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  5. What is the best time frame for a wave count?

What is the best time frame for a wave count?

Elliott Wave analysis works well at any time frame, and so does WaveBasis.

So, the time frame(s) that you use for your wave counts is highly dependent on your trading style and objectives, particularly your preferences for trading time horizons (how long you intend to hold a trade). Other important factors to consider are the amount of time a market has been trading and whether or not it is a 24-hour market.

For example, an 8-hour chart wouldn’t make much sense for a US stock since the trading day is less than 8 hours, however, it is not uncommon to analyze 8-hour charts for forex or crypto markets. Further, a montly chart would not include much wave information for a market with a shorter (2-4 years or so) trading history, so a daily or weekly wave count would be more effective in most of these cases. So, there is no single “best” timeframe that works for all markets or all traders.

So, if you like longer-term trades (weeks, months, years), a daily, weekly, or monthly wave count will likely work best for you as a starting point to determine the longer-term wave count context.

If you prefer faster trades (minutes, hours, or days), then starting with a larger timeframe to determine the market context then drilling down to smaller timeframes (5, 15, 30, 60 minutes, etc.) to refine your analysis will tend to give you the best results.

As noted above, regardless of the timeframe that you choose to refine your trade entries and exits, for best results, it is recommended that you begin your analysis with a larger timeframe wave count, which will help to determine the overall context of a particular market. This approach will help you answer important questions such as “are we in a long term correction? or “are we near the top of a long term uptrend?”, for example. Once you have clarity on the larger context, you can work your way down to smaller time frame(s) as desired, according to your specific objectives.

 

A helpful article on our blog explores multi-timeframe analysis.

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