Algorithm and Portfolio Stats - 03/19/2024 - 03/22/2024
We rolled out a new version of our algorithm this week, and after some hiccups on Monday, we got it started on Tuesday. Over the last 4 trading days, we’ve seen 33 trades from the system (19 long, 14 short) on 21 unique tickers.
So far, our new system is seeing negative returns - down about 0.19%. This isn’t a point of concern for us yet for a couple of reasons. First off, we’re forecasting a Sharpe in the low-mid 2’s, meaning that we aren’t expecting it to be consistently profitable on a weekly basis (though it should be when examined on a monthly basis). Additionally, we’re looking at a fairly small number of trades thus far, which further makes this number unreliable as an indicator of long term performance.
To calculate total returns, we’re examining the average number of tickers traded per day. Since our system can’t take on multiple simultaneous positions on the same ticker, this gives us an estimation of how aggressive a user can be in their allocations without requiring margin. On average, our system has traded 5.25 tickers per day, so to calculate total returns, we’re assuming the user allocates 19.05% of their portfolio into each position.
There’s one problem I have with performance, beyond its negative returns. The system is recommending a smaller number of trades than we’d like to see. On one hand, this means that every trade it’s recommending should meet our standards exactly (barring some preferences which are difficult to evaluate algorithmically). On the other hand, this negatively impacts its usefulness to users. In my opinion, it also means we’re leaving money on the table, performance-wise. If we can increase the number of trades we see each day, without significantly compromising on our requirements, we should be able to make performance much more consistent.
Research is ongoing on this front. Our main goal is to find a way to increase the number of trades recommended per day, without compromising performance - or even improving it. One approach we’re trying is incorporating more tickers. Our current system is restricted to S&P 500 constituents, to avoid trading tickers with volume too low or price action too choppy.
Right now, we’re experimenting on a system that handles this expanded ticker list well in backtests. Should a few more tests go well, we hope to move this into a closed beta on our staff-only server. If things don’t go poorly on this method, we’ll continue looking into loosening the strict rules our current algorithm has, to allow more trades through.
Now then, let’s look at our biggest winner and loser of the week.
Our most profitable trade of the week was a long position on PANW that we took on Thursday. This trade had no re-entries, and returned us 0.91%. There’s a lot to like here. The MACD looks very strong when we enter, and only goes negative after we exit the position. The momentum going in here is very strong, and only falters briefly. We do get close to getting stopped out at around 11:45, but even if we had, we would have gotten back in and still profited.
Our biggest loser of the week is relatively benign. This short position on HPE had 2 re-entries, and lost 0.39% between them. The main problem I have with this trade, and one I think most of our users would have recognized, is that the stock was fairly stagnant prior to our entry. On paper, the stock had short-term momentum when we made our first entry, in that movement was consistently downwards, but it was small enough relative to prior movements, and the Kijun-Sen line was flat enough that this entry was largely unappealing. At very least, I wouldn’t have taken either re-entry, which would have capped losses to 0.15%.
Overall, we feel this system has a lot of potential, and will continue to utilize and improve it. As a reminder, this is NOT financial advice. We never advise taking on a position without doing your due diligence and reaching your own conclusion. Further, this system is designed to be easily cherry-picked. A confident trader will have a myriad of opportunities to make profit, and to out-perform it.
Now then, let’s examine our portfolio.
Our long term portfolio had another great week - beating SPY by 0.72%!
The return attributions are pretty unsurprising here - tech was the hero of the portfolio this week, as well as the market overall.
We’ve done a slight re-balance on our portfolio this weekend. Our allocations by sector will look pretty similar, but with a few changes to bring us closer to the S&P overall. Notably, we’re now over-allocating on several sectors, whereas just a few weeks ago we were only over-weighting technology. We’re expecting our current layout to bring us a little closer to the S&P’s performance, while still giving us an edge from our fundamental analysis.
Our current portfolio is included below. For the sake of brevity, all positions under 0.5% allocation are omitted.
That’s all I have for you tonight. Thank you for reading, and happy trading!
