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The financial markets are dynamic and constantly fluctuating. Many trading opportunities are fleeting — and do not last for more than a few seconds or minutes. This makes it nearly impossible to manually track and identify such price changes, plan your trades and execute them promptly — before the opportunity passes.
Here is where algorithmic trading can help. An algorithmic trading strategy involves using computer programs that offer a set of predefined instructions to identify triggers in the market and execute a trade based on such signals. They essentially automate the trading process, improve the speed and accuracy of your trades and even reduce the cost of trading in the long run.
In this article, we explore some of the best algorithmic trading strategies you can use to tap into price changes in the financial market.
The Best Algorithmic Trading Strategies to Use
Algorithmic trading is only as effective as the fundamentals of the strategy you use. Different strategies work in different market conditions. So, to improve the outcome of your trades, you need to find the right trading strategies for the specific trend or phase prevailing in the financial markets. Here are some effective strategies to consider for different goals.
1. Trend-Following Trades
In algorithmic trading, trend-following trades aim to identify and follow prevailing market trends. The algorithms used in these trading strategies analyse vast arrays of historical data and real-time market movements to spot ongoing trends. Based on such analysis, trades that align with the direction of the trend are executed. The aim is to capture profits from the current market movement.
So, such algorithms will typically buy during uptrends and sell when there is a downtrend. Key technical indicators like moving averages, trend lines and momentum indicators are integral to trend-following algorithmic trading. The strength of such a trade depends on how well the algorithm analyses market data and captures market movements. With predefined rules, these algorithms should be able to initiate trend-following trades swiftly, before any reversals occur.
2. Momentum Trading
Momentum trading involves buying and selling stocks or securities based on the strength of their recent price movements. Algorithmic trading strategies can help you identify and act on such fast-moving trends in stock prices. You can use algorithms that scan the financial market for signs of strong price movements coupled with high trading volumes. Based on the results, trades can be initiated to capitalise on the ongoing momentum.
Algorithms used in this trading strategy rely on technical indicators like the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD) and volume indicators to gauge the strength and potential sustainability of a trend. When a suitable momentum is detected, the algorithm ensures that a trade is initiated to profit from the continuing trend. The position is typically closed as soon as signs of a reversal are detected.
3. Mean Reversion
Mean reversion is based on the idea that despite any significant volatility, the prices of stocks eventually tend to revert to their mean or average price levels. Algorithmic trading can help you leverage this phenomenon in the financial market. This is because algorithms used for mean reversion trades operate on the assumption that high and low prices are temporary. They are designed to identify stocks that have moved significantly away from their historically average prices.
These algorithms then execute trades based on the expectation that the prices will revert to their historical averages. Technical indicators like Bollinger Bands, moving averages and the standard deviation of stock prices can be crucial in creating algorithms that can define the average price of a stock and identify potential mean reversion opportunities.
4. Index Fund Rebalancing
Investing in index funds is a passive investment strategy that aims to replicate the constituents and weightage in a benchmark index. These funds are periodically rebalanced to minimise the tracking error and realign their portfolios with the index they are tracking. Index fund rebalancing can result in significant purchases or redemption of certain stocks, leading to noticeable price movements. With an algorithmic trading strategy, you can capitalise on these price changes.
Algorithms can be configured to leverage such large-scale rebalances. You can develop an algorithmic trading strategy to identify when such rebalancing may occur and which stocks are likely to witness substantial buy or sell movements. This makes it easier to initiate trades ahead of a potential rebalancing event, so you can gain from the subsequent price movements in the stocks concerned.
5. Arbitrage
Arbitrate trading is the practice of capitalising on small price discrepancies in the same asset that is trading in two different financial markets. One of the most common examples of such an arbitrage opportunity is a stock that is trading at two slightly different prices on the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE). However, to leverage this price difference, you need to pinpoint such opportunities with speed and accuracy.
Here is where an algorithmic trading strategy can be a game changer. By configuring an algorithm to track and identify such price discrepancies, you can initiate your buy and sell orders promptly, before the fleeting price gap closes. Algorithms can efficiently monitor multiple market segments and exchanges simultaneously, identify price gaps and even execute trades as per your predefined instructions.
6. Black Swan Catchers
Black swan events are unexpected and rare market movements that result in significant adverse or downward price movements. In other words, they are like market crashes that cannot be easily predicted or anticipated based on prevailing economic indicators. Some examples of such black events include the global financial crisis in 2008 and the COVID-19 pandemic in 2020.
A black swan catcher is a trading strategy that attempts to tap into the steep market volatility after such an unexpected event. With algorithmic trading, you track and monitor price changes in the derivatives market or other speculative segments — which often record heightened activity during or after a black swan market event. This is manually not possible given the lack of historical data to correlate such developments.
7. Risk-On/Risk-Off Trading
In risk-on/risk-off trading, your market positions are tailored to suit your changing risk tolerance levels based on the current market sentiment. This means that your investment preferences will oscillate between safe and risky assets. In a risk-on scenario, you may favour high-risk stocks because the systematic or market risk may be low. In a risk-off scenario, you may move towards safer investments because the financial market may be inherently riskier.
You can use algorithmic trading to track macroeconomic data, local and global markets and even company-specific data. This can be pivotal to accurately identifying risk-on and risk-off phases. Your trading algorithms can also be configured to effect quick transitions between risk-on and risk-off positions, so you can capitalise on the various market developments as and when they arise.
8. Inverse Volatility Trading
Inverse volatility trading involves adjusting your market positions based on the prevailing market volatility. Here, you typically increase your exposure to the market as the volatility decreases and reduce your exposure to the financial market when the volatility rises. An algorithmic trading strategy can help you leverage inverse volatility techniques more effectively because it can process complex market data to accurately assess changes in volatility levels.
These algorithms typically rely on volatility indices, market trends and historical data to predict volatility shifts in the market. The algorithm can be configured to increase your investments in a particular stock or security when the volatility is expected to decrease. Conversely, if an increase in volatility is expected, the algorithm may prioritise safer investments in your portfolio’s asset allocation.
9. News-Based Trading
News-based trading using algorithms involves tracking and acting on news stories, economic reports and even media feeds in real time. These algorithms typically use Natural Language Processing (NLP) to analyse relevant news items and monitor how the market reacts to such developments. Then, based on how stock prices are affected, the algorithmic trading strategy can initiate a new trade or close an existing position.
For example, when a news item breaks that is expected to have a positive impact on a company’s stock, the algorithm may automatically execute buy orders in that company’s stock. Conversely, if a negative development occurs, the algorithm can be configured to exit any positions you have in that stock. This algorithmic trading strategy can also help you keep pace with emerging trends like sustainable investing by tracking ESG factors and news.
Conclusion
These algorithmic trading strategies rely on the same technical and fundamental principles that the average trader adopts. The difference is that when you use algorithmic trading strategies, you can execute more traders at a faster pace, thereby capitalising on market opportunities as and when they arise.
As a beginner to trading, algorithms may seem daunting and difficult to grasp. However, with a little bit of research, you will discover the world of free and easy-to-use APIs that can elevate your algorithmic trading strategies to a different level. That said, when you engage in algo trading, ensure that you have adequate hedging and stop-loss measures in place to limit the downside risk and keep the losses, if any, within an acceptable level.
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