tclf
A scikit-learn compatible classifier to perform trade classification in Python.
Science Score: 57.0%
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○Scientific vocabulary similarity
Low similarity (12.7%) to scientific vocabulary
Keywords
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Repository
A scikit-learn compatible classifier to perform trade classification in Python.
Basic Info
- Host: GitHub
- Owner: KarelZe
- License: bsd-3-clause
- Language: Python
- Default Branch: main
- Homepage: https://karelze.github.io/tclf/
- Size: 2.93 MB
Statistics
- Stars: 19
- Watchers: 3
- Forks: 1
- Open Issues: 1
- Releases: 7
Topics
Metadata Files
README.md
Trade Classification With Python
Documentation ✒️: https://karelze.github.io/tclf/
Source Code 🐍: https://github.com/KarelZe/tclf
tclf is a scikit-learn-compatible implementation of trade classification algorithms to classify financial markets transactions into buyer- and seller-initiated trades.
The key features are:
- Easy: Easy to use and learn.
- Sklearn-compatible: Compatible to the sklearn API. Use sklearn metrics and visualizations.
- Feature complete: Wide range of supported algorithms. Use the algorithms individually or stack them like LEGO blocks.
Installation
pip
console
pip install tclf
uv⚡
console
uv add tclf
Supported Algorithms
- (Rev.) CLNV rule[^1]
- (Rev.) EMO rule[^2]
- (Rev.) LR algorithm[^6]
- (Rev.) Tick test[^5]
- Depth rule[^3]
- Quote rule[^4]
- Tradesize rule[^3]
For a primer on trade classification rules visit the rules section 🆕 in our docs.
Minimal Example
Let's start simple: classify all trades by the quote rule and all other trades, which cannot be classified by the quote rule, randomly.
Create a main.py with:
```python title="main.py"
import numpy as np
import pandas as pd
from tclf.classical_classifier import ClassicalClassifier
X = pd.DataFrame( [ [1.5, 1, 3], [2.5, 1, 3], [1.5, 3, 1], [2.5, 3, 1], [1, np.nan, 1], [3, np.nan, np.nan], ], columns=["tradeprice", "bidex", "ask_ex"], )
clf = ClassicalClassifier(layers=[("quote", "ex")], strategy="random")
clf.fit(X)
probs = clf.predict_proba(X)
Run your script with
console
$ python main.py
```
In this example, input data is available as a pd.DataFrame with columns conforming to our naming conventions.
The parameter layers=[("quote", "ex")] sets the quote rule at the exchange level and strategy="random" specifies the fallback strategy for unclassified trades.
Advanced Example
Often it is desirable to classify both on exchange level data and nbbo data. Also, data might only be available as a numpy array. So let's extend the previous example by classifying using the quote rule at exchange level, then at nbbo and all other trades randomly.
```python title="main.py" hllines="6 16 17 20" import numpy as np from sklearn.metrics import accuracyscore
from tclf.classical_classifier import ClassicalClassifier
X = np.array( [ [1.5, 1, 3, 2, 2.5], [2.5, 1, 3, 1, 3], [1.5, 3, 1, 1, 3], [2.5, 3, 1, 1, 3], [1, np.nan, 1, 1, 3], [3, np.nan, np.nan, 1, 3], ] ) ytrue = np.array([-1, 1, 1, -1, -1, 1]) features = ["tradeprice", "bidex", "askex", "bidbest", "askbest"]
clf = ClassicalClassifier(
layers=[("quote", "ex"), ("quote", "best")], strategy="random", features=features
)
clf.fit(X)
acc = accuracyscore(ytrue, clf.predict(X))
``
In this example, input data is available as np.arrays with both exchange ("ex") and nbbo data ("best"). We set the layers parameter tolayers=[("quote", "ex"), ("quote", "best")]to classify trades first on subset"ex"and remaining trades on subset"best". Additionally, we have to setClassicalClassifier(..., features=features)` to pass column information to the classifier.
Like before, column/feature names must follow our naming conventions.
Other Examples
For more practical examples, see our examples section.
Development
We are using tox with uv for development.
bash
tox -e lint
tox -e format
tox -e test
tox -e build
Citation
If you are using the package in publications, please cite as:
latex
@software{bilz_tclf_2023,
author = {Bilz, Markus},
license = {BSD 3},
month = nov,
title = {{tclf} -- trade classification with python},
url = {https://github.com/KarelZe/tclf},
version = {0.0.1},
year = {2023}
}
Footnotes
[^1]:
Owner
- Name: Markus Bilz
- Login: KarelZe
- Kind: user
- Location: Germany
- Company: Atruvia
- Website: http://www.markusbilz.com
- Repositories: 37
- Profile: https://github.com/KarelZe
Citation (CITATION.cff)
cff-version: 1.2.0
title: tclf
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Markus
family-names: Bilz
email: web@markusbilz.com
orcid: 'https://orcid.org/0009-0009-6833-4393'
repository-code: 'https://github.com/KarelZe/tclf'
url: 'https://karelze.github.io/tclf/'
abstract: >-
`tclf` is a scikit-learn-compatible implementation of
trade classification algorithms to classify financial
markets transactions into buyer- and seller-initiated
trades.
keywords:
- clnv
- lee-ready
- rule-based classifier
- scikit-learn
- trade-classification
license: BSD 3
version: 0.0.3
date-released: '2024-01-06'
GitHub Events
Total
- Watch event: 2
- Delete event: 11
- Issue comment event: 89
- Push event: 71
- Pull request review comment event: 4
- Pull request review event: 5
- Pull request event: 55
- Create event: 13
Last Year
- Watch event: 2
- Delete event: 11
- Issue comment event: 89
- Push event: 71
- Pull request review comment event: 4
- Pull request review event: 5
- Pull request event: 55
- Create event: 13
Committers
Last synced: over 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Markus Bilz | m****l@m****m | 59 |
| pre-commit-ci[bot] | 6****] | 21 |
| dependabot[bot] | 4****] | 18 |
| Markus Bilz | g****b@m****m | 7 |
| github-actions[bot] | 4****] | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 14
- Total pull requests: 221
- Average time to close issues: 6 days
- Average time to close pull requests: 6 days
- Total issue authors: 3
- Total pull request authors: 4
- Average comments per issue: 0.21
- Average comments per pull request: 2.11
- Merged pull requests: 190
- Bot issues: 0
- Bot pull requests: 130
Past Year
- Issues: 0
- Pull requests: 56
- Average time to close issues: N/A
- Average time to close pull requests: 14 days
- Issue authors: 0
- Pull request authors: 3
- Average comments per issue: 0
- Average comments per pull request: 1.95
- Merged pull requests: 40
- Bot issues: 0
- Bot pull requests: 54
Top Authors
Issue Authors
- KarelZe (12)
- strcat32 (1)
- michibau (1)
Pull Request Authors
- KarelZe (91)
- pre-commit-ci[bot] (70)
- dependabot[bot] (54)
- github-actions[bot] (6)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 51 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 9
- Total maintainers: 1
pypi.org: tclf
Classify trades using trade classification algorithms 🐍
- Homepage: https://github.com/KarelZe/tclf
- Documentation: https://tclf.readthedocs.io/
- License: bsd-3-clause
-
Latest release: 0.0.9
published almost 2 years ago
Rankings
Maintainers (1)
Dependencies
- actions/checkout v2 composite
- conda-incubator/setup-miniconda v2 composite
- numpy ^1.25.1
- pandas ^2.0.3
- python ^3.10
- scikit-learn ^1.3.0
- actions/checkout v4 composite
- actions/download-artifact v3 composite
- actions/setup-python v5 composite
- actions/upload-artifact v3 composite
- pypa/gh-action-pypi-publish release/v1 composite
- sigstore/gh-action-sigstore-python v2.1.0 composite