Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSS

This book serves two purposes. First, it teaches the importance of using sophisticated yet accessible statistical methods to evaluate a trading system before it is put to real-world use. In order to accommodate readers having limited mathematical background, these techniques are illustrated with step-by-step examples using actual market data, and all examples are explained in plain language. Second, this book shows how the free program TSSB (Trading System Synthesis & Boosting) can be used to develop and test trading systems. The machine learning and statistical algorithms available in TSSB go far beyond those available in other off-the-shelf development software. Intelligent use of these state-of-the-art techniques greatly improves the likelihood of obtaining a trading system whose impressive backtest results continue when the system is put to use in a trading account. Among other things, this book will teach the reader how to: Estimate future performance with rigorous algorithms Evaluate the influence of good luck in backtests Detect overfitting before deploying your system Estimate performance bias due to model fitting and selection of seemingly superior systems Use state-of-the-art ensembles of models to form consensus trade decisions Build optimal portfolios of trading systems and rigorously test their expected performance Search thousands of markets to find subsets that are especially predictable Create trading systems that specialize in specific market regimes such as trending/flat or high/low volatility More information on the TSSB program can be found at TSSBsoftware dot com.

這本書有兩個目的。首先，它教導使用複雜但易於理解的統計方法在實際應用之前評估交易系統的重要性。為了迎合數學基礎有限的讀者，這些技巧將通過使用實際市場數據的逐步示例來說明，並且所有示例都以簡單的語言解釋。其次，本書展示了如何使用免費的TSSB（交易系統合成和增強）程序來開發和測試交易系統。TSSB中提供的機器學習和統計算法遠遠超出其他現成開發軟件的範圍。聰明地使用這些最先進的技術可以大大提高獲得令人印象深刻的回測結果的交易系統在實際交易中持續表現的可能性。除其他事項外，本書還將教讀者如何：使用嚴謹的算法估計未來表現；評估回測中好運的影響；在部署系統之前檢測過度擬合；估計由於模型擬合和選擇看似優越的系統而產生的性能偏差；使用最先進的模型集合形成共識交易決策；構建最佳的交易系統組合並嚴格測試其預期表現；搜索數千個市場以找到特別可預測的子集；創建專門適用於特定市場環境（如趨勢/平穩或高/低波動性）的交易系統。有關TSSB程序的更多信息，請訪問TSSBsoftware dot com。