In our previous article, we took a look at trading strategies in regards to Components and Methods of Creating Strategies. in this article we shall be taking a closer look at a wide range of trading strategies, highlighting key examples of popular types, trends, and major breakthroughs.
Types of Trading Strategies
There are five main categories of trading strategies, and they are as follows:
- Trend-Trading strategies
- Mean-Reverting strategies
- Breakout trading strategies
- Carry trade strategies
- Event-based trading strategies
1. Trend-Trading Strategies
According to the stock’s trend, trend trading strategies produce entry and exit conditions. The trending trading strategy calls for buying an asset when its price is trending upward and selling it when it’s trending downward on the assumption that the price will continue to move in the same direction. When a reversal of trend occurs, the trade is closed.
Based on technical analysis indicators like moving average crossovers, relative strength index (RSI), and average directional index (ADX), a trend trading strategy is developed (ADX).
The following is an illustration of a trend trading strategy based on technical analysis.
Moving Average Crossover
When a stock’s price is rising, the moving average of that price will also rise at an upward-sloping angle. If you follow the rules of the moving average crossover strategy, you’ll buy a stock whenever the shorter moving average drops below the longer one.
A shorter period moving average is one with a shorter time frame, such as the 21-day moving average, while a longer time frame moving average is one with a longer time frame, such as the 50-day moving average, for generating short-term trading signals.
When trading over the long term, the 50-day moving average and the 200-day moving average are considered the shorter and longer period moving averages, respectively.
Trading signals are generated using a long term moving average crossover strategy in the daily chart of AAPL shown below. The 50-day moving average went above the 200-day moving average, signaling a buy, on September 2, 2016. This sell signal was triggered on December 20th, 2018, when the 50-day MA dropped below the 200-day MA.
Quantitatively speaking, trend trading strategies include both cross-sectional and time-series momentum strategies.
The goal of cross-sectional momentum strategies is to construct a long-short portfolio based on an analysis of the securities’ relative performance over a predetermined time frame.
As an illustration, think about the S&P 500 index. Our goal is to develop a cross-sectional momentum strategy by analyzing the quarterly performance of 500 different stocks. To construct a long-short portfolio, one must first take a long position in the top 20% of a market and a short position in the bottom 20%. Every three months, the portfolio will be adjusted to reflect market changes.
When developing a time-series momentum strategy, we will also construct a long-short portfolio based on the absolute performance of securities over time. Using the securities’ returns over a time period of your choosing, a long-short portfolio will be constructed based on a predetermined cutoff.
For the stocks in the S&P 500 index, for instance, a long position is opened in those that have returned more than 5% over the past three months, and a short position is opened in those that have returned less than -5% over the same time period.
Fundamental analysis includes trend trading methods like factor-based investing. The stocks to be invested in are chosen using the factors that are believed to best explain the stocks’ returns.
Value, size, volatility, momentum, and expansion are all relevant indicators. However, these aren’t the only economic variables taken into account; things like inflation, interest rates, and GDP are taken into account as well.
Long-term market outperformance is anticipated for stocks chosen using these criteria. Whether or not to invest in a stock is determined by how these variables are trending.
2. Mean-Reverting Strategies
The premise upon which mean reverting strategies are built is that prices and economic indicators tend to revert to their respective means over long enough time horizons. As with most things in life, stock prices, volatility, etc. are assumed to have mean reversion properties.
An elementary example of a mean-reverting trading strategy would be to buy a stock after a sudden sharp drop below the mean. Several mean-reverting trading strategies are developed with the help of technical analysis. Here are some particularly striking instances:
- Pullbacks and retracements trading strategies
- Oversold and overbought trading strategies
- Range trading systems
When markets are trading in a tight range, mean reversion strategies tend to do well.
Consider a popular mean reversion strategy that makes use of Bollinger bands as an illustration.
The upper and lower Bollinger bands indicate the overbought and oversold price levels when the bands are not moving in any trends.
- Trading near the lower band results in a buy signal,
- While trading near the upper band results in a sell signal.
The TSLA case study is provided below. As we’ve already established, range-bound markets are ideal for the mean reversion strategy. Bollinger bands that are flat indicate a range-bound market and can be used to generate buy and sell signals at oversold and overbought levels, respectively.
Mean-reverting trading strategies include pair trading and statistical arbitrage, according to quantitative research.
- Statistical arbitrage strategies are created to take advantage of mean reversion characteristics of the price and also the market microstructure anomalies.
- Pairs trading is a market-neutral trading strategy where the long and short positions are taken in the stocks with high positive correlation.
The value investing approach is a classic example of a mean-reverting strategy in the field of fundamental analysis.
An investment’s true worth is determined through fundamental analysis. If the market price of a security is less than its true worth, it is said to be undervalued. Those who practice value investing seek to profit from the undervaluation of stocks.
3. Breakout Trading Strategies
With a breakout trading strategy, you’ll make a purchase or sale once the asset’s price breaks through a key price level, such as the price of a stock’s long-term support or resistance. An example of a breakout trading strategy in technical analysis would be to buy after a price breaks through a resistance level or sell after a price breaks through a support level.
Breakout strategy encompasses a wide variety of technical analysis methods, including opening range strategy, dual thrust strategy, and strategies based on the breakout of patterns like wedges, flags, head-and-shoulders, and triangles.
An illustration of a falling wedge breakout strategy is provided below. In a falling wedge pattern, the price action takes place between two convergent, downward-sloping lines. Buying occurs once price breaks out above the wedge’s resistance level.
You can see a falling wedge pattern on a daily timeframe for the security GOOGL in the months of February and March in the following chart. The buy signal was triggered on April 1, 2021, when the wedge’s upper line was broken.
Quantitative analysis utilizes complex quantitative models like time-series regime switching and hidden Markov models to plan for successful breakouts.
Breakout strategies are often thought of as relative value strategies in fundamental analysis. Taking a long or short position in a security depends on comparing its fundamental ratio to the fundamental ratio of the sector. Microsoft (MSFTP/E )’s ratio is compared with that of the information security industry, for instance.
It is believed that Microsoft (MSFT) is undervalued if its price-to-earnings ratio (P/E) is lower than the sector average. Also, if Microsoft’s P/E ratio is higher than the industry average, investors may decide to sell their shares of MSFT short because the stock is overpriced.
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4. Carry Trade Strategies
To capitalize on the spread between interest payments and receipts, traders employ a strategy known as “carry trade.” As such, it finds extensive application in the foreign exchange market.
The carry trading strategy is implemented in the foreign exchange market by selling a low-yielding currency and purchasing a high-yielding currency. If the interest rate on the base currency is higher than the interest rate on the quote currency, then the carry strategy will be implemented by purchasing the pair.
If the interest rate on currency A is 6% and the interest rate on currency B is 4%, then the pair A/B would be unfavorable to the investor. When you buy the A/B currency pair, you are engaging in a carry trade. There is a term for this: “carry trade.”
Assuming the interest rate spread is positive after the trade is completed, your broker will pay you the spread. On the other hand, you’d lose money. When the interest rate of currency B rises above the interest rate of currency A, the interest rate differential turns negative.
Examples of carry trade strategies in quantitative analysis include market making and cash-future arbitrage. A common form of carry trading in technical analysis is the use of volatility selling strategies, also known as short gamma.
5. Event-Based Trading Strategies
Following the release of economic and corporate events, price inefficiencies can form, and this is where event based trading strategies come in.
Event-based trading, such as stock trading based on breaking news, is categorized as such in quantitative analysis. Learn more about trading stocks based on news by reading the article Quantified News Analytics: Profitability vs. Pitfalls.
The term “event-based trading strategies” refers to the practice of investing in the wake of news and economic events.
ML-Based Trading Strategies
Most recently, trading strategies based on machine learning have become the norm. Each trading strategy, including trending, mean reverting, break out, carry, and event based, uses a combination of quantitative, technical, and fundamental methods.
There are three main application areas for ML-based strategies:
- Supervised learning trading strategies
- Unsupervised learning trading strategies
- Reinforcement learning trading strategies
Conclusion
In order for automated trades to be successful, trading strategies must first be developed. Trading strategies aid in the systematic execution of trades with the goal of improving the likelihood of a positive outcome.
Traders can choose from a variety of trading approaches, each of which has the potential to yield profits in different market conditions. We covered technical, quantitative, fundamental, and machine learning trading strategies and their respective use cases.
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