Advanced Algorithmic Trading

English | 2017 | 517 Pages | PDF | 10 MB

Machine Learning Applied To Real World Quant Strategies
Finally…implement advanced trading strategies using time series analysis, machine learning and Bayesian statistics with the open source R and Python programming languages, for direct, actionable results on your strategy profitability.
I’m sure you’ve noticed the oversaturation of beginner Python tutorials and stats/machine learning references available on the internet.
Few tutorials actually tell you how to apply them to your algorithmic trading strategies in an end-to-end fashion.
There are hundreds of textbooks, research papers, blogs and forum posts on time series analysis, econometrics, machine learning and Bayesian statistics.
Nearly all of them concentrate on the theory.
What about practical implementation? How do you use that method for your strategy? How do you actually program up that formula in software?
I’ve written Advanced Algorithmic Trading to solve these problems.
It provides real world application of time series analysis, statistical machine learning and Bayesian statistics, to directly produce profitable trading strategies with freely available open source software.

What Topics Are Included In The Book?

• Time Series Analysis
• Time Series Models
• Cointegrated Time Series
• State-Space Models and Kalman Filters
• Hidden Markov Models
• Machine Learning
• Linear Regression
• The Bias-Variance Tradeoff
• Tree-Based Methods
• Kernel Methods
• Unsupervised Methods
• Natural Language Processing
• Bayesian Statistics
• Markov-Chain Monte Carlo
• Bayesian Stochastic Volatility

What Technical Skills Will You Learn?

• R: Time Series Analysis
• Strategy Decay
• Python: Scikit-Learn
• Robust Backtesting
• Python: PyMC3
• Risk Management