Book : Hands-on Financial Trading With Python A Practical..

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Discover how to build and backtest algorithmic trading strategies with ZiplineKey FeaturesGet to grips with market data and stock analysis and visualize data to gain quality insightsFind out how to systematically approach quantitative research and strategy generation/backtesting in algorithmic tradingLearn how to navigate the different features in Pythons data analysis librariesBook DescriptionAlgorithmic trading helps you stay ahead of the markets by devising strategies in quantitative analysis to gain profits and cut losses.The book starts by introducing you to algorithmic trading and explaining why Python is the best platform for developing trading strategies. Youll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources. Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. Youll also focus on time series forecasting, covering pmdarima and Prophet.By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization.What you will learnDiscover how quantitative analysis works by covering financial statistics and ARIMAUse core Python libraries to perform quantitative research and strategy development using real datasetsUnderstand how to access financial and economic data in PythonImplement effective data visualization with MatplotlibApply scientific computing and data visualization with popular Python librariesBuild and deploy backtesting algorithmic trading strategiesWho this book is forThis book is for data analysts and financial traders who want to explore how to design algorithmic trading strategies using Pythons core libraries. If you are looking for a practical guide to backtesting algorithmic trading strategies and building your own strategies, then this book is for you. Beginner-level working knowledge of Python programming and statistics will be helpful.Table of ContentsIntroduction to algorithmic tradingExploratory Data Analysis in PythonHigh-speed Scientific Computing using NumPyData Manipulation and Analysis with PandasData Visualization using MatplotlibStatistical Estimation, Inference, and PredictionFinancial Market Data Access in PythonIntroduction to Zipline and PyFolioFundamental algorithmic trading strategies Review Financial Trading with Python is an excellent introduction to Python, time-series analysis, and algorithmic trading. It deserves a place in every systematic traders bookshelf. --Andreas F. Clenow, CIO Acies Asset Management and author of Following the Trend and Trading Evolved About the Author Jiri Pik is an artificial intelligence architect & strategist who works with major investment banks, hedge funds, and other players. He has architected and delivered breakthrough trading, portfolio, and risk management systems, as well as decision support systems, across numerous industries. Jiris consulting firm, Jiri Pik RocketEdge, provides its clients with certified expertise, judgment, and execution at the speed of light.Sourav Ghosh has worked in several proprietary high-frequency algorithmic trading firms over the last decade. He has built and deployed extremely low latency, high throughput automated trading systems for trading exchanges around the world, across multiple asset classes. He specializes in statistical arbitrage market-making, and pairs trading strategies for the most liquid global futures contracts. He works as a Senior Quantitative Developer at a trading firm in Chicago. He holds a Masters in Computer Scienc
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