Neural Networks - page 25

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Predicting the direction and movement of stock index prices is difficult, often leading to excessive trading, transaction costs, and missed opportunities. Often traders need a systematic method to not only spot trading opportunities, but to also provide a consistent approach, thereby minimizing trading errors and costs. While mechanical trading systems exist, they are usually designed for a specific stock, stock index, or other financial asset, and are often highly dependent on preselected inputs and model parameters that are expected to continue providing trading information well after the initial training or back-tested model development period. The following research leads to a detailed trading model that provides a more effective and intelligent way for recognizing trading signals and assisting investors with trading decisions by utilizing a system that adapts both the inputs and the prediction model based on the desired output. To illustrate the adaptive approach, multiple inputs and modeling techniques are utilized, including neural networks, particle swarm optimization, and denoising. Simulations with stock indexes illustrate how traders can generate higher returns using the developed adaptive decision support system model. The benefits of adding adaptive and intelligent decision making to forecasts are also discussed.


 
Predicting the market’s behavior to profit from trading stocks is far from trivial. Such a task becomes even harder when investors do not have large amounts of money available, and thus cannot influence this complex system in any way. Machine learning paradigms have been already applied to financial forecasting, but usually with no restrictions on the size of the investor’s budget. In this paper, we analyze an evolutionary portfolio optimizer for the management of limited budgets, dissecting each part of the framework, discussing in detail the issues and the motivations that led to the final choices. Expected returns are modeled resorting to artificial neural networks trained on past market data, and the portfolio composition is chosen by approximating the solution to a multi-objective constrained problem. An investment simulator is eventually used to measure the portfolio performance. The proposed approach is tested on real-world data from New York’s, Milan’s and Paris’ stock exchanges, exploiting data from June 2011 to May 2014 to train the framework, and data from June 2014 to July 2015 to validate it. Experimental results demonstrate that the presented tool is able to obtain a more than satisfying profit for the considered time frame.
 

The Foreign Exchange Market is the biggest and one of the most liquid markets in the world. This market has always been one of the most challenging markets as far as short term prediction is concerned. Due to the chaotic, noisy, and non-stationary nature of the data, the majority of the research has been focused on daily, weekly, or even monthly prediction. The literature review revealed that there is a gap for intra-day market prediction. Identifying this gap, this paper introduces a prediction and decision making model based on Artificial Neural Networks (ANN) and Genetic Algorithms. The dataset utilized for this research comprises of 70 weeks of past currency rates of the 3 most traded currency pairs: GBP\USD, EUR\GBP, and EUR\USD. The initial statistical tests confirmed with a significance of more than 95% that the daily FOREX currency rates time series are not randomly distributed. Another important result is that the proposed model achieved 72.5% prediction accuracy. Furthermore, implementing the optimal trading strategy, this model produced 23.3% Annualized Net Return. (c) 2013 Elsevier Ltd. All rights reserved.


 

Neural Network Indicators Development

Hi!

Im trying to make some neural network indicators for metatrader4, and would like some sugestions, mostly regarding inputs and outputs of the net, and maybe the structure or type of net that you consider the best for this application.

As far as in know the best outputs for financial series forecasting, are price range forcasting, tops or bottoms forecasting, and that tipe of things. Forecasting directly the price (open, close) doesnt get good results because to numerous reasons, for example a little shift on the time between the open time and the close time could change their values considerately.

If anyone has a sugestion i'll be glad to listen to it and try it.

By the way, im no expert neural network programmer, i just have a good overall idea on the subject =P.

Thanks in advance,

JCC

 

The Foreign Exchange Market is the biggest and one of the most liquid markets in the world. This market has always been one of the most challenging markets as far as short term prediction is concerned. Due to the chaotic, noisy, and non-stationary nature of the data, the majority of the research has been focused on daily, weekly, or even monthly prediction. The literature review revealed that there is a gap for intra-day market prediction. Identifying this gap, this paper introduces a prediction and decision making model based on Artificial Neural Networks (ANN) and Genetic Algorithms. The dataset utilized for this research comprises of 70 weeks of past currency rates of the 3 most traded currency pairs: GBP\USD, EUR\GBP, and EUR\USD. The initial statistical tests confirmed with a significance of more than 95% that the daily FOREX currency rates time series are not randomly distributed. Another important result is that the proposed model achieved 72.5% prediction accuracy. Furthermore, implementing the optimal trading strategy, this model produced 23.3% Annualized Net Return.


 
This paper studies whether it is possible to exploit the nonlinear behaviour of daily returns on the Spanish Ibex-35 stock index returns to improve forecasts over short and long horizons. In this sense, we examine the out-of-sample forecast performance of smooth transition autoregression (STAR) models and artificial neural networks (ANNs). We use one-step (obtained by using recursive and nonrecursive regressions) and multi-step-ahead forecasting methods. The forecasts are evaluated with statistical and economic criteria. In terms of statistical criteria, we compared the out-of-sample forecasts using goodness of forecast measures and various testing approaches. The results indicate that ANNs consistently surpass the random walk model and, although the evidence for this is weaker, provide better forecasts than the linear AR model and the STAR models for some forecast horizons and forecasting methods. In terms of the economic criteria, we assess the relative forecast performance in a simple trading strategy including the impact of transaction costs on trading strategy profits. The results indicate a better fit for ANN models, in terms of the mean net return and Sharpe risk-adjusted ratio, by using one-step-ahead forecasts. These results show there is a good chance of obtaining a more accurate fit and forecast of the daily stock index returns by using one-step-ahead predictors and nonlinear models, but that these are inherently complex and present a difficult economic interpretation.
 
We predict stock markets using information contained in articles published on the Web. Mostly textual articles appearing in the leading and the most influential financial newspapers are taken as input. From those articles the daily closing values of major stock market indices in Asia, Europe and America are predicted. Textual statements contain not only the effect (e.g., stocks down) but also the possible causes of the event (e.g., stocks down because of weakness in the dollar and consequently a weakening of the treasury bonds). Exploiting textual information therefore increases the quality of the input. The forecasts are available real-time via www.cs.ust.hk/~beat/Predict daily at 7:45 am Hong Kong time. Hence all predictions are available before the major Asian markets start trading. Several techniques, such as rule-based, k-NN algorithm and neural net, have been employed to produce the forecasts. Those techniques are compared with one another. A trading strategy based on the system...
 
Based on the trend background of financial development in China in recent years, and statistical analysis of trend line, this paper establishes the quantitative trading strategy through the BP Neural Network Algorithm and the Fisher Linear Discriminant. Firstly, the data is linearly regressed into equal-length trend lines and the slope is fuzzified to build the matrix of upward trend and downward trend. And then use BP Neural Network Algorithm and Fisher Linear Discriminant to carry on the price forecast respectively and take transaction behavior, and correspondingly we take Shanghai and Shenzhen 300 stock index futures as an example to carry on the back test. The result shows that, firstly, the initial price trend is well retained by fitting; secondly, the profitability and risk control ability of the trading system are improved through the training optimization of Neural Network and Fisher Linear Discriminant.
 

OpenNN (Open Neural Networks Library) is a software library written in the C++ programming language which implements neural networks, a main area of deep learning research..

OpenNN implements data mining methods as a bundle of functions. These can be embedded in other software tools using an application programming interface (API) for the interaction between the software tool and the predictive analytics tasks. In this regard, a graphical user interface is missing but some functions can support the integration of specific visualization tools.

The main advantage of OpenNN is its high performance. This library outstands in terms of execution speed and memory allocation. It is constantly optimized and parallelized in order to maximize its efficiency.

http://www.opennn.net/
 

Neural Network

Neural Network: discussion/development threads

  1. Better NN EA development thread with indicators, pdf files and so on.
  2. Better NN EA final thread
  3. Neural Networks thread (good public discussion)
  4. How to build a NN-EA in MT4: usefull thread for developers.
  5. Radial Basis Network (RBN) - As Fit Filter For Price: the thread 

Neural Network: Indicators and systems development

  1. Self-trained MA cross!: development thread for new generation of the indicators
  2. Levenberg-Marquardt algorithm: development thread

Neural Network: EAs

  1. CyberiaTrader EA: discussion thread and EAs' thread.
  2. Self learning expert thread with EAs' files here.
  3. Artificial Intelligence EAs threads: How to "teach" and to use the AI ("neuron") EA thread and Artificial Intelligence thread
  4. Forex_NN_Expert EA and indicator thread.
  5. SpiNNaker - A Neural Network EA thread. 

Neural Network: The Books

  1. What to read and where to learn about Machine Learning (10 free books) - the post.

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