Computing Trend-Following Indicators on Homomorphically Encrypted Data

Summary

This study examines whether a trend-following indicator can be computed on encrypted market data, allowing an aggregator to keep its data private while analysts produce trading signals. It implements the indicator with two homomorphic encryption libraries, SEAL and HEAAN, and compares the encrypted calculations with a plaintext implementation. The motivation is a market-data arrangement in which an intermediary aggregates trading decisions and shares encrypted inputs with analysts, limiting their access to raw data.

The reported indicator outputs closely match the plaintext version: the study gives percentage errors of 0.14916% for SEAL and 0.00020% for HEAAN. It also describes an attempt to build and analyze a trading strategy, though the supplied summary gives no strategy-level performance results. The authors note that homomorphic encryption imposes implementation limitations. The evidence therefore supports numerical similarity for this indicator implementation, but does not establish trading profitability, broad applicability, or operational suitability across other indicators and data settings.

Key ideas

• The study implements a trend-following indicator over encrypted market data using two homomorphic encryption libraries.
• It compares encrypted indicator outputs against a plaintext implementation.
• The reported percentage errors are 0.14916% for SEAL and 0.00020% for HEAAN.
• The description mentions a trading-strategy analysis but provides no performance findings for it.
• Encryption constraints limit the implementation, and indicator similarity alone does not demonstrate profitability.

Tags

# A Trend-following Trading Indicator on Homomorphically Encrypted Data


# A Trend-following Trading Indicator on Homomorphically Encrypted Data









Algorithmic trading has proliferated the area of quantitative finance for already over a decade. The decisions are made without human intervention using the data provided by brokerage firms and exchanges. There is an emerging intermediate layer of financial players that are placed in between a broker and algorithmic traders. The role of these players is to aggregate market decisions from the algorithmic traders and send a final market order to a broker. In return, the quantitative analysts receive incentives proportional to the correctness of their predictions. In such a setup, the intermediate player - an aggregator - does not provide the market data in plaintext but encrypts it. Encrypting market data prevents quantitative analysts from trading on their own, as well as keeps valuable financial data private. This paper proposes an implementation of a popular trend-following indicator with two different homomorphic encryption libraries - SEAL and HEAAN - and compares it to the trading indicator implemented for plaintext. Then an attempt to implement a trading strategy is presented and analysed. The trading indicator implemented with SEAL and HEAAN is almost identical to that implemented on the plaintext, the percentage error is of 0.14916% and 0.00020% respectively. Despite many limitations that homomorphic encryption imposes on this algorithm's implementation, quantitative finance has a high potential of benefiting from the methods of homomorphic encryption.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.