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![Tuning LLMs with Your Own Personalized Data and Integrating into EA (Part 5): Develop and Test Trading Strategy with LLMs(I)-Fine-tuning](https://c.mql5.com/2/88/Tuning_LLMs_with_Your_Own_Personalized_Data_and_Integrating_into_EA_Part_5__LOGO.png)
With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
![Integrate Your Own LLM into EA (Part 4): Training Your Own LLM with GPU](https://c.mql5.com/2/82/Integrate_Your_Own_LLM_into_EA_Part_4____LOGO.png)
With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
![Integrate Your Own LLM into EA (Part 3): Training Your Own LLM with CPU](https://c.mql5.com/2/79/Integrate_Your_Own_LLM_into_EA__Part_3_-_Training_Your_Own_LLM_with_CPU_____LOGO.png)
With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
![Data label for time series mining (Part 6):Apply and Test in EA Using ONNX](https://c.mql5.com/2/64/Data_label_for_time_series_mining_1Part_60_Apply_and_Test_in_EA_Using_ONNX____LOGO.png)
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
![Data label for time series mining (Part 5):Apply and Test in EA Using Socket](https://c.mql5.com/2/64/Data_label_for_time_series_miningbPart_50_Apply_and_Test_in_EA_Using_Socket_____LOGO.png)
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
![Data label for time series mining (Part 4):Interpretability Decomposition Using Label Data](https://c.mql5.com/2/61/Data_label_for_time_series_mining_nPart_45Interpretability_Decomposition_Using_Label_Data_LOGO.png)
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
![Integrate Your Own LLM into EA (Part 2): Example of Environment Deployment](https://c.mql5.com/2/59/penguin-image.png)
With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
![Integrate Your Own LLM into EA (Part 1): Hardware and Environment Deployment](https://c.mql5.com/2/59/Hardware_icon_up__1.png)
With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
![Data label for time series mining (Part 3):Example for using label data](https://c.mql5.com/2/58/data-label-for-time-series-mining-avatar.png)
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
![Data label for timeseries mining (Part 2):Make datasets with trend markers using Python](https://c.mql5.com/2/58/Make_datasets_with_trend_markers_using_Python_Avatar.png)
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
![Data label for time series mining(Part 1):Make a dataset with trend markers through the EA operation chart](https://c.mql5.com/2/57/data-label-for-time-series-mining-avatar.png)
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!