Quantitative trading is gaining significant traction. A recent study has shown that over 60% of the derivatives trading volume is taking place through algorithms. As quantitative trading becomes the norm, it is essential to understand what it is, how it works, and the various trading strategies employed in this approach.
What is Quantitative Trading?
Quantitative trading, also known as “quant trading”, is a different approach to trading compared to traditional methods. Quant trading involves the use of mathematical models, algorithms, and statistics to make trading decisions.
Instead of relying on chart patterns, quant trading makes use of data and logic. The models are built to analyse large amounts of data – such as price, volume, and margins to predict the market movement and execute the trades. In quant trading, emotion is replaced by data, and algorithms help lower the burden of manual trading.
For example, D. E. Shaw & Co. is an international investment management firm known for their quantitative trading strategies. They use algorithms to analyse the market, and predict short-term movements in the stock price. The results are then used to decide trades, which are executed at high speeds through automation.
2. How Quantitative Trading Works
Quantitative Trading is a systematic process that follows a step-by-step routine. Here is how it works:
2.1 Data inputs: price, volume, alternative datasets
The first step in quantitative trading is collection of data. Data is the driving force behind every model. Clean and accurate data is essential for consistent results in quant trading.
The data on the stock’s price and trading volume is collected to understand its market value and trading frequency. Apart from this, data on the following is collected:
- Macroeconomic indicators such as GDP, inflation, per capita income,etc.
- Market sentiment
- Company fundamentals like liquidity ratio, profitability, financial leverage, etc.
2.2 Model building & statistical analysis
The data inputs are analysed to identify different patterns and logics. Some of the techniques used for analysis are:
- Linear Regression: To understand the relationship between two variables, how one variable affects the other
- Time-series analysis: Making use of the previous data in order to forecast future movements
- Machine Learning Models: To capture non-linear patterns, as in real-world use cases, all data sets may not follow a linear relationship
The insights from the analysis are used to formulate a model according to the trading strategy.
2.3 Backtesting and validation
After the model is built, we now need to check if it is actually working. For this, the model is run on historical data to see how it would have performed in the past. This is known as a backtesting strategy.
The model is validated to see if it is generating consistent returns. A well-built model is essential for the quant trading strategy to work effectively. However, a well-performing backtest doesn’t always guarantee real-world success due to changing market conditions.
2.4 Execution & automation
After getting validation, the model is converted into a fully automated system connected to a brokerage platform. Automation allows the model’s instructions to be carried out at high speeds across various markets.
Automation also helps in managing the transaction costs and latency, which makes trading more precise and scalable.
3. Quantitative Trading vs Algorithmic Trading
The two terms, quantitative trading and algorithmic trading, are often used interchangeably. But they differ on the following parameters:
| Aspect | Quantitative Trading | Algorithmic Trading |
| Focus | Using mathematical models and statistics to find out trading opportunities | Automating the execution of trades |
| Decision Basis | Based on data analysis, probability and statistics | Based on pre-set rules and logic |
| Goal | Identification of trade opportunities | Efficiency in trade execution |
| Key Skills | Data analysis and modelling | Programming and automation |
4. Core Strategies in Quantitative Trading
There are a variety of strategies in quantitative trading. Some of the most common ones are:
4.1 Trend-following / momentum models
It follows the approach that if the stocks have been following a trend, they will most likely continue their momentum for some time. Tools such as moving averages and price momentum indicators are used in this strategy.
A trend following strategy is easier to understand and execute, making it suitable for retail investors.
Example: The quant model is programmed to buy Adani Green shares after the stock breaks its resistance level, supported by consecutive weeks of positive performance. It will continue to ride the momentum until it weakens and then take an exit to capture the profit.
4.2 Statistical arbitrage / mean reversion
This strategy is based on the belief that the prices revert to their historical averages or ‘means’ after any significant deviations. This strategy requires strong statistical and programming knowledge to calculate correlations and co-integrations.
A common form of “mean reversion” is done with two highly correlated stocks. It involves shorting the overperforming stock and buying the underperforming one. When prices eventually correct, they generate profits for the trader.
Example: HDFC bank and ICICI bank are highly correlated. If the price of HDFC rises abnormally, HDFC shares are shorted and ICICI shares are bought. When prices realign, the investor earns profit.
4.3 High-frequency trading (HFT) & microstructure
HFT strategies focus on profiting from extremely small changes in price. Positions in HFT are held for seconds or even microseconds. This strategy exploits the tiny price differences using powerful computers and algorithms to execute the trade.
It requires the use of advanced technology outside the reach of retail investors, making it suitable for institutions.
The tiny profits are made in enormous volumes, showcasing the edge quantitative trading has over traditional methods.
Example: A HFT algorithm finds a price gap of ₹0.50 between NSE and BSE for MRF shares. It quickly executes a trade of 10,000 shares to capture a profit of ₹5,000.
5. Advantages and Disadvantages
The advantages of quantitative trading are:
Consistency: The models stick to their inputs and algorithms to deliver consistent returns, eliminating the aspect of human error.
Efficiency: Thousands of trades can be made within a second, which makes trading highly efficient.
Backtesting: The models can be tested on previous data before using them for real trading.
Scalability: Once the model is validated, it can be scaled to deliver large amounts of trade, far beyond an individual trader’s capacity.
The disadvantages of quantitative trading are:
Data Dependency: Subpar data leads to poor models and hampers efficiency.
Complexity: Deep knowledge of finance, mathematics, and programming is required for quantitative trading.
Set-Up Cost: The infrastructure for quantitative trading requires powerful computers, cloud servers, and access to quality data, all of which are expensive.
Overfitting Risk: Past returns do not guarantee future success. The model should be adaptable to changing market conditions in order to maintain its efficiency.
In short, quantitative trading offers the benefit of efficiency, scalability, and backtesting, but it is not a guarantee for success. The goal is to create adaptable financial models that respond well to market changes and deliver consistent returns.
6. Who uses Quantitative Trading: Institutions vs Retail
Although Quantitative Trading is generally used by large financial institutions such as hedge funds, its popularity among retail investors is also increasing.
Institutional Traders
Hedge funds and investment banks are the institutions that dominate the field of quant trading. They have access to high-speed data, advanced computers, and algorithms, which put them at an advantage in trade execution.
Institutions involved in qualitative trading prefer people from non-finance backgrounds, such as data science, machine learning, and statistics.
Retail Investors
New platforms such as QuantConnect and MetaTrader are providing retail investors with an opportunity to explore retail trading. Tools such as Python and Excel can be used to run simulations before using any actual money for quant trading.
While the retail investors can’t match the scale of financial institutions, they can still devise a trading strategy of their own through quantitative means.
7. Skills & Tools Needed to Get Started
The world of quantitative trading is highly competitive and requires a mix of the following skills:
- Mathematics and statistics
- Calculus
- Time-series analysis
- Probability distributions and statistical inference
- Optimization techniques (linear, non-linear, quadratic)
- Numerical methods like Monte Carlo simulation
- Programming: To transform mathematical models into an efficient trading system
- Python: widely used for data analysis, machine learning, and strategy backtesting .
- C++ : For high-frequency trading (HFT) and making low-latency execution systems
- SQL: For managing and querying large datasets
- Financial Knowledge
- Financial instruments – equities, derivatives, commodities, etc.
- Risk management principles
- Market microstructure and trading mechanisms
- Market dynamics and economic cycles
8. Real-World Examples / Case Studies
Renaissance Technologies
The Medallion Fund from Renaissance Technologies is the most well-known example of quant trading. Jim Simons, the founder, is famous for employing statisticians and data scientists instead of traditional traders and MBAs. The fund has been returning an annualised return of 39% (after investor fees) for over three decades now.
Two Sigma Investments
They are an investment firm that treats trading as a science based on data and computation rather than human instinct. They combine a mix of machine learning, alternative data, and cloud computing to find out any hidden patterns or signals.
Their model evolves automatically with new information to provide a highly efficient and low-latency trading system.
Mean Reversion Example
A mathematical model is used to find out two highly correlated stocks- Stock A and Stock B. Stock A has been outperforming Stock B for the last seven days by a significant difference.
The trader decides to short the shares of Stock A and buy the shares of Stock B.
After 4 days, the correction happens, the price of Stock A falls rapidly, and the Stock B price increases to a similar level as Stock A’s fallen price.
The trader gains on both ends: profit from the short selling of Stock A, which has fallen, and profit from buying Stock B, which has risen.
Conclusion
Quantitative trading is the next big leap in the world of finance. It represents a blend of financial knowledge with data science and programming to provide efficient and accurate trading systems.
Although it requires a high setup cost due to advanced infrastructure for computation and cloud servers, new platforms such as QuantConnect and MetaTrader are making quantitative trading more accessible for the common man.
By experimenting with different tools and models for retail investors, you can come up with a quantitative strategy of your own, which will lead to financial success.
FAQ‘s
Quantitative trading means using mathematical models, statistics, and algorithms to make trading decisions.
Data is collected and analysed to build mathematical models. The models are then backtested, and after getting validation, they are used for trading.
Quantitative trading means identifying trade opportunities with the help of mathematical models and data analysis. Algorithmic trading means efficient execution of trades using automation, which enhances speed and reduces latency.
The main strategies used in quantitative trading are: trend-following / momentum models, statistical arbitrage / mean reversion, and HFT.
The advantages of quantitative trading are high speed, consistency, scalability, and elimination of human error. The disadvantages are its complexity, dependency on data, and its adaptability to change with market conditions.
To become a quantitative trader, you need mathematical skills, programming skills, and knowledge of finance and markets.
Yes, with the help of tools and platforms such as Python, MetaTrader, and Quant Connect, traders can also make use of quantitative trading strategies.
