  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.
