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i learned more than the machine... the sad truth is that the method i was using didnt pan out of course there are other methods and other angles to try... i was never expecting a magic wand, so its not so bad... the next thing i will be looking at is different kind of net.. the one i was using was more about patterns while a RNN net and LSTM is more about time series... there is even the idea of converting the information into a visual representation to see how a CNN network may go... one thing i was thinking of was something actually simpler... trying to find forward indicator pairs... might even be possible on think or swim scanning... though a stat analysis is probably better accross a huge number of stocks and etfs.. to explain... lets say a stock goes up on monday... is there a stock that will follow that price a day or two later? again, nothing new under the sun... its akin to finding trading pairs... asking the question, are there stocks that follow other stocks movements at a high enough percentage rate that one could use the action of one to get an edge on the action of another... part of my problem is that unlike a finance company, i have good data, not great data.. they have much better datasets that they spent time making consistent adjustments and tweaks.. its an interesting challenge... the results on the net never got higher than about 8% accuracy too low to depend on... way too low... but for fun i generated random numbers and the outcome was even worse... so its a kind of measure of the accuracy possible within the boundaries of the way the question was set up in terms of the data and problem... wish i had more time without more worry... given i am not working, that takes a lot of my mental resource where if this was my job, or i was retired and had cash to live on, i could do a lot better have lots of interesting ideas to try.. including a kind of tierra based genetic algorithm version ie. create critters with large ability to make rule sets about stock... and evolve them to survive on earnings... that was one i wanted to try for ages... i thought that would be a productive one for sure... think of it as the kind of thing people do when they use a small set of indicators and rules to trade successfully... it solves for the idea of what is the best or most productive rules set to trade by... that would probably work out better than complicated neural nets.. though i was able to solve the preatorian challenge as a test of my nets working that challenge was to take snippets of binary code and identify what machine it was from or for... i solved it in two days... but sadly, it didnt get me a job... ha ha! ps.. thanks for asking!!! i should have lead with the thanks...
I tried something like this with a genetic programming rules generator. The result would be rules like Code: rule 1 SPYror001BarsAhead_02 <- /fitness 1.29702 /numWins 1104 /numHits 1687 /netWins 521 /mean 0.283658 /perfMeasure 527.289 /winPct 65.4416 /hitPct 31.6214 /numTxns 5335 /numInstr 300 /crc d45583c9f85fbec2 0000: if -0.817802 < QQQpobr005 0001: if 0.0386963 >= XARpobr005 0002: if XLPpobr003 > -0.578102 0003: if QQQpobr003 < -0.477997 0004: if XESpobr005 > -0.834 0005: if -0.795197 <= XBIpobr003 0006: if -0.394997 >= XLEpobr002 0007: if XLFpobr002 <= -0.259201 0008: return 3.19151 0009: if XLEpobr002 < 0.842499 0010: if 0.805809 > XLFpobr001 0011: if XLKpobr003 <= 0.178802 0012: if 0.932503 < IWMpobr001 0013: return 3.19151 0014: if -0.0888977 >= XLKpobr005 0015: if XLIpobr004 <= -0.649399 0016: if XLIpobr001 >= 0.210907 0017: if XLVpobr003 < 0.545502 0018: if -0.75 < MDYpobr002 0019: if -0.528297 <= XLYpobr002 0020: return 3.19151 0021: if 0.637993 >= XLYpobr003 0022: if 0.839996 < MDYpobr001 0023: if SPYpobr005 <= SPYpobr003 0024: if MDYpobr005 < -0.0628967 0025: if MDYpobr002 > -0.228203 0026: return 3.19151 ... return NAN where the rule classifies whether or not to take a trade. The trade in this example is go long SPY at the next trading day's close, and exit at the following trading day's open. The rule's instructions are either if statements (indentation shows nesting), a return of a numeric value which means the trade should be taken, or return NAN at the end which means the trade should not be taken. The arguments for the if statements compare an oscillator value that ranges from -1 through 1 to a constant or a different oscillator value. For example, "0000: if -0.817802 < QQQpobr005" means if the oscillator value for symbol QQQ with a period of 5 trading days is greater than or equal to -0.817802, continue to the next nested instruction. Otherwise, skip to the next non-nested instruction ("0009: if XLEpobr002 < 0.842499"). For this example, out of 5335 trading days simulating potentially entering trades from 1998-12-31 through 2020-03-12, the rule would have taken the trade 1687 times while predicting the correct class 1104 times. The class predicted is the top third of the values of percentage changes from the next day's close to the following day's open price. This rule resulted in a simulated mean gain of 0.28%. The mean for all 5335 trading days is 0.03%, so the rule shows significantly better results. Is this similar to your ideas?
Here is another example that simulates buying at the next day's open price and exiting at that day's close price. The generated rule has similar performance to the previous example. For the history period entering (and exiting) trades from 1998-12-31 through 2020-03-16, the simulated result is a mean gain of 0.43%. The mean for all 5337 trading days is 0.00%, so the rule shows significantly better results. Code: rule 1 SPYror000BarsAhead_02 <- /fitness 1.20881 /numWins 1036 /numHits 1578 /netWins 494 /mean 0.426047 /perfMeasure 506.344 /winPct 65.6527 /hitPct 29.5672 /numTxns 5337 /numInstr 300 /crc 77825b3ef8a99c2d 0000: if 0.531403 < XSDpobr004 0001: if 0.318192 <= XMEpobr003 0002: if 0.792603 <= XOPpobr003 0003: if XLEpobr005 >= -0.103798 0004: if XESpobr005 <= XESpobr003 0005: if QQQpobr002 > 0.303307 0006: return 4.38555 0007: if XLKpobr003 > -0.897697 0008: if SPYpobr003 < -0.631897 0009: if -0.569504 >= XLEpobr005 0010: if -0.222298 >= XLKpobr001 0011: if -0.800003 <= XLPpobr004 0012: if -0.801102 <= XSDpobr003 0013: return 4.38555 ... 0296: if XLIpobr002 <= 0.3479 0297: if XRTpobr001 > 0.840401 0298: if XSDpobr002 <= -0.181396 0299: return 4.38555 return NAN