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Practical Aspects of
Algorithmic Trading.
Bitcoin hedge fund.
Gatis Eglitis, Managing Partner
Sergey Troshin, Ph.D., Head ...
2
Algorithmic trading
 Modeling the strategy
 Implementing the model
 Result analysis
 Production
Choosing a broker
 ...
Algorithmic trading
Automatic trading
HFT – high
frequency trading
Strategy implementation
Automatic trading
Buy-side Sell-side
Statistical
arbitrage VWAPMarket Making / HFT
Trend
following
Arbitrage
Smart order
r...
Arbitrage
GAZPRU
(MICEX)
On new tick:
ogzd_rub = convert(ogzd, usd_rub)
spread = normalize(ogzd_rub/gazpru)
changedSpread(...
Volume Weighted Average Price
Data Guess Model Approval
Algorithm creation cycle
Historical Data
Completeness
Symbols
Exchanges
News
Depth
Past
Precision
Order Book
Quality
Splits etc.
Gaps
Timestamps
Data Rendering
Huge
Volume
Processing
Speed
Technical
analysis
Иллюстрация с panopticon.com
Guess and Knowledge
Intuition
EmpiricalFundamental
Trading Model
Alfa Algorithm Risks Expenses
Approval. Pre-trade Analysis
Input
•Historical data
•Market influence
modeling
•Configuration
parameters
Backtesting
• Pro...
Backtest Results
Param 1 Param 2 … Income Expenses
X1 Y1 … 10% 4%
X1 Y2 … 15% 11%
… … … … …
Xn Yn … 5% 2%
Execution with:
...
Computational Technologies
Software Overclocking FPGA
Multi-core GPGPU Cloud
x32 x200 x30000
Data Guess Model Approval
Algorithm creation cycle
Ready to Production?
17
Choosing a broker
Prime
broker
…
Retail
broker
Broker
Volumes Delays Commissions
Broker Connection
Broker
Client Computer
Trading
terminal
Algorithm
Control
Trading
Internal
Protocol
Broker Connection
Broker
Client ComputerWidely used protocols:
FIX, Plaza2, Technology
providers, Custom
TraderAlgorithm
Broker Connection
Broker Client Computer
RDP, SSH
Trader
Algorithm
General
Exchanges/Markets coverage
Trading volumes
Regulation issues
Legal issues
Commissions and fees
Technology
Market data
Delays
Market Depth
Best Bid Offer
Trades
Execution
Delays
Client side /
Server side
Pre-trade
risk...
Servers and locations
25
What is next?
Post trade analysis
Excel Program
Charts Model
Export Trade
Results
Compare with model
Algorithmic Trader
Technology
Math
Finance
Questions?
Sergey Troshin, Director
+356 2015 0000, st@exante.eu
29
NEXT GENERATION
PRIME BROKER
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EXANTE: Practical aspects of algorithmic trading. Bitcoin hedge fund. SSE Riga lecture 23.01.2014. Part2.

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Lecture of Sergey Troshin, Ph.D., Director at EXANTE, covers the main aspects of practical aspects of algorithmic trading. Main points of presentation include process of modeling the strategy, implementation and evaluation of results.
The recording is available at the following link: https://www.youtube.com/watch?v=8JToXHeqElA. Enjoy and share the video!

Publicada em: Economia e finanças
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EXANTE: Practical aspects of algorithmic trading. Bitcoin hedge fund. SSE Riga lecture 23.01.2014. Part2.

  1. 1. 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
  2. 2. 2 Algorithmic trading  Modeling the strategy  Implementing the model  Result analysis  Production Choosing a broker  Prices  Technologies  Stability What is next? Contents
  3. 3. Algorithmic trading Automatic trading HFT – high frequency trading Strategy implementation
  4. 4. Automatic trading Buy-side Sell-side Statistical arbitrage VWAPMarket Making / HFT Trend following Arbitrage Smart order routing Algo trading strategies
  5. 5. 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
  6. 6. Volume Weighted Average Price
  7. 7. Data Guess Model Approval Algorithm creation cycle
  8. 8. Historical Data Completeness Symbols Exchanges News Depth Past Precision Order Book Quality Splits etc. Gaps Timestamps
  9. 9. Data Rendering Huge Volume Processing Speed Technical analysis Иллюстрация с panopticon.com
  10. 10. Guess and Knowledge Intuition EmpiricalFundamental
  11. 11. Trading Model Alfa Algorithm Risks Expenses
  12. 12. Approval. Pre-trade Analysis Input •Historical data •Market influence modeling •Configuration parameters Backtesting • Prototype (R / Python / Java / C++ / Mathlab / Erlang / …) • GPU • Cluster / Cloud / … Result •Possible income •Risks •Real Expenses
  13. 13. Backtest Results Param 1 Param 2 … Income Expenses X1 Y1 … 10% 4% X1 Y2 … 15% 11% … … … … … Xn Yn … 5% 2% Execution with: osame input data odifferent set of parameters values
  14. 14. Computational Technologies Software Overclocking FPGA Multi-core GPGPU Cloud x32 x200 x30000
  15. 15. Data Guess Model Approval Algorithm creation cycle
  16. 16. Ready to Production?
  17. 17. 17 Choosing a broker
  18. 18. Prime broker … Retail broker Broker Volumes Delays Commissions
  19. 19. Broker Connection Broker Client Computer Trading terminal Algorithm Control Trading Internal Protocol
  20. 20. Broker Connection Broker Client ComputerWidely used protocols: FIX, Plaza2, Technology providers, Custom TraderAlgorithm
  21. 21. Broker Connection Broker Client Computer RDP, SSH Trader Algorithm
  22. 22. General Exchanges/Markets coverage Trading volumes Regulation issues Legal issues Commissions and fees
  23. 23. 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
  24. 24. Servers and locations
  25. 25. 25 What is next?
  26. 26. Post trade analysis Excel Program Charts Model Export Trade Results
  27. 27. Compare with model
  28. 28. Algorithmic Trader Technology Math Finance
  29. 29. Questions? Sergey Troshin, Director +356 2015 0000, st@exante.eu 29
  30. 30. NEXT GENERATION PRIME BROKER

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