IRCLogs for #armagetron in 2022-11 • ArmaNelgTron.tk

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[2022-11-03 05:37:15] <Lucifer_arma_> yep.  No surprise.  Broke up over religion.  :/
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[2022-11-09 09:26:15] <Bombo> hm
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[2022-11-13 20:14:16] <Lucifer_arma> It's amusing to me that the verification problems Twitter has been having are basically all problems we predicted back in the day when people wanted verified names in arma

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[2022-11-15 01:26:14] <Lucifer_arma> ok, the breakup has finally played out and I deleted her from my phone
[2022-11-15 01:26:18] <Lucifer_arma> fucking christians
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[2022-11-19 02:20:35] -!- silver.libera.chat set mode #armagetron +nt
[2022-11-19 02:20:35] -!- Channel #armagetron created on 2021-05-20 17:23:14 UTC
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[2022-11-19 02:24:43] -!- Topic for #armagetron is "Armagetron Advanced | http://www.armagetronad.org/ | Welcome to IRC"
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[2022-11-19 02:24:44] -!- Channel #armagetron created on 2021-04-20 19:56:37 UTC
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[2022-11-20 23:38:26] <Lucifer_arma> When analyzing accuracy over a large dataset, can you average the accuracies together?
[2022-11-20 23:38:59] <Lucifer_arma> The specific case here is that I've been training a bunch of different AIs to analyze stock price history, and I generate an overall accuracy of predictions for each individual stock
[2022-11-20 23:39:25] <Lucifer_arma> I want to gauge the accuracy of the bot over many stocks, so I want to average the accuracy of each prediction step over all of the stocks the bot studied
[2022-11-20 23:40:39] <Lucifer_arma> Each stock history dataset is different, because the stock itself could come from different industries, has different lengths of data (some companies haven't been publicly traded for long and may only have a few months history, while others have been trading for 20+ years and the dataset only goes back a maximum of 20 years)
[2022-11-20 23:41:19] <Lucifer_arma> Arguably, that makes each dataset unique, and so trying to combine them is wrong, but I'm trying to measure the general purpose accuracy of the bot in predicting any random stock I give it
[2022-11-20 23:42:07] <Lucifer_arma> For reference, I'm trying to measure accuracy in predicting both how much the stock price changed and also how close the predicted price was
[2022-11-20 23:42:23] <Lucifer_arma> I'm working on a different metric to determine how accurate each bot was in predicted the direction of the stock price changes

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[2022-11-23 12:05:17] <Z-Man> Lucifer_arma: Yes, of course you can do that. You just need to be aware of the different ways to average and what they mean. Optimize your average to the outcome you want.
[2022-11-23 12:06:39] <Z-Man> At the very least, you need to normalize the accuracy to the stock value. It's relative errors that count, not absolute ones. It is more useful to correctly predict a raise from 10$ to 15$ than from 100$ to 120$.
[2022-11-23 12:10:17] <Z-Man> I'd simply put into the average what I intend to do with the predictions. Say you plan that you buy 1000$ worth of stock when your system says it is going to rise 10% over the next month, and sell it all after a month. Or when it did rise 10%. Or when it fell 20% instead. Whatever you plan, put it in, simulate what you would do with the AIs predictions, and then your measure is how mch you would make or lose.
[2022-11-23 12:11:26] <Z-Man> (I would not do anything like that at all. It is a good idea, and the problem with that is that someone with bigger pockets, mode data and fatter AI systems probably already tried it)
[2022-11-23 12:12:27] <Z-Man> (If it worked, they are still doing it and you can't compete. If it did not work, your raspii powered AI won't work either.)
[2022-11-23 18:08:01] <Lucifer_arma> Over 80% of transactions on the stock market are already done by AIs by those people with big pockets.  The competition is irrelevant.  :)
[2022-11-23 18:08:34] <Lucifer_arma> I'm still trying to measure how good the bots are at predicting while I'm zeroing in on a more or less final configuration that I'll put into realtime use, so these averages are just a way to measure performance
[2022-11-23 18:09:31] <Lucifer_arma> The basic idea is to predict the lower value stocks that I can afford now over the course of the next 2-5 days, and write a trading algorithm that works on that
[2022-11-23 18:10:15] <Lucifer_arma> so a stock may only increase from $5 to $5.50, but if I trade on that .50/stock/day, I can turn $100 into $120 in a week, and then it snowballs until I *am* that deep pocket person
[2022-11-23 18:11:02] <Lucifer_arma> anyway, yeah, over 80% of stock trades are already automated with AIs, specifically deep learning RNNs, and *that's* why the stock market didn't react to the last couple of presidential elections.  Not because investors liked Trump or Biden.
[2022-11-23 18:12:17] <Lucifer_arma> the specific average I'm looking at, though, is already normalized.  It's a 0-1 average, you know, before you multiply by 100 to turn it into a percentage.  It's the accuracy of the prediction in the spread
[2022-11-23 18:13:14] <Lucifer_arma> so if a stock changed from $5 to $5.50, and my bot predicted it would move only 40 cents, but it predicted the right direction, then the accuracy would be 0.8.  That same bot working on another stock may have the real change of $2.00, but predicted $1, again in the right direction
[2022-11-23 18:13:42] <Lucifer_arma> so that would give 0.5 for accuracy.  So my question is "Can I average the 0.8 and the 0.5 to get an overall measurement of the bot's accuracy?"
[2022-11-23 18:14:23] <Lucifer_arma> I'm only using it to compare different bot configurations (different optimizer, loss, and activation functions, and also different numbers of nodes in the network, and also different decay values for each layer)
[2022-11-23 18:15:14] <Lucifer_arma> in production (and starting in simulation), I'll track accuracy of predictions on a stock by stock basis, but the trading algorithm won't know the accuracy of a current prediction, obviously, because the final price isn't known
[2022-11-23 18:15:33] <Lucifer_arma> but it should be able to look at the trend in prediction accuracy over the last, say, 7 predictions for a given stock
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[2022-11-24 13:30:25] <Z-Man> If predictions in the wrong direction give increasingly negative accuracy, then that is a sensible value that can be averaged.
[2022-11-24 13:31:31] <Z-Man> There is another caveat: If one week you make a gain of 20%, the next a loss of 20%, those don't cancel out and you end up with a net negative.
[2022-11-24 13:35:31] <Z-Man> The effect is probably not strong enough to matter over all of the other noises you would be getting. If you do want to account for it, the easiest way is to not use the normalization you have, but simply to look at the change of the log of the stock price. Base does not matter.
[2022-11-24 13:36:40] <Z-Man> Say you pick log base 2, then a doubling of the price will be +1 in the log, and halving would be -1. Doubling and halving cancel each other out, as do +1 and -1 if you average.
[2022-11-24 18:49:23] <Lucifer_arma> well, talking about managing gains and losses is an entirely different conversation, but the basic algorithm in my head goes like this:
[2022-11-24 18:50:10] <Lucifer_arma> First, pull all the best predictions out of the database.  Each prediction will get a score (or more than one score) based on overall accuracy of the bot, actual real stock price, and the expected differences between open/close and high/low
[2022-11-24 18:50:56] <Lucifer_arma> This should pull a decent-sized list of stocks that *should* be profitable.  Included in the results here will be some measure of volatility, because that's a way of assessing risk
[2022-11-24 18:52:02] <Lucifer_arma> ok, say this happens as soon as the market opens.  Now the algorithm finds the "best" stocks for the amount of money available and buys them.  It'll determine this by calculating the net gain in actual dollars, because the percentages don't matter that much
[2022-11-24 18:52:45] <Lucifer_arma> then it puts in the sale order, and the price used for the sale order can basically be written as Predicted Price - accuracy of prediction - margin of error
[2022-11-24 18:53:24] <Lucifer_arma> so a stock that starts at $5 and is predicted to rise to $5.50 may be determined to be sold at $0.35.  As long as the price rises to that amount, the sale is guaranteed and the profit is guaranteed
[2022-11-24 18:54:07] <Lucifer_arma> At the same time, a sale order can be placed for, say $4.90, so if the price drops below that, it'll sell.  So maximum loss is $0.10/share, while the gain will be $0.35/share
[2022-11-24 18:54:16] <Lucifer_arma> Multiply that by number of shares and you get total profit
[2022-11-24 18:55:00] <Lucifer_arma> But wait!  There's no "just buy one stock and see what happens" happening.  The whole point of using a computer for all this is because I can put in $100 and have it do this same thing with 10-20 different stocks
[2022-11-24 18:55:49] <Lucifer_arma> Anyway, then we just watch (the computer, that is, does the watching).  If the stock price doesn't get high or low enough to trigger one of the sales, look at tomorrow's prediction and do the contingency plan.
[2022-11-24 18:56:57] <Lucifer_arma> So let's assume that happens and it's tomorrow.  For some reason the stock opens at $5.20.  Go ahead and sell it all and take the smaller gain.  Or, maybe the predicted high will be $6, so go ahead and put in a new sale order (after canceling the other one) for $5.50 and make a bigger gain, but now it took two days.  So average gain is $0.25/day/share
[2022-11-24 18:57:26] <Lucifer_arma> This very basic description of the algorithm will work, provided the predictions themselves are solid to begin with
[2022-11-24 19:00:18] <Lucifer_arma> But the details of the algorithm don't matter if I don't have bots that can make solid predictions.  So, for a bot to make a solid prediction, it must correctly predict the direction the stock price moved, also correctly predict that High > Low.  It also has to come within like 2% accuracy on the difference between open/close prices and High/Low prices.
[2022-11-24 19:00:37] <Lucifer_arma> It's the accuracy of that difference that I want to average together for different stocks.
[2022-11-24 19:03:17] <Lucifer_arma> Meanwhile, as soon as the market closes, download all the new prices and retrain all the bots on each one.  This should be a quick operation, once all the bots have been trained initially on all existing historical data.  The initial training takes about 1-3 minutes per bot, but once it only has to update for new values, that should be extremely fast.
[2022-11-24 19:04:15] <Lucifer_arma> Also, it's worth pointing out that while *one* bot may not get to that 2% accuracy that I want, if, say, four bots averaged together can get it, that's fine, too
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[2022-11-29 01:19:55] -!- Topic for #armagetron is "Armagetron Advanced | http://www.armagetronad.org/ | Welcome to IRC"
[2022-11-29 01:19:55] -!- Topic set by ChanServ!services@services.oftc.net on 2022-08-14 19:47:42 UTC
[2022-11-29 01:19:56] -!- weber.oftc.net set mode #armagetron +nt
[2022-11-29 01:19:56] -!- Channel #armagetron created on 2021-04-20 19:56:37 UTC
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