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Vovik, we might be making a logical error with this comparison since the holdings are different (note FDN and TLH). It would be a bit like saying: "The Distilled Bear algo that Leandro posted here made 9,532%, so the "Distilled Bear" is better than the "Dual Momentum with Out Days" and the "In & Out".
In addition, I think that there could be value in moving beyond the comparison of individual in & outs and working on the question of whether it might be beneficial to combine them. Of course, be aware that this is 'rich data modelling' at its finest.

Peter,

Using definitions in your previous post:
 
> [parameter minimalists]  Menno  <----  Vladimir  ---->  Peter  [rich data modelists] 

I have the following questions/comments:

How did you determine that Menno belongs to "parameter minimalist" category?

Appreciate!

Liam Op, no worries. Check out page 2 of this thread, searching (Ctrl + F) for the term "Gedankenexperiment".

Peter,

For comparison with Leandro Maia version Vladimir posted his v1.9

Interim stock taking
It’s been about half a year since the post below on Quantopian. Since then, much work has been done on multiple fronts. Merging this discussion into QuantConnect also has fired things up substantially.

This discussion has brought about multiple threads. An overview can be useful for people who want to explore all or some of the work that has been done. So here is a current list, approximately in chronological order:
- This thread. Focuses on developing in & out algos which then can be used to trade great stock selection strategies. It is an incubator for in & out-related ideas that are then further developed in dedicated threads.
- Amazing returns = superior stock selection strategy + superior in & out strategy (link). Focuses on combining in & outs with stock selection strategies, trying to find optimal combinations. Mostly uses the In & Out and Distilled Bear algos from this thread, combining it with leveraged ETFs (TQQQ and bonds), stock selections based on valuation (‘Valuation Rockets’), and stock selections based on quality fundamentals and momentum (‘Quality Companies in an Uptrend’).
- Dual Momentum with Out Days (link). Focuses on combining different in & outs (In & Out, Intersection of ROC comparison using Out_Day approach) with different equity ETFs (QQQ, FDN, IWF). Also, a focus on reducing the number of out signals (e.g. only using USD or Gold vs Silver and Utilities vs Industrials) as well as using leverage to boost returns.
- Intersection of ROC comparison using OUT_DAY approach (link). Focuses on combining the ‘Intersection of ROC comparison using OUT_DAY approach’ in & out algo with different equity holdings (e.g., combining QQQ, MSFT, and NFLX; FNGS; top 10 tech gainers). Also, discussion of sensitivity, leveraging, adding (trailing) stop losses, portfolio optimization, identifying 2x/3x bear and 2x/3x bull regimes.
- A very profitable version of IN and OUT, and why it is likely to fail in real life trading like its siblings (link). Argues that the in & out algos are overfitted and predicts that they will generate negative alpha in the future. Discusses strategies to test for overfitting and out-of-sampling testing. Also includes results from a parameter optimization run for the Dual Momentum In & Out.
- ROC comparison Utilities and Industrials (link). Focuses on minimizing the number of out signals to only one pair comparison (utilities vs industrials) and tests out-of-sample, compared to the other in & out backtests, by including the period from 1998-2007.

Great stuff, keep it up!

Welcome to the discussion, Strongs. My two cents regarding whether the system will continue to work:
- of course, nobody can predict this with certainty
- one thing to consider: The critical component of the system in my view is not the bond side, but the equity side. You need to make a great selection here for the system to perform. For example, see our discussions in Amazing returns = superior stock selection strategy + superior in & out strategy.
- currently, bonds are going down since inflation and interest expectations are increasing (= economic recovery expectations). Currently, the algo is in equity not in bonds. When the algo does a solid job, then we only go in bonds when there is (substantial) jitter in the equity market. Jitter could occur because of doubts concerning the speed of the economic recovery. When there are these kinds of doubts, then bond prices will increase (= yields will fall) and, if the system switches accurately, we will benefit from this uptick to some extent.

Hi everybody! I deployed a slightly modified version of the algorithm for a client on IBKR US. But the client had an error some days ago and the strategy stopped.

Can anyone suggest the reason?

Thanks

Hi Emiliano Fraticelli, thanks for the note. Just speculating here: This could be an error at IB's end, that they could not unambiguously identify the stock that the algo is referring to (?). I know that some people are live-trading the algo, so they may have experienced something similar (I still trade them manually but may convert to auto-trading at some point). An additional thought: You could contact the QC team (via support) directly and they may be able to help. If you have a solution that worked, it would be great if you could share it here, so that others know what to do in a similar situation. Fingers crossed.

Hi Peter Guenther 

The latest notebook is within the context of the IN & OUT trading strategies (all 5 versions). It states that the rebalancing schedule is responsible for most of the trading by these strategies and not necessarily the contraptions designed as trade triggering mechanism.

It is a different understanding from what is usually expressed in these 5 variants of the trading strategy. The math presented would also apply to other strategies using scheduled rebalancing as part of their trading design.

Hope some find it useful.

P.S.:

I am unable to attached a notebook or simply do not know how.

Here is that notebook as an HTML file: https://alphapowertrading.com/QC/Portfolio_Math.html

It can also be viewed in my latest article: Basic Portfolio Math

https://alphapowertrading.com/index.php/2-uncategorised/404-basic-stock-portfolio-math

Guy Fleury You are god damn right! I had been scratching my head past few days as irrespective of what 'stock selection' component I used in my modified version of 1.3 Intersection of ROC comparison using OUT_DAY approach by Vladimir and Leandro Maia , I more or less got same P/L with gains marginally within same range. It got me thinking that the ETF signals for IN/OUT and Volatility calculation for OUT DAYS played a nice role in defining the chart.

For example from period 2016-2021:

The Alpha I cooked past few weeks have stats like this with monthly periodic rebalance:

Adding the modified IN/OUT on varying timeframe rebalance gave something like this:

As you can see CAR nudged only by 6% but rest of the metrics seems to drastically fall under sweet spot or may be I might have overfitted my Alpha.

Thanks for sharing it was a good read. 

Welcome to the discussion, Jack Whisky. You are right and this is still true regarding the In & Out: The exit signals are derived from substantial drops in the signal ETFs' prices. Specifically, we are looking for drops that are so extreme that they fall into the 1% most extreme percentile of a returns sample that we are pulling from the ETFs' price history. Regarding the list of signals and differences between the various in & out algos, the best approach to get our head around these differences probably is to check out the Initialize section and what ETFs are loaded there. And true: in the In & Out, we could be staying out for more than 15 days if there are negative return flips in certain asset pairs (i.e. Gold vs Silver, Utilities vs Industrials, and Safe haven currency vs Risky currency). These return flips temporarily increase the number of days that we would be staying out for. See the post here.

Thanks for sharing, Guy Fleury.
Harsh Patelan explanation for similar returns can be that the Intersection of ROC Comparison Using OUT_DAY was mainly mixed with tech stock selections (summary above). We did a similar thing in Amazing returns = superior stock selection strategy + superior in & out strategy. However, we also mixed in a value/quality stock selection (see page 2, QualUp). So, this might be of interest to get additional diversity regarding the stock selection component.

Hi Peter Guenther and everyone, as I wrote in my last comment, I'm still a nabbo in coding here on quantconnect. I had the idea of implementing a momentum filter on these stocks and using the same logic as the ROC, select in this container of shares, only the one with the most momentum of the last six months, hold it for a month and then run the process again. I tried to change the code but I can't understand what mistakes I make. Can someone help me?

Please find an implementation attached, Jack Whisky. I reckon, key lines are the following:

- Lines 26-28: variables for your return momentum lookback period (self.mom_lookback), for the selected stock(s) (to save the selection between rebalancing dates; self.stock_selection), and for saving the month (to see when it changes; self.ret_reb_month).

- Line 57: since they are fixed (i.e., not dynamically selected), add the stocks to the consolidator to continuously update their stock price data

- Lines 92-94: on month change, calculate the stocks' momentum and select the best stock (note: one can increase the 1 in line 93 to select more stocks)

- Lines 111-123: the function that performs the returns ranking and selects the best stock(s)

Clearly, the total return is incredible: +37,532%. Annual growth 56.59%. Of course, part of this is because the algo was riding on the substantial Tesla stock price increase.


Additional examples that might be of interest: For dynamic (instead of fixed) stock selections, see Amazing returns = superior stock selection strategy + superior in & out strategy.

The In & Out Trading Strategy - Analysis

This is a follow-up to my last article Basic Stock Portfolio Math, trying to provide a different look at the inner workings of the In & Out stock trading strategy which is freely available on QuantConnect where you can modify it at will. The intent is to show how this strategy is making its money. It should prove interesting. The strategy is composed of only a few parts: a stock selection process, a trend definition section, and a trade execution method. Nothing very complicated.

I examined and tested the 5 published versions of In & Out and some of their variations. Basically viewing them as templates where the math of the game could provide ways to raise performance almost at will. The strategies still have some weaknesses but they can be corrected or at least alleviated.

For the rest of the article, follow the link below. As just said, it should prove interesting.

https://alphapowertrading.com/index.php/2-uncategorised/406-the-in-out-trading-strategy-analysis

I have a free account and run this algo (cloned it) and want to know if you similar experiences - it is extremly slow (I am maybe spoiled for the quantopian times..). 

I had to change the start time to 2020-1-1 and even then it run over 7 minutes (I think running it from 2008 it will run over an hour).

Do you have the same durations?

Sharing this research on Bond-Stock correlation, as it seems relevant to this topic.

Article Excerpt:

The correlation between stock and bond returns is an integral component of hedging strategies, risk assessment, and minimization of risk in allocation decisions.  In the context of those strategies, the stock-bond correlation is typically estimated using monthly return data over a recent previous period.  This is a reasonable approach but has turned out to be an unreliable indicator for forecasting purposes.   ....   In this paper, the authors conduct an incremental analysis of three innovations for reliably forecasting the long-term correlation of stocks and bonds.

Article Link:

Guy Fleury , I finally got around to reading the article you shared above, and it was a great read! Thanks for taking the time to do, and document, the analysis.  

This is timely for me because I am exploring OLPS (online portfolio selection), and these dynamics (rebalancing,  ranking, etc) are critical to understand.

Hope to see more similar writing from you. Subscribed.   

@.ekz., thanks for the kind words.

Another look at the In & Out strategy. This time, trying to show rebalancing might be the most important line of code in this strategy. Also, that the trade mechanics has a major impact on final results.

Hope it will help some.

Link: Making Money with no Fault of Your Own

Thank you very much Peter Guenthern the last few weeks I've been practicing a lot on quantconnect and I'm slowly understanding how it works. I'm here one more time to ask for information.. Reading the initial description of the algos where there are written the reasons for the choice of some assets as a warning of a stock collapse. However, I cannot understand after the subsequent changes what is the underlying reason why if the performance of GLd> SLV should be a bearish market signal?  in the same way also that of the pairs XLU> XLI and UUP> DBB.

That's great to hear, Jack Whisky. Keep on hacking!
I might not be the perfect person to speak to the economic interpretation of the pairs, since these logics are based on Vladimir's algo (Gold return > Silver return and Utilities returns > Industrials returns) and Dan Whitnable's algo version (adding Metals return < Dollar return) on Quantopian (for the discussion, see this link here).

Anyway, here is my take:
Investments in Gold and investments in Utilities can be safe-haven type of moves (= indications that something might be going pear-shaped in the equity market). However, these investments might also just indicate ordinary investments in a growing economy/market (i.e., more available capital then is invested into all kinds of assets that therefore increase in value). To tease out the safe-haven move, we compare with an alternative asset of the same type. For example, silver is similar to gold and industrials are similar to utilities. The logic is then: Usually the pair components should have similar returns. However, when a safe-haven move occurs, the safe-haven asset (i.e., gold or utilities) should generate a comparably higher return.

The third pair may be a bit more difficult to interpret. It combines falling metals prices (i.e., decreases in resource demand that may be early signs of drops in economic activity) and an increasing dollar (i.e., safe-haven move). Either move would create a signal in the third pair.

Let's see there might be different takes on the pairs and others may chip in with their own interpretations.

Hi Peter Guenther, Thank you for your reply. First of all I am interested in understanding the basic theoretical model, so as to be able to formalize it mathematically. For example, the one described by you explains in detail the reasons why you go into bond. Now I will try to write to Vlad another time.

Hi  Peter Guenther  and everyone, after a few months I have been working on the various systems exposed here. I have some observations to make on the whole. I remain of the opinion that they are robust systems that can work. I have tried all the versions and I have formed an opinion. In & out classic or rather the version: Flex_5, it seems to have a really good timing for entries and exits, if we take into account the collapse of March 2020, the algorithm managed to exit correctly but what I think is even more incredible is the entry timing for the equity component that arrives in correspondence with the rises, while both the distilled bear version and the ROC remain in bonds until July, losing most of the rises. On balance the version I mentioned of in & out is also the one with the lowest drawdown and standard deviation, if we go and see, it would have been released on February 26, 2021 so lacking the timing of the decline on qqq and spy and still holds bonds, in reality it could be wrong to think that this entry is wrong, we should wait for the next few months which in my opinion may not be so bright. So we still don't know if this exit was completely wrong, what instead was objectively a problem is the bond counterpart that collapsed due to the inflation nightmare and therefore leading the system to a loss. On the other hand, the distilled bear and the ROC remained in equities, but suffered all the volatility of the last few months. So I have a few points I want to bring to attention. It is necessary to understand which is the best system among those exposed in terms of risk return, in terms of the number of correct exits. Another important thing is the macro situation of the last few months, namely inflation. A condition that Algos has never experienced so strongly during the backtest. I think it is interesting to consider a possible solution that is to impose an additional filter on the system: once the exit from the equity component has been validated, it might be a good idea to evaluate the situations of the interest rate, so as to have a third option of exit, not in bonds but in an alternative asset against inflation. Instead, returning to the discussion to find a selection of shares or ETFS, I believe that it is necessary to explore other possibilities, especially for ETFs, further brainstorming is needed. We could also create a telegram group for those who are interested in improving this system more actively which, as I repeat, I consider valid.

Thanks for the thoughtful comment, Strongs. I agree that an inflation-proof alternative is with pursuing. Interested to hear more from people on this. 

My only suggestion: rather than telegram, we use the QuantConnect Slack group. Many of us are already there, and many of us, I'm sure, don't use Telegram (myself included). Ideally we don't want to splinter / fragment the community over this discussion. 

Hi everyone,

I've learnt a lot from this thread and I've been trying to add a SPY Put Option, but I've getting an error on most backtests.

Specifically I think it's on this line.

Self.history.pct_change(period).iloc[-1] 

And the error is,

ValueError : cannot convert float NaN to integer

Has anyone else been getting this error when backtesting the algorithms above?

Hi Kamal, do you have a backtest or code for the above? 

Thanks for replying Miguel. I know this algorithm is from another thread but the problem happens sporadically on algorithms in this thread too.
On one backtest the error was the below (I cant attach a backtest with an error).

BacktestingRealTimeHandler.Run(): There was an error in a scheduled event EveryDay: SPY: 100 min after MarketOpen. The error was ValueError : cannot convert float NaN to integer

And then on the following backtest with no changes made, the backtest ran successfully.

Great analysis, Strongs, thanks for sharing it with us! I was musing about whether one should follow 50:50 the Distilled Bear or ROC on the one hand and the In & Out on the other hand. To participate a bit in both worlds. The Distilled Bear and ROC outperformed leading up to 2020, while the In & Out outperformed in 2020. So, one could create a tiered in & out regime, which is 0% in (when both in & outs are 'out'), 50% in (when one of the in & out is in/out), or 100% in (when both in & outs are in).

Great point regarding inflation. I have used the ETF RINF before. It might be useful to provide a signal and funnel us into, say, gold instead of bonds. Alternatively, one could go into TIPS (Treasury Inflation-Protected Security). I will definitely give that a shot.

Thanks for sharing this issue, Kamal G!
I was wondering, since it specifically mentions the conversion to an integer, that the issue might be related to the calculation of the volatility (vola) and subsequent steps, ie the following code:

vola = self.history[[self.MKT]].pct_change().std() * np.sqrt(252)
wait_days = int(vola * BASE_RET)
period = int((1.0 - vola) * BASE_RET)

We try to convert to an integer to calculate wait_days and period.
Not sure why the error occurs, since it actually should work alright, but maybe it could help to use a dropna() again when calculating vola, ie along these lines:

vola = self.history[[self.MKT]].pct_change().dropna().std() * np.sqrt(252)

Just a little update; we've been using this as a benchmark to optimize a real-world application of python on QC. We've made it run 100% faster over the last 2 months. Behind the scenes, we've migrated to a new framework (.net) and optimized the bridge to python 55%. 🏎 

833.69 seconds at 36k data points per second. Processing total of 30,088,220 data points.

1138.08 seconds at 26k data points per second. Processing total of 29,948,756 data points.

1657.74 seconds at 18k data points per second. Processing total of 29,948,756 data points.		

Wow, this is amazing! Thanks a lot, Jared Broad and team, for all your relentless work. Fantastic innovations and improvements!

Kamal G, I think you are right about the line. We can try the same thing here, ie remove the nans.
See the attached backtest. Let me/us know whether this fixes the issue or whether it continues to occur.

@Peter Guenther, That didn't work either, but I worked it out with the help of @Alex Catarino 

Basically the entire self.history(*) was returning nan values on some backtests. Alex advised that it was due to multiple time resolutions on the same equity. In this case “SPY”

I was initially using Hour for the Asset, Daily for the MKT and Minute for the SPY Put Option. Changing them all to Hour, only on SPY, resulted in no failed backtests!!! (It was driving me up the wall). The backtests return very similar returns and drawdowns after the change too, so that's pleasing.

spy = self.AddEquity("SPY", Resolution.Minute)
self.STK1 = self.AddEquity('SPY', Resolution.Hour).Symbol
self.MKT = self.AddEquity('SPY', Resolution.Daily).Symbol

Thanks all.

Thanks for sharing these details, Kamal G! This is really useful since others may be scratching their heads about the very same thing, so thanks again for reporting back to this thread.

On The Use of a Rebalancer, a Flipper, and a Flusher

I published a new article dealing with the IN & OUT trading strategy. In it, I try to provide a better understanding of the trade mechanics in order to better “control” the future outcome of this trading strategy.

Follow the link below:

https://alphapowertrading.com/index.php/2-uncategorised/408-on-the-use-of-a-rebalancer-a-flipper-and-a-flusher

Often, a different look at a problem can help us better understand our own methods.

Guy,

This looks rather vacuous. For what it's worth, in my opinion a little bit of math notation and a lot of superfluous words do not together (nor apart) amount to a meaningful furtherance of the discussion. In this post on ‘AlphaPowerTrading’ (by the way, what could that possibly mean??) exactly nothing is added to the conversation. To be fair, a very circuitous and nonsensical evaluation of the logic of this algorithm is rendered, but it seems to be far below the level of the conversation here.

Hey Abbi, 

When you have the chance, explore the development of this trading strategy since its inception on quantconnect and quantopian, as well as an individual's intellectual and technical contributions to this trading strategy. I might be wrong here, but Guy's blog dates back to 2010 and he's produced other written works, some of which can be purchased as books. This could mean that he knows what he's talking about. Most people would think so.

@Abbi,

The origin of 'AlphaPowerTrading' comes from a tribute to the 'alpha' as defined by Jensen in the late '60s as the excess return over and above the average market return. However, his paper concluded that the average alpha was negative (by about -1.7%) meaning that professional money manages, on average, did not even cover trading expenses. 

A lot of fund managers, at the time, did not like his conclusions since it was putting little value on their money management skills. And from his premises, we saw the emergence of index funds. A 'if you cannot beat them, at least, join them' kind of mentality. Trillions are now managed that way.

The 'power' part comes from the alpha's compounding impact over time as illustrated in the following formula: F(t) = F_0 ∙ (1 + r_m + α – exp)^twhere the 'alpha' is added to the average market return r_m. An 'expense' component was added to represent the operating costs of gaining that added return. It is easy to see that if the alpha is greater than the expenses incurred: α > |exp|, it would improve performance over the long term, even for a small positive alpha value. The impact gets greater as time increases. To get the higher alpha requires a probabilistic edge, some sustainable skills, or better long-term methods of play.

The Jensen alpha is different from the 'alpha streams' described and used in QC where any source of profit becomes an 'alpha stream' even if it turns out that a portfolio might produce less than the average market return. Jensen's idea was simple: if there are management skills, it will show in a positive alpha α > |exp|. The average market return was easily obtainable using mutual or managed funds. So, r_m was a trivial component of the equation since it was available with practically no effort or skills.

I like Mr. Buffett's methods of play where compounding and time are put to the forefront. He has managed an 'alpha' of about 10 points over his 54 years or so on the job with a CAGR close to 20% (10% coming from the average market return and 10% from his alpha skills after trading expenses). 

So, for me, he is the benchmark. If over the long term your trading strategy cannot outperform Mr. Buffett's investment methods, you technically missed the boat since there was an easy solution available that would have outperformed your own trading methods. A long-term structured trading plan is what is needed, and backtesting can help you find it. At least, that is how I use this strategy and many others.

You might want to read my 2007 paper on the subject. It is an old paper but still relevant. It is titled appropriately: Alpha Power 

There has been discussions across this and other threads regarding bonds (TLT / SHY) and what we don't know about the future.  I didn't want to side track the focus of this thread so I created a new thread and  posted a simple tactical bond strategy for the community that pulls from bond ETFs across maturities, credit quality and yield.  It's a simple and automated way to play the yield curve.  

Welcome to the discussion, Todd. Fantastic work on this bond strategy and thanks a lot for sharing it with the community! You are absolutely right, there is some debate in this thread and the “Amazing returns = …” thread regarding that the ‘out’ side of the in & out strategy has been relatively neglected and that there might be room for improvement/optimization. So, your strategy is definitely timely. Some comments recommend to also look beyond bonds and consider additional alternative assets (e.g., gold), so there might even be room for an ‘alternative asset rotator’ which then could be combined with / plugged into the in & out algo.

Hi Peter Guenther thanks so much for starting this thread. I've briefly looked through the historical discussion from Quantopian times, and also at the newer updates. I have a small point to make about the use of the one-percentile for determining the threshold values for extreme_b. You used the 1% statistical significance to explain why the bottom one percentile was taken to be the threshold. However, if that were the case, I think a normal distribution should have been assumed, and the threshold taken to be -2.58 standard deviations (z-score threshold for a one-tail test requiring 1% confidence-level) away from the mean of the rolling window. I performed a backtest replacing line 225:

pctl_b = np.nanpercentile(returns_sample, 1, axis=0)

with the following:

pctl_b = np.mean(returns_sample, axis=0)-2.58*np.std(returns_sample, axis=0)

I've attached a backtest (code taken from the algo you posted on Jan 2021 on the Amazing returns = … thread), but there doesn't seem to be a huge difference. Nonetheless I think there is a slight misuse of the statistical confidence concept that I wish to clarify. Or I could have misunderstood why the one-percentile was used, in which case I would also like to clarify :)

I also would like to ask if there is any reason why the initial ‘Debt’ and ‘Tips’ (obtained when self.dcount ==0) are always used in comparison with the current ‘Debt’ and ‘Tips’ to obtain the median, which in turn will be used to determine if there is any inflation at the current moment. It seems more intuitive to me to use compare the current prices with prices perhaps a year or two ago. Considering that the algo runs from 2008 to present, I would not expect prices from 10 years ago to be relevant to making trading decisions in the present. I also do not understand why there was division involved, so I hope I could get some clarification on this.

Thanks so much for sharing this with the community! I think it's the first time I've seen an algo that makes use of price signals from a variety of sources to make trading decisions, so it's very interesting!

Thanks for sharing these observations, Han Ruobin!

Valid idea regarding changing to a ‘mean minus x-times standard deviation’ logic. 
In terms of your question concerning misuse, or different use, of stats concepts: Using the 1% extreme from the observed returns sample can have the advantage that we do not need to make any assumption about the distribution of the underlying returns. In contrast, when using the ‘mean – 2.58*sd’ we need to assume that stock returns are normally distributed which does not always hold. For instance, see ‘fat tails’ in returns distributions. Using the 1% extreme of the observed returns takes the returns distribution as is and does not require any distributional assumptions. Not sure whether the following is a completely sound comparison, but if you are into statistics, you could compare typical significance testing (estimate/sd) which is usually based on a normal distribution assumption vs significance testing based on bootstrapping which, similar to the In & Out algo approach, is based on the empirical distribution of the observed data (i.e., also holds for non-normal data).

In terms of ‘Debt’ and ‘Tips’, annual resets are also a valid idea and worth a try. If the backtest starts not earlier than Jan 2012, the preference may be to use RINF (see an earlier algo version) since it directly measures inflation. Regarding why to divide by the base level, this is to calculate a return (relative to the base level). For example, if the base level is $100 and, after a year, the ETF lists at $110, the calculation yields 1.1 (i.e. 10% above base level). Expressing as returns helps making ETFs with different price levels comparable. To measure inflation, we subtract the ‘Tips’ return (bond yield without inflation) from the ‘Debt’ return (bond yield plus inflation). The reason for this calculation is that when inflation expectations are increasing in the market, then bond yields increase while TIPS do not. Regarding using the 2008 base level: Both the ‘Debt’ underlying (SHY) and the ‘Tips’ underlying (TIP) do not increase extensively over time, so that using the 2008 base level vs annual resets might be valid to some degree. Anyway, it’s definitely worth a try to use annual resets.

Good observations, keep them coming and let me know if things don’t add up and how your tests played out!

SHY
 

TIP
 

Hi Peter Guenther thank you for your replies!

1. Regarding assumption of distribution of data: I do think that no matter what sort of statistical methods  we use to obtain a threshold value, we are still basing that on some assumptions (proven or otherwise) about the distribution of returns value. In the case of using percentiles, we are assuming that the returns values can be found with equal probability anywhere between the max and min value in the rolling window. I have not yet read enough to say for sure what distribution fits our returns values the best, so I will stick with this for now.
2. Regarding the reference to initial values of TIPS and SHY: I suppose what I am confused about is whether or not the inflation rate that we are concerned about is short-term or long-term. To discern short-term inflation, I would just refer to the rolling window returns. If (latest_SHY_returns - latest_TIPS_returns) > median(SHY_returns-TIPS_returns) then it is an indicator of short-term inflation. If I wanted to determine if there is any long-term inflation, I would probably just take (median(rolling_SHY_prices)-median(rolling_TIPS_prices))/(median(initial_SHY_prices)-median(initial_TIPS_prices)). Considering the trading timeframe and that the longest trade held was about half a year or so (information taken from somewhere in this thread), I would be concerned about the short-term inflation prospects.
   • Related to the determination of inflation, should we not use TIPS-SHY instead of SHY-TIPS (which is what we are doing right now)? If I understand how TIPS works, the principal value of the security should inflate / deflate along with currency right? So when there is imminent inflation, shouldn't TIPS prices (and by extension returns) increase as well, while SHY will just remain as it is? Of course using the inflation ETF would circumvent this problem but I was just curious since how the algo determines inflation is opposite to my intuition.

I have downloaded some data for backtesting on my PC instead of using the QC engines. In my backtest, I decided to go with my definition for short-term inflation using SHY-TIPS but only looking at the 187 most recent returns. I also assumed no slippage and trading fees (not very realistic oops, but I don't think those would be a big problem when performing so few trades and trading very liquid ETFs).

I decided to investigate how the width of the rolling window of prices (in the original algo this is 11) and the lookback period (in the original algo this is 55? I think) used to determine returns (returns=hist/hist_shift) affects the performance of the in-out strat. I compared it against just holding TQQQ from the same starting period (sometime in January 2010), and I have attached an image of the results. I would attach the file if anyone can tell me how I can do it.

The appearance of a band is rather interesting, and hopefully this could be helpful in dealing with the issue of having fixed constants (why was a window size of 11 lagged by 55 days used to determine returns?). It seems to me that in general rule for the band would be k_min<window width + lookback<k_max. I suppose strictly speaking the lookback period is also partially determined by the size of the window (in the above data set, the lookback period is defined as the number of trading days between the most recent end of the window and current day), so maybe by presenting the data as such I might get a clearer picture of what is happening. I'll update it here sometime later. I'll explore to see if the waiting time would affect the band or not.

What I think would be helpful / interesting would be an explanation about why there is such a band. My intuitive explanation for why returns for window_width+lookback > k_max is disappointing is because the algo looks too far back (window_width also affects how far back the algo looks) and data in the past would have become irrelevant. I do not yet have an explanation for why window_width+lookback < k_min could also yield disappointing results. I don't think it is noise because if it were, I should be seeing a lot more greens at wider windows.

Really great work there, Han Ruobin! Nice illustration using the green coloured returns. Since this strategy tries to time the market, I reckon that there will always be a ‘perfect timing’ in terms of lookback/window (and other) settings. The green diagonal band is indeed quite interesting and somewhat reassuring, since it shows that there is not only one perfect lookback/window combo, but there seem to be several combos that work similarly well. This is definitely a new interesting insight. Also, great regarding formalizing the diagonal band. Yes, it’s interesting to mused about this a bit more. One could argue that, by the end of the day, we need to select a specific combo from the band—so then, a selection criterion would be needed to determine this selection. Alternatively, you could select multiple (or all, if the calculation effort is worth it) combos from the band (according to your formula for the band) and use the results from these combos to determine whether to go ‘out’ of the market or stay ‘in’, e.g. as a majority call based on the different results from the band combos.

Regarding percentiles and distribution assumptions: Hmm ... I would argue that percentiles have no distribution assumption, not explicit nor implicit, because the, say, 1% extreme observations are the 1% extreme observations no matter what the returns distribution looks like. They are the 1% most extreme observations on the left-hand side, always, no matter whether the returns follow a normal distribution, Chi2, Poisson, uniform distribution, or any other distribution. In contrast, when we work with -2.58*sd, we only really know what we will be getting if the returns are normally distributed. For any other distribution, -2.58*sd could be anything, really. For a normal distribution, it means the 1% most extreme (smallest) observations, but for a Chi2 distribution, it means something else, and again it means something different for a uniform distribution etc.

Regarding TIPS and SHY: I think you are right that this should be the other way around, but for a different reason. As I understand it, inflation expectation changes do not move TIPS since these have an inflation protection guarantee. In contrast, changes in inflation expectations move SHY. Specifically, and this is the reason why the calculation needs to be reversed, SHY (i.e. bond prices) goes down as inflation expectations go up since investors demand additional returns to be compensated for inflation. So, let’s say inflation expectations increase by 1%, then SHY (i.e., bond prices) should drop by 1% (i.e., the bond yield increases by 1% to compensate for inflation) while TIPS should stay constant, meaning that more negative values in (SHY-TIPS) indicate higher inflation expectations. So, when we check for above-median inflation expectations, we should either check for (SHY-TIPS) below its historic median or, equivalently, check for whether the reverse-coded difference (i.e., -(SHY-TIPS) = (TIPS-SHY)) is above its historic median. Thus, since we currently work with an above-median type of check in the algo, the easiest fix seems to be to calculate the return difference reverse-coded via TIPS-SHY instead of SHY-TIPS.

Actually, percentile has an implicit assumption of a normal distribution. If you want to customize your own distribution, then you need to use the first, second, third, etc. moments to obtain non-normal distributions.

No, they do not, Chak 😊 You can use them on a normal distribution, yes, but they mean the same thing, no matter what the distribution is. For example the 1% percentile means the 1% left-most extreme observations … this is true in any distribution, not only a normal distribution.

https://numpy.org/doc/1.20/reference/generated/numpy.percentile.html

Moving forward, I'm suggesting that perhaps using np.percentile has some limitations to finding accurate outliers.

Hi Chak , now I don't know exactly what data you are talking about, I know how it works in & out with the percentile. But from what I understand from the discussion, taking a certain percentile level doesn't change the type of distribution. The random variable taken into consideration are equity returns, and it is the random variable itself that assumes a certain type of distribution, which usually in the financial markets tends to be a T-student. By placing yourself on the most extreme percentile, you are only observing what happens in the distribution queues, which is the least likely event.

Chak, thanks for the link. I am not sure where this suggests that percentiles assume an underlying normal distribution. They do not. We should not confuse a ‘normalized ranking’ (mentioned in the link) with a normal distribution. These are very different things. To determine percentiles we just rank order the observed values (e.g. historical returns) to determine the percentile of a given observation. What the algo uses this for is to check whether a given, current return is falling into the 1% most extreme (left hand side) observed returns. If it is, this creates an ‘out’ signal. The distribution of the historical returns does not need to be of any particular distribution type. This approach works with all kinds of distributions – the distribution simply does not matter for the approach to work. Hope this helps / clarifies.

I have read and followed this strategy since it was introduced on quantconnect 10 months ago. While my academic training lies in physics, neuroscience, statistics, and behavioral economics, I didn't really understand enough of the market's implicit undertones to suggest anything. Now, I believe I'm somewhat comfortable enough with this topic to begin to really talk about the limitation of each strategy's variation.

My point in bringing up the limitations of setting a hard rule, in this case 1%, is that it causes the strategy to exit out too late or too soon, and subsequently, miscalculates the suggested n number of wait days. When you take a combinatorial approach to the number of out signals, GLD>SLV, XLI>XLU, DBB>UUP, the chances of getting an incorrect signal multiplies. Sure, bonds go up when one/two of the signals are TRUE, but the market remains “bullish” as well. What one needs is a malleable cutoff point.

As a corollary example, the PE ratio of growth stocks post covid market crash is 25+, while precovid it's an entirely different number. Further, the PE ratio for a growth stock fluctuates across time and perhaps, in some cases, might not even be a leading indicator at all!

Thanks Peter Guenther! I agree with you on the effects of inflation expectations on the prices of SHY and TIP. I believe I mistakenly analysed the effects of inflation that has already happened on the prices of SHY and TIP. Thank you for the clarification:)

Regarding the use of the 1% threshold, I agree that this can be applied to any type of probability distributions. However, this ‘1% of all values’ do not necessarily, and I believe probably would never correspond directly to, a ‘99% confidence interval’ in the typical statistics definition unless the distribution was rectangular like I described earlier. I have attached a graph that I plotted on python to investigate the difference between using the 2.58 std as a threshold for the 99% confidence interval, and using just the bottom 1% as the threshold. The results are strikingly different.

On this note, I think I want to ask if the assumption that I have had about the use of the 1% threshold is right, which is that it is motivated by the requirement of 99% confidence that the observation is above the mean.

Chak About your point on the hard cutoff, while I agree that a 1% threshold, and any hardcoded number, would have a certain rigidity to them, they already do have some degree of malleability to them. A more rigid criteria would perhaps be a rule that is like ‘if day-to-day returns are below x, out-signal generated’. This is different from a rule like ‘if day-to-day returns are within the bottom 1% of returns in the most recent 187 days, out-signal generated'. The 1% threshold used in tandem with the moving window allows this signal-generating threshold to change based on the most recent ‘state’ of the market (defined in the algo as the most recent half a year? I think), and I see this as a form of malleability. Using your example of PE ratios, while selecting stocks based on a fixed PE would yield very different results pre and post covid, if we selected the top  / bottom 25 stocks by PE, we would not expect this stock selection to differ very much pre and post covid. the idea of using the most extreme 25 stocks is the same as using the most extreme 1% of returns, I think.

Of course, this does not address the point you made about indicators remaining as leading indicators in the long run. I think this would remain a rather difficult problem to solve. However, considering that there is a good deal of economics theory backing the algorithm (e.g. the use of diverging SHY and TIP returns to forecast inflation expectations and thus the impact on whatever ETF we are trading), I do not see the loss of predictive power of the indicators any time soon, if they have any currently. I myself am also still in the midst of evaluating this.

The stocks are sorted by returns. So, if you take 100 stocks sorted in this manner, the 1% will take the one stock at the end of the sample, the highest or lowest, no matter what the distribution may be. This last one, or first one, could be 5, even 10+ standard deviations from the mean (an outlier).

Nice work there again, Han Ruobin, and valid point, Guy Fleury!
The missing puzzle piece might be the following: We have to be careful that we do not equate a ‘99% confidence interval’ with a ‘99% confidence interval for a normally distributed variable’. The -2.58 std that you are comparing against in your chart is the cut-off value (1% bottom) for normally distributed variables. It is important to note that this cut-off value does not give us the 99% confidence interval cut-off for just any variable, but only for normally distributed variables. Now, stock returns are often not normally distributed, especially in small samples. When an underlying variable does not follow a normal distribution, then resampling and using the 1% percentile will give you an appropriate estimate for the 99% confidence level cut-off value. Therefore, the distances displayed in your chart are a result of the ‘99% confidence interval for a normally distributed variable’ not applying in this context, since the sampled returns are not normally distributed, so that using -2.58 sd creates an error/deviation. That is, the distances are not created by the 1% percentile deviating, but by the -2.58 sd deviating since it assumes a normal distribution that does not show neatly in the data.
Not sure if you have a background or interest in statistics, but to find an equivalent for this point, one example that comes to mind is the test for mediating effects in statistics (variable A affects variable B which affects variable C). Call the effects a (A on B) and b (B on C). The total mediation effect (A on C) can be calculated as a * b. This total effect does not follow a normal distribution (due to the multiplication), irrespective of the fact that the effect components (i.e., a and b) may be normally distributed. To test the statistical significance of this effect (or to create a confidence interval), we cannot use the logics we would use for normally distributed variables (e.g., t = effect / sd) since the assumption of a normal distribution does not hold. Instead, we can use bootstrapping (random draws from the original sample) to create observations of the total mediation effect in many different scenarios and, thereby, generate a distribution. To determine the ‘99% confidence interval’, we would look at the cut-off level provided by the bottom 1% percentile (similar to what the algo does) of the bootstrapped distribution. Again, we cannot use -2.58 sd in this context because the assumption of a normal distribution does not hold (as can be the case for real-life stock returns, especially in small samples). For more details, e.g. see the description and online tool for total mediating effects here: http://quantpsy.org/medmc/medmc.htm

Good reply regarding Chak’s note, reminding us that a 1% percentile is not a hard cut-off, since it is responsive to the actual data that is being sampled. I agree.

Trivia: 1% of 10 stocks is zero. 1% of 100 stocks is one. 1% of 199 stocks is one. 1% of 299 stocks is 2. If you trade less than 100 stocks, use a top or bottom function instead of the percentile function to at least have one stock selected. It is nonsensical to use the 1-percentile function if the number of stocks traded is less than 100...

We do not have fractional stocks. The number of stocks traded is an integer. There is no such thing as 0.6 Apple or any other stock for that matter. We have started to have fractional shares, but that applies only to the number of shares bought or sold on this integer stock.

The greater the number of stocks traded, the more the >|2.58σ| will contain some stocks. The more the portfolio has a large number of stocks, the more the distribution will approach a normal distribution, even if the distribution is not normal.

We cannot predict with any accuracy what will happen to 100 stocks tomorrow, and yet, we want to cut hairs to the 100th. All that is very fuzzy, and any approximation will not make that much of a difference on a 10-year trading strategy.

Thanks for sharing, Guy Fleury.
Maybe a thought to add, only a nuance really: The 1% percentile is used to find extreme returns within a given instrument (e.g., say, for the ETF tracking metals). That is, it is a time-series view (e.g., 100 historic returns) instead of a cross-sectional view (e.g., 100 stocks). The 1% percentile tracks whether a given current return is ‘extreme’, i.e. sits within the 1% percentile of the sampled returns (including the current return).

Your point is generally correct that we need to ensure that we have at least 100 returns in the sample, which is constantly fulfilled in the algo considering the lookback of 252 trading days. Now, it seems that the algo would still work if we forget to pay attention to this technical requirement and reduce the lookback period below 100 observations: numpy.nanpercentile() even gives us the most extreme observation when we specify a percentile of 1% and the sample includes fewer than 100 observations. If you have python at hand (e.g. via Research) and want to give it a try, the following code can be used:

import numpy as np
test = np.array(range(1,51))
np.nanpercentile(test, 1)

The test sample includes 50 observations, from 1 to 50. We request the 1% percentile. It gives us 1.49 as the output, meaning that the most extreme (smallest) observation (i.e., 1) would be categorized as ‘extreme’ since it is smaller than the percentile cut-off value.
Therefore, we would still be within our conceptual logic of checking for extreme returns/observations, although of course a case can be made that the sample size should be made more consistent with the percentile that we are checking for. 

Curious with all the work that has been done on these, how popular are these when it comes to live trading? Or combining this strategy with another.

My latest article: The Makings of a Stock Trading Strategy – PART I deals with the mathematical structure of stock trading strategies. It is related to the IN & OUT strategy, especially the upcoming PART II. 

The article is looking at stock portfolios from an equation point of view. From the expected endpoints, it tries to re-fabricate what could have been required to get there to then include these findings in the trade triggering mechanics to ensure a profitable outcome.

The objective is to gain control over the evolution of a trading strategy as well as increase performance.

The article is also in HTML format.

Hope some will find it interesting.

Here is PART II of The Makings of a Stock Trading Strategy

This HTML file can be very helpful to anyone designing automated short-term stock trading strategies. It has deep implications. It deals with correcting for long-term portfolio return degradation, on how to fix it, and even how to reverse it.

It builds on mathematical equations used in describing the outcome of our trading portfolios and shows how easy it is to improve on these designs with simple trade triggering techniques. As if saying that your trading procedures can be greatly improved just by requesting more and letting long-term compounding do its job.

The main idea is to stop long-term return degradation which tends to make trading strategies break down or fail going forward. Not only can we stop return degradation, but we can also easily reverse it to then make our strategies prosper and thrive instead of their overall return degrade and die. As always, it is only if you want to do it. But regardless, you should be aware of how to compensate for return decay, overcome it, and reverse its detrimental effects.

Take the time to play with the equations provided, adapt them to your own trading strategies. Study the limits of what you could do or can do. It will open portfolio building and portfolio reengineering to anyone wanting to make it big for whatever purpose. Evidently, there will be work to do, but it will be based on a solid foundation from which one can soar. And yet, you should find it so simple. Make the following trade imbalance hold: (1 + fW) ∙ (1 – fL) > 0.

Click tor the HTML version. 

The article is a little long, I agree. It is there to protect your portfolio, not mine. So, you be the judge.

Related Articles:

The Makings of a Stock Trading Strategy – PART I 

Your Automated Stock Trading Portfolio

On The Use of a Rebalancer, a Flipper, and a Flusher

Guy Fleury : thank you for sharing this. Seems like it will make for a great read.

Given the content / relevant of the article, I suggest sharing this in its own forum post. It will surely not get the best exposure, buried in this In&Out thread.  

This will likely reach, and benefit, more people in its own post. Further, we can have focused discussions around it on a dedicated thread. 

My reflections on the IN & OUT strategy.

I consider the IN & OUT strategy as operating on market noise, especially when rebalancing on a daily basis. We all know we cannot win a heads or tails process except, of course, by luck. So, why put so much emphasis on this strategy which under other trading procedures might be classified as rather ordinary. 

It operates as if a positive outlier or an unexpected sore thumb depending on the point of view. Who would risk so much on market noise in the first place?

The answer is: it is not totally market noise. As in any stochastic process equation, there is a drift part and a random-like component, which by itself does, in fact, have a zero expectancy. So, it is not from the stochastic part of the equation that will come the expected benefits, but from the drift component which is easy to get. The drift component of the equation is often defined as the underlying trend. 

Any trend-following strategy should capture this underlying trend. Not because it is prescient, but just because it participated in the game. Not surprisingly, the average long-term portfolio return tends to the market's long-term expected average: E[r_m]. 

That seems like taking the merit of designing such a trading system out of the equation. As if saying, you won, but it was not entirely your fault, you were buying in an upward market and were simply in it for the ride. Nothing more. You would still get the merits of participating and would collect the benefits of such a ride. That is perfectly OK. It was what you wanted in the first place: the profits, pretty please, the same here. I do not mind so much how or why I profit as long as (over the long term) my strategy can profit substantially from the game. And I suspect you would conclude to the same.

It remains that trading over market noise appears as an opportunity bet in the sense that you know why you got in a trade based on some reason, but that your gain is still connected to a partially random-like exit strategy. This is where you should take advantage of price variance. 

You know prices will swing all over the place, that you like it or not. It is what it is. So, your advantage would be to exit a trade while variance is also to your advantage (∆σdW > 0) even if this variance is the result of market noise. That is what is exploited in the IN & OUT strategy (all its versions, btw). 

Is this wrong? I do not think so. Why should it be? You entered the game to make some money. You determined that this type of daily rebalancing could provide an advantage, an edge, even if small since mostly dealing with the delta of the drift (∆rdt). 

The variation of the underlying trend from day to day is in the order of 0.04% over the long term. It is not much. It is 4 cents on a $100 dollar stock or 1 cent on a $25 dollar stock. But it is still there, it adds up day by day, and is given free of charge. All you have to do is participate while you identify the trend as going up. This trend has been mostly up since March 2009, or, with a longer perspective, for over 240 years.

Almost any trend-following strategy following this underlying trend could have profited during those past 12 years (in fact, all the IN & OUT variants did).

In a recent simulation of mine, I had the average win rate at 0.06% thereby catching part of the long-term drift as well as a little bit extra, the result of the trade mechanics associated with the rebalancing process itself. Note that the average trade duration was more than one day. It is like taking advantage of this stochastically moving statistical blob of fluctuating variance penny by penny.

Yet, I consider the trading strategy's trend declaration to be wrong most of the time (~85%). I also noted that the strategy by its structure had an overall safeguard measure, thereby becoming a desirable protective “feature”. 

This all-in at all times strategy was winning due to its repeated flushing which served as its universal stop-loss. The strategy would not accumulate falling knives even though it was intrinsically designed to do so (buying the dips and the dips and the dips). The flip-flopping would force the execution of the stop-losses before they could be considered troublesome or too detrimental to one's portfolio. And since the strategy dealt with 100 stocks, any one stock's downfall would be considered to have only a small impact on the overall portfolio.

You want to win? Understandably. Then know why you win. Know what your trading strategy is really doing. A simulation is just there to confirm your trade mechanics. If it didn't work on past data, you have your answer for going forward even if you do not know what the future might bring. However, if your trade mechanics give you an edge, exploit it.

Should the IN & OUT strategy warrant more investigation as to why it can be made even more profitable? I think so. How about you?

Thank you to all of the contributors who have made this forum post (and a couple of other related ones) an extremely educational read. I have played with a few variations of the In & Out algo, and it seems that there have been no trades since about April-May timeframe. Is that correct that the algo has been “out of the market” for the last 3-4 months?

Is there another thread that focuses on live trading that I may have overlooked? It would be great to hear about some live trading experience and results given that we've had about 10 months of real-world trading since the algo was first proposed by Mr. Guenther in October 2020.

Again, thank you all for the great work and especially the knowledge sharing in this forum.

Guys I read the whole discussion. Super interesting. My concern is that while the back tests look incredible, I suspect they are incredible because its overfit. 2008 to 2021 has been an incredible bull run. So if we just dodge couple of drawdowns, we can “beat” the market quite handily. However i suspect this might just be overfit rather than a signal. My main criticism is that as name suggests its an “in or out” strategy. So are inferring a lot just based on the signal. Its not a situation where we are probabilistically changing our weights given expectations of next day returns, but instead we completly rotate out. So this makes me think thats its very easy to overfit because its a very binary signal. If you get some of the big moves wrong you could lose a lot of money. So its not a question of slightly underperforming the markets, because the system is slightly overfit. Anyways I was curious to hear your reasons for why you believe that the system is not overfit.

Is “IN & OUT” overfitted or not? 

We all have “opinions” on that since we cannot “prove” if it is or not. We have no tools to effectively demonstrate either yes or no. 

The first question that should come to mind is: overfitted, compared to what? 

Maybe, here is a better question: is “IN & OUT” outperforming long-term market averages? Because, if it is not, who would want to play with it? It would have lost all its potential value since it would then be underperforming market averages.

I am of the “opinion” that “IN & OUT” is operating, for the most part, on the fringe, the fumes, of market noise. And that cannot be overfitted due to its random-like nature. You cannot consistently overfit randomness. Every day, the price variations are, structurally, surprises that are highly unpredictable. You could, nonetheless, take a bet on the long side based on the underlying long-term market uptrend. 

Any trading strategy should be biased toward its goal. Meaning that in an upward market, most trades should be biased to the long side. Should the upside last for 10 years or more, you still should favor, for the duration, the long side for your “average” trade. Moreover, that average trade should generate a profit, not because you anticipated a market rise (even if you should have) but just because you participated in some market upswing while it was going up.

The only prediction we can make going forward is that market noise will be there, that we like it or not. And it stops there. As for predicting tomorrow's prices, your guess is as good as mine, although I will continue to favor the upside. This is what the “IN & OUT” was designed to do over the last 12 years.

Regardless, the strategy still needs some added protection and will need to change its upward stance for when the market really turns down again. However, it is already quite sensitive to market fluctuations, to the point that its average universal “stop-loss” is relatively low (my average loss rate is below -0.1%). The trading signal goes down a fraction of a percent and its flusher kicks in which could explain why it is wrong so often. At least, it will catch all downtrends of significance. It is a rare strategy where being wrong often (~85%) becomes a positive protective measure... 

“IN & OUT” wins the game because it was wrong most of the time. That, in itself, is interesting. Note that its rebalancing procedures (flipper, switcher, and flusher) had a major structural role in making this strategy win over its simulation period. It won mostly due to its trade mechanics. And that is also quite interesting.

one way to check if this startegy is overfit would be run the backtest from 2000-2008 time period. We could simplify the code so that we can run the code from 2000-2008 and see how the backtest performs. What do you guys think?

Thanks for sharing your observation, Jake Rocket. I agree that that is quite a long time for being out of the market, although it's also indeed true that the in & outs are sometimes out of the market for several months. Anyway, I have looked into an In & Out version that exists the market less frequently. My approach was to try and find a better balance between the size of the returns sample and the critical cut-off percentile and at the same time, ideally, reduce the number of required signals. 

So, attached is the latest version of these efforts (v7) – work in progress. Some additional notes regarding this algo:
(1) It saves important state information (dcount and outday) to the ObjectStore which ensures recovery of its state in live trading when the algo is interrupted.
(2) It creates a chart called ‘Out return’ that helps to track the return delta generated by the decision to exit the market instead of stay in the market. This chart can be quite instructive in terms of whether the out decision indeed was a good one … or a lousy one.

As always: Looking forward to any comments and improved versions.

As a follow up to my prior post;
Since it's difficult to see in the backtest charts above, below is a picture of the Out return chart which some may find useful. The chart value is zero when we are in. As soon as we are out, the chart value starts to fluctuate according to the following difference: out portfolio return minus return if we had stayed in. In science they would call the latter component the counterfactual, which here is the return in the parallel-world scenario where we would have remained invested in the market instead of going into bonds. When we go back in, the Out return chart value jumps back to zero, giving us a clear cliff's edge (sometimes we may have to zoom in a little) indicating the eventual return differential (out return - counterfactual in return) of the out decision. If positive, the out decision was a good one. If negative, we would have been better off remaining invested in the market.