没有合适的资源?快使用搜索试试~ 我知道了~
首页算法交易实战:计算机程序征服金融市场的指南
算法交易实战:计算机程序征服金融市场的指南
![](https://csdnimg.cn/release/wenkucmsfe/public/img/star.98a08eaa.png)
《机器交易:利用计算机算法征服市场》是一本由Ernest P. Chan撰写的实战指南,他是拥有大型金融机构经验的交易员。本书深入浅出地介绍了量化交易的实践步骤和技术,涵盖了最新的市场技术和工具。作者不仅分享了如何在华尔街范围内运用这些技术,还探讨了如何在股票、期权、期货、外汇以及比特币等多元化市场中实施定量策略。书中特别强调了易于使用的最新交易平台,如提供软件代码下载的配套网站,以及使用MATLAB设计个性化交易工具的方法。 书中内容涵盖了作者作为从自营交易员到基金经理的职业生涯演变,这为不同层次的投资者提供了宝贵的经验教训,不仅包括金融业务层面的洞察,还包括对系统性交易和风险管理的深入理解。《机器交易》系列书籍由John Wiley & Sons出版,该公司是美国最古老的独立出版商之一,其全球网络使其能够开发并推广旨在提升读者专业和个人知识的产品和服务。 该书适合那些希望在不断变化的市场环境中寻找稳定盈利策略的交易者,无论是初级交易者还是资深专业人士,都能从中获得实用的建议和策略,以应对当前和未来的挑战。通过阅读本书,读者将学会如何利用计算机算法来优化交易决策,实现市场的征服。同时,作者丰富的实战经验和深入的理论分析,使得本书成为理解和掌握量化交易不可或缺的参考资料。
资源详情
资源推荐
![](https://csdnimg.cn/release/download_crawler_static/10564519/bg10.jpg)
Trim Size: 6in x 9in Chan ast.tex V2 - 12/11/2016 12:44 A.M. Page xiv
k
k k
k
![](https://csdnimg.cn/release/download_crawler_static/10564519/bg11.jpg)
Trim Size: 6in x 9in Chan c01.tex V2 - 12/11/2016 12:44 A.M. Page 1
k
k
1
k
k
CHAPTER 1
The Basics
of Algorithmic
Trading
A
n algorithmic trading strategy feeds market data (historical or live) into
a computer (backtest or automated execution) program. The program
then submits orders to the broker through an API, and receives order status
notications back from the broker. The owchart in Figure 1.1 illustrates
this process.
Notice that I deliberately use the same box to indicate the computer pro-
gram that generates backtest results and live orders: This is the best way to
ensure we are trading the exact same model that we have backtested.
In this chapter, I will discuss the latest services, products, and their ven-
dors applicable to each of the blocks in Figure 1.1. In addition, I will describe
my favorite performance metrics, the way to determine the optimal lever-
age, and the simplest asset allocation method. Though I have touched on
many (but not all) of these issues in my previous books, I have updated them
here based on the state of the art. The FinTech industry has not been stand-
ing still, nor has my understanding of issues ranging from brokers’ safety to
subtleties of portfolio optimization.
![](https://csdnimg.cn/release/download_crawler_static/10564519/bg12.jpg)
Trim Size: 6in x 9in Chan c01.tex V2 - 12/11/2016 12:44 A.M. Page 2
MACHINE TRADING
k
k
2
k
k
Historical market
data
Live market
data
Computer
program
Backtest results
Order Order status
Broker API
Broker’s server
Order Order status
FIGURE 1.1 Algorithmic trading at a glance
■ Historical Market Data
For daily historical data in stocks and futures, I have been using CSI
(csidata.com) for a long time. CSI has a very exible, and robust, desk-
top application. The beauty of this application is that we can set a time
in the evening when the data are automatically updated through an Inter-
net connection with CSI’s server. Also, the data can be stored in various
convenient formats such as .txt, .csv, or .xlsx. We can ask it to automati-
cally adjust historical stock (and ETF) prices for splits and dividends. For a
little extra, CSI can also provide delisted stocks’ historical data, so that you
can have a survivorship-bias-free data set.
1
(By the way, CSI data powers
Yahoo! Finance’s historical stock data.) For futures, we can choose dierent
rollover methods to create continuous contracts. Or not, since the origi-
nal contract prices are also available, and many professional traders prefer
to backtest futures using original contract prices instead of back-adjusted
continuous contract prices. This is because the latter depends on a particu-
lar roll method, and may have look-ahead bias embedded (see Chan, 2013,
![](https://csdnimg.cn/release/download_crawler_static/10564519/bg13.jpg)
Trim Size: 6in x 9in Chan c01.tex V2 - 12/11/2016 12:44 A.M. Page 3
THE BASICS OF ALGORITHMIC TRADING
k
k
3
k
k
for a detailed exploration of this issue). Finally, CSI has excellent customer
support through email and phone.
An alternative to CSI is Quandl.com, which is a consolidator of many
kinds of data from many dierent vendors. It also provides an API in dierent
languages (including MATLAB, which I use in this book, or Python, which
many other traders use) that we can use for data selection and download.
Some of Quandl’s data are free (daily data for stocks is one example), and
others require payment. I have purchased, for example, fundamental stock
data from them (see Chapter 2, Factor Models), and they are much more
economical than established vendors such as Compustat.
Serious traders or academic nance researchers may prefer stock, ETF,
and mutual fund data from CRSP (www.crsp.com). Their historical data
are carefully compiled to be survivorship-bias-free, and dividends and splits
are provided separately so you can decide how to utilize them in a back-
test. But most importantly, they provide the best bid and oer (BBO) prices
at the close. These are important because, as is explained in Box 6.2 in
Chapter 6, using the usual consolidated closing prices from CSI or Quandl
can inate backtest performances for certain strategies. A similar issue arises
from using the consolidated opening prices. The best open and close prices
to use are the auction prices from the primary exchange. (See also Box 6.2
for an explanation of how we can extract such auction prices from tick data.)
The second best open and close prices to use are the BBO prices that can be
used to compute the midprices at the open and close. Unfortunately CRSP
does not provide the BBO prices at the open, so one must use intraday data
for that purpose. For academic researchers, CRSP data can be obtained at
a lower cost through WRDS (wrds-web.wharton.upenn.edu), which is a
consolidator of many high quality historical databases for scholarly research.
Of course, those serious traders who can aord to buy data from CRSP
may also be able to aord a Bloomberg terminal subscription. One advantage
of a Bloomberg terminal is that we can download the ‘‘primary exchange’’
close price for US stocks. Of course, a Bloomberg subscription also includes
access to many historical and live data spanning a wide variety of instruments
and, importantly, breaking news on every stock. I have found Bloomberg’s
news service to be superior to many other vendors’. Often, we will see a
stock moves suddenly, and are not able to nd the reason anywhere else but
on Bloomberg’s news feed. They do capture the most obscure news on the
most obscure stocks in the shortest time frame, which is important when
you have an event-driven strategy. Bloomberg’s historical US stock data are
also survivorship-bias-free. (To be fair to Thomson Reuter’s Eikon platform,
![](https://csdnimg.cn/release/download_crawler_static/10564519/bg14.jpg)
Trim Size: 6in x 9in Chan c01.tex V2 - 12/11/2016 12:44 A.M. Page 4
MACHINE TRADING
k
k
4
k
k
which is a keen competitor to Bloomberg’s, I have not tested its news feed.
So it is possible that it provides just as wide and timely coverage as well.
There is one feature on Eikon that impressed me in a demo: I was able to
see the geographical locations of individual oil tankers and where they were
headed. Apparently, this is useful to oil traders to predict short-term oil
inventory, supply, and demand.)
For futures traders, daily data does not present much of a problem.
CSIs and the free data on Quandl are as good as any.
Daily options data can be obtained from ORATS.com as well as
ivolatility.com. Both oerings are survivorship-bias-free. The institutional
trader or academic researcher may also purchase from Option Metrics,
which is often part of the WRDS package (see above). One nice feature
of all these databases: They do not include just option closing prices, but
also the bid-ask quote at the close as well. This is important because some
options, especially ones that are out-of-the-money or have long tenor,
may be traded infrequently. So the last trade price of the day may be very
dierent from the bid-ask quotes at the close, and is not indicative of the
price we can actually trade at or near the close. These databases also include
auxiliary but important information such as the Greeks and implied volatil-
ities. Furthermore, they may include an implied volatility surface. This uses
implied volatilities computed on actual options and interpolates them to
yield implied volatilities for strikes and tenors that did not actually exist.
Options historical data tend to be more expensive than stock or futures
data, due to their voluminous nature. However, it can be cheaper to rent
intraday option prices from QuantGo.com than to buy daily option prices
from other vendors. We will talk more about QuantGo when we discuss
intraday data in general. It would be a trivial programming exercise to
extract the end-of-day quotes from the intraday data, by looking for the
quotes with timestamps at or just before the daily closing time.
Beyond daily price data, there are, of course, fundamental nancial
data for companies. I already mentioned that Quandl carries such data.
Institutional traders would most likely look to Compustat for such data. For
corporate earnings estimates by analysts, Thomson Reuters’ IBES database
is the standard. Both Compustat and IBES are available from WRDS.
Meanwhile, crowd-sourced earnings estimates are available from Estimize.
There is some research that suggests Estimize’s contributors can more
accurately forecast actual earnings than traditional sell-side analysts (Wang
et al., 2014). An example strategy using Estimize’s data is discussed
in Deltix (2014). Short interest data are available from Compustat and
剩余266页未读,继续阅读
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://profile-avatar.csdnimg.cn/ce2d057183474645a1fe000eac996f37_qq_22231931.jpg!1)
qq_22231931
- 粉丝: 0
- 资源: 3
上传资源 快速赚钱
我的内容管理 展开
我的资源 快来上传第一个资源
我的收益
登录查看自己的收益我的积分 登录查看自己的积分
我的C币 登录后查看C币余额
我的收藏
我的下载
下载帮助
![](https://csdnimg.cn/release/wenkucmsfe/public/img/voice.245cc511.png)
最新资源
- Hadoop生态系统与MapReduce详解
- MDS系列三相整流桥模块技术规格与特性
- MFC编程:指针与句柄获取全面解析
- LM06:多模4G高速数据模块,支持GSM至TD-LTE
- 使用Gradle与Nexus构建私有仓库
- JAVA编程规范指南:命名规则与文件样式
- EMC VNX5500 存储系统日常维护指南
- 大数据驱动的互联网用户体验深度管理策略
- 改进型Booth算法:32位浮点阵列乘法器的高速设计与算法比较
- H3CNE网络认证重点知识整理
- Linux环境下MongoDB的详细安装教程
- 压缩文法的等价变换与多余规则删除
- BRMS入门指南:JBOSS安装与基础操作详解
- Win7环境下Android开发环境配置全攻略
- SHT10 C语言程序与LCD1602显示实例及精度校准
- 反垃圾邮件技术:现状与前景
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
![](https://img-home.csdnimg.cn/images/20220527035711.png)
![](https://img-home.csdnimg.cn/images/20220527035711.png)
![](https://img-home.csdnimg.cn/images/20220527035111.png)
安全验证
文档复制为VIP权益,开通VIP直接复制
![](https://csdnimg.cn/release/wenkucmsfe/public/img/green-success.6a4acb44.png)