Skill Sheet: What You Will Learn Here
- What is quantitative trading in technical analysis?
- Advantages of quant trading
- Risks of quant trading
- Know various types of quant strategies
Financial markets have evolved quite a lot over the years and so have the techniques that people use to trade in them. Apart from the traditional techniques of time frame, price movement, breakout trading, momentum trading, etc., there is one more technique called quantitative trading, which is quite new compared to traditional techniques. It is also quite advanced and is steadily catching the fancy of traders.
What is quantitative trading in technical analysis? Quantitative trading makes use of mathematical and statistical models, measurement and research to represent a given situation in terms of a numerical value. This can also be applied to the measurement and performance evaluation of a company or a financial instrument and also to predict crucial economic events like a country’s GDP, inflation, etc.
In financial markets, quantitative trading was initially used by large institutions or fund houses involving large transactions for the purchase and sale of shares to the tune of hundreds and thousands. In the last few years, the technique is becoming popular with individual investors as well. Investors are becoming more and more proficient with this technique when they use key financial ratios like P/E multiple, earning per share (EPS) and return on capital employed (ROCE) to make their investment decisions. The technique can be used for analysing simple data like revenue trends and conducting more complex mathematical calculations.
How quantitative trading works
Traders use modern-day technology, mathematical equations and databases to make their trading decisions. Quantitative trading is a strategy where traders use mathematical equations to create a model around it. Based on this, they develop a computer program which is then applied to the historical data from the markets. For example, a trader might write a program based on momentum or trend trading that would identify the stocks based on the upward trend in the market. The program will look for market uptrends and pick stocks automatically based on parameters set in the program. A combination of fundamental analysis and technical tools can be used to create an efficient program to pick stocks.
Traders then painstakingly do the back-testing and optimisation of their model/program to check the results. The success rate during the back-testing will determine whether the system can be implemented for actual trading with real money.
Different trading strategies
Every trading strategy aims to generate profitable trades for the users based on a wide range of parameters, but as is with everything else in life, it is impossible to guarantee success for every trade.
Since algorithmic trading ( also known as algo-trading) uses computer programs that are basically a defined set of instructions to execute a trade, it results in more liquid markets and more systematic trading as it rules out the impact of human emotions while trading.
Algorithmic trading strategies can be of the following types:
Trend following: Using this strategy, traders make trading decisions based on the formation of desired trends, which are easy to identify and straightforward to implement with simple algorithmic calculations. The trends can be easily identified using the moving averages over a period of time, like a 50-day or a 200-day moving average.
Arbitrage trading: This can be applied to stocks that are listed in more than one market so that profit can be generated by buying stocks at a cheaper in one market and selling them at a higher price in another market. Executing a trade by using an algorithm to identify such price differential can generate good profitable opportunities.
Index fund rebalancing: Index funds rebalance themselves at regular intervals to bring themselves to par with the benchmarks. Algo traders use algorithms to identify profitable trades just before the rebalance of an index fund.
Model-based strategy: Traders use mathematical models that allow trading on a combination of options, like the delta–neutral strategy, by leveraging the model-based strategy.
Mean reversion (trading range): As the name suggests, this strategy is based on the principle that no matter how high or low the price of an underlying security may move, it ultimately always reverts to its average or mean value. Using an algorithm based on these defined price ranges can help execute the trades whenever there is a movement of prices beyond this range.
Other prominent algo-trading strategies include VWAP (volume-weighted average price), TWAP (time-weighted average price), per cent of volume (POV) and implementation shortfall.
Advantages of quant trading
Emotions have no place in stock trading, and this is one of the major quantitative trading benefits as it completely eliminates the emotional factor. The mistakes that a trader usually commits out of greed and fear are entirely addressed by this technique, as computer programs do not have any emotions.
A human mind can get overwhelmed by a large amount of data which might result in slow or wrong calculations, thereby impacting the success of the trades. This problem is eliminated by the usage of computers, and the processes of monitoring, analysing, and decision-making get automated.
Limitations of quantitative trading
There are also some risks of quant trading that we should be aware of before indulging in it. There are cases when the algorithms developed by quantitative traders have been successful in a particular market condition but have failed when the market scenario changes. Thus it is very important for the developers to generate algorithms that are as dynamic as the stock markets to remain profitable.
Points to remember:
- Quantitative trading makes use of mathematical and statistical models in the process of trading.
- Depending on the parameters, various quantitative strategies can be used like trend following or arbitrage strategies.
- Quantitative trading removes human emotions from trading though the algorithms require constant monitoring in case the market scenario changes.