Ultra-short-term trading system using a neural network-based ensemble of financial technical indicators

2021 ◽  
Author(s):  
Theodoros Zafeiriou ◽  
Dimitris Kalles
Keyword(s):  
Neural Network ◽  
Short Term ◽  
Trading System ◽  
Technical Indicators

SHORT-TERM TREND PREDICTION OF FOREIGN EXCHANGE RATES WITH A NEURAL-NETWORK BASED ENSEMBLE OF FINANCIAL TECHNICAL INDICATORS

2013 ◽  
Vol 22 (03) ◽  
pp. 1350016 ◽  
Author(s):  
THEODOROS ZAFEIRIOU ◽  
DIMITRIS KALLES
Keyword(s):  
Neural Network ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Short Term ◽  
Trend Prediction ◽  
Technical Indicators ◽  
The Neural Network ◽  
Exchange Markets ◽  
Short Term Forecasting ◽  
One Year

This paper is about designing, developing and training a neural network for short-term forecasting of buy-sell trends in foreign exchange markets. We use a set of established financial technical indicators as inputs to the neural network and we develop the architecture to predict a trend and then train the network based on the accuracy of the prediction. We perform extensive real time testing with the closing prices (one per minute) of the USD/EUR exchange rates for a one-year period. The overall approach delivers a system that predicts trends substantially better than individual technical indicators.

scholarly journals Devizaárfolyam előrejelzés neurális háló modell segítségével = Foreign Exchange Rate Forecast Using Neural Network

2021 ◽  
Vol 16 (1) ◽  
pp. 117-137
Author(s):  
Zsolt Lakatos
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Foreign Exchange Rate ◽  
Neural Models ◽  
Short Term ◽  
Neural Network Models ◽  
Technical Indicators ◽  
The Neural Network ◽  
Master's Thesis ◽  
Technical Indicator

Modelljeimben a technikai indikátorok használatát kapcsolom össze a neurális hálós modellek előrejelző képességeivel. A technikai indikátorok használata mellett szól, hogy rövid távon a pénzügyi idősorok autokorreláltak, a neurális modellek pedig nemlineáris kapcsolatok modellezésére alkalmasak. A kapott eredmények révén azt a következtetést vontam le, hogy ugyan a neurális háló modellek optimalizációs képessége nagyon jó és alkalmazásukkal a megfelelő technikai indikátorok is meghatározhatók, de csak lassan képesek rátanulni az adatokra, így még a legkisebb tranzakciós költség alkalmazása mellett is csak a kezdeti befektetésünk elvesztését tudjuk halogatni. My present paper is the shortened version of my master's thesis in finance presented in November 2015, in which I presented the results of the research implemented in the Training Center for Bankers. In my models I combine the use of technical indicators with predictive capabilities of neural network models. The use of a technical indicator suggests that in the short term the financial timeseries are autocorrelated, and neural models are suitable for modeling nonlinear relationships. Based on the results I concluded that although the optimization capabilities of the neural network models are very good and their application can be determined by the appropriate technical indicators, but learning from timeseries data takes too much time, so even with the smallest transaction cost we can only delay the loss of our initial investment.

Short–Term Prediction of Available Parking Space Based on Improved Wavelet Neural Network

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Jinfen Wang ◽  
Xiaofei Ye ◽  
Zhen Yang ◽  
Qiming Ye ◽  
Chang Yang
Keyword(s):  
Neural Network ◽  
Wavelet Neural Network ◽  
Parking Space ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

Convolutional and recurrent neural network based model for short-term load forecasting

2021 ◽  
Vol 195 ◽  
pp. 107173
Author(s):  
Hosein Eskandari ◽  
Maryam Imani ◽  
Mohsen Parsa Moghaddam
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting
Sign in / Sign up

Export Citation Format

Share Document