This document discusses algorithmic trading and provides guidance on developing an algorithmic trading strategy. It covers modeling the strategy, implementing it, analyzing results, and moving it to production. Key aspects addressed include choosing a broker based on prices, technologies, and stability. Various algorithmic trading strategies like arbitrage, trend following, and market making are examined. Implementing the trading model involves backtesting it using historical data to analyze potential income, risks, and real expenses. Moving the strategy to production requires connecting to a broker using protocols like FIX and considering factors like market data delivery, order execution, and server locations. Post-trade analysis compares actual results to the model.

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

2. Algorithmic trading




Modeling the strategy
Implementing the model
Result analysis
Production
Choosing a broker



Prices
Technologies
Stability
Contents
What is next?
2

3. Strategy implementation
Algorithmic trading
Automatic trading
HFT – high
frequency trading

4. Algo trading strategies
Arbitrage
Automatic trading
Buy-side
Sell-side
Trend
following
Statistical
arbitrage
Smart order
routing
Market Making / HFT
VWAP

5. Arbitrage
London Server
Arbitrage strategy
On new tick:
GAZPRU
(MICEX)
ogzd_rub = convert(ogzd, usd_rub)
spread = normalize(ogzd_rub/gazpru)
changedSpread()
OGZD
(LSE)
On change spread:
if (spread > threshold) place_limit(OGZD, price, size)
USD/RUB
(FOREX)
On limit fill:
If (limit_is_filled) place_market(GAZPRU, size)
LIMIT (LSE)
Filled (size)
MARKET (MICEX)
Filled (price)
Parameters: threshold

6. Volume Weighted Average Price

7. Algorithm creation cycle
Data
Guess
Model
Approval

8. Historical Data
Completeness
Depth
Quality
Symbols
Past
Splits etc.
Exchanges
Precision
Gaps
News
Order Book
Timestamps

9. Data Rendering
Technical
analysis
Huge
Volume
Processing
Speed
Иллюстрация с panopticon.com

10. Guess and Knowledge
Fundamental
Empirical
Intuition

11. Trading Model
Alfa Algorithm
Risks
Expenses

12. Approval. Pre-trade Analysis
Input
Backtesting
Result
• Historical data
• Market influence
modeling
• Configuration
parameters
• Prototype (R / Python
/ Java / C++ / Mathlab
/ Erlang / …)
• GPU
• Cluster / Cloud / …
•Possible income
•Risks
•Real Expenses

13. Backtest Results
Execution with:
o same input data
o different set of parameters values
Param 1
Param 2
…
Income
Expenses
X1
Y1
…
10%
4%
X1
Y2
…
15%
11%
…
…
…
…
…
Xn
Yn
…
5%
2%

14. Computational Technologies
Software
Overclocking
FPGA
Multi-core
GPGPU
Cloud
x32
x200
x30000

15. Algorithm creation cycle
Data
Guess
Model
Approval

16. Ready to Production?

17. Choosing a broker
17

18. Broker
Prime
broker
Volumes
Delays
…
Commissions
Retail
broker

19. Broker Connection
Client Computer
Control
Internal
Protocol
Broker
Trading
terminal
Trading
Algorithm

20. Broker Connection
Widely used protocols:
FIX, Plaza2, Technology
providers, Custom
Client Computer
Broker
Algorithm
Trader

21. Broker Connection
Client Computer
Broker
RDP, SSH
Trader
Algorithm

22. General
Exchanges/Markets coverage
Trading volumes
Regulation issues
Legal issues
Commissions and fees

23. Technology
Market data
Execution
Connection
Control
Delays
Delays
Protocol
Trades Export
Market Depth
Best Bid Offer
Client side /
Server side
Virtual
Machines
Interface
Trades
Pre-trade
risks, Software
Co-location
Error handling

24. Servers and locations

25. What is next?
25

26. Post trade analysis
Excel
Program
Export Trade
Results
Charts
Model

27. Compare with model

28. Algorithmic Trader
Math
Technology
Finance

29. Questions?
Sergey Troshin, Director
+356 2015 0000, st@exante.eu
29

30. NEXT GENERATION
PRIME BROKER