  The process of continuously reallocating funds into financial assets, aiming
to increase the expected return of investment and minimizing the risk, is known
as portfolio management. Processing speed and energy consumption of portfolio
management have become crucial as the complexity of their real-world
applications increasingly involves high-dimensional observation and action
spaces and environment uncertainty, which their limited onboard resources
cannot offset. Emerging neuromorphic chips inspired by the human brain increase
processing speed by up to 1000 times and reduce power consumption by several
orders of magnitude. This paper proposes a spiking deep reinforcement learning
(SDRL) algorithm that can predict financial markets based on unpredictable
environments and achieve the defined portfolio management goal of profitability
and risk reduction. This algorithm is optimized forIntel's Loihi neuromorphic
processor and provides 186x and 516x energy consumption reduction is observed
compared to the competitors, respectively. In addition, a 1.3x and 2.0x
speed-up over the high-end processors and GPUs, respectively. The evaluations
are performed on cryptocurrency market between 2016 and 2021 the benchmark.
