A story about clustering time series data of foreign exchange

Summary of this article

Development environment

Data preparation

Using USD / JPY from 2018.01 to 2019.04, The entry point of the golden cross of the moving average on the 5-minute bar was used as sample data. (2482 data)

labeling

Labeling was done according to the following rules.

Result Label
Profit 1
Loss -1
Settlement by holding time 0

This time, we set the loss cut and profit taking lines so that they are roughly divided into three equal parts.

Clustering

Expected result

As shown in the graph below, I expected that "profit taking" / "loss cut" / "settlement by holding time" would be separated for each cluster.

2002_1_hoped.png

With this, in the case of cluster 2, it can be judged that it is not good and the trade can be forgotten.

result

We clustered using scikit-learn's TimeSeriesKMeans, illustrated the percentage of labels in each cluster, and sorted them in order of winning percentage.

2002_2_OHLC_and_RSI.png

Not good enough. .. The highest win rate was 45% and the lowest win rate was 22%. Since the original is almost divided into 3 equal parts (33%), it seems that it can be divided a little, but I would like it to be divided a little more beautifully.

Add upper leg

Aiming for improvement, we decided to add the following longer timeframe information to the features.

The result is below. 2002_3_with_long_term_indicator.png

The highest win rate was 63% and the lowest win rate was 14%. By adding the information of the upper legs, it has improved a lot. I think it was good because I was able to confirm again that the information on the upper legs is useful. With such a result, it seems difficult to avoid damaging, but I personally thought that it could be used to adjust the quantity of positions.

Thank you for reading the article.

reference

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