带时间的趋势指标

MT4版本的多周期K线指标，很实用的指标(MT4 version of the multi-cycle K-line index, very practical indicators)

说明：  任意均线金叉死叉报警，均线周期自己可以调整，均线类型也可以自己调整。(Any average golden fork dead fork alarm, average cycle can be adjusted by itself, average type can also be adjusted by itself.)

先测试下，通达信公式学习教程，初学者很有用，建议收藏，谢谢！！(Fourthly early rising gives us enough time to get ready for our worksuch as to wash our faces and hands and eat our breakfast properly.)

java写的股票技术分析 软件,包括分时图,k线图,技术分析指标,功能强大(Stock Technical java written analysis software, including sharing plans, k chart, technical analysis indicators, powerful)

MT4编程入门详解，内容详见细致，中文版本。(Detailed Introduction to MT4 programming, content, see the detail, the Chinese version.)

manage sales history, it s written in C# with SQL server database

说明：  MT4编程手册技术大全，实用的编程参考工具书(MT4 programming manual technology, practical programming reference book)

说明：  量化投资模型,期货品种量化策略源码.1.海龟图,2.机器学习(股票) 量化策略源码.3.做市商交易(期货) 量化策略源码.4.跨期套利(期货) 量化策略源码.5.跨品种套利(期货)量化策略源码.6.网格交易(期货) 量化策略源码. 7.alpha对冲(股票+期货) 量化策略源码(Quantitative investment model)

通达信集成版股票系统公式编程教程，简述很全面，有例子分析。我找了很久才找到，希望对大家有用。(Access to the stock version of the letter system integration formula programming tutorial, outlining a very comprehensive, there are examples of analysis. I am looking for a long time to find, I hope useful for all of us.)

经验模态分解（Empirical Mode Decomposition，简称EMD))方法被认为是2000年来以傅立叶变换为基础的线性和稳态频谱分析的一个重大突破?，该方法是依据数据自身的时间尺度特征来进行信号分解，无须预先设定任何基函数。 该方法的关键是经验模式分解，它能使复杂信号分解为有限个本征模函数（Intrinsic Mode Function，简称IMF），所分解出来的各IMF分量包含了原信号的不同时间尺度的局部特征信号。经验模态分解法能使非平稳数据进行平稳化处理，然后进行希尔伯特变换获得时频谱图，得到有物理意义的频率。(The Empirical Mode Decomposition (EMD) method is considered as a major breakthrough in the linear and steady state spectrum analysis based on Fu Liye transform in 2000. The method is based on the time scale characteristics of the data itself to decompose the signal without setting any base functions in advance. The key of this method is the empirical mode decomposition, which can decompose the complex signal into a limited eigenmode function (Intrinsic Mode Function, for short, IMF), and the decomposed IMF components include the local characteristic signals of the different time scales of the original signal. The empirical mode decomposition method can smooth the non-stationary data, and then obtain the time-frequency spectrum by Hilbert transform to get the physical meaning frequency.)