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Rajiv Malhora •I think Tom on this Forum has an Automated trading machine. I think it is called G-Bot.Isn't it Tom?
Guy R. Fleury •You're looking for your stock portfolio to outperform and realize early that the game is not a one trade game: take a profit and run kind of thing. You need more. The market you want to trade is so complex that you could view it from many different angles and still justify your unique vision of the game.
You want it to be a short term game, no problem it will accommodate. You prefer a swing trade type of game, again the market can answer your wishes. You're a long term investor looking at the big picture, there is a place for you too, and on your terms. Technically, it is up to you to design the game you want to play based on your belief system and temperament. I think the only caveat would be to be consistent within the type of game you want to play. Please understand, not consistent in trading or win/loss ratios, but being consistent methodology wise.
Can you win based on your playbook preferences? I say yes, as long as you delimit your trading environment, set up your trading rules to be consistent within this environment, and make provisions for the unexpected. A long term investor worrying about a nickle wiggle spread should be considered schizophrenic to say the least.
The market does not care what you think. It's there to spread the risk of business ventures. It's mission is not to respond to your program, it's mission is just to be there, provide a market place, give you the opportunity to exchange cash for shares if you want to take the risk somebody else does not want to take anymore, or shares for cash when you are no longer willing to hold that risk. The time and the price of this exchange is in your hands. I describe this exchange function using a payoff matrix: Σ(H.*ΔP).
Mark Brown mark@markbrown.com •i have been doing fully automated trading for a couple of decades now. i fought the ignorance vigorously that it could even be done and now it's accepted and mainstream. there is no denying that high frequency trading is not being done by humans on a discretionary basis, i tried it - i know. however that said and i don't know why? i have never been able to achieve financially what my mentor accomplished as a discretionary trader.
ren tech however is certainly an example of quant surpassing typical discretionary trading abilities, don't know if they topple the best discretionary traders but they seem formidable. there is no denying it however at some point to be wildly successful the required influx of committed capital will be needed. then the marketing machine is brought out and the real endeavor of raising capital begins and becomes a run away train. i have been with an organization and witnessed this myself first hand - when your hot your hot. ~m
Guy R. Fleury •What's your payoff matrix? What are the characteristics of your present trading method? What are the numbers? You must have traded long enough to know these numbers by now: average win per trade, average number of trades per period, etc...
The average long term market return over the past century has been around 9 to 10% compounded. To achieve this long term level of performance, all that might be required is a lot of time and a dart to initiate things. But when you look at it closely, and see what is implied, you could almost achieve this performance level blindfolded, or even go as far as letting your machine do the job.
To achieve 10% per year on your investment, requires an average net move of $0.02 per day on a $50 stock equivalent. Imagine the concept here: two cents to be made in a single day trading a $50 stock. Even if you take a week to make your $0.10 net, you are still on track to reach your annual goal. How many 10 cents move in a week, or even a day can you make? All your machine has to do is: get in, get out, collect $0.10, thank you, next. And then the question becomes: how many next?
Oh, you want 30% plus per year, no problem! You're looking for a net move of $0.06 per day or $0.30 per week on your $50 stock. The variance per day on a $50 stock is about $1.50, on a weekly basis I would guesstimate in the vicinity of $2.50 (variance is a random variable here). Over 800 stocks will move by more than $1.00 in a single day. How many $0.50 moves are there in the market in a single day? There is no lack of opportunities out there.
I know, it is boring to accumulate nickels and dimes, then go for quarters! It is up to each one of us to design better mouse traps, better trading strategies; that they be boring or not is irrelevant. Your job is to figure out ways for your machine to accomplish these mundane tasks, and then watch your machine do its intended job properly. In the end, it's your trading strategy H(C), and your mission is to fill up your own payoff matrix: Σ(H(C).*ΔP?).
Chris C Yu •@Guy, have you ever compiled a statistical variance analysis on a single name stock such as google or othe momentum stocks on automated trading vs. a buy-and-hold? We all know the run away success of this beta stock relative to overal market performance. If we empirically derive GOOG long term buy-and-hold strategy and determine its alpha performance during a designated trading period of 1500 candle period, statistically speaking an automated algorithmic trading should outperform a buy-and-hold strategy on the same stock name. By how much % points, of course it all depends on the architecture of algo. Is there a sweet spot you prefer over other in term of frequency? I tend to scale up/down and customize trading frequency after some number crunching and market conditions. Afterall company's fundamental is its driver to success. To a larger extent that's the basis of discretionary trading. Technical trading is also vital. Combination of the two is recipe for success.
Radi Cholakov •Think 80% profit in the past year is out preforming a lot of things mainly with automated trade so I think that it's possible to be stressful and create algorithms that work in the long run. At least algorithms are based on successful on historical data if properly back tested so they allow you to learn what would've been your most profitable rules for the tested period which hopefully should be successful in the future too. But it's not a one time act of creating something and then forgetting about it and just profit. Algorithmic trade requires time and effort just like any other type of trade.
Guy R. Fleury •@Chris, technically I do not know how to answer your questions. Your argument seems to rely on: now that we have hindsight, it is easy to design trading strategies that can outperform the Buy & Hold on past data. I would have to answer this question the same you would: yes. But then, designing such trading strategies would be kind of self defeating.
If I look at Google's next 6 years of data (1,500 trading days), I really don't know how it will behave. All I can say is: look at how it behave in the past. There is anecdotal evidence of all sorts over those price movements. For sure, I can not determine what will be the sweet spot over the next 6 years; I am not looking for any. And if I was looking for it, I do think that it probably would be the wrong quest. That prices will fluctuate: yes; in what manner, I don't know. Can I profit from that: yes.
Daniel Boutrin •@Guy R. Fleury
You are quite confusing few things, in the world of High Frequency Trading, most of your computation is wrong.
1. Liquidity is a discret quantity, which means you can only assume your orders aren't deforming the market during your back testing.
2. Back testing on historical values only demonstrate how stable might be your automate , not how it will perform, as the core idea behind doing an automate is to realized an asymetric output from pseudo stochastic input.
3. Cointegration, Correlation, ... are all at a point a sensitive value on how stable the market is, assuming in a peacefull world nothing will change and asset should provide a reward on the investment. That's idea is an assumption and not true. During crisis, investissors prefer to secure their money rather than leveraging it, which explain negative rate of TBILL.
Having say that , H(C) = S(C) .- B(C) isn't the correct form to evaluate a high frequence trading automate.
This is what we call a a Call ( H(C(t), H(C,T)), as you will roll around a certain worstof/bestof barrier the composition. In this equation you have to analyze a composite volatility under discret constraint ( cointegration, autocorrelative barrier, ..) and a risk measure far different than the other which lead to something like Σ(H(C)-B(C)=theta((H(C(t))-H(C(T))+)-error(C(t,T))
Arf, Ok I give up, It's way too hard to explain this on linkedin, sorry to bother you
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发表于 2013-3-10 12:40
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