By Hitesh Mittal, founder and chief executive officer of BestEx Research, and Kathryn Berkow, managing director of BestEx Research

Some execution algorithm providers approach the design of futures algorithms by starting with an equity algo base and tailoring the details to the specificities of trading futures. While this approach may create a fair foundation, on average, it leads to unnecessarily high trading costs—sometimes in extreme fashion—because futures come with their own unique market structure and market microstructure challenges. Here, we include just a few examples of the unique challenges specific to futures trading that make repurposed equity algorithms problematic.

Daily volume estimation isn’t straightforward

A fundamental example of the failure of equities algorithms repurposed for futures is volume prediction. In order to make educated decisions around order size and timing, algorithms need estimates of the volume they will encounter throughout the day. Equities algorithms often rely on historical volume, taking an average of the last month of daily trading volumes to estimate the next day’s volume, for example. Using this strategy for futures, however, can be dangerous because of volume changes during the “roll” period of a contract.

Each futures contract represents an agreement over a specific period of time, with an expiration date. While some traders are interested in a single contract expiration, many want to continue to hold exposure to a contract after it expires and must “roll” their position into the next active contract to expire.

As a contract nears expiration, its trading volume declines as traders sell off their positions. Trading volume in the next active contract increases as positions roll from a soon-to-be-expired contract into the next one. Relying on historical volume for a nearly expired contract that is declining in volume can severely overpredict volume, creating more market impact than expected when trading on these estimates. Relying on historical volume for a rising contract can likewise underpredict volume and result in the transition of an investment more slowly than is necessary.

As a result, a simple average of historical volume as a daily volume estimate ignores important features of futures market structure. Unrealistic volume estimates render the foundation of an algorithm’s decision-making unreliable, yielding poor trading decisions and increased costs on average.

Long queues can reduce passive executions

Like volume estimation, the diverse queue times of futures’ limit order books can drive increased costs if mishandled.

Each futures contract trades on a single exchange, and algorithms aiming to earn the spread must wait in a single queue to buy or sell. An order may be waiting with many other orders at the same price—thousands or millions, depending on the contract—for the pleasure of earning the corresponding spread premium.

Because bid-offer spreads are proportional to a contract’s volatility and inversely proportional to its volume, volatility and volume should be used to determine minimum tick size—the minimum allowed spread increment. Futures exchanges determine their own minimum tick size requirement, which may be far larger than the “fair” bid-offer spread of a contract based on its volatility and volume. When the fair spread is narrower than the minimum allowed spread, the cost of crossing the spread (and reward for providing liquidity) is higher. As a result, many market participants’ limit orders wait at the prices aligned with the minimum tick increments, forming long queues.

For long-queue contracts, algorithms’ limit orders often wait endlessly in a queue of other orders, never making it to the front of the queue for execution. The algorithm must then cross the spread to remain on its planned trading schedule—yielding execution costs that are heavy in spread costs with few passive executions.

If algo designers customise behaviors to long-queued futures contracts and apply a broad-brush approach to all contracts, over-representing orders in the queue to increase passive executions, overall performance will still be poor. In contracts with sparse limit order books, placing large orders at the best bid or offer has the opposite effect, increasing costs. Large orders often create market impact and come with increased adverse selection, as fills are likely received when prices are moving in the orders’ favour.

For optimal futures execution, algorithms must dynamically address the contract-specific relationship between fair spread (which depends on volatility and volume) and minimum tick size—handling contracts with uniquely long queues, sparse limit order books, and everything in between.

Volatility isn’t one size fits all

Another failure of equities algorithms for trading futures is their inability to handle wildly diverse volatility profiles across contracts; the distribution of inherent volatility across futures contracts is very different from that of equities. While for most stocks annualised volatility can be around 20-40%, for futures contracts it can range from very close to 0% to over 100%.

As described above, tick sizes determined by exchanges often do not take into account the volatility of the underlying product. For some products, tick sizes are too large with respect to volatility, leading to long queues and few passive fills. Meanwhile, for products with very high volatility, managing adverse selection costs and market impact becomes extremely challenging. Algorithms that give themselves a lot of “flexibility” around a trade plan tend to improve performance for low volatility products, but not for high volatility products where the cost of adverse selection and market impact is extremely high, requiring more skill around limit order placement. Algorithms must dynamically adjust, optimising for volatility across products and for each product throughout the day.

It’s not all FIFO execution

Equities exchanges operate in a “First In, First Out” (FIFO) style across the board, where those limit orders posted first in the queue at each price level receive priority. But some futures contracts execute in a pro-rata execution style, where a subgroup of parties (or all parties) waiting in the queue receive some portion of an arriving limit order. In that event, a limit order waiting deep in a queue for ten contracts may receive a partial fill of one or more contracts while the remainder of the order continues to wait in the queue. As this matching structure does not exist for equities, an equity algorithm is not equipped to handle this style of execution—or more importantly, to handle it strategically. This is a clear case where a futures-specific market structure difference has the potential to increase costs if not properly accommodated. And the style of execution is not exchange-specific, but rather, contract-specific, making each futures contract’s optimal execution strategy highly distinguished.

Contracts trade around the clock, globally

A critical data management issue in the design of effective futures algorithms is market timing. In the US equities market, exchanges operate from 9:30am to 4:00pm ET each day, with only about 3% of daily market volume trading outside of continuous market hours. Futures markets trade around the clock, globally; there tends to be higher volume during US, European and APAC trading hours when trading in other asset classes is most active, with lower volumes between these sessions.

When institutions trade outside the liquid trading hours for a particular contract, volume can be very low and spreads very wide, leading to increased execution costs. As a result, even designing algorithms specific to each contract is not sufficient. Futures algorithms must dynamically adjust for intraday volume, volatility, spread and depth. While this is always the case for any execution algorithm, when volume is particularly low, poor pre-trade analytics (for example, volume estimation) or failure to adjust to current market conditions can result in dramatically increased execution costs.

Poor accommodation of futures market structure leads to increased costs

The issues detailed above are part of a long list of trading challenges associated with the unique market structure of futures and the idiosyncratic behaviour of individual contracts. We recommend asking your execution algorithm providers how they tailor execution strategies to futures and how their algorithms dynamically adjust to each contract, market, time zone, queue-length, and exchange matching rules, among other important adjustments.

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--------------------------------------------------------------------------------------------------------------------------------------------------------------------------This research paper reflects the views and opinions of BestEx Research Group LLC. It does not constitute legal, tax, investment, financial, or other professional advice. Nothing contained herein constitutes a solicitation, recommendation, endorsement, or offer to buy or sell securities, futures, or other financial instruments or to engage in financial strategies which may include algorithms. This material may not be a comprehensive or complete statement of the matters discussed herein. Nothing in this paper is a guarantee or assurance that any particular algorithmic solution fits you, or that you will benefit from it. You should consider whether our research is suitable for your particular circumstances and needs and, if appropriate, seek professional advice.