## Overall Winning Percentage

The database of trades currently has 654 trend trade examples. These trades occurred from 9/06/19 to 12/05/19. This data will be updated on a monthly basis.

As additional data points are added, this page will be updated, so be sure to bookmark it and check it a few days after the 1st of the month.

**As explained on the previous page, the information does not constitute a trading strategy or trading advice. Past performance is not necessarily indicative of future results.**

The purpose of the page is to use the information to prepare a trading strategy. For example, use of a data point may reduce the number of losses, but it may also reduce the overall number of wins, and reduce overall profits. You may also find that use of a data point may also reduce the number of trades that don’t reach your profit target.

All of the this information needs to be evaluated and balanced to inform your trading decisions.

**There were 504 wins in the 654 trades, which gives us a 77% winning average. The average win was 16 ticks.**

## Broken Reversal

There were 465 trades when no Broken Reversal Pattern was in play. **There were 361 wins in the 465 trades, which gives us a 77.6% winning average. The average win was 17 ticks. **

This does not seam like much of an improvement. In the first 3 weeks of the data collection, this number was much higher. Then on 9/24/19 there was breaking (bad) news involving trade talks that introduced volatility due to illiquidity. After that news event all of the statistical averages suffered.

The win rate of a trend trade after a Broken Reversal pattern was 75.6%. The average win was 16 ticks.

## Exhaustion

There were 568 trades when the Trend Trade did not follow an Exhaustion Pattern. **There were 449 wins in the 568 trades which gives us a 79% winning average. The average win is about the same at 16.8 ticks.**

There is sone important commentary here. When we look at the data point there were 86 trend trades that followed an exhaustion pattern. The winning percentage of these trades was only 63.9%. The average win was 15.6 ticks. This is completely expected and validated the data point.

What is also interesting is the statistic for the previous KP2 Ratio. For losing trades the average was 86 and for winning trades it was 96. I would have expected the opposite. The KP2 Ratio is measuring limit order density.The previous density was opposing our trade. The current density statistic was 95.3 for winning trades and 83.1 for losing trades. This is what would be expected.

## Broken Reversal and Exhaustion

When both the Broken Reversal and Exhaustion data points are combined we have a total of 379 trades. **There were 306 wins which gives us an 80.7% winning average. The average winning ticks was 17.1 ticks.**

## Trading With Classification

Trading with the classification system had 204 trades. This meant that there was no opposing previous or current KP2 or KP3 values. **There were 163 wins giving us a 79.9% winning average. The average win was 16.9 ticks.**

The market traded 14 points higher (12/12/19) after the trade on the right. So another point of discussion to developing a trading plan is the following. In order to develop a trading plan, you should determine if staying in a trade leads to a more favorable outcome or should you pull out of the trade at break even.

## Trading With Divergences

Trading with divergences had 188 trades. **There were 144 wins giving us a 76.5% winning average. The average win was 16.2 ticks. **

A divergence in the current trade reflects a accumulation / distribution pattern or a special divergence pattern. The overall majority were accumulation and distribution. By definition there is usually heavy trading against out trend trade position in an accumulation / distribution pattern.

## No Broken Reverals or Exhaustion

Trading with divergences but not during a Broken Reversal or Exhaustion pattern had 113 trades. **There were 93 wins giving us a 82.3% winning average. The average win was 16.8 ticks. **

Again using the Broken Reversal and Exhaustion data point does a great job in increasing the winning average.

Here is an example of the special divergence, which was very uncommon.

Here is an example of the more common accumulation pattern.

Here is an example of the more common distribution pattern.

Note the high KP2 and KP3 numbers going into the trade. This is why this trade is a very good pattern. When the numbers are higher, the buyers were wrong and got stopped out, increasing your profit.

Also the KP2 Ratio was very high at 218. The sellers were not getting short on market orders, because the KP2 would have been significantly lower. They were getting short on limit orders.

## Combining Classification with Divergences

When we combine the trading with classification along with divergences that do not include broken reversals or exhaustion patterns gives the most consistent trading system.

When combining these two ideas there were 312 trades. **There were 254 wins which gives us a 81.4% winning average. The average winning ticks was 16.7 ticks.**