Quant technology

I'm a technology guy (software, EE, math) partnered with a couple of other other technology guys (no WS, general finance, analyst, trading experience) and we've built a black box system that generates near-term trading signals (long and short) for equities, ETFs, and indices (and probably commodities and currencies - but we've not tested that yet). The accuracy levels for both win/loss rates and percent return per signal appears to be very high (to us, anyway). Anyone have any insights on how to get such a system in front of people who could direct funding and investment capital toward us?

While the core analysis engine is sufficiently complete for us to test/backtest the system there is substantial work that can be done (and should be done) to improve performance, scale up, automate and integrate, etc. We'd consider taking funding for these reasons - and ideally we'd want a partner who could compliment our system with all the skills and resources we lack.

I appreciate any thoughts you might have.

 

Well first you want to make sure that you have a legitimate system and not something that will be blown out in actual trading. Lots of ideas that look good on paper/backtesting get absolutely killed when it comes to factoring in slippage, transacation costs, errors/biases in backtesting, and just simply overfitting the data.

Does your model work on a wide range of ETFs, equities? That would probably avoid overfitting bias. Does it generate a sufficently large pnl/trade to make up for slippage and commissions? Then you can probably start thinking about the next step.

 
Best Response

Great comments and questions. Let me take a shot at answering them and you (and others) can comment on next steps, if applicable.

The “signals” our system generates are buy/sell signals that are intended to identify a trading opportunity that would last days. A typical “buy and hold” trade might be 10 days (while we are not limited to this, let’s assume a 10 day buy-and-hold strategy for this discussion). Ten days of movement is, on average, a decent size move – I think enough to overcome commissions and to reduce the impact of slippage.

We can run against any security with serial (typically daily) data. We’ve found that higher liquidity securities tend to yield greater accuracy. And we can run the system (FYI… it’s not a “model” – it’s not an algorithm or even a set of algorithms that are static) and see excellent results against a wide range of equities, ETFs and indices. Of course we can also generate bad results, so a key part of the system is a method to differentiate good securities/signals from bad, a priori.

So… I think the longer trades (multi-day) and the high liquidity securities helps address some of the issues you are raising (volume/liquidity, transaction costs, slippage).

On overfitting and general performance evaluation… we evaluate all of our results (signals) relative to chance. If a particular ETF traded up 55% of the days over a backtest period we need to see results that radically exceed a 55% “win rate”, as this is achievable with random trading. The same rules apply to the average returns for our signals – they need to be 2X or more above chance trading returns rates.

We apply what I think is a very high standard to our results. But of course the ultimate standard is account value growth, and we haven’t got there yet. We’d like to further refine/enhance the core engine in our system; address automation and integration (back office stuff) functionality and generally scale our capability (e.g., run thousands of securities, not tens of securities) before we get to a place where we would actively/heavily trade. There are also skills issues… we are not WS guys and we don’t know how to optimally trade, manage risks, etc.

One model for “next steps” is to partner with a firm that can bring a) venture capital to develop/complete the system, b) investment capital to run a small fund based on our signals and c) expertise and resources to run such a fund effectively. Other approaches exist, too, of course, including a pure bootstrap approach. I’m not sure we have the financial means to go that route - again, we’re tech guys not highly compensated WS guys :-).

Thoughts?

 

Wiseclam -- I'm right there with you, buddy! I'm a stats guy and my partners are stats guys and we've built a quant system that backtests extremely well, and I'll just tell you what hurdles we've encountered so far.

(1) Find a professional trader you know, through a friend or whatever, who can look over your backtesting and make sure you're not leaving out any important factors, like transaction costs and slippage. This will be one of the first things people ask you...over and over and over again.

(2) No one believes backtesting in finance. Why? Because most of it isn't rigorous, because most finance people aren't well versed enough in statistical rigor to backtest with strictly out-of-training-set data. Overfit is rampant. Our solution to this was to raise $70k from friends and family for a proof-of-concept fund, which we've been running for two months and is up 15.3% so far. We're trying to raise money with this track record, and our extensive, rigorous backtesting, but most places want to see 6 months - 1 year of real-world PnL before investing; and that's the ones that are friendly to start-ups.

(3) Legal fees and back office support. It takes 15-20k to get a hedge fund set up, at the low end of the range. Then you have to think about whether you need to be or want to be registered investment advisors (registration takes a while and costs another few thousand). Then you need back office support, like auditors/accounting and infrastructure. Fund of Funds and Seeders will look for this stuff. There are places that provide funding as well as backoffice support, but the more infrastructure you have, the easier it will be. We have none, and it's rough going.

(4) Develop marketing materials. They need to look slick and include a shitload of detail. We have some decent ones but literally every time we send them out we end up revising them to include more detail because people ask for things we didn't think to include. Not just your backtesting, not just your live testing, but also methodology, personal bios, philosophy, strategy, process, ratios (beta, Sharpe, Sorentino, etc), peak-to-trough analysis, volatility, etc.

(5) Pound the pavement. Network incessantly and go to every finance/hedge fund event you can find -- crash them if you have to. Get your model in front of high net worth individuals that you know or your friends/family know, and get your model in front of Fund of Funds and Seeder Funds. Give out a lot of business cards. Get a lot of business cards. Keep at it until you find that crucial anchor investor willing to take a risk on an unproven system and give you that first $1mil in AUM, and grow from there.

(6) Everyone wants you to already have money, already have investors, already have backoffice support. It's one of those "I need a car to get a job but I need a job to get a car" type of situations. This is why when starting up it's easier to focus on high net worth individuals than Fund of Funds and Seeders, because individuals are less stringent generally, though still very hard to get.

We've been building our system for eight months, live testing for two, and raising money for two, and we're basically nowhere. It's rough out there, but if we keep at it we know that we'll eventually break through and get some real AUM, and once our legal shit and back office shit is taken care of and we have 1-5mil under our belt, building on it will be MUCH easier.

Edit: It's funny, your system sounds somewhat similar to ours, actually. I'd be interested in learning more about it, feel free to PM me.

 

wiseclam,

I can't answer any of the questions relating to funding and setting up a HF, just trading related concerns.

You're right with 10 days holding slippage is almost not a concern, just commission costs. Depending on how often you rebalance this can add up.

Anyways some things prospective investors will ask and that you should be able to answer.

  1. How long is the backtesting period (IS/OOS) ?
  2. What is the sharpe ratio, worst month, best month?
  3. Probably the most important, what is the worst drawdown you have seen? Specifically overall and for each individual trade. Stop loss, etc? What will you do when it goes 5%,10%,20% against you?
  4. Are the signals being generated from close to close prices? OHLC? Fundamentals, technicals?
 

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