Trading FAANG Stocks: A Breakout Trading System For Google Stocks

In this article, we will show you how to trade the U.S. stock market using a simple but effective trading system based on a break-out approach and able to exploit the main upward movements of this market.

For this example, we will focus on the leading stocks that drive the performance of the U.S. stock market, namely the so-called FAANG stocks. The acronym FAANG refers specifically to the five companies Meta META (formerly Facebook), Amazon AMZN, Apple AAPL, Netflix NFLX, and Google GOOGL. Over the years, this acronym has become a common term that refers generally to high-growth stocks in the technology and consumer goods sector found on the world's major stock exchanges. Goldman Sachs later coined its acronym, FAAMG, adding Microsoft MSFT stock and crossing out Netflix to refer to the top 5 technology companies driving growth in the U.S. stock market.

We will therefore focus on Google stocks, arbitrarily selected from the FAANG stocks, which has performed positively from 2010 to the present, albeit with a decidedly negative 2022, as has been the case for almost all of the world's stock markets. However, the same approach shown in this article could be applied to any other stock.

Figure 1 – Google "buy&hold" trend

As mentioned earlier, we will use a trading system based on a break-out approach to exploit the large upward movements typical of a market like the Nasdaq while limiting the exposure in the event of significant retracements. The goal is to achieve more regular results than by "buy&hold," and to restrict the large swings that the latter approach would force us into, even though it will most likely result in smaller absolute gains.

The Strategy

Assuming a fixed capital of $10,000 per trade, the strategy we will use will wait 30 minutes after the opening of trading (which occurs at 9:30 a.m. exchange time) for the market to establish a high and low for the day. Then, a long position is taken from 10:00 a.m. to 3:30 p.m. if the high is broken. The trade is kept open until it falls below the low of the current session or the stop loss and take profit values are reached, set at 2% and 4%, respectively, of the capital invested in the respective trade. All these values are, of course, a first attempt and can be optimized later.

The strategy is tested on a 15-minute time frame, and a data history from January 2010 to January 2023. The test provides the metrics shown in Figures 2, 3, and 4. The results look pretty good when compared to the performance of the stock, and it seems that the strategy has been able to resist the strong shocks that have characterized the last two years.

Figure 2 – Strategy performance report

Figure 3 – Equity curve

Figure 4 – Total Trade Analysis

On the other hand, the average trade value is relatively marginal (0.11% of the trade value), and operational costs could eat up a large part of the profit. Therefore, optimization of the strategy parameters should be done to see if there is room for improvement and to make the system usable for live trading.

The Optimization

Let us try to improve the metrics of the strategy by acting on the most critical parameters.

First, we will optimize the entry time in 15-minute increments. We said that we would wait 30 minutes after the market opens to allow prices to define a daily high, which will then be our entry-level: let's see if waiting a little longer improves things:

Figure 5 – Entry time optimization

From the optimization results, waiting until about 12:30 p.m. increases trading efficiency by doubling the average trade and reducing the number of total trades by almost half. This confirms that most of the losing trades have been eliminated, which also reduces the operating costs with a net profit that is even slightly higher than the previous one.

One can try to further refine the strategy by introducing a filter that allows trading only when a specific price pattern occurs. For example, one could limit the entries to the days of indecision when the body of the previous day's candlestick (Open - Close) is less than a certain percentage of the candlestick range (High - Low). After defining this percentage as Daily Factor (DF), we can optimize it between 0 (no trade) and 1 (no filter) in steps of 0.1.

Figure 6 – Daily Factor Optimization

From the results, although the value of 0.9 does not filter the trades very much, being close to 1, it eliminates another 18 less effective trades from the total 714. The value of 0.6, which filters out the trades even further, could also have been evaluated, but at the expense of the overall profit, which would perhaps decrease a bit too much without increasing the average trade.

At this point, looking at the equity curve shows that it has become more constant than the original and certainly much more constant than the buy&hold. The net profit has increased, and the average trade has reached an acceptable value, although one could try to improve it further by working on stop loss and take profit.

Figure 7 – Optimized Equity Curve

As a final proof of the goodness of this approach and to demonstrate that diversification over a portfolio of stocks makes for even more consistent trading, we show you the equity of this system applied to all 5 FAANG stocks, leaving it to you to experiment and further improve the strategy.

Figure 8 – FAANG Portfolio Equity

Conclusion

In summary, the breakout logic explained in this article and applied to FAANG stocks has produced more consistent results than a simple "buy&hold" and saved the trader many heartaches. It can be used for other stocks or stock portfolios with appropriate adjustments, but we leave it up to you to experiment, always being open to new ideas.

Until next time, and happy trading!

This content is for informational purposes only and is not intended to be investing advice.