With Quantitative Trading, you can purchase and sell stock market assets using only mathematical and numeric data without any emotion. Quantitative Trading (or 'Quants') is the process of using mathematical calculator systems, over your feelings, to profit from stock market investments more effectively. By using automated systems to trade, individuals will typically earn a greater return on their investment by using an organised, systematic method than by relying solely on their instincts or feelings.
The following Guide is designed to give you a better understanding of Quantitative Trading. It will explain how Quantitative Trading Systems work, the Strategies used within these systems, and how you can begin your pathway towards becoming a Quantitative Trader.
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Core Components of a Quantitative Trading System
In total, there are four interconnected components that make up a successful Quantitative Trading System and create a disciplined, systematic trading cycle.
1. Strategy Identification (The Alpha)
In order to identify your strategy (or alpha), you need to first identify statistically significant market imbalances or trading opportunities. To do this, you would first need to form a Hypothesis.
Sources of Data: Quant Traders will typically collect a vast amount of historical price and volume data and will often utilise other macro-economic data, as well as alternative data sources such as satellite data to form an opinion on their trades.
Developing the Model: After formulating a hypothesis, a Quant Trader will develop/create a mathematical model to define when to purchase, sell or hold each asset. Rules on how to implement the model are determined using statistical and quantitative finance techniques.
2. Strategy Backtesting (The Validation)
Prior to deploying funds, you will want to conduct thorough historical testing of your strategy. Backtesting shows whether or not the strategy would have achieved profitability in the past.
The objective is to evaluate the performance of the strategy using metrics from the past, such as annualised return, maximum drawdown and Sharpe ratio.
The risk of excessive optimisation. The major concern is the potential for overfitting, i.e. developing a model that fits perfectly with past data (in the backtest) but fails in real-time trading. Quantitative analysts mitigate overfitting through the use of out-of-sample data and designing models for simplicity.
If you want to learn more about the subject of validation, check out our article on Backtest of Trading Strategies.
3. Execution System (The Automation)
The next step after validating your strategies is to have them implemented into an automated trading system, also known as 'Algorithmic Trading System' or 'Algo Trade System,' so that all execution will take place automatically.
The purpose of Algorithms: The programs used in the trading systems communicate directly with the exchanges, using Application Programming Interfaces (APIs), to generate orders that are automatically submitted when triggered by the signals produced by the trading models.
Latency Troubles: When using algorithms for trading strategies such as High Frequency Trading (HFT), keeping the latency at a minimum is extremely important.
4. Risk Management (The Control)
This may be the most important aspect. The risk models dictate how much you can risk and what your potential loss could be.
The Key Controls for quant trading are to develop position sizing rules, set up automatic stop-losses and manage model risk (what happens when the underlying market fundamentals change and the model no longer works).
The importance of having proper risk controls is critical.
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Quantitative Trading Strategies
There are several categories of quantitative trading strategies based on the behaviour of the market.
Mean Reversion:
As the name suggests, the idea behind mean reversion is that asset prices tend to return to their historical mean once they deviate significantly from their mean.
As an example, statistical arbitrage (stat arb) and pairs trading use statistically correlated assets and provide a way to capitalise when the price relationships temporarily diverge.
Trend Following / Momentum:
The idea behind trend following is to buy assets that have strong upward momentum and sell those that have downward momentum, with the expectation that the momentum will continue.
Some of the tools typically used for this style of trading are technical indicators such as moving averages or the relative strength index (RSI).
Market Making:
The market maker will buy and sell at the same time, which is how they provide liquidity and make a profit by taking advantage of the narrow bid-ask spread. This is an essential component of how high-frequency traders operate.
Volatility Strategies:
Volatility trading involves trading based on the expected size of price fluctuations without regard to the direction of the price. Most volatility trading is done in the derivatives market.
Machine Learning (ML) & AI:
The ability of advanced algorithms (like Neural Networks) to work with Large nonlinear data Sets helps identify patterns which are often Too Complex for Traditional Linear Models. This represents the new frontier of Quantitative Finance.
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Quant Trading vs. Traditional Trading
The Major Difference Between Quantitative Trading and Traditional Discretionary Trading is How Decisions About Buying/Selling are made - i.e. Quantitative Trading is more about Systematic Logic and Less About Human Intuition & Decision Making.
Key Advantages and Challenges
Some Key Advantages of Quantitative Trading.
Objectiveness - Quantitative Trading Removes Human Emotion from the Trading Process (Fear/Greed), Leading to More Disciplined Responses to Market Movement.
Scalability - One Quantitative Trading Model Can Be Used to Trade Hundreds of Assets and/or Markets Simultaneously.
Speed - With the Use of Algorithms, Trades Can Be Made at a Speed That No Human Could Achieve. We can capture short-lived opportunities before they are gone.
Some Key Challenges of Quant Trading.
Model Failure - Quantitative Models are Built with Historical Data. Many Times, Such Models Fail Completely During Black Swan Events and/or During Sudden Market Regime Changes.
High Data & Technology Costs - For Quantitative Trading to Be Successful, It Requires a Well-Constructed, Low Latency Infrastructure Along with Access to Clean/High Quality Market Data.
Complexity of Quantitative Trading and Quantitative Analysis in General - The Field of Quantitative Analysis, as well as the Field of Programming, is a highly Specified Discipline. The Language That is Most Often Used for Quant Trading is Python.
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How to Start Quantitative Trading?
The good news is that the barriers to entry are lower than ever, especially for those interested in algorithmic trading.
1. Master the Required Skills
Math & Statistics - Learn the principles of probability, regression analysis & time series analysis.
Programming - Python is the preferred programming language in the industry. It has a large selection of data analysis and manipulation libraries, known as NumPy, Pandas & SciPy, to facilitate the creation of a data analysis and modelling system.
Finance Knowledge - Gain an understanding of how financial markets operate, the types of financial products available (e.g., Derivatives) & general concepts of equity markets.
2. Learn to Backtest Effectively
You should first choose a simple, well-documented strategy (such as a basic moving average crossover) to start testing your strategies via back testing. After creating the strategy, implement it into a programming language, like Python, & then run an extensive back test on the strategy that includes accounting for slippage & transaction fees.
3. Practice, Practice, Practice
A good method to continue improving your strategy execution is through the use of a paper trading application. Paper trading enables you to test the execution of your strategy without risking any capital. It allows you to learn how to fix your code execution system without having to worry about making costly logistical mistakes.
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Conclusion
Quant trading represents the future of finance and has transformed the modern trading floor from one of verbal bids to that of advanced computing systems. Quant traders apply disciplined, systematic processes to replace the feeling of human emotion through the application of complicated mathematical models and rigorous statistical analysis, along with an algorithmically generated execution of orders. The quantitative trader emerges from this combination of qualified trading skill and a data analysis environment.
The time is now to begin establishing your analysis of the data-driven trading strategy. Through a deep understanding of the Analysis of Quantitative Finance and a consistently increasing knowledge of the multiple variables that impact this analysis, you will build your quantitative trading knowledge.
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