[PRE REVIEW]: StockAIT : Python Machine Learning Framework for Stock Artificial Intelligence Trader

[editorialbot]

Submitting author: !--author-handle-->@ag-su<!--end-author-handle-- (EUNSU KIM) Repository: https://github.com/stockAI-py/stockait Branch with paper.md (empty if default branch): Version: v1.0.0 Editor: Pending Reviewers: Pending Managing EiC: Arfon Smith

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[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.88  T=0.06 s (390.6 files/s, 349512.6 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
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Jupyter Notebook                12              0          16403           2286
Python                           6            419            153            770
Markdown                         3            115              0            194
TeX                              1             33              0            183
YAML                             1              1              4             18
-------------------------------------------------------------------------------
SUM:                            23            568          16560           3451
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gitinspector failed to run statistical information for the repository

[editorialbot]

Wordcount for paper.md is 1478

[editorialbot]

Failed to discover a Statement of need section in paper

[editorialbot]

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.1109/ICICCS53718.2022.9788231 is OK
- 10.1109/BDEIM55082.2021.00062 is OK
- 10.1109/TKDE.2005.176 is OK

MISSING DOIs

- 10.1016/j.procs.2020.03.326 may be a valid DOI for title: Stock closing price prediction using machine learning techniques
- 10.1007/s10462-019-09754-z may be a valid DOI for title: A systematic review of fundamental and technical analysis of stock market predictions
- 10.1016/j.eswa.2022.118581 may be a valid DOI for title: A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost
- 10.1109/iccons.2018.8663035 may be a valid DOI for title: Stock price forecasting using data from Yahoo finance and analysing seasonal and nonseasonal trend
- 10.1016/j.knosys.2021.107119 may be a valid DOI for title: Technical analysis strategy optimization using a machine learning approach in stock market indices
- 10.1109/icses52305.2021.9633961 may be a valid DOI for title: Technical Analysis of Pattern Based Stock Prediction Model Using Machine Learning
- 10.1145/3447548.3467297 may be a valid DOI for title: Accurate multivariate stock movement prediction via data-axis transformer with multi-level contexts
- 10.1007/978-3-030-56485-8_3 may be a valid DOI for title: Random forests

INVALID DOIs

- None

[editorialbot]

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

[editorialbot]

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⚠️ Note to editors: If these papers look like they might be a good match, click through to the review issue for that paper and invite one or more of the authors before before considering asking the reviewers of these papers to review again for JOSS.

[arfon]

@editorialbot query scope

@ag-su – many thanks for your submission to JOSS. I'm going to start a scope review amongst the JOSS editors to decide if this submission is research software as defined by JOSS. This does not mean that it is not software that is useful in research, but just that JOSS might not consider it in scope for review as research software.

This process will likely take a week or so to complete.

[editorialbot]

Submission flagged for editorial review.

[arfon]

@ag-su – thanks for your submission to JOSS. Unfortunately, after review by the JOSS editorial team we've determined that this submission doesn't meet our substantial scholarly effort criterion. In addition, there were concerns whether this submission fits our scope (i.e., whether or not it is research software).

One possible alternative to JOSS is to follow GitHub's guide on how to create a permanent archive and DOI for your software. This DOI can then be used by others to cite your work.

[arfon]

@editorialbot reject

[editorialbot]

Paper rejected.