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a7414eac233cbf3587eced76fb175fd6fdc20b98
* feat: initial wandb configured. Sweep parameters aren't configured yet. * feat: Wandb logs now results. * feat: gitignore. * fix: Took out print() * feat: Changed default value of wandb to False. * feat: Added wandb to turn of automatically if there is no environment variable to start it (when we push it). Added environment configuration aswell. * fix(Dependencies): the package name seems to be python-dotenv Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
refactor(Project): move out load_data to utils, rename fetch_data to
run_fetch_data, got classifiers to work (#38)
refactor(Project): move out load_data to utils, rename fetch_data to
run_fetch_data, got classifiers to work (#38)
refactor(Project): move out load_data to utils, rename fetch_data to
run_fetch_data, got classifiers to work (#38)
feat(Data): added feature extractors, and feature extractor presets, removed a bunch of custom arguments from load_data (#42)
fix(Training): major bug in scaling, we only scaled training data, not the ones we're predicting from (#45)
feat(Core): ensemble models, correct forward returns calculation, scaling, only train from when asset returns are available, major bug fixed in walk_forward_train_test (#35)
refactor(Project): move out load_data to utils, rename fetch_data to
run_fetch_data, got classifiers to work (#38)
Financial time series prediction models
Installation
Use the conda environment file attached!:)
Description
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Python
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Jupyter Notebook
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