This document provides an overview of algorithmic trading, including: - Algorithmic trading uses computer programs to automate trading processes like analysis, signal generation, and execution. Profits drive these systems through cost savings, commissions, or proprietary trading. - For broker trading, the objective is to minimize costs, while proprietary trading aims to maximize profits. Trading firms that take on risk stand to gain the largest share of profits. - The trade process involves pretrade analysis using fundamental, technical, or quantitative methods, generating trading signals based on entry and exit strategies and risk management, and then executing trades.

1. Algorithmic Trading
Giuseppe Nuti, Mahnoosh Mirghaemi, Philip Treleaven, and
Chaiyakorn Yingsaeree
UK Centre in Financial Computing, London
IEEE Computer Society, Nov. 2011
Advisor : DR. Jenq-Shiou Leu
Student : Chia-Yun Chan
Date : 2014/10/27

2. Outline
• Introduction
• Trade Objective
• Trade Execution
• Trade Process
o Pretrade Analysis
o Trading Signal Generation
• Conclusion

3. Introduction
• In electronic financial markets, algorithmic
trading is the use of computer programs to
automate one or more stages of the trading
process: pretrade analysis (data analysis),
trading signal generation (buy and sell
recommendations), and trade execution.
• Profits drive any algorithmic trading system—
whether in the form of cost savings, client
commissions, or proprietary trading.

4. Trade Objective
• Broker Trading
o Minimize the cost of trading
• Proprietary Trading
o Maximize the profit
• The Clients V.S Trading Firm
o Who takes on the trading risk, takes the lion’s
share of the profits

5. Trade Execution
• Order list
• Buy and Sell
• Rank by price
and order
arrival time
• First in, first out
Time Price Quantity
09:00:01 99 20
09:00:11 99 4000
09:00:13 99 980

6. Trade Process

7. Algorithmic Trading
System Components

8. Pretrade Analysis
• Fundamental analysis
• Technical analysis
o Technical analysis aims to predict future price
movements based on asset price history
• Quantitative analysis
o Quantitative analysis treats asset prices as
random and uses mathematical and statistical
analysis to find a suitable model for describing
this randomness

9. Trading Signal Generation
• Entry strategies
• Exit strategies
• Risk management

10. Algorithmic Trading
Example

11. Conclusion
• Trading strategies
• Ultrahigh-frequency trading
o Buying and selling of stocks at extremely fast
speeds with the help of powerful computers.
They can scan dozens of markets simultaneously,
execute thousands of orders a second, and alter
strategies in a matter of milliseconds

12. Thanks for
Your Attention !