Automated quantitative investing systems represent a very sophisticated area of quant finance which it requires extensive programming expertise. These systems include the following components:
- strategy identification;
- strategy back testing;
- execution system ;
- risk management.
Automated Quantitative Investing System Types
All automated quantitative investing systems conduct research to obtain data. This is necessary for identifying the strategy for higher returns or lower risks. These strategies fall into two categories:
- Mean-reversion-tries to identify the long-term mean on price
- Momentum strategy- uses both big fund structure and investor psychology to gather momentum in one direction and follow the trend.
After identifying strategies, back-testing is used to test the profitability of the strategy or to set strategies based on historical data. Through the execution system, lists of trades are sent and executed by the broker. The execution mechanism can be fully automated, semi-manual or manual depending on the strategies. Manual or semi-manual mechanisms are commonly used for LFT strategies, while HFT strategies need fully automated execution mechanisms. The final component of automated quantitative investing systems is the risk management. This includes technology and brokerage risk.
Risk management also includes optimal capital allocation to a set of several strategies and to the trades according to those strategies.
Today the majority of investors and traders are using these investing systems because they prevent emotional behavior in trades and provide great results. These systems also give the opportunity to identify unique market conditions. Automated quantitative investing systems use the best indicators for investment which allow investors to conduct precise timing and execution but also give the opportunity to intervene whenever they desire according to the market’s volatility. These strategies control the downside risk and give the opportunity to invest in a simple and automatic manner. They also bring potential profits by taking advantage of market conditions when there are momentum shifts between oversold and overbought.
Although these automated quantitative investing systems are implemented by those who specialize in math and computer science, the results of analysis can be used by all the quantitative investors. In order to make their investment successful, those investors should develop, experiment and carry out a verified investment strategy regardless of the fact that they are sometimes applying a quantitative investing system or trading
All investors should implement ideal risk management in order not to have negative outcomes. In order to generate a successful quantitative investing system we should know what doesn’t works and what works, and know the issues the imminent issues.