R_selflearning

Repository for the Udemy course. Please support this project by joining this on-line course:

https://www.udemy.com/self-learning-trading-robot/?couponCode=SELF-LEARN-BOT

Note: this course is the part of the series of step-by-step tutorials alowing to build more comprehensive Automated Trading System based on Decision Support System approach. Check out other courses of the series. Contact the author for questions.

Goal

Create model-based self-testing artificially intelligent trading system.

Motivation

There are probably just 2 possible types of mechanical trading systems:

Human Idea[captured from the screen pattern] -> Indicator[parameters] + fix trading rules + Fresh Data = trading decision
Past Data + Algorithm[based on some idea] -> Model[hyperparameters] + Fresh Data = trading decision [via predicted probability]

The first approach relies on finding suitable parameters via optimization [bruteforcing many trades scenarios to find the best] and testing to confirm. This is typically done using MT4 terminal with optimization module

The second approach, developed in this course, relies on the model that would be trained to recognize the dominant pattern to generate trading decision. Approach will involve use of neural network model on h2o frame. The CPU is not 'bruteforcing' trades execution but follows the algorithm rules trying to find best parameters of the deep learning model. Technically use of NN models is more efficient as modern algorithms can use all CPU cores. As apposed to MT4 platform that can only use 1 CPU. At the same time training NN is more challenging due to reproducibility, overfitting issues, etc.

Main Features

Repository contains functions and scripts capable to:

import financial data: (price levels and indicator)
transform data for modelling
perform deep learning
test deep learning model
use model and fresh data to generate new predictions

Particularity of this project is that data and modelling is coming from 28 forex currency pairs. Data used for modelling is then multiplied to increase dataset.

Advantages

More efficient trading generation tool:

Both reduced time and energy spent to perform modelling and test.
Fully automated execution.

Challenges

Trading decisions are moved to the separate Decision Support System fully coded in R. This will imply the following: