Contents
- What is Quantitative Trading?
- History of Quantification
- Pros and cons of Quant Trading
- Strategy Identification
- Quantitative Trading Strategies
- Strategy Backtesting
- Execution Systems
- Risk Management
- Quantitative vs Algorithmic Trading
- Quantitative Trading, Developing Automated Trading Systems
- Conclusion
- FAQs
What is Quantitative Trading?
Quantitative trading, also referred to as Quant trading is a type of marketing strategy that focuses on the use of quantitative analysis to identify trading opportunities. This use of quantitative analysis simply means the trading opportunities would have to be identified using research, measurement, statistical, and mathematical models.
As a trader, it is essential to have a good knowledge of what Quantitative trading entails. Times are changing hence there has been a shift from the use of quantitative trading strategies by financial institutions and hedge funds to private Individuals referred to as Quant traders.
To own a successful quantitative trading business, always remember that four essential components are needed. These components include strategy identification, strategy backtesting, execution systems, and risk management. These components are step by step so one cannot function without the other.
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Also Read: What Is Position Trading
History of Quantification
To fully understand any model, it is always important to learn the historical data of what brought about the strategy as well as how it was developed. Quantitative trading history dates back to the 1970s. This was the period investors in the financial market started using algorithmic trading and mathematical models to purchase stocks and bonds which was achievable due to the thesis developed by Harry Markowitz.
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Harry Markowitz published a doctoral thesis in the Journal of Finance in which he applied the use of statistical models and mathematical computations to show how variables can be computed for a stock portfolio. This paper by Markowitz was written in 1952 but it wasn’t until the 1970s that quant traders began using these strategies in their investment firms. This is why most quant traders often refer to Harry Markowitz as the father of quantitative trading.
The designated order turnaround (DOT) system developed in the 70s is also a notable part of quant trading because it made it possible for the New York Stock Exchange (NYSE) to take orders electronically for the first time, and this development also provided real-time market data to traders using Bloomberg terminals.
The use of quantitative trading systems kept rising as years went by and in 2009, 60% of US stock trades were executed by HFT firms, who relied on quantitative trading techniques and mathematical models to back their strategies.
It is however important to note that although brokerage firms and investment banks were making use of quantitative models, quantitative trading was still widely criticized by major financial institutions because some said the use of automated systems was not 100% accurate in the purchase of stocks.
Despite this, quant trading has continued to grow. Institutional investors and hedge funds still rely on Quantitative trading strategies for trading decisions.
Pros and cons of Quant Trading
No marketing strategy has pros without cons. Quantitative trading is not an exception to this. One of the major advantages of quantitative trading is that it involves the use of purely mathematical models thereby eliminating bias and emotional decision-making and instead focusing on available data.
Asides from this, quantitative trading is faster. It limits the problem of time spent analyzing the number of financial market data available. This is because, without quantitative trading, the quant trader becomes biased and would find it hard to make decisions without involving emotions but with the help of quantitative trading work, this problem can be easily solved.
Quantitative trading is not a perfect trading system without risks, however. A major disadvantage or con of using quantitative trading is that it has limitations because it works best when other quantitative traders don’t know if the strategy because if they do, the trading strategy loses its effectiveness.
Additionally, the quantitative trader constantly has to cope with the ever-changing financial markets conditions. A profitable trading strategy today can result in losing money rapidly the next day. It is highly unpredictable.
To be a successful quant trader, you would need a detailed knowledge of statistical arbitrage and the quantitative trading model and strategies.
Strategy Identification
Quantitative trading consists of four major components. One of the major components is strategy identification. Here, the trader seeks to identify trading opportunities by exploiting a hedge and also figuring out how to decide the best trading frequency.
The process of identifying the best strategy for quant trading starts with research. It is only through research that some key considerations can be factored in. These key considerations involve finding a strategy, grouping the strategy into the right portfolio, using data mining to test the selected strategies, and optimizing such a strategy to get higher returns.
A good way of finding strategies with high returns or good profits is through multiple sources. Trade journal is a good example of this because they contain trading strategies required by a quantitative researcher. The most popular strategies include mean reversion, statistical arbitrage amongst others.
It is however important to note an essential part of quant trading and this is known as the frequency of the trading. The frequency could be high or low depending on the technology risk. High-frequency trading focuses on holding assets intraday while low-frequency trading hinges on holding assets longer than a trading day.
Ultra-high-frequency trading strategies on the other hand involve holding assets for seconds or milliseconds. Once a strategy has been identified, historical databases must be tested to ensure profitability. The process of doing this is known as backtesting,
Quantitative Trading Strategies
There are several Quant strategies every quant trader should be aware of but it would be impossible to explain them all. Here are five quantitative strategies to adopt to guide your trading decisions.
1. Mean reversion strategy
Mean reversion strategy has to do with using advantage of the dynamic changes that occur when pricing a stock or security. This trading strategy capitalizes on the theory that asset prices will always return to average levels anytime there is an extreme price move.
It gets its name from the price fluctuation around the mean price but despite the fluctuation, the price always returns to its average price. This strategy is effective because it allows the quant trader to profit anytime the price goes up and also save when the price is abnormally low. The only downside to this strategy is that it doesn’t work in all financial markets.
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The way it works is that the Quantitative trader writes codes which makes it possible to find markets that have a long-standing mean. Once it’s found, it highlights it anytime it diverges from it. If it diverges up, the system will calculate the probability of a profitable short trade. If it diverges down, it will do the same for a long positio
2. Statistical arbitrage
This is a trading strategy based on the mean reversion strategy. This is a profitable strategy that relies on the fact that anytime a group of stocks are similar, they will always have similar performance in the financial markets and so when there is a stock that doesn’t perform like these group of stocks, it shows that there is a huge likelihood of profits.
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For this strategy to work, the average price of each stock in the group of stocks subject to similar market conditions would have to be calculated. After this, a table would have to be created showcasing stocks that underperformed the average price and stocks that over performed above the average price. Anytime any of these stocks revert to the average price, then it means that both positions are closed for profits.
As with every strategy, the disadvantage to statistical arbitrage is that sometimes, there are factors that can be applied to an individual asset that cannot be applied to the rest of the group. Due to this, long-term deviations occur which prevents the reversion of the average price. As a risk management strategy, the trader would have to use HFT algorithms to exploit extremely short-term market inefficiencies.
3. Trend Following
also referred to as Momentum trading is a very straightforward strategy. As the name suggests, it uses emotional trends to identify a significant market movement once it starts and rides it until it ends. A very good example showcasing this strategy is monitoring the sentiment among traders at a major financial institution. Through this monitoring, a model can then be developed to predict the period institutional investors would likely buy or sell a stock in large quantity.
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4. Algorithmic pattern recognition
has to do with creating a model that can identify when large institutional firms or institutional traders plan to make a large trade, with the sole purpose of trading against them. It can also be referred to as high-tech front running. Institutional trading is usually done through algorithms and with this strategy, the custom execution patterns of institutional investors are easily recognized and isolated.
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It is important to note that the Algorithm pattern recognition strategy is often used by HFT firms and they help market makers get ahead of sales.
5. ETF rule trading strategy.
ETF is an acronym that stands for Exchange Traded Funds. The ETF rule trading strategy is based on profiting from the relationship between an index and the ETFs that track it. Anytime a new stock is added to an index, the ETFs representing that index often have to buy that stock as well. By understanding the rules of index additions and subtractions and utilizing ultra-fast execution systems, quant funds can capitalize on this rule and trade ahead of forced buying.
Strategy Backtesting
Strategy Backtesting is the second component of Quantitative trading. It is very essential because it involves that the strategy that has been identified is profitable with historical and non-study data. After the best strategy has been identified in the quantitative trading system, the trader gains knowledge of the strategies to use based on historical and out-of-sample data, however, there is a need to crosscheck how these strategies would work in the real world. This is where strategy backtesting comes into place.
For Backtesting to be successful, the following is essential: availability and cleanliness of historical data, imputing realistic transaction costs, and deciding the best efficient backtesting platform. Every backtesting procedure uses a software platform. Good examples of these include Excel, Platform, Trade station, Python amongst others.
Obtaining the historical data can costs sometimes depend on the quality, depth, and timeliness of the data so for traders that are new to quantitative trading (especially retail investors), it is advisable to use the free data set provided by Yahoo Finance.
Once a strategy has been backtested and is deemed to be free of biases (although backtesting cannot be completely guaranteed to be successful because it involves a lot of biases even though these biases are eliminated as much as possible) and has a good Sharpe (this is the average of the excess returns divided by the standard deviation of excess returns which is the return of the strategy above a pre-determined benchmark), with minimized drawdowns ( the largest peak-to-trough drop in the account equity curve over a particular time), the next course of action is to build an execution system.
Also Read: Best Forex Strategy For Consistent Profits
Execution Systems
To execute means to act therefore an execution system involves the means brokers use to send and carry out a list of trades after the strategy has been developed and backtested. It is an essential component of Quantitative trading.
The execution systems require technical indicators which traders have to take note of. These technical indicators include the interface to the brokerage, minimization of transaction costs, and divergence of performance of the live system from back-tested performance. The execution system is the only way brokers send and execute the trading list generated by the strategy. This could be carried out manually, semi-automatic, or fully automatic. Most Low-Frequency Tradings (LFT) techniques use manual or semimanual techniques.
In execution system, it is important to consider transaction cost minimization and this involves commissions (tax collected by brokerage), slippage (the difference between the intended filled order versus the actual filled order), and spread (the difference between the asking price of the security that is being traded).
The divergence of strategy performance from the backtested performance can however serve as an issue that affects the execution system because of the bias that occurs when backtesting amongst other reasons. Also, another hindrance is the fact that there may be bugs in the system as well as the trading strategies which don’t show during backtesting but come up during live trading.
Risk management
Risk management is an essential component of quantitative trading because it helps to weigh factors that could affect the success of the trading. One of such risks that could occur is bias during backtesting, as well as the risks of a computer system malfunction due to being offline. Also, there is the brokers’ risk of which brokers can go bankrupt.
Asides from this, the quantitative trader also needs to factor in the technological risk of hard disks failing in the final component of quantitative trading. Other factors to consider which have not been mentioned include optimal capital allocation and dealing with one’s psychological profile.
Quantitative vs Algorithmic Trading
Most times, people tend to mix up Quantitative trading with Algorithmic trading. The two are very similar but both have distinctive features. The major difference between the two however is that Quantitative trading techniques involve using a mathematical model to identify trading opportunities.
While an Algorithmic trading business involves the use of automated trading systems or traditional technical analysis to analyse chart patterns and find new positions. Due to this, algorithmic trading can automate trading decisions and business making it faster and more accurate than Quant.
Quantitative trading, Developing Automated Trading Systems
Quantitative trading has to do with automated systems so quantitative traders are required to have an idea of the elements of automated trading systems which include:
1 finding the right market to trade. This has to do with selecting appropriate markets and instruments to trade. Once this has been decided, you would need to find historical data for the instruments that have been selected. Some of these instruments include stocks, options, and futures.
2 building the required features and trading signal. The two work together because to be able to identify a trading signal, the required features would need to be built. Features here mean moving averages or ratios of the price of data.
The signals could be open, close, high, or low and can be combined to build new features. You would also need a knowledge of the trading strategies mentioned above to successfully build these required features and trading signals.
3 deciding the best trade execution strategy as well as the trading costs because trading costs and strategy can determine if it would be a profitable strategy or not.
4 Backtesting and performance metrics to observe historical data on how the strategy worked in the past. Backtesting also help to optimize systems for trading. Te secret
Conclusion
Quantitative trading is interesting but very complex to learn. To be successful, quantitative traders are required to have a good knowledge of mathematical computations. You would also need to broaden your programming knowledge as well as do a lot of machine learning. Now that you have an idea of what quantitative trading is about, you’ll need to read more books and take more courses on quantitative trading before starting live trading if you’re a beginner.
Once you have a good background in statistics and econometrics, you can start data collection, backtesting, and other components and strategies of Quantitative trading before you are ready to own quantitative trading business.
The financial markets can be very unpredictable, traders and investors should make use of proper risk management when applying quantitative trading techniques. Risk tolerance and risk management should be properly implemented.
FAQs
What is Quantitative trading in simple terms?
Quantitative trading simply means making use of computer algorithms, statistics, and programming to identify the best and available trading opportunities. It involves extensive research work on historical data to be able to identify these profitable opportunities.
What is backtesting in trading?
Backtesting is a component of quantitative trading that involves checking the extent of how well a strategy can perform ex-post. It does so by evaluating the viability of a trading strategy using historical and out-of-sample data.
Is there a difference between Quantitative trading and Algorithm trading?
The major difference between Quantitative trading and algorithmic trading is that Quantitative trading uses advanced mathematical methods while Algorithmic trading relies on more traditional analysis for example chart analysis.
What do I need to know to start Quantitative trading?
You would need extensive knowledge of programming, statistics, how an automated trading system works, the components of quantitative trading, and the available quantitative strategies you can learn. Read books and take online courses on quantitative trading and find out more about risk management and execution system
