A new multi-period investment strategies method based on evolutionary algorithms

This work introduces a new algorithmic trading method based on evolutionary algorithms and portfolio theory. The limitations of traditional portfolio theory are overcome using a multi-period definition of the problem. The model allows the inclusion of dynamic restrictions like transaction costs, portfolio unbalance, and inflation. A Monte Carlo method is proposed to handle these types of restrictions. The investment strategies method is introduced to make trading decisions based on the investor’s preference and the current state of the market. Preference is determined using heuristics instead of theoretical utility functions. The method was tested using real data from the Mexican market. The method was compared against buy-and-holds and single-period portfolios for metrics like the maximum loss, expected return, risk, the Sharpe’s ratio, and others. The results indicate investment strategies perform trading with less risk than other methods. Single-period methods attained the lowest performance in the experiments due to their high transaction costs. The conclusion was investment decisions that are improved when information providing from many different sources is considered. Also, profitable decisions are the result of a careful balance between action (transaction) and inaction (buy-and-hold).

     

Building Technical Trading System with Genetic Programming: A New Method to Test the Efficiency of Chinese Stock Markets

Testing whether technical trading rules can beat buy-and-hold strategy is a common approach to study the efficiency of stock markets. Noticing that the common approach of evaluating popular technical trading rules’ profitability would result in the biases of data snooping and incomplete test, we build a technical trading system with genetic programming to test the efficiency of Chinese stock markets. This system takes historical prices and volumes as inputs, randomly generates treelike structured technical trading rules composed of basic functions, and optimizes the rules using genetic programming according to the inputs. Using daily prices and volumes of Shenzhen Stock Exchange 100 index from January 2, 2004 to March 12, 2010, we find out that the optimal technical trading rules generated by our technical trading system have statistically significant out-of-sample excess returns compared with buy-and-hold strategy considering realistic transaction costs. Therefore, we conclude that Chinese stock markets have not achieved weak-form efficiency.     

基于FP-Tree 的快速选择性集成算法

选择性集成通过选择部分基分类器参与集成,从而提高集成分类器的泛化能力,降低预测开销.但已有的选择性集成算法普遍耗时较长,将数据挖掘的技术应用于选择性集成,提出一种基于FP-Tree(frequent pattern tree)的快速选择性集成算法:CPM-EP(coverage based pattern mining for ensemble pruning).该算法将基分类器对校验样本集的分类结果组织成一个事务数据库,从而使选择性集成问题可转化为对事务数据集的处理问题.针对所有可能的集成分类器大小,CPM-EP算法首先得到一个精简的事务数据库,并创建一棵FP-Tree树保存其内容;然后,基于该FP-Tree获得相应大小的集成分类器.在获得的所有集成分类器中,对校验样本集预测精度最高的集成分类器即为算法的输出.实验结果表明,CPM-EP算法以很低的计算开销获得优越的泛化能力,其分类器选择时间约为GASEN的1/19以及Forward-Selection的1/8,其泛化能力显著优于参与比较的其他方法,而且产生的集成分类器具有较少的基分类器.     

A genetic programming model to generate risk-adjusted technical trading rules in stock markets

Technical trading rules can be generated from historical data for decision making in stock markets. Genetic programming (GP) as an artificial intelligence technique is a valuable method to automatically generate such technical trading rules. In this paper, GP has been applied for generating risk-adjusted trading rules on individual stocks. Among many risk measures in the literature, conditional Sharpe ratio has been selected for this study because it uses conditional value at risk (CVaR) as an optimal coherent risk measure. In our proposed GP model, binary trading rules have been also extended to more realistic rules which are called trinary rules using three signals of buy, sell and no trade. Additionally we have included transaction costs, dividend and splits in our GP model for calculating more accurate returns in the generated rules. Our proposed model has been applied for 10 Iranian companies listed in Tehran Stock Exchange (TSE). The numerical results showed that our extended GP model could generate profitable trading rules in comparison with buy and hold strategy especially in the case of risk adjusted basis.     

A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs

A memetic approach that combines a genetic algorithm (GA) and quadratic programming is used to address the problem of optimal portfolio selection with cardinality constraints and piecewise linear transaction costs. The framework used is an extension of the standard Markowitz mean–variance model that incorporates realistic constraints, such as upper and lower bounds for investment in individual assets and/or groups of assets, and minimum trading restrictions. The inclusion of constraints that limit the number of assets in the final portfolio and piecewise linear transaction costs transforms the selection of optimal portfolios into a mixed-integer quadratic problem, which cannot be solved by standard optimization techniques. We propose to use a genetic algorithm in which the candidate portfolios are encoded using a set representation to handle the combinatorial aspect of the optimization problem. Besides specifying which assets are included in the portfolio, this representation includes attributes that encode the trading operation (sell/hold/buy) performed when the portfolio is rebalanced. The results of this hybrid method are benchmarked against a range of investment strategies (passive management, the equally weighted portfolio, the minimum variance portfolio, optimal portfolios without cardinality constraints, ignoring transaction costs or obtained with L₁ regularization) using publicly available data. The transaction costs and the cardinality constraints provide regularization mechanisms that generally improve the out-of-sample performance of the selected portfolios.     

Energy trading scheme based on consortium blockchain and game theory

With the gradual opening of the electricity sales market, distributed energy trading is becoming an important research topic. However, it is not easy to design practical energy trading schemes in distributed scenario. In particular, known distributed energy trading schemes do not address the security of transaction data and the maximization of benefits among all the participants. In this paper, we propose a distributed energy trading scheme based on consortium blockchain and game theory. In our scheme, a peer-to-peer trading platform is constructed to realize direct transactions among all the participants by the property of decentralization in consortium blockchain. The direct transactions greatly reduce operating costs of energy trading, and at the same time, the security of transaction data can be obtained by the cryptographic techniques such as digital signatures and hash functions associated with the underlying blockchain. Moreover, we design an energy transaction matching mechanism by game theory in our scheme. In the matching mechanism, we construct a game model among all the participants and design an equilibrium solving algorithm, which are the key techniques to realize the maximization of benefits among all the participants in energy trading. The security analysis and experimental results show that our scheme can realize the best transaction price and quantity in the transaction matching and has high security in distributed energy transaction scenarios.     

An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms

Discovering intelligent technical trading rules from nonlinear and complex stock market data, and then developing decision support trading systems, is an important challenge. The objective of this study is to develop an intelligent hybrid trading system for discovering technical trading rules using rough set analysis and a genetic algorithm (GA). In order to obtain better trading decisions, a novel rule discovery mechanism using a GA approach is proposed for solving optimization problems (i.e., data discretization and reducts) of rough set analysis when discovering technical trading rules for the futures market. Experiments are designed to test the proposed model against comparable approaches (i.e., random, correlation, and GA approaches). In addition, these comprehensive experiments cover most of the current trading system topics, including the use of a sliding window method (with or without validation dataset), the number of trading rules, and the size of training period. To evaluate an intelligent hybrid trading system, experiments were carried out on the historical data of the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. In particular, trading performance is analyzed according to the number of sets of decision rules and the size of the training period for discovering trading rules for the testing period. The results show that the proposed model significantly outperforms the benchmark model in terms of the average return and as a risk-adjusted measure.     

An adaptive stock index trading decision support system

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.     

基于群体智能的选择性决策树分类器集成

尽管选择性集成方法的研究和应用已取得了不少重要成果,然而其实现方法计算复杂度高、效率低仍是应用该方法的一个瓶颈。为此,提出了一种新的高速收敛的选择性集成方法。该方法使用C4.5决策树分类器作为基学习器,利用高速收敛的群体智能算法来寻找最优集成模型,并在UCI数据库的多值分类数据集上进行了实验。实验结果表明,该方法计算效率高,其精度和稳定性比Bagging方法都要高,可以成为一种高效的选择性集成的实现方法。     

Ensemble of support vector machines for land cover classification

This letter presents the results of two different ensemble approaches to increase the accuracy of land cover classification using support vector machines. Finite ensemble approaches, based on boosting and bagging and infinite ensemble created by embedding the infinite hypothesis in the kernel of support vector machines, are discussed. Results suggest that the infinite ensemble approach provides a significant increase in the classification accuracy in comparison to the radial basis function kernel‐based support vector machines. While using finite ensemble approaches, bagging works well and provides a comparable performance to the infinite ensemble approach, whereas boosting decreases the performance of support vector machines. Comparison in terms of computational cost suggests that finite ensemble approaches require a large processing time in comparison to the infinite ensemble approach.     

Trading strategy design in financial investment through a turning points prediction scheme

Turning points prediction has long been a tough task in the field of time series analysis due to its strong nonlinearity, and thus has attracted many research efforts. In this study, the turning points prediction (TPP) framework is presented and further employed to develop a novel trading strategy designing approach to financial investment. The TPP framework is a machine learning-based solution incorporating chaotic dynamic analysis and neural network modeling. It works on the ground of a nonlinear mapping deduced in financial time series through chaotic analysis. An event characterization method is created in TTP framework to characterize trend patterns in ongoing financial time series. The main contributions of this paper are (1) it presents an ensemble learning based TPP framework, within which the nonlinear mapping is approximated by the ensemble artificial neural network (EANN) model with a new parameters learning algorithm; (2) a genetic algorithm (GA) based threshold optimization procedure is described with a newly defined performance measure, named TpMSE, which is used as a cost function; and (3) a trading strategy designing approach is proposed based on the TPP framework. The proposed approach was applied to the two real-world financial time series, i.e., an individual stock quote time series and the Dow Jones Industrial Average (DJIA) index time series. Experimental results show that the proposed approach can help investors make profitable decisions.     

基于动机分析的区块链数字货币异常交易行为识别方法

当前区块链数字货币被众多恶意交易者利用,导致了"粉尘"注入、"空投"操作、勒索、骗局等一系列异常交易行为.因此,研究区块链数字货币异常交易行为的识别方法对于规范交易行为、保障网络空间安全具有重要意义.在众多区块链数字货币中,比特币市值超过所有区块链数字货币市值和的一半,具有高代表性.比特币系统的用户数量多、交易规模大、...     

在线投资组合选择的半指数梯度策略及实证分析

针对传统投资组合策略的高频资产配置调整产生高额交易成本从而导致最终收益不佳这一问题，提出基于机器学习与在线学习理论的半指数梯度投资组合（SEG）策略。该策略对投资期进行划分，通过控制投资期内的交易量来降低交易成本。首先，基于仅在每段分割的初始期调整投资组合而其余时间不进行交易这一投资方式来建立SEG策略模型，并结合收益损失构造目标函数；其次，利用因子图算法求解投资组合迭代更新的闭式解，并证明该策略累积资产收益的损失上界，从理论上保证算法的收益性能。在纽约交易所等多个数据集上进行的仿真实验表明，该策略在交易成本存在时仍然能够保持较高的收益，证实了该策略对于交易成本的不敏感性。     

基于区块链的数字版权交易系统

在当前数字化、网络化时代中,数字版权交易需求越来越大,传统的中心化版权保护系统存在注册成本高、作品受理时间长、容易遭受破坏者的篡改等问题。区块链技术作为一个以P2P网络为基础,以密码技术为核心的去中心化网络结构,能够在网络上以纯数学方法建立信任关系,无需依托中间平台就能够缓解上述问题。借助区块链技术的自我监管、可追溯、去中心化的特性,结合数字版权交易场景,设计了一个基于联盟链的数字版权交易系统模型,利用当前 IBM 提供的最新联盟链技术,做了版权注册和版权交易的实现,能够保证版权信息不可篡改性和可溯源性。最后,测试了链码部署安装时间。结果表明,系统安装简单,维护成本低。相比传统的基于可信第三方版权认证机制,基于区块链的数字版权交易系统注册时间短,无需注册费,具有更好的架构安全性和可扩展性。     

Computational learning techniques for intraday FX trading usingpopular technical indicators

We consider strategies which use a collection of popular technical indicators as input and seek a profitable trading rule defined in terms of them. We consider two popular computational learning approaches, reinforcement learning and genetic programming, and compare them to a pair of simpler methods: the exact solution of an appropriate Markov decision problem, and a simple heuristic. We find that although all methods are able to generate significant in-sample and out-of-sample profits when transaction costs are zero, the genetic algorithm approach is superior for non-zero transaction costs, although none of the methods produce significant profits at realistic transaction costs. We also find that there is a substantial danger of overfitting if in-sample learning is not constrained.     

A Forex trading expert system based on a new approach to the rule-base evidential reasoning

Currently FOREX (foreign exchange market) is the largest financial market over the world. Usually the Forex market analysis is based on the Forex time series prediction. Nevertheless, trading expert systems based on such predictions do not usually provide satisfactory results. On the other hand, stock trading expert systems called also “mechanical trading systems”, which are based on the technical analysis, are very popular and may provide good profits. Therefore, in this paper we propose a Forex trading expert system based on some new technical analysis indicators and a new approach to the rule-base evidential reasoning (RBER) (the synthesis of fuzzy logic and the Dempster–Shafer theory of evidence). We have found that the traditional fuzzy logic rules lose an important information, when dealing with the intersecting fuzzy classes, e.g., such as Low and Medium and we have shown that this property may lead to the controversial results in practice. In the framework of the proposed in the current paper new approach, an information of the values of all membership functions representing the intersecting (competing) fuzzy classes is preserved and used in the fuzzy logic rules. The advantages of the proposed approach are demonstrated using the developed expert system optimized and tested on the real data from the Forex market for the four currency pairs and the time frames 15 m, 30 m, 1 h and 4 h.     

一种面向交易过程的虚拟人情绪模型

虚拟人在电子交易中具有重要的应用价值,但现有交易模型对情绪因素考虑不足。为此,建立一种面向交易过程的虚拟人情绪模型,该模型综合虚拟人的情绪和个性。引入概率函数表述交易过程中的信息不确定性,构建情绪公式。仿真实验结果表明,该情绪交易模型能够反映现实中交易的规律,较好地模拟实际交易过程。     

A fast ensemble pruning algorithm based on pattern mining process

Ensemble pruning deals with the reduction of base classifiers prior to combination in order to improve generalization and prediction efficiency. Existing ensemble pruning algorithms require much pruning time. This paper presents a fast pruning approach: pattern mining based ensemble pruning (PMEP). In this algorithm, the prediction results of all base classifiers are organized as a transaction database, and FP-Tree structure is used to compact the prediction results. Then a greedy pattern mining method is explored to find the ensemble of size k. After obtaining the ensembles of all possible sizes, the one with the best accuracy is outputted. Compared with Bagging, GASEN, and Forward Selection, experimental results show that PMEP achieves the best prediction accuracy and keeps the size of the final ensemble small, more importantly, its pruning time is much less than other ensemble pruning algorithms.     

聚类集成时机的确定

使用集成学习技术可以提高聚类性能。在实验中发现,当各聚类成员聚类迭代到中后期时进行集成所得的结果会优于其迭代完全停止时进行集成所得的结果。利用集成网络泛化能力的偏差-方差分解理论对聚类集成过程中的上述现象进行解释,将提高集成网络间泛化能力的早期停止准则应用于聚类集成过程,并提出聚类集成时机的概念。对比实验表明,基于早期停止准则的聚类集成得到的结果较好,且更节约聚类集成的时间,为寻求聚类集成的最佳时机提供了可行性建议和方法。     

Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model.