Algorithmic and Mechanical Forex Strategies | OneStepRemoved

Retail trader disadvantage

by Leave a Comment

Michael Halls-Moore invited a reply to one of my tweets last week, “Retail traders have an advantage over the pros? Me thinks not.” He wrote a great overview of why the institutional traders look longingly at the retail crowd and all the hoops that they don’t have to jump through.

His points are all valid, but he overlooked the big picture. Pricing is everything to a trader. Retail traders get the short end of the stick when it comes to the cost of doing business.

The cost of trading is massively disproportionate

Let’s say that you’re a would be quantitative trader and that you’re looking for opportunities. Let’s say you trade mini lots in the forex market with 60% accuracy and 1:1 risk reward ratios. If you’re not familiar with what a typical trading system looks like, those numbers means that you have an enormous edge.

Some of the less reputable forex brokers out there charge 3 pip fixed spreads. If you’re trading with a broker offering fixed spreads, I urge you to start price shopping. Fixed spreads are wildly overrated. You pay a huge premium for the certainty of a fixed spread. I can’t think of anything remotely plausible to justify them.

The larger forex brokers charge typical spreads in the neighborhood of 2 pips on the largest majors. Although most seem to find this reasonable, the comparison between a 2 pip average spread and institutional spreads is night and day.

Do you know what the average EURUSD spread looks like on the interbank market? It’s often 0.2-0.5 pips. Retail traders pay an average markup of over 300% on their trades.

retail trader pricing

Retail traders facing the institutions is a bit like David and Goliath.

Retail forex prices have declined in recent years. A few brokers like MB Trading and Pepperstone offer raw spreads with commissions tied to the dollar volume traded. These are, in my opinion, are about the fairest prices available to low balance traders running an expert advisor.

The best deal available to semi-institutional forex traders (CTAs, large balance retail traders, etc) is Interactive Brokers. The customer support is famously poor; they’re cheap for a reason. IB also offers raw spreads with a commission.

My experience with IB has been excellent, but you need to trade size for the economics to work. A 0.5 pip typical spread is great, but the 2 mini-lot minimum trade size and $2.50 minimum commission really adds up. Trading with IB doesn’t approach institutional type pricing until your average trade size approaches 1 standard lot.

So, how does pricing affect the final outcome with our 1:1 risk reward strategy that wins 60%?

  • Free trading: After 100 trades, you’ve earned $600 and lost $400. The hypothetical net profit is $200.
  • Fixed spread: You’ve spent $300 in spread costs to enter 100 trades. The total net profit is -$100 ($200-$300).
  • Average retail: You’ve spent $200. There is no profit because you breakeven ($200 hypothetical profit – $200 in costs). However, your broker loves you for doing that many trades.
  • Good retail pricing: Let’s say the average cost of a trade is 1.3 pips after commissions. You’ve spent ~$130 placing 100 trades. The total profit is $70.

Even with good strategies, the profitability of your algorithm is as simple as choosing the cheapest broker.

Equities pricing

Trading stocks is even more expensive than forex. I remember back in the day when I thought Scottrade was cheap for offering $7 commissions. It gets worse and worse when you go through the list of stock brokers. Most of them try to get away with charging $7-10 per trade. If customer service is important to you, then those are the shops to look at.

If your top priority is trading profitably, then again, broker selection is critical. The only way that a small guy can make it is by chipping away at the costs. Interactive brokers is again a great option, charging fractions of a penny per share traded. If you decide to trade 2 shares of Google (GOOG: $1,017 per share) or 1,000 shares of Fannie Mae (FNMA: $2.35 per share), the transaction costs are tiny. Two ticks in your favor is all it takes to cover the trade.

You might be thinking that I said two ticks in forex is expensive. How can I say that two ticks in equities is reasonable?

Volatility. Two ticks in the stock market is a little hiccup. It’s not at all uncommon to see highly liquid stocks move 2-3% in a single day. Forex is only interesting because of the leverage. The currency pairs themselves rarely move more than 1%, and that’s usually on major news.

Risk Management

Every employee knows that they’re only one mistake away from getting fired. That’s the reason that everyone hates having a boss. There’s a single person with unilateral authority to financially murder you. Who’s going to look upon that as a good thing?

Well, the truth is that bosses exist for a reason. It’s someone that calls you out when you do something stupid. More importantly, the boss has the power and influence to ensure that you stop doing stupid things.

The dream of entrepreneurship is not having a boss. You go on vacation when you can, you don’t have to play office politics, you don’t have to waste time selling good ideas. You just go out and do them.

Even with good strategies, the profitability of your algorithm is as simple as choosing the cheapest broker

I can tell you as a small business owner that the negatives stand out strongly in my mind. When you don’t have someone to hold you accountable, even if it’s a mentor, you make many more dumb mistakes than you should. It takes incredible discipline to hold the line consistently. Knowing that I’m not going to look stupid or have to explain myself to anyone probably gives me a lot more false confidence than I really need.

Self-employed traders working at home experience the same thing. Who calls you out when you’re trading just because you’re bored?

The decline in the trading account points out the obvious, but that’s not enough to necessarily stop the bad behavior. We’re social creatures. Most people need to speak with other people to maintain their sanity. When you’re trading at home alone, it takes a lot of effort to ensure that you’re getting enough social contact. A good boss prevents you from indulging in bad behaviors.

Conclusion

Selecting the right broker is enough to determine whether or not a good strategy will wind up making money or not. It’s expensive to trade. The bigger you are, the better your pricing.

Retail trading prices have reached a point where it’s at least possible to trade profitably. Nonetheless, the number of strategy types out there is limited because the lower, shorter term strategies are prohibitively expensive to trade.

The quantitative traders and hedge funds get the more active strategy space to themselves. Their trading costs are so low that they’re really the only people that can afford to trade actively.

Limit Orders at Best Bid Best Offer

by 2 Comments

Earlier this year I was testing market making ideas in detail. To quickly recap, the idea of making a market entails placing limit orders either at the current best bid and best offer (BBBO) or further away. When the limit order gets filled, the trader achieves a major advantage. Spread costs no longer weigh on the returns.

A failed trading experiment with MB Trading to earn commissions segued into a trading project that I ran on behalf of a client. The key issue was that the strategy could only profit if trading costs were kept to an absolute minimum. The easiest way to reduce cost is to not pay the spread.

Creating a limit order at the best bid/best offer accomplishes that. Whenever a buy limit placed at the current bid price receives a fill, the trader can immediately sell the trade at the bid free of charge.

That’s the theory, anyway. The BBBO idea makes the assumption that another bid or offer would continue to stand behind my own at the same price. Like most trading experiments, that assumption flopped. The NinjaTrader strategy would post the order, then leave it at the original price without changing. Sometimes the order sat for 10-20 minutes. It was only when the price crossed a custom indicator line in the opposite direction that the strategy cancelled the order and placed a new one at the best bid/best offer (BBBO).

The most informative insight that I learned about the experience was the fill rate on the orders. Curiously, placing them at BBBO only resulted in execution 75-80% of time on several hundred attempted trades.  MB Trading’s ECN program is rather small. I chalked the low execution rate up to a lack of price takers, even on a major pair like the EURUSD.

So, we switched to Interactive Brokers and tried again. I was quite surprised to find the same result; orders placed at BBBO only filled 75-80% of the time. The really profitable trades always got skipped over as the market found its stride and zoomed upwards.

I don’t know how much of this falls in line with normal market mechanics and what responsibility high frequency algorithms might play in the low execution. It appears commonly accepted that HFT algos routinely engage in games where false orders are displayed with the intention of lifting the market prices, only to slam them back down again when a sucker accepts the lifted price. An interview with a high frequency trader made me wonder if perhaps the HFT algos were lifting the price hoping that my strategy would be their sucker. An interview from two months ago added to my lingering suspicion.

A second example: HFTs can model other traders’ behavior. When someone trades through Scottrade or Interactive Brokers, their order has a unique number attached to it – the same number every time a client places an order. This number is bundled with all relevant trade information (time, price, etc.) and sold as an encrypted “enhanced data feed.” An HFT can then use those past results to predict the trader’s behavior.

I don’t believe this happens at MB Trading, although my order sizes were admittedly so small it’s easy to see individuals among the order stack. I routinely look at the market depth and feel like I can identify the orders of individual retail traders.

I can’t help but wonder how many people experience a surprisingly low fill rate using limit orders at BBBO. Use the comment section below to share any relevant stories that you may have.

Dark Pools

by Leave a Comment

I picked up Dark Pools by Scott Paterson on Friday evening and finished Sunday afternoon. That ought to say something for the book’s readability. I recommend it to anyone that trades, especially equities traders.

The structure of the stock market is far more complicated than I ever expected. Trading at Interactive Brokers, I always noticed that the execution venue would vary between different acronyms like ISLAND, ARCA and BATS. I knew that they were ECNs, but I never really understood what linked them together.

The stock market is not an exchange in the sense of a centralized location where all transactions occur. It is more like a listing entity where public companies go to list their shares. Actual trading occurs on any network plugged into the system.
The “exchange” is really a complex network of networks with varying degrees of favoritism shown to high volume clients.

Electronic Communication Networks (ECN) originally started in the mid 1980s with the idealistic goal of eliminating the corrupt practices of the NASDAQ floor specialists and market makers. These guys were notorious (and later heavily fined) for colluding to artificially widen spreads on stocks for their own profits. ECNs would cut out the hated middle man while reducing errors, increasing transparency and dramatically decreasing execution time.

As one can imagine, the ECNs took off rather quickly. Not only did they offer much faster execution, but they were also about 60% cheaper to execute a trade.

They corruption that ECNs sought to eliminate inadvertently replaced one problem with another. Networks looking for liquidity offered trading rebates for limit orders that added orders into the system. The setup, which came to be known as maker-taker, created perverse trading incentives for participants. The more trades executed, the more the profits would add up.

Firms sought to become something like Walmart is to wide screen TVs. The more you sell, the more you make. The liquidity providers started fighting aggressively for inside placement of the spread to facilitate ever more trades.

The system spun out of control in two ways. It created a computing arms race where firms focused on purchasing cutting edge technology that shaves microseconds off of calculation time. Firms with the deepest pockets could literally buy an advantage through their computing hardware over the average Joe.

More importantly, the system itself bred its own corruption. The ECNs grew addicted to the liquidity fees. The more trades that fired off, the more money they made. They naturally started catering to their most important clients.

How they did it, though, is what sickens me as a trader. The ECNs started creating order types that were effectively secret. They allowed high speed firms to jump in line over retail chumps using vanilla limit orders. The ECNs offered colocation access at exorbitant fees to give the machines an edge. They allowed the creation of dark liquidity where some players could literally hide orders for execution while others displayed theirs 100% of the time. It makes a complete mockery of the idea of a level playing field.

Artificial intelligence played a big role in how the algorithms operate. What encouraged me, however, was how Patterson chose to wrap up the book. Many of the funds covered near the end start out as relatively small fish working with various types of AI to build predictive trading systems. Their initial results appear encouraging.

One of our biggest projects here is to use various models of fractal markets to build an automated trading system on behalf of the client that I go visit in Ireland so often. Andy and I meet on a near daily basis to discuss our genetic algorithm and how best to encourage the network to behave as we want it to. We are about a month from running our first predictive tests with our in house model. Reading this book encourages me that we are blazing down the right path.