《统计套利》什么是统计套利？假设，工商银行和建设银行股票价差大于1元的情况很少，那么当市场的价格波动导致建设银行的股票价格比工商银行高出1元时，你可以通过卖出建设银行股票、买进工商银行股票构建投资组合；当两家公司的股票价差回归到1元以内时，做相反的交易，从而获得交易收益。如何通过股指期货和股票联动，利用市场暂时的失灵，获得无风险收益，是很多人的想法。如果想长期获得收益，需要通读本书，理解统计套利的精髓。《统计套利》作者具有多年运作统计套利对冲基金的管理经验，通过阅读本书，你可以探究统计套利的真正含义，了解统计套利的发展历程。更重要的是，在明了统计套利的运作方式和获利机理后，敏锐的投资者可以借此在金融市场中捕捉获利的机会。 ·匹配交易的基本原理； ·重要的时间序列模型，从最基本的加权移动平均模型，到复杂的动态因子分析模型； ·爆米花理论、反转理论...(展开全部)

The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. In this textbook, the authors develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. These models are grounded on how the exchang...(展开全部)

If you want to find out how you can build a solid foundation in algorithmic trading using Python, this cookbook is here to help.Starting by setting up the Python environment for trading and connectivity with brokers, you’ll then learn the important aspects of financial markets. As you progress, you’ll learn to fetch financial instruments, query and calculate various types of candles and historical data, and finally, compute and plot technical indicators. Next, you’ll learn how to place various types of orders, such as regular, bracket, and cover orders, and understand their state transitions. Later chapters will cover backtesting, paper trading, and finally real trading for the algorithmic strategies that you've created. You’ll even understand how to automate trading and find the right strategy for making effective decisions that would otherwise be impossible for human traders.By the end of this book, you’ll be able to use Python libraries to conduct key tasks in the algorithmic trading ecosystem.Note: For demonstration, we're using Zerodha, an Indian Stock Market broker. If you're not an Indian resident, you won't be able to use Zerodha and therefore will not be able to test the examples directly. However, you can take inspiration from the book and apply the concepts across your preferred stock market broker of choice.

Praise for Algorithmic Trading: "Algorithmic Trading is an insightful book on quantitative trading written by a seasoned practitioner. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory. Concepts are not only described, they are brought to life with actual trading strategies, which give the reader insight into how ...(展开全部)

An A to Z options trading guide for the new millennium and the new economy Written by professional trader and quantitative analyst Euan Sinclair, Option Trading is a comprehensive guide to this discipline covering everything from historical background, contract types, and market structure to volatility measurement, forecasting, and hedging techniques. This comprehen...(展开全部)

Algorithmic trading and Direct Market Access (DMA) are important tools helping both buy and sell-side traders to achieve best execution. This book starts from the ground up to provide detailed explanations of both these techniques: - An introduction to the different types of execution is followed by a review of market microstructure theory. Throughout the book examples from emp...(展开全部)

The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evalu...(展开全部)

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research.This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples.By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research.This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples.By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.

One of the most widely read books among active option traders around the world, "Option Volatility & Pricing" has been completely updated to reflect the most current developments and trends in option products and trading strategies. It covers pricing models, volatility considerations, basic and advanced trading strategies, and risk management techniques. Written in clear, easy-...(展开全部)