Allow me to write a few words before the central part.

I recapped what I wrote here and published it on a Japanese website where Japanese coders gather. Thankfully, many people read the article. I am pleased about this. Thank you very much.

**Plan**

Okay, we did everything we could easily do.

The question here is, what should we do to improve this model and make it more practical?

There are mainly two ideas now.

1. To put another indicator on pictures in the dataset.

Ex) long-term trend and trading volume information.

2. To test several models automatically and save only the best result (This will save possibly one with the highest accuracy.)

**Problem 1.** 67% of the data was labelled as “Up” in the dataset. My computer might think to answer “up” a lot is the solution for enhancing its accuracy.

Well no. First, the computer is not headed to maximize accuracy but to minimize the loss function.

**Reference 1.** I made the ratio of “Up” and “Down” in dataset 1:1 by eliminating some of the “Up” pictures, and we’ve got the following result after 75 epochs.

**Reference 2.**

*"Up" accounts for n(former)% of the whole dataset * AI answers "Up" with a chance of n(latter)%. The expected accuracy of AI completely randomly generates answers.***Problem 2.** Since it is a significantly simplified model, the only function is to predict the price precisely two months later, and it does not consider the price fluctuation during the period at all. Practical operation with this model might be too dangerous.

I can solve this problem by improving the model and idea step by step. See you soon.

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