automated trading Archives - Algorithmic and Mechanical Forex Strategies

How Automated Should Your Trading Strategy Be?

December 31, 2013 by Andrew Selby Leave a Comment

One of the most attractive things about quantitative trading approaches is that many of them have the capability to be completely automated. That leads many traders to believe that they will be able to simply program a strategy into their platform and turn their computer into a virtual ATM.

As with most things in life, automated trading isn’t as simple as it appears. The performance of different strategies changes over time as markets adjust. Continually evolving technology exposes our strategies to continually evolving biases. The fact that a strategy worked well last year is no guarantee that it will work well this year.

With all of the different ways that markets could fundamentally change, do you really feel comfortable designing a fully automated strategy?

Completely automated trading sounds like a great idea, but you may see some advantages to keeping some manual aspects in your strategy.

An article that appeared on Forex Crunch earlier this month discussed the pros and cons of automated and manual trading. After breaking down both sides, the article concluded that the best approach is usually to develop a strategy that lies somewhere between the two extremes.

The fact that you want to build a fairly automated strategy does not have to mean that you can’t override that strategy if markets suddenly change. On the other hand, the discretionary approach you are working on might be improved with an expert advisor to help you identify setups.

Advantages of Automated Trading

The biggest advantage of using an automated trading approach is the reduction in slippage through flawless execution. If your strategy doesn’t have to wait for you to confirm an entry, it can jump into a position the instant that it sees a signal. This improvement in order entry also allows a trader to avoid being glued to a computer screen all day.

Some of the other advantages of automated trading that the article covered include the ability to process large amounts of data and the ability to trade around-the-clock. The article points out that automated strategies can monitor far more markets than humans can, and automated strategies never have to sleep.

Advantages of Manual Trading

The biggest advantage of manual trading, according to the article, is having the ability to call and audible. The article suggests that if a crash in Japanese Yen is due to a large typhoon, a manual trader can simply shut down his strategy until the weather clears up.

Another advantage that manual traders have is the ability to scale the aggressiveness of their strategy up or down depending on gut feelings or discretionary judgements. This may not be helpful if your gut feelings are not very accurate, but traders with extensive experience will be much more comfortable trusting their instincts.

As you can see, there are advantages to both sides of this discussion. The best approach for any trader is to find an approach that works well with their own personality.

Automated Trading

December 28, 2012 by Shaun Overton 2 Comments

Nathan Orange contacted me in early 2012 looking for advice about automating a grey box strategy. Through the course of our conversation, it turned out that he was a profitable trader with a multiyear track record. Nathan has gone on to found his own forex signal service at Global Trend Capital.

Nathan conducted this interview with the intention of informing his readers about automated trading. You’ll have to pardon the vanity of publishing his interview of me, but I believe it’s useful for my own readers.

(Nathan):
Shaun, good to talk to you again and I appreciate you taking the time to discuss what I consider a very important topic. Before we jump into the specific questions, let’s fill everyone in on your background.

(Shaun):
I led the sales effort for the Sentiment Fund at FXCM, which was a fully automated strategy based on the market positioning of retail clients. I needed to understand how it worked in order to answer client questions. That interaction with the systems desk gave me access to one of the tiny handful of people in the forex industry that really knew anything about systems trading and analysis.

I tried trading manually during work hours, but as a broker, it was really difficult to manage trading accounts and to squeeze in 100+ attempted phone calls per day. I also suffered from the usual sob story that every trader endures. Account #1 blew up in 3 months. Account #2 blew up in 6 months. That was the first $5,000 thrown down the pit.

Technical analysis with its trend lines and other tools are hocus pocus pseudo-science. I traded like that for nearly a year, but I never felt confident or comfortable with the idea that subjectively drawing lines on the chart leads to any useful information.

The idea of quantitatively defining a strategy allows for testing and analyzing an idea to determine whether or not it really held any merit. The first non-technical analysis idea I had was to look at unusually big bars with the idea of fading those moves. Access with the FXCM Systems desk helped shape my idea from a subjective idea like “big bar” into a mathematical parameter like “standard deviation”. They also explained trading platforms to consider and recommended a few programmers to help develop the idea.

My experience working with programmers was uniformly terrible. I tend to dive into projects, so rather than depending on the clown-car brigade to half-develop my ideas, I wanted ultimate control over the development process. That eventually led to 20+ hours per week programming and analyzing strategies at home after working all day. The system design bug bit hard and never let go.

(Nathan):
One of the most common concerns when discussing back-testing is over-optimization. From your perspective, what are some of the common mistakes that most system developers make? I have my own list, but we can discuss those further when we turn the tables.

(Shaun):
The basic kernel of the idea either has merit or it does not. There is no secret set of magical inputs that turns a bad strategy into a good one. Bad inputs, however, can turn a good strategy into a bad one.

Optimization fails to differentiate between “profitable” and “good”. I flog this dead horse constantly, but the most confusing thing about trading is that you can trade by flipping a coin and setting a 50 pip stop, 50 pip take profit and actually come out a winner – sometimes. Most of the winners will show small profits. A tiny handful of them would show gigantic profits purely as the result of luck. What’s worse is that most of the profitable traders will actually believe that they are the reason for their success when it’s really just dumb luck.

Optimization is usually the process of finding the luckiest accidental winner. It’s no wonder that optimized strategies almost universally fail going forward. The real task is to distinguish between ideas that are inherently non-random versus strategies or expert advisors that coincidentally make money from a random process.

(Nathan)
Based on your experience and knowledge, if someone sends you a system to code can you quickly determine potential issues with their logic, or even over-optimization red flags? For example, you might a get a system a trader or hedge fund wants coded that has so many specific variables that you know immediately it won’t be robust. I can usually spot these issues from my own system development experience, but from your perspective as a coder is it fairly easy to recognize?

What do you do in those cases? Are most clients bull headed, avoiding any feedback or are they more open minded to listen?

(Shaun):
We see our primary role as that of a construction worker. If you want to build an ugly house, that’s your affair. On the flip side, if you solicit my opinion, I won’t hold back telling you it’s the ugliest house I’ve ever seen.

People frequently ask, “Do you think this will work?” I almost always answer no, and then they hire us to build it anyway.

Interestingly, strategy development is very similar to trading in that people get emotionally attached to ideas. Even in the face of strong warnings, they charge ahead. A dear friend of mine opined on the subject, saying, “A handful of people don’t try. An even smaller handful listen to good advice. The rest of us learn the hard way.” Most people require the experience of falling flat on their face before they learn the lesson behind the advice.

If you’re motivated enough to ask a programmer to build a strategy for you, it’s because you already know that it is something that you really want to try. I could bluntly say, “This is going to wind up in tears.” 95% of people go ahead with the project, anyway.

Despite my knowledge of markets and systems, I’m not an oracle, either. I’ve told people that I thought their ideas were bad, only to have them come back a year later and tell me they’re making money.

…….Stay tuned for Part II when we discuss HFT, more back-testing issues (including those unique to Metatrader) and if there are common themes to successful systems.

Fixed Fractional Money Management

April 10, 2012 by Shaun Overton 8 Comments

Fixed fractional money management changes the overall outcome of your trades. Remember that trading is the net outcome of several hundred trades. The power of money management comes into play after several hundred trades or more.

Trading totally at random with a 50% winning percentage and an R multiple of 1 yields no advantage, as I discussed last week in modeling money management. Remember that an R multiple is the average win to the average loss. Such a system poses neither an advantage or disadvantage. The average outcome should come out extremely close to the starting balance.

Fixed fractional money management stretches some portions of the bell curve and compresses other regions. Before we get into that, it’s important to remember what fixed fractional money management means. It’s a complicated name for a simple concept. It stands for the idea of risking a set percentage of the current account equity rather than the starting equity.

Most traders focus on risking a set dollar amount such as $1,000 on a given trade. This method updates that dollar figure after every single trade.

Consider an example where the account balance starts at $100,000 risking 1%. Both methods risk the same amount on the first trade, $1,000. The next trade, however, will yield a different risk amount. A win on the previous trade would increase the account equity to $101,000. One percent of a 101 grand is $1,010 of risk on the next trade. A whopping ten dollar change.

That may seem trivial. It is most certainly not over the long run.

Examples

Consider a trader that plays the coin toss game and has a system with the following characteristics:

He starts with a $100, 000 account balance
His R multiple is 1.0
He wins 50% of the time with no trading costs
He risks 1%

The absolute worst outcome of playing the coin toss with a fixed dollar risk of $1,000 is a loss of $46,000. Adding fixed fractional money management during that difficult drawdown improves the drawdown to a less substantial loss of $37,500. The worst drawdown goes from -46% to -37.5%. The method drags the absolute worst case scenario and pulls it closer to the average. When an unlucky, devastating drawdown kicks in, the technique reduces the losses that the trader experiences.

The best case scenario for fixed dollar risk is a $58,000 (58%) return. Adding money management to the system dramatically stretches the best case scenario further to the right. It improves to a $76,000 return (76%). The good times get a lot better without changing anything at all about the trading system. The method stretches positive returns away from the average. The trader walks away with more money in his pocket.

The natural instinct is to conclude that fixed fractional money management is the way to go. I agree. It improves the risk reward profile of a totally random strategy. Adding it to a real trading system should help control parameters that most traders consider critical like drawdowns and maximizing the return.

An important consequence of using fixed fractional money management, however, is that the odds of receiving a below average return increase somewhat. The coin toss game suffered a below average return 47% of the time. Applying fixed fractional money management increased the likelihood of a below average return to 53%. The effect is not all that much. Losing is more likely. But when it happens, the “loss” is so negligible that it can be thought of as breaking even.

Random numbers occasionally follow a seemingly non-random pattern such as loss-win-loss-win. When this occurs, the size of the trade on the losses is bigger than the trade size of the winners. Even if the winning percentage comes out at precisely 50%, those wins get slightly overshadowed by the losers. That micro effect of slightly larger losses than gains shows up as a slightly increased risk of not making as much money as expected.

Graphing all outcomes

$Fixed fractional curves$

Fixed fractional money management changes the shape of the returns curve. This example is exaggerated to highlight the effect

Red areas represent the losing outcomes while green areas represent the winners. Money management is really about maximizing the ratio of green area to red area. Random trades with no expectation of profit yield a bell curve, which appears on the left.

Fixed fractional moves the highest density of returns slightly to the left. Doing so creates the trivial disadvantage of a slightly increased risk of negligible loss. Importantly, the far left side (the worst case loser) gets dragged much closer to the average. The far right side (the best case winner), gets stretched much further from the average. The green area is now larger than the red area. The random system has a minor expectation of generating a positive return!