# CFRM 523: Advanced Trading Systems > Reading Material: > > * *Expected Returns: An Investor’s Guide to Harvesting Market Rewards*, A. Ilmanen, 2011. > * *Inside the Black Box: a Simple Guide to Quantitative and High-Frequency Trading, R. Narang*, 2013. > * *Evaluation and Optimization of Trading Strategies*, R. Pardo, 2008. > * *Time Series Forecasting in Python*, M. Peixeiro, 2022. > * *Trading Systems*, U. Jaekle, 2009. This course will provide a detailed research process and tools for replicating, assessing, conceptualizing, and developing systematic trading strategies. Students will apply their knowledge in hands-on projects to replicate and evaluate existing research and to create and evaluate a new strategy model. Development of systematic trading strategies should follow a highly scientific and repeatable process. This course will start by reviewing categories of systematic strategies, drawing out patterns followed throughout the industry. We will demonstrate a repeatable process for evaluating ideas, constructing hypotheses, building each of the strategy components, and evaluating and improving the strategy at each step. Students will use the Python to replicate academic research and evaluate the claims made in papers. Students will also construct a non-trivial strategy from scratch, evaluate the power of each of its components, and examine the likelihood of overfitting. The strategy will be documented and presented in lieu of a final exam. The first half of the quarter will focus on the structure of quantitative strategies, and on the different types of strategies used in production by trading firms and asset managers. The second half of the quarter will focus on more advanced techniques for model evaluation, feature engineering, and using modern methods such as machine learning, as well as practical application of these skills to strategy building. Throughout the class, we will be doing project work to apply the learned skills. Projects are designed to mimic as closely as possible the day-to-day research activities of working strategy quants, so that students will have practical experience building, testing, and evaluating quantitative models. 1. Introduction to Replication 2. Formulating and Testing Hypotheses 3. Strategy Styles 1. Momentum and Trend 2. Mean Reversion 3. Statistical Arbitrage 4. Liquidity Provision (Market Making) 5. Proprietary Trading 6. Factors 4. Structure of Quantitative Strategies 1. Constraints, Benchmarks, & Objectives 2. Walk Forward Analysis 3. Parameter Optimization 5. Machine Learning --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://cfrm.franco.biz/program/third-quarter/cfrm-523-advanced-trading-systems.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.