lazytrade: Learn Computer and Data Science using Algorithmic Trading

Provide sets of functions and methods to learn and practice data science using idea of algorithmic trading. Main goal is to process information within "Decision Support System" to come up with analysis or predictions. There are several utilities such as dynamic and adaptive risk management using reinforcement learning and even functions to generate predictions of price changes using pattern recognition deep regression learning. Summary of Methods used: Awesome H2O tutorials: <>, Market Type research of Van Tharp Institute: <>, Reinforcement Learning R package: <>.

Version: 0.5.3
Depends: R (≥ 3.6.0)
Imports: readr, stringr, dplyr, tibble, lubridate, ggplot2, grDevices, h2o, ReinforcementLearning, openssl, stats, cluster, lifecycle
Suggests: testthat (≥ 2.1.0), covr, magrittr, data.table, bit64
Published: 2021-12-15
DOI: 10.32614/CRAN.package.lazytrade
Author: Vladimir Zhbanko
Maintainer: Vladimir Zhbanko <vladimir.zhbanko at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: lazytrade results


Reference manual: lazytrade.pdf


Package source: lazytrade_0.5.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): lazytrade_0.5.3.tgz, r-oldrel (arm64): lazytrade_0.5.3.tgz, r-release (x86_64): lazytrade_0.5.3.tgz, r-oldrel (x86_64): lazytrade_0.5.3.tgz
Old sources: lazytrade archive


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