I've recently learned a lot about quantitative trading, stock trading done by algorithms rather than people, and I think it's pretty interesting. Even retail investors could benefit from understanding the principles behind quant trading. I'll give a brief primer:
A quant trading system is comprised of five modules:
Alpha Model: Predicts the future of the instruments the quant trades
Risk Model: Limits exposure to factors which could drive loss
Transaction Cost Model: Calculates the cost of a trade and compares it to the expected return of the trade
Portfolio Construction Model: Gets the data from other models and uses it to create the ideal portfolio
Execution Model: Compares the current portfolio to the ideal one and determines the most cost effective way to acquire the ideal portfolio
I'll talk a bit more about the alpha model because it's the most interesting.
Trend Following/Momentum Strategy
The idea behind this strategy is that the market sometimes moves in a single direction for a long enough period of time that one can identify the trend and ride it. Quants look to the Moving Crossover Trend to find such phenomena in the market. This compares the average price of a stock over the short term (60 days) with the average price over the long term (200 days). If the short term price is lower than the long term price this is a negative trend.
Mean Reversion Strategy
While some traders bet that trends will continue, others bet that they will reverse. The idea is that each stock has a “center of gravity” around which its price will fluctuate. Buyers and sellers will occasionally be unbalanced, causing the company to be temporarily over or under valued. The mean reversion strategy buys up shared when this occurs and sells them when the shares return to what the strategy considers its true price. This is called statistical arbitrage.
Value/Yield Strategy
This strategy buys “cheap” assets and sells “expensive” assets based on ratios such as earnings to price and so on.
Growth Strategy
This strategy trades based on growth using metrics such as GDP and earnings.
Quality Strategy
This strategy focuses on buying quality assets. Indicators are how diversified a company’s sources of earnings are.
Data Driven Strategies
All of the above strategies have been based upon understood market phenomena and a company’s fundamentals. Those strategies fall under the theoretical approach. A data driven strategy is an empirical one in which machine learning is used to find correlations. The strategy uses the correlations to make trading decisions.
There's a lot more to it, like how quants blend strategies, the parameters that set one quant trading system from another, and the advantages of quant trading over discretionary trading. If you're interested in learning more, you can read Inside the Black Box by Rishi K. Narang, or my medium.com post on the topic.
Thanks for reading. Let me know if you have any questions.
Submitted March 24, 2017 at 11:53AM by Huxley_Mindset http://ift.tt/2nkC11x
