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A couple questions about your service/models: 1. It seems likely your models work internally with probabilities vs binary buy/sell decisions. Why do you not ha

by Chamix 5y ago

A couple questions about your service/models: 1. It seems likely your models work internally with probabilities vs binary buy/sell decisions. Why do you not have the option to expose this probability, vs just simple buy sell signal? I would think this would pair very nicely with asset allocation. Have you investigated performance when using a sliding asset adjustment (even if just sp500 future & cash) that corresponds to model confidence, vs the binary win/lose bet system? Does slippage from frequent adjustment dominate the gains, no matter the adjustment threshold? 2. At a lazy glance, it seems trivial to boost performance with leverage on buy + shorting on sell, assuming the model really retains its backtest-heavy predictive power. Is this ignored just to remove black swan risk, or is there something more fundamental? Obviously a 0 leverage buy sell signal is very marketable and low friction, fitting for a SAAS product, but it seems you could do even better with skills you obviously would possess if actually capable of making such a "god" level market predictor. Your posts are full of other seemingly more complicated strategies with somewhat contradictory capital levels throughout the years so it further adds to the main crux of confidence here: That you discovered a top 1% hedge fund caliber model individually as a mid tier SWE early retiree and suddenly offer it to all for an attractive price, without already being sure you can fuck off to a private island with the power of 50%+ CAGR, ignoring the rest of the world. You are also very sparse on details regarding your "proprietary anti-overfitting" methodology, when overfitting is of course the well-memed downfall of 99% of algo trading techniques that claim to meaningfully beat the market. To say nothing of the backtesting window conveniently starting after 2008, lack of VIX hourly data or not. Disclaimer: I've spent the last 8 hours or so digging through your entire SA, reddit, and HN post history. Initially I dismissed your site as 80% likely scam, 19% naïve backfitted and overfitted waiting-to-be-raped-by-bear-market drivel, etc. But now I'd say I'm operating on a more optimistic 5-10% chance there is something legit here. You might actually just have researched sufficiently in all the right places, implemented a legitimate edge through extensive pareto culling via the yet virgin power of code, and had the perfect balance of libertarian desires to motivate you and yet progressive ideals to democratize it. As well of course as this being a genuine passion project that you are excited to share with others. I currently work overemployed at FAANG, capital accumulation focused, but I've always held attempting to pareto market timing with a ~99-1 EMH assumption as my main FIRE project in the back of my mind for the past couple of years. I came to the weak conclusion with my pareto^pareto research of the idea that attempting to limit drawdown via algorithmic tactical allocation on SP futures (for liquidity) via macro + TA indicators seemed the best method. So, I am very intrigued to see someone who essentially appears to be me born 5 years earlier doing that exact thing. Rereading this, I don't mean to come as aggressive/hateful; regardless of how the future treats your models this is a very thought provoking and exciting project!

throwawaysea 5y ago
> I currently work overemployed at FAANG, capital accumulation focused What does this mean?
- Chamix 5y ago
  Instead of getting good at 1 big tech job making 300k in 20-30 hours a week you get good at 2-3 and make 800k+ in 60 hours a week. Though I think the actual best way to optimize labor-to-income is F500 Cloud SRE via B2B contract, but its hard to have to balls to pivot to that after spending so long perfecting SWE interview skills.
  - throwawaysea 5y ago
    Wait so you literally work at multiple companies? How do you manage meetings and deadlines and things like that? Surely that creates conflicts between them.
    - deleted 5y ago
      [deleted]
    - grog454 5y ago
      Not sure how well this ever works in practice, but I just realized you could work remotely for drastically different time zones if your employers were spread across multiple countries.
pyrrhotech 5y ago
Thanks for the detailed response, I'll do my best to answer: 1. Internally, the model does work on a sort of weightings / probability system but it's not so simple as boiling down to a single probability figure, i.e. if it hits over 60% we trigger buy or less than 40% trigger sell. Instead there are a few phases in signal change identification starting with an attempt to classify overall fundamentals and market move and moving down to the more granular indicators based on each level of abstraction. Certain low level indicators mean very different things based on the higher level classifier output. However, I could feasibly attempt to boil down to some sort of confidence or probability figure and expose it. I'll need to do some rigorous internal testing to ensure that it conveys accurate information first, but it's a good suggestion. 2. I think the fairest way to report the results is with a 1x/0x (long with no leverage on buy signal, market neutral on short) and then let the users decide whether to use leverage or to short the market during sell signals. Personally I have used leverage here and there and I tend to dial it up or down based on how well the model seems to be performing at a given time. However, I think it's important to note that no model can ever be 100% free from overfitting, and while a great deal of effort has been made to generalize and widen params and heuristics, there's bound to still be some and even I don't know to what degree. This, plus the fact that algotrading becomes more competitive every year means that I think it's prudent to conservatively expect returns somewhere in the 50-100% wide range of the past, which could also mean drawdowns a bit larger than the backtest shows. And of course, black swan risk is ever present. Finally, the models only go back to 2009, so we don't have great data to indicate how they might perform in a more harsh bear market such as 2007-2009 or 2000-2003. This is an unfortunate coincidence as one of the key indicators, Vix Futures, only came to be in late 2004 and the earliest I could find intraday data broken into distinct contracts (not just continuous front month) was early 2009. As far as my occasional contradictory past reporting of my own financial position. There's a couple reasons this could be--at times I've included less liquid assets (private company shares, family business interest, 401k, home equity, etc), and at others I've only reported what's immediately available to me and highly liquid (bot account + emergency fund, etc). I feel I've probably erred on the side of sharing too much personal financial details over the years, but it is what it is. Secondly, I've not always been a stable investor. I hate to admit that back in the day I was a bit of a WSBer at times. Never consistently, but sometimes I'd get caught up in the FOMO and make a rash (and often too large) bet on an earnings report or something and (though I won a few times) usually end up losing my ass. As such my net worth was pretty volatile between 2013-2018. I've since given that all up and the bot has helped assuage my inner gambler. I also used to follow some other folks' timing systems back then and learned the hard way that most don't work, so all skepticism is warranted. Even I like to keep a skeptical mind about the future of my own models which is why I reported the wide range of expected outcome above

by Chamix 5y ago

throwawaysea 5y ago

> I currently work overemployed at FAANG, capital accumulation focused What does this mean?

Chamix 5y ago
Instead of getting good at 1 big tech job making 300k in 20-30 hours a week you get good at 2-3 and make 800k+ in 60 hours a week. Though I think the actual best way to optimize labor-to-income is F500 Cloud SRE via B2B contract, but its hard to have to balls to pivot to that after spending so long perfecting SWE interview skills.
- throwawaysea 5y ago
  Wait so you literally work at multiple companies? How do you manage meetings and deadlines and things like that? Surely that creates conflicts between them.
  - deleted 5y ago
    [deleted]
  - grog454 5y ago
    Not sure how well this ever works in practice, but I just realized you could work remotely for drastically different time zones if your employers were spread across multiple countries.

pyrrhotech 5y ago

Thanks for the detailed response, I'll do my best to answer: 1. Internally, the model does work on a sort of weightings / probability system but it's not so simple as boiling down to a single probability figure, i.e. if it hits over 60% we trigger buy or less than 40% trigger sell. Instead there are a few phases in signal change identification starting with an attempt to classify overall fundamentals and market move and moving down to the more granular indicators based on each level of abstraction. Certain low level indicators mean very different things based on the higher level classifier output. However, I could feasibly attempt to boil down to some sort of confidence or probability figure and expose it. I'll need to do some rigorous internal testing to ensure that it conveys accurate information first, but it's a good suggestion. 2. I think the fairest way to report the results is with a 1x/0x (long with no leverage on buy signal, market neutral on short) and then let the users decide whether to use leverage or to short the market during sell signals. Personally I have used leverage here and there and I tend to dial it up or down based on how well the model seems to be performing at a given time. However, I think it's important to note that no model can ever be 100% free from overfitting, and while a great deal of effort has been made to generalize and widen params and heuristics, there's bound to still be some and even I don't know to what degree. This, plus the fact that algotrading becomes more competitive every year means that I think it's prudent to conservatively expect returns somewhere in the 50-100% wide range of the past, which could also mean drawdowns a bit larger than the backtest shows. And of course, black swan risk is ever present. Finally, the models only go back to 2009, so we don't have great data to indicate how they might perform in a more harsh bear market such as 2007-2009 or 2000-2003. This is an unfortunate coincidence as one of the key indicators, Vix Futures, only came to be in late 2004 and the earliest I could find intraday data broken into distinct contracts (not just continuous front month) was early 2009. As far as my occasional contradictory past reporting of my own financial position. There's a couple reasons this could be--at times I've included less liquid assets (private company shares, family business interest, 401k, home equity, etc), and at others I've only reported what's immediately available to me and highly liquid (bot account + emergency fund, etc). I feel I've probably erred on the side of sharing too much personal financial details over the years, but it is what it is. Secondly, I've not always been a stable investor. I hate to admit that back in the day I was a bit of a WSBer at times. Never consistently, but sometimes I'd get caught up in the FOMO and make a rash (and often too large) bet on an earnings report or something and (though I won a few times) usually end up losing my ass. As such my net worth was pretty volatile between 2013-2018. I've since given that all up and the bot has helped assuage my inner gambler. I also used to follow some other folks' timing systems back then and learned the hard way that most don't work, so all skepticism is warranted. Even I like to keep a skeptical mind about the future of my own models which is why I reported the wide range of expected outcome above