Forum Replies Created
S3 allocations this month:
TSLA 5% – I’m leery of Tesla right now, almost 9% shorts at this price, so replacing with one of (NTES, MSFT, AMZN)
@Mark Vincent – not sure what the cause would be. I’ll post another version with the complete set of .ini files later this week, that may help.
@Horizon60 – LQD is similar to IEF, 5-7 year US Treasuries, in terms of returns and volatility. It’s pretty highly correlated most of the time, although it does move as a hedge sometimes. There’s nothing particularly great about it honestly, I’m looking for a better substitute, but for now the IEF/LQD/GLD combo seems fair as an alternate “low vol” strategy that can be rotated against the “high vol” main strategy.
@bobh – I tried to use a custom range in QT that ends before the present, but it threw an error. It seems the idea is to use a variable start of range, not so much the end of range. I should report that as a bug. In any case, I have a slightly lower drawdown version that I’m working on, but the risk is always that it becomes too complex, and it’s just a curve fitting exercise. That’s why the LI team tries to keep their strategies very simple (I think), because it avoids at least the appearance of a simple backwards-optimized curve fitting exercise. I’ve had many much more complex versions of the “Super Simple Strategy”, all of which I have dumped for progressively simpler versions, ironically. Even the one I shared above is still a bit on the complex side, with multiple sub-strategies.
@Korving99 – sure, I would be happy to talk to you on Teams or whatever. I don’t know if there’s anything really special about this strategy, aside from it’s (on paper, at least) longer-term apparent reliability and durability, which is what I’m looking for. I’d like to be able to book 5-15% per year reliably, but that’s a big challenge.
I’ve played around for quite some time with S3 variants of all sorts. I now have something that I think works well. Here are the required .ini files:
It uses the built-in “Hedge”, “GLD-USD”, and “Nasdaq 100 balanced without hedge” strategies. It is restricted to the following symbols:
FVD, QQQ, PCY, TLT, GLD, LQD, IEF, TIP, GSY
It is similar in performance to the built-in US Market Strategy hedged, except that it has far better performance over the full 20-year timespan (all values rounded):
1 yr: CAGR 22.1, Sharpe 1.5, Vol 15, Draw -15.3
5 yr: CAGR 17.4, Sharpe 2.0, Vol 8.7, Draw -15.3
10 yr: CAGR 16.0, Sharpe 2.0, Vol 8.2, Draw -15.3
20 yr (Jan ’01 – present): CAGR 11.4, Sharpe 0.9, Vol 12.7, Draw -41.0
1 yr: CAGR 29.1, Sharpe 2.0, Vol 14.3, Draw -14.1
5 yr: CAGR 16.8, Sharpe 2.0, Vol 8.6, Draw -14.1
10 yr: CAGR 16.0, Sharpe 2.0, Vol 8.2, Draw -14.1
20 yr: CAGR 15.9, Sharpe 1.8, Vol 8.6, Draw -14.1
From the start of the strategy (Jan ’01) to present, there is no year-end with negative returns – though there are several that are nearly flat. The simplest mix of the strategy is labeled S3 – 1 – 3. That uses even fewer symbols, avoiding using the “braking” sub-strategies. The second main sub-strategy, S3 – 2 – 4, does make use of the brakes. Either of those two sub-strategies could be used independently. The top-level strategy, S3, rotates these two sub-strategies. Each of these sub-strategies is composed of several sub-strategies:
FIRST MAIN SUB-STRATEGY:
S3 – 1 – 1 – LONG OPT: Rotates FVD, PCY, and QQQ with long (20-yr) optimum
S3 – 1 – 1 – SHORT OPT: Rotates FVD, PCY, and QQQ with short (<=10-yr) optimum
S3 – 1 – 1: Rotates S3 – 1 – 1 short and long opts
S3 – 1 – 2 – LONG OPT: Rotates S3 – 1 – 1 and Hedge with long opt
S3 – 1 – 2 – SHORT OPT: Rotates S3 – 1 – 1 and Hedge with short opt
S3 – 1 – 2: Rotates S3 – 1 – 2 short and long opts (already achieves 20-yr CAGR 14.4, Sharpe 1.58, Draw -14.13)
S3 – 1 – 3 – LONG OPT: Rotates S3 – 1 – 2 and Nasdaq 100 balanced without hedge (N100-4) with long opt
S3 – 1 – 3 – SHORT OPT: Rotates S3 – 1 – 2 and N100-4 with short-opt
S3 – 1 – 3 – NO OPT: A fixed-ratio of 80% S3 – 1 – 2 and 20% N100-4
S3 – 1 – 3: Rotates S3 – 1 – 2 short, long and no-opt strategies (20-yr CAGR 15.7, Sharpe 1.7, Draw -14.13) *can be used stand-alone
SECOND MAIN SUB-STRATEGY:
S3 – 2 – 1 – LONG OPT: Rotates GLD-USD (GLD and GSY), IEF, and LQD with long opt
S3 – 2 – 1 – SHORT OPT: Rotates GLD-USD (GLD and GSY), IEF, and LQD with short opt
S3 – 2 – 1: Rotates S3 – 2 – 1 long and short opts
S3 – 2 – 2 – CASH BRAKE: Rotates S3 – 2 – 1 and GSY
S3 – 2 – 2: Rotates S3 – 2 – 1 and S3 – 2 – 2 – CASH BRAKE – The idea here is to use lower-volatility market, gold and US bonds ETFs (LQD, GLD-GSY, IEF) to construct a “braking” strategy that targets lower returns with lower volatility, and that moves differently from the higher volatility strategy, so it can be used as an effective fallback. This strategy is a little ugly to use on its own, it’s really intended as a low-vol “lightning rod” or “brake” for the main strategy.
S3 – 2 – 3 – LONG OPT: Rotates S3 – 1 – 2 and S3 – 2 – 2 (“the brakes”) with a long opt
S3 – 2 – 3 – SHORT OPT: Rotates S3 – 1 – 2 and S3 – 2 – 2 with a short opt – this ended up never being used so could be eliminated.
S3 – 2 – 3: Rotates S3 – 1 – 2 long and short opts (but only uses the long opt)
S3 – 2 – 4 – LONG OPT: Rotates S3 – 2 – 3 and N100-4 with long opt
S3 – 2 – 4 – SHORT OPT: Rotates S3 – 2 – 3 and N100-4 with short opt
S3 – 2 – 4: Rotates S3 – 2 – 4 long and short opts. This strategy achieves 20-yr CAGR 15.5, Sharpe 1.9, Vol 8.2, Draw -12.24 and can be used on its own.
S3: Rotates S3 – 1 – 3 and S3 – 2 – 4. Total return 1845.5% from late ’00 to present, CAGR 15.872%, Sharpe 1.844, Vol 8.609, Draw -14.13%.
@StefanM if you use the CASH strategy, it’s ultra-low volatility will act like a “lightning rod”. Try setting the volatility multiplier for CASH to about 25x in the Strategy Manager and observe the results. I’ve found using extreme settings like that causes instability in QT, so the cash stops in S3 are instead implemented using the volatility and cash Sharpe limits. I apply those limits only when they benefit the overall returns or reduce volatility/drawdowns, and only after the initial strategy is optimized. To answer your other question about holding stocks through earnings, yes, you can just manually inspect the list of stocks and check their recent performance. It’s perfectly fine to substitute a highly-ranked symbol for another. I sometimes choose alternatives just so I’m not all-in on tech stocks for example, or so I can avoid stocks with freaky looking charts.
Overall, S3 is just a typical combination of hedge and risk sub-strategies, with a dash of Nasdaq-100 top 4 to boost returns.
The hedge is constructed mostly of a mix of GLD and TLT, along with a few other symbols. I’ve been back and forth on using the inverse ETFs, but I’ve decided they are useful because they level out the hedge over time. Even if treasuries and gold are going down, the hedging sub-strategy is still able to move up. The inverse ETFs are used sparingly, because I don’t like their decay, but there are some periods of time when hedges are misbehaving when they prove useful.
The risk sub-strategies are mostly based upon QQQ and SPLV, although I use a few other alternate symbols that have good long-term behavior. I hedge each individual risk symbol, apply the cash stops when they’re useful, then rotate between them using the SRE method. I’m not really inclined to share the .ini files because of the amount of work I’ve done on it. To get similar returns, you could just use the Maximum Sharpe LI strategy. Of course, I prefer my own QT strategy, which sits at around Sharpe ratio 2.3 over long and short timeframes, consistently returning about 16%. It plows straight through the 2000 and 2009 markets with solid returns. It’s a thing of beauty.
If you have a higher risk appetite, just use any degree of leverage you’re comfortable with in your investment account.
I’ll try to post some more info soon, not trying to ignore your comments.09/04/2018 at 9:47 pm in reply to: Using synthetic data or external data sources in QuantTrader #54889
FYI I’ve tried many times to get synthetic pricing to work and it just doesn’t. It’s one of those things that really could use some attention in the program, just my 2 cents. I gave up again tonight =(09/04/2018 at 8:33 pm in reply to: Using synthetic data or external data sources in QuantTrader #54887
I just added UGLD_SYN to the collection.
Just add recent data to the others to bring them up-to-date. I’m not having the best of luck getting Quant Trader to recognize synthetic prices, it’s very sporadic. It would be nice if there were a built-in feature that would allow synthetic prices for leveraged funds based upon the underlying – i.e. allow a checkbox in symbol manager “use synthetic price history” that would download all available actual data, and then designate a related symbol – say GLD for UGLD, and a leverage multiplier (3x), and QT could create 2 files, one would be _UGLD.csv, the other would be _UGLD_SYN.csv. Both files would be used to construct backtests for any strategy that used those symbols, but only if the portfolio were configured in Portfolio Manager to “use synthetic prices when available”.
Another approach would be, instead of using stops and other calculations, a simple “phase transition” that allows one strategy to operate during say VIX < 20 markets, and another to immediately take over any time a simple parameter like that occurs, so we essentially have a simple rule around volatility that forces a “gear shift”. We would only really want to evaluate the gear shift strategy’s outcomes during the times when it is in force.
Of course this can also be done manually, by simply creating two different strategies. I would stick with a monthly reallocation on the primary, but perhaps switch to a weekly reallocation on the high vol strategy. It would still not respond to a single-day event, but it wouldn’t let you sit on accumulating losses for most of a month while you get slaughtered, because neither treasuries nor gold are currently behaving as effective hedges to market risk. They are only dropping “less quickly”. In this case, the only remaining “hedge” is a zero-loss asset, cash. So, we need a better way to introduce cash as an explicit stop-loss in cases like the current market.
I just read an article that compares the current environment to the 1987 and 1994 events, when there is a significant market correction, but bond yields are actually increasing, and therefore bond prices are also dropping. This is a very rare combination that isn’t accounted for in any of the QT models, because it breaks all of the assumptions the hedging approach is based upon. If ZIV were to be closed by Credit Suisse in the same way they’ve now closed XIV, the typical expectation would be for TMF to make up some or much of the loss. That has completely broken down.
If a VIX spike to 30 was sufficient to completely crash XIV, I am not certain ZIV is really a suitable investment vehicle at all at this point. We might be much better off focusing on SPXL/TQQQ or Nasdaq strategies. Unfortunately, the triple-leveraged growth strategies are amplifying market losses well beyond the actual correction, because the hedges in them have completely failed at the same time.
Therefore, I need an alternate strategy that takes over and uses a nicely constructed set of rules that prefer cash and reallocate on a perhaps weekly basis. It would avoid using explicit stops, but it would require some programming effort in QT to reflect as a single parent strategy with an overall performance value.
I just mean if we could model the existence of trailing stops that would trigger at any time, rather than only at the reallocation frequency. If there were “triggers” that we could design around VIX spikes or non-negative correlations between the assets we define as “hedges” (TLT/TMF/GLD/UGLD) and those we define as “risk” (ZIV/QQQ/TQQQ/SPY/SPXL/Nasdaq100), then we could test the effect they might have. They could trigger a move to cash until the next reallocation on whichever assets they are configured. In the trading platform, they would be implemented as one-triggers-other sales using trailing stops or other conditional orders.
I still think there should be provision for a ruleset in QT that would allow us to experiment with intra-allocation trailing stops based upon VIX and movements in the hedge/risk designated assets. At least we could test it. I’ve struggled for quite a while to find a way to create a “cash out” rule, but there are really only 2 settings in QT, “Volatility limit” and “Cash Sharpe Limit”.
Alternatively, maybe it would be a good idea to have a weekly reallocating and much more conservative strategy, but that would only be considered if volatility in the main risk strategy is over some limit.
Ok, so the premise of the whole momentum trading system is that there are asset classes that are anti-correlated over a monthly timeframe. That seems to work, except when it doesn’t, like in the last week or two. Now, what if the QT software had a way to test for the trailing correlations between your hedging assets. So, you designate certain investments as your hedges, and others as your risk assets, and the software computes their lookback correlation. When the correlation has been reliably and sufficiently negative, then allocate as expected. However, when the correlation is insufficient, below whatever threshold, then we substitute go ahead and allocate according to the normal rules, but we also add a cash stop. The rule would be enabled when both the hedge and the asset move downwards within a single monthly investment cycle by more than whatever threshold for pain we set. Is that a bad idea, or a good idea?08/23/2017 at 10:37 am in reply to: Using synthetic data or external data sources in QuantTrader #44825
I have now also added SPXL and TQQQ synthetic data, generated from SPY and QQQ, respectively. I looked at the relationship of the two over some time and they do seem to track pretty closely to 3x, so I used that value instead of 2.8x as in the TMF calculations. I’ll keep adding more synthetic data as I try to do longer-term analysis of other strategies. For now, I have:
and these 4 enable analysis back to 2004 for the main growth driver strategies MYRS, Nasdaq 100, UIS 3x, and perhaps a few others.08/22/2017 at 5:02 pm in reply to: Using synthetic data or external data sources in QuantTrader #44808
I just tested these synthetic price files in the latest version of QT and they work fine. The ZIV data is from Alex’s file above, just merged with up-to-date actuals. The TMF data is computed from -2.8x daily TLT returns starting from 4/15/2009.
I put simple test data into Excel that compounds at a consistent rate, and the scatter plot allows for an exponential curve to be fit to the line, and includes an R2 calculation. I had thought would only calculate for a straight line, but it looks like it can just as easily do it for a curve or even a polynomial. So, you wouldn’t need to use a logarithmic scale to plot your data, you can calculate R2 directly from “curved” data. Just thought it might be worth mentioning.
This may not be the correct forum to propose this, but do you think it would be worthwhile exploring a different kind of optimization? I think it would be very interesting to minimize the “time to recovery” as an alternative optimization strategy. In other words, any time the strategy achieves a “high water mark” in value, we measure the amount of time it requires for the strategy to exceed that level, regardless of volatility. Rather than minimizing volatility per se, we are minimizing the time to recovery for the strategy or portfolio. As soon as the previous high water mark is surpassed, a new one is set. The optimization would search for the lowest average TTR value in days.
I have no idea if it would produce good results, but it would most definitely be an interesting test. Thoughts?
I actually managed to get it to run on the entire list of S&P 500 stocks several times before it became unstable. It takes a while. I’m not 100% sure the program is multi-threaded and using all 4 CPU cores, if it isn’t then that would help, along with using a 64-bit architecture, so larger chunks of data could be processed in memory.
You really only need the few new symbols from [StockItems], and then copy/paste the updated $Nasdaq100 list from [StockLists] in the file I attached above over the current entry in your .ini file.
I’ve updated the Nasdaq 100 stock list from the Nasdaq website as of today.
All of the legacy symbols have been left in the stock list, and new symbols have been added. The attached text file includes only the necessary .ini file line items from the [StockItems] and [StockLists] tags.
In Symbol Manager, I suggest adding an “effective start date” and “effective end date”, so listing and delisting can be tracked accurately. The strategies would employ symbols only during their effective interval. This would be useful for maintaining a stock list like Nasdaq 100.
It would also be extremely slick if there were a way to automatically update a stock list with current membership and effective dates.
314S the min/max allocation logic is not working correctly.
Biotech Rotation Strategy
My latest creation is inspired by the Nasdaq 100 Hedged strategy, and follows the same recipe with a few modifications. It uses the symbols currently held in the IBB biotechnology ETF. I’ve attached the symbol list as a .csv that can be imported using Stock List Manager. I created a stock list called Biotech, and included it along with IBB and QQQ in a strategy with the same name (Biotech) to view as benchmarks. The strategy uses semi-monthly rebalancing, 16d lookback, SRE 4, volatility attenuator 7. 5-year CAGR 46.4%, Sharpe 2.42, volatility 19.1%, drawdown -21.2%. I then created a strategy called Biotech Hedged, which includes Biotech and TMF with a 30% maximum allocation limit. The hedged strategy uses monthly rebalancing, 40d lookback, SR 2, max/min allocation 100%/0%, volatility attenuator 2.5. 5-year CAGR 45.7%, Sharpe 2.7, Volatility 16.9%, drawdown -15.2%.
The results are not as exciting as Nasdaq 100 Hedged, but certainly nothing to disregard. Over the 5-year optimization window, the strategy returns 655%, compared to a paltry 245% returned by its baseline, IBB. It also reduces drawdown from -39.2% to -15.2% and volatility from 25.2% to 16.9%.
If biotech is your thing, this would be a great strategy. Current allocations 80% Biotech, 20% TMF. Within Biotech, allocations are AMGN, CBPO, CELG, GILD 25% each.
[quote quote=40279]Regarding the strategy “The Beast” you are working on. I have a couple of questions:
1. Since it is a Portfolio of Portfolios, is there an easy way to get down to the Stocks/ETF’s and percentages you need to invest in? the Top portfolio just shows the sub portfolios and percentages. I assume you then have to run the sub portfolios and work the percentages which I can do, but looking for a better way.
2. Just curious – have you put any real money on this strategy?
1) Not that I’ve found. I do think it would be nice to have a “resolve to stock list” button that would redisplay the Current Allocation as a list of base-level symbols with their resulting percentages. It’s easy enough to walk it backwards, though. I built my own spreadsheet that I’m using to track it.
2) Yes, I currently have a solid amount of money invested using this strategy.
I have 3 suggestions for optimization:
1) Prior to optimizing a strategy, offer an “optimization goal” that can be any of
a) maximize overall return
b) minimize volatility
with target thresholds, like 20%, 15%, 10%, 5%. It may be that the strategies and other settings already accomplish this though, so it might be redundant.
2) Offer a checkbox that would search for optima with stable nearby values (a large hot spot on the heat map)
3) I suggest allowing the option to “continuously reoptimize”, with a selected optimization period, rather than force optimization over the currently-selected historical display range. Those 2 things should be separate, with the historical display for presentation only.
In my experiments with the software, I noticed that it is possible to optimize over a shorter time period and achieve better results than using the optima over a very long time period. I’d like to see a simulation over a long period of time hat uses a continuous optimization approach, so the lookback period is slowly changing as the underlying instruments go through different high/low volatility and returns regimes.
I’ve worked on this strategy quite a bit and I think it’s getting close to the point that I will probably implement it soon. There is a top-level strategy made of 2 sub-strategies, one that is lower-return, lower-volatility, and the other is higher-return, higher-volatility. The two can be combined in a range of values, depending upon risk aversion.
Sub-strategy 1 combines #BRS with another sub-strategy that uses #GLD-USD and #Treasury hedge (M, 30d, SR, top 2, Max/A 100%, V/A 2). Sub-strategy 1 uses the settings (M, 20d, SR, top 2, Max/A 100%, V/A 10), with 5yr CAGR 12.3%, vola 6%, draw/range -5.7%.
Sub-strategy 2 combines #Nasdaq 100 hedged, #UIS SPXL-TMF 3x leveraged, and Ritter’s MYRS variant (see above) as a sub-strategy using #MYRS ZIV-TMF and ZIV (M, 66d, SRE, top 1, Max/A 100%, V/A 1). Sub-strategy 2 uses the settings (M, 132d, SR, top 2, Max/A 80%, V/A 0.5), with 5yr CAGR 47%, vola 19.5%, draw/range -11.9%.
The top-level strategy at its most aggressive settings (M, 122d, DR, top 1, Max/A 80%, V/A 0) results in 5yr CAGR 40.1%, vola 16.1121%, draw/range -8.5%. A conservative setup (M, 122d, DR, top 1, Max/A 50%, V/A 0) results in 5yr CAGR 28.9%, vola 11.5%, draw/range -6.4%. Anything more conservative simply uses cash as a brake. So, for example, by changing only the maximum allocation, the following 5yr outcomes are possible:
Max/A%, CAGR, Sharpe, vola%, draw/range%
10%, 5.3%, 2.3, 2.3%, -1.3%
20%, 10.8%, 2.3, 4.6%, -2.6%
30%, 16.5%, 2.4, 6.9%, -3.9%
40%, 22.4%, 2.4, 9.2%, -5.1%
50%, 28.9%, 2.5, 11.5%, -6.4%
60%, 32.6%, 2.5, 13%, -7.3%
70%, 36.3%, 2.5, 14.5%, -8.5%
80%, 40.1%, 2.5, 16.1%, -9.7%
90%, 44%, 2.5, 17.7%, -10.8%
100%, 47.9%, 2.5, 19.3%, -12%
I wouldn’t ever use anything higher than the 80% setting since the hedging sub-strategy remains almost unused. At 80%, using the maximum available history range, starting July, 2010, CAGR 43.3%, Sharpe 2.6, Vola 16.7%, Draw/Range -9.7%.
One thing I like about this strategy is that it minimizes the number of concurrent investments. For example, the current allocation is:
Sub-Strat 1 (80%) (ZIV, MU, NFLX, NVDA, SYMC, TMF)
> ZIV (80%)
> Nasdaq 100 hedged (20%)
>> Nasdaq 100 (80%) (25% each MU, NFLX, NVDA, SYMC)
>> TMF (20%)
Sub-Strat 2 (20%) (PCY, CWB, YCS, GLD, TIP)
> BRS (80%) (PCY 40%, CWB 60%)
> GLD-USD hedged (20%)
>> GLD-USD (80%) (YCS 30%, GLD 70%)
>> Treasury hedged (20%) (TIP 100%)
1 year results with these settings are CAGR 47%, Sharpe 3.1, Vola 15.4%, Draw/Range -6.14%.
IMHO this strategy is a beast.
As I’ve been playing with QT, I’ve noticed that the optimizer sometimes will fall on a highly optimal set of values that are surrounded by suboptimal solutions. If the heat map were “all white”, then it indicates there is very little sensitivity of the strategy to the lookback period or volatility limit. If the heat map is checkered randomly, then it is very sensitive. I’ve seen mention of the optimized results being “curve fit” in such a case, when the optimal solution lands on a single “bright white” square in the middle of a checkered field of landmines, and of the solution being somewhat resilient when surrounded by similar shades of white and gray.
It might be interesting to program in an optimizer strategy that looks not for a single optimal value, but rather the largest cluster of stable values, because that represents a range of lookback periods and volatility limits that is resilient, meaning I would obtain similar results if I were to choose 95 or 100 or 90 days. That way, the recommendation is for the center-point of a cluster of stable values.
You might want to create a bug report email and add it to QT so we can report issues as they are found. A detailed report with screenshots would be difficult to track on a forum. Mantis (https://www.mantisbt.org/) is a good open source issue tracking system that will help you stay organized as you continue to improve the software, and I believe it can accept email submissions.
I’ve just built a strategy with CAGR 49.3%/10yr, vola 19%, draw/range 13.15%
It uses 2 sub-strategies and #Treasury hedge with a long lookback period.
Use top 3
The sub-strategies are very different. The first includes #CurrenciesUSD, #GLD-USD, and #BRS (M, 22d, SR, top 2, 100% max). The second includes #Nasdaq100, #MYRS ZIV-TMF, #UIS SPXL-TMF 3x (M, 96d, SR, top 2, 90%).
The 10-year return is 1422%. I wish I could backtest through 2007 to stress test the results. ~20% volatility is pretty freaky stuff, but I just thought I’d post this for fun.
On another note, I’ve also been trying to find an ultra-low volatility strategy with the highest possible CAGR. One thing I’ve noticed in my own investing is that I can stomach a short turn drop, even a pretty severe one, but that I lose hope if the duration of the negative return is too long. It could be easily quantified, similar to maximum drawdown, but would rather be called the “maximum negative return duration” in days. A high-volatility strategy that tries to contain that measure might actually be quite powerful, since it “jumps up” after it gets “knocked down”. It’s the psychology of being beaten down and losing hope that causes me to make bad trading decisions.
Hope this is of interest.
Thanks for the strategies. I’ve been playing with QT a bit trying to get some good results, but I love 00RichardQT6 the SRRP strategy better than anything I’ve come up with so far. It would be great to see how it back-tests through the 2009 fiasco, that’s the real stress test for any of these strategies.02/27/2015 at 11:13 pm in reply to: Strategy: Global Market Rotation Strategy Enhanced #17807
Interesting, I thought you felt the exchange rates were unpredictable and worked against the strategy, but apparently not. Thanks for your response.02/26/2015 at 5:13 pm in reply to: Strategy: Global Market Rotation Strategy Enhanced #17732
Have you considered using currency hedged ETFs in the rotation when FX markets are moving? The cost of the hedge will reduce some gains for sure, but it might help neutralize the effect of a strong trend in FX markets affecting the intention of the rotation strategy to capture relative momentum. Thoughts?11/20/2014 at 10:30 pm in reply to: Quants: More technical details, facts and discussion #14175
I don’t see any information on the SPY-TLT strategy. Do you plan to post that soon?