While Quant trading focuses on statistics, you must understand a few Quantitative Trading strategies to get the best out of this trading model.
This article comprehensively examines the 5 major quantitative trading strategies you can adopt to better your trading decisions in 2023.
Without any further ado, let’s dive right into the details!
Top 5 Quantitative Trading Strategies For 2023
1. Mean Reversion Strategy
The Mean Reversion Strategy involves taking advantage of the dynamic changes that occur when pricing a stock or security. It’s based on the belief that asset prices will always return to average levels anytime there is an extreme price move.
As a Quantitative trader, you’ll write codes that make finding markets with a long-standing mean possible. 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. It will do the same for a long position if it diverges down.
This strategy is effective because it allows the quant trader to profit anytime the price goes up and save when it is abnormally low. The only downside to this strategy is that it doesn’t work in all financial markets.
2. Statistical Arbitrage
The Statistical Arbitrage trading strategy is based on the mean reversion strategy.
It 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. So when there is a stock that doesn’t perform like this group of stocks, it shows that there is a huge likelihood of profits.
For this strategy to work, you’ll have to calculate the average price of each stock in the group of stocks subject to similar market conditions. After this, you’ll create a table showcasing stocks that underperformed the average price and those that overperformed above the average price. Anytime any of these stocks revert to the average price, then it means that both positions are closed for profits.
The disadvantage to statistical arbitrage is that sometimes, factors 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.
In simpler words, it’s somewhat trickier to use this strategy to exploit extremely short-term market inefficiencies.
3. Trend Following
The Trend Following Strategy is also referred to as Momentum trading, and it’s 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 good example is monitoring the sentiment among traders at a major financial institution. By monitoring, you can develop a model to predict when institutional investors would likely buy or sell a stock in large quantities. With any information you get, then you can act accordingly.
You can use the Trend Following Strategy with the Mean Reversion Strategy for better precision.
4. Algorithmic Pattern Recognition
This strategy involves 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.
The Algorithmic Pattern Recognition trading strategy can also be called high-tech front running.
Institutional trading is usually done through algorithms, and with this strategy, you can easily recognize and isolate the custom execution patterns of institutional investors.
Note that HFT firms often use this strategy, 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 Exchange Traded Funds Rule trading strategy is based on profiting from the relationship between an index and the ETFs that track it.
That is, anytime a new stock is added to an index, the ETFs representing that index often have to buy that stock. By understanding the rules of index additions and subtractions and utilizing ultra-fast execution systems, as a quant trader, you can capitalize on this rule and trade ahead of forced buying.
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