profit_factor_data

datasets

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

profit_factor_data function

A dataframe with several columns<dl>
<dt>X1</dt>
<dd>Unique identifiers of the Trading Robots</dd><dt>X2</dt>
<dd>Ticket Number of closed position</dd><dt>X3</dt>
<dd>Date and Time when order started</dd><dt>X4</dt>
<dd>Date and Time when order closed</dd><dt>X5</dt>
<dd>Monetary result of the trade</dd><dt>X6</dt>
<dd>Symbol of the Asset e.g. EURUSD</dd><dt>X7</dt>
<dd>Order Type 0 - buy, 1 - sell</dd>
</dl>

Format

Table with Trade results samples — profit_factor_data

A dataframe with several columns<dl>
<dt>X1</dt>
<dd>Unique identifiers of the Trading Robots</dd>

<dt>X2</dt>
<dd>Ticket Number of closed position</dd>

<dt>X3</dt>
<dd>Date and Time when order started</dd>

<dt>X4</dt>
<dd>Date and Time when order closed</dd>

<dt>X5</dt>
<dd>Monetary result of the trade</dd>

<dt>X6</dt>
<dd>Symbol of the Asset e.g. EURUSD</dd>

<dt>X7</dt>
<dd>Order Type 0 - buy, 1 - sell</dd>


</dl>

profit_factor_data: Table with Trade results samples

Description

Usage

Arguments

Format