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Trend trading model in the financial market using machine learning algorithms. The machine learning algorithm predicts the result of the transaction of the base trading model and predicts the price of the next timeframe.
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Okrugnosti/Forex-and-Stock-Python-Trade-Model
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Структура каталога ├── LICENSE ├── Makefile <- Makefile with commands like `make data` or `make train` ├── README.md <- The top-level README for developers using this project. ├── data │ ├── external <- Data from third party sources. │ ├── interim <- Intermediate data that has been transformed. │ ├── processed <- The final, canonical data sets for modeling. │ └── raw <- The original, immutable data dump. │ ├── docs <- A default Sphinx project; see sphinx-doc.org for details │ ├── models <- Trained and serialized models, model predictions, or model summaries │ тут храняться готовые скомпилированные модели, с подобранными параметрами, | либо специально сохраненные модели, которые сразу можно загрузить в код | ├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), │ the creator's initials, and a short `-` delimited description, e.g. │ `1.0-jqp-initial-data-exploration`. │ ├── references <- Data dictionaries, manuals, and all other explanatory materials. │ Словари данных, руководства и все другие пояснительные материалы | ├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. │ └── figures <- Generated graphics and figures to be used in reporting │ ├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g. │ generated with `pip freeze > requirements.txt` │ ├── src <- Source code for use in this project. │ ├── __init__.py <- Makes src a Python module │ │ │ ├── data <- Scripts to download or generate data │ │ └── make_dataset.py │ │ │ ├── features <- Scripts to turn raw data into features for modeling │ │ └── build_features.py │ │ │ ├── models <- Scripts to train models and then use trained models to make │ │ │ predictions │ │ ├── predict_model.py │ │ └── train_model.py │ │ │ └── visualization <- Scripts to create exploratory and results oriented visualizations │ └── visualize.py │ └── tox.ini <- tox file with settings for running tox; see tox.testrun.org Настройка окружения: 1) pip install -r requirements.txt 2) pip install -r requirements.txt --find-links=wheels --no-index
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Trend trading model in the financial market using machine learning algorithms. The machine learning algorithm predicts the result of the transaction of the base trading model and predicts the price of the next timeframe.
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