In this post, you are going to find the 21 most popular basic trading strategies that have influenced a lot of successful algorithmic and quantitative trading systems. These strategies are proven systematic trading methods, that have been implemented not only by individual algo traders but have also been used by the quant trading desks of established hedge funds and other financial institutions.
As you have probably found by now, quite often it is very hard to find inspiration when trying to design your next automated trading system. Sometimes you just get stuck with what you already know, and your mind just cannot go out of these limits.
If you have been there, don’t worry, we’ve got you covered!
Below you will find the list of the 21 most popular trading strategies you can use to jump-start your ideas again.
The price-momentum strategy is based on buying the best-performing stocks and selling the worst-performing stocks, according to a predefined criterion. The performance criterion can be cumulative return, mean return, or risk-adjusted return. The strategy can be long-only – you open long positions in the top 10% of the best-performing stocks, or long-short, where you buy the top 10% best performing and sell short the 10% of the worst-performing stocks.
The earnings-momentum strategy follows the same logic as the price-momentum strategy above – buying or selling the top/bottom 10% stocks according to their performance. What is different is the performance criteria. In the price-momentum strategy, the performance criterion is return, while in the earnings-momentum strategy the criterion is based on earnings.
This strategy is also based on buying the top winners and selling the bottom losers, like the price-momentum and the earnings-momentum strategies above. The difference is that the performance selection criterion is based on book-to-price value (B/P ratio). The portfolio in this strategy consists of buying the top 10% stocks with the highest B/P ratio and selling short the bottom 10% with the lowest B/P ratio.
The low-volatility anomaly trading strategy relies on the observation that the future returns of low-return-volatility portfolios outperform the returns of high-return-volatility portfolios. While this is counter intuitive because the general notion that higher risk yields higher returns the low-volatility anomaly strategy shows quite good returns.
The implied volatility strategy is based on an observation on the put/call implied volatility of stock options. The observation suggests that stocks with the largest increases in call options implied volatilities over the previous month on average tend to have higher future returns. On the other side it of the observation, stocks with the largest increases in put options implied volatilities over the previous month on average tend to have lower future returns. As a result, the trader can open long positions in the stocks in the upper 10% according to these criteria and short positions in the stocks in the lower 10%.
The multifactor strategy relies on buying and selling short stocks based on more than one factor. The observed factors can be value, momentum, volatility, etc. As a result, a trader can combine uncorrelated factors and extract additional value for the portfolio.
Pairs trading strategies
Pairs trading is a classic example of a mean-reversion strategy. The first step in the pairs trading strategy is based on identifying a pair of stocks with highly correlated historical performances. The next step of the strategy is to monitor how the correlation between the two stocks changes over time. When mispricing is observed, the trader sells short the overpriced stock and buys the underpriced stock.
Single moving average
The single moving average is one of the very basic trading strategies. It is based on the price of an asset (stock, futures contract, currency pair, etc.) crossing up or down a moving average. The conditions in this system are quite basic. If the price crosses up a moving average, the trader opens a long position. Vice-versa if the price crosses down a moving average, the trader opens a short position. It can be run as a long-only, short-only, or long-short strategies in single or multi-asset setups.
Moving averages crossover
The moving averages crossover is another very popular trading strategy. It relies on two moving averages – a fast one (short period) and a slow one (long period). The trading logic of the moving average crossover is very similar to the single moving average strategy. What is different is that the trader is looking for a cross-over of the fast and the slow moving averages instead of just the market price and a single moving average.
Read more about Moving Averages and MA trading strategies in the following article: How to use Moving Average to successfully analyze market trends?
Multiple moving averages crossover
The multiple moving averages crossover strategy adds additional moving averages with different durations in addition to the fast and the slow moving averages in the strategy above. The additional indicators can be used to filter false signals. For example, instead of opening a long position when the fast-moving average crosses above the slow one, the trader waits for a third moving average also to cross before opening his position.
Pivot points support and resistance
The pivot points support, and resistance strategy is based on the pivot points trading indicator. This popular indicator has three basic levels – center, support, and resistance. The center level is calculated as the average of the previous day’s high, low, and close prices. The resistance level is calculated as two times the center level minus the previous day’s low. The support level is calculated as two times the center level minus the previous day’s high. In the strategy, the trader opens a long position when the market price crosses the central level upwards and liquidates it when it reaches the resistance level. The short position trigger signal is when the market price crosses the center level downwards and the target is hit when the support level is reached.
Channel trading strategies
There are several trading indicators that consist of channels, with the most popular being Donchian channel, Bollinger bands, and Keltner channel. The concept of a channel trading strategy is to buy or sell short an asset when it reaches the bottom or the upper level of a channel. A channel consists of two lines forming a band withing the price fluctuates. There are two signal conditions in channels trading – the price would bounce back from a channel, so the trader would expect it to remain in the channel or the price would break through the channel which signals the emergence of a new trend.
Merger arbitrage or also known as risk arbitrage is a trading strategy aiming to capture an excess return that is occurring due to corporate actions such as mergers and acquisitions. You can find a merger arbitrage opportunity whenever one publicly trader company is trying to acquire another public company at a price that is different from the current market price. There are two types of mergers – cash and stock merger. The strategy in the case of a cash merger is to establish a long position in the target company stock. In the case of a cash merger, the strategy is to establish a long position in the target company stock and a short position in the acquiring company stock.
The market making strategy is one of the most popular ones in algorithmic and quantitative trading. It is as simple as capturing the bid-ask spread for a given trading instrument – you buy at the bid and sell at the ask. However, like many things in life there is much more than that. It relies on the fact that most of the order flow on the market is “dumb” money (uninformed retail investors). While it can work well in some markets, the strategy hits its limits when it meets the “smart” money (informed investors).
Alpha trading strategies generation
The alpha generation is a strategy where the traders are trying to gain an edge by data mining and machine learning methods. An alfa is every trading strategy resulting from this generation which has reasonable expected return. Quite frequently these alphas are weak and cannot be traded on their own, so they are combined in a combination of alphas, also known as “alpha combo” strategy.
The carry trade is one of the most popular FX trading strategies. It is based on gaining from the interest rate differentials between two currencies. In the carry trade strategy implies that high interest rate currencies should lose value versus low interest rate currencies. In the basic carry trade strategy, the trader sells short forwards on currencies that are quoted at a premium (the forward FX rate exceeds the spot FX rate) and vice-versa. This strategy is not a risk-free arbitrage, because there is a chance that the FX rate suddenly changes, and the trader is exposed to exchange rate risk.
Forex triangular arbitrage
The Forex triangular arbitrage is a trading strategy based on opening positions in 3 currency pairs. For example, EURUSD, USDJPY, and EURJPY. The system is relying on catching discrepancies (arbitrage opportunities) occurring by opposing positions where the rate of one currency pair diverges from the cross rate between, the other two. For example, if the result of exchanging EUR to USD is different than the result from exchanging EUR to JPY and JPY to USD there is an arbitrage opportunity.
Commodity futures contracts roll yields
Commodity futures contracts roll yields is a strategy that aims to gain from the natural backwardation or contango occurring between the different futures contracts’ maturities. Roll yields occur from rebalancing futures positions. When a futures contract is about to expire, it needs to be replaced with another futures contract with longer expiration. If the price of the front futures contract is higher than the following month futures price, there is backwardation. If it is the opposite – the price of the front futures contract is lower than the price of the following month futures price, then there is contango.
In commodity futures markets the near-month contracts react to supply and demand changed quicker than further-month contracts most of the time. As a result, a trader can implement a trading strategy, called calendar spread which aims to gain from the difference. There are two types of calendar spread. The bull futures spread is based on buying a near-month futures contract and selling the further-month contract. The bear futures spread is the opposite – you sell the near-month contract and buy the further-month one.
The convertible arbitrage strategy is based on convertible bonds. A convertible bond is a security with hybrid properties that gives the investor the option to convert the bond from a fixed-income instrument to equity. This transformation happens according to a predefined conversion ratio of bonds to stocks when the stock price reaches a certain level (called conversion price). The convertible arbitrage strategy is based on buying a convertible bond and selling short the underlying stock.
The sentiment analysis strategy is based on extracting trading signals using machine learning algorithms applied to social media data. The process starts with the collection of social media posts (most frequently tweets) that are containing at least one keyword listed in vocabulary over a predefined time frame. The second step is cleaning the data. After this is completed the data is further processed by the machine learning algorithms aiming to extract models that can be used to predict the price movements according to the public sentiment.
We hope that this list of 21 most popular trading strategies was useful for you! Don’t forget to check out our series of educational articles on quantitative finance and systematic trading or sign up for our newsletter for additional great content!
This article is a part of a series on quantitative finance developed by “Quantitative Strategies Academy” Foundation according to its mission for the benefit of people who want to know more about quantitative analysis and automated systems.
1) 151 Trading Strategies, 2018, Kakushadze, Zura (et al.)
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