Algorithmic Trading Strategies“If you want to have a better performance than the crowd, you must do things differently from the crowd.” – John Templeton
Here is why I find it reliable:
Trading strategies in general are difficult to implement and succeed for a few reasons:
- Lack of backtests cannot confirm how a trading strategy perform during different market periods, so it’s too easy for someone to give up a potentially good strategy (when it underperforms the market during a certain period);
- Human emotions (greed, fear) prevents one to build the proper temperament to stick to the plan. Even if one is convinced that their strategy is solid, it’s very difficult to separate emotions to make decisions (buy / sell) during turbulent times;
- No one wants to rely on gut feelings for trading signals, and no one wants to spend hours analyzing what to buy and sell.
These issues are addressed with Algorithmic Trading Strategies (or Quantitative Strategies) – complex mathematical models to detect investment opportunities. This is implemented as an algorithmic trading model that executes a series of rules (based on fundamentals and/or technical analysis) to provide a buy and sell signal based on the objectives of a given model. By using models from a reliable and robust platform, the issues above can be addressed:
- Algo trading models are backtested with a non-survivalship bias platform, to evaluate how a given idea / strategy performed in a period that involved at least 2 recessions (since 1999);
- Algo trading models allow one to have discipline when trading, since there are no emotions when a buy or sell signal is issued, making it easier to stick to the plan;
- Low turn-over models allow one to enjoy short term gains of the market with little effort;
- Computing power provides an edge to the retail investor, by exploiting inefficiencies of the markets based on quantitative data;
- Successful strategies can pick up on trends in their early stages as the computers constantly run scenarios to locate inefficiencies before others do. The models are capable of analyzing a very large group of investments simultaneously, where the traditional analyst or retail investor may be looking at only a few at a time. This makes the actual trading process very straightforward by investing in the highly rated investments and selling the low-rated ones.
Trading different models provide diversification to different market conditions. Trading models can take advantage of mathematical formulas to time the market – either by micro events, such as earnings decline or by macro events, such as economic indicators.
Trading requires luxuries like minimizing drawdown and being able to liquidate a portfolio at any time, so different rules and temperament are required, when compared to investing for the long term. Hence, I firmly believe that a trading portfolio greatly complements an investing portfolio.