Quantitative Trading Strategy
Quantitative trading is the use of sophisticated mathematical and statistical models and computation to identify profitable opportunities in the financial markets. Quantitative trading is known to implement advanced modern technologies on huge databases so as to provide comprehensive analyses of the opportunities present in the market. For quantitative british traders, price and volume are the most important variables, and the bigger the dataset, the better.
Trading has always been likened to forecasting the weather, and if that is the case, quantitative traders in UK are the equivalent of modern-day real-time meteorologists using the latest state-of-the-art equipment to determine the weather of any particular place at any given time. Granted, the results may not be accurate all the time, but the success rate is usually more than respectable, and any predictions will be based on both a huge historical and present database.
How Quantitative Trading Works
Quantitative trading is largely data-driven and uses purely statistical and mathematical models to establish the probability of certain outcomes. It requires a lot of computational power to extensively research and it makes conclusive hypotheses out of numerous numerical data sets. This is why quantitative trading has, for a long time, been a preserve of top financial institutions and high-net-worth individuals. Nonetheless, in recent days it is increasingly being utilised by retail investors too.
An example of a quantitative model would be analysing the bullish pressure experienced on the McDonald’s Stock (MCD) on the NYSE during lunch hours. A quant would then develop a program to analyse this pattern over the entire history of the stock. If it is established that this pattern happens over 90% of the time, then the quantitative trading model developed will predict that the pattern will be repeated 90% of the time in the future.
Quantitative vs Algorithmic Trading
The idea of quantitative trading is to generate solid trade ideas purely by using mathematical models. A quant trader in UK will research, and analyse historical data, and then proceed to apply advanced mathematical and statistical models to pick out trading opportunities in the market. The trade ideas can then be executed manually or automatically in the market. As mentioned earlier, quantitative trading has always been more common with financial institutions because of the computational power it demands. But advancement in technology, especially in cloud computing resources, has opened the doors for average retail traders to also try their hands in this space.
In contrast, algorithmic trading involves the use of algorithms to pick out and take advantage of trading opportunities in the market. This basically means that algorithmic trading is turning a trading idea into a trading strategy using coded algorithms. Algorithms, therefore, serve the role of auto trading software. Algorithmic traders can automate all aspects of trading activity from market scanning and signal generation to order execution and market exit. There is no human intervention required at any stage.
Whereas quantitative traders utilise mathematical models to generate trading signals, algorithmic traders often use traditional technical analysis methods, such as candlestick patterns and a combination of technical indicators. Furthermore, quantitative traders apply sophisticated methods, but algorithmic traders can implement both simple and advanced strategies in the market. There is an obvious overlap between quantitative and algorithmic trading, but the subtle differences can play a significant part.
There is also HFT (high-frequency trading), where the idea is to take advantage of execution speed using top-notch technologies. Essentially, HFT seeks to gain a mechanical advantage in the market. HFT is principally a subset of quantitative trading, but it is very fast. Quantitative trading, however, isn’t bound to super high order execution speed. Instead, it can be slow, medium or fast. It is not unheard of for quantitative traders to place positions in the market that can last as long as a year.
Quantitative Trading Systems
Quants develop systems to help them find the best mathematical probabilities in the market. There are numerous different quantitative trading systems, but they all have 4 core components: Strategy, Backtesting, Execution and Risk Management.
This is essentially the research phase of a quantitative trading system. The type of strategy must suit the portfolio that the trader wants to apply. For instance, a stock trader may implement a medium-term strategy that will seek to take advantage of earnings and dividend reports, whereas a forex trader may apply a short-term strategy. The frequency of trading is an important aspect of quantitative trading. There are different types of strategies that can be developed, such as mean reversion, trend following or momentum trading. The idea of this phase is to gather all the necessary data required to optimise the strategy for maximum returns and minimal risk in the market. It is effectively turning a strategy into a mathematical model.
Backtesting is conducted to qualify the identified strategy. This is where the gathered data comes in handy. Backtesting involves applying the strategy to historical data to determine how reliable it would have performed in the market. Granted, success here is no guarantee of future performance, but it is a good indicator of the kind of returns the strategy can be expected to generate in the real market.
Backtesting allows for the strategy to be tweaked and optimised because it can expose inherent flaws. Flaws can be unpredictable drawdown levels or even high volatility in its performance levels. To achieve accurate backtesting results, the historical data must be of high quality, just like the software platform that is utilised.
Every trading system must have an execution element, which is how generated trade signals will be placed in the market. Execution can be manual (every detail is keyed in by the trader); semi-manual (one-click trading prompt); and automated (no human intervention needed). The key considerations for execution include trading costs (spreads, commissions or tax), slippage and broker interface. Good execution allows a trading system to operate at its optimal best, with the best prices achieved in the market at all times.
Trading financial markets is an inherently risky endeavour. Thus, an important component of quantitative trading systems is risk management. Risk is essentially anything that can interfere with the successful performance of a quantitative trading system. In the market, quants face different types of risk. There is, of course market risk, which means that price changes of underlying financial assets can be fast and dynamic such that losing trades are generated.
This kind of risk is what traders focus on most, and it can be mitigated by installing parameters such as stop losses, stake amount, trading times, tradable markets or even leverage level. But this is not the only risk quants are exposed to. There is also efficient capital allocation to diverse assets, technology risk, broker risk, and even personality risk (but this can be mitigated with automation). Like in any business, strict risk management in quantitative trading will keep you protected but still sufficiently open to numerous profitable opportunities for the long run.
Pros and Cons of Quantitative Trading
The major benefit of quantitative trading is that massive computational power gives investors access to broader opportunities in the market. Investors can trade numerous markets at the same times using multiple trading strategies without loss of quality or consistency. There will be no worry about not being able to monitor or track trading risks actively in the market. Quantitative trading also eliminates the risk of subjective trading in the market. The danger of human emotions and bias is eliminated by the use of mathematics in the trading activity.
Traders should find confidence in a trading system that has been thoroughly tested to ensure that it makes objective trading decisions in the market at all times. In an age where data flows freely, it is hard to keep tabs with how this can affect our portfolios. Quantitative trading ensures that there’s no need to monitor every single piece of data, while at the same time ensuring that every high probability trading opportunity is taken full advantage of in the fast and dynamic financial markets.
But there are also some downsides. Quantitative trading has mainly been a preserve of institutional british traders because of the high costs it requires to set up a reliable trading system. From research and quality data collection to testing and optimisation, developing a good quantitative trading system is a time consuming and capital-intensive endeavour. It also requires a great deal of mathematical and programming knowledge and skill, which the average retail investor does not always possess.
There are beginner-friendly templates around, but such solutions may not be enough. It is also worth noting that a quant trading system is as good as its creator. Automating a profitable strategy can enhance its performance, but it will be difficult to improve upon a mediocre strategy in a market that is perennially fast, dynamic and unpredictable.
Quantitative Trading Strategies
Here are some of the most common strategies that quantitative traders use:
This is a strategy designed to take advantage of the mispricing of assets in the market. Statistical arbitrage trades happen within a few seconds or minutes when an underlying exchange or service has failed to price an asset according to its true value. The trades must take a short time so that there is less exposure to market risks.
This is a strategy designed to make money out of the bid-ask spreads. Market making is simply buying the best bid and selling the best ask prices. Market makers thus act as wholesalers in the financial markets, with their prices reflecting demand and supply in the market. They are not necessarily brokerage firms, but large market participants that provide more liquid market for investors.
Mean reversion is based on the idea that extreme prices are rare cases and temporary and that prices of financial assets will always tend to have average prices in the long run. Defined deviations from the average prices represent an opportunity to trade the underlying market. A mean can be represented by a complex mathematical formula or simply the average of prices in the last X periods, like the Simple Moving Average. If prices are below the average price by the stated deviation, it is an invitation to buy; similarly, sell opportunities will come up when prices are above the average by a predetermined deviation.
These are strategies designed to take advantage of definitive direction in the market. In markets such as long-term bonds and selected stocks or indices, quantitative trading systems can determine when there is genuine upward or downward momentum so that they can ride the wave. Market direction can be forecasted using past pricing information and volume data; then the appropriate directional strategies can be implemented in the market.
Economic events, such as mergers and acquisitions in the stock market, can create short term opportunities that quantitative traders can exploit. In the case of M&A, the idea is usually to sell the stock of the buying company while simultaneously buying the company to be acquired. The danger of event arbitrage is that there is exposure to market risk in case a deal is called off, which can happen due to legal challenges or other complications.
This is a controversial trading technique that continues to date even though it is considered outlawed. It involves placing limit orders outside the bid-ask range with no intention of them being executed. For instance, if the price of EURUSD is 1.2000/1.2005, an order can be placed at 1.2010 for a buy position. This creates an illusion of increased market demand, but the disruptive algorithm will cancel the trade before it is executed. The intention would simply have been to get a higher selling price than the prevailing prices. This is usually associated with big institutions in some traditional markets, but in modern markets, it is difficult for a single entity to manipulate prices.