NLP project
This is an attempt to build a reinforcement learning model for generating trading signals. It uses a limited vocabulary such as 'go-long', 'go-short' and 'do-nothing'.
Usage
Install the project with:
pip install defirl
Then:
from defirl.defi import main as RLmodel
Warning
This is not financial advise. NLP project is entirely on its preliminary stages. Use it at your own risk.
1 maintainer
Metadata
1 maintainer
Release files for defirl 1.3.52
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| defirl-1.3.52.tar.gz | 157.3 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| defirl-1.3.52-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 314.0 kB
Release files / defirl-1.3.52.tar.gz
| Download URL | defirl-1.3.52.tar.gz |
|---|---|
| Size | 157.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.11.14
|
Release files / defirl-1.3.52-py3-none-any.whl
| Download URL | defirl-1.3.52-py3-none-any.whl |
|---|---|
| Size | 156.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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|
|
BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.11.14
|
Security for defirl 1.3.52
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None of the files in this release were uploaded with provenance metadata.
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