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by Ernest P. Chan
Machine Trading
Cover
Series Page
Title Page
Copyright
Dedication
Preface
Chapter 1: The Basics of Algorithmic Trading
Historical Market Data
Live Market Data
Backtesting and Trading Platforms
Brokers
Performance Metrics
Portfolio Optimization
Summary
Exercises
Endnotes
Chapter 2: Factor Models
Time‐series Factors
Cross‐sectional Factors
A Two‐Factor Model
Using Option Prices to Predict Stock Returns
Short Interest
Liquidity
Statistical Factors
Putting Them All Together
Summary
Exercises
Endnotes
Chapter 3: Time‐Series Analysis
AR(p)
ARMA(p, q)
VAR(p)
State Space Models
Summary
Exercises
Endnotes
Chapter 4: Artificial Intelligence Techniques
Stepwise Regression
Regression Tree
Cross Validation
Bagging
Random Subspace and Random Forest
Boosting
Classification Tree
Support Vector Machine
Hidden Markov Model
Neural Network
Data Aggregation and Normalization
Application to Stocks Selection
Summary
Exercises
Endnotes
Chapter 5: Options Strategies
Trading Volatility without Options
Predicting Volatility
Event‐Driven Strategies
Gamma Scalping
Dispersion Trading
Cross‐Sectional Mean Reversion of Implied Volatility
Summary
Exercises
Endnotes
Chapter 6: Intraday Trading and Market Microstructure
Latency Reduction
Order Type and Routing Optimization
Adverse Selection Reduction
Backtesting Intraday Strategies
Order Flow
Order Book Imbalance
Summary
Exercises
Endnotes
Chapter 7: Bitcoins
Bitcoin Facts
Time‐Series Techniques
Mean Reversion Strategy
Artificial Intelligence Techniques
Order Flow
Cross‐Exchange Arbitrage
Summary
Exercises
Endnotes
Chapter 8: Algorithmic Trading Is Good for Body and Soul
Your Mind and Your Health
Trading as a Service
Does This Stuff Really Work?
Keeping Up with the Latest Trends
Managing Other People's Money
Conclusion
Endnotes
Bibliography
About the Author
Index
End User License Agreement
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