MetaTrader 5 Python User Group - the summary - page 30

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Data Science and ML (Part 23): Why LightGBM and XGBoost outperform a lot of AI models? 

Data Science and ML (Part 23): Why LightGBM and XGBoost outperform a lot of AI models?

Gradient Boosted Decision Trees (GBDT) are a powerful machine learning technique used primarily for regression and classification tasks. They combine the predictions of multiple weak learners, usually decision trees, to create a strong predictive model.

The core idea is to build models sequentially, each new model attempting to correct the errors made by the previous ones.

Have gained much popularity in the machine learning community as the algorithms of choice for many winning teams in machine learning competitions. In this article, we are going to discover how we can use these accurate models in our trading applications.

Data Science and ML (Part 23): Why LightGBM and XGBoost outperform a lot of AI models?
Data Science and ML (Part 23): Why LightGBM and XGBoost outperform a lot of AI models?
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These advanced gradient-=boosted decision tree techniques offer superior performance and flexibility, making them ideal for financial modeling and algorithmic trading. Learn how to leverage these tools to optimize your trading strategies, improve predictive accuracy, and gain a competitive edge in the financial markets.
 

Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models

Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models

Time series forecasting is the process of using past data to predict future values in a sequence of data points. This sequence is typically ordered by time, hence the name time series.

Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models
Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models
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In the forex markets It is very challenging to predict the future trend without having an idea of the past, Very few machine learning models are capable of making the future predictions by considering past values. In this article, we are going to discuss how we can use classical(Non-time series) Artificial Intelligence models to beat the market
 

Forum on trading, automated trading systems and testing trading strategies

All (not yet) about Strategy Tester, Optimization and Cloud

Sergey Golubev, 2024.06.29 16:37

Automated Parameter Optimization for Trading Strategies Using Python and MQL5

Readers will understand the importance of auto-optimization, different algorithms used, and see practical examples in Python and Expert Advisor (EA) scripts. They’ll learn how to set up auto-optimization, compare results, and properly configure parameter optimization, enhancing their trading strategy efficiency.

Self-optimization algorithms for trading strategies include parameter optimization, evolutionary algorithms, heuristic methods, gradient-based techniques, machine learning, and simulation-based optimization. Each has unique pros and cons, tailored for different trading needs and market conditions.

Automated Parameter Optimization for Trading Strategies Using Python and MQL5

Python programs are an excellent tool to try ideas, create graphics quickly and confirm theoretical statements with historical trading data. Python allows to develop and adjust models agilely, which facilitates experimentation with different strategies and parameters. Its ability to generate detailed graphs and visualizations helps interpret the results more intuitively. In addition, the possibility of integrating historical data allows verifying how strategies would have worked in past scenarios, providing practical validation to the theories raised. This combination of speed, flexibility and analytical capacity makes Python an invaluable tool for any trader that seeks to optimize their strategies and better understand financial markets.

 

Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)

Recurrent Neural Networks (RNNs) are artificial neural networks designed to recognize patterns in sequences of data, such as time series, language, or video. Unlike traditional neural networks, which assume that inputs are independent of each other, RNNs can detect and understand patterns from a sequence of data (information).

A basic understanding of Python, ONNX in MQL5, and Python machine learning is required to understand the contents of this article fully.
Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)
Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)
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Recurrent neural networks (RNNs) excel at leveraging past information to predict future events. Their remarkable predictive capabilities have been applied across various domains with great success. In this article, we will deploy RNN models to predict trends in the forex market, demonstrating their potential to enhance forecasting accuracy in forex trading.
 

Sentiment Analysis and Deep Learning for Trading with EA and Backtesting with Python

Integrating deep learning and sentiment analysis into trading strategies in MetaTrader 5 (MQL5) represents a sophisticated advancement in algorithmic trading. Deep learning, a subset of machine learning, involves neural networks with multiple layers that can learn and make predictions from vast and complex datasets. Sentiment analysis, on the other hand, is a natural language processing (NLP) technique used to determine the sentiment or emotional tone behind a body of text. By leveraging these technologies, traders can enhance their decision-making processes and improve trading outcomes.

Can we perform a test in Python to understand the results of combining sentiment analysis and deep learning? The answer is yes, and we will proceed to study the code.
Sentiment Analysis and Deep Learning for Trading with EA and Backtesting with Python
Sentiment Analysis and Deep Learning for Trading with EA and Backtesting with Python
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In this article, we will introduce Sentiment Analysis and ONNX Models with Python to be used in an EA. One script runs a trained ONNX model from TensorFlow for deep learning predictions, while another fetches news headlines and quantifies sentiment using AI.
 

Reimagining Classic Strategies in Python: MA Crossovers

Reimagining Classic Strategies in Python: MA Crossovers

Many of today's trading strategies were conceived in vastly different market landscapes. Assessing their relevance in contemporary markets dominated by algorithms is crucial. This article delves into the moving average crossover strategy to evaluate its effectiveness in today's financial environment.

Reimagining Classic Strategies in Python: MA Crossovers
Reimagining Classic Strategies in Python: MA Crossovers
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In this article, we revisit the classic moving average crossover strategy to assess its current effectiveness. Given the amount of time time since its inception, we explore the potential enhancements that AI can bring to this traditional trading strategy. By incorporating AI techniques, we aim to leverage advanced predictive capabilities to potentially optimize trade entry and exit points, adapt to varying market conditions, and enhance overall performance compared to conventional approaches.
 

Portfolio Optimization in Python and MQL5

Introducing two innovative portfolio optimization programs designed to revolutionize trading strategies and maximize returns while minimizing risk The first a Python-based solution leverages the power of MetaTrader 5 integration alongside advanced libraries such as pandas Numpy and cvxpy to analyze historical data optimize asset allocation and visualize results with Matplotlib. The second a similar implementation crafted in MQL5 harnesses the native capabilities of the MetaTrader 5 platform offering traders a seamless experience directly within their preferred trading environment. Both programs exemplify the cutting-edge intersection of quantitative finance and technology empowering traders with sophisticated tools to make data-driven decisions in an ever-evolving market landscape.
Portfolio Optimization in Python and MQL5
Portfolio Optimization in Python and MQL5
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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.
 

Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks

Long Short-Term Memory(LSTM), is a type of recurrent neural network designed for sequence tasks, excelling in capturing and utilizing long-term dependencies in data. Unlike vanilla Recurrent Neural Networks(simple RNNs) discussed in the previous article of this series (a must-read). Which can't capture long-term dependencies in the data.

Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks
Data Science and ML (Part 26): The Ultimate Battle in Time Series Forecasting — LSTM vs GRU Neural Networks
  • www.mql5.com
In the previous article, we discussed a simple RNN which despite its inability to understand long-term dependencies in the data, was able to make a profitable strategy. In this article, we are discussing both the Long-Short Term Memory(LSTM) and the Gated Recurrent Unit(GRU). These two were introduced to overcome the shortcomings of a simple RNN and to outsmart it.
 

Data Science and ML (Part 27): Convolutional Neural Networks (CNNs) in MetaTrader 5 Trading Bots — Are They Worth It?

Data Science and ML (Part 27): Convolutional Neural Networks (CNNs) in MetaTrader 5 Trading Bots — Are They Worth It?

Convolutional Neural Networks (CNNs) are a class of deep learning algorithms specifically designed to process structured grid-like data, such as images, audio spectrograms, and time-series data. They are particularly well-suited for visual data tasks because they can automatically and adaptively learn spatial hierarchies of features from input data.

CNNs are the extended version of artificial neural networks (ANN). They are predominantly used to extract the feature from the grid-like matrix dataset. For example, visual datasets like images or videos where data patterns play an extensive role.

Data Science and ML (Part 27): Convolutional Neural Networks (CNNs) in MetaTrader 5 Trading Bots — Are They Worth It?
Data Science and ML (Part 27): Convolutional Neural Networks (CNNs) in MetaTrader 5 Trading Bots — Are They Worth It?
  • www.mql5.com
Convolutional Neural Networks (CNNs) are renowned for their prowess in detecting patterns in images and videos, with applications spanning diverse fields. In this article, we explore the potential of CNNs to identify valuable patterns in financial markets and generate effective trading signals for MetaTrader 5 trading bots. Let us discover how this deep machine learning technique can be leveraged for smarter trading decisions.
 

Forum on trading, automated trading systems and testing trading strategies

Self learning expert

Sergey Golubev, 2024.07.29 19:27

Build Self Optimizing Expert Advisors With MQL5 And Python

Build Self Optimizing Expert Advisors With MQL5 And Python

This article demonstrates how we can intelligently achieve our goal by using a transition matrix to model market behavior and determine whether to employ trend-following or mean-reverting strategies. We start by developing a high-level understanding of transition matrices. Then, we explore how these mathematical tools can be used to create intelligent trading algorithms with enhanced decision-making abilities.

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