Let’s see, what is Algorithmic Trading? and How can it help you to improve your investment strategy.
To do this, we analyze its origins, the development methodology and its application in a specific case, which can stimulate your interest in generating your own investment algorithms.
The reasons for Algorithmic Trading
Every minute of every day has the potential to generate new information that changes the valuation of the financial assets listed on the planet.
Thousands of shares are traded on Wall Street alone.
Processing information to make decisions at the required speed, buying or selling, has become increasingly difficult for our brains.
The amount of information available many times exceeds us, even if we integrate a qualified professional team and have good software and hardware support.
Reasons like these have led institutional investors for decades to develop algorithmic trading techniques, also called quantitative trading.
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How are investment algorithms generated?
It is about generating techniques that allow trading financial assets based strictly on buy-sell decisions generated by algorithms programmed on computers.
It is a process that takes place in several steps:
a) Find a reasonable a priori trading strategy that generates accurate decisions against specific, measurable data
b) Subject that strategy to the test of the past (backtesting), to ensure that at least the historical performance of the strategy is good
c) Organize the means to process the data required by the strategy and execute market operations
d) Adopt the necessary policies for this strategy to generate controlled positions on yields and risks
It is important to note that algorithms are simply tools for systematizing an investment policy.
A strategy is developed, and it is sought to be implemented on objective and automatic basis.
And avoid, in this way, the effect of emotions, which alter even the most experienced of investors in times of strong fluctuations in the market.
What is Algorithmic Trading? The Case of AL SIMPLE
We will see the case that we developed on this site, to exemplify the process of developing an algorithmic trading system.
In recent years, algorithmic trading has piqued the interest of individual investors, those who manage their own money.
AL SIMPLE is an answer to that interest.
We are looking for a strategy that will work for those who cannot spend the day in front of trading screens, but who have sufficient knowledge and experience to evaluate our algorithms.
You can learn from our experience to understand What is Algorithmic Trading?
STEP 1: A reasonable a priori strategy
As described in the introductory section of this site, we attach supreme relevance to the correct choice of market in which we will invest, and explain why we choose Wall Street.
We also refer to the importance of selecting a good investment portfolio, and chose the Nasdaq 100 to generate our algorithms.
That way, we have a good portfolio through a single asset, QQQ, the ETF developed by Invesco to emulate the performance of the Nasdaq 100.
From that decision, we acquire QQQ and only operate buy or sell options on QQQ.
(Here you can analyze QQQ portfolio)
The QQQ units we buy keep them in our portfolio, we never sell them (the «passive investment» strategy).
Periodically, we reinvest the gains earned by purchasing more QQQ units.
Based on the yields and risks (VXN) we monitor daily in QQQ, our algorithms determine the moments of executing Aggressive or Defensive Strategies.
We use only accurate and measurable data: market prices and risk levels, so it was easy to program investment algorithms.
(Here you can dive into VXN, the volatility of the Nasdaq 100)
Step 2: Algorithmic Trading Backtesting
The trading strategy was tested for the 12-year period between the beginning of 2007 and the end of 2018, with the results we presented on this site.
In these results we need to distinguish the importance of different parts of the strategy.
The choice of QQQ as a portfolio allowed to record results well above the market average, as measured by the performance of the S&P 500.
(This chart summarizes 2007-2018, 12 years backtesting period, «Rendimiento Anual Promedio» meaning «Avg Annual Returns»)
Aggressive Strategies explain most of the results aggregated by algorithms, which is natural because they are a 12-year period dominated by bullish market periods.
Defensive Strategies, on the other hand, contribute less to total average performance, but have been key in maintaining the balance of the strategy during the years of market corrections.
Since 2019, when the algorithms become to be published, these where the results:
You can see more information about strategies in the following articles:
Step 3: Processing and Implementation of the Strategy
At AL SIMPLE we monitor the market in real time (during options trading hours on Wall Street) and process the algorithms.
When algorithms indicate that it is time to initiate or terminate a strategy, either aggressive or defensive, we immediately notify our users.
Step 4: Position and risk management
The investment unit in AL SIMPLE is equivalent to 100 units of QQQ.
For each investment unit we will notify you of the operations to be carried out.
It will always be trading 1 call or 1 put for each investment unit.
Depending on how much money you decide to invest following the algorithmic trading, you may have more QQQ units, and you will need to adapt our instructions to the size of your position.