As we run our CQNS service more often, we do more targeted R&D (industrial engineering) on our algorithm & Chicago Quantum Net Score model.
We analyze two questions:
Q: Should we continue to use the SPY (S&P 500 ETF) as our benchmark for calculating BETA for use in calculating expected returns? How about QQQ (NASDAQ ETF)?
A: It does not seem to significantly impact the results. We ran BETAs against the QQQ last night and came up with similar portfolios (for a 64 stock portfolio) as when we used SPY. We may make this a parameter for clients, but it likely won't change much.
Q: Should we manually change CQNS_power from how we automatically calculate it today?
A: Not sure it matters much. The CQNS_Power is a way to balance risk and return in our runs, and it naturally changes with BETA values, market returns and market volatility. When we have changed this manually (in the past week), it has not made a significant difference.
How is the Simulated Bifurcation Machine working? Better each time we advance our tuning expertise. Two days ago we were able to use our bifurcator (custom coded) to find a very good portfolio of 64 stocks out of 4,000+ stocks. It yielded a CQNS score that was in the ballpark (not best, but good) with our best solvers on this run.
One interesting anecdote. When we are running 4,000+ stocks, things take much longer to run. Our tuning takes on more importance. For example, one of our genetic algorithm runs (this is one small part of our client analysis process) took 2,262 seconds to run and provide a deep and 'best' CQNS score, before we moved to phase 2 code and run on the quantum computer. There must be a way to do this more quickly (in this case, we could have eliminated 4 generations with hindsight). When we move to phase 2, and look at 64 stocks, we can run almost every solver to full explanatory power and not run out of time.
Jeffrey Cohen, President US Advanced Computing Infrastructure, Inc. Chicago Quantum (SM)
Dec 7, 2020 1pm CT
A few updates to share on our run over this past weekend.
We include more shares in our analysis: (from ~3,250 to 4,529 US common shares on Dec 6, 2020). We analyze 4,529 stocks, perform data validation to remove missing or incomplete adjusted close prices, and BETA values (1-year vs. SPY ETF) outside 'extreme' ranges, and analyze 4,163 US common stocks (up from 3,171 in our most recent publication). We made improvements and adjustments. We now include both NASDAQ Q and G stocks in our largest analyses. We improve our FTP download and validation of tickers to enable more 'tricky tickers' to be included. For example, we now include stocks with tickers that include '-' or '.' or ' ' when they are US common stocks. Before, we pull data from NASDAQ or a static spreadsheet from NYSE. Now, we pull all tickers via FTP from both NASDAQ and NYSE.
The second change is that we automate the downloading and validation processes. We eliminated a source of human error (and time consumption). This process still takes a very long time depending on the hardware we use, and begins to show the speed advantage of our HP Z420 server (multi-core, 3.6GHz processor with 48GB ECC RAM) over our iMAC 2013 with 16GB non-ECC RAM).
We also varied our CQNS_power setting across two parallel runs (same data, different systems). The CQNS_power (one ran at 6.5, and the other at 4.3), did not provide significant difference in the analysis we run. Therefore, we remove the CQNS_power adjustment parameterization from our roadmap.
negative or near-zero BETA values.
Just had a minor epiphany. Our model enables swing trading. We help find portfolios of stocks that have shown 'destructive behavior of individual stock volatility' so one can keep the BETA exposure and reduce the portfolio volatility below that of the individual stocks.
Our Chicago Quantum Net Score model, which analyzes stocks to determine a portfolio to hold for up to 25 trading days, enables swing trading.
So, we Tweeted on it, and added the phrase 'swing trading' to our website in a few places.
We enable swing trading. We do not suggest stocks that will do well on any one day, but over a period of up to 25 trading days (and we have seen the effects last up to 35 trading days - or 7 weeks).
When I look at how I use the model, I started by swing trading for a few days. Going to hold the current position (and any additional stocks identified to add to those portfolios) for a few weeks...let the winners run a bit and balance between offsetting positions.
We bought four evenly weighted stock positions this week, in two pairs of positions:
On Dec 7, we bought into one new stock (will disclose later), and added to two of our positions.
If you would like to learn more about our quantum algorithm or platform, or to buy a stock analysis, please click our portfolio tab.
Strategic IT Management Consultant with a strong interest in Quantum Computing. Consulting for 29 years and this looks as interesting as cloud computing was in 2010.