High-Performance Futures Algorithms

Problem

Most algorithms use simple heuristics to decide when to place orders and at what price, leading to high spread costs, especially in less liquid contracts.
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The illustration above shows average spread for a variety of futures contracts.

Solution

The heart of our algorithms is our multi-step fill probability model that prices each limit order as an option based on historical and real-time liquidity characteristics of the contract you’re trading, yielding higher spread savings. And it doesn’t matter whether you’re using a VWAP strategy or any other algorithm we offer.
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Our simulated performance versus interval VWAP for a variety of futures contracts, with orders less than 1% of average daily volume. Performance vs. interval VWAP measures spread costs because an order’s market impact is incorporated into the VWAP price.
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Problem

Most algorithms have a serious design flaw—timing actions by the clock when the market’s memory is in volume. A trade can take a few minutes in an illiquid contract or less than one second in a highly liquid contract. A heuristic that cancels and replaces limit orders every 10-15 seconds will create extreme information leakage for less liquid contracts, while achieving very few passive fills.

A simplified limit order book evolving over 25 seconds. In each 5-second interval, the right side represents the back of the queue and the left side is the front. Here, 10 seconds was not enough time for a limit order in this contract to move to the front of the queue and execute. Similarly, 10 seconds was not enough time for an order placed at midpoint to be executed.

Solution

Our algorithms make every decision—including cancel and replacement of limit orders or pausing after a fill—in contract-specific volume time, reducing information leakage and increasing passive fill rates.
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This illustration shows simulated passive fill rates when BestEx Research trades a variety of futures contracts, with order sizes less than 1% of each contract’s average daily volume.

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Problem

Futures exchanges often have long queues waiting to trade highly liquid contracts. Waiting only 30 seconds or a few trades before canceling and replacing a limit order would never allow for passive fills in many contracts. Most brokers ignore this challenge and simply pay the spread when their standard wait times expire.

Time between trades for five example contracts. Eurodollar is a common example of a long-queue contract, for which traders must wait several times longer than is typical to execute limit orders. Limit orders placed at the bid for the E-mini or crude oil are likely to execute more quickly.

Solution

Our algorithms naturally accommodate long queues, deviating from the schedule as needed and layering orders appropriately in the book to increase passive fills and reduce spread costs.
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BestEx Research algorithms place limit orders (shown in green) according to contract-specific queue lengths, layering orders to optimize passive fills. Above, we show example order books for FIFO and pro-rata trading—with two very different behaviors. For FIFO we place multiple orders at the same price, but for pro-rata we place larger orders to get a larger share of executions.

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Problem

Most algorithms use aggressive logic only to catch up on a trade plan without accounting for varying depth and spread, resulting in higher costs for taking liquidity.

This image illustrates spreads widening and narrowing throughout the trading day. Overly simplistic aggressive logic executes market orders instantly as needed to get back on schedule, risking execution when spreads are at their widest and yielding high spread costs.

Solution

Our algorithms monitor every quote change and utilize forecasts of spread, depth, and short-term alpha to take liquidity opportunistically, significantly minimizing your spread costs and temporary market impact.

BestEx Research forecasts spreads and depth to decide whether waiting to take will reduce spread costs. If spreads are likely to tighten, we’ll wait to take liquidity when the time is right, creating substantial savings.

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Problem

While passive orders earn spread, they also tend to fill when prices are moving in your favor. Improved prices after your fills are referred to as “adverse selection” and the cost of adverse selection can be much higher than a half spread.

The price of this currency pair is moving down over time. This example shows adverse selection costs exceeding spread costs; prices are declining rapidly during your buy order, by more than the size of the quoted half spread.

Solution

Our algorithms do what market makers do, canceling limit orders when our proprietary short-term alpha model points to a high probability that the price will move in your favor.

This illustration shows limit orders to buy and sell in the limit order book at an ECN. Our short-term alpha model detects the thinning liquidity at the BBO and below, and our algorithms will cancel those orders and replace with new limit orders deeper in the order book.

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