Getting Started with Forex Trading Using Python: Beginner’s guide to the currency market and development of trading algorithms

Cover
Title Page
Copyright and Credits
Contributors
Table of Contents
Preface
Part 1: Introduction to FX Trading Strategy Development
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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,
		UK Brexit, June 23,
	Industry-specific information
		Crude oil and the Canadian dollar
	Summary
Chapter 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
Chapter 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
Chapter 9: Trading Strategies and Their Core 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
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