Monday 30 December 2019

Failure 3. Tried to improve accuracy by modifying and adding more data to the dataset.

First attempt.
I did use only drastically-up and drastically-down data to build a model this time. I got something like this. 

And in the test, we’ve got this


52% accuracy is no better than an average human.

This result indicates either, Machine learning, as far as I tested, supports that the stock market is random walking, or there is something wrong with my code or model.

The latter case is likely. I need to research further.

Second attempt.

Okay, the first one is not working. This time I will do a basic like a simple "up" and "down" analysis but this time with a more significant amount of data, including the following in a period of 2001-01-01 to today :

^N225 nikkei 225

AAPL Apple Inc.

^GSPC S%P 500


GOOGL Alphabet Inc Class A

BA Boeing Company



TWTR Twitter, Inc.

V Visa Inc.

I got this result.

Third attempt.

In that training dataset in the second attempt, price up by even 1 or 2 cents could cause promotion from “down” to “up” and vice versa.

Data with minor price changes might confuse the algorithm. This time only price movement of 1% or more can be considered up or down. I will ignore slight changes.

Now, I got this.

I eliminated a possibly confusing factor from the dataset, and accuracy decreased for some reason.
I might have a problem with the model or layers.

In conclusion, Machine learning suggests the difficulty of predicting the stock market's future. However, it also implies that something can make the prediction possible behind the candlestick chart since 60% of accuracy is something. 

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