Embedded Systems in Algorithmic Trading - Peoplealanmi/presentations/... · 2015-03-29 ·...

Algorithmic Trading and Embedded Systems

Sat Chatterjee ([email protected])

Embedded Systems Week / Oct 15th 2014

IMPORTANT DISCLAIMER AND DISCLOSURE INFORMATION

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This document has been prepared by the author listed on the first page and represents only the opinions of the author. This document is exclusively for informational and educational purposes only and may not be copied, disseminated or distributed to any other person in any fashion without the express prior written consent of the author and Two Sigma Securities, LLC (“TSS”). Under no circumstances should this document or any information herein be construed as investment research, or as an offer to sell or the solicitation of an offer to buy any securities or other financial instruments, including an interest in any investment fund sponsored or managed by TSS, Two Sigma Investments, LLC, Two Sigma Advisers, LLC, or any other affiliate of TSS (each, a “TSS Affiliate”). Further, this document does not constitute and shall not be construed as an advertisement, or an offer or solicitation for any brokerage or investment advisory services, by TSS or any TSS Affiliate. While the information herein was obtained from or based upon sources believed by the author to be reliable, neither the author nor TSS and its affiliates have independently verified the information and provide no assurance as to its accuracy, reliability, suitability or completeness. All information is provided as of the date referenced on the first page, and neither the author nor TSS and its affiliates have an obligation to update the information herein. In no event shall the author, TSS, any TSS Affiliate, or their respective officers, employees or representatives, be liable for any claims, losses, costs or damages of any kind, including direct, indirect, punitive, exemplary, incidental, special or, consequential damages, arising out of or in any way connected with any information contained herein. This limitation of liability applies regardless of any negligence or gross negligence of the author, TSS, its affiliates or any of their respective officers, employees or representatives.

Sat Chatterjee / Algorithmic Trading

A Little History

1792 1797

1987 Today

3 Sat Chatterjee / Algorithmic Trading What is different about the last picture?

The US Equity Market Cloud

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This picture is not entirely accurate

What is driving this?

Lower Trading Costs

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Market Participants and Interaction

• Buy-side – retail

– mutual funds

– pensions

– insurance

– non-financial

• Sell-Side – execution brokers

– market makers

– exchanges

– dark pools

Different utility curves, horizons

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Interact through the order book


Lower Market Impact


Lower Spreads


(spread data: C. Jones, What do we know about High Frequency Trading?, SSRN 2236201)

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• More efficient price discovery

• Lower operational expenses


What are the technical challenges?

Performance

Correctness


Very High Message Rates

(source: marketdatapeaks.com)

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Highest 1-second message rate by minute across a set of data

feeds for US Equities, Futures, and Options (30 Sep 2014)


Low Latency: Soft Real-time Systems

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Order acknowledgement latency at BATS (a US Equity exchange)


(source: batstrading.com)

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Bugs can be very costly in short order

(source: nytimes.com)


A Simplified

Algorithmic Trading System


A Simplified Algorithmic Trading System

Market Data Processor

(MD)

Order Management System (OMS)

Trading Logic

Market Cloud


Market Data Processing

MD OMS Trading

Logic

Market Cloud

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Responsibilities: filtering, normalization, summarization, logging

• Performance (volume and spikes, O(1) WCET, partitioning, HW/SW co-design)

• Self-healing (connection issues, fail-over, outlier detection)

• Reliable logging (accurate timestamps, compression, integration with storage)


Order Management System

MD OMS Trading

Logic

Market Cloud

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Responsibilities: risk management, regulatory, (de-)normalization

• Distributed algorithms (borrow allocation, fill allocation, risk checks)

• Correctness (trusted component, formal verification)

• Self-monitoring (resilient to internal and external bugs)

• Performance (order bursts, maximal inline checks, HW/SW co-design)


Trading Logic

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MD OMS Trading

Logic

Market Cloud

Responsibilities: buy low, sell high

• Algorithms (machine learning, numerical optimization, state management)

• Time to market (modifiability, exploration, custom DSLs)

• Performance (need to react quickly, for back testing, machine learning)

(Cartoon: JP Rini, New Yorker)


The Reality:

Systems of Systems


Architectural Challenges

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Market Cloud

MD OMS Trading

Logic

MD OMS Trading

Logic

MD OMS Trading

Logic

MD OMS Trading

Logic

• Buffer bloat, deadlock, livelock (Facebook IPO)

• Quality of service, fairness

• Load balancing, HW/SW partitioning

• Availability


Engineering Challenges

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Market Cloud

MD OMS Trading

Logic

MD OMS Trading

Logic

MD OMS Trading

Logic

MD OMS Trading

Logic

• Early Architectural Exploration

• Back-Testing (fast, accurate simulation)

• QA (continuous integration)

• Deployment and Security

• Measurement and Benchmarking


Takeaway

Embedded Systems Research should inform the Engineering of Algorithmic Trading Systems

Hard problems in Algorithmic Trading could be good benchmarks for Embedded Systems Research

in HW/SW Co-Design,

High-level HW Synthesis,

Soft Real-Time SW Synthesis,

and Formal Verification


Backup

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Markets are linked

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Contagion spread from the Futures Market to Equities

Flash Crash of 2010


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(source: nytimes.com)

An ongoing process


Trading Costs as a Percentage of Transaction Volume

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(spread data: C. Jones, What do we know about High Frequency Trading?, SSRN 2236201)

Embedded Systems in Algorithmic Trading - Peoplealanmi/presentations/... · 2015-03-29 ·...

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