Amazing returns = superior stock selection strategy + superior in & out strategy

Derek Melchin thank you for this!

My 2 Cents guys! :) 

As starting point I've took the latest release published by Derek Melchin 
Reviewed Coarse Selection of stocks - Improved Total Return, Drawdown and Sharpe

Enjoy it :) 

Great work everyone! I took Simone's and did the following:

• Lowered the volatility timeframe down to 100 from 252.
• Lowered the stocks to trade to 5 from 10.
• Raised the screener from 10 to 20
• Raised coarse filtering to stocks >$5 from >$2

Drawdown is now down to -19% and annual return about double that at 38%.

Mark,

thanks for sharing your results. I just wanted to make a couple of comments.

First the indicator calculating the dollar volume average in the universe selection is not updating daily, but only when the algo is about do the rebalance. The indicator is also not being warmed-up.

Regarding the change in volatility timeframe, the self.lookback variable was supposed to be 126 and not 252. I think this was copy and paste mistake from someone, which used the other I&O algo.

The other place you changed the volatility was in the annualized volatility calculation, where sqrt(252) is used for anualization. To keep to code more understandable I would suggest you to keep 252 and instead apply a factor corresponding to sqrt(100) / sqrt (252) or 0.6. 

Thanks for that Leandro, I didn't know the sqrt 252 was for annualized vol. 

Derek Melchin, Simone Pantaleoni, mark hatlan, and Leandro Maia: Fantastic work there on the algo (SEL[EarningsRockets] + I/O[DistilledBear])!!

To add to our pool of stock selection strategies combined with I/O strategies: An old classic from Quantopian combining the “Quality Companies in an Uptrend” stock selection with the In & Out.

SEL[QualUp] + I/O[In & Out]
Chris Cain posted the original “Quality Companies in an Uptrend” strategy on Nov 22 2019 (Quantopian). It has seen some early tweaks which the attached algo builds on. The idea is to select high-quality stocks based on several fundamentals: (1) cash return, (2) free cash flow yield, (3) return on invested capital (roic), and (4) leverage. Basically, firms are ranked on each of these factors and these ranks are aggregated. There is a certain hierarchy: Factors (1) and (2) are combined first (i.e. sum ranks and calculate a new rank) into a value factor. The final quality factor results from combining the value ranking with the roic and leverage rankings.
Then the quality stocks are ranked according to their momentum and the top 20 stocks are bought.

Backtesting period: Jan 1 2008 to now
Total return: 3,597%
Sharpe: 1.25, Compounding annual return: 31.89%, Max drawdown: 33.00%

Some specifics regarding the selection process:
- start with the top 500 dollar volume stocks
- select the top 250 stocks according to the fundamentals ranking
- select the top 20 stocks based on the momentum ranking

Like SEL[EarningsRockets], SEL[QualUp] has the advantage that we do not need to commit to a certain sector (e.g. Tech) since the selection is based on fundamentals, giving all sectors an equal chance to make it into the final selection. The stock vector changes dynamically over time depending on which sectors/stocks are doing the greatest.

I still have to give Jonathon Tzu ’s modified QualUp a shot.

Follows another version of Distilled Bear with Stock Selection based on the findings of Mark.

Total return: 9532%

Drawdown: 23%

CAGR: 42%

Sharpe: 1.641

Great work there again, Leandro Maia and mark hatlan (and others)!
I performed the 'Damiano Bolzoni test' :) and removed TSLA from the universe (if you want to try yourself, see the attached backtest; lines 39-41 and 145-148). The returns are holding up quite nicely: 6,675.80% total return. Also the reduction to 5 stocks from 10 stocks in the final holdings vector holds up nicely. For 10 stocks the total return would be 3,222.34% i.e. 5 stocks perform better. Great stuff!

Returns also look good from the perspective of the annual saw tooth chart (see In & Out thread; here: versus holding QQQ). Of course, three years substantially contribute to the total return: 2008, 2010 and 2020. Anyway, the strategy (blue line) hardly underperforms QQQ (black line), only in 2009 and 2013. In all the other years you have overperformance. This looks very good!

Thats a cool returns chart, how did you display that?

As for the returns yes 2020 dramatically improves overall profitability. If you remove 2020 then you get around 2100% return, drawdown 20% and CAR 29%. Which still isn't that bad, mind that it still uses TLT instead of TMF.

Only other thought I have is to hedge the stocks with IEF, but in order to preserve profitability it needs to use margin for that hedge.

mark hatlan: Attached is an algo that includes the relevant code to create the chart. Feel free to just copy and paste the relevant code snippets since the algo also includes the TSLA test I preformed earlier:
lines 74-81: initialize a few variables for the chart(s)
line 244: call the chart function as part of the daily in/out signal check
lines 315-324: a few calculations for the saw tooth chart (Portfolio return vs QQQ return)
line 334: a variable that saves the current year. At year change (line 317) the base to calculate the return is reset, creating the saw tooth view.

Great Stuff Peter Guenther  Derek Melchin, mark hatlan, and Leandro Maia: ! This is rocking hard now with the latest changes! :D 

I have another idea to include in the project, so let's keep posted! :D

Here we go again. Is not the last release I've "promised", but I've seen somebody forgot to include the weekly volume based Coarse Selection I've posted out a few comments ago :)
Performance wise it improve Total Return, Sharpe heavily (up to 1.814), and also bring in a slightly lower Drawdown.

 

Simone,

in you implementation the price volume average indicator is being update only when the algo is about to rebalance and the universe selection is executed. So it's not a 5 day average, but the average of the last 5 times when rebalance happend. If you make it update daily you see that there's no almost no change in the results but the backtest takes much longer to run.  

Gotcha! Thanks for spotting it Leandro :) was early morning in my defense :P :) 

Some quick findings to share:
When we have the In & Out underlying, the total return is at ~2,000%, so much lower. A stricter universe updating regime (4th Feb and then every three months) can get us to 3,300%. The universe updating timing appears to play a key role. It seems that updating less often (Distilled Bear) versus more often (In & Out, which is ‘out’ more often and then updates the universe before getting back in) leads to higher total returns. The DistilledBear generally appears to have the better timing in the backtest. I will have a look whether this is specific to the stock selection or can be generalized to other stock selections (e.g. QualUp).

  Peter Guenther is there a way you can share the latest versions of both Distilled Bear and In&Out versions? It's difficul to go back in the thread and find the "right one" :-)

Thanks!

In general, for momentum-based strategies rebalcing once a month is optimal. So I would change the 60-day rebalancing baked in right now. Furthermore, it is well known that the date picked for rebalancing can have huge influence n the returns ("timing luck"). Rebalancing at the very beginning of the month bears higher returns compared to middle of the month. I never found a reliable explenation, I would say it's because the big funds will rebalance around the end or beginning of the month.

Damiano Bolzoni: Thanks for your comments!

Attached is an algo that includes both the In & Out and Distilled Bear algos, with a switch in line 55 (self.go_inout_vs_dbear set to 1 vs 0). The ‘engine rooms’ of the in & out algos are the signal checks which are sitting in the functions def signalcheck_inout(self) and def signalcheck_dbear(self), respectively. This set up may allow to efficiently compare ‘in & out plus stock selection’-combos.

The stock selection strategy is the Valuation Rockets (slightly relabelled compared to earlier since the selection is mainly based on a valuation multiplier plus momentum).

It also includes the saw tooth chart (Portfolio vs QQQ; lines 377-381), which I recoded a bit to make it more compact.

I have replaced the leverage line with a chart showing the cash level (settled and unsettled). We want to avoid going into the negative territory here (for an unleveraged play) and reduce the amount of unused cash.

Regarding the actual results of the previous post’s attached algo: It shows that by merely switching the in & out logic between the two in & out algos which yield similar results when holding the SPY or QQQ (In & Out thread), the total return goes from about 9,000% (Distilled Bear) to 1,500% (In & Out). Open for discussion: This sensitivity may be a warning sign regarding very lucky timing in the Valuation Rockets & Distilled Bear combo (esp. the timing of the universe reshuffle)? I mean, I could still imagine to trade the combo but possibly not with the weight I initially planned to. Any thoughts/something that I am not seeing?