Trade Smarter with Multi-Timeframe Analysis: A Comprehensive Approach | Quantra Classroom

Today, we dive into the fascinating world of multi-timeframe analysis. Understanding how different timeframes can impact your trading decisions is a crucial skill for any trader, and we're here to guide you through it. Let’s explore how you can trade using multiple timeframes.

Strategy Returns

The result of the multiple timeframes strategy on Apple stock from the year 2012 to the year 2021 is shown below:

The cumulative returns from the strategy remained flat during the initial years. But in mid-2020 there was a sharp increase in returns. By the year 2021, the strategy has gained a cumulative return of 2.76x and with this, we can conclude that the strategy has performed quite well, especially in recent years.

The Power of Multiple Timeframes

To begin, let's consider a stock you're familiar with—Apple.

By examining its weekly chart from May to September 2022, we observe a strong and consistent uptrend during that period. However, when we shift our focus to the daily chart for the same timeframe, we notice that the uptrend is not as smooth, with occasional pullbacks. This discrepancy arises because different timeframes provide different perspectives on the market.

Using a single timeframe limits your analysis and understanding of the market. Therefore, traders often opt for multiple timeframes to overcome this drawback.

Multi-timeframe analysis involves studying price charts across more than one timeframe. By examining various timeframes simultaneously, you can gain a holistic view of the market and make well-informed trading decisions.

It's important to note that no timeframe is inherently superior to others. Your choice of timeframe should align with your trading style and preferred holding period.

Understanding Timeframe Categories

To make the most of multi-timeframe analysis, it helps to categorise the timeframes into three categories:?higher, medium, and lower timeframes:

• The higher timeframe represents the longest period and provides insight into major trends.
• The medium timeframe helps identify minor trends and serves as a better timeframe for establishing stop-loss levels.
• The lower timeframe is useful for generating trading signals and identifying optimal entry points.

Selecting Timeframes and Implementing the Scaling Factor

Now that you’ve seen the categories of timeframes, the only question remaining is - how can we ascertain these timeframes? Well, you can do this by using a scaling factor. A scaling factor is a value that is multiplied or divided with other quantities to change their magnitude or size.

The steps through which you can select timeframes is as follows:

Step 1:?Select the Medium Timeframe

Consider your trading style and desired holding period to determine the most suitable medium timeframe. For instance, if you're a swing trader, you might opt for an hourly, 4-hourly, or daily chart.

Step 2:?Obtain the Higher and Lower Timeframes

The higher and lower timeframes depend on the medium timeframe, and we'll use a scaling factor to calculate them. Typically, a scaling factor in the range of 3 to 5 is applied. Here's how it works:

Example:

Medium timeframe: 1 hour

Scaling factor: 4

Higher timeframe: Medium timeframe (1 hour) × Scaling factor (4) = 4 hours

Lower timeframe: Medium timeframe (1 hour) ÷ Scaling factor (4) = 15 minutes

By implementing this scaling factor, you can effectively obtain higher and lower timeframes that complement your medium timeframe.

Strategy Implementation

To set the entry and exit points, you can use any technical indicator and confirm its signal on multiple timeframes. In this example, we will be using the moving average crossover strategy and confirm its signals over?two timeframes, i.e., the higher and lower timeframes.?

Step 1: Select Timeframes

We are going for intraday trading hence we want a timeframe that captures the price behaviour over the day, hence the higher timeframe should be less than 1 day, 240 minute chart (4 hours) is the most used higher timeframe by intraday traders for their multiple timeframe strategies.

Now, we will select the lower timeframe based on a scaling factor of 4. If we divide 4 hours by 4 (scaling factor) it works out to 1 hour. But using an hourly timeframe would generate very few signals throughout the day. So let's divide it further by 4. Therefore the lower timeframe works out to 15 minutes.

Step 2: Set the entry and exit conditions

Here's the SMA crossover strategy logic:

• When the short SMA crosses the long SMA from below, it indicates an uptrend.
• When the short SMA crosses the long SMA from above, it indicates a downtrend.

Therefore, the?condition to enter?a long trade is:

Bullish crossover in the higher timeframe

Bullish crossover in the lower timeframe

After the bullish crossover is confirmed in the longer timeframe, we will check the lower timeframe for another bullish crossover, and enter only when both of these conditions are met.

When can you?exit?this trade?

You can exit the long trade when a bearish crossover takes place in the lower timeframe.

Note: We have used the moving average indicator, but you can use any technical indicator of your choice and confirm its signals on multiple timeframes.

The returns are more or less flat during the initial years. Here, an entry signal was not generated because the bullish crossover was not confirmed in both the timeframes. The returns picked up in 2014 to 2016. And in 2020-2021, the strategy gave exponential returns.

The Python code for multi-timeframe strategy is covered in this?unit?of the?Technical Indicators Strategies in Python?course. You need to take a Free Preview of the course by clicking on the green-coloured Free Preview button on the right corner of the screen next to the FAQs tab and go to Section 23 and Unit 9 of the course.

Conclusion

Here’s a quick summary of how the three timeframes can be used:

• The higher timeframe determines the overall trend direction in which we should trade
• The medium timeframe serves well for establishing stop-loss levels.
• The lower timeframe assists in confirming the trend, generating trading signals and identifying suitable entry points.

While we have discussed the benefits of utilising three timeframes and its implementation in this email. The strategy that we implemented above has generated a cumulative return of 2.76x. To learn more about multi-timeframes and a few more strategies based on technical analysis, head to our course on Technical Indicators Strategies in Python.

IMPORTANT DISCLAIMER:?This is for educational purposes only and is not a solicitation or recommendation to buy or sell any securities. Investing in financial markets involves risks and you should seek the advice of a licensed financial advisor before making any investment decisions. Your investment decisions are solely your responsibility. The information provided is based on publicly available data and our own analysis, and we do not guarantee its accuracy or completeness. By no means is this communication sent as the licensed equity analysts or financial advisors and it should not be construed as professional advice or a recommendation to buy or sell any securities or any other kind of asset.