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I wrote a masters thesis related to machine learning in finance, and during this process I surveyed about 200 of the research papers that were written about the topic since 2018. This is the distribution of the algorithms used in the research papers: LSTM is by far the most used machine learning algorithm used to predict financial time series and used in ...


QuestDB is also another option. Billed as "the fastest open source time series database".


I don't think that there is one right way to approach this problem. However, I will give an example which I found quite interesting. The JP-Morgan risk-metrics approach was (or still is I don't know) quite popular in the industry. They use an EWMA $$ \sigma_{t}^2=(1-\lambda)r_{t-1}^2+\lambda\sigma_{t-1}^2 $$ to predict daily or monthly volatility. For daily ...


The logic used by the library is to combine all trades equal or above the threshold and to never split one trade over two bars. So 6 becomes its own bar and 2 and 7 combine to be one bar. I think this makes more sense than your approach. In the second table you show two bars with the same time. When using time on the x-axis there is no good way to even plot ...

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