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I am asking this question because I want to research some variables. An example is the RSI where the current RSI is updated every tick. This means that the value of the RSI is fluctuating a lot inside an individual candle. Lets say I want to research what happens after the RSI reaches a value. But if I only take the close prices and not the tick-prices I don't get exactly what I want but a value that is close to the real value (assumption).

A reason for me not to work with tick-data is that is makes the data set very big. Imagine that I want to analyse 4 years of price data using tick-data. That is an immense amount of rows.

Is there someone who tested the close price and tick-data and found a major difference?

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You don't necessarily need to use tick data to accomplish what you want. If you have OHLC data you can just calculate RSI values using the extremes of the H and L values to get the boundary conditions of a density distribution and then use this distribution to do your testing.

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  • $\begingroup$ Thanks for the reply. And lets say I want to train my model with OHLC data. Doesn't that give me a false model to work further with? Lets hypothetical say it validates the validation and confirmation data. If these two data's approve my OHLC RSI model is it still a false model? $\endgroup$
    – Bob hhhuh
    Commented Oct 15, 2020 at 17:39
  • $\begingroup$ It'll depend on the model I suppose. If you train a probabilistic model (Bayesian?) there is no "correct" model anyway - just a probabilistic output given the data. $\endgroup$
    – babelproofreader
    Commented Oct 17, 2020 at 13:30

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