CODEWITHSUNDEEP
algorithmfinancequantitative-financecandlestick-chart
badcoder
badcoder

Reputation: 3834

Algorithm to find areas of support in a candlestick chart

I am in the process of designing an algorithm that will calculate regions in a candlestick chart where strong areas of support exist. An "area of support" in this case is defined as an area in the chart where the price of a stock rises by a large amount in a short period of time. (Please see the diagram below, the blue dots represent these strong areas of support)

enter image description here

The data I am working with is a list of over 6000 TOHLC (timestamp, open price, high price, low price, close price) values. For example, the first entry in this list of data is:

[1555286400, 83.7, 84.63, 83.7, 84.27]

The way I have structured the algorithm to work is as follows:

1.) The list of 6000+ TOHLC values are split into sub-lists of 30 TOHLC values (30 is a number that I arbitrarily chose). The lowest low price (LLP) is then obtained from each of these sub-lists. The purpose behind using this method is to find areas in the chart where prices dip.

2.) The next step is to determine how high the price rose from each of these lows. For this, I take the next 30 candlestick values from the low and determine what the highest high price (HHP) is. Then, if HHP / LLP >= 1.03, the low price is accepted, otherwise it is discarded. Again, 1.03 is a value that I arbitrarily chose, by analysing the stock chart manually and determining how much the price rose on average from these lows.

The blue dots in the chart above represent the accepted areas of support by the algorithm. It appears to be working well, in terms of that I am trying to achieve.

So the question I have is: does anyone have any improvements they can suggest for this algorithm, or point out any faults in it?

Thanks!

Upvotes: 2

Views: 164

Answers (1)

user7340499
user7340499

Reputation:

I may have understood wrong, however, from your explanation it seems like you are doing your calculation in separate 30-ish sub lists and then combining them together.

So, what if the LLP is the 30th element of sublist N and HHP is 1st element of sublist N+1 ? If you have taken that into account, then it's fine.

  1. If you haven't taken that into account, I would suggest doing a moving-window type of approach in reading those data. So, you would start from 0th element of 6000+ TOHLC and start with a window size of 30 and slide it 1 by 1. This way, you won't miss any values.

  2. Some of the selected blue dots have higher dip than others. Why is that? I would separate them into another classifier. If you will store them into an object, store the dip rate as well.

  3. Floating point numbers are not suggested in finance. If possible, I'd use a different approach and perhaps classifier, solely using integers. It may not bother you or your project as of now, but surely, it will begin to create false results when the numbers add up in the future.

Upvotes: 1

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