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ETF - IDMM HP RT IDMM-Article IDMM-ETFs IDMM-SP500 QAT Systems, LLC The Intra-Day Momentum Method The Intra-Day Momentum Method - ETFs The Intra-Day Momentum Method - S & P 500

Improving the Accuracy of The Intra-Day Momentum Method

In an effort to improve the accuracy of The Intra-Day Momentum Method levels, I have made a few adjustments to the approach. After creating the methodology, I needed a way to demonstrate it as well as a way to track it’s progress. I developed web-based applications to perform these tasks. After a few years, the web-based applications were created. At that time, I also created a way to track the performance of the methodology. The reason for doing this was to create a way to answer questions. Two of the many questions that I wanted the answer in regards to trading were:

  1. Does trend-following give a trader an edge?
  2. Do Fast Moves come from False Moves?

Does Trend-Following Give a Trader an EDGE?

The answer to the first question is provided in this article I wrote a while back: Does Trend Following Give a Trader an EDGE? In this article, I use the Intra-Day Momentum Method (as applied to over 400 stocks from the S & P 500) to demonstrate mathematically that trend-following would likely give a trader an edge.

Because I have been able to mathematically demonstrate, that trend following would likely give a trader an edge. I have updated the approach of the method. I now determine the trend on a longer-term time period, to determine how to trade intra-day. Keep in mind that the speed of the move is not relevant, in this research. The idea is that because the trend is likely to increase the edge intra-day, it may be magnified on a longer-term time period. My previous research demonstrates that this would likely increase the accuracy of the method.

Fast Moves, False Moves?

Do Fast Moves come from False Moves is currently a project that is still in the works. This is research and therefore, it is constantly evolving. The goal of this research is to determine what to do in the case of a Fast Move. We need to identify when a Fast Move is ‘tradeable.’ This will help us to determine when to ‘fade’ a fast move, as well.

Applying a Trend-Following Filter

In the following graphs, you will see that adding a trend-following filter would likely increase the probability of The Intra-Day Momentum Method levels. For this research, the trend for each stock was not identified. Instead, we took a look at the overall market, using the SPY.

The goal was to pick two different time periods when the SPY was trending. One data set was when the SPY was in an up-trend. The other was when the SPY was in a down-trend. The goal was to review the results for The Intra-Day Momentum Method levels on over 400 stocks from the S & P 500, for each time period. If we see an increase in the probability of success from the levels, in the direction of the trend, it suggests we should filter for the trend daily. Not all stocks would be in the same trend. We also know that different stocks are often in different phases of a trend. It is quite possible for a stock to be going in the complete opposite direction of the market. This leads us to believe that filtering each individual stock would increase the percentages even greater.

Up-Trending Market

In an Up-trending market, as defined by a traditional technical indicator. The trend was not mathematically defined. What this data shows us is that we get a higher probability of a close above the ML1 Level. Combine that with a lower probability of a close below the MS1 Level. It clearly indicates that if we can identify the trend, then we should get a more accurate approach. Using this approach, we will determine the trend on the daily and trade in that direction, intra-day. In an uptrend, both the ML1 and MS1 levels are good entry points for LONG positions.

Down-Trending Market

In a Down-Trending market, the method demonstrated the following results. A traditional technical indicator was used to identify the downward trend on the daily chart of the SPY, not each individual stock.

As you can see, the intra-day reversals were greater from the ML1 to the MS1. A larger percentage of the stocks that reached the MS1 level closed below that level as well as below the open. A smaller percentage of stocks that reached the ML1 level, closed above the ML1 level as well as the open.

Conclusion

In conclusion, you can see that applying a trend-following filter increases the probability of the success of the levels. Even though each individual stock’s trend is not the factor for determining the trend, in this research. Instead, the overall market trend, in this case, the SPY. In an up-trending market we see more reversals from the MS1 level to the ML1 level. We also see a higher percentage of closes above the ML1 level as well as the Open. In a down-trending market, we see the exact opposite. We see a larger percentage of closes below the MS1 level as well as the Open. We also see a larger percentage of reversals from the ML1 to the MS1 level. These statistics demonstrate that a trend on a larger timeframe would increase the accuracy of The Intra-Day Momentum Levels success.

What’s Next

The goal now for The Intra-Day Momentum Method is to determine the trend and trade in that direction. Currently, there is one exception. The exception appears after an intra-day reversal pattern. The best opportunity appears to be a trade in the direction of the signal that day. This is due to the fact that the intra-day reversal does not repeat all that often. Our focus will continue to be on additional patterns that indicate a change in direction may be likely. The goal is to identify more mathematical patterns that indicate overbought and oversold conditions. This will give us clues as to when changes in direction are more likely to occur.

I will be sharing more statistics on the trend-following approach in the coming weeks.

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IDMM-SP500 The Intra-Day Momentum Method The Intra-Day Momentum Method - S & P 500

The Intra-Day Momentum Method

I discuss the historical results for The Intra-Day Momentum Method. This model applied to over 400 stocks from the S & P 500. The results are from April 2015 through October 2020.