Roman Korotchenko / 个人资料
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http://itmozg.ru/resume/show/?id=572d5c129f5255221bb43c3f
Using the original SSA codes from the ALGLIB site, I programmed the indicator program with a forecast and an expert for MQL5. AlglibSSA.dll is connected to them, so the programs are not on the market. Who is interested - contact please.
有关金融数据分形行为的研究表明,在经济时间序列看似混乱的行为背后,存在着参与者集体行为的隐性稳定机制。这些机制可以导致交易所出现价格动态,从而定义和描述价格序列的具体属性。应用于交易中,能够有效、可靠地估计尺度和时间框架内的分形参数的指标,具有一定的实用价值。
在2015年升级了 MATLAB 包之后,有必要考虑一种现代的创建 DLL 库的方法。本文利用样本预测指标,说明了在目前使用的64位平台上关联 MetaTrader 5 和 MATLAB 的特点。通过探讨连接 MATLAB 的整个过程,MQL5 开发人员将能够更快地创建具有高级计算能力的应用程序,从而避免“陷阱”。
本文研究建立高效交易的推荐制系统的思想和方法, 结合了贝叶斯定理基础之上的重要机器学习方法, 以及奇异频谱分析 (SSA) 的预测能力。
An analogue of the Stochastic oscillator based on algorithms of singular spectrum analysis (SSA) SSA is an effective method to handle non-stationary time series with unknown internal structure. It is used for determining the main components (trend, seasonal and wave fluctuations), smoothing and noise reduction. The method allows finding previously unknown series periods and make forecasts on the basis of the detected periodic patterns. Indicator signals are identical to signals of the original
SSACD - Singular Spectrum Average Convergence/Divergence This is an analogue of the MACD indicator based on the Caterpillar-SSA ( Singular Spectrum Analysis ) method. Limited version of the SSACD Forecast indicator. Limitations include the set of parameters and their range. Specificity of the method The Caterpillar-SSA is an effective method to handle non-stationary time series with unknown internal structure. The method allows to find the previously unknown periodicities of the series and make
This indicator extracts a trend from a price series and forecasts its further development. Algorithm is based on modern technique of Singular Spectral Analysis ( SSA ). SSA is used for extracting the main components (trend, seasonal and wave fluctuations), smoothing and eliminating noise. It does not require the series to be stationary, as well as the information on presence of periodic components and their periods. It can be applied both for trend and for another indicators. Features of the
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