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: <https://github.com/h2oai/awesome-h2o>, Market Type research of Van Tharp Institute: <https://vantharp.com/>, Reinforcement Learning R package: <https://CRAN.R-project.org/package=ReinforcementLearning>.
Version: 0.5.4 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: 2024-07-16 DOI: 10.32614/CRAN.package.lazytrade Author: Vladimir Zhbanko Maintainer: Vladimir Zhbanko <vladimir.zhbanko at gmail.com> BugReports: https://github.com/vzhomeexperiments/lazytrade/issues License: MIT + file LICENSE URL: https://vladdsm.github.io/myblog_attempt/topics/lazy%20trading/, https://github.com/vzhomeexperiments/lazytrade NeedsCompilation: no Language: en-US Materials: README, NEWS CRAN checks: lazytrade results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=lazytrade to link to this page.
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