Table with aggregated trade results

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.

Vladimir Zhbanko

lazytrade

Learn Computer and Data Science using Algorithmic Trading

DFR function

A dataframe with one column<dl class="dl-horizontal">
 <dt>MagicNumber</dt><dd>Unique identifiers of the Trading Robots from Trade Log</dd>
 <dt>TicketNumber</dt><dd>Ticket Number of closed position</dd>
 <dt>OrderStartTime</dt><dd>Date and Time when order started</dd>
 <dt>OrderCloseTime</dt><dd>Date and Time when order closed</dd>
 <dt>Profit</dt><dd>Monetary result of the trade</dd>
 <dt>Symbol</dt><dd>Symbol of the Asset e.g. EURUSD</dd>
 <dt>OrderType</dt><dd>Order Type 0 - buy, 1 - sell</dd>
 <dt>CUMSUM_PNL</dt><dd>Cumulative sum of the ordered data</dd></dl>

DFR: Table with aggregated trade results

Description

Usage

Arguments

Format