Nicely done.

A couple of pointers. One on data, one on that RAM usage.

First I’ll go with the RAM usage because this is hackernews and everyone loves algorithms.

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There are a lot of libraries out there that do technical analysis, and most of them are designed for batch processing. TALib is an example - it works on large data sets but is not appropriate for live trading because it repeats calculations over and over and over again. If you have 10000 datapoints and calculate indicators, it’ll calculate 10000 of them. Add one more bar, now you have a dataset of 10001 items, which TALib will calculate the indicators on from scratch. Or maybe you just feed the last 10000, and still perform that calculation over all of those, but save that 10001st oldest one. Either way, it’s bad. Same goes for every library I’ve seen, presumably because no one would open source a production grade indicator generation system.

The production approach to this is somewhat different. Turn your features into state engines. Most features are just running calculations that are very easy to perform once per bar.

Moving averages are a perfect example - for a moving average of N bars, store N items in a fixed size array on the stack and keep track of the latest index to be written to. When a new value (X) comes in, increment that index, and grab the value (Y). Modify your old mean by adding (X-Y)/N. Then write X over the old value Y (It’s a ring buffer).

For EMAs, it’s even easier, because you just need to keep track of a numerator and a denominator - nothing else is needed. On a new value X with the scaling factor A (such that A^halflife = 0.5), the numerator N becomes NA+X and the denominator D becomes DA+1. Divide the two and you’ve got your new value.

If an indicator depends on another, don’t recalculate it. Break the indicators down into fundamental calculations and you’ll often find a lot of redundant calculations being done. Rearrange it all so it fits. Automate that process if you enjoy that kind of thing like I do.

Most indicators can be handled this way. The indicators that can’t - are rarely useful. After all, what you’re tracking is the evolving state of the market, and if you’re doing gymnastics over an indefinite number of bars, it probably doesn’t mean much.

The end result is that you don’t end up accumulating memory throughout the day. You receive a bar, you throw it through your indicator generators (all of which using a fixed size of memory), then you discard the bar and wait for the next. Save state at the end of the day and load that on the following market day.

The result will be a speed up like you couldn’t imagine. I promise. I run an indicator generation engine in a container capped at 40MB ram on one CPU, and it generates hundreds in much less than a millisecond after the bar arrives.

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Now, onto data. I recommend cleaning your data. You have a screenshot of a Tesla chart in there with some funky highs/lows every so often. It has been a long time since I’ve worked with US equities (and gladly so, it’s a mess of a system!) but the following is the best of my recollection.

The trades you receive will come from several sources. For US stocks, there are several different venues that operate their own order books. These will operate as typical markets between the open and close of the day. By typical I mean that the bid and ask represent what you’d get if you market order instantly (which you can’t), and the market trades on them have to take from the bid and ask side of the order book (formed by people placing limit orders).

However. There’s also the ADF - the Alternative Display Facility. This is the DIY of trade reporting (and quote reporting, but no one does). If someone sells some shares to their grandmother for a low price in exchange for the recipe to her famous apple pie, the ADF is where they can tell other participants about that trade, manually, subject to fat finger errors, prices of weird fractions of cents, and very relaxed constraints on timing.

It’s also where dark pools post trades.

The problem with this is that this data has no direct relationship to the rest of the market. This is why, every so often, you’ll see those blips.

If you don’t clean them, they’ll play havoc with any indicator that uses highs and lows.

The other problem is that ADF trades - at least when I last analysed this very issue - are not rare. They make up a large fraction of trades. So my approach was to clean ADF trades more rigorously than the venues by matching them against prior prices from an ADF-free background.