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FIVE Stock Study (2-9-23)

Posted by on November 10, 2022 at 07:26 | Last modified: March 6, 2023 13:34

I recently* did a stock study on Five Below Inc. (FIVE) with a closing price of $199.04.

Value Line writes:

     > Five Below, Inc. offers a broad range of merchandise targeted
     > to the teen and pre-teen customer. Most products are priced
     > at $5 or below, with some in the $6-$10 range (Five Beyond).
     > Products are in the following category worlds: Style, Room,
     > Sports, Tech, Create, Party, Candy, and Now.”

This medium-sized company has grown sales and earnings at annualized rates of 22.5% and 26.6% over the last 10 and nine (excluding a loss in ’12) years, respectively. Lines are mostly up, straight, and parallel except for an EPS decline in ’20. PTPM over the last decade has ranged from 7.8% (’20) to 12.9% (’21) with a last-5-year average of 11.5%. This is higher than peer and industry averages.

ROE has fallen from 35.9% (’13) to 26.7% (’21) with a last-5-year average of 25%, which is better than peer and industry averages. The debt-to-capital ratio has averaged 33% over the last five years, which is lower than peer and industry averages. FIVE has zero total debt and a Current Ratio of 1.5.

I assume long-term annualized sales growth of 12% based on the following:

I assume long-term annualized EPS growth of 17.3% based on the following:

2022 earnings are in decline and because I believe this to be an isolated event, I will look to exclude the recent quarters. I can do this by projecting from the 2021 data point with my desired 13% growth rate. This is equivalent to projecting from the last quarterly data point ($4.11/share) with a 17.3% growth rate. Doing the latter keeps with the default approach, which will probably be more commonly applied in Member Sentiment (see MS below) studies.

While this type of manual override is more aggressive than my usual methodology, I am still lower than all but one (YF) of six long-term analyst estimates (mean 19.5%).

My Forecast High P/E is 36. Since 2013, High P/E has ranged from 39.5 (’15) to 93.7 (’13) with a last-5-year average of 55.3. I expect that to come down to earth at some point.

My Forecast Low P/E is 25. Since 2013, Low P/E has ranged from 20.2 (’17) to 58.7 (’13) with a last-5-year average of 25.1. I would typically project 20 in such a case, but I’m electing to be more aggressive.

My Low Stock Price Forecast is the default value of $102.80. Although more than the 20% “rule of thumb” below the previous closing price [-48%], I think it reasonable being only 6.1% less than the 52-week low. Raising the Low Stock Price Forecast would effectively be raising the Forecast Low P/E, which is already at my upper limit of comfort.

All this computes to an U/D ratio of 1.3, which makes FIVE a HOLD. The Total Annualized Return (TAR) computes to 10.5%.

PAR, which is based on Forecast Average (not High) P/E, is 6.9%: lower than I want to see for a medium-sized company.

I use MS to assess margin of safety (MOS) by getting an idea how likely the company may be to outperform my estimates. Out of 345 studies over the past 90 days (my own and two others with projected low prices above last closing price excluded), projected sales, projected EPS, Forecast High P/E, and Forecast Low P/E average 15.2%, 16.1%, 39.2, and 25, respectively. My inputs are not substantially different, which means my study has negligible MOS. Value Line projects an average annual P/E of 30, which is just lower than mine (30.5) and lower than MS (32.1).

I will look to revisit FIVE under $159/share.

*—Publishing in arrears as I’ve been doing one daily stock study while posting only two blogs per week.

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Backtester Development (Part 3)

Posted by on November 7, 2022 at 07:07 | Last modified: June 22, 2022 08:36

In moving forward to eliminate the lower bound, I encountered a bug described at the end of Part 2. Today I want to finish discussing my debugging effort.

I tried all kinds of things during the next three hours. I can’t remember the exact order of what I tried or even the results of everything I did.

The bug occurred before the dataframe was printed to .csv file, which prevented me from viewing in Excel. I was able to display in JN, and that helped immensely. This revealed a long option with 28 DTE at trade inception. Of course that’s going to be a problem because it means the short option would be 0 DTE and missing upon subsequent update. I later noticed many missing short options, which I never understood.

Before looking at the dataframe, I added lines in the program to print different things as a way to track footprints. I needed to better understand what branches the program traveled through. In order to see the log file, I had to enter in JN:

     > strike_file.close()

Because the ValueError occurred before the program completed, the results file remained open in Python. Closing it manually with this line made viewing the log file easier. After studying the log file and making changes, I learned to close the log file to avoid an error upon subsequently running the program again (PermissionError: [Errno 13] Permission denied).

Printing out different variables helped to determine where the bug arose and what steps the program took to get there. The list of variables I looked at grew long:

I even had to create some new variables to better interpret the old ones. raw_trade_date_list is one example because the data file—something I studied to understand on what row the program stopped due to error—shows the raw date rather than a more familiar format (see end of this post for explanation).

In many places, I inserted counters to determine how many times the program executed particular branches. This line proved particularly useful because it creates a detailed log of variables upon every iteration:

My debugging effort had many fits and starts because I would find something that seemed like a smoking gun only to make changes and see the bug remain. I became saddled with contradictory information. For example, assignment to current_date meant the program executed one branch while so-and-so flag True meant the program couldn’t have executed said branch.

I eventually noticed an instance of == when it should have been = . This led me to do a search for equals signs and verify every single one of over 350 in the code. This revealed:

See a pattern here? Clearly I need to improve with regard to usage of = versus == .

With these all fixed, the program runs as expected even without the lower bound.

Mission [finally] accomplished!

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OLLI Stock Study (2-8-23)

Posted by on November 4, 2022 at 07:00 | Last modified: March 5, 2023 13:30

I recently* did a stock study on Ollie’s Bargain Outlet Holdings (OLLI) with a closing price of $56.75.

M* writes:

     > Ollie’s Bargain Outlet Holdings Inc is a retailer of brand name
     > merchandise at drastically reduced prices. It offers customers a
     > selection of brand name products, including housewares, food,
     > books and stationery, bed and bath, floor coverings, toys, and
     > hardware. It operates stores across the Eastern half of the
     > United States. Its differentiated go-to-market strategy is
     > characterized by a unique, fun and engaging treasure hunt
     > shopping experience, compelling customer value proposition and
     > witty, humorous in-store signage and advertising campaigns.
     > These attributes have driven rapid growth and strong and
     > consistent store performance for the company.

This medium-sized company has grown sales and earnings at annualized rates of 16.9% and 32.4%, respectively, over the last nine years. Lines are mostly up, straight, and parallel until 2021 when sales dipped and EPS started to fall. Over the last 10 years, PTPM has been higher than peer (stated as PSMT, DOL.TO, and BIG) and industry averages, increasing from 5.7% in ’13 to 15.4% in ’20 before starting to decline. The last-5-year average is 12.9%.

Since 2015, ROE has increased from 7% to 19% (’20) before heading lower. The last-5-year average is 15.2%, which is lower than peer and industry averages. The debt-to-capital ratio has fallen from 37.7% in ’13 to 25.1% in ’21. This is just over the last-3-year average and lower than peer and industry averages. Long-term debt is minuscule with most debt being uncapitalized leases. Current ratio is 2.83.

I assume long-term annualized sales growth of 5% based on the following:

I assume long-term annualized EPS growth of 6% based on the following:

While my forecast is lower than the average of five longer-term growth rates (10.5%), I am overriding projection from the last annual (not quarterly) data point. The latter is a more aggressive calculation.

My Forecast High P/E is 29. Over the last seven years, high P/E has ranged from 29.8 (’17) to 48.1 (’19) with a last-5-year average of 39.8.

My Forecast Low P/E is 15. Over the last seven years, low P/E has ranged from 7.8 (potentially a downside outlier in ’20) to 24.7 (’18 and ’19) with a last-5-year average (excluding the 7.8) of 20.6.

My Low Stock Price Forecast is $36.50 based on the annual (not quarterly) earnings as mentioned above. This is just below the 52-week low price and 35.7% below the previous closing price.

All this results in an U/D ratio of 1.9, which makes OLLI a HOLD. The Total Annualized Return (TAR) computes to 10.7%.

PAR is 4.7%, which is lower than desired for a medium-sized company.

To assess margin of safety (MOS), I compare my inputs with those from Member Sentiment (MS). Out of 200 studies over the past 90 days (excluding my own along with 14 others with Low Stock Price Forecast above last closing price), projected sales, projected EPS, Forecast High P/E, and Forecast Low P/E average 10.9%, 11.8%, 31.1, and 18.3, respectively. I am lower on all inputs. Value Line projects an average annual P/E of 24, which is slightly lower than MS (24.7) but higher than mine (22).

Overall, I appear to have a decent MOS built into this study, but I need to see the stock fall ~10% before considering a purchase for longer-term investment

*—Publishing in arrears as I’ve been doing one daily stock study while posting only two blogs per week.

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Backtester Development (Part 2)

Posted by on November 1, 2022 at 07:24 | Last modified: June 22, 2022 08:36

I resume today with my detailed approach to check_second, which I provided at the end of Part 1.

In case you had trouble following all that, I concur because it’s a mess! check_second increases the find_long branch from 17 to 45 lines. In the process, I encountered a bug that took over two hours to identify.*

I also had great difficulty in keeping track of indentation:

Getting the indentation wrong can screw up a program something fierce. I really need to think carefully about what’s going to happen where depending on how the conditional criteria evaluate.

Even getting the indentation correct, I am not entirely happy with the solution. I feel it’s sloppy Python, but I may not know any better at this point. The elapsed time is about three seconds longer, but I feel more confident that nothing will slip through the cracks. Perhaps a couple user-defined functions would make the nested ifs more readable.

Starting an ELSE with the same indentation whenever I begin an if statement might also help the effort. This will allow me to more easily see the proper indentation up front even if I don’t have it filled in yet. When there is nothing to do in the ELSE branch, I can always use PASS. All this is to make the code more readable, which I’m guessing would be advised by PEP 8 (Python style guide), and to decrease the risk of sloppy mistakes.

To that end, my next steps were to eliminate the redundant 10-multiple truth tests and to make sure every if has a corresponding ELSE statement. In most cases for this program, the ELSE block simply gets CONTINUE to move to the next iteration (row of data file). I am encouraged to say no bugs were encountered at this stage, which added 10 lines and included quite a bit of code manipulation aided by the Atom editor folding feature (see footnote here).

Maybe I can do right in Python after all! I also feel much better about the code with things seemingly better organized.

This confidence boost was very short-lived, however, as my next step to eliminate the lower bound (see fifth paragraph here for a refresher) resulted in the previous day’s check_second bug profile all over again:

I will discuss this more next time.

*—Incidentally, the bug was in (2) because at the bottom of find_long, I assigned find_short to control_flag.
     The whole idea of check_second is to hold off on taking steps to completing the spread (find_short) until
     verifying the first qualifying DTE to be the only (else I want the shorter DTE).

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GNRC Stock Study (2-6-23)

Posted by on October 27, 2022 at 06:40 | Last modified: March 2, 2023 14:33

I recently* did a stock study on Generac Holdings Inc. (GNRC) with a closing price of $122.44.

M* writes:

     > Generac Power Systems designs and manufactures power generation
     > equipment serving residential, commercial, and industrial markets.
     > It offers standby generators, portable generators, lighting,
     > outdoor power equipment, and a suite of clean energy products.

This medium-sized company has grown sales and earnings at annualized rates of 11.2% and 18.6%, respectively, over the last 10 years. Lines are mostly up and parallel except for sales/EPS declines in ’14 and ’15 along with EPS decline in recent quarters. PTPM has been cyclical—ranging from 9.3% (’15) to 18.8% (’13) with a last-5-year average of 15.7%. This is higher than peer (stated as SPXC, IR, and GTES) and industry averages.

Excluding an upside outlier of 66.9% in ’13, ROE has ranged from 15.1% (’15) to 38.3% (’14) with a last-5-year average of 30%. This leads peer and industry averages. Debt-to-Capital has trended lower since 2012 from 65.8% to 37.4% with a last-5-year average of 49.1%. This is mostly higher than peer and industry averages. Interest Coverage is 14 and Current Ratio is 2.1 (sans inventory, the Quick Ratio is 0.70).

I assume long-term annualized sales growth of 7% based on the following:

I assume long-term annualized EPS growth of 5% based on the following:

My projected growth rate is somewhat of a compromise. Seeing that ’22 is not complete, my guess is that analyst long-term estimates are based on the last annualized number. As this effectively raises the High Stock Price Forecast, I will do so with a lower growth forecast: 5% rather than 6%.

My Forecast High P/E is 22. Over the last 10 years, high P/E has ranged from 17.1 (’18) to 63.2 (upside outlier in ’21) with a last-5-year average (excluding the outlier) of 26.7.

My Forecast Low P/E is 12. Over the last 10 years, low P/E has ranged from 12 (’19) to 26.8 (upside outlier in ’21) with a last-5-year average (excluding the outlier) of 12.8.

My Low Stock Price Forecast of $79.80 is the default value. This is about 35% below the last closing price and 7.5% lower than the 52-week low price.

All this results in an U/D ratio of 2.6, which makes GNRC a HOLD. The Total Annualized Return (TAR) computes to 13.7%.

PAR is 8%, which is a bit lower than I’d like to see for a medium-sized company.

I use member sentiment (MS) to assess margin of safety (MOS) in deciding how likely it is that TAR rather than PAR can be realized. Out of 378 studies over the past 90 days (excluding my own along and 50 others with invalid low prices), projected sales growth, projected EPS growth, Forecast High P/E, and Forecast Low P/E average 11%, 12%, 24.8, and 14.3, respectively. I am lower on all inputs. Value Line’s estimate, perhaps the most optimistic of all, seems like an upside outlier. They project a future average annual P/E of 27.5, which far outpaces my 17 and is also higher than MS 19.5.

MOS appears to be alive and well in this study.

MS Low Stock Price Forecast is $84.33: 5.7% above my own. This is not unreasonable and would put me just on the cusp of the BUY zone. I am sticking with my Forecast Low P/E, however, which results in a HOLD on these shares at $118 or higher.

*—Publishing in arrears as I’ve been doing one daily stock study while posting only two blogs per week.

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Backtester Development (Part 1)

Posted by on October 24, 2022 at 06:40 | Last modified: June 22, 2022 08:35

This mini-series will be a chronicle of backtester development activities. Because so many things are a Python learning experience for me, I think it’s worth documenting them for later review.

I want to start by revisiting the problem I was having with data type conversion. I noted ignorance as to why the problem occurs. My partner filled me in:

     > I think this is an excel issue. Do a right click on the file and use the “open
     > with” option. Use Notepad. You will see that everything is displayed as float…
     > When you open either file with excel, excel creates what looks like an integer.

In other words, this is not actually a problem. Excel makes things look nice, which in this case created an unintentional headache. Having to reformat data from a commercialized vendor source to serve my own purpose should not be surprising.

In the post linked above, I also presented some additional lines to record program duration in a log file. I couldn’t understand why the output data separated by commas was not printing to separate cells in the Excel .csv.

The explanation is the difference between the following two lines:

The commas in L305 are visible only to the Python program. In order to be visible to the application displaying the .csv file, I need to include quotation marks around the commas as if I were printing them (L307).

I enclosed the entire program in a while loop with a new variable timing_count in order to have the program run three times and log multiple elapsed times.

I then assigned current_date in find_long and reset the variable as part of the else block in L77. These were suggestions from Part 11. The program output appears unchanged. Run duration appears consistent and relatively unchanged (as best as I can tell when comparing two sets of three elapsed times each).

The next step is to add the check_second flag (discussed in Part 5), which ended up as a total mess because subsequent debugging took over two hours to seek out a “stupid mistake” (grade-school math terminology) I made in the process.

To refresh your memory especially with regard to the DTE criterion, see second paragraph of Part 6.

My approach to the check_second flag is as follows:

  1. The previous find_long block is split in two: check_second == False and ELSE (implies check_second == True).
  2. False includes a nested if statement that checks DTE criterion (ELSE CONTINUE), 10-multiple criterion, then strike price and 10-multiple (redundant) criteria followed by complete variable assignment including flipping check_second to True.
  3. True (ELSE block) includes a nested if statement that checks for a change in DTE (ELSE CONTINUE), DTE criterion, 10-multiple criterion, then strike price and 10-multiple (redundant) criteria followed by partial variable assignment (SPX price and strike price already assigned), resetting of check_second, assigning find_short to control_flag, and CONTINUE.
  4. The DTE criterion of True has ELSE block that includes: check_second reset, control_flag = find_short, and CONTINUE.

I will resume next time.

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PKG Stock Study (2-3-23)

Posted by on October 21, 2022 at 07:04 | Last modified: March 1, 2023 14:48

I recently* did a stock study on Packaging Corp. of America (PKG) with a closing price of $145.09.

M* writes, “Packaging Corp of America is the third-largest containerboard and corrugated packaging manufacturer in the United States.”

This medium-sized company has grown sales and earnings at annualized rates of 6.8% and 9.8%, respectively, over the last 10 years. Lines are mostly up and parallel except for EPS declines in ’14, ’19, and ’20. PTPM has trended higher over the last 10 years from 11.3% to 16.1% with a last-5-year average of 13.4%. This is significantly higher than peer (stated as CAS.TO, SEE and REYN) and industry averages.

ROE has trended mostly flat since 2014 with a last-5-year average of 23%. Debt-to-Capital has trended lower since 2013 with a last-5-year average of 46.1% (data not available for ’22), which is significantly lower than peer and industry averages. Quick Ratio and Interest Coverage seem acceptable at 1.92 and 11, respectively.

I assume long-term annualized sales growth of 1% based on the following:

I assume long-term annualized EPS growth of 1% based on the following:

My Forecast High P/E is 14. Over the last 10 years, high P/E has ranged from 14.4 (’13) to 28.7 (upside outlier in ’20) with a last-5-year average (excluding the outlier) of 16.4.

Forecast Low P/E is 8. Over the last decade, low P/E has ranged from 8.5 (’13) to 14.7 (’20) with a last-5-year average of 12.

My Low Stock Price Forecast of $88.10 is default. This is 39% below the last closing price. The low of the last two years is $110.60, but given such dim growth prospects, the Forecast Low P/E seems reasonable.

All this results in an U/D ratio of 0.3, which makes PKG a SELL. The Total Annualized Return computes to 4.7%.

Over the last 10 years, Payout Ratio has ranged from 33.8% (’13) to 69.6% (upside outlier in ’20) with a last-5-year average (excluding the outlier) of 42.5%. I used 34% as a conservative estimate.

Although the current yield (3.4%) is a bright point for this stock, a PAR (using Forecast Average, not High, P/E) of 0.5% is an exclamation point for what is otherwise a depressing stock study. One of BI’s core principles is to buy stock in high-quality growth companies. While PKG has demonstrated consistent historical growth, the outlook for future growth is muddy at best. With the stock up about 17% in just over three months, it’s now far past the BUY zone.

I like to assess margin of safety (MOS) by comparing my inputs with Member Sentiment (MS). Out of 68 studies over the past 90 days, projected sales, projected EPS, Forecast High P/E, and Forecast Low P/E average 5.4%, 6.3%, 17.8, and 11.8. I am dramatically lower on all inputs. Value Line also projects an average annual P/E of 19, which is higher than MS 14.8 and much higher than my 11. I do see a large MOS in this study, but with the wide range of long-term EPS estimates on either side of zero, I also see good reason to be conservative.

MS has a Low Stock Price Forecast of $107.55, which seems reasonable being 20%+ below the last closing price. I just cannot be convinced to raise mine at this time, however [which would increase the U/D ratio]. Seven long-term EPS growth estimates average 1.7%. My forecast is not much lower, and I would not be surprised to see P/E fall to the bottom of its 10-year range given such anemic growth.

*—Publishing in arrears as I’ve been doing one daily stock study while posting only two blogs per week.

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Data Type Conversions with the Automated Backtester

Posted by on October 18, 2022 at 06:45 | Last modified: June 22, 2022 08:36

I’ve struggled mightily trying to figure out how to handle types for the option data. I think the current solution may be as good as any and today I’m going to discuss how I got to this point.

The data file is .csv with the following [skipping position zero] fields:

  1. Date (e.g. 17169)
  2. DTE (e.g. 1081)
  3. Stock Price (e.g. 2257.83)
  4. Option Symbol (e.g. SPX 191220P02025000)
  5. Expiration (e.g. 18250)
  6. Strike (e.g. 2025)
  7. Mean Price (e.g. 203.2)
  8. IV (e.g. 0.209406)
  9. Delta (e.g. -0.30844)
  10. Vega (e.g. 13.1856)
  11. Gamma (e.g. 0.000417)
  12. Theta (e.g. -0.12758)

For now, I need the following fields as integers (floats for the decimal portion): 1, 2, 5, 6 (3, 7, 8, 9, 12).

Iterating over the first line of data in the file (skipping the header) with L59 (seen here) yields:

Every field I need as an integer comes up with a .0 at the end. When I try to convert to integer with int(), I get a traceback:

     > ValueError: invalid literal for int() with base 10: ‘17169.0’.

I can use int(float())) every time I need to encode data from these columns. I do this 21 times in the program. It may seem like an a lot of unnecessary conversion, but I don’t want to see those trailing zeros in the results file.

If I understood why this happens in the first place, then I might be able to nip it in the bud.

Here’s a short code that works:

Note that each trailing zero (highlighted) is eliminated. This seems like a lot of preprocessing. The fields remain as strings (note the single quotes) and I still have to convert them to integers.

Is all this faster than 21 instances of int(float())?

I mentioned timers a couple times in this post discussing backtester logic. The following is one approach:

     import time
     start_time = time.time()
     elapsed_time_log = open(r”C:\path\timelog.csv”,”a”)
     ——————-BODY OF PROGRAM——————-
     now = datetime.now()
     dt_string = now.strftime(“%m/%d/%Y %H:%M:%S”)
     end_time = time.time()
     elapsed_time = end_time – start_time
     comment = ‘v.9′
     print(dt_string,’ ‘, elapsed_time,’ ‘, comment, file=elapsed_time_log)
     elapsed_time_log.close()

This code snippet appends a line to a .csv file with time and date, elapsed time, and a comment describing any particular details I specify about the current version. This will give me an idea how different tweaks affect program duration.

As a final note, the code screenshot shown above does not work if L1 reads stats_copy = stats because the original list then changes. This gave me fits, and is probably something every Python beginner encounters at least once.

What’s the problem?

With stats_copy = stats, I don’t actually get two lists. The assignment copies the reference to the list rather than the actual list itself. As a result, both stats_copy and stats refer to the same list after the assignment. Changing the copy therefore changes the original as well.

Aside from the slicing implemented in L1, these methods should also work:

NFLX Stock Study (2-2-23)

Posted by on October 13, 2022 at 07:04 | Last modified: July 25, 2023 10:51

I recently* did a stock study on Netflix Inc. (NFLX) with a closing price of $361.99.

CFRA writes, “Netflix is the world’s largest Internet subscription service for accessing TV shows and movies.”

This large-sized company has grown sales and earnings at annualized rates of 26.6% and 58.4%, respectively, over the last 10 years. Lines are mostly up and parallel except for EPS declines in ’15 and ’22. PTPM has trended higher over the last 10 years from 3.9% to 16.6% with a last-5-year average of 13.4%. This trails peer (stated as PARA and FOX) and industry averages.

ROE has trended up from 9.2% to 21.6% over the last 10 years with a last-5-year average of 26%. Debt-to-Capital increased from 27.3% in ’13 to 66.4% in ’18 before declining to 40.9% in ’22. The last-5-year average is 56.5%, which is higher than desired. Interest Coverage is somewhat reassuring at 8, but Quick Ratio offers little comfort at 0.96. As of Q2 2022, the M* analyst describes the company “in a decent position” with $14.2B long-term debt and $7.8B cash. Management has stated the firm should not need to tap the credit market in the future to fund its ongoing content spending, but this can always change.

I assume long-term annualized sales growth of 8% based on the following:

I assume long-term annualized EPS growth of 8% based on the following:

I’m forecasting near the bottom of the long-term-estimate range (8.9% – 26%). Because a rebound is forecast following a sharp [quarterly] EPS drop in ’22, I decided to override projection from the last annual (vs. quarterly) data point.

My Forecast High P/E is 35. High P/E has come down from 210 in ’13 to 61.3 in ’22 with a last-5-year average trending lower at 93.9. At some point, I expect P/E to fall into a “normal” range.

My Forecast Low P/E is 25. Low P/E has come down from 49.1 in ’13 to 16.4 in ’22. The last-5-year average is trending lower at 48.1. Again, at some point I expect this to fall into a “normal” range and we may already be starting to see this.

My Low Stock Price Forecast of $247.7 is default. This is 31.2% below the last closing price. The 52-week low price is $162.7.

All this results in an U/D ratio of 1.3, which makes NFLX a HOLD. The Total Annualized Return (TAR) is projected at 7.2%.

A PAR (using Forecast Average, not High, P/E) of 3.9% dictates waiting for a lower price. I certainly see room for downside stock volatility as my Low Stock Price Forecast is > 50% above the 52-week low.

If I can glean any current optimism for buying prospects then it would be in the margin of safety, which I can assess through comparison with Member Sentiment (MS). Out of 333 studies over the past 90 days, projected sales, projected EPS, High P/E, and Low P/E average 12.9%, 11.8%, 65.3, and 54.3, respectively. I am lower on all inputs—especially on P/E. Value Line projects a future average annual P/E of 35.5, which is also higher than my 30.

MS has a Low Stock Price Forecast ~$239, which is lower than mine. A closer look reveals some projected lows over $300 (some much higher than the current price) and some under $100. When I exclude these 67 studies, the MS Low Stock Price Forecast drops to $198.69. While that would pull NFLX even farther from the Buy zone in my study, at -31.2% I think my Low Stock Price Forecast is sufficient.

Projected High Price is where my study really diverges from MS: $511 vs. their $1,135.

*—Publishing in arrears as I’ve been doing one daily stock study while posting only two blogs per week.

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Backtester Logic (Part 11)

Posted by on October 10, 2022 at 06:45 | Last modified: June 22, 2022 08:35

Moving on through the roadmap presented at the end of Part 9, let’s press forward with trade_status.

trade_status operates as a column in btstats to indicate trade progress and as a control flag to direct program flow.

You may have noticed the former by looking closely at the dataframe code snippet from Part 10. In the results file, this describes each line as ‘INCEPTION’, ‘IN_TRADE’, ‘WINNER’, or ‘LOSER’ (also noted in key).

As a control flag, trade_status is best understood with regard to the four branches of program flow (i.e. control_flag). The variable gets initiated as ‘INCEPTION’. This persists until the end of the find_short branch when it gets assigned ‘IN_TRADE’. At this point, the program no longer has to follow entry guidlines but rather match existing options (see third paragraph of Part 2). Once the whole spread has been updated at the end of update_short, the program can evaluate exit criteria to determine if trade_status needs to be assigned ‘WINNER’ or ‘LOSER’. In either of those cases, the trade is over and:

One variable not reset is current_date. This is needed in L65 (see Part 2) along with wait_until_next_day. For completeness, I should probably reset current_date as part of that else block (L68) and assign it once again as part of find_short or in the find_long branch with trade_date in L75.

With regard to exit logic, I have the following after variables are calculated and/or assigned in update_short:

The only exit criteria now programmed are those for max loss and profit target, but this must be expanded. At the very least, I need to exit at a predetermined/customizable DTE or when the short option expires. This may be done by expanding the if-elif block. If the time stop is hit and trade is up (down) money then it’s a ‘WINNER’ (‘LOSER’).

I will also add logic to track overall performance. I can store PnL and DIT for winning and losing trades in separate lists and run calculations. Just above this, I can check for MAE/MFE and store those in lists for later plotting or processing. I can also use btstats. Ultimately, I’d like to calculate statistics as discussed in this blog mini-series. I will take one step at a time.

I conclude with a quick note about data encoding. Following the code snippet shown in Part 1, I have:

That imports each option quote into a list—any aspect of which can then be accessed or manipulated by proper slicing.

That ends my review of the logic behind a rudimentary, automated backtester. My next steps are to modify the program as discussed, make sure everything works, continue with further development, and to start with the backtesting itself.

As always, stay tuned to this channel for future progress reports!

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