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

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Younes Chihab ◽  
Zineb Bousbaa ◽  
Marouane Chihab ◽  
Omar Bencharef ◽  
Soumia Ziti
Keyword(s):  
Random Forest ◽  
Foreign Exchange ◽  
Growth Index ◽  
Trading Strategy ◽  
Good Prediction ◽  
Currency Markets ◽  
Probit Regression ◽  
Trading Systems ◽  
Currency Market ◽  
Minimum Requirements

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.

Do the calendar anomalies still exist? Evidence from Indian currency market

2016 ◽  
Vol 42 (2) ◽  
pp. 136-150 ◽  
Author(s):  
Satish Kumar ◽  
Rajesh Pathak
Keyword(s):  
Least Square ◽  
January Effect ◽  
Currency Markets ◽  
Content Type ◽  
Currency Market ◽  
Before And After ◽  
Calendar Anomalies ◽  
Ordinary Least Square Regression ◽  
The 2008 Financial Crisis ◽  
Using Data

Purpose – The purpose of this paper is to examine the presence of the day-of-the-week (DOW) and January effect in the Indian currency market for selected currency pairs; USD-(Indian rupee) INR, EUR-INR, GBP-INR and JPY-INR, from January, 1999 to December, 2014. Design/methodology/approach – Ordinary least square regression analysis is used to examine the presence of DOW and January effect to test the efficiency of the Indian currency market. The sample period is later divided into two sub-periods, that is, pre- and post-2008 to capture the behavior of returns before and after the 2008 financial crisis. Further, the authors also use the non-parametric technique, the Kruskal-Wallis test, to provide robustness check for the results. Findings – The results indicate that the returns during Monday to Wednesday are positive and higher than the returns on Thursday and Friday which show negative returns. The returns during January are found to be higher than the returns during rest of the year. Further, all currencies exhibit significant DOW and January effects in pre-crisis period, however, post-crisis; these effects disappear for all currencies indicating that the markets have become more efficient in the later time. The findings can be further attributed to the increased intervention in the forex markets by the Reserve Bank of India after the crisis. Practical implications – The results have important implications for both traders and investors. The findings suggest that the investors might not be able to earn excess profits by timing their positions in some particular currencies taking the advantage of DOW or January effect which in turn indicates that the currency markets have become more efficient with time. The results are in conformity with those reported for the developed markets. The results might be appealing to the practitioners as well in a way that they can consider the state of financial market for financial decision making. Originality/value – The authors provide the first study to examine the calendar anomalies (DOW and January effect) across a range of emerging currencies using 16 years of data from January, 1999 to December, 2014. To the best of the authors’ knowledge, no study has yet examined these calendar anomalies in the currency markets using data which covers two important periods, pre-2008 and post-2008.

Return Dynamics and Trading Strategy in Alternative Trading Systems

2012 ◽  
Vol 7 (3) ◽  
pp. 52-65
Author(s):  
Jeffrey R. Black ◽  
Hong Miao ◽  
Sanjay Ramchander
Keyword(s):  
Trading Strategy ◽  
Trading Systems ◽  
Return Dynamics

scholarly journals Grid Trading System Robot (GTSbot): A Novel Mathematical Algorithm for trading FX Market

2019 ◽  
Vol 9 (9) ◽  
pp. 1796 ◽  
Author(s):  
Rundo ◽  
Trenta ◽  
di Stallo ◽  
Battiato
Keyword(s):  
High Frequency ◽  
High Performance ◽  
Foreign Exchange Market ◽  
Financial Time Series ◽  
Trading Strategy ◽  
High Frequency Trading ◽  
Trading System ◽  
Trading Systems ◽  
Time Series Forecast ◽  
Financial Transactions

Grid algorithmic trading has become quite popular among traders because it shows several advantages with respect to similar approaches. Basically, a grid trading strategy is a method that seeks to make profit on the market movements of the underlying financial instrument by positioning buy and sell orders properly time-spaced (grid distance). The main advantage of the grid trading strategy is the financial sustainability of the algorithm because it provides a robust way to mediate losses in financial transactions even though this also means very complicated trades management algorithm. For these reasons, grid trading is certainly one of the best approaches to be used in high frequency trading (HFT) strategies. Due to the high level of unpredictability of the financial markets, many investment funds and institutional traders are opting for the HFT (high frequency trading) systems, which allow them to obtain high performance due to the large number of financial transactions executed in the short-term timeframe. The combination of HFT strategies with the use of machine learning methods for the financial time series forecast, has significantly improved the capability and overall performance of the modern automated trading systems. Taking this into account, the authors propose an automatic HFT grid trading system that operates in the FOREX (foreign exchange) market. The performance of the proposed algorithm together with the reduced drawdown confirmed the effectiveness and robustness of the proposed approach.

scholarly journals Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6756
Author(s):  
DongHyun Ko ◽  
Seok-Hwan Choi ◽  
Sungyong Ahn ◽  
Yoon-Ho Choi
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Indoor Localization ◽  
Artificial Noise ◽  
Good Prediction ◽  
Localization Method ◽  
Good Prediction Accuracy ◽  
Attack Scenario ◽  
Natural Noise

With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it. So far, two representative methods, i.e., the filtering-based method and deep learning-based method, have shown a significant effect in removing natural noise. However, the prediction accuracy of these methods severely decreased under artificial noise caused by adversaries. In this paper, we introduce a new media access control (MAC) spoofing attack scenario injecting artificial noise, where the prediction accuracy of Wi-Fi-based indoor localization system significantly decreases. We also propose a new deep learning-based indoor localization method using random forest(RF)-filter to provide the good prediction accuracy under the new MAC spoofing attack scenario. From the experimental results, we show that the proposed indoor localization method provides much higher prediction accuracy than the previous methods in environments with artificial noise.

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