EXANTE: Practical aspects of algorithmic trading. Bitcoin hedge fund. SSE Riga lecture 23.01.2014....

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Practical Aspects of Algorithmic Trading. Bitcoin hedge fund. Gatis Eglitis, Managing Partner Sergey Troshin, Ph.D., Head of IT Strategy and Operations Part 2: Algo trading

Transcript of EXANTE: Practical aspects of algorithmic trading. Bitcoin hedge fund. SSE Riga lecture 23.01.2014....

Page 1: EXANTE: Practical aspects of algorithmic trading. Bitcoin hedge fund. SSE Riga lecture 23.01.2014. Part2.

Practical Aspects of Algorithmic Trading.Bitcoin hedge fund.

Gatis Eglitis, Managing PartnerSergey Troshin, Ph.D., Head of IT Strategy and Operations

Part 2: Algo trading

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Algorithmic trading Modeling the strategy Implementing the model Result analysis Production

Choosing a broker Prices Technologies Stability

What is next?

Contents

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Algorithmic trading

Automatic trading

HFT – high frequency trading

Strategy implementation

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Automatic trading

Buy-side Sell-side

Statistical arbitrage VWAPMarket Making / HFT

Trend following

Arbitrage

Smart order routing

Algo trading strategies

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Arbitrage

GAZPRU(MICEX)

On new tick:

ogzd_rub = convert(ogzd, usd_rub)spread = normalize(ogzd_rub/gazpru)

changedSpread()OGZD(LSE)

USD/RUB(FOREX)

LIMIT (LSE)

London Server

Filled (size)

MARKET (MICEX)

Filled (price)

On change spread:if (spread > threshold) place_limit(OGZD, price, size)

On limit fill:If (limit_is_filled) place_market(GAZPRU, size)

Arbitrage strategy

Parameters: threshold

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Volume Weighted Average Price

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Data Guess Model Approval

Algorithm creation cycle

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Historical Data

Completeness

Symbols

Exchanges

News

Depth

Past

Precision

Order Book

Quality

Splits etc.

Gaps

Timestamps

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Data Rendering

Huge Volume

Processing Speed

Technical analysis

Иллюстрация с panopticon.com

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Guess and Knowledge

Intuition

EmpiricalFundamental

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Trading Model

Alfa Algorithm Risks Expenses

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Approval. Pre-trade AnalysisInput•Historical data•Market influence modeling•Configuration parameters

Backtesting•Prototype (R / Python / Java /

C++ / Mathlab / Erlang / …)•GPU•Cluster / Cloud / …

Result•Possible income•Risks•Real Expenses

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Backtest Results

Param 1 Param 2 … Income Expenses

X1 Y1 … 10% 4%

X1 Y2 … 15% 11%

… … … … …

Xn Yn … 5% 2%

Execution with:o same input dataodifferent set of parameters values

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Computational Technologies

Software Overclocking FPGA

Multi-core GPGPU Cloudx32 x200 x30000

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Data Guess Model Approval

Algorithm creation cycle

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Ready to Production?

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Choosing a broker

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Prime broker

… Retail broker

Broker

Volumes Delays Commissions

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Broker Connection

Broker

Client Computer

Trading terminal

Algorithm

Control

Trading

Internal Protocol

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Broker Connection

Broker

Client ComputerWidely used protocols:

FIX, Plaza2, Technology providers,

Custom

TraderAlgorithm

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Broker Connection

Broker Client Computer

RDP, SSH

Trader

Algorithm

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General

Exchanges/Markets coverageTrading volumesRegulation issuesLegal issues

Commissions and fees

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Technology

Market data

Delays

Market Depth Best Bid Offer

Trades

Execution

Delays

Client side / Server side

Pre-trade risks, Software

Connection

Protocol

Virtual Machines

Co-location

Control

Trades Export

Interface

Error handling

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Servers and locations

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What is next?

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Post trade analysis

Excel

Program

Charts

Model

Export Trade Results

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Compare with model

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Algorithmic Trader

Technology

Math

Finance

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Questions?Sergey Troshin, Director

+356 2015 0000, [email protected]

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