what ema

Parameters   Input parameters Input amount G       Output Parameters Gradient N   Unit U       Note Truerange Tr =

V God: Vol http://www.55188.com/thread-3371557-1-1.html   Change hands: If (Capital> 0, V/capital * 100, v), linethick, precision2;HS: = change hands;K: = SMA (c-llv (L, 9)/(HHV (H, 9)-llv (L, 9) *, 3, 1), color00ff00, linethick0, precision2;D: =

0, Matplotlib--Introduction. NoteOfficial website:http://matplotlib.org/1, Matplotlib--draw the polynomial function. Note#-*-coding:utf-8-*-#绘制多项式函数import numpy as Npimport matplotlib.pyplot as plt# coefficients of the natural number sequence as a

Facade: provides a consistent interface for a group of interfaces in the subsystem. This mode defines a high-level interface, which makes the subsystem easier to use.Mode description:SubSystem (SubSystem)Responsible for handling complex logic

Types of moving AveragesThe moving average can be divided into "arithmetic moving average", "weighted moving average", "exponential smoothed moving average" three kinds.1. Arithmetic moving average (MA)The arithmetic moving average is a simple and

Moving Average convergence and diver-gence) Similar to moving average indicators, the difference is that the exponent needs to be processed smoothly. In the application, macd should first calculate the moving average value of the fast (12-day)

First nonsense: Before coming to the company, the project is done by the outsourcing company, interview at the beginning, no contact with too much time figure K line this piece, feel very difficult, I can handle not! But after a period of time,

Ma/sma/dma/ema moving Average algorithm Formula 1, simple moving average MA usage: MA (x,n): X's N-day simple moving Average algorithm (X1+X2+X3+...+XN)/n2, moving average SMA usage: SMA (X,N,M), N-day moving average for x, m/ N is the weight given

Monograph "The method of pole symmetry mode decomposition--The new way of data analysis and scientific exploration" has been published in 2015.5, please pay attention to.Http://blog.sciencenet.cn/blog-686810-898163.html The latest free esmd

TensorFlow Neural Network Optimization Strategy Learning, tensorflow Network Optimization During the optimization of the neural network model, we will encounter many problems, such as how to set the learning rate. We can quickly approach the optimal

This example, through numpy analysis of stock price CSV file reading and writing CSV (comma-separated value, comma separated values) is a common file format, usually the database is the file is a CSV format, each field in the file corresponds to the

        MACD called is developed from a double exponential moving average, with a fast exponential moving average (EMA12) minus the slow exponential moving average (EMA26) to get the fast line dif, then 2x (Fast line Dif-dif's 9th weighted moving

One setting of basic technical analysis parameters I have made some necessary changes to the technical parameters of the Money Dragon dynamic. : 1, EMA system Parameters Day candlestick three 5, 10, 30 is changed to 10, 15, 21. 30 the three 5,

Basic usage 1. macd Golden Cross: Diff breaks through the DEA from bottom up, which is a buy signal. 2. macd dead cross: Diff breaks through the DEA from top to bottom to sell signals. 3. macd green to red: The macd value changes from negative to

TensorFlow implements Batch Normalization, I. BN (Batch Normalization) Algorithm 1. Importance of Data normalization The essence of the neural network learning process is to learn Data Distribution. When training data is different from test data

Parameter Selection via exponential moving Average When training a classifier via gradient decent, we update the current classifier ' s parametersθθviaθt+1=θt+αδθt,θt+1=θt+ Αδθt, Whereθtθt is the ' parameters andδθtδθt is the ' update step proposed

Drawing timing Diagram Tools Timingdesigner 9.2 Installation InstructionsFirst on file download link: http://bbs.eetop.cn/viewthread.php?tid=250446&;highlight=timing%2Bde%24ignerInstallation, crack method:First, installation1) after extracting

Here's the book. No matter what the stock, before buying shares, first look at the 250-day average, only buy 250 moving averages above the stock, preferably in the stock price from under the 250 ema across to the 250 ema when buying A stop

Let me explain some historical stories ...... When I thought about the game of 3DM hanhua I community, Mufeng website administrator war3andc. S (agent) often stole 3DM's chinhua, which gave the Chinese group a headache. After cracking the 3DM

Create an attribute: Public class entitymappingattribute: attribute {Public String tablename {Get; set;} Public String sortorder {Get; Set ;}} Create a class: Public class user {[entitymapping (tablename = "test", sortorder = "5")] public