Traditionally, there have been two general ways of analyzing market data:
- fundamental analysis – focused on underlying fundamental data
- technical analysis – focused on charts and price movements
In recent years, computer science and mathematics revolutionized trading, it has become dominated by computers helping to analyze vast amounts of available data. Algorithms are responsible for making trading decisions faster than any human being could. Machine learning and data mining techniques are growing in popularity, all that falls under one broad category called ‘quantitative trading’ or ‘algorithmic trading’.
Below, I intend to provide you with basic tools for handling and analyzing market data with aim of generating profit from buying and selling financial instruments.
Python Programming Language
Currently, among the hottest programming languages for finance, you’ll find R and Python, alongside languages such as C++, C# and Java. I think Python or R is the right choice for many traders today. In this post, I assume you’re more or less starting from scratch or with very basic knowledge of Python, which by the way is one of the more approachable languages.
It is good to get the feeling of general Python programming before moving on with application to trading, there is a number of books and tutorials most available free or almost free:
- A Byte of Python by Swaroop C H
- Python for Everybody – Prof.Charles Severance
- Python Programming by Wikibooks
- Think Python: How would you Think Like a Computer Scientist by Allen Downey
- Dive Into Python 3 by Mark Pilgrim
All blow examples of code are for Python 3.5 with Anaconda distribution available there – www.continuum.io
The whole point of trading is to predict with certain probability what will be market behavior in future and take advantage of that. Very often it can be as simple as ‘go long’ while expecting market prices to go up or ‘go short’ while expecting market prices to drop.
Defining our ‘view’ on market or expectations about future price changes usually takes some kind of market data analysis, to do it we need data first.
There are many ways to import data to python, one of most common is using pandas-datareader package (starting with Pandas 0.19 on), it allows to import data from multiple sources like Yahoo Finance, Google Finance, Quandl, World Bank or OECD.
The easiest way to install it is:
pip install pandas-datareader
conda install -c anaconda pandas-datareader
When pandas-datareader is installed getting historical data takes only a few inputs, start and end date of a period we require the data for, ticker symbol and source of data.
Something like this:
import pandas_datareader.data as web import datetime start = datetime.datetime(2000, 1, 1) end = datetime.datetime(2017, 1, 1) data = web.DataReader('AAPL', 'yahoo', start, end)
Let’s check what we have:
Yahoo Finance gives back:
- Date – quotation date
- Open – open price
- High – highest price for the day
- Low – lowest price of the day
- Close – close price
- AdjClose – close rice with adjustments eg.stock split or dividend.
- Volume – trade volume for the day
Let’s check Google Finance, we need as above Apple for a period of 01/01/2000 to 01/01/2017.
import pandas_datareader.data as web import datetime start = datetime.datetime(2000, 1, 1) end = datetime.datetime(2017, 1, 1) data = web.DataReader("AAPL", 'google', start, end) print (data.tail())
We get an exact same set of data, except AdjClose value, so stock splits or dividends are not included.
Another very valuable source of financial/economic data can be Quandl.com.
import pandas_datareader.data as web symbol = 'WIKI/AAPL' # or 'AAPL.US' data = web.DataReader(symbol, 'quandl', "2000-01-01", "2017-01-01") print(data.tail())
Again just like Yahoo Finance, Quandl delivers AdjClose information. There is plenty of other sources accessible via pandas-datareader, for more detailed information and examples pelase go there:
Pandas-datareader is very useful and offers plenty of options, although not the only solution, you can also use libraries like Quandl.
Running this line of code installs the package:
pip install quandl
Getting data is very similar to Pandas-datareader:
import quandl data = quandl.get("WIKI/AAPL", start_date="2000-10-01", end_date="2017-01-01") print(data.tail())
After downloading the data it is always useful to save a local copy for further work, as generating online query every time data is required may be very time consuming for larger data sets eg. 100 tickers.
import pandas_datareader.data as web import datetime start = datetime.datetime(2000, 1, 1) end = datetime.datetime(2017, 1, 1) data = web.DataReader('AAPL', 'yahoo', start, end) data.to_csv('data.csv')
After securing an own copy of the data, we can quickly read it in using pandas:
import pandas as pd data = pd.read_csv('data.csv', index_col='Date', parse_dates=True) print(data.tail())
Working with Data
Having all data saved, we can start looking at them in more detail, for purpose of this intro, we will use Adjusted Close values only. Let’s select that from the whole dataset:
AdjClose=data['Adj Close'] print(AdjClose.tail())
Next thing useful for trading would be to try and plot it, we can do it using pandas:
As we are about to use quantitative methods, let’s see some statistics about the Adjusted Close price values:
For a variety of reasons that are out of scope for this text, it is better to work with daily returns rather than nominal prices of financial instruments. There is a very simple way to returns using pandas:
Rets = AdjClose.pct_change(1)
We can also plot returns:
Sometimes instead of simple returns, we may like to use log returns, it is easy to do it using Numpy:
import numpy as np LogRets = np.log(AdjClose.pct_change()+1)
Lets plot log returns:
In next part of this introduction, we will move on to code first trading strategy.
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If you want to look for more information on python for trading, check some online courses available at udemy.com.
Recommended reading list:
|Machine Trading: Deploying Computer Algorithms to Conquer the Markets (Wiley Trading)
Dive into algo trading with step-by-step tutorials and expert insight
Machine Trading is a practical guide to building your algorithmic trading business. Written by a recognized trader with major institution expertise, this book provides step-by-step instruction on quantitative trading and the latest technologies available even outside the Wall Street sphere. You'll discover the latest platforms that are becoming increasingly easy to use, gain access to new markets, and learn new quantitative strategies that are applicable to stocks, options, futures, currencies, and even bitcoins. The companion website provides downloadable software codes, and you'll learn to design your own proprietary tools using MATLAB. The author's experiences provide deep insight into both the business and human side of systematic trading and money management, and his evolution from proprietary trader to fund manager contains valuable lessons for investors at any level.
Algorithmic trading is booming, and the theories, tools, technologies, and the markets themselves are evolving at a rapid pace. This book gets you up to speed, and walks you through the process of developing your own proprietary trading operation using the latest tools.
Utilize the newer, easier algorithmic trading platforms
Access markets previously unavailable to systematic traders
Adopt new strategies for a variety of instruments
Gain expert perspective into the human side of trading
The strength of algorithmic trading is its versatility. It can be used in any strategy, including market-making, inter-market spreading, arbitrage, or pure speculation; decision-making and implementation can be augmented at any stage, or may operate completely automatically. Traders looking to step up their strategy need look no further than Machine Trading for clear instruction and expert solutions.
|Applied Quantitative Methods for Trading and Investment
This much-needed book, from a selection of top international experts, fills a gap by providing a manual of applied quantitative financial analysis. It focuses on advanced empirical methods for modelling financial markets in the context of practical financial applications.
Data, software and techniques specifically aligned to trading and investment will enable the reader to implement and interpret quantitative methodologies covering various models.
The unusually wide-ranging methodologies include not only the 'traditional' financial econometrics but also technical analysis systems and many nonparametric tools from the fields of data mining and artificial intelligence. However, for those readers wishing to skip the more theoretical developments, the practical application of even the most advanced techniques is made as accessible as possible.
The book will be read by quantitative analysts and traders, fund managers, risk managers; graduate students in finance and MBA courses.
|Quantitative Technical Analysis: An integrated approach to trading system development and trading management
This book, the fifth by Dr. Howard Bandy, discusses an integrated approach to trading system development and trading management.
It begins with a discussion and quantification of the several aspects of risk.
1. The trader's personal tolerance for risk.
2. The risk inherent in the price fluctuations of the issue to be traded.
3. The risk added by the trading system rules.
4. The trade-by-trade risk experienced during trading.
An original objective function, called "CAR25," based on risk-normalized profit potential is developed and explained. CAR25 is as near a universal objective function as I have found.
The importance of recognizing the non-stationary characteristics of financial data, and techniques for handling it, are discussed.
There is a general discussion of trading system development, including design, testing, backtesting, optimization, and walk forward analysis. That is followed by two parallel development paths -- one using traditional trading system development platform and the second machine learning.
Recognizing the importance of position sizing in managing trading, an original technique based on empirical Bayesian analysis, called "dynamic position sizing" and quantified in a metric called "safe-f," is introduced. Computer code implementing dynamic position sizing is included in the book.
56 fully disclosed, ready-to-run, and downloadable programs are included.
|Finding Alphas: A Quantitative Approach to Building Trading Strategies
Design more successful trading systems with this practical guide to identifying alphas
Finding Alphas seeks to teach you how to do one thing and do it well: design alphas. Written by experienced practitioners from WorldQuant, including its founder and CEO Igor Tulchinsky, this book provides detailed insight into the alchemic art of generating trading signals, and gives you access to the tools you need to practice and explore. Equally applicable across regions, this practical guide provides you with methods for uncovering the hidden signals in your data. A collection of essays provides diverse viewpoints to show the similarities, as well as unique approaches, to alpha design, covering a wide variety of topics, ranging from abstract theory to concrete technical aspects. You'll learn the dos and don'ts of information research, fundamental analysis, statistical arbitrage, alpha diversity, and more, and then delve into more advanced areas and more complex designs. The companion website, www.worldquantchallenge.com, features alpha examples with formulas and explanations. Further, this book also provides practical guidance for using WorldQuant's online simulation tool WebSim® to get hands-on practice in alpha design.
Alpha is an algorithm which trades financial securities. This book shows you the ins and outs of alpha design, with key insight from experienced practitioners.
Learn the seven habits of highly effective quants
Understand the key technical aspects of alpha design
Use WebSim® to experiment and create more successful alphas
Finding Alphas is the detailed, informative guide you need to start designing robust, successful alphas.
|Quantitative Trading with R: Understanding Mathematical and Computational Tools from a Quant's Perspective
Quantitative Finance with R offers a winning strategy for devising expertly-crafted and workable trading models using the R open source programming language, providing readers with a step-by-step approach to understanding complex quantitative finance problems and building functional computer code.
|Quantitative Momentum: A Practitioner's Guide to Building a Momentum-Based Stock Selection System (Wiley Finance)
The individual investor's comprehensive guide to momentum investing
Quantitative Momentum brings momentum investing out of Wall Street and into the hands of individual investors. In his last book, Quantitative Value, author Wes Gray brought systematic value strategy from the hedge funds to the masses; in this book, he does the same for momentum investing, the system that has been shown to beat the market and regularly enriches the coffers of Wall Street's most sophisticated investors. First, you'll learn what momentum investing is not: it's not 'growth' investing, nor is it an esoteric academic concept. You may have seen it used for asset allocation, but this book details the ways in which momentum stands on its own as a stock selection strategy, and gives you the expert insight you need to make it work for you. You'll dig into its behavioral psychology roots, and discover the key tactics that are bringing both institutional and individual investors flocking into the momentum fold.
Systematic investment strategies always seem to look good on paper, but many fall down in practice. Momentum investing is one of the few systematic strategies with legs, withstanding the test of time and the rigor of academic investigation. This book provides invaluable guidance on constructing your own momentum strategy from the ground up.
Learn what momentum is and is not
Discover how momentum can beat the market
Take momentum beyond asset allocation into stock selection
Access the tools that ease DIY implementation
The large Wall Street hedge funds tend to portray themselves as the sophisticated elite, but momentum investing allows you to 'borrow' one of their top strategies to enrich your own portfolio. Quantitative Momentum is the individual investor's guide to boosting market success with a robust momentum strategy.
|Quantitative Trading: Algorithms, Analytics, Data, Models, Optimization
The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and management. The second part covers market impact models, network models, multi-asset trading, machine learning techniques, and nonlinear filtering. The third part discusses electronic market making, liquidity, systemic risk, recent developments and debates on the subject.
|Python for Finance: Analyze Big Financial Data
The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance.
Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include:
Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices
Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression
Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies
|A Guide to Creating A Successful Algorithmic Trading Strategy (Wiley Trading)
Turn insight into profit with guru guidance toward successful algorithmic trading
A Guide to Creating a Successful Algorithmic Trading Strategy provides the latest strategies from an industry guru to show you how to build your own system from the ground up. If you're looking to develop a successful career in algorithmic trading, this book has you covered from idea to execution as you learn to develop a trader's insight and turn it into profitable strategy. You'll discover your trading personality and use it as a jumping-off point to create the ideal algo system that works the way you work, so you can achieve your goals faster. Coverage includes learning to recognize opportunities and identify a sound premise, and detailed discussion on seasonal patterns, interest rate-based trends, volatility, weekly and monthly patterns, the 3-day cycle, and much more—with an emphasis on trading as the best teacher. By actually making trades, you concentrate your attention on the market, absorb the effects on your money, and quickly resolve problems that impact profits.
Algorithmic trading began as a "ridiculous" concept in the 1970s, then became an "unfair advantage" as it evolved into the lynchpin of a successful trading strategy. This book gives you the background you need to effectively reap the benefits of this important trading method.
Navigate confusing markets
Find the right trades and make them
Build a successful algo trading system
Turn insights into profitable strategies
Algorithmic trading strategies are everywhere, but they're not all equally valuable. It's far too easy to fall for something that worked brilliantly in the past, but with little hope of working in the future. A Guide to Creating a Successful Algorithmic Trading Strategy shows you how to choose the best, leave the rest, and make more money from your trades.
|Building Winning Algorithmic Trading Systems, + Website: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Trading (Wiley Trading)
Develop your own trading system with practical guidance and expert advice
In Building Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Training, award-winning trader Kevin Davey shares his secrets for developing trading systems that generate triple-digit returns. With both explanation and demonstration, Davey guides you step-by-step through the entire process of generating and validating an idea, setting entry and exit points, testing systems, and implementing them in live trading. You'll find concrete rules for increasing or decreasing allocation to a system, and rules for when to abandon one. The companion website includes Davey's own Monte Carlo simulator and other tools that will enable you to automate and test your own trading ideas.
A purely discretionary approach to trading generally breaks down over the long haul. With market data and statistics easily available, traders are increasingly opting to employ an automated or algorithmic trading system—enough that algorithmic trades now account for the bulk of stock trading volume. Building Algorithmic Trading Systems teaches you how to develop your own systems with an eye toward market fluctuations and the impermanence of even the most effective algorithm.
Learn the systems that generated triple-digit returns in the World Cup Trading Championship
Develop an algorithmic approach for any trading idea using off-the-shelf software or popular platforms
Test your new system using historical and current market data
Mine market data for statistical tendencies that may form the basis of a new system
Market patterns change, and so do system results. Past performance isn't a guarantee of future success, so the key is to continually develop new systems and adjust established systems in response to evolving statistical tendencies. For individual traders looking for the next leap forward, Building Algorithmic Trading Systems provides expert guidance and practical advice.