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Here is back test result for lonewolf EA which is a heiken Ashi smooth based Expert Advisor. EA can be download from here post #304 where Igorad helped to correct the EA to use with new MT4 build.
It still profitable but may be a fine tune will be better. Best use on H4 chart.
Hi
I was wondering if there are any good premium AI / ML EAs available in the market ? using lstm or any new prediction methods?
Thanks
You can try to find them by yourself using premium wiki threads.Quote:
Originally Posted by newguy123
Because the coder of the most indicators and Eas are Igorad (Igor), and he is having his own coding style and coding preferences.
-----------------------
The premium section -
Indicators and so on
- Indicators' section http://newdigital-world.com/trading-systems-tools/
- Wiki thread for this section http://newdigital-world.com/trading-...ools-wiki.html
- Request's thread http://newdigital-world.com/trading-...-requests.html
EAs in premium section
- EAs http://newdigital-world.com/automated-trading/
- Wiki thread for this section http://newdigital-world.com/automate...ding-wiki.html
- Request's thread for EAs' section - http://newdigital-world.com/automate...-requests.html
Hi Igorad, ND
Can you modified the indicator into MTF version ?
Thanks in advance
Regards,
chartartist
Attachment 44256
My problem appears only in real time, not in the strategy tester.
My EA works fine with currencies.
I am using PriceChannelStopv9 to set the initial StopLoss. This works.
When trading the DAX my EA moves the SL to 7 digits difference to Bid or Ask (e.g. if Short SL 15007, BID price 15000), as soon as the profit is > ~€7.50 for one contract.
Its lot * 7.50, i.e. for 3 contracts the profit must be > ~€ 23 before the EA moves the SL. Then it behaves like a trailing stop.
Stripped my code to the minimum and have no idea what could trigger this tight SL. Does it also in SP500 trades, but 7 digits difference of 15000 vs 4700 makes a huge difference.
Would be very grateful for any hints.
I was wondering if there are any good premium AI / ML EAs available in the market ? using lstm or any new prediction methods?
Best Regards
have tried to understand the code and put the alerts myself, however it is something out of my understanding
The point is that it gives an alert when it is going to change from red to green or vice versa and also another alert when it changes to gray, in the thick line of the indicator
Thank you very much for your attention.
Attachment 46701
Hello,
Is there a MT5 version of the "London Breakout Box" indicator?
Thank you
I did not find it for MT5 (seems, it was not coded).Quote:
Originally Posted by Eris
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more...Quote:
In the previous article Creating an EA that works automatically (Part 02): Getting started with the code, we started to develop an order system that we will use in our automated EA. However, we have created only one of the necessary functions.
Now ..., we can add other necessary functions to the EA, which will cover more than 90% of the cases.
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more...Quote:
In the previous article "Creating an EA that works automatically (Part 03): New functions" we finished covering the orders system. If you haven't read it or do not completely understand its contents, I suggest that you go back to that article. Here we will no longer discuss the order system. We will proceed to other things, in particular, to triggers.
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more...Quote:
In the previous article entitled Creating an EA that works automatically (Part 04): Manual triggers (I) I have shown how, with a bit of programming, to send market orders and to place pending orders using a combination of keys and mouse.
Well, to make the use of our EA in the manual mode more comfortable, we need to do a few things. This work is simple and easy for programmers, so we can get straight to the point. Namely, we will create lines indicating the location of order limits for the orders that we send to the trading server.
more...Quote:
In the previous article Creating an EA that works automatically (Part 06): Account types (I), we started developing a way to ensure that the automated EA works correctly and within its intended purpose. In that article, we created the C_Manager class, which acts as an administrator, so that in case of strange or incorrect EA behavior the EA will be removed from the chart.
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more...Quote:
In previous articles: Creating an EA that works automatically (Part 06): Account types (I) and Creating an EA that works automatically (Part 07): Account types (II), we were focusing on the importance of being careful when designing an EA that trades automatically.
Before we can really understand how the EA code for automation should work, we need to understand how it interacts with the trade server.
more...Quote:
In the previous article Creating an EA that works automatically (Part 08): OnTradeTransaction, it was explained how we can take advantage of the MetaTrader 5 platform by using a rather interesting event handling function. We will start now with building the first level of automation in our EA.
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more...Quote:
In a previous article Creating an EA that works automatically (Part 9): Automation (I), we looked at how to create a breakeven and trailing stop system that uses two different modes. One of them uses a stop line on OCO positions, while the other uses a pending order as a stop level.
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more...Quote:
In the previous article "Creating an EA that works automatically (Part 10): Automation (II)", we looked at a way to add EA operation schedule control. While the entire EA system has been built to prioritize autonomy, before moving on to the last phase where we will get a 100% automated EA, we need to make some minor changes to the code.
more...Quote:
In the previous article Creating an EA that works automatically (Part 11): Automation (III), we looked at how we can create a robust system, minimizing the failures and loopholes that can affect a program.
In the previous article, I raised this question and left it for you to understand where this flaw was, and how it could cause problems, so that we could not automate our EA at 100% yet. Did you manage to understand where the failure was and how it could have been triggered? Well, if the answer is no, it is ok.
So, to understand what it's about, let's divide things into topics. I think it will be easier for you to notice something seemingly unimportant that can cause you great annoyances.
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more...Quote:
Now that we have finished creating the basic skeleton, we can finally automate the EA to make it operate 100% automatically while following the operational rules that we have defined. The purpose of the article is not to build an operational model, but to show and prepare you to use the system proposed here, by turning a manual Expert Advisor into an automated one.
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more...Quote:
In the previous article Creating an EA that works automatically (Part 13): Automation (V), I explained how a trader even without any programming knowledge can create the required basis for converting a trading system into an automated EA. This is what we have been doing throughout this series of articles. These concepts and information apply to any EA including any you create. In this article, we will consider one of the many ways to accomplish this task.
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MQL5 Freelance is a specialized service for trading application developers. Traders come here when they need custom-made trading robots, indicators, and other utility apps developed in MQL5/MQL4, Python, C++, and other modern programming languages.
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We have already implemented a lot of interesting things in the previous articles. We have a trading strategy or several trading strategies that we can implement in the EA. Besides, we have developed a structure for connecting many instances of trading strategies in a single EA, added tools for managing the maximum allowable drawdown, looked at possible ways of automated selection of sets of strategy parameters for their best work in a group, learned how to assemble an EA from groups of strategy instances and even from groups of different groups of strategy instances. But the value of the results already obtained will greatly increase if we manage to combine them together.
Let's try to outline a general structure within the article framework: single trading strategies are fed into the input, while the output is a ready-made EA, which uses selected and grouped copies of the original trading strategies that provide the best trading results.
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In the previous article, I have outlined a general plan for developing the EA, which includes several stages. Each stage generates a certain amount of information to be used in the stages that follow. I decided to save this information in a database and created a table in it, in which we can place the results of single passes of the strategy tester for various EAs.
In order to be able to use this information in the next steps, we need to have some way of creating the necessary objects (trading strategies, their groups and EAs) from the information stored in the database. There is no option to save objects directly to the database. The best thing that can be suggested is to convert all the properties of objects into a string, save it in the database, then read this string from the database and create the required object from it.
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CatBoost is an open-source software library with gradient-boosting algorithms on decision trees, it was designed specifically to address the challenges of handling categorical features and data in machine learning.
It was developed by Yandex and was made open-source in the year of 2017, read more.
Despite being introduced recently compared to machine learning techniques such as Linear regression or SVM's, CatBoost gained massive popularity among AI communities and rose to the top of the most used machine learning models on platforms like Kaggle.
What made CatBoost gain this much attention is its ability to automatically handle categorical features in the dataset, which can be challenging to many machine learning algorithms.
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Forecasting time series of future prices is critical in various financial market scenarios. Most of the methods that currently exist are based on certain autocorrelation in the data. In other words, we exploit the presence of correlation between time steps that exists both in the input data and in the predicted values.
Among the models gaining popularity are those based on the Transformer architecture that use Self-Attention mechanisms for dynamic autocorrelation estimation. Also, we see an increasing interest in the use of frequency analysis in forecasting models. The representation of the sequence of input data in the frequency domain helps avoid the complexity of describing autocorrelation and improves the efficiency of various models.
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In this article, we examine the limitations of conventional stepwise feature selection, such as its potential for overfitting and its challenges in capturing interactions between features. We then introduce an enhanced algorithm designed to address these issues, implemented in MQL5, which provides flexible integration with various supervised learning methods.
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A common approach when processing time series is to keep the original arrangement of the time steps intact. It is assumed that the historical order is the most optimal. However, most existing models lack explicit mechanisms to explore the relationships between distant segments within each time series, which may in fact have strong dependencies. For example, models based on convolutional networks (CNN) used for time series learning can only capture patterns within a limited time window. As a result, when analyzing time series in which important patterns span longer time windows, such models have difficulty effectively capturing this information. The use of deep networks allows to increase the size of the receptive field and partially solves the problem. But the number of convolutional layers required to cover the entire sequence may be too large, and oversizing the model leads to the vanishing gradient problem.
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In this article, we delve into the integration of the Strategic Golden and Death Cross strategies into the Trend Constraint Expert Advisor, unlocking the potential of these time-tested moving average crossover techniques. Our goal is to enhance trend-following capabilities in algorithmic trading by automating these strategies, ensuring precision, consistency, and seamless compatibility with broader trading systems.
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The Kalman filter is a recursive algorithm used in algorithmic trading to estimate the true state of a financial time series by filtering out noise from price movements. It dynamically updates predictions based on new market data, making it valuable for adaptive strategies like mean reversion. This article first introduces the Kalman filter, covering its calculation and implementation. Next, we apply the filter to a classic mean-reversion forex strategy as an example. Finally, we conduct various statistical analyses by comparing the filter with a moving average across different forex pairs.
