Algo-Trading Strategy for Intraweek Foreign Exchange Speculation Based on Random Forest and Probit Regression

Abstract

In the Forex market, the price of the currencies increases and decreases rapidly based on many economic and political factors such as commercial balance, the growth index, the inflation rate, and the employment indicators. Having a good strategy to buy and sell can make a profit from the above changes. A successful strategy in Forex should take into consideration the relation between benefits and risks. In this work, we propose an intraweek foreign exchange speculation strategy for currency markets based on a combination of technical indicators. This system has a two-level decision and is composed of the Probit regression model and rules discovery using Random Forest. There are two minimum requirements for a trading strategy: a rule to enter the market and a rule to exit it. Our proposed system, to enter the currency market, should validate two conditions. First, it should validate Random Forest access rules over the following week while in the second one the predicted value of the next day using Probit should be positive. To exit the currency market just one negative warning from Probit or Random Forest is enough. This system was used to develop dynamic portfolio trading systems. The profitability of the model was examined for USD/(EUR, JYN, BRP) variation within the period from January 2014 to January 2016. The proposed system allows improving the prediction accuracy. This indicates a good prediction of the behavior market and it helps to identify the good times to enter it or to leave it.

A Client-Centric Evaluation System to Evaluate Guest’s Satisfaction on Airbnb Using Machine Learning and NLP

“…Support vector machine was introduced by Vapnik and colleagues in 1992 initially to resolve classification issues. It is applied in many fields of business, science, and industry to classify and recognize patterns [54]. Its principle has been extended to regression in order to establish predictions.…”

Designing Strategies for Autonomous Stock Trading Agents using a Random Forest Approach

“…Among these techniques, supervised learning algorithms efficiently capture the nonlinear relationships between the inputs and outputs, inferring functional relationships from the data. Machine learning algorithms for stock forecasting include the support vector machine approach [46][47][48], ANNs [18][19][20][21][22], genetic algorithms [23][24][25][26][27], GP [28-33, 43, 44], support vector regression models [49], RF [15][16][17], extreme gradient boosting (XGB) [11,17], and reinforcement learning [50]. Two reviews [34,35] cover the recent significant studies on machine learning algorithms for stock market forecasting.…”

“…Two reviews [34,35] cover the recent significant studies on machine learning algorithms for stock market forecasting. The most relevant studies are [14,16,17]; and [51] who initially applied machine learning (particularly RF) to financial forecasting.…”

“…The authors in [16] proposed an algorithmic trading strategy for the intraweek FX market trading based on an RF model and Probit regression. The RF model forecasted the medium-term evolution, whereas the Probit model was applied to the technical analysis indicators for predicting the price value of the next day.…”

“…Machine learning techniques identify the patterns in financial time series using a data driven approach. Common machine learning techniques for forecasting financial time series are decision trees [14], random forest (RF) [15][16][17], artificial neural networks (ANNs) [18][19][20][21][22] and evolutionary algorithms, such as genetic algorithms [23][24][25][26][27] and genetic programming (GP) [28][29][30][31][32][33]. Machine learning applications of financial time-series forecasting are reviewed in [34][35].…”