In previous discussions, we analyzed an S&P 500 trading strategy employing high-weight stocks in the index. An alternative strategy involves using Treasury Note yields. Historically, risk-averse investors shift funds from stocks to safer bonds and Treasury notes, creating a negative correlation between stock investments and Treasury yields. High bond yields suggest low demand, indicating investors are leaning towards stock investments.

To validate this strategy, we examined the statistical significance of using Treasury yield data. Multiple models, including the SGD Regressor, were trained using S&P 500 OHLC data and 5-year Treasury Note data. Results indicated the models performed better with just S&P 500 data, questioning the reliability of Treasury yield correlations.

Data was collected using Python scripts to export from MetaTrader 5. Analysis showe...
#MQL5 #MT5 #trading #finance

Read more...

Discover the power of non-linear methods in financial time series analysis! This article delves into innovative techniques for creating non-linear indicators on MetaTrader 5, transforming linear indicators using logarithmic price transformations to provide robust solutions where linear indicators fall short. Learn to utilize measures like the median and Bayesian smoothing to enhance sensitivity to price changes. Explore practical applications like robust trend analysis with the Weighted Median Ichimoku and improved pattern recognition using ordinal statistics. Ideal for both traders and developers, this comprehensive guide enhances algorithmic trading strategies and indicator accuracy comprehensively.
#MQL5 #MT5 #Trading #Finance

Read more...

In the previous article, we examined merging MetaQuotes Language 5 (MQL5) with Telegram for signal generation and relay. This streamlined the sending of trading signals to Telegram. We now take a further step in integrating MQL5 with Telegram by sending a screenshot of the trading signal chart.

Instead of only the text portion, a visual representation, such as a chart screenshot, can add context and clarity to the signal. This article focuses on converting image data into a format compatible for embedding in HTTPS requests. This conversion is essential for including images in our Telegram bot.

The article is organized into four parts: overview of image encoding and transmission over HTTPS, implementation in MQL5, testing the integration, and conclusion. By the end, we will have developed an Expert Advisor (EA) that sends chart screenshots with trading i...
#MQL5 #MT5 #trading #finance

Read more...

Markov chains are effective for modeling and forecasting time series data in finance. Their simplicity is a primary advantage, relying on probabilistic models without complex assumptions. They are beneficial for financial data, which often exhibit non-stationary behavior.

There are four main types of Markov chain models: discrete-time, continuous-time, Hidden Markov models, and Switching Markov models. The primary types are discrete-time, modeling a system over discrete steps, and continuous-time, modeling over an interval. Probability estimation, often via expectation maximisation, is key for using these models.

Markov chains predict future states based on current status and transition probabilities, making them suitable for various fields like finance, weather, and biology.
#MQL5 #MT5 #finance #forecasting

Read more...

Review of "Conformer: Embedding Continuous Attention in Vision Transformer for Weather Forecasting"

Study on the potential of utilizing meteorological forecasting algorithms to predict financial market behavior, focusing on the Conformer algorithm. It combines Continuous Attention with Neural ODE, allowing the model to process weather variables over time. The architecture utilizes multi-head attention encoded as a differentiable function to model complex weather dynamics.

Technical implementation using MQL5 involves creating the CNeuronConformer class derived from CNeuronBaseOCL. The structure includes convolutional layers for Query, Key, and Value entities, differentiation over time, and initialization of feedforward and differential equation blocks.

Detailed implementation involves defining partial derivatives within the Continuous Attention mechanism, en...
#MQL5 #MT5 #Finance #AI

Read more...

Chaos theory's application in financial markets offers a distinct perspective beyond conventional models, focusing on non-linear and complex dynamics. Key concepts include attractors, fractals, and the butterfly effect. Attractors represent recurring patterns or levels toward which markets gravitate. Fractals show consistent patterns across different timeframes, relevant to technical analysis. The butterfly effect highlights sensitivity to initial conditions, complicating long-term forecasts.

Chaos theory aids in volatility analysis, with models like phase space reconstruction providing insights into market behavior. The Lyapunov exponent measures chaos, indicating a system's sensitivity to change. Positive values show unpredictability, while negative values suggest stability. Implementing the Lyapunov exponent in MQL5 can enhance trading strategies,...
#MQL5 #MT5 #ChaosTheory #Finance

Read more...

Gating methods offer a dynamic approach to model selection in complex environments, crucial for financial forecasting where economic trends affect prediction accuracy. These techniques utilize gate variables to adaptively weight model outputs based on context, outperforming traditional ensemble methods. There are two main strategies: selecting a single model through predefined rules or using context-dependent weights to combine multiple models, the latter being more robust. Key innovations include learned specialization, where gating parameters are derived directly from data, enhancing model performance despite computational demands. Practical applications demonstrated by testing scenarios highlight the potential of such methods in adaptive algorithmic trading systems.
#MQL5 #MT5 #Algorithm #Finance

Read more...

The first article provided foundational insights into chaos theory, emphasizing its relevance in financial market analysis. Key themes included attractors, fractals, and the butterfly effect, with a practical focus on the Lyapunov exponent for analyzing market dynamics. It compared traditional chaos theory with Bill Williams' approach, highlighting differences in theoretical and practical applications. Practical examples using the EURUSD pair demonstrated the applicability of chaos theory in deciphering trading trends.

The second article delves into fractal dimension as a complexity measure in markets. This metric quantifies the complexity and randomness of price movements, aiding in volatility assessment, market mode identification, and strategy optimization. Box-counting method implementation in MQL5 facilitates real-time chart analysis, enabling trade...
#MQL5 #MT5 #Finance #Trading

Read more...

Unlock sophisticated asset prediction with Multitask-Stockformer, a cutting-edge framework leveraging deep learning for stock forecasting. Combining wavelet transforms with self-attention networks, this model captures both high-frequency market fluctuations and long-term trends. By decomposing historical data into distinct components, it accurately forecasts returns using dual-frequency encoders and decoders. Ideal for traders and developers, it employs graph attention networks to map complex market relationships, ensuring refined trading strategies. The seamless integration with MQL5 underlines its practical applicability in algorithmic trading environments. Whether you're refining multifactor strategies or venturing into deep learning, Multitask-Stockformer represents a significant technical advancement.

👉 Read | VPS | @mql5dev

#MQL5 #MT5 #Finance