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I was proving a very simple strategy with Bollinger Bands for a intraday timeframe (1 minute) that buy on lower band and sell in a higher band (Very common strategy), but in backtesting in E-Mini SP 500 at 1999 I think that it generated interesting results. This the graphic:

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It's not winner but for a long period of the year it was profitable. First, considering that I'm a newbie i could like that you tell me if it can achieve to something profitable with some optimization, and if it so, what kind of optimization can I implement? or if it's a rubbish and I should try with other stuff

I was thinking that maybe I could implement some kind of machine learning for detect abrupt raises or falls prices which are the main problem as show in the image below

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Or something with statistics that I should learn (my statistic knowledge it's not very widely) and prove

Any advice it would be greatly appreciated

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    $\begingroup$ I absolutely would not approach this as a tradeable strategy, but it could be a good project to learn statistics with. $\endgroup$
    – steveo'america
    Nov 24, 2020 at 1:04
  • $\begingroup$ I have to give you credit for testing this far away... a millennium ago. Have you checked that the index methodology and the contract terms were the same in 1999 as it they are now, apples to apples? $\endgroup$
    – Sergei Rodionov
    Mar 15, 2021 at 17:01
  • $\begingroup$ No, I didn't know that was relevant for test this simple algo $\endgroup$
    – Caeta
    Mar 17, 2021 at 15:04

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This might not be a direct response, but just some general advise / ideas.

I think giving you "optimal" parameters is not a straight realistic response. It depends on the security you are analizing. For instance, what you can do is grab a set of historical years, run multiple simulations on different parameters, and check which ones provide you the higher return (you can use a risk-adjusted performance metric, e.g., sharpe ratio).

There are other strategies you can also try, such as the Momentum one. If you want to explore machine learning on it, you can check out Marcos Lopez de Prado's book on advances in financial machine learning. You can also backtest using Quantopian.

Bollinger Bands is a common strategy, thus you might find some implementations of it already, to use as reference.

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