We remember that normalizing is needed for machine learning algorithms.
The principles of data mining and machine learning have been the topic of part 4.
Economic indicators help you consider trades in the context of economic events and understand price actions during these events. By following indicators for GDP, for instance, or inflation and employment strength, you can anticipate market volatility and gain potential trading opportunities in good time. Below you can see the most important economic indicators at a glance. It measures average consumer confidence and spending power for instance, a drastic decrease in consumer confidence can indicate a weakening economy.
CPI is used as a measure of inflation, as it reports price changes in over categories. Durable Goods Orders A monthly released key indicator of future manufacturing activity with indications to new orders placed with domestic manufacturers for the upcoming delivery of durable goods.
Employment Cost Index ECI A quarterly economic series that indicates the rising and falling tendencies in employment costs. It measures inflation in salaries, wages and employer-paid benefits in the US. Gross Domestic Product GDP It indicates the economic growth of a country, and it is determined by product output, income and expenditure. GDB is often correlated with the living standard. It is the market value of all services and goods produced in a country during a certain time period.
Gross Domestic Product Deflator A measure of price levels for all goods and services in an economy. If this sounds right to your necessities, we endorse you speak with the support team to completely understand the functionality and features available here.
WebTrader This is online trading platform. It is well-matched with Mac, PC and any other apparatus accomplished of running a browser. No Download is essential. Just open the platform from XM. Traders can take benefit of a number of tools to increase their trading skills and trade with no rejections and no requotes. Like the Metatrader4 platform, the WebTrader 4 platform offers one-click trading, market analysis, an economic calendar and streaming news.
It comes extremely optional. It offers admittance to your Metatrader4 account and presents actual interactive charts with scroll and zoom functionality. For our experiment we do not preselect or preprocess the features, but you can find useful information about this in articles 1 , 2 , and 3 listed at the end of the page. Select the machine learning algorithm R offers many different ML packages, and any of them offers many different algorithms with many different parameters.
Even if you already decided about the method — here, deep learning — you have still the choice among different approaches and different R packages. Most are quite new, and you can find not many empirical information that helps your decision.
You have to try them all and gain experience with different methods. This keeps our code short. There are other and more complex deep learning packages for R, so you can spend a lot of time checking out all of them. As to my knowledge, no one has yet come up with a solid mathematical proof that it works at all.
Anyway, imagine a large neural net with many hidden layers: Training the net means setting up the connection weights between the neurons. The usual method is error backpropagation. But it turns out that the more hidden layers you have, the worse it works.
The backpropagated error terms get smaller and smaller from layer to layer, causing the first layers of the net to learn almost nothing. Which means that the predicted result becomes more and more dependent of the random initial state of the weights. This severely limited the complexity of layer-based neural nets and therefore the tasks that they can solve.
At least until 10 years ago. In scientists in Toronto first published the idea to pre-train the weights with an unsupervised learning algorithm, a restricted Boltzmann machine. This turned out a revolutionary concept. It boosted the development of artificial intelligence and allowed all sorts of new applications from Go-playing machines to self-driving cars.
Meanwhile, several new improvements and algorithms for deep learning have been found. A stacked autoencoder works this way: Select the hidden layer to train; begin with the first hidden layer.
Feed the network with the training samples, but without the targets. Train it so that the first hidden layer reproduces the input signal — the features — at its outputs as exactly as possible. The rest of the network is ignored. Now feed the outputs of the trained hidden layer to the inputs of the next untrained hidden layer, and repeat the training process so that the input signal is now reproduced at the outputs of the next layer.
Repeat this process until all hidden layers are trained. Now train the network with backpropagation for learning the target variable, using the pre-trained weights of the hidden layers as a starting point.
The hope is that the unsupervised pre-training process produces an internal noise-reduced abstraction of the input signals that can then be used for easier learning the target. And this indeed appears to work. No one really knows why, but several theories — see paper 4 below — try to explain that phenomenon.
Generate a test data set We first need to produce a data set with features and targets so that we can test our prediction process and try out parameters.
The features must be based on the same price data as in live trading, and for the target we must simulate a short-term trade. So it makes sense to generate the data not with R, but with our trading platform, which is anyway a lot faster. Our target is the result of a trade with 3 bars life time. Trading costs are set to zero, so in this case the result is equivalent to the sign of the price difference at 3 bars in the future.
The adviseLong function is described in the Zorro manual ; it is a mighty function that automatically handles training and predicting and allows to use any R-based machine learning algorithm just as if it were a simple indicator.
In our code, the function uses the next trade return as target, and the price changes and ranges of the last 4 bars as features. Calibrate the algorithm Complex machine learning algorithms have many parameters to adjust. Some of them offer great opportunities to curve-fit the algorithm for publications.
Still, we must calibrate parameters since the algorithm rarely works well with its default settings. A fourth function, TestOOS, is used for out-of-sample testing our setup. Otherwise we would get a slightly different result any time, since the neural net is initialized with random weights. The matrix containing the features and target is passed to the function as second parameter.
If the XY data is not a proper matrix, which frequently happens in R depending on how you generated it, it is converted to one.
Then it is split into the features X and the target Y , and finally the target is converted to 1 for a positive trade outcome and 0 for a negative outcome. The network parameters are then set up. Some are obvious, others are free to play around with:
Economic calendar includes most important economic indicators and events from ministries and agencies of different countries. The Calendar is useful for traders in the forex market, stock exchanges and other financial markets.
Economic calendar - real-time news and reports, as well as the schedule of forthcoming world economy. The economic calendar provides useful information on upcoming macroeconomic events by means of pre-scheduled news announcements and government reports on economic indicators that influence the financial markets.
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Pair Action Status Stop Loss Take Profit EUR/USD BUY Active USD/CHF SELL Active GBP/USD SELL Active . Install your software by double-clicking on the MT4 client terminal exe file.