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Risk manager for algorithmic trading
Risk manager for algorithmic trading
The objectives of this article are to prove the necessity of using a risk manager and to implement the principles of controlled risk in algorithmic trading in a separate class, so that everyone can verify the effectiveness of the risk standardization approach in intraday trading and investing in financial markets. In this article, we will create a risk manager class for algorithmic trading. This is a logical continuation of the previous article in which we discussed the creation of a risk manager for manual trading.
How to add Trailing Stop using Parabolic SAR
How to add Trailing Stop using Parabolic SAR
When creating a trading strategy, we need to test a variety of protective stop options. Here is where a dynamic pulling up of the Stop Loss level following the price comes to mind. The best candidate for this is the Parabolic SAR indicator. It is difficult to think of anything simpler and visually clearer.
Multiple Symbol Analysis With Python And MQL5 (Part I): NASDAQ Integrated Circuit Makers
Multiple Symbol Analysis With Python And MQL5 (Part I): NASDAQ Integrated Circuit Makers
Join us as we discuss how you can use AI to optimize your position sizing and order quantities to maximize the returns of your portfolio. We will showcase how to algorithmically identify an optimal portfolio and tailor your portfolio to your returns expectations or risk tolerance levels. In this discussion, we will use the SciPy library and the MQL5 language to create an optimal and diversified portfolio using all the data we have.
Comet Tail Algorithm (CTA)
Comet Tail Algorithm (CTA)
In this article, we will look at the Comet Tail Optimization Algorithm (CTA), which draws inspiration from unique space objects - comets and their impressive tails that form when approaching the Sun. The algorithm is based on the concept of the motion of comets and their tails, and is designed to find optimal solutions in optimization problems.
Neural Networks Made Easy (Part 88): Time-Series Dense Encoder (TiDE)
Neural Networks Made Easy (Part 88): Time-Series Dense Encoder (TiDE)
In an attempt to obtain the most accurate forecasts, researchers often complicate forecasting models. Which in turn leads to increased model training and maintenance costs. Is such an increase always justified? This article introduces an algorithm that uses the simplicity and speed of linear models and demonstrates results on par with the best models with a more complex architecture.
Data Science and Machine Learning (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal
Data Science and Machine Learning (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal
In the fast-paced world of financial markets, separating meaningful signals from the noise is crucial for successful trading. By employing sophisticated neural network architectures, autoencoders excel at uncovering hidden patterns within market data, transforming noisy input into actionable insights. In this article, we explore how autoencoders are revolutionizing trading practices, offering traders a powerful tool to enhance decision-making and gain a competitive edge in today's dynamic markets.
MQL5 Trading Toolkit (Part 2): Expanding and Implementing the Positions Management EX5 Library
MQL5 Trading Toolkit (Part 2): Expanding and Implementing the Positions Management EX5 Library
Learn how to import and use EX5 libraries in your MQL5 code or projects. In this continuation article, we will expand the EX5 library by adding more position management functions to the existing library and creating two Expert Advisors. The first example will use the Variable Index Dynamic Average Technical Indicator to develop a trailing stop trading strategy expert advisor, while the second example will utilize a trade panel to monitor, open, close, and modify positions. These two examples will demonstrate how to use and implement the upgraded EX5 position management library.
MQL5 Wizard Techniques you should know (Part 40): Parabolic SAR
MQL5 Wizard Techniques you should know (Part 40): Parabolic SAR
The Parabolic Stop-and-Reversal (SAR) is an indicator for trend confirmation and trend termination points. Because it is a laggard in identifying trends its primary purpose has been in positioning trailing stop losses on open positions. We, however, explore if indeed it could be used as an Expert Advisor signal, thanks to custom signal classes of wizard assembled Expert Advisors.
Developing a Replay System (Part 46): Chart Trade Project (V)
Developing a Replay System (Part 46): Chart Trade Project (V)
Tired of wasting time searching for that very file that you application needs in order to work? How about including everything in the executable? This way you won't have to search for the things. I know that many people use this form of distribution and storage, but there is a much more suitable way. At least as far as the distribution of executable files and their storage is concerned. The method that will be presented here can be very useful, since you can use MetaTrader 5 itself as an excellent assistant, as well as MQL5. Furthermore, it is not that difficult to understand.
MQL5 Wizard Techniques you should know (Part 39): Relative Strength Index
MQL5 Wizard Techniques you should know (Part 39): Relative Strength Index
The RSI is a popular momentum oscillator that measures pace and size of a security’s recent price change to evaluate over-and-under valued situations in the security’s price. These insights in speed and magnitude are key in defining reversal points. We put this oscillator to work in another custom signal class and examine the traits of some of its signals. We start, though, by wrapping up what we started previously on Bollinger Bands.
Using PSAR, Heiken Ashi, and Deep Learning Together for Trading
Using PSAR, Heiken Ashi, and Deep Learning Together for Trading
This project explores the fusion of deep learning and technical analysis to test trading strategies in forex. A Python script is used for rapid experimentation, employing an ONNX model alongside traditional indicators like PSAR, SMA, and RSI to predict EUR/USD movements. A MetaTrader 5 script then brings this strategy into a live environment, using historical data and technical analysis to make informed trading decisions. The backtesting results indicate a cautious yet consistent approach, with a focus on risk management and steady growth rather than aggressive profit-seeking.
Turtle Shell Evolution Algorithm (TSEA)
Turtle Shell Evolution Algorithm (TSEA)
This is a unique optimization algorithm inspired by the evolution of the turtle shell. The TSEA algorithm emulates the gradual formation of keratinized skin areas, which represent optimal solutions to a problem. The best solutions become "harder" and are located closer to the outer surface, while the less successful solutions remain "softer" and are located inside. The algorithm uses clustering of solutions by quality and distance, allowing to preserve less successful options and providing flexibility and adaptability.
Neural Networks Made Easy (Part 84): Reversible Normalization (RevIN)
Neural Networks Made Easy (Part 84): Reversible Normalization (RevIN)
We already know that pre-processing of the input data plays a major role in the stability of model training. To process "raw" input data online, we often use a batch normalization layer. But sometimes we need a reverse procedure. In this article, we discuss one of the possible approaches to solving this problem.
MQL5 Wizard Techniques you should know (Part 38): Bollinger Bands
MQL5 Wizard Techniques you should know (Part 38): Bollinger Bands
Bollinger Bands are a very common Envelope Indicator used by a lot of traders to manually place and close trades. We examine this indicator by considering as many of the different possible signals it does generate, and see how they could be put to use in a wizard assembled Expert Advisor.
Applying Localized Feature Selection in Python and MQL5
Applying Localized Feature Selection in Python and MQL5
This article explores a feature selection algorithm introduced in the paper 'Local Feature Selection for Data Classification' by Narges Armanfard et al. The algorithm is implemented in Python to build binary classifier models that can be integrated with MetaTrader 5 applications for inference.
Portfolio Optimization in Python and MQL5
Portfolio Optimization in Python and MQL5
This article explores advanced portfolio optimization techniques using Python and MQL5 with MetaTrader 5. It demonstrates how to develop algorithms for data analysis, asset allocation, and trading signal generation, emphasizing the importance of data-driven decision-making in modern financial management and risk mitigation.