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Arbitrage spread prediction can provide valuable insights into the identification of arbitrage signals and assessing associated risks in algorithmic trading. However, achieving precise forecasts by increasing model complexity remains a challenging task. Moreover, uncertainty in the development and maintenance of model often results in extremely unstable returns. To address these challenges, we propose a K-fold cross-search algorithm-optimized LSTM (KCS-LSTM) network for arbitrage spread prediction. The KCS heuristic algorithm incorporates an iterative updating mechanism of the search space with intervals as the basic unit into the traditional ant colony optimization. It optimized the hyperparameters of the LSTM model with a modified fitness function to automatically adapt to various data sets, thereby simplified and enhanced the efficiency of model development. The KCS-LSTM network was validated using real spread data of rebar and hot-rolled coil from the past three years. The results demonstrate that the proposed model outperforms several common models on sMAPE by improving up to 12.6% to 72.4%. The KCS-LSTM network is shown to be competitive in predicting arbitrage spreads compared to complex neural network models.

The intention of the spread trader is to implement a position that will reflect changes in the price difference between contracts rather than changes in outright price levels. To achieve such a trade, the two legs of a spread must be equally weighted. Although for most for most intramarket and intermarket spreads, equal weighting simply implies an equal number of contracts long and short, for intercommodity spreads, which can differ significantly both in contract size and price levels, the most sensible definition for equal weighting is a spread that is indifferent to equal percentage price changes in both markets. It can be demonstrated this condition will be fulfilled if the spread is initiated so the dollar values of the long and short positions are equal. An equal dollar value spread can be achieved by using a contract ratio that is inversely proportional to the contract value ratio. if intercommodity spreads are traded using an equal‐dollar‐value approach—as they should be—the price difference between the markets is no longer the relevant subject of analysis. Rather, such an approach is most closely related to the price ratio between the two markets. This fact means that for intercommodity spreads, chart analysis and the definition of historical ranges should be based on the price ratio, not the price difference.

A spread trade involves the simultaneous purchase of one futures contract against the sale of another futures contract either in the same market or in a related market. Spreads can provide additional trading opportunities that are not available in outright position trading. There are three basic types of spreads: intramarket (interdelivery), intercommodity, and intermarket. For most but not all commodity type markets, as a general rule near months will gain ground relative to distant months in a bull market and lose ground in a bear market. Gold and silver will behave inversely to the general rule, while cattle and hogs will bear no relationship to the general rule. The limited risk spread is a long nearby/short forward spread in a market in which the maximum premium that a more distant month can command over a nearby contract is roughly equal to the cost of taking delivery, holding the commodity for the length of time between the two expirations, and then redelivering. The cost for this entire operation is referred to as full carry. The chapter concludes with a five‐step process for analyzing spreads and a list of caution points for spread trading.

Chapter 2 examines how long currency hedge positions are used to lock in the future costs of purchasing currency, while short hedges are used to lock in the price on the future sale of a currency. The chapter also explains how futures and forward contracts are used for exchange‐rate speculation and how arbitrage governs the prices of currency futures.