Just a sample RM using a subset of IPO stocks from the past 36 Months to demonstrate the effect of such a custom universe.

Price Momentum Oscillator is used to identify stocks on a go-go growth trajectory. It does reasonably well with either weekly or monthly rebalancing and either weekly or monthly Data Scale settings, and seems best with the NASDAQ 100 (a fair place to look for the go-go growth jet set), and anywhere from 5-15 stocks selected. Fewer stocks gives you a bumpier ride with mildly higher returns. Default PMO params and Weekly settings (both for rebalancing and indicator) looks promising for deeper investigation.... a decent win rate, but significantly bigger average winning trades than losers.

Breakout model for NASDAQ (other universes have fewer similar breakouts). Looks good as is for past 10 years of bull market. The caveat is this breakout like others is weakest during market retreats, hence a 20-year backtest looks crappy.

Homework: This is where a whole-market indicator (like Death/Golden Cross) is useful. Add that filter to both the buy and sell sides to get a better 20 year record (that beats SPY with lower drawdown) but underperforms SPY over the last 10 years. There are other market filters.

I liked the smooth look of 32's equity curve, rather nice for a simple mean reversion model. But I wanted it to perform a little better, so I tweaked three things. First, in Settings I changed the Max Open Positions to 0 instead of 10. For unknown reasons, this lets the model properly hold up to 10 positions :-) which helps a lot here. Second, I wanted to add another Condition block to the Buy, such that the stock had to be on a good upswing overall. I used 1-month momentum ROC(20) > 1.5% for this. But this also cuts down on the number of trades available more than I'd like. So third, I went to the Qualifier for N Times in N Bars and loosened it up a little, to 12 bars instead of 8 bars in the original.

What did I get? Overall CAGR is up, in line with SPY now. Exposure is only slightly higher (still under 50%). Sharpe ratio has improved a bit to up around 1.0, and MDD is about the same (a little less than SPY). Nice!

But... hmmm, did I overfit this simple strategy? Well, I did play around with some of those params, and tried and rejected some things (like the 10 month momentum should also be positive), so technically, maybe I did. So what happens when we extend the timescale to the full 20 year backtest? Definitely a degraded performance relative to the original. Model 32 shines from 1999-2009 compared to this overfit misfit. You can run the numbers yourself if you want to see just how humbling this simple exercise was for me. At least it beats SPY. But that's about all you can say for it.

Connors TPS from the book 'High Probability ETF Trading' does combine time, price and scaling-in to give the highest percent correct on the ETFs of any strategy they created or ever traded. It basically identifies when an ETF is overbought or oversold and then averages into the position as it becomes more oversold. TPS was first taught to the Chairman’s Club members in 2008 and has since been expanded. As part of this expansion, full protection for the ETF positions and literally tens of thousands of variations of TPS have been published and presented to the Chairman’s Club members. To put this in perspective it would take thousands of pages to publish the complete TPS findings and the many ways to trade it.

1. The ETF is above the 200-day.
2. The 2-period RSI is below 25 for two days in a row.Buy 10% of your position on the close.
3. If prices are lower on the close than your previous entry price, any day you're in the position, buy 20%
4. If prices are lower on the close than your previous entry price, any day you're in the position, buy 30%
5. If prices are lower on the close than your previous entry price, any day you're in the position, buy 40%
6. Exit on the close when the 2-period RSI closes above 70.