Data Aggregation - Bar Building¶

This notebook demonstrates how to transform raw tick-level data into structured bars using various aggregation methods provided by the Quantreo library.

Starting from synthetic tick data, you'll learn how to:

• Build time-based bars, tick-based bars, and volume-based bars
• Construct advanced bars like tick imbalance and volume imbalance bars
• Visualize the results using candlestick charts with volume overlays

These methods are essential for normalizing market data and preparing it for backtesting, signal generation, or machine learning models.

Let’s dive in. 👇

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 [3]:

# 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[3]:

	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

In [4]:

import plotly.graph_objects as go
from plotly.subplots import make_subplots

def plot_candlestick_with_volume(df):
    """
    Plot a candlestick chart with a volume bar chart below it.
    
    Parameters
    ----------
    df : pd.DataFrame
        DataFrame with datetime index and the following columns:
        - 'open', 'high', 'low', 'close', 'volume'
    """
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.10, row_heights=[0.8, 0.2],
        subplot_titles=("Price", "Volume")    )

    # Candlestick
    fig.add_trace(go.Candlestick(x=df.index, open=df["open"], high=df["high"], low=df["low"], close=df["close"],
        name="OHLC"), row=1, col=1)

    # Volume
    fig.add_trace(go.Bar(x=df.index, y=df["volume"], name="Volume", marker_color="lightblue"), row=2, col=1)

    fig.update_layout(xaxis_rangeslider_visible=False, showlegend=False, height=500, margin=dict(t=30, b=30)    )

    fig.update_yaxes(title_text="Price", row=1, col=1)
    fig.update_yaxes(title_text="Volume", row=2, col=1)
    fig.update_xaxes(type='category', row=2, col=1)
    fig.update_xaxes(type='category', row=1, col=1)
    fig.update_layout(
        xaxis=dict(tickangle=45, tickfont=dict(size=9), nticks=15),
        xaxis2=dict(tickangle=45, tickfont=dict(size=9), nticks=15))

    fig.show()

import plotly.graph_objects as go from plotly.subplots import make_subplots def plot_candlestick_with_volume(df): """ Plot a candlestick chart with a volume bar chart below it. Parameters ---------- df : pd.DataFrame DataFrame with datetime index and the following columns: - 'open', 'high', 'low', 'close', 'volume' """ fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.10, row_heights=[0.8, 0.2], subplot_titles=("Price", "Volume") ) # Candlestick fig.add_trace(go.Candlestick(x=df.index, open=df["open"], high=df["high"], low=df["low"], close=df["close"], name="OHLC"), row=1, col=1) # Volume fig.add_trace(go.Bar(x=df.index, y=df["volume"], name="Volume", marker_color="lightblue"), row=2, col=1) fig.update_layout(xaxis_rangeslider_visible=False, showlegend=False, height=500, margin=dict(t=30, b=30) ) fig.update_yaxes(title_text="Price", row=1, col=1) fig.update_yaxes(title_text="Volume", row=2, col=1) fig.update_xaxes(type='category', row=2, col=1) fig.update_xaxes(type='category', row=1, col=1) fig.update_layout( xaxis=dict(tickangle=45, tickfont=dict(size=9), nticks=15), xaxis2=dict(tickangle=45, tickfont=dict(size=9), nticks=15)) fig.show()

Time Bars¶

The ticks_to_time_bars function aggregates raw tick data into fixed-time bars (e.g., 1-second, 1-minute, etc.). This is the most common form of bar construction, used in nearly all trading platforms.

The function will group ticks by time intervals and compute OHLCV values per bar.

In [5]:

time_bars = da.bar_building.ticks_to_time_bars(df, resample_factor="4H", col_price="price", col_volume="volume", additional_metrics=[])
time_bars

time_bars = da.bar_building.ticks_to_time_bars(df, resample_factor="4H", col_price="price", col_volume="volume", additional_metrics=[]) time_bars

Out[5]:

	open	high	low	close	volume	number_ticks	high_time	low_time
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
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
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
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
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
...	...	...	...	...	...	...	...	...
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
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
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
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
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

70 rows × 8 columns

In [6]:

plot_candlestick_with_volume(time_bars)

plot_candlestick_with_volume(time_bars)