ETMA_The Evolution

Trend Following Drawdowns are hitting all time High .

State of Trend Following: Drawdown Levels Comparison


A couple of months ago, we published a study on the performance of trend following after drawdowns, as the State of Trend Following index was hitting high levels of drawdown (about 2/3 of the historical maximum). These periods - to be expected due to the cyclical nature of trend following performance - always trigger the debate whether "trend following is dead" (so far it's always stayed alive) or whether the timing represents a good opportunity to invest in the strategy.

We showed that in 80% of cases, the post-drawdown performance is positive, showing that investing in trend following strategies during periods of under-performance can be beneficial.

Since the article was published, the performance has kept on the negative side. The drawdown now nearly equals the max historical value (30.3% vs 31.9% as of December 5th), so we decided to keep a close eye on the performance of the index, by putting it in historical context.

Below is a comparison of the short-term performance of the index in the 6 months following the drawdown hitting 2/3 of the max historical value.

https://ci6.googleusercontent.com/pr...comparison.png

The blue line is the current performance line, from September 9th this year, when the drawdown hit 21% (2/3 of the historical maximum). Two months in, it now stands towards the bottom of the chart, where the arrows are located. It is pitted against other past drawdown instances for comparison, and stands in their lower vicinity.

It is the only instance that broke the "90's line" (-10%) within the 6-month window (the May-2011 instance actually did break that line too, but taking a longer time). It is quite obvious on the following chart, with the drawdown curve at the bottom showing different paths to -30% drawdown:

https://ci3.googleusercontent.com/pr...wnGraph_P1.png

The 2011 instance took about 3 years to bottom out at -30%, while this year's instance went there much quicker, in about 10 months. Is it a simple case of "slow and steady" vs. "hard and fast" to reach similar levels? Looking at the drawdowns over the whole length of the back-test shows that there is a vast array of drawdown paths (quick spike down, prolonged downtrend, etc.). It'll be interesting to see which of the up or down arrow the performance follows in the next few months, especially given the levels we're at.

Stay tuned. We'll keep you updated.

Hi bassramy

Can you recommend me, a software that can test multiple strategies on one instrument at the same time, or a software that can test one strategy on multiple instruments at the same time.

All the big names can do all of this, in the end it all come down to your personal preferences :

- Tradestation is excellent but need you to open an account to be able to use it on sim or live, you can not trade spot Fx on this platform .
- Ninja is great and let you play Endlessly with all the Features enabled on Sim, Kinetick gives you Live EOD (D1) Free feed to test to your Hearts Desire and you can open a Demo with a lot of Brokers Including and not Limited To Ninjatrader Brokerage Services .
- Multicharts also can be a good option similar to Tradestation but with a lot More Freedom for Downloading the Platform and opening Demo or sim accounts .
- Amibroker is a lower end platform but equally Powerful to the Above mentioned if you learn how to use it .
- Matlab, Wealthlab and Rightedge are more complex platforms which r used also for testing and optimizing Trading Models by a lot of pro's .

The big boys are creating their own Testing and Optimization Platforms using R and Python Free coding Languages if you are willing to learn how to code which in turn gives u greater Flexibility and control .

I personally use Metatrader, Ninja and Tradestation.

Hope it Helps.
bass

A Very Different Kind of Trend Model
Posted by: Andreas Clenow November 28, 2016

Trend following is all about following the price. Typically the only input we need for a trend following model is the price. But what if I told that we could make a kind of trend following model which does not use the price direction as an input at all? It also has no stops and no targets.
In this article, I’d like to introduce you to a different way of thinking about futures trading models. It may sound a little strange at first, but I can assure you that it’s perfectly logical. What we’ll be doing is constructing a model that uses only the implied yield of the term structure. No, that’s not as complicated as it sounds. In fact, it’s something that you really should understand if you trade futures.

Let’s start with a quick recap of what term structure means. It’s really quite important and you shouldn’t be trading futures if you don’t understand that part.
If you for instance want to buy gold futures, you’ll need to pick a specific delivery. As of writing this, you could trade the December 2016, February 2017, April 2017 etc. Which months are available varies for different markets. There is usually one delivery month which is by far the most liquid one, and that’s usually where almost all futures traders have their positions. Right now, the primary month just shifted from December 2016 (Z6) to February 2017 (F7).
Looking at the usual futures chain, in most market data systems it would look something like this image below. The chain will show you what deliveries are listed and the key data for them.

http://www.followingthetrend.com/wp-...n-1024x256.png

Gold Futures Chain
What is important to understand here is that the price for two different deliveries of the same market will always be different. Sure, gold is gold, but we’re not trading actual gold bars here. We’re trading a contract that requires a delivery of gold at some point in the future. How far in the future this point is, will be a factor in pricing that contract.
The graph below shows this relationship. The green dots, connected by a line, shows the price for the different deliveries. As you can see, gold is currently cheaper for a December delivery than for the later deliveries. This is what’s called contango.

http://www.followingthetrend.com/wp-...e-1024x660.png

Gold Term Structure
If the prices are lower further out in the future, that would be called backwardation. In the example below, you see the term structure graph for orange juice. Note how the green price markets are lower and lower for each successive delivery. This is because this market is in backwardation.

http://www.followingthetrend.com/wp-...t-1024x683.png

Orange Juice Term Structure
There are several factors at play in the price differences. Interest rates, storage costs etc. For financial futures, like equity index futures or currency futures, the price difference is minor. For commodities and fixed income, the difference tends to be more significant.
So far, none of this should be new to you. If you trade futures or have in interest in the subject, this is all old news. After all, this is the reason why we need to adjust futures continuations.
Now if you understand the logic so far, you should also understand that the term structure impacts the direction of the price. No, no, that’s not at all a stretch of the logic. Here’s how to think about it.
In this example, the price of spot gold is now exactly $1,000. The February futures contract is trading at $1,020. We know that by the very definition, the futures price will at expiry settle at the same price as the spot market. From the above, you should understand why the price might be a little different in the futures. But here’s the kicker: If the actual futures price does not move at all, and stays at exactly $1,000 the entire time, the price of the futures contract has to, by mathematical certainty, fall $20 until it goes to delivery.
Below is a chart that someone who seems to have the same level of artistic skills as I do has contributed to Wikipedia. The logic shown here is correct, but I can’t help but think that the label “Expected future spot” is both incorrect and misleading. It would be more clear if it just said “Spot”. Also, this graph has the word forward in the labels, but the logic is exactly the same for futures.

https://upload.wikimedia.org/wikiped...kwardation.png
Ok, are you with me so far? What did we learn here that is of real value?
We learnt that a market in contango has a bearish bias and a market in backwardation has a bullish bias. Now we’re getting to the juicy parts.
This of course doesn’t mean that a market in contango is always falling. It is very much dependent on the price movements of the underlying asset, but it does put a bias on the price. A market in contango will need to see a price increase in the underlying just for the futures contract to remain flat. It can be a powerful driver of long term market moves if the term structure is steep enough.
For some markets, the term structure is so steep and so persistent over time, that it’s really all that matters. This is the situation we had for so many years in natural gas, and something that is very clearly visible in STIR markets.
A couple of paragraphs ago, I threw out the phrase “if the term structure is steep enough”. That begs the question about how you could measure the steepness. If you have a background in finance, you already know the logical answer. Calculate an implied annual yield!
Simple stuff, people. Take the price difference between two contracts. Calculate the percentage difference. Check how many days difference there is in the two deliveries. Now use that to annualize the difference to standard money market yield conventions. And now you have a number that you can relate to and compare across all markets. It tells you the implied annual yield between two points in the term structure.
Is this information useful?
Let’s find out. Let’s build a model that only uses this information. Nothing else. We’ll just trade on the shape of the term structure.
The only indicator we use is a time series of the implied yield between the active contract and the next. We’re just looking one point out in this simple example. Nothing fancy, we just want to see if there’s any informational value to this.
The rules are very simple. If the implied yield between the second and first contract is more than 3%, we go long. If it’s below -3%, we’ll go short. Allocation is standard volatility parity and these rules are applies on a large set of futures markets, covering agricultural commodities, metals, energies, fixed income and STIRs.
Keep in mind that this is a highly simplistic demo model. It’s not meant for live trading but it is something that holds a great educational value.

The results?
http://www.followingthetrend.com/wp-...ctureModel.png

A simulation starting in 1990 saw an annual return of 30.7% with a maximum drawdown of 50%. Not bad for a model as simple as this. Did I pick some over optimized settings? No, not really. Let’s change the number up a bit. Let’s run it again, and set the yield threshold at 10% instead of 3%. Just to make an extreme change and see if we break the logic.

http://www.followingthetrend.com/wp-...ureModel10.png
Now we get an annualized yield of 16%, at a max drawdown of 38%. Note how most of the return was made in the 90’s, with returns leveling out a bit in the past decade. That’s not too strange, given the change in interest rate climate. Yes, that means that you may want to model different thresholds based on the yield situation at the time, but there you need to do your own homework. I just want to get your interest in the idea here.
What we can see here is that seemingly crazy rules like this, actually has a clear value. Now again, no one in their right mind would launch a model in this shape, but now we know that there is a value to be found in the implied yield.
By standardizing the term structure shape into what resembles an indicator, you have a easily accessible way to base models on this data. There are plenty of more things you can do with this information of course. This is just a simple demo to show the value.
Getting the Data
So why isn’t everyone using this data as an input factor? Well, a lot of people do. But not on the retail side. It’s really not that hard to get this data, but it may require a little more work than just punching up a chart on a website.
All the data you need is actually available for free, if you’re willing to do the work yourself. You need end of day time series for the currently trading contracts as well as data on price, expiration dates and preferably some data on volume and open interest. This is all available from the exchanges for free. Now you just need to crunch the data and automate the process.
Once you have the time series for the implied yield, you can put that into your simulation platform and start developing models. In this article, I made a simple demo model that only uses the structure as input. You might also want to construct models that use the structure as one of several inputs.
I’ve already automated this process of course, and it’s available on this site. You can check the implied yield between different points and download a full time series history that can be imported into your simulation platform.
http://www.followingthetrend.com/wp-...s-1024x775.png
Daily updated data, view online or download csv files.

The automated daily data is currently only available to subscribers of course. Sorry about that. But that seems reasonable, I think, given that I actually explain above how you can do it yourself. I will write more on this topic going forward and I plan to publish some sample model code in the premium section as well for making use of the term structure data. Feel free to sign up and give it a spin.

I’m also trying out this article as a bit of an experiment. In my own view, this is likely one of the most useful and valuable articles on this site. But I also suspect it will be one of the least read. It’s a funny internet phenomenon, I suppose. If I write an article about how Elliot Waves are for people who believe in chem trails, I get 100,000 people reading it even though it’s just really obvious stuff. Usually when I actually write something of value, very few people read it. Let’s see what happens to this one. If there’s a real interest, there’ll be more useful articles. Otherwise, I’ll go back to picking on the mathematically challenged…

Trading System Rules

Core Trend Following Rules

There are not a whole lot of different ways that trend following can be done. The minor tweaks may have positive results but the effect is usually very minor. If you spend too much time looking at minor variations of entry rules you risk missing the important parts. The truth is that most trend following system rules do the same thing. They show highly similar results simply because they attempt to achieve the same thing. By all means, play around with the detailed rules. Just make sure you do that after you have tried the basic strategy. Understand where the value come from first and you’ll realize just how little the entry rules really mean.

The value in professional trend following strategies come from the diversification. The rules presented here are good enough to achieve results on par with the large trend following futures hedge funds. Making the rules more complex does not aid your performance. The most common amateur mistake is to spend all the time tweaking entry and exit rules and not enough analyzing position sizing and investment universe.

The simple rules presented here are good enough to replicate the performance of many large name trend following hedge funds with high precision and correlation. In my book I detail several ways this can be further enhanced and improved upon. Make no mistake though. The trading system rules is the least important component of your trend following trading strategy.

Position Size
Some markets are inherently more volatile than others. To give each position an equal chance to impact the bottom line, positions must be larger for less volatile markets. This can be achieved many different ways. My core strategy uses Average True Range (ATR) for this purpose. ATR measures the average daily price movement of a market. This can serve as a proxy for volatility. Set a target desired daily impact per position. Then calculate how many contracts you need to trade to achieve that based on the ATR. This naturally assumes that volatility remains roughly the same. This is not always the case of course. It’s an approximation and as such it does the job.
For the core strategy on this website I use a desired daily impact of 20 basis points.

Trend Filter
Long positions are only allowed to be opened if the 50 day moving average is above the 100 day moving average and vice versa. This is to ensure that we don’t put on trades counter to the dominant trend. It reduces the number of trades and lessens the risk of getting caught in whipsaw markets.

Entry Signal
Enter long positions on a new 50 day high. Vice versa for shorts. Go with the breakout and ride the trend. Nothing else. Signals are generated on daily closing data and the trade taken on the open the following day. Slippage is accounted for of course as well as trading costs.

Exit Signal
Exit on three average true range moves against the position from its peak reading. Thereby we have a theoretical loss of 60 basis points. No intraday stops are used, so a close beyond three ATR units are needed for a stop to be triggered the following day.

http://www.followingthetrend.com/wp-...de-300x198.png

Investment Universe

Trading a trend following system on a single market or only a few different markets is suicidal. There may be long periods, even years, where there simply are no trends in any given market or asset class. The key idea is to trade many markets covering all asset classes at the same time. If you fail to do so, this strategy will simply not work.

The investment universe you chose will have a much greater impact than tweaking buy and sell rules, so choose wisely. You should chose a broad set of markets and avoid too high concentration in any single sector. In the long run, a healthy balance between all major market sectors yields the best results.
You can study a broad range of markets on the Trends of the World page, which is updated daily with charts and analytics.

thank you for the answer.

Comparison: MetaTrader, NinjaTrader, TradeStation
JULY 19, 2013 BY SHAUN OVERTON

Pros and Cons of MetaTrader, NinjaTrader and Trade Station
NinjaTrader, MetaTrader and TradeStation are by far the most popular platforms among the retail trading crowd. Each one tends to focus on a special market niche. MetaTrader primarily caters to forex market. NinjaTraders fan base mainly comes from futures. TradeStation earns most of its business from equities traders.

Despite their popularity among certain markets, each platform is fully capable of handling the different types of markets. It all comes down to which platform suits your needs. They all offer automated trading as well as backtesting capabilities. If popularity plays a role in your decision making, then you may want to review Google Trends.

Done, The EU has officially broken his 20 Months Range to the downside and retesting it today, if it holds today and Finish the week as it is then we will probably be looking to the 1.00 (Parity) Level as the next Psychological Target, Hooray .

https://soundcloud.com/search?q=hans%20zimmer

RED NOVEMBER FOR MANAGED FUTURES

December 20, 2016 by Shane Wisdom
This month’s managed futures report is overall negative with only a handful of funds we track showing positive performance for November, with the YTD performance being slightly negative too.

Th Euro Ending the Year on a Break.
Courtesy of Factor Trading
Peter Brandt‏@PeterLBrandt

Managed Futures Performance Ranking : www.managedfutures.com

Clenow ....... A voice of reason in this mad trading world. Thanks for sharing Bass :-)

....and have a great Chrissy mate. :-)

C

Thank you C. and

https://soundcloud.com/chat-with-tra...-turtle-trader

https://soundcloud.com/user267218583...lowing-legends

EUR/USD jumps a big figure higher - thin liquidity a factor

Same old, same old, the big boys are gunning stops with thin liquidity as a weapon of choice .

EUR/USD jumps a big figure higher - thin liquidity a factor

Euro Jumps 1.6 Percent in Minutes as Algo Orders Surprise Market

Same old, same old, the big boys are gunning stops taking advantage of thin liquidity in the absence of majors Banks and Financial Institutions ( Balancing the Books sort of Speak), its exactly the same every year, in the end of the Year so Happy New Year to all of you and sorry if u had losses today (U shouldn't have any open positions this Week anyway) .

Happy New Year .

A very happy New Year to you too Bass and your family. It has been a sweltering day here in Brisbane.....time to get the beach gear on and find the surf. C :-)

Vice Versa, 3 - 5 °C Here in Beirut with Multiple Winter Storms, Not usual in this time of the Year, well at least the Ski Resorts are open Early this year So i guess we will be surfing also but on the Snow .

Why Some Technical Analysis May No Longer Be Effective: An Interview With Michael Harris


Summary of Core Research Themes in 2016

1. Alpha is not risk-less. In fact, for an active strategy to have the potential to outperform in the long run, we believe it must be difficult to hold in the short-run. As a zero-sum measure, alpha is passed from investors who foldduring times of volatility to those who have the fortitude to hold.

1. Risk cannot be destroyed, only transformed. Every investment decision we make should be viewed through the lens of a relative risk trade-off.

1. Active management should be an allocation, not a trade. To harvest the long-term out performance potential of active investment strategies, we need to look well beyond 3-year track records and create long term factor exposure allocation in our portfolios.