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Your thinking is correct: bars are simply for aggregation. Within the realm of aperiodic data (tick data), bars make the data periodic, which in turn makes certain types of analysis easier to perform. I wouldn't call bars a "sampling" technique since that implies random selection. Cross-validation checks a predictive model's sensitivity to overfitting by ...


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I will prefix my answer with the following: I've not read the holy wars stuff in the other answers in detail. I think its missing the point. I've implemented exactly this project more than once in small buy side firms. Your main issue is getting the data in the first place. I have a recommendation - S&P Capital IQ. You can get a deployment where you ...


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Markowitz does not require time series of equal length. Markowitz does not veen require that the covariance matrix be based on time series. Markowitz just requires a covariance matrix. The covariance matrix could, for all Markowitz cares, be based entirely on judgment, on proxies, etc. So your question really is, how do we construct a covariance matrix (non-...


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It's not about timestamps. You just need to assign the same meaning to each bar. Choose a fixed percentage of daily volume each bar should represent. Then for each individual day, compute the bar size from that percentage: today's bar size = today's total volume * chosen fixed percentage For example, I can choose that each bar should be 2% of the daily ...


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As mentioned in my comment, tick data is the individual quotes and trades; Yahoo only has daily data. As an analogy, you can always make a high-definition photo more blurry and pixelated, but you can't add detail and definition to a bad picture. Daily data is just an aggregate of individual ticks, so you can't get the individual ticks from daily data. You ...


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a tick is a change in the price, it is not a second or minute at regular tine intervals, it's frequency is driven by market moves whilst daily data is aggregated, therefore by definition it is impossible to deduct tick data from any other time frequency. to extract this in python, there are multiple ways, I personally use Dukascopy, where you can download ...


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To solve your multicollinearity problem I would first perform a regularization technique such as Ridge or Elastic Net. If you choose Ridge for example, once you have tuned your hyperparameter through cross validation (for time series a forward walk approach is preferable) you can fit after your simple OLS by choosing the predictors with the biggest ...


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There's a simple open-source JavaScript/TypeScript library made by our team in Github: Technical Analysis library for JavaScript To build charting interfaces for JavaScript, you can use WebGL accelerated LightningChart JS


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There is a MATLAB code developed recently to handle the multivariate MS GARCH model, check this link


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