Last Updated on October 25, 2022
Chapter 6 of Larry Connors‘ High Probability Trading contains a trading strategy called Multiple Days Up (MDU) And Multiple Days Down (MDD). The book was published in 2009, the trading tests were done until 31st of December 2008, and it’s time to test and check how the strategy has performed since then.
You can find the four previous strategies from Larry Connors, plus many more, on this page:
The rules of the strategy:
The main idea of the strategy is that ETFs revert to their mean. Because ETFs consist of many stocks or holdings, they are unlikely to go to zero. Thus, they should perform better for mean reversion than for single stocks, which can go to zero.
In this strategy, we look to buy when an ETF has dropped 4 out of the last 5 trading days. The rules are like this:
- The close is above the 200-day moving average.
- The close must be below the 5-day moving average.
- The ETF must have dropped at least 4 days out of the last 5 trading days.
- If 1-3 above is true, then enter at the close.
- Sell on the close when the ETF closes above its 5-day moving average.
- No stop-loss.
If you like to know the code of Connors’ strategy plus the code for all the other free strategies on this website, click here:
For more on why we use Amibroker, you might want to read this article:
The results of the Multiple Days Up strategy:
Below are the results for the original 20 ETFs Connors used, and an additional five ETFs we added (results are from the inception of the ETF until December 2020):
|Result by Connors||Average gain since||Profit|
|Average gain in %||inception to Nov.2020||factor||Difference:|
The last five ETFs were not part of Connors’ original test.
How does the strategy perform as a portfolio?
Most traders don’t tradie just one instrument, so let’s test how the strategy performs as a portfolio.
The simulation is done by having a maximum of five open positions at any time, which equals 20% of the equity for each position. This also means the capital is frequently in cash. The portfolio is simulated from the year 2000 until December 2020, and the capital is compounded (no commissions or slippage).
The equity curve looks like this:
The results can be summarized like this:
- CAGR: 6.4%
- The number of trades: 2210
- Exposure: 28.5%
- The profit factor: 1.46
- Average gain per trade: 0.31%
- Maximum drawdown: 14.5%
Compared to the other four mean reversion strategies from Connors, this one has the lowest profit factor. Several of the ETFs are most likely not suited for mean reversion, so the results can be improved.
The results in SPY and QQQ:
If we change the strategy to only trade SPY and QQQ, having 50% of the equity in each position, we get this compounded equity curve:
There are 287 trades, the profit factor is 1.82, the max drawdown is 12%, and the CAGR is only 2.4%. The low CAGR can partly be explained by the low time spent in the market: 7.7%.
How does the strategy perform on the short side
Short is always much more difficult than the long side. The reason is that stock indices have a positive tailwind overnight:
As a portfolio of a maximum of five positions the equity curve looks like this (only shorts):
This is not tradeable, of course. If we change to only trade SPY and QQQ (having max two positions), we get this equity curve:
Much of the gain happened in 2008, but still the results are surprisingly good: the win ratio is 65% and the average gain is 0.68%.
Larry Connors’ other trading strategies
We have backtested many other of Connors’ strategies:
- Larry Connors’ Double Seven strategy (Double 7 trading strategy)
- Larry Connors’ %b strategy (Bollinger Bands)
- Larry Connors’ R3 strategy
- Larry Connors’ RSI 25 & RSI 75 strategy
- Larry Connors’ 3-day high/low strategy
Multiple days up (MDU) and multiple days down (MDD) – conclusion:
Common for all strategies is that you can’t expect them to perform well on all asset classes. Each ETF has its own distinct features, and you must tweak (without data mining) to find what works on each. Hence, the Multiple Up Days and Multiple Down Days strategy can most likely be improved. Moreover, optimization is likely to gain some more insights into the strategy.