Getting Started with Forex Trading Using Python

Description

Discover the inner workings of today's forex market, the essential risks in forex algo trading, and how to mitigate them Key Features • Build trading applications with research and without advanced Python programming skills • Dive into professional fx trading while enhancing your trading apps to be more accurate • Develop simple yet efficient backtesting applications to help keep your expectations realistic Book Description Algorithm-based trading is a popular choice for Python programmers due to its apparent simplicity. However, very few traders get the results they want, partly because they aren't able to capture the complexity of the factors that influence the market. Getting Started with Forex Trading Using Python helps you understand the market and build an application that reaps desirable results. The book is a comprehensive guide to everything that is market-related: data, orders, trading venues, and risk. From the programming side, you'll learn the general architecture of trading applications, systemic risk management, de-facto industry standards such as FIX protocol, and practical examples of using simple Python codes. You'll gain an understanding of how to connect to data sources and brokers, implement trading logic, and perform realistic tests. Throughout the book, you'll be encouraged to further study the intricacies of algo trading with the help of code snippets. By the end of this book, you'll have a deep understanding of the fx market from the perspective of a professional trader. You'll learn to retrieve market data, clean it, filter it, compress it into various formats, apply trading logic, emulate the execution of orders, and test the trading app before trading live. What you will learn • Explore the forex market organization and operations • Understand the sources of alpha and the concept of algo trading • Get a grasp on typical risks and ways to mitigate them • Understand fundamental and technical analysis • Connect to data sources and check the integrity of market data • Use API and FIX protocol to send orders • Translate trading ideas into code • Run reliable backtesting emulating real-world market conditions Who this book is for This book is for financial traders and python developers who are interested in forex trading. Academic researchers looking to focus on practical applications will find this book useful. This book can also help established fx market professionals who want to take the first steps in algo trading. Familiarity with Python and object-oriented programming within the scope of an online course or self-study is a must. Knowledge of network protocols and interfaces is a plus but not a prerequisite, as is specific knowledge about markets and trading. Preface Part 1: Introduction to FX Trading Strategy Development 1 Developing Trading Strategies – Why They Are Different Trading strategies – it’s all about how you manage risk Trading strategies – how we make money in financial markets Trading app – what can be simpler? So, what about that risk? Automated trading – operational risk and regulatory limitations Retrieving market data – quality and consistency as keys to success Receiving data – when size does matter Received data – looking at it from a critical angle Making actual trading decisions – trading logic and credit risk Ordering – transactional risk The general architecture of a trading application Summary 2 Using Python for Trading Strategies Technical requirements The advantages of using Python in trading strategy development Memory management Interactive computing Ease of integration and routing Native language structures – lists and dictionaries Libraries Modeling – forecasting the future doesn’t necessarily mean practical benefits Paper trading and backtesting – an essential part of a systemic trader’s risk management What are paper trading and backtesting? Backtesting and paper trading in Python The disadvantages of using Python in trading strategy development Live trading – where Python faces its limits Summary 3 FX Market Overview from a Developer's Standpoint Trading venues – where money meets… other money Organizing chaos – types of trading venues Auction (open outcry) Exchange and order book OTC markets Liquidity and order limitations Trade mechanics – again, some terminology Market makers – comfortable, sophisticated, expensive Counterparties that provide liquidity How do market makers make money? Market risk and how market makers mitigate it Reasons to trade with market makers Liquidity providers – the whales that support this planet Liquidity and volatility – how one transforms into another ECN – looks like a fair game, but is it? The organization of ECNs ECN – not all market participants were created equal Aggregation – in search of the best price Trading the FX market – what and how FX instruments Naming conventions How do I place a trade? Why do I need all this? Summary Part 2: General Architecture of a Trading Application and A Detailed Study of Its Components 4 Trading Application: What’s Inside? Technical requirements Have your app talk to the world – the gloomy world of communication protocols FIX – universal but too flexible Retrieving data – garbage in, garbage out Tick sequence Time gaps Price gaps Trading logic – this is where a small mistake may cost a fortune Risk management – your safety belt Ordering – make sure you are understood correctly Summary 5 Retrieving and Handling Market Data with Python Navigating through data Tick data and snapshots Timestamps – be careful comparing apples to apples! Storing and retrieving tick data Order book (depth of market) Storing and retrieving order book data Data compression – keeping the amounts to a reasonable minimum Retrieving compressed data Compressing market data in Python Working with saved and live data – keeping your app universal Universal data connector Data compression revisited Summary 6 Basics of Fundamental Analysis and Its Possible Use in FX Trading Fundamental analysis – intuitive but of little practical use Economic news US non-farm payrolls Gross domestic product (GDP) Economic news from a fundamental analysis perspective Political events US presidential elections, November 8, 2016 UK Brexit, June 23, 2016 Industry-specific information Crude oil and the Canadian dollar Summary 7 Technical Analysis and Its Implementation in Python Technical analysis – ideal for computing, but missing real market processes Velocity of the market Digital filters Range indicators Volatility indicators Key takeaways – what TA indicators are all about and how to use them Implementation of TA indicators in Python Sliding windows Peeking ahead The ultimate solution to the peek-ahead issue Sliding window as a queue Moving average – implementation Stochastic oscillator – implementation Summary 8 Data Visualization in FX Trading with Python Technical requirements The basics of charting with Python Graphical backends Static versus real-time data and related issues Installing Matplotlib Using Matplotlib in your code Simple plots of market data Visualizing static market data with pandas Installing pandas Reading data from CSV files with pandas Setting index for a dataframe Creating simple bar charts using pandas and mplfinance Making slices and subsets in pandas Plotting market charts with mplfinance Visualizing live market data Plotting live tick data Plotting live bar or candlestick charts Adding objects to price charts Summary Part 3: Orders, Trading Strategies, and Their Performance 9 Trading Strategies and Their Core Elements Elements Alpha and beta – widely used, widely confused Alpha – earn from changes in price Using currency rates as a benchmark Common FX benchmarks – the USDX Bank indices – more details, more confusion Beta – earn on volatility Options – stable income with unlimited risk Alpha classics – trend-following, mean reversion, breakout, and momentum Trend following Mean reversion Breakouts Arbitrage – let’s earn from others’ mistakes Statistical arbitrage Event-driven trading strategies Market-making – profiting on liquidity provision and associated risks High frequency, low latency – where Python fails Summary 10 Types of Orders and Their Simulation in Python Order ticket – what you send is what you get Market orders – the way to get maximum control over transactional risk Possible execution issues Limit orders – guaranteed price, but not execution Possible execution issues Time in force – better control over execution Immediate or cancel Fill or kill Good for a day (GTD) and good till canceled (GTC) Stop orders – maximum uncontrolled risk Possible execution issues Compound orders Summary 11 Backtesting and Theoretical Performance Trading app architecture – revised and improved Market data component Data aggregation component Trading logic component Order execution component Advantages of the modular architecture Multithreading – convenient but full of surprises Trading application with live data feed Backtesting – speeding up the research Syncing threads using events Backtesting platform with a historical data feed Emulating order execution during backtesting Equity curve and statistics Summary – where do we go now? Part 4: Strategies, Performance Analysis, and Vistas 12 Sample Strategy – Trend-Following Trend-following revisited – trading setup Determining a trend, part 1 – market model Determining a trend, part 2 – moving averages Entry and exit rules Money management Choosing the market and preparing data Compressing data to a daily timeframe Be careful with time! Trend-following strategy – implementation Summary 13 To Trade or Not to Trade – Performance Analysis Trade analysis Average trade and trading costs Spread Slippage Commission Overnight swaps Measuring performance – alpha and beta revisited Alpha in investment and trading Beta Net profit versus buy and hold Drawdown Risk/reward and return on drawdown The power of leverage – how much do I need to trade it? Summary 14 Where to Go Now? Implementing limit and stop orders The correct way to calculate the number of trades From trading ideas to implementation – another sample strategy using limit and stop orders Contingent orders Money management and multiple entries Strategy performance revisited – more metrics More about the risks specific to algo trading Classical technical trading setups Optimization – the blessing and the curse of algo trading Final words Index