The Trading Platform is the trader's working tool, providing all the necessary features for a successful online trading. It includes trading, technical analysis of prices and fundamental analysis, automated trading and trading from mobile devices. In addition to Forex symbols, options futures and stocks can be traded from the platform.
This manual will help you to get to know your bike and includes some useful information and detail. We recommend that you read it with care. MANUAL CONTENT In this manual, you will find all the necessary information to assemble, set up and maintain your bike correctly. You will also find the different security warnings and the warranty. This is a manual for Wikimedia chapters.It is divided into several parts corresponding to the tasks in a chapter. Please contribute to this manual, by inserting answers, but also questions.
All Types of Orders, Price Charts, Technical and Fundamental Analysis, Algorithmic and Mobile Trading
Trading The platform provides a wide set of trading tools. It supports four order execution modes: Instant, Request, Market and Exchange execution. All types of orders are available in the platform, including market, pending and stop-orders. With such a diversity of order types and available execution modes, traders can implement various trading strategies for successful performance in the currency markets and stock exchanges. The platform also features One-Click Trading and provides functions for trading straight from the chart. | |
a | Analytics The trading platform provides powerful analytical functions. 82 different analytical tools are available for analyzing currency and stock prices, including technical indicators and graphical objects. The analytical resources of the trading platform are not limited to the built-in indicators only. The trader can additionally use the free Code Base of technical indicators and the Market of trading applications. There are 21 timeframes, from a minute to a month one, available for each financial instrument. Up to 100 charts of financial instruments can be open at the same time. |
Algorithmic Trading Virtually any trading strategy can be formalized and implemented in the form of an Expert Advisor, which can automatically trade for you. A trading robot never gets tired or suffers from stress; it accurately follows its algorithm and is much more responsive to market changes. The trading platform provides all the necessary tools for Expert Advisor development: the powerful MQL5 language with an integrated development environment, a multicurrency tester for testing and optimizing strategies, and the Code Base of free trading robots. | |
Mobile Trading Smartphones and tablets are indispensable in trading when you are away from your computer. Use special trading platform versions on your iPhone/iPad and Android devices to trade in the financial markets on the go. You will certainly appreciate the functionality of the mobile trading platforms that include the full support for the trading functions, broad analytical capabilities with technical indicators and other graphical objects. Of course, all these features are available from anywhere in the world 24 hours a day. Use the mobile platform to read financial news and internal emails, as well as for instant messaging with the participants of the most popular website for traders MQL5.community. | |
Additional Services The platform provides powerful trading tools and a variety of additional services. Social trading is available through the Signals service. This is an easy way to copy deals of experienced traders. Choose from thousands of signal, subscribe in a couple of clicks and the selected provider's deals will be automatically copied to your account. Market is a store where you can purchase or download for free cutting-edge trading robots and technical indicators. An application can be purchased straight from the platform. The purchase procedure is simple and secure. If you cannot find the desired app, order one from professional developers in Freelance. The service provides secure cooperation between the customer and the developer — a payment for an application is transferred only after the approval of the resulting program. To ensure 24/7 operation of your trading robots and copied signals, rent a Virtual Hosting straight from your platform. |
Conceptually, Meta-Regression does not differ much from a subgroup analysis. In fact, subgroup analyses with more than two groups are nothing more than a meta-regression with categorial predictors. However, meta-regression does also allow us to use continuous data as predictors and check whether these variables are associated with effect size differences.
The idea behind meta-regression
You may have already performed regressions in regular data where participants or patients are the unit of analysis. In typical meta-analyses, we do not have the individual data for each participant available, but only the aggregated effects, which is why we have to perform meta-regressions with predictors on a study level. This also means that while we conduct analyses on participant samples much larger than usual for single studies, it is still very likely that we do not have enough data for a meta-regression to be sensible. In Chapter 7, we told you that subgroup analyses make no sense when (k<10). For meta-regression, Borenstein and colleages (Borenstein et al. 2011) recommend that each covariate should at least contain ten studies, although this should not be seen as an iron-clad rule.
In a conventional regression, we want to estimate a parameter (y) using a covariate (x_i) with (n) regression coefficients (beta). A standard regression equation therefore looks like this:
[y=beta_0 + beta_1x_1 + ...+beta_nx_n]
In a meta-regression, we want to estimate the effect size(theta) for different values of the predictor(s), so our regression looks like this:
[hat theta_k = theta + beta_1x_{1k} + ... + beta_nx_{nk} + epsilon_k + zeta_k]
You might have seen that when estimating the effect size (theta_k) of a study (k) in our regression model, there are two extra terms in the equation, (epsilon_k) and (zeta_k). The same terms can also be found in the equation for the random-effects-model in Chapter 4.2. The two terms signify two types of independent errors which cause our regression prediction to be imperfect. The first one, (epsilon_k), is the sampling error through which the effect size of the study deviates from its “true” effect. The second one, (zeta_k), denotes that even the true effect size of the study is only sampled from an overarching distribution of effect sizes (see the chapter on the Random-Effects Model for more details). In a fixed-effect model, we assume that all studies actually share the same true effect size and that the between-study heterogeneity(tau^2 = 0). In this case, we do not consider (zeta_k) in our equation, but only (epsilon_k).
As the equation above includes fixed effects (the (beta) coefficients) as well as random effects ((zeta_k)), the model used in meta-regression is often called a mixed-effects-model. Mathematically, this model is identical to the mixed-effects-model we described in Chapter 7 where we explained how subgroup analyses work.
Indeed, as mentioned above, subgroup analyses are nothing else than a meta-regression with a categorical predictor. For meta-regression, these subgroups are then dummy-coded, e.g.
Meta Manual Differential
[ D_k = {begin{array}{c}0:ACT 1:CBT end{array}]
[hat theta_k = theta + beta x_{k} + D_k gamma + epsilon_k + zeta_k]
In this case, we assume the same regression line, which is simply “shifted” up or down for the different subgroups(D_k).
Figure 8.1: Visualisation of a Meta-Regression with dummy-coded categorial predictors
Meta Alarm System Manual
Assessing the fit of a regression model
To evaluate the statistical significance of a predictor, we a t-test of its (beta)-weight is performed.
[ t=frac{beta}{SE_{beta}}]
Which provides a (p)-value telling us if a variable significantly predicts effect size differences in our regression model. If we fit a regression model, our aim is to find a model which explains as much as possible of the current variability in effect sizes we find in our data.
In conventional regression, (R^2) is commonly used to quantify the goodness of fit of our model in percent (0-100%). As this measure is commonly used, and many researchers know how to to interpret it, we can also calculate a (R^2) analog for meta-regression using this formula:
Meta Manual Differential
[R^2=frac{hattau^2_{REM}-hattau^2_{MEM}}{hattau^2_{REM}}]
Where (hattau^2_{REM}) is the estimated total heterogeneity based on the random-effects-model and (hattau^2_{REM}) the total heterogeneity of our mixed-effects regression model.