Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2109.10662

Evaluation of Dynamic Cointegration-Based Pairs Trading Strategy in the Cryptocurrency Market

Masood Tadi·Irina Kortchmeski
View original source ↗Read source document ↗Export metadata ↓

Abstract

This research aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market, and evaluate its return and risk by applying three different scenarios. We employ the Engle-Granger methodology, the Kapetanios-Snell-Shin (KSS) test, and the Johansen test as cointegration tests in different scenarios. We calibrate the mean-reversion speed of the Ornstein-Uhlenbeck process to obtain the half-life used for the asset selection phase and look-back window estimation. By considering the main limitations in the market microstructure, our strategy exceeds the naive buy-and-hold approach in the Bitmex exchange. Another significant finding is that we implement a numerous collection of cryptocurrency coins to formulate the model's spread, which improves the risk-adjusted profitability of the pairs trading strategy. Besides, the strategy's maximum drawdown level is reasonably low, which makes it useful to be deployed. The results also indicate that a class of coins has better potential arbitrage opportunities than others. This research has some noticeable advantages, making it stand out from similar studies in the cryptocurrency market. First is the accuracy of data in which minute-binned data create the signals in the formation period. Besides, to backtest the strategy during the trading period, we simulate the trading signals using best bid/ask quotes and market trades. We exclusively take the order execution into account when the asset size is already available at its quoted price (with one or more period gaps after signal generation). This action makes the backtesting much more realistic.

Explore related subjects

q-fin.TRq-fin.CPq-fin.MFq-fin.ST

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Masood Tadi, Irina Kortchmeski. 2021-09-22. Evaluation of Dynamic Cointegration-Based Pairs Trading Strategy in the Cryptocurrency Market. https://doi.org/10.1108/sef-12-2020-0497

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The Marginal Effects of Ethereum Network MEV Transaction Re-Ordering

Two MEV builders now produce nearly 80\% of Ethereum blocks. Block builders have the ability to reorder transactions on the blockchain in a way that can be harmful to participants. We estimate participants would pay in the aggregate nearly \$7.2 million per month to guarantee that they remained in the first quartile of the block. Sandwich attacks, in which a transaction is front run, are frequent, averaging more than one every two blocks. Gas fees on these transactions pay for nearly 9.6\% of the MEV payments to the validator. Reforms such as gas fee priority or private transaction pools might be helpful.

q-fin.TR↗
arXiv2026

Computable Countermarkets and the Limits of Universal Trading

We explain why no trading algorithm can guarantee profit in every market. For each deterministic program that always returns a finite-precision position, we construct a fixed, algorithmically generated price path on which every active position loses and inactivity earns nothing. This holds with positive, continually changing prices, costless trading, and unlimited computation time. Separate arguments limit learning market rules, certifying future events, and establishing randomness from finite data. Useful strategies may exploit market structure, information, or compensation for risk, while benchmark performance need not imply profit. Reversing and rearranging price histories within the assumed market class provide practical stress tests, distinguishing conditional success from universal guarantees.

q-fin.TR↗
arXiv2026

Adapting the Actor Model of Concurrency for High-Frequency Trading: Synchronous Message Delivery (fast_send) and a Tick-to-Book Latency Study

The actor model - state isolation, data-race freedom, deadlock resistance, and sequential single-message reasoning - has long been dismissed as unsuitable for high-frequency trading (HFT): actors seem to imply many threads, a mailbox per actor, and a heap-allocated message plus a context switch per interaction, overhead incompatible with a microsecond budget. This paper argues the dismissal is wrong for co-located actors, and supports it both analytically and with a deployed, measured implementation: kaspar-hft, an open-source C++20 framework. Four extensions adapt the model for HFT: fast_send, a synchronous delivery mechanism in which the sending thread runs the receiver's handler inline and returns the reply as a value; actor groups, which co-schedule actors on one thread behind a shared mailbox; per-actor selectable mailbox queues; and a memory pool. fast_send has receiver transparency: the handler cannot tell whether delivery was synchronous or asynchronous, or which thread runs it. A grouped synchronous chain runs on one thread, cutting scheduler context switches from O(N) to O(1), and a thread-local call-chain test catches cyclic invocation before any lock is taken. Microbenchmarks put the synchronous round trip at tens of nanoseconds. On a live CME market-data feed (ES, NQ, ZN futures), socket-to-book latency decomposes into a ~7 microsecond decode-and-book floor plus a per-message slope; the framework's own contribution is under 1% of the floor. The tail is set not by the actor machinery but by the market's non-Poisson, clustered arrival process, characterized in a companion paper. The shared-queue group also yields a production/simulation duality: the same actor code runs unchanged in live trading and deterministic backtest.

q-fin.TR↗