023-03-04 01:54:15	2023-03-04 00:36:35	-0.042187	-0.900129	115.115431	0.65	1330.0	115.284433
2023-03-04 04:00:00	115.157326	115.382338	115.057242	115.288936	26443.0	14400	2023-03-04 05:27:52	2023-03-04 07:39:06	-0.617723	-0.124820	115.268555	0.65	1011.0	115.330631
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2023-03-14 08:00:00	118.248409	118.263260	117.510691	117.664435	28025.0	14400	2023-03-14 08:00:06	2023-03-14 11:20:01	0.191413	-0.906341	117.623576	0.15	1401.0	117.636825
2023-03-14 12:00:00	117.664435	117.997454	117.635474	117.843508	25745.0	14400	2023-03-14 15:15:01	2023-03-14 12:06:39	0.061016	0.139561	117.834563	0.55	562.0	117.878979
2023-03-14 16:00:00	117.843508	117.970998	117.649717	117.910534	24350.0	14400	2023-03-14 19:53:27	2023-03-14 16:39:29	0.364729	-0.470253	117.762165	0.35	316.0	117.783954
2023-03-14 20:00:00	117.910534	118.255959	117.811763	118.226044	28402.0	14400	2023-03-14 23:58:51	2023-03-14 21:11:00	0.725995	-0.699551	117.922812	0.25	936.0	117.932291
2023-03-15 00:00:00	118.226044	118.722931	118.175237	118.672084	21583.0	12196	2023-03-15 03:14:41	2023-03-15 01:11:08	0.954973	0.185180	118.366930	0.35	454.0	118.625803

70 rows × 14 columns

In [11]:

tick_bars = da.bar_building.ticks_to_tick_bars(df, tick_per_bar=10_000, col_price="price", col_volume="volume", additional_metrics=[
    (da.bar_metrics.skewness, "price", ["skewness"]),
     (lambda px,v:da.bar_metrics.volume_profile_features(px,v,n_bins=10), "price_volume",["poc", "poc_distance"]),
])
tick_bars

tick_bars = da.bar_building.ticks_to_tick_bars(df, tick_per_bar=10_000, col_price="price", col_volume="volume", additional_metrics=[ (da.bar_metrics.skewness, "price", ["skewness"]), (lambda px,v:da.bar_metrics.volume_profile_features(px,v,n_bins=10), "price_volume",["poc", "poc_distance"]), ]) tick_bars

Out[11]:

	open	high	low	close	volume	number_ticks	duration_minutes	high_time	low_time	skewness	poc	poc_distance
time
2023-03-03 13:36:36	114.806983	114.821643	114.519924	114.661382	17732.0	10000	166.65	2023-03-03 13:39:22	2023-03-03 15:42:52	0.130231	114.685869	0.55
2023-03-03 16:23:16	114.661382	115.055958	114.577370	115.048522	17867.0	10000	166.65	2023-03-03 19:09:50	2023-03-03 16:54:07	0.541432	114.649159	0.15
2023-03-03 19:09:56	115.048522	115.063405	114.411137	114.666460	16616.0	10000	166.65	2023-03-03 19:10:23	2023-03-03 20:54:14	0.810642	114.639430	0.35
2023-03-03 21:56:36	114.666460	114.928161	114.564990	114.873724	16559.0	10000	166.65	2023-03-04 00:21:44	2023-03-03 23:27:27	0.041901	114.692100	0.35
2023-03-04 00:43:16	114.873724	115.313589	114.845063	114.969048	19984.0	10000	166.65	2023-03-04 01:54:15	2023-03-04 00:47:55	-0.010750	115.102752	0.55
...	...	...	...	...	...	...	...	...	...	...	...	...
2023-03-14 13:29:56	117.833813	117.997454	117.701412	117.730518	18495.0	10000	166.65	2023-03-14 15:15:01	2023-03-14 16:14:55	0.822873	117.775423	0.25
2023-03-14 16:16:36	117.723186	117.925650	117.649717	117.868810	16815.0	10000	166.65	2023-03-14 18:52:12	2023-03-14 16:39:29	0.252527	117.718700	0.25
2023-03-14 19:03:16	117.868810	118.007704	117.718963	117.919602	18407.0	10000	166.65	2023-03-14 21:37:33	2023-03-14 19:20:44	-0.640993	117.935518	0.75
2023-03-14 21:49:56	117.919602	118.378553	117.874878	118.341288	19526.0	10000	166.65	2023-03-15 00:28:01	2023-03-14 22:04:57	0.089221	118.101532	0.45
2023-03-15 00:36:36	118.334049	118.722931	118.175237	118.672084	17692.0	10000	166.65	2023-03-15 03:14:41	2023-03-15 01:11:08	0.708433	118.366930	0.35

100 rows × 12 columns

Create New Metrics¶

Sometimes, standard OHLCV data isn’t enough.

You might want to extract advanced metrics from the raw ticks inside each bar like skewness, volume profile peaks, or volatility spikes.

That’s exactly what additional_metrics is for. It lets you plug in your own logic and enrich every bar with custom, computed features.

In [15]:

from numba import njit
import numpy as np
from typing import Tuple

# Example of additional metric functions 
@njit
def median_volume(x: np.ndarray) -> float:
    """
    Compute the median of a 1D array (e.g., volumes within a bar).

    Parameters
    ----------
    x : np.ndarray
        Input 1D array of numerical values.

    Returns
    -------
    float
        Median value of the input array.
    """
    n = len(x)
    if n == 0:
        return 0.0
    sorted_x = np.sort(x)
    mid = n // 2
    if n % 2 == 0:
        return 0.5 * (sorted_x[mid - 1] + sorted_x[mid])
    else:
        return sorted_x[mid]


@njit
def min_max(x: np.ndarray) -> tuple:
    """
    Compute the minimum and maximum of a 1D array.

    Parameters
    ----------
    x : np.ndarray
        Input 1D array of numerical values.

    Returns
    -------
    tuple
        A tuple (min_value, max_value) of the array.
    """
    n = len(x)
    if n == 0:
        return (0.0, 0.0)
    
    min_val = x[0]
    max_val = x[0]
    
    for i in range(1, n):
        if x[i] < min_val:
            min_val = x[i]
        elif x[i] > max_val:
            max_val = x[i]
    
    return (min_val, max_val)


@njit
def max_traded_volume(prices: np.ndarray, volumes: np.ndarray) -> Tuple[float, float]:
    """
    Return the maximum traded volume and the associated price.

    Parameters
    ----------
    prices : np.ndarray
        1D array of price values corresponding to each tick.
    volumes : np.ndarray
        1D array of traded volume at each tick.

    Returns
    -------
    Tuple[float, float]
        - max_volume : Highest volume exchanged on a single tick.
        - price_at_max_volume : Price level at which the maximum volume occurred.
    """
    n = len(volumes)
    if n == 0:
        return 0.0, 0.0

    max_idx = 0
    max_vol = volumes[0]

    for i in range(1, n):
        if volumes[i] > max_vol:
            max_vol = volumes[i]
            max_idx = i

    return max_vol, prices[max_idx]

from numba import njit import numpy as np from typing import Tuple # Example of additional metric functions @njit def median_volume(x: np.ndarray) -> float: """ Compute the median of a 1D array (e.g., volumes within a bar). Parameters ---------- x : np.ndarray Input 1D array of numerical values. Returns ------- float Median value of the input array. """ n = len(x) if n == 0: return 0.0 sorted_x = np.sort(x) mid = n // 2 if n % 2 == 0: return 0.5 * (sorted_x[mid - 1] + sorted_x[mid]) else: return sorted_x[mid] @njit def min_max(x: np.ndarray) -> tuple: """ Compute the minimum and maximum of a 1D array. Parameters ---------- x : np.ndarray Input 1D array of numerical values. Returns ------- tuple A tuple (min_value, max_value) of the array. """ n = len(x) if n == 0: return (0.0, 0.0) min_val = x[0] max_val = x[0] for i in range(1, n): if x[i] < min_val: min_val = x[i] elif x[i] > max_val: max_val = x[i] return (min_val, max_val) @njit def max_traded_volume(prices: np.ndarray, volumes: np.ndarray) -> Tuple[float, float]: """ Return the maximum traded volume and the associated price. Parameters ---------- prices : np.ndarray 1D array of price values corresponding to each tick. volumes : np.ndarray 1D array of traded volume at each tick. Returns ------- Tuple[float, float] - max_volume : Highest volume exchanged on a single tick. - price_at_max_volume : Price level at which the maximum volume occurred. """ n = len(volumes) if n == 0: return 0.0, 0.0 max_idx = 0 max_vol = volumes[0] for i in range(1, n): if volumes[i] > max_vol: max_vol = volumes[i] max_idx = i return max_vol, prices[max_idx]

In [16]:

time_bars = da.bar_building.ticks_to_time_bars(df, resample_factor="4H", col_price="price", col_volume="volume", additional_metrics=[
    (median_volume, "volume", ["volume_median"]),
    (min_max, "price", ["low_price", "high_price"]),
    (max_traded_volume, "price_volume", ["max_vol", "price_max_vol"])

])
time_bars

time_bars = da.bar_building.ticks_to_time_bars(df, resample_factor="4H", col_price="price", col_volume="volume", additional_metrics=[ (median_volume, "volume", ["volume_median"]), (min_max, "price", ["low_price", "high_price"]), (max_traded_volume, "price_volume", ["max_vol", "price_max_vol"]) ]) time_bars

Out[16]:

	open	high	low	close	volume	number_ticks	high_time	low_time	volume_median	low_price	high_price	max_vol	price_max_vol
time
2023-03-03 12:00:00	114.806983	114.821643	114.519924	114.640622	15573.0	8604	2023-03-03 13:39:22	2023-03-03 15:42:52	1.0	114.519924	114.821643	1111.0	114.650636
2023-03-03 16:00:00	114.640622	115.063405	114.577370	114.681267	24813.0	14400	2023-03-03 19:10:23	2023-03-03 16:54:07	1.0	114.577370	115.063405	936.0	114.957178
2023-03-03 20:00:00	114.681267	114.896731	114.411137	114.859736	24455.0	14400	2023-03-03 23:58:56	2023-03-03 20:54:14	1.0	114.411137	114.896731	478.0	114.702053
2023-03-04 00:00:00	114.859736	115.313589	114.747424	115.149874	26909.0	14400	2023-03-04 01:54:15	2023-03-04 00:36:35	1.0	114.747424	115.313589	1330.0	115.284433
2023-03-04 04:00:00	115.157326	115.382338	115.057242	115.288936	26443.0	14400	2023-03-04 05:27:52	2023-03-04 07:39:06	1.0	115.057242	115.382338	1011.0	115.330631
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2023-03-14 08:00:00	118.248409	118.263260	117.510691	117.664435	28025.0	14400	2023-03-14 08:00:06	2023-03-14 11:20:01	1.0	117.510691	118.263260	1401.0	117.636825
2023-03-14 12:00:00	117.664435	117.997454	117.635474	117.843508	25745.0	14400	2023-03-14 15:15:01	2023-03-14 12:06:39	1.0	117.635474	117.997454	562.0	117.878979
2023-03-14 16:00:00	117.843508	117.970998	117.649717	117.910534	24350.0	14400	2023-03-14 19:53:27	2023-03-14 16:39:29	1.0	117.649717	117.970998	316.0	117.783954
2023-03-14 20:00:00	117.910534	118.255959	117.811763	118.226044	28402.0	14400	2023-03-14 23:58:51	2023-03-14 21:11:00	1.0	117.811763	118.255959	936.0	117.932291
2023-03-15 00:00:00	118.226044	118.722931	118.175237	118.672084	21583.0	12196	2023-03-15 03:14:41	2023-03-15 01:11:08	1.0	118.175237	118.722931	454.0	118.625803

70 rows × 13 columns