rangedf

Transform Tick Data into OHLCV Range Dataframe!

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Installation

   pip install rangedf

or just copy rangedf.py file to your project!

• rangedf requires pandas, numpy and mplfinance

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Content

• Usage
• Animation/Realtime Range Chart
• Is the Range chart calculated correctly?

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Usage

There are two classes available:

• Range(df, range_size, keep_inner_gap, add_columns)
  To create Range OHLCV dataframe with existing Ticks data.
• RangeWS(timestamp, price, range_size, keep_inner_gap, external_df, external_mode)
  To create real-time Range charts, usually over a WebSocket connection.

Let's start with the first class, load a Pandas DataFrame containing Ticks Data, for example:

import pandas as pd
df_ticks = pd.read_parquet('examples/data/EURGBP_T1_cT.parquet')
df_ticks.rename(columns={'bid': 'close'}, inplace=True)
df_ticks.head(3)
df_ticks.tail(3)

	ask	close	spread
datetime
2025-05-08 00:00:00.678	0.85047	0.85043	0.00004
2025-05-08 00:00:00.848	0.85047	0.85045	0.00002
2025-05-08 00:00:02.000	0.85046	0.85045	0.00001

	ask	close	spread
datetime
2025-05-15 23:59:46.238	0.84130	0.84126	0.00004
2025-05-15 23:59:50.844	0.84129	0.84125	0.00004
2025-05-15 23:59:54.240	0.84130	0.84125	0.00005

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Only two columns are required:

• close: Mandatory.
• datetime: If is not present, the index will be used.
  You can add other columns if you want, just put a list with their names in the add_columns parameter.

After importing rangedf and setting range_size, just call range_df() with the chosen mode name.
See all available modes in rangedf_modes.ipynb

from rangedf import Range
r = Range(df_ticks, range_size=0.0003)
df = r.range_df() # 'normal' = default
df.head(3)
df.tail(3)

	open	high	low	close	volume
datetime
2025-05-08 00:20:02.244	0.85052	0.85077	0.85047	0.85077	560.0
2025-05-08 00:37:55.846	0.85077	0.85103	0.85073	0.85103	358.0
2025-05-08 00:55:00.448	0.85105	0.85112	0.85082	0.85082	532.0

	open	high	low	close	volume
datetime
2025-05-15 21:39:16.453	0.84065	0.84091	0.84061	0.84091	185.0
2025-05-15 22:01:40.657	0.84091	0.84094	0.84064	0.84094	432.0
2025-05-15 23:53:39.843	0.84095	0.84108	0.84078	0.84108	1367.0

You can use mpf.plot() or r.plot(), as in the example below.

import mplfinance as mpf
mpf.plot(df.iloc[:130], type='candle', volume=True, style="charles",
         title=f"Range: normal\nrange_size: 0.0003")
mpf.show()
# same as:
# r.plot('normal')

As described in rangedf_modes.ipynb, we can have multiple dataframes of different modes from the same instance.

df_normal = r.range_df()
df_nongap = r.range_df('nongap')

fig = mpf.figure(style='charles', figsize=(12.5,9))
fig.subplots_adjust(hspace=0.1, wspace=0.01)
ax1 = fig.add_subplot(2,2,1)
ax2 = fig.add_subplot(2,2,2)

mpf.plot(df_normal.iloc[:130],type='candle',ax=ax1,axtitle='normal')
mpf.plot(df_nongap.iloc[:130],type='candle',ax=ax2,axtitle='nongap')
mpf.show()

Animation/Real-time Range Chart

To run the animation examples, clone this repository, then into the rangedf/examples folder, run:

• python ws_animchart_demo.py
• python ws_multichart_demo.py
• python ws_externaldf_demo.py

NOTE: There are comments, in each of the above example files, talking about situations or recommendations to be applied in real cases.

Is the Range chart calculated correctly?

I also asked myself the same question, how about we see for ourselves?
We are going to do this based on Spotware's FX/CFD Trading Platform called cTrader,
using IC Markets/Trading as a Price Provider.

Let's go!

RESUME: Despite the possible occurrence of more/less bars in cases of gap, the range calculation is practically the same or very approximate.

Non-Affiliation Disclaimer

I'm not endorsed by, directly affiliated with, maintained, authorized, or sponsored by any company previously mentioned. All product and company names are the registered trademarks of their original owners. The use of any trade name or trademark is for identification and reference purposes only and does not imply any association with the trademark holder of their product brand.