Momentum Trading: Types, Strategies and More

Momentum trading, a popular approach in short-term trading, revolves around spotting changing asset prices and capitalising on these changes by aligning positions with the anticipated price movement. Traders anticipate continued price momentum in the chosen direction, driving their investment decisions.

With this blog, you will learn all about this dynamic strategy driving success in financial markets. Moreover, we will discuss the intricacies of this approach, from its fundamental principles to its practical applications.

We will delve into the basics of momentum trading, the trading example with Python ending the blog with the FAQs to clarify and navigate the world of momentum trading with finesse.

Before moving ahead, you must know that the underlying principle for momentum trading is to “buy high and sell higher”, and vice-versa.

Let us find out more about momentum trading with this blog that covers:

• What is momentum trading?
• How does momentum trading work?
• Types of momentum trading
• Factors affecting momentum trading
• Long term vs. Short term momentum trading approaches
• Technical analysis tools or indicators for momentum trading
• A simple momentum trading strategy in Python
• How to start momentum trading?
• Risks involved while implementing momentum trading strategy
• Key takeaways while implementing momentum trading strategy
• FAQs about momentum trading

What is momentum trading?

Momentum trading involves buying or selling assets based on recent price trends. Traders seek assets with strong upward or downward momentum over a specified period, such as days or weeks.

This strategy relies on the idea that market trends persist before reversing, allowing traders to maximise returns by following the trend. Hence, you can analyse assets in the short-term and buy the assets whose price is rising. Then sell those assets when the price trend seems to become weak.

Successful momentum trading requires careful analysis of price movements and market conditions to identify opportunities and manage risk effectively. ⁽¹⁾

Fact:
Richard Driehaus, a famous investor, is considered the Father of Momentum Investing and his investing techniques have become the basics of Momentum Trading. Driehaus believed in selling the losers and letting the winners ride while reinvesting the money from the losers in other stocks that were beginning to show momentum.

Next, we will find out the working of momentum trading to learn the intricacies of this trading practice.

How does momentum trading work?

Let us take an example here to learn about the working of momentum trading.

Think of momentum trading as a moving car. The speed is slow as you start moving forward. This is when you identify a stock which is increasing in price.

As the car accelerates, the speed increases. If you have identified the stock and purchased it, your investment now starts to grow.

On seeing a red traffic signal, the car decelerates and the speed reduces. This is similar to when you exit your position at a profit on seeing a momentum loss in the asset price. ⁽²⁾

Going forward, let us see the steps below that take place in momentum trading.

• Identifying Strong Price Trends: Momentum traders look for assets that have exhibited strong price movements in a particular direction over a defined period, such as days, weeks, or months. They typically focus on assets with significant price momentum, either upwards (bullish) or downwards (bearish).
• Using Technical Indicators: Momentum traders often use technical indicators to confirm price momentum and identify potential entry and exit points. Common indicators include moving averages, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and stochastic oscillators. These indicators help traders gauge the strength and direction of the price trend.
• Establishing Entry and Exit Criteria: Momentum traders establish specific criteria for entering and exiting trades based on their chosen indicators and trading strategy. For example, they may enter a long position (buy) when the price breaks above a certain moving average or when the RSI indicates oversold conditions. Conversely, they may enter a short position (sell) when the price breaks below a moving average or when the RSI indicates overbought conditions.
• Managing Risk and Position Sizing: Risk management is crucial in momentum trading to protect against losses. Traders typically set stop-loss orders to limit potential losses and use position sizing techniques to manage the amount of capital allocated to each trade relative to their overall portfolio size and risk tolerance.
• Adapting to Changing Market Conditions: Momentum traders continuously assess market conditions and adjust their strategies as needed to capitalise on new opportunities or mitigate risks. They remain flexible and responsive to changes in price trends, volatility, and other market factors.

Going forward, let us learn about the types of momentum trading.

Types of momentum trading

There are two main types of momentum trading which are:

Time-Series Momentum

• Application: In time-series momentum trading, traders assess the historical performance of individual assets over a specific period.
• Methodology: Traders identify assets that have achieved a certain percentage profit threshold over a specified historical period, such as the past 3, 6, or 12 months.
• Execution: Assets exceeding this threshold are bought, anticipating that their positive momentum will continue. The focus is solely on the performance of each asset relative to its own historical performance.
• Example: If a stock has gained more than 10% over the past 6 months, it may trigger a buy signal under a time-series momentum strategy.

Cross-Sectional Momentum

• Application: In cross-sectional momentum trading, traders compare the performance of assets relative to each other within a portfolio or universe of assets.
• Methodology: Traders rank assets based on their recent performance relative to other assets in the same universe. For instance, they may rank stocks based on their returns over the past 3 months.
• Execution: The top-performing assets (e.g., top 10) are bought, while the bottom-performing assets are either sold short or avoided. The focus is on selecting assets with the strongest relative performance compared to their peers.
• Example: If a stock ranks among the top 10 performers in terms of returns over the past 3 months compared to other stocks in the same sector, it may be considered for inclusion in a cross-sectional momentum strategy.

In summary, time-series momentum strategies focus on individual asset performance relative to their historical performance, while cross-sectional momentum strategies compare the performance of assets relative to each other within a portfolio or asset universe. Both types of momentum strategies aim to capitalise on trends and momentum in asset prices, but they differ in their approach to selecting assets for trading.

Certain factors affect momentum trading and it is important to know these factors to take necessary actions for lessening the harmful impacts of the same.

Factors affecting momentum trading

The short-term price change of an asset is affected by several factors. Some of the major factors as well as the sub-factors are as follows.

Market Environment

Market Conditions: The overall market environment, including trends, volatility, and liquidity, can significantly impact momentum trading. Strong trending markets with low volatility often provide favourable conditions for momentum strategies to thrive, as trends are more likely to persist. Conversely, choppy or range-bound markets with high volatility may result in false signals and challenges for momentum traders.

Time Horizon: The choice of time horizon or the duration over which momentum signals are evaluated can affect trading outcomes. Shorter-term momentum strategies may capture more frequent but smaller price movements, while longer-term strategies may aim to capitalise on more significant trends. Traders need to align their time horizons with their risk tolerance, trading style, and market conditions.

Asset Selection: The choice of assets to trade can significantly impact momentum trading performance. Different assets exhibit varying levels of momentum and volatility, and certain markets may be more conducive to momentum strategies than others. Traders need to select assets that demonstrate clear and persistent trends and avoid assets with choppy price action or low liquidity.

Risk Management

Risk Management Techniques: Effective risk management is critical for mitigating losses and preserving capital in momentum trading. For example, trend following strategies can be inherently risky, as they rely on trends continuing in the expected direction. Traders need to implement risk management techniques such as setting stop-loss orders, position sizing, and diversification to manage downside risk and protect against adverse market movements.

Psychological Factors: Trader psychology plays a significant role in momentum trading, as it can influence decision-making, risk-taking behaviour, and emotional responses to market fluctuations. Overconfidence, fear of missing out (FOMO), and reluctance to cut losses can lead to poor trading decisions and undermine the effectiveness of momentum strategies. Traders need to maintain discipline, manage emotions, and adhere to their trading plan to avoid behavioural biases.

Information and Analysis

News and Events: Market-moving news, economic data releases, geopolitical events, and corporate announcements can impact asset prices and disrupt existing trends. Momentum traders need to stay informed about relevant news and events that could affect their positions and adjust their trading strategies accordingly. Event-driven momentum traders may actively trade around specific catalysts to capitalise on short-term price movements.

Market Sentiment: Market sentiment, including investor sentiment and sentiment indicators, can influence the direction and strength of price trends. Bullish sentiment may fuel upward momentum, while bearish sentiment may lead to downward momentum. Momentum traders often monitor sentiment indicators, such as surveys, sentiment indices, and options positioning, to gauge market sentiment and identify potential trading opportunities.

Regulatory Factors: Regulatory changes, policy decisions, and interventions by central banks or government authorities can impact market dynamics and disrupt existing trends. Momentum traders need to stay informed about regulatory developments and anticipate their potential implications for asset prices. Regulatory announcements or interventions can trigger sudden reversals or accelerations in momentum, requiring traders to react swiftly and adjust their positions.

Let us now see the difference between long term and short term momentum trading.

Long term vs. Short term momentum trading approaches

Below is a detailed explanation of the difference between long and short term momentum trading approaches in a tabular format.

Now, let us see the technical analysis tools or indicators that help identify and confirm momentum trading opportunities.

Technical analysis tools or indicators for momentum trading

Here are some commonly used technical analysis tools and indicators for momentum trading: ⁽¹⁾

Moving Averages:

• Simple Moving Average (SMA): Helps smooth out price data by averaging closing prices over a specified period. Crossovers of shorter-term SMAs above SMAs can signal bullish momentum, while crossovers below can indicate bearish momentum.
• Exponential Moving Average (EMA): Similar to SMA but gives more weight to recent prices, making it more responsive to short-term price changes.

Relative Strength Index (RSI):

• Measures the speed and change of price movements to identify overbought or oversold conditions. RSI values above 70 indicate overbought conditions and potential selling opportunities, while values below 30 suggest oversold conditions and potential buying opportunities.

Moving Average Convergence Divergence (MACD):

• Consists of two lines: the MACD line and the signal line. Crossovers between these lines can indicate changes in momentum. When the MACD (one of the trading indicators) line crosses above the signal line, it may signal bullish momentum, and vice versa.

Stochastic Oscillator:

• Measures the current closing price relative to the high-low range over a specified period. Values above 80 indicate overbought conditions, while values below 20 suggest oversold conditions. Crosses of the %K and %D lines can signal changes in momentum.

Bollinger Bands:

• Consist of a middle band (SMA) and upper and lower bands representing standard deviations of price volatility. Price moves beyond the bands can signal potential momentum shifts. Narrowing bands may precede explosive price moves, while widening bands may indicate increased volatility.

Volume Analysis:

• Helps confirm the strength of price movements. Increasing volume during price advances or declines can confirm momentum, while decreasing volume may suggest weakening momentum.

Price Patterns:

• Chart patterns such as flags, pennants, triangles, and channels can indicate potential momentum continuation or reversal patterns. Breakouts from these patterns often signal momentum entry points.

Average True Range (ATR):

• Measures market volatility by calculating the average range between high and low prices over a specified period. Higher ATR values may indicate increased volatility and potential momentum opportunities.

Ichimoku Cloud:

• Consists of multiple lines that provide support, resistance, and trend direction signals. The cloud's thickness and the position of price relative to the cloud can indicate momentum, strength and direction.

Traders often combine multiple indicators to confirm momentum signals and reduce false signals. It's essential to understand each indicator's strengths, weaknesses, and interpretation methods to effectively use them in momentum trading strategies. Additionally, backtesting and practising with different combinations of indicators can help traders identify the most suitable tools for their trading style and preferences.

Moving ahead to building a simple momentum trading strategy, we must first discuss how the detection of momentum takes place.

A simple momentum trading strategy in Python

The detection of a momentum trading opportunity is very important so that you can time your entry position in an asset. To detect momentum, you can either use the technical indicators mentioned above or use statistical analysis.

Statistical analysis like the Hurst Exponent test can be used here. The Hurst Exponent relates to the autocorrelation of the asset and can be used to identify if the asset is trending or not. Here you will get to know what AutoCovariance and AutoCorrelation functions are.

The various methods to detect momentum are covered with detailed examples in the momentum trading strategies course on Quantra. For complex trading strategies, you can even combine signals from these indicators to obtain a more reliable momentum detection algorithm.

You can read more about Five Indicators To Build Trend-Following Strategies here.

A Simple Momentum Trading Strategy

Let’s study a simple momentum trading strategy using moving averages now. Here you will see the implementation of the famous Golden Cross and Death Cross algorithm. This algorithm uses two moving average lines.

The two moving averages are the slow-line, or the slow-moving average with a larger lookback period, say 50 days. And the fast-line, or the fast-moving average with a smaller lookback period, say 10 days.

• The golden cross is a chart pattern which indicates a bullish price trend. A golden cross occurs when the fast-line crosses the slow line in an upward direction (i.e. from below to above).
• A death cross is indicative of a bearish trend. This occurs when the fast-line crosses the slow line in a downward direction (i.e. from above to below).

A simple strategy can be built to long the asset when a golden cross occurs, and short it when a death cross occurs.

The Python logic for finding signals using the moving averages is as follows.

Output:

In the Python code above, the buy and sell signals are determined based on the fast moving average (MA) of the stock price. Here are the observations:

• Buy Signal: The buy signal occurs when the fast moving average crosses above or is above the slow moving average.
• Sell Signal: The sell signal occurs when the fast moving average crosses below or is below the slow moving average.

There is an instance of each “buy signal and sell signal” shown in the graph plotted above.

Now, let us see the cumulative returns below.

Output:

In the above plotted graph, the cumulative returns are shown for the stock AAPL. It can be seen that the cumulative returns of the strategy are going in the upward direction over a period of time.

It is important to note that backtesting results do not guarantee future performance. The presented strategy results are intended solely for educational purposes and should not be interpreted as investment advice. A comprehensive evaluation of the strategy across multiple parameters is necessary to assess its effectiveness.

Now that we have explored important concepts around momentum trading, let us see how to start momentum trading.

How to start momentum trading?

To initiate momentum trading, it's essential to thoroughly grasp the concepts of momentum trading, including how to identify momentum, understand the factors influencing it, and recognise the associated risks. Additionally, platforms like Blueshift offer an excellent opportunity to create, backtest, and execute your trading strategies live.

To study momentum trading in detail, you can check out the Quantra course on momentum trading strategies where the concepts are explained with examples and worked out in Python code. The course imparts a multitude of trading strategies that empower you to seize diverse momentum types employing indicators. It also guides you in executing momentum trading via asset futures and effectively utilising event-driven trading strategies. The momentum trading strategies are implemented on Blueshift and templates for IBridgePy are provided too!

Risks involved while implementing momentum trading strategy

Similar to any trading strategy, momentum trading carries inherent risks that traders must be mindful of.

Some of these risk factors include:

• Timing of Entry and Exit: Momentum trading strategies rely heavily on precise timing. Delayed entry positions may result in losses, while premature exits can lead to missed profit opportunities even after correctly identifying momentum shifts.
• High Transaction Costs: Due to the frequent trading nature of momentum strategies, transaction costs can accumulate quickly compared to long-term investment strategies like value investing or buy-and-hold approaches, impacting overall profitability.
• Time-Intensive Nature: Momentum traders must diligently monitor market opportunities and stay abreast of relevant news and developments related to the assets being traded, requiring significant time and attention.
• Market Sentiment: Momentum trading tends to perform optimally in bullish market conditions, as bearish sentiment can lead to herd behaviour and diminish profit potential. This phenomenon can be observed in the decreased profitability per trader during adverse market conditions.
• Trend Reversals: While momentum strategies assume that trends will persist, there is always the risk of trend reversals. Unexpected reversals can invalidate the momentum strategy and result in losses.

To mitigate these risks, it's crucial to implement effective risk management techniques when engaging in live market trading. Utilising proper position sizing and trailing stop-loss orders can help minimise drawdowns and limit exposure to market fluctuations, enhancing overall risk management and preserving capital.

We will now see the key takeaways from all the concepts we have gone through with regard to momentum trading.

Key takeaways while implementing momentum trading strategy

Here are some of the key takeaways for implementing a momentum trading strategy.

• Understand the Strategy: Grasp the basics of momentum trading and key technical indicators.
• Define Clear Criteria: Establish entry/exit signals, position sizing, and risk management rules.
• Select Assets Wisely: Choose assets with strong trends and volatility.
• Use Technical Indicators: Employ indicators to confirm signals and avoid reliance on one.
• Manage Risk Effectively: Set stop-loss orders, diversify, and avoid over-leveraging.
• Stay Disciplined: Stick to your plan and avoid emotional decision-making.
• Stay Informed: Stay updated on market news and events impacting your trades.
• Continuous Learning: Adapt and refine your strategy based on market dynamics.
• Backtest and Evaluate: Test your strategy thoroughly before live trading.
• Start Small and Scale Up: Begin with a small capital allocation and grow cautiously.

Coming to the end of the blog, let us find the answers to some frequently asked questions which will help clarify your understanding of momentum trading.

FAQs about momentum trading

Here are some frequently asked questions (FAQs) that can help you with a common query regarding momentum trading:

Q: What are some common technical indicators used in momentum trading?
A: Popular technical indicators used in momentum trading include moving averages, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), stochastic oscillator, and Bollinger Bands, among others.

Q: What types of assets are suitable for momentum trading?
A: Liquid assets with high trading volumes and volatility are often preferred for momentum trading. Stocks, currencies (forex), commodities, and certain derivatives such as futures and options can be suitable for momentum trading.

Q: What are the risks associated with momentum trading?
A: Risks of momentum trading include mistiming trades, high transaction costs, market reversals, overbought or oversold conditions, and psychological biases such as fear of missing out (FOMO) or fear of losses.

Q: How do traders manage risk in momentum trading?
A: Traders manage risk in momentum trading by using stop-loss orders to limit potential losses, diversifying their portfolios, sizing positions appropriately, and implementing proper risk management techniques.

Q: Can momentum trading be combined with other strategies?
A: Yes, momentum trading can be combined with other trading strategies such as trend following, mean reversion, or breakout trading to create hybrid strategies that suit individual trader preferences and market conditions.

Q: Is momentum trading suitable for beginners?
A: Momentum trading requires a solid understanding of technical analysis and market dynamics. While beginners can learn and practice momentum trading, it's essential to start with proper education, risk management, and realistic expectations.

Q: Are there any specific timeframes for momentum trading?
A: Momentum trading can be applied across various timeframes, including intraday, short-term (days to weeks), and medium-term (weeks to months). Traders choose timeframes based on their trading style, preferences, and market conditions.

Q: How to use Machine Learning for momentum trading?
A: Momentum trading relies on the idea that assets that have performed well in the past can perform well in the future as well, and vice versa. Machine learning can be a powerful tool in identifying and exploiting momentum patterns in financial markets.

Here's how Machine Learning can be utilised:

• Feature Selection: Machine learning algorithms can help identify relevant features or variables that contribute to momentum in asset prices. These features could include historical price data, trading volume, market sentiment indicators, economic data, and more.
• Model Training: Various machine learning algorithms, such as decision trees, random forests, support vector machines, and neural networks, can be trained to predict future price movements based on historical data. The models can learn complex patterns and relationships in the data to identify potential momentum opportunities.
• Algorithm Implementation: Once trained, the machine learning models can be used to generate trading signals. These trading signals can indicate when to buy, sell, or hold assets based on predicted momentum trends.
• Risk Management: Machine learning algorithms can also be employed to optimise risk management strategies by dynamically adjusting position sizes, setting stop-loss levels, and incorporating other risk factors into the trading strategy.

Q: How do you compare other trading styles with momentum trading?
A: Momentum trading is just one of many trading styles, each with its own characteristics and strategies.

Here's a comparison with some other common trading styles:

• Value Investing: Value investors seek to identify undervalued assets trading below their intrinsic value and hold them for the long term. Momentum trading focuses on short-term price trends and does not necessarily consider fundamental valuation metrics.
• Day Trading: Day traders buy and sell assets within the same trading day, aiming to profit from intraday price movements. Momentum trading can involve holding positions for longer periods, ranging from days to weeks, depending on the strength of the momentum signal.
• Trend Following: Trend followers aim to capture trends in asset prices, whether upward or downward. While momentum trading also exploits price trends, it specifically focuses on the acceleration of those trends rather than the direction alone.
• Arbitrage: Arbitrageurs exploit price discrepancies between different markets or assets to make risk-free profits. Momentum trading does not necessarily rely on price discrepancies but rather on the persistence of price trends over time.

Q: Is momentum trading risky?
A: Yes, momentum trading carries risks due to market volatility, potential overvaluation, false signals, liquidity concerns, behavioural biases, regulatory changes, and leverage usage. Effective risk management is crucial for mitigating these risks.

Q: Where can I learn more about momentum trading?
A: There are numerous resources available, including books, online courses, articles, forums, and trading platforms that offer education and insights into momentum trading strategies and techniques. It's essential to seek reputable sources and continue learning and adapting to evolving market conditions.

Conclusion

Momentum trading offers traders a powerful strategy to capitalise on existing market trends and generate maximum returns by buying high and selling higher, or vice versa. By identifying assets with strong price momentum and employing effective technical indicators and strategies, traders can navigate volatile markets and seize lucrative opportunities.

With careful risk management and continuous learning, momentum trading can be a rewarding endeavour for traders. Whether you're a beginner or a seasoned trader, understanding the concepts, strategies, and risks associated with momentum trading is essential for success in today's dynamic financial markets.

If you wish to learn more about momentum trading, you must enroll in the course on Momentum Trading Strategies. This course will help you with the knowledge needed to create time series and cross sectional momentum strategies on stock, stock indices, fixed income, and commodities futures. Moreover, you can learn to quantitatively analyse time series, portfolio returns and risks, and design and backtest momentum trading systems.

Author: Chainika Thakar (Originally written by Gaurav Singh)

Note: The original post has been revamped on 18th March 2024 for recentness, and accuracy.

Disclaimer: All data and information provided in this article are for informational purposes only. QuantInsti^® makes no representations as to accuracy, completeness, currentness, suitability, or validity of any information in this article and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. All information is provided on an as-is basis.