# A Review on Recent Advancements in FOREX Currency Prediction
## 6 Brief Review of FOREX Price Prediction Techniques
### Optimization Techniques  
[PERSON], [PERSON], and [PERSON] [51] proposed a forecasting model that combines the Jaya optimization technique with extreme learning machines, which is capable of predicting currency exchange rates. They used two currency pairs, USDEUR and USDINR. They evaluated their proposed model's performance using MAPE, ARV, Theil's U, and MAE. They compared their model's performance with other neural network models. They tested their model against other models based on ELM, NN, and FLANN. They found that regarding ELM, NN, and FLANN, ELM shows the best optimization. According to their evaluation data, for MAPE evaluation, ELM DE provides the lowest error. For MAE, ARV, and Theil's U models ELM TLBO, ELM PSO, and ELM Jaya provided the best results respectively. [PERSON] and [PERSON] (2018) proposed a technical system for FOREX that was inspired by the Donchian channel method. The main purpose of their strategy was to create profitable portfolios for FOREX trading. They first constructed the modified Renko bars (MRBs) by combining their trading rules. Their modified MRBs proved to be more accurately responsive than the normal candlesticks used in FOREX. They created an optimization stage used by eight currency pairs: NZD/USD, USD/JPY, GBP/USD, EUR/USD, AUD/USD, EUR/JPY, GBP/JPY, and USD/CAD. To achieve their optimization stage, they used three search-derivative-free global optimization techniques. These algorithms were the swarm optimization algorithm, also known as dividing a hyperrectangle (DIRECT), along with multilevel coordinate search (MCS) and pity beetle algorithm (PBA). They tested their optimization strategy and based on the return they constructed two kinds of portfolios: an equally weighted portfolio and a Kelly criterion-based portfolio. They evaluated the performance of their system based on the geometric return, arithmetic mean, and Sharpe ratio. The performance was then matched with the popular Barclay CTA index, S&P500 benchmarks. They combined the total return of currency pairs on seven years of data from 2010 to 2016; GBP/JPY provided the highest return at 217.59%. EUR/JPY was the second best with 208.74% total return. The worst was the USD/CAD currency pair with only 29.10% total return. They found out that the proposed model is not appropriate for three currency pairs: AUD/USD, NZD/USD, and USD/CAD, which provided low returns on their system. Thus, they excluded them and built two additional portfolios: one based on the [PERSON] criterion and a second equally weighted one, with five currency pairs that had an average return greater than 200%. The result showed that the newly created portfolio based on the Kelly criterion has high stability in its performance and performs better than the old system. [PERSON] and [PERSON] (2018) suggested a model for FOREX prediction that was based on a quantile regression neural network and particle swarm optimization. They used PSO to train the QRNN and named the model PSO-QRNN. They used eight currencies: USDINR, EURUSD, GBPUSD and USDJPY, oil price, gold price, NSE India stock index, and standard and poor 500 stock index as datasets for their model. They used seven different algorithms: group method of data handling (GMDH), multilayer perceptron (MLP), random forest (RF), a quantile regression neural network (QRNN), generalized autoregressive conditional heteroskedasticity (GARCH), quantile regression random forest (QRRF), and a general regression neural network (GRNN) for the performance analysis of their model. Their suggested model provided better results than all of the compared models. The results gotten for test data using MSE were as follows: for USD-JPY 0.0000001027, for USD-GBP 0.00000098, for USD-EUR 0.0000000612, for USD-INR 0.000000838, for gold price 0.000002050, for crude oil 0.00000369, for S&P stock 0.000000815, and for NSE Indian stock 0.00000537. When they performed the Diebold-Mariano (DM) evaluation test on all the test results, they found that their proposed PSO-QRNN model performed better than all models on two datasets.