In this chapter, we will take you through a brief history of trading and explain in which situations manual and algorithmic trading each make sense. Additionally, we will discuss financial asset classes, which are a categorization of the different types of financial assets. You will learn about the components of the modern electronic trading exchange, and, finally, we will outline the key components of an algorithmic trading system.
In this chapter, we will cover the following topics:
- Walking through the evolution of algorithmic trading
- Understanding financial asset classes
- Going through the modern electronic trading exchange
- Understanding the components of an algorithmic trading system
Walking through the evolution of algorithmic trading
The concept of trading one possession for another has been around since the beginning of time. In its earliest form, trading was useful for exchanging a less desirable possession for a more desirable possession. Eventually, with the passage of time, trading has evolved into participants trying to find a way to buy and hold trading instruments (that is, products) at prices perceived as lower than fair value in the hopes of being able to sell them in the future at a price higher than the purchase price. This buy-low-and-sell-high principle serves as the basis for all profitable trading to date; of course, how to achieve this is where the complexity and competition lies.
Markets are driven by the fundamental economic forces of supply and demand. As demand increases without a commensurate increase in supply, or supply decreases without a decrease in demand, a commodity becomes scarce and increases in value (that is, its market price). Conversely, if demand drops without a decrease in supply, or supply increases without an increase in demand, a commodity becomes more easily available and less valuable (a lower market price). Therefore, the market price of a commodity should reflect the equilibrium price based on available supply (sellers) and available demand (buyers).
- Human traders are inherently slow at processing new market information, making them likely to miss information or to make errors in interpreting updated market data. This leads to bad trading decisions.
- Humans, in general, are also prone to distractions and biases that reduce profits and/or generate losses. For example, the fear of losing money and the joy of making money also causes us to deviate from the optimal systematic trading approach, which we understand in theory but fail to execute in practice. In addition, people are also naturally and non-uniformly biased against profitable trades versus losing trades; for instance, human traders are quick to increase the amount of risk after profitable trades and slow down to decrease the amount of risk after losing trades.
- Human traders learn by experiencing market conditions, for example, by being present and trading live markets. So, they cannot learn from and backtest over historical market data conditions – an important advantage of automated strategies, as we will see later.
With the advent of technology, trading has evolved from pit trading carried out by yelling and signaling buy and sell orders all the way to using sophisticated, efficient, and fast computer hardware and software to execute trades, often without much human intervention. Sophisticated algorithmic trading software systems have replaced human traders and engineers, and mathematicians who build, operate, and improve these systems, known as quants, have risen to power.
- Computers are extremely good at performing clearly defined and repetitive rule-based tasks. They can perform these tasks extremely quickly and can handle massive throughputs.
- Additionally, computers do not get distracted, tired, or make mistakes (unless there is a software bug, which, technically, counts as a software developer error).
- Algorithmic trading strategies also have no emotions as far as trading through losses or profits; therefore, they can stick to a systematic trading plan no matter what.
However, algorithmic trading is not always better than manual trading:
- Manual trading is better at dealing with significantly complex ideas and the complexities of real-world trading operations that are, sometimes, difficult to express as an automated software solution.
- Automated trading systems require significant investments in time and R&D costs, while manual trading strategies are often significantly faster to get to market.
- Algorithmic trading strategies are also prone to software development/operation bugs, which can have a significant impact on a trading business. Entire automated trading operations being wiped out in a matter of a few minutes is not unheard of.
- Often, automated quantitative trading systems are not good at dealing with extremely unlikely events termed as black swan events, such as the LTCM crash, the 2010 flash crash, the Knight Capital crash, and more.
In this section, we learned about the history of trading and when automated/algorithmic is better than manual trading. Now, let's proceed toward the next section, where we will learn about the actual subject of trading categorized into financial asset classes.
Understanding financial asset classes
The major financial asset classes are as follows:
- Equities (stocks): These allow market participants to invest directly in the company and become owners of the company.
- Fixed income (bonds): These represent a loan made by the investor to a borrower (for instance, a government or a firm). Each bond has its end date when the principal of the loan is due to be paid back and, usually, either fixed or variable interest payments made by the borrower over the lifetime of the bond.
- Real Estate Investment Trusts (REITs): These are publicly traded companies that own or operate or finance income-producing real estate. These can be used as a proxy to directly invest in the housing market, say, by purchasing a property.
- Commodities: Examples include metals (silver, gold, copper, and more) and agricultural produce (wheat, corn, milk, and more). They are financial assets tracking the price of the underlying commodities.
- Exchange-Traded Funds (ETFs): An EFT is an exchange-listed security that tracks a collection of other securities. ETFs, such as SPY, DIA, and QQQ, hold equity stocks to track the larger well-known S&P 500, Dow Jones Industrial Average, and Nasdaq stock indices. ETFs such as United States Oil Fund (USO) track oil prices by investing in short-term WTI crude oil futures. ETFs are a convenient investment vehicle for investors to invest in a wide range of asset classes at relatively lower costs.
- Foreign Exchange (FX) between different currency pairs, the major ones being the US Dollar (USD), Euro (EUR), Pound Sterling (GBP), Japanese Yen (JPY), Australian Dollar (AUD), New Zealand Dollar (NZD), Canadian Dollar (CAD), Swiss Franc (CHF), Norwegian Krone (NOK), and Swedish Krona (SEK). These are often referred to as the G10 currencies.
- The key Financial derivatives are options and futures – these are complex leveraged derivative products that can magnify the risk as well as the reward:
In this section, we learned about the financial asset classes and their unique properties. Now, let's discuss the order types and exchange matching algorithms of modern electronic trading exchanges.
Going through the modern electronic trading exchange
The first trading exchange was the Amsterdam Stock Exchange, which began in 1602. Here, the trading happened in person. The applications of technology to trading included using pigeons, telegraph systems, Morse code, telephones, computer terminals, and nowadays, high-speed computer networks and state-of-the-art computers. With the passage of time, the trading microstructure has evolved into the order types and matching algorithms that we are used to today.
Financial trading strategies employ a variety of different order types, and some of the most common ones include Market orders, Market with Price Protection orders, Immediate-Or-Cancel (IOC) orders, Fill and Kill (FAK) orders, Good-'Till-Day (GTD) orders, Good-'Till-Canceled (GTC) orders, Stop orders, and Iceberg orders.
These orders will execute against all available orders on the opposite side at the order's price until all the quantity asked for is executed. If it runs out of available liquidity to match against, it can be configured to sit in the order book or expire. Sitting in the book means the order becomes a resting order that is added to the book for other participants to trade against. To expire means that the remaining order quantity is canceled instead of being added to the book so that new orders cannot match against the remaining quantity.
So, for instance, a buy market order will match against all sell orders sitting in the book from the best price to the worst price until the entire market order is executed.
These orders may suffer from extreme slippage, which is defined as the difference in the executed order's price and the market price at the time the order was sent.
IOC orders cannot execute at prices worse than what they were sent for, which means buy orders cannot execute higher than the order's price, and sell orders cannot execute lower than the order's price. This concept is known as limit price since that price is limited to the worst price the order can execute at.
- The entire quantity on the IOC order is executed.
- The price of the passive order on the other side is worse than the IOC order's price.
- The IOC order is partially executed, and the remaining quantity expires.
An IOC order that is sent at a price better than the best available order on the other side (that is, the buy order is lower than the best offer price, or the sell order is higher than the best bid price) does not execute at all and just expires.
Limit order books
The exchange accepts order requests from all market participants and maintains them in a limit order book. Limit order books are a view into all the market participant's visible orders available at the exchange at any point in time.
Buy orders (or bids) are arranged from the highest price (that is, the best price) to the lowest price (that is, the worst price), and Ask orders (that is, asks or offers) are arranged from the lowest price (that is, the best price) to the highest price (that is, the lowest price).
The highest bid prices are considered the best bid prices because buy orders with the highest buy prices are the first to be matched, and the reverse is true for ask prices, that is, sell orders with the lowest sell prices match first.
Orders on the same side and at the same price level are arranged in the First-In-First-Out (FIFO) order, which is also known as priority order – orders with better priority are ahead of orders with lower priority because the better priority orders have reached the exchange before the others. All else being equal (that is, the same order side, price, and quantity), orders with better priority will execute before orders with worse priority.
The exchange matching engine
The matching engine at the electronic trading exchange performs the matching of orders using exchange matching algorithms. The process of matching entails checking all active orders entered by market participants and matching the orders that cross each other in price until there are no unmatched orders that could be matched – so, buy orders with prices at or above other sell orders match against them, and the converse is true as well, that is, sell orders with prices at or below other buy orders match against them. The remaining orders remain in the exchange matching book until a new order flow comes in, leading to new matches if possible.
In the FIFO matching algorithm, orders are matched first – from the best price to the worst price. So, an incoming buy order tries to match against resting sell orders (that is, asks/offers) from the lowest price to the highest price, and an incoming sell order tries to match against resting buy orders (that is, bids) from the highest price to the lowest price. New incoming orders are matched with a specific sequence of rules. For incoming aggressive orders (orders with prices better than the best price level on the other side), they are matched on a first-come-first-serve basis, that is, orders that show up first, take out liquidity and, therefore, match first. For passive resting orders that sit in the book, since they do not execute immediately, they are assigned based on priority on a first-come-first-serve basis. That means orders on the same side and at the same price are arranged based on the time it takes them to reach the matching engine; orders with earlier times are assigned better priority and, therefore, are eligible to be matched first.
In this section, we learned about the order types and exchange matching engine of the modern electronic trading exchange. Now, let's proceed toward the next section, where we will learn about the components of an algorithmic trading system.
Understanding the components of an algorithmic trading system
The core infrastructure of an algorithmic trading system
A core infrastructure handles communication with the exchange using market data and order entry protocols. It is responsible for relaying information between the exchange and the algorithmic trading strategy.
Its components are also responsible for capturing, timestamping, and recording historical market data, which is one of the top priorities for algorithmic trading strategy research and development.
The core infrastructure also includes a layer of risk management components to guard the trading system against erroneous or runaway trading strategies to prevent catastrophic outcomes.
Finally, some of the less glamorous tasks involved in the algorithmic trading business, such as back-office reconciliation tasks, compliance, and more, are also addressed by the core infrastructure.
Market participants have trading servers that receive these market data updates. While, technically, these trading servers can be anywhere in the world, modern algorithmic trading participants have their trading servers placed in a data center very close to the exchange matching engine. This is called a colocated or Direct Market Access (DMA) setup, which guarantees that participants receive market data updates as fast as possible by being as close to the matching engine as possible.
Once the market data update, which is communicated via exchange-provided market data protocols, is received by each market participant, they use software applications known as market data feed handlers to decode the market data updates and feed it to the algorithmic trading strategy on the client side.
Once the algorithmic trading strategy has digested the market data update, based on the intelligence developed in the strategy, it generates outgoing order flow. This can be the addition, modification, or cancellation of orders at specific prices and quantities.
The order requests are picked up by an, often, separate client component known as the order entry gateway. The order entry gateway component communicates with the exchange using order entry protocols to translate this request from the strategy to the exchange. Notifications in response to these order requests are sent by the electronic exchange back to the order entry gateway. Again, in response to this order flow by a specific market participant, the matching engine generates market data updates, therefore going back to the beginning of this information flow loop.
The quantitative infrastructure of an algorithmic trading system
A quantitative infrastructure builds on top of the platform provided by the core infrastructure and, essentially, tries to build components on top to research, develop, and effectively leverage the platform to generate revenue.
The research framework includes components such as backtesting, Post-Trade Analytics (PTA), and signal research components.
Other components that are used in research as well as deployed to live markets would be limit order books, predictive signals, and signal aggregators, which combine individual signals into a composite signal.
Finally, trading strategies themselves have a risk management component to manage and mitigate risk across different strategies and instruments.
Profitable trading ideas have always been driven by human intuition developed from observing the patterns of market conditions and the outcomes of various strategies under different market conditions.
For example, historically, it has been observed that large market rallies generate investor confidence, causing more market participants to jump in and buy more; therefore, recursively causing larger rallies. Conversely, large drops in market prices scare off participants invested in the trading instrument, causing them to sell their holdings and exacerbate the drop in prices. These intuitive ideas backed by observations in markets led to the idea of trend-following strategies.
It has also been observed that short-term volatile moves in either direction often tend to revert to their previous market price, leading to mean reversion-based speculators and trading strategies. Similarly, historical observations that similar product prices move together, which also makes intuitive sense have led to the generation of correlation and collinearity-based trading strategies such as statistical arbitrage and pairs trading strategies.
Since every market participant uses different trading strategies, the final market prices reflect the majority of market participants. Trading strategies whose views align with the majority of market participants are profitable under those conditions. A single trading strategy generally cannot be profitable 100 percent of the time, so sophisticated participants have a portfolio of trading strategies.
Trading signals are what drive algorithmic trading strategy decisions. Signals are well-defined pieces of intelligence derived from market data, alternative data (such as news, social media feeds, and more), and even our own order flow, which is designed to predict certain market conditions in the future.
Signals almost always originate from some intuitive idea and observation of certain market conditions and/or strategy performance. Often, most quantitative developers spend most of their time researching and developing new trading signals to improve profitability under different market conditions and to improve the algorithmic trading strategy overall.
The trading signal research framework
A lot of man-hours are invested in researching and discovering new signals to improve trading performance. To do that in a systematic, efficient, scalable, and scientific manner, often, the first step is to build a good signal research framework.
This framework has subcomponents for the following:
- Data generation is based on the signal we are trying to build and the market conditions/objectives we are trying to capture/predict. In most real-world algorithmic trading, we use tick data, which is data that represents every single event in the market. As you might imagine, there are a lot of events every day and this leads to massive amounts of data, so you also need to think about subsampling the data received. Subsampling has several advantages, such as reducing the scale of data, eliminating the noise/spurious patches of data, and highlighting interesting/important data.
- The evaluation of the predictive power or usefulness of features concerning the market objective that they are trying to capture/predict.
- The maintenance of historical results of signals under different market conditions along with tuning existing signals to changing market conditions.
A very simple aggregation method would be to take the average of all the input signals and output the average as the composite signal value.
Readers familiar with statistical learning concepts of ensemble learning – bagging and boosting – might be able to spot a similarity between those learning models and signal aggregators. Oftentimes signal aggregators are just statistical models (regression/classification) where the input signals are just features used to predict the same final market objective.
The execution of strategies
Slippage is the difference between market prices and execution prices and is caused due to the latency experienced by an order to get to the market before prices change as well as the size of an order causing a change in price once it hits the market.
The quality of execution strategies employed in an algorithmic trading strategy can significantly improve/degrade the performance of profitable trading signals.
Limit order books
Limit order books are built both in the exchange match engine and during the algorithmic trading strategies, although not necessarily all algorithmic trading signals/strategies require the entire limit order book.
Sophisticated algorithmic trading strategies can build a lot more intelligence into their limit order books. We can detect and track our own orders in the limit book and understand, given our priority, what our probability of getting our orders executed is. We can also use this information to execute our own orders even before the order entry gateway gets the execution notification from the exchange and leverage that ability to our advantage. Other more complex microstructure features such as detecting icebergs, detecting stop orders, detecting large in-flow or out-flow of buy/sell orders, and more are all possible with limit order books and market data updates at a lot of electronic trading exchanges.
Position and PnL management
Let's explore how positions and PnLs evolve as a trading strategy opens and closes long and short positions by executing trades.
From a flat position, if a buy order executes, then it is referred to as having a long position. If a strategy has a long position and prices increase, the position profits from the price increase. PnL also increases in this scenario, that is, profit increases (or loss decreases). Conversely, if a strategy has a long position and prices decrease, the position loses from the price decrease. PnL decreases in this scenario, for example, the profit decreases (or the loss increases).
From a flat position, if a sell order is executed then it is referred to as having a short position. If a strategy has a short position and prices decrease, the position profits from the price decrease. PnL increases in this scenario. Conversely, if a strategy has a short position and prices increase, then PnL decreases. PnL for a position that is still open is referred to as unrealized PnL since PnL changes with price changes as long as the position remains open.
A long position is closed by selling an amount of the instrument equivalent to the position size. This is referred to as closing or flattening a position, and, at this point, PnL is referred to as realized PnL since it no longer changes as price changes since the position is closed.
Similarly, short positions are closed by buying the same amount as the position size.
When a long or short position is composed of buys or sells at multiple prices with different sizes, then the average price of the position is computed by computing the Volume Weighted Average Price (VWAP), which is the price of each execution weighted by the quantity executed at each price. Marking to market refers to taking the VWAP of a position and comparing that to the current market price to get a sense of how profitable or lossy a certain long/short position is.
A backtester uses historically recorded market data and simulation components to simulate the behavior and performance of an algorithmic trading strategy as if it were deployed to live markets in the past. Algorithmic trading strategies are developed and optimized using a backtester until the strategy performance is in line with expectations.
Backtesters are complex components that need to model market data flow, client-side and exchange-side latencies in software and network components, accurate FIFO priorities, slippage, fees, and market impact from strategy order flow (that is, how would other market participants react to a strategy's order flow being added to the market data flow) to generate accurate strategy and portfolio performance statistics.
PTA is performed on trades generated by an algorithmic trading strategy run in simulation or live markets.
When applied to trades generated from live trading strategies, PTA can be used to understand strategy performance in live markets as well as compare and assert that live trading performance is in line with simulated strategy performance expectations.
Bad risk management cannot only turn a profitable trading strategy into a non-profitable one but can also put the investor's entire capital at risk due to uncontrolled strategy losses, malfunctioning strategies, and possible regulatory repercussions.
In this chapter, we have learned when algorithmic trading has an advantage over manual trading, what the financial asset classes are, the most used order types, what the limit order book is, and how the orders are matched by the financial exchange.
We have also discussed the key components of an algorithmic trading system – the core infrastructure and the quantitative infrastructure which consists of trading strategies, their execution, limit order book, position, PnL management, backtesting, post-trade analytics, and risk management.
In the next chapter, we will discuss the value of Python when it comes to algorithmic trading.