Prompt Title: retrain model

Your approach of retraining the model with the new data every 6 months and testing it on the next 6 months seems reasonable. This is a form of incremental learning, where the model is trained on new data periodically, instead of retraining the entire model from scratch.

However, there are a few things to consider:

1. The frequency of updating the model: 6 months is a good starting point, but you may want to adjust the frequency depending on how quickly the market is changing. If the market is volatile and changes rapidly, you may want to update the model more frequently.

2. The number of epochs: One epoch may not be enough to train the model on new data. The number of epochs needed depends on the amount of new data and how much the market has changed. You may need to experiment with different numbers of epochs to find the optimal value.

3. The size of the new data: 1500 samples every 6 months may not be enough to capture all the changes in the market. You may want to consider increasing the size of the new data to get better results.

4. Overfitting: When updating the model with new data, you need to be careful not to overfit the model to the new data. You can avoid overfitting by using techniques such as early stopping, regularization, and cross-validation.

Overall, your approach of incremental learning is a good strategy for updating your stock market prediction model. Just be mindful of the potential issues and adjust your approach accordingly.

It depends on how much the market has changed since the last time the model was trained, and how much new data you have. If the market has changed significantly and you have a large amount of new data, it may be necessary to train the model for more epochs to capture the changes. However, it's important to use early stopping to avoid overfitting to the new data.

Early stopping is a technique that monitors the performance of the model on a validation set during training. If the validation loss starts increasing after a certain number of epochs, the training is stopped early to prevent the model from overfitting to the training data. In this way, early stopping can help you find the optimal number of epochs for training the model.

So, if you decide to train the model for more epochs, you should use early stopping to prevent overfitting to the new data. The key is to find the balance between training for enough epochs to capture the changes in the market, while avoiding overfitting to the new data.

Given that the new data for each

Thank you for the clarification.

In this case, 1500 samples of new data may not be sufficient to significantly affect the existing model, especially if the original model was trained on a much larger dataset. Training the model for more epochs with early stopping may not be necessary if the new data is not significantly different from the original data.

Instead, you may consider retraining the model on a larger amount of new data, such as every 12 months instead of every 6 months. This would allow you to collect more data and make more significant updates to the model, while also reducing the computational burden of training the model too frequently.

However, if you do decide to train the model on the new data every 6 months with 1500 samples, you can experiment with different numbers of epochs and use early stopping to find the optimal number of epochs for the updated model.

Yes, you are correct. Each time you update the model with new data, you should also update the MinMaxScaler with the new data to ensure that the scaling is consistent across all the data.

When updating the scaler, you should fit it on the combined original and new data, and then transform both the original and new data using the updated scaler. This will ensure that the scaling limits are consistent across all the data, including the new data.

It's also a good practice to save the scaler along with the model, so that you can easily reuse it the next time you need to update the model.

Fitting the MinMaxScaler on the new data alone without incorporating the original data could result in a scaling range that is different from the range used to scale the original data. This could lead to inconsistency in the scaling and negatively impact the performance of the model.

Regarding outliers, it is important to handle them appropriately during training and prediction. If there are outliers in the new data that fall outside the range of the MinMaxScaler, you could choose to exclude them from the training data or adjust the scaling limits of the MinMaxScaler to include these outliers. However, if the outliers are too extreme and cannot be reasonably included in the scaling limits, then it may be best to exclude them from the prediction.

It's important to note that handling outliers is a delicate balance between improving the performance of the model and avoiding overfitting to extreme data points. You may want to experiment with different strategies for handling outliers and evaluate the impact on the model's performance.

Yes, you can use the 2019 data as well when creating the new scaler. Since the 2019 data was used only for validation and not for training the original model, it can be considered as part of the original data distribution and used to create the new scaler.

Including the 2019 data in the scaler will ensure that the scaling is consistent across all the data, including the new data that was collected after the original model was trained. However, it's important to keep in mind that the 2019 data should not be used for training or tuning the updated model, as this would introduce data leakage and invalidate the model's performance evaluation.

So, in summary, you can include the 2019 data when creating the new scaler, but it should not be used for training or evaluation of the updated model.

Ideally, when you initially created the MinMaxScaler, you should have fitted it on the entire original dataset, including the 2019 data, to ensure that the scaling is consistent across all the data.

If you did not include the 2019 data when creating the original scaler, it may be better to re-create the scaler with the entire original dataset, including the 2019 data, before proceeding with the update. This will ensure that the scaling is consistent across all the data, and there is no mismatch between the scaling of the original and new data.

However, if you are unable to re-create the scaler with the entire original dataset, you can still use the updated scaler with the 2019 data included to scale both the original and new data for the update. This should still result in consistent scaling across all the data.

In summary, it is important to ensure that the scaling is consistent across all the data, so if the original scaler was not created with the 2019 data included, you may want to consider re-creating the scaler with the entire original dataset, including the 2019 data, to ensure consistency.

You are correct that in the real world, you would not know the minimum and maximum values of the data beforehand. However, the purpose of the validation set is to evaluate the performance of the model and tune hyperparameters, not to create the MinMaxScaler.

When creating the MinMaxScaler, you should use only the training data, not the validation set. The purpose of the scaler is to normalize the data before feeding it into the model, and it should be based only on the training data to ensure that the model is trained on consistent data.

If the data distribution in the new data is significantly different from the original data, you may need to adjust the scaling limits of the MinMaxScaler to accommodate the new data. This can be done by fitting the scaler on the combined original and new data and adjusting the scaling limits accordingly. However, you should avoid including the validation set in the scaler as it may introduce data leakage and bias the performance evaluation of the model.

I apologize for any confusion. Let me clarify my previous responses.

When creating the MinMaxScaler, it is generally a good practice to fit it on the entire dataset, including both the training and validation sets, to ensure that the scaling is consistent across all the data. This is because the scaler is used to normalize the data before feeding it into the model, and it should be based on the entire dataset to ensure that the model is trained on consistent data.

However, when training the model, it is important to use only the training set for training and not to include the validation set. This is to avoid data leakage and ensure that the model is evaluated on unseen data.

Therefore, to answer your question directly, you can include the 2019 data in the scaler used in the train process, but you should not use the validation set in the actual training of the model. The scaler should only be used to normalize the data before feeding it into the model.

I hope this clarifies things for you.

I understand your point, but it's important to ensure that the model is trained on consistent and unbiased data. By including the validation set in the scaler, you risk introducing data leakage and biasing the performance of the model.

Ideally, you want to create the scaler based only on the training set and ensure that it is applied consistently to all the data, including the validation set and any new data. If the distribution of the new data is significantly different from the original data, you may need to adjust the scaling limits of the scaler accordingly.

Regarding your point about using information from the future to predict stock prices, it's important to note that any information that is not available at the time of prediction should not be used in the model. Otherwise, the model may overfit to the training data and perform poorly on new data. To avoid this, you should ensure that the validation set and any new data are not used in the actual training of the model.

Yes, that sounds like a good approach. To summarize:

• Train the model on tagged historical data from 2008-2018, with 2019 as the validation set for early stopping.
• Use the original scaler based on the data until the validation set (i.e., exclude 2019).
• Every 6 months, retrain the model with new data (excluding the 2019 validation set) and check it on the validation set for several epochs with early stopping.
• For each iteration of retraining, create a new scaler with all the historical data and the new data (excluding 2019).

This approach should help ensure that the model is trained on consistent and unbiased data, while also incorporating new data as it becomes available.

Yes, if the model doesn't perform well on the validation set after an iteration, it's a good idea to investigate why and potentially try different approaches to improve it. Depending on the specific situation, you could try adjusting hyperparameters, changing the architecture of the model, or adding/removing features.

If you're unable to get satisfactory results even after multiple iterations, you could consider going back to the original model and trying to improve it. However, keep in mind that the original model may not be sufficient to handle the new data, so it may still require adjustments.