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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 ...


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


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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 ...


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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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