Data science in high density temporal data

1. Challenging aspects of High
Frequency Trading
Academic or Industrial Research

2. The essence of HFT is to react to incoming data
by actions meant to make markets efficient.
● Efficiency
○ Minimum turbulence, Temporal
● Actions
○ Asymmetric cost and benefit of failure and success in actions
● Reactive
○ By definition the system only responds to disturbances or events
● Infinite dimensional input
○ Any history of data has some relevance
● Hidden Inputs
○ Every participant is a part of the input to the system
● Need for speed
○ Many effects of an impulse are easily calculable and hence we can’t take too
much time to effect the simpler patterns
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3. Control flow
Public Market
Data from
Exchanges
+
Private Order
Information
Translate From Exch API
Update Trading Signal
Switch
10G Network
Interface
Card
Trading Process
Translate change in
trading disposition to
actions like sending
order
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4. Modular Research Aspects
●
Automated feature learning
○
●
The majority of people in this industry are working in feature learning. As with other machine learning
disciplines like image recognition, this should morph into automated feature learning
Deep learning to identify the major structures of price + time movements in
market
○ This might be synonymous to the previous topic to some but this has been virtually not touched at all by
the industry. This is similar to finding patterns in temporal data like recognizing what is happening in a
video.
●
●
What is a good loss function for measuring the utility of learning methods,
that best matches trading profitability
The benefit of algorithmic optimizations and a faster round-trip time
○
●
●
Quantifying the merits of speed is very relevant here since methods that are slow are often worse than
methods that are somewhat suboptimal due to the loss function.
The computer architecture aspects of setting up processes for fastest
round trip performance
○ Kernel bypass, CPU Affinity, Shared Memory, Cache usage, Raw Ethernet QPair, FPGA
Signal processing aspects of cleaning data to find patterns
○
Many traditional signal processing methods of handling noisy data or data with hidden inputs have been
underused due to the predominantly computer science background of participants.
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