rl_record_policy

Function will write a policy 'decision' to the csv file specific for each Expert Advisor
<a href="https://lifecycle.r-lib.org/articles/stages.html#stable"><img src="figures/lifecycle-stable.svg?package=lazytrade&version=0.5.4" alt="[Stable]"></a>

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>.

Vladimir Zhbanko

lazytrade

Learn Computer and Data Science using Algorithmic Trading

rl_record_policy function

<dl><dt>x</dt>
<dd><ul>
<li>Dataframe containing columns MarketType and Policy</li>
</ul></dd>
<dt>last_result</dt>
<dd><ul>
<li>character vector of the last result of the trade</li>
</ul></dd>
<dt>trading_system</dt>
<dd><ul>
<li>character vector of length 1 with Trading System Magic Number information</li>
</ul></dd>
<dt>path_terminal</dt>
<dd><ul>
<li>path to the sandbox where this Policy/Decision must be written</li>
</ul></dd>
<dt>fileName</dt>
<dd><ul>
<li>string, desired control file prefix e.g. 'SystemControl'</li>
</ul></dd></dl>

Arguments

Author

Function will write a policy 'decision' to the csv file specific for each Expert Advisor
<a href='https://lifecycle.r-lib.org/articles/stages.html#stable'><img src='figures/lifecycle-stable.svg' alt='[Stable]' /></a>

Record Reinforcement Learning Policy. — rl_record_policy

<dl>

<dt>x</dt>
<dd><ul>
<li>Dataframe containing columns MarketType and Policy</li>
</ul></dd>


<dt>last_result</dt>
<dd><ul>
<li>character vector of the last result of the trade</li>
</ul></dd>


<dt>trading_system</dt>
<dd><ul>
<li>character vector of length 1 with Trading System Magic Number information</li>
</ul></dd>


<dt>path_terminal</dt>
<dd><ul>
<li>path to the sandbox where this Policy/Decision must be written</li>
</ul></dd>


<dt>fileName</dt>
<dd><ul>
<li>string, desired control file prefix e.g. 'SystemControl'</li>
</ul></dd>

</dl>

rl_record_policy: Record Reinforcement Learning Policy.

Description

Usage

Value

Arguments

Author

Details

Examples