ETMA_The Evolution

Creating a Trading System within Trading System Lab.

Trading System Lab will automatically generate Trading Systems on any market in a few minutes using a very advanced computer program known as a AIMGP (Automatic Induction of Machine Code with Genetic Programming). Creation of a Trading System within Trading System Lab is accomplished in 3 easy steps. First, a simple preprocessor is run that automatically extracts and preprocesses the necessary data from the market you wish to work with. TSL accepts CSI, MetaStock, AIQ, TradeStation, Free Internet data, ASCII, TXT, CSV, CompuTrac, DowJones, FutureSource, TeleChart2000v3, TechTools, XML, Binary and Internet Streaming data. Second, the Trading System Generator (GP) is run for several minutes, or more, to evolve a new Trading System. You may use your own data, patterns, indicators, intermarket relationships or fundamental data within TSL. Third, the evolved Trading System is formatted to produce new Trading System signals from within TradeStation or many other trading platforms. TSL will automatically write Easy Language, Java, Assembler, C code, C# code and WealthLab Script Language. The Trading System may then be manually traded, traded through a broker, or automatically traded. You may create the Trading System yourself or we can do it for you. Then, either you or your broker may trade the system either manually or automatically.

Friday, October 28, 2016
Jerry Parker on volatility

It's been a while since I read Michael Covel's The Complete Turtle Trader - the story of the famous Turtles experiment with Richard Dennis and William Eckhardt and their band of trend followers in the mid-1980's.

Whenever you read an old favourite after a while you tend to discover some little nugget that you may have previously overlooked. As an example, today I came across the following excerpt featuring quotes from Jerry Parker:

"Jerry Parker found that his best trends often start with very low volatility at the initial breakout entries. He said "If the recent volatility is very low, not $5 in gold, but $2.50 in gold, then we're going to throw in a very large position.

Parker's analysis kept showing that a low 'N' measurement at the time of entry was a good thing. He said "I can have on a really large position. and when volatility is low, it usually means that the market has been dead for a while. Everyone hates the market, has had lots of losers in a row, tight consolidation. And then as it motors through those highs, we get on board.""

Now, as explained in this post I place volatility contraction prior to entry as a key part of my own trading. However, I learned this from reading the likes of Thomas Stridsman, and also the following quote from Paul Tudor Jones' Market Wizards interview:

"The basic premise of the system is that markets move sharply when they move. If there is a sudden range expansion in a market that has been trading narrowly, human nature is to try and fade that price move. When you get a range expansion, the market is sending you a very loud, clear signal that the market is getting ready to move in the direction of that expansion."

While looking at volatility contraction was not part of the original Turtle rules, I found it highly interesting that Jerry Parker picked up on this, and some of the other Turtles recognized this too.

In the original Turtle rules document floating around on the internet, it states that "...if the volatility of a given market was lower, any significant trend would result in a sizeable win because the Turtles would have held more contracts of that lower volatility commodity."

In my own trading, I've taken this basic concept a stage further. Because I place a great deal of emphasis (rightly or wrongly) on measuring volatility, if I deem current price action to be too volatile, then I won't take the trade - indeed, my own scans now include this element as one of the conditions.

This is something that Larry Hite also talked about:

"The fourth thing Mint does to manage risk is track volatility. When the volatility of a market becomes so great that it adversely skews the expected risk/return ratio, we will stop trading that market."

Generally speaking, as I use an Average True Range measurement to determine initial stop placement, then the lower the volatility, the bigger the potential position size while respecting your monetary risk per trade.

That also means that price has to move a shorter distance in your favour to generate each unit of R profit. Or put another way, the more volatile the stock or instrument at the point of entry, the furtherprice has to move to make each unit of R profit.

It is generally accepted that the ideal market state for a trend follower is a trending (obviously), quietstate. When you enter a new position based on a new high or low being made, you never know if a trend will develop. However, by looking for 'quiet' setups, it means that the inherent level of volatility is currently low.

By looking to trade markets that are quiet at the intended point of entry, I'm hoping to skew the odds further in my favour, while avoiding the more choppy, volatile setups.

Weekly Snapshot of the EU, make or break levels .

Peter Brandt ‏@PeterLBrandt Oct 24
My Euro Dollar Blog post from earlier today. http://buff.ly/2eo5h5S

https://www.peterlbrandt.com/euro-do...ampaign=buffer

Peter Brandt ‏@PeterLBrandt Oct 25
#FACTORMEMBERS This is a chart to keep your eye on

A License To Print Money
OCTOBER 24, 2016 5:00 AM

When it comes to legends in the trading world, the name Larry Williams comes up often. Larry is the creator of the ultimate oscillator and the Williams %R Indicator. Furthermore he has written many popular books. His claim to fame is taking a $10,000 account to $1,100,000 during the 12-month competition known as the World Cup Championship of Futures. Interestingly his daughter, Michelle Williams won the same contest at age 17. I bet a little help from Dad went a long way with that one! While Larry’s impressive story is well known to many there is another trader who has done equally as well when it comes to taking a small account to large sums. That trader is Sheldon Knight.

In 1986 Sheldon took a $75,000 trading account and grew it to more than $1 million in a year. He would later double his account in 1989. Sheldon was actually a seminar student of Larry in 1985. Back in those days personal computer power and trading platforms were not what we have today. Sheldon was working in the aerospace industry when developing his trading ideas. He would use his work computers to test his trading concepts and models. Sheldon was a systematic trader who uses a computer program to quantify his trading methods performance. Clearly he was way ahead of most people in the field of quantifiable trading.
So why do I bring up these amazing stories? It’s not simply for inspiration. Instead these stories are an introduction to highlight the trading style both Larry and Sheldon used. Both traders used a similar style to take a small seed account into something much bigger in a very short period of time. In my opinion there are two important aspects to these stories which can have an impact to your own trading today. The first is their trading method. We’ll be going over this throughout the article. The second aspect is the position sizing algorithms they used. I won’t be going over position sizing in this article, but it was critical to their success. For now, let’s see how they did it by looking at the trading method both Larry and Sheldon used.

How They Did It

Clearly to take an account from $10,000 to over one million there is some luck involved. More importantly, you have to take on a lot of risk. You have to be willing to endure large drawdowns. Perhaps as much as 50% or 60% at times. That’s really psychologically difficult to do. You must be completely detached from your money and willing to lose most of it. In fact, you have to be willing to lose it all! Such a game is not mentally within the reach of everyone. Blowing $10,000 would not be OK for most people. But can we take any information from Larry and Sheldon to use in our more restrained or conservative approach to trading? Of course.

So what did Larry and Sheldon use as the basis for their trading system? Was it a complicated trading system with many rules and filters? Or was it a clever trading system that analyzed market order flow and made predictions into the future with a neural net? Maybe a complex shorter term scalping system that allowed them to take many trades throughout the day? Well, no. It was none of these. In fact, the trading concept they used is rather simple and well known. So what was Larry’s and Knight’s big secret? The heart of their trading method was Opening range/Volatility Breakouts.

There are many variations on this type of method. In essence all the concepts attempt to buy at a level above the market open and sell-short at a given level below the market open. This predetermined level is known as the “breakout level”, the “stretch”, or “offset”. For example, maybe at the market’s open we place a buy stop at yesterday’s high and place a stop sell-short order at yesterday’s low. Then we simply wait for our order to get filled and manage the trade. As you can imagine, there are many formulas for determining a breakout level.

Another famous trader we did not talk about yet who was less aggressive but became a legend in his own right was Toby Crabel. Toby became a money manager and managed billions of dollars using this method. He also wrote a series of articles in the late 1980’s on opening range breakout and then a book, “Day Trading With Short Term Price Patterns and Opening Range Breakout”. This book is such a cult classic that even though it has been out of print for almost 20 years copies which show up on Amazon are often sold for as much as $1,000.00! Sheldon, Larry, and Toby have their own way of calculating this offset. Let’s now look into each of these methods. The main concept of each of these methods is to calculate a noise level and leave the breakout level just outside of that.

The Crabel Stretch offset is calculated as follows:
BreakoutLevel = Average( minlist( Open of data2 – Low of data2, High of data2 – Open of data2), 10 )
It’s basically the minimum of how far on average the market moves off of the open on a given day. In the TradeStation formula above let’s assume that Data 2 is daily data.

Next, Sheldon Knights offset was a bit different because he used the differences between the Highest High and Lowest Low over a given number of days.
BreakoutLevel = Highest( High data2, 10 ) – Lowest( Low data2, 10 );

Finally Larry’s method was simply using a percentage of the daily average range or true range.
RangMult*Average(TrueRange of Data2,Period)

The period normally used is 3 days.

These systems were developed as patterns with setups triggered using opening range breakouts. They often had target profits, stops, and filters. They also used different breakout levels depending if you were in a bull or bear market.

A License To Print Money – The Good Old Days!
During the years our famous traders were generating massive returns with their opening range breakout trading systems on the commodity futures markets, the commodity pits ruled. Looking back during this time some would say that opening range breakout systems were a license to print money. Naturally within the wider trading community there was a surge of many trading systems which utilized some type of opening range breakout method. But nothing lasts forever. More people had access to computing power which allowed them to perform their own market analysis. This allowed more backtesting to be performed on different ideas including opening range breakouts. This in turn resulted in more people able to test and implement opening range breakout trading methods. On top of these developments a radical change began to happen with the markets. That is, the advent of the electronic market.

The Electronic Market Killed Open Range Breakouts
With the dawn of electronic markets and the death of the pits, the traditional market open has lost its meaning. Remember each of the futures markets opened at slightly different times, from 7:30 a.m. to 10:30 a.m. all of the commodity markets opened so there was not just one time that made sense so once the pit closed there was no opening time. The stock indexes and stocks still had the 9:30 New York open to key one so they did not totally break down like the commodities did. In the commodity world we know we had 24-hour markets which remained close for 45 minutes to a few hours. This destroyed the power of this trading method.
Another likely factor in the reduced performance of this trading model is how well known this strategy is. This trading technique is not unique or unknown. In fact, it’s widely known and understood. To help demonstrate the fading edge of this strategy you can use the published information found at Crabel Capital Management. There you will find monthly and annual returns going back to 1998.

I created a simple graph which displays the annual returns going back to 1998. I then applied a linear trend line through the data (red line). The trend is clear.
http://systemtradersuccess.com/wp-co...ce-500x337.png

click to enlarge

Can We Fix The Opening Range Breakouts To Work Again?

Can we apply a twist to the opening range breakout to improve the results? Or, is the opening range breakout simply going to fade into oblivion? In next week’s article I’m going to point out a clever piece of code that brings the opening range breakout concept into the modern age of electronic trading. This technique uses a proprietary method to calculate a dynamic market open instead of relying on a fixed market open based upon a static time. It can do wonders in resurrecting these opening range breakout systems. Keep an eye out for next week’s article!

The ORB (Open Range Breakout).

Trading System Development: Trading the Opening Range Breakouts .

ORB Tradestation Code :
http://quanteer.blogspot.com/2012/10...out-range.html

ORB Trading System and Indicator Set for TradeStation™
A quantitative analytic approach to trading stocks, ETFs and futures.

Data Sheet

1. Excels at trading instruments that track the S&P 500 Index
2. Works with many markets including stocks and ETFs
3. Swing Trading System
4. All orders placed end of day
5. Ability to run in fully automated mode so you don't have to check the system each day
6. Written in 100% Easy Language with all source code provided to you
7. Seeks high net profit with comfortable profit factor
8. Includes two systems
9. Easy to customize for different markets

Unique Features of The ORB Trading System

1. Uses quantitative analysis to determine trading opportunities.
2. Uses a unique way to identify trading opportunities.
3. Easy to use. Discover and place trades in seconds.
4. Full source code and disclosure of all trading rules.
5. TradeStation Show Me indicator to scan for ORB signals and for use with RadarScreen.
6. TradeStation strategy, TradeStation indicator and TradeStation workspace.

We’ve developed and performance tuned ORB to run well on daily timeframes. ORB is parameterized such that it can be tuned by the user to change it's behavior.
The default settings are tuned for daily timeframe trading of the S&P 500 futures contract ( e-mini commonly the ticker symbol ES ). In addition to the default settings we provide three additional tunings of the ORB trading system:
1. A high probability version for the ES
2. A high profit version for the ES
3. A stock trading version tuned for the high volatility of stocks
The high probability version of the ORB system increases the profit factor by eliminating higher risk trades.

https://www.automatictradingsignals....2015.08.01.png

This is the equity curve for the standard version. No changes have been made to the system since the original publication in May 2013.

The following performance results tables are for the standard ORB and the high probability version trading the E-mini S&P 500 futures contract. The system has an optional second entry and consequently approximately 50% of the trades end up holding 2 contracts in this sample. Commission of $5 per plus $25 slippage per round turn are accounted for in these results.
https://www.automatictradingsignals....2015.08.01.png

The ORB trading system can be applied to any market including individual stocks. By using the parameters included with the system, we will also show you how you can tune the ORB system to perform well with the higher volatility of stock markets.

Below you will find some example performance reports created using the ORB trading system applied to individual stocks. The report includes all trades for each stock between 01/01/00 and 04/11/13.

http://www.automatictradingsignals.c...RB/Stocks2.png

A Systematic approach to Building and Developing a Trading Model or Strategy.

Testing A Euro Currency Futures Scalping Strategy, Part 5
NOVEMBER 7, 2016 5:00 AM

Its been a couple of years since I reviewed this potential trading idea of a Euro currency futures scalping strategy. Over the series of articles, which are listed below, Ive been combing filter to demonstrate how I add different filters to a system based on market conditions.

1. Testing A Euro Currency Futures Scalping Strategy
2. Testing A Euro Currency Futures Scalping Strategy, Part 2
3. Testing A Euro Currency Futures Scalping Strategy, Part 3
4. Testing A Euro Currency Futures Scalping Strategy, Part 4

With regards to testing different filters, youll notice that I always go back to the baseline system when testing a new filter to help reduce over-fitting the system to the historical data. It is on the baseline system where it will determine if a given rule is worthy of consideration within the final trading system.
First, lets go back to our second article where we discovered that our 1% envelope was far from an optimal value and far from an outlier- which is good. We also discovered that as we increased the size of our envelope each trade generated more profit per trade at the expense of generating fewer trades. Ive concluded to leave our 1% value as it is but Im going to add this as an input parameter to the next version of the system (available below). The reason why Im doing this is for when I use TradeStations Walk Forward Optimizer down the road, I would want to use this parameter as one of the values to optimize.
Next, we added out timing rules which demonstrated the quiet hours as defined by my Central timezone dramatically improved results. Finally in the last article, we determined to remove the volatility extremes enhanced performance. All these filters make some type of logical sense. The market tends to be more choppy during the quiet hours and avoiding high volatility times reduces losing trades.

But there seems to be a problem as no trades appear to be taken.

No Trades Being Taken

It was pointed out to me that if you download the current version of the Euro Currency Futures Scalping Strategy and TradeStation WorkSpace no trades are taken. I narrowed the problem down to the volatility filter. In short our filter was disabling all trades because none fell within our desired volatility range. That rage was 60 < X <240.

The Euro range varies, as measured by our trading model code, has ranges well above that. Often values of 900, 1,000, or even 2,000. Im not currently aware of why the scale apparently changed but, it did. The end result is no trades were being taken. Thus, we need to test the volatility filter again.

Testing Environment and Baseline
Before we do that I want to just review the environmental settings and the baseline results.

1. In-sample period 2000 2011.
2. Starting account size of $10,000.
3. 1 contract per trade.
4. The P&L is not accumulated to the starting equity.
5. $17.50tiny deducted per round trip for commissions and slippage.
6. There are no stops.

Below is the baseline results starting with the equity curve followed by a performance report.
http://systemtradersuccess.com/wp-co...ve-500x441.png

Euro Scalp Baseline Equity Curve

http://systemtradersuccess.com/wp-co...ne-500x520.png

Euro Scalp Baseline Performance

Now lets get back to testing the volatility filter.

Testing Volatility Filter Again
To accomplish our volatility test I need to first capture the volatility on the daily chart. Remember, we are trading on a 5-minute chart so how do we do this? Well, in TradeStation there are built-in EasyLanguage functions which can grab the daily price elements such as open, high, low, and close from the daily timeframe. However, Im going to show a technique that requires adding the daily price data into our existing 5-minute chart. Im doing this because this is a valuable skill inserting different timeframes into a single chart. Knowing how to do this allows you to build trading systems that can access various timeframes within a single chart. This will allow you to generate signals on a daily chart and trade on a 5-minute chart, for example. To learn how to insert another timeframe into a single chart, please read the article, Access Two Timeframes In EasyLanguage.

The following code is executed when the Euro session ends. It simply computes the closing days range and then computes the 12-day average of the daily ranges. This average value will be our volatility score which will determine if we take trades or not.

If ( Time = SessionEndTime(1,1) ) Then
Begin
ocRange = ( HighD(0) - lowD(0) ) * Pricescale;
ocRangeAvg = Average( ocRange, 12 );
volFilter = ocRangeAvg > ivol and ocRangeAvg <= ivol+200;
End;

We then set a boolean flag, volFilter, based upon a specific volatility range. That is, if the volatility is between a specific range of values, we set our flag indicating its OK (true) to take trades in the current market environment.

Using TradeStations optimization feature Im going to execute the trading system over the historical data 20 times. For each iteration the volatility filter will be altered to produce a specific 200 point range where trades will be taken. The first iteration will test the volatility range between 200-400, then next between 400-600, and so on. This will give us an idea if volatility plays a part in the success of the trading model.

Please note, Im applying the volatility filter to the baseline trading model not the trading model with the time filter. I want to independently test the volatility filter to see how it alone affects the performance. At this time, I dont want to stack filters on top of each other. Below are the results of the volatility study. The x-axis contains the volatility range as measured by our calculation explained above. The y-axis contains the net profit in dollars.
http://systemtradersuccess.com/wp-co...dy-500x374.png

This is a bit choppy. There is no clear winning range. Generally speaking the most profitable bars all fall within the range of 600-3,000 yet, within that range there are three of the largest losing bars as well. I went ahead and selected that range, and allowed the strategy to execute. The results are below.
http://systemtradersuccess.com/wp-co...ve-500x452.pnghttp://systemtradersuccess.com/wp-co...ly-500x529.png

In the end, this does not appear to be much of a change. I cant justify keeping this additional filter. Moving on, I did notice something interesting when looking at the length of time between winning trades and losing trades.

Time Based Exit
By reviewing the performance report of the baseline I noticed that many of the losing trades were trades that had long hold times. Winning trades were often profitable within a few bars. You can see that by looking at the performance report below.
http://systemtradersuccess.com/wp-co...-Bar-Count.png

Winning trades required holding for about five bars on average. On the other hand, losing trades were held around 24 bars on average. Perhaps we can add a time limit on all our open trades. Since winning trades tend to have a much shorter holding period maybe we can exit a trade after X number of bars. This might reduce some profits but may also reduce drawdown. Its definitely worth testing.

I created a new input called maxHoldTime which will be the number of bars to hold an open trade before closing it out. For example, if maxHoldTime is three, this means once a trade is opened it will only be held for three bars (15-minute). If its still an open trade after that period, it will be closed. I use TradeStations optimization feature to iterate between 2-20 bars. Below are the results. The x-axis represents the maximum bars to hold a trade while the y-axis contains the net profit in dollars.
http://systemtradersuccess.com/wp-co...it-500x370.png

Well, that does not appear to help. Even at the optimal hold time of 11 bars, we make far less profit and the average profit per trade goes down to $8.16. I really thought I was on to something here but, it looks like another dead end.

Summary
In closing this is where we stand with our Euro currency futures scalping strategy. We only have a time-based filter after determining that the newly tested volatility filter is not improving the baseline results. If you recall, the time-based filter limits trading to what I call the quiet hours. For more information on this filter, seeTesting A Euro Currency Futures Scalping Strategy, Part 2.
Below are the results of our current trading model on the in-sample data.
http://systemtradersuccess.com/wp-co...2016-11-02.png
http://systemtradersuccess.com/wp-co...02-500x441.png

In the next article Im going to continue to tackle this trading model to see if I can improve the results at all. After that, its time to test this on the out-of-sample data and see how it holds up.
Downloads

1. Strategy Code (TradeStation ELD, text)
2. TradeStation WorkSpace (TWS)

A proven framework for BUILDING ROBUST TRADING STRATEGIES With Bob Pardo

http://downloads.bettersystemtrader....-Workbook1.pdf

Tuning up the turtle

https://anthonyfjgarner.files.wordpr...garner0210.pdf

https://sway.com/urTA5hDn759RYZES

Closing a 1 Year and 3 Months CHFJPY trade, 15100 pip Profit, the power of patience, going long trying to repeat it, according to the turtles its Highly unlikely that it will happen again but who knows !!! ...

The EU is Fluffing his lines, a break would probably lead to the test of 1.00 (Parity) psychological Level (These r unusual times ), its really more of a USD Strength than Euro Weakness .

Inside a Moneymaking Machine Like No Other

The fabled fund, known for its intense secrecy, has produced about $55 billion in profit over the last 28 years, according to data compiled by Bloomberg, making it about $10 billion more profitable than funds run by billionaires Ray Dalio and George Soros. What’s more, it did so in a shorter time and with fewer assets under management. The fund almost never loses money. Its biggest drawdown in one five-year period was half a percent......

The eternal Trend Following Question :

Should i exit Early with Profits or Follow the Trend, ride my profits and then exit after a reversal and give back some profits in some cases experienced by me up to 50% ?

Exits as important as entry points? A critical issue

"I made my fortune by selling too early", a quote from Baron Rothschild used for an investment game by John Hussman. John Hussman has invented a game called "Baron Rothschild" as a teaching tool which is based on a simple process of drawing returns from a set of cards The sequence of cards can generate a performance record. Think of this as a form of Monet Carlo simulation from a return distribution. The player can call stop after any number of picks or he can continue to pull return cards to compound his performance.

Hussman finds that a limited number of picks will do better than drawing more cards in order to run up the returns. There is some interesting math here on optimal stopping times and whether all or a portion of money is invested, but we will leave that for another discussion. See Spencer Jakab's Heads I Win, Tails I Win for the reference and further description of the game. This game and the Hussman conclusion lead to one of the age old issues with trend-following and investing in general. Should you get out early from winning trades?

In trend-following, the idea is that you hold your long positions in any trend as long as the market is going up. You may have a trailing stop, but you should be willing to give up returns on a short-term reversal in order to stay in a position. Because you don't know when the top or the bottom of the market will occur, just hold onto positions even if it means giving back some profits. The more general investment advice is simple, hang onto your winners and sell your losers. Hussman is willing to say that walking away early before tops or bottoms is always better that waiting for the peak or trough and then giving back some profits.

This question is fundamental to all investment strategies, yet there has been limited work and providing a clear answer. Clearly, the momentum risk premium factor research suggests that following past behavior with holding positions until momentum reverses has been a successful strategy. Nevertheless, there is also work that shows that momentum strategy are subject to crash risk or a large reversal. Some have argued that the risk premium is received because there is crash risk with momentum strategies. Higher returns come with higher risk.

Our simple graph tries to address the key problem. Should you continue to play the game and hold a position when you don't know when there will be a reversal? I can argue that this issue of profit-taking or exit management and risk management is more critical than the question of how do you identify a trend.

Trend Following Wizards – October
November 29th, 2016

The Wizards were well in the red last month, and two months before the end of the year, the YTD performance is negative too.

Another piece of news from last month was the closure of a Turtle trader fund Saxon Investment, as mentioned on their website. Saxon Investment was set up by Howard Seidler, part of the Turtle trading experiment.

Below are the full results as of end October 2016:

Quantopian.com,

The Tutorials : https://www.quantopian.com/tutorials/getting-started
The lectures, learn to code and Develop Trading Models In Python .

https://www.quantopian.com/lectures

A Strong Buy — All Major USD Currencies Revealing Long-Term Strength,
by Gabriel Grammatidis


Impatience or even euphoria are two emotions most detrimental to trading success. The ability of knowing when to “Stay out of the Market” is one of Van’s Top Tasks of Trading (see Critical trading habit needed). In that last article, I wrote about the prolonged but healthy sideways consolidation in the EURUSD. It lasted for over a year after the EUR had fallen so hard against the USD the year before. With this typical trend-following consolidation about to complete, the USD shows strong bullishness now — it just needed some time-out to regain strength on its further way up. The USD is growing stronger in relation to what? Generally, stronger in relation to many currencies and specifically in relation to the Euro.

EURUSD Continuing Its Long-Term Downtrend

The break of the critical level 1.0910 in the EURUSD pair (see purple line in the chart) created a strong momentum move down to the lower range of the sideways consolidation in the last few weeks (read above article pre-indicating this critical level). The break of 1.0910 has also locked-in the price target of 0.8000. Please be aware this target is more than 2000 pips (or 25%) lower than the current price which offers investors and traders excellent trade opportunities (from the weekly chart down to the 5min chart).

In any case price should not close above the high of the red rejection bar (3rd bar from the right). This very important level was created when the markets learned of the US presidential election results. A lot of market participants were caught on the wrong foot and that price represents an ideal place to put a stop for a long-term position to ride a EURUSD short towards its’ target level.

In the short term, there are two possible price moves:

 

1. Price finds resistance most probably at the consolidation low and will pull back towards the critical level. This would be a healthy pullback — or —
2. Price might break through the consolidation low immediately and increase its downward momentum. A failure to pullback from this strong consolidation low would indeed be a sign of a very strong USD.

Multiple EURUSD Trades in November

These last 2-3 weeks offered excellent trading opportunities for all three systems that I trade (and that I teach in the Forex Trading Workshop). Look at the numerous trade setups labeled by system number on the 60min chart below. The grey boxes show each individual trade and indicate entry and initial stop levels. On an hourly basis, more than +10R were available within a few weeks – in just this one pair. This shows the power of trading with the trend in the higher timeframe. Once a higher timeframe gives you a clear directional edge you can easily scale-in building a position-trade or grow a day-trade into a large R swing-trade.

Can you see the weekly consolidation low (the horizontal gray line at 1.0546) representing strong support? The failure of the last S1 pattern to deliver a winning trade at that exact level is a good counter-trend long signal indicating the beginning of an overall pullback (described earlier). Due to the fractal nature of the markets, patterns like this appear regularly in several timeframes with good trends like this. In these kind of conditions, you should definitely trade systems that help you stay “in the markets.”

I am always interested to hear all of the reasons that people use to explain why certain price moves happened (Trump won the election, etc.). I have a strong belief that news does not make price move but that price moves from one level to the next one because of the inherent dynamics of the chart. This is why reading price chart patterns is so important — charts really do tell you what is going on in the markets.

What is the USD Index Doing Now?

The USD Index is an index calculated as the weighted average of 6 important currencies against the USD (being EUR, JPY, GBP, CAD, SEK and CHF). The USD Index has been calculated since 1973 and is listed as a currency future on the ICE futures exchange. You can also trade the USD Index through the ETF called UUP (Powershares) that tracks the index very well. The Index is said to be a leading indicator reflecting the market dynamics of the Forex market. It just sent out a signal that the Forex market overall is in strongly trending mode again!

For most of the time since 2008, the USD Index has been in a sideways range (see monthly chart below). The up move in early 2015 which broke prior highs was a first sign of a developing uptrend. A one-leg move, however, does not yet make an uptrend. The consolidation that followed formed a long-term setup signal in a classic Busted Breakout pattern (System S1). The entry and stop levels for this long-term trade are indicated in the chart. That trade is currently running for other peoples’ stops above the prior high (see orange line) and the trade is break-even now as a worst case. If a Busted pattern occurs in a beginning trend, it gives you good early signal of a strong trend developing. Target 1 at the 115 level is the projected target coming out of the S1 entry signal. Target 2 above 130 is the more aggressive target that clusters with the high of the USD Index in 2002. These levels act like magnets pulling the price higher.

Now that the USD Index made a higher high, it is technically in early uptrend mode with a lot of room to go to meet its’ target levels. As an early indication to the Forex market as a whole, this S1 Busted pattern gives traders a high probability that other FX pairs are going into trend mode soon. Get prepared now to trade the multiple Forex trends in the coming months and years. This monthly US Index chart can be a guide to you revealing the trendiness of the FX markets (read more about the many advantages Forex trading offers — Ten Reasons to Trade Forex).

Here’s another currency pair with a high likelihood to start trending again shortly.

EURCHF — About to Resume Downtrend

EURCHF has been in “refresher mode” for quite some time. If you have been following my articles, you might remember that I outlined the likely scenario after the unpeg occurred about 1½ year ago (see Starting Signal for Extended Currency Trends). The unpeg created a huge move down of the EURCHF in trend direction. That pronounced move, similar in scope to the EURUSD, required price to pause and regain strength again. By now prices have pulled back nicely to the Big number of 1.1000 as well as rejoined with the 21EMA (see monthly chart below).

Actually you can clearly see how the monthly bars have found support and resistance at the 8EMA respecting the 21EMA and how the price bars have become very tight. These signs indicate that the very healthy pullback is nearing its’ end. The rounded consolidation is showing the first signs of ending with the breaking lower of the last small red bar (see red line). This triggered a short entry based on System 3 (3TMA). Being able to set a tight stop protected by the 21EMA above as well as the Big number (1.1000) represents two edges already helping this trade. This pair and the other CHF pairs are back on my list of very promising long-term trends.

Over the coming weeks and months, many excellent short, medium and long-term trading opportunities for low risk Forex trades will present themselves. Now is the time to put together your game plan with multiple edges to profit from these trending currency pairs. Now is the time to more actively stalk the markets for easy to identify low-risk setup patterns. Most of the CAD, JPY and AUD pairs have already been trending nicely for quite some time. Now the USD and CHF pairs are about to join the other pairs in major trend moves.

You can see these long-term trends beginning in the charts above. Whether you want to open investment positions (using the weekly or daily chart), swing trade the 4-hour chart, or day trade the 5 minute charts, the opportunities are going to be plentiful. With these nascent trends, the leverage, the liquidity, and the 24-hour-trading the Forex market offers, you have to ask yourself: why aren’t you trading currencies yet?

(Note: Even if you are not set up to trade Forex yet, you could still trade these trends using currency ETFs. You could short the EURUSD through EUFX or the double leveraged EUO. Likewise, you could simply go long the USD with the ETF symbol UUP. For the EURCHF trade mentioned above, Europeans could short a 5x leveraged EURCHF ETF - though that offering may not be available in other geographies.)

For more information on how to scale-into a trade pyramid (position-trade) or transfer a day-trade into a swing-trade please click here to view my “Trade of the Week” video.

Good trading,
Gabriel Grammatidis

Inside a Moneymaking Machine Like No Other

https://www.bloomberg.com/1d7d3c8c-7...f-cc8ce01ef14a

The Medallion Fund, an employees-only offering for the quants at Renaissance Technologies, is the blackest box in all of finance.
by Katherine Burton - November 21, 2016

Sixty miles east of Wall Street, a spit of land shaped like a whale’s tail separates Long Island Sound and Conscience Bay. The mansions here, with their long, gated driveways and million-dollar views, are part of a hamlet called Old Field. Locals have another name for these moneyed lanes: the Renaissance Riviera.
That’s because the area’s wealthiest residents, scientists all, work for the quantitative hedge fund Renaissance Technologies, based in nearby East Setauket. They are the creators and overseers of the Medallion Fund—perhaps the world’s greatest moneymaking machine. Medallion is open only to Renaissance’s roughly 300 employees, about 90 of whom are Ph.D.s, as well as a select few individuals with deep-rooted connections to the firm.

The fabled fund, known for its intense secrecy, has produced about $55 billion in profit over the last 28 years, according to data compiled by Bloomberg, making it about $10 billion more profitable than funds run by billionaires Ray Dalio and George Soros. What’s more, it did so in a shorter time and with fewer assets under management. The fund almost never loses money. Its biggest drawdown in one five-year period was half a percent.

“Renaissance is the commercial version of the Manhattan Project,” says Andrew Lo, a finance professor at MIT’s Sloan School of Management and chairman of AlphaSimplex, a quant research firm. Lo credits Jim Simons, the 78-year-old mathematician who founded Renaissance in 1982, for bringing so many scientists together. “They are the pinnacle of quant investing. No one else is even close.”
Few firms are the subject of so much fascination, rumor, or speculation. Everyone has heard of Renaissance; almost no one knows what goes on inside. (The company alsooperates three hedge funds, open to outside investors, that together oversee about $26 billion, although their performance is less spectacular than Medallion’s.) Apart from Simons, who retired in 2009 to focus on philanthropic causes, relatively little has been known about this small group of scientists—whose vast wealth is greater than the gross domestic product of many countries and increasingly influences U.S. politics 1 —until now. Renaissance’s owners and executives declined to comment for this story through the company’s spokesman, Jonathan Gasthalter. What follows is the product of extensive research and more than two dozen interviews with people who know them, have worked with them, or have competed against them.

https://assets.bwbx.io/images/users/.../-999x-999.gif
This story appears in the December 2016 / January 2017 issue of Bloomberg Markets.
Renaissance is unique, even among hedge funds, for the genius—and eccentricities—of its people. Peter Brown, who co-heads the firm, usually sleeps on a Murphy bed in his office. His counterpart, Robert Mercer, rarely speaks; you’re more likely to catch him whistling Yankee Doodle Dandy in meetings than to hear his voice. 2 Screaming battles seem to help a pair of identical twins, both of them Ph.D. string theorists, produce some of their best work. Employees aren’t above turf wars, either: A power grab may have once lifted a Russian scientist into a larger role within the highly profitable equity business in a new guard vs. old guard struggle.
For outsiders, the mystery of mysteries is how Medallion has managed to pump out annualized returns of almost 80 percent a year, before fees. “Even after all these years they’ve managed to fend off copycats,” says Philippe Bonnefoy, a former Medallion investor who later co-founded Eleuthera Capital, a Switzerland-based quantitative macro firm. Competitors have identified some likely reasons for the fund’s success, though. Renaissance’s computers are some of the world’s most powerful, for one. Its employees have more—and better—data. They’ve found more signals on which to base their predictions and have better models for allocating capital. They also pay close attention to the cost of trades and to how their own trading moves the markets.
But as computing power becomes ever cheaper and competitors sharpen their skills, will Medallion continue to mint money?
Quants seem like saviors to investors disappointed with how mere mortals have managed their money of late. In 2016 clients plugged $21 billion into quant hedge funds, while pulling $60 billion from those that do everything else. One noteworthy quant shop, Two Sigma, managed just $5 billion during the financial crisis and has seen assets jump to $37 billion. Even old-fashioned traders such as Paul Tudor Jones and Steve Cohen are adding to their computer scientist ranks in hopes of boosting returns.
Renaissance’s success, of course, ultimately lies with the people who built, improved upon, and maintain Medallion’s models, many of whom met at IBM in the 1980s, where they used statistical analysis to tackle daunting linguistic challenges. This is their story.

imons is already well-known: math genius, professor at MIT and Harvard, recipient of the Oswald Veblen Prize in Geometry, and co-creator of the Chern-Simons theory. He was also a code breaker for the Institute for Defense Analyses, where he worked finding messages amid the noise.
The goal of quant trading is similar: to build models that find signals hidden in the noise of the markets. Often they’re just whispers, yet they’ll help predict how the price of a stock or a bond or a barrel of oil might move. The problem is complex. Price movements depend on fundamentals and flows and the sometimes irrational behavior of people who are doing the buying and selling.
Although Simons lost the IDA job after denouncing the Vietnam War in a letter to theNew York Times, the connections he made through his work in cryptography helped create Renaissance and, a few years later, Medallion. Over the next decade, while chairing the math department at Stony Brook University, Simons dabbled in trading commodity futures. In 1977 he left academia for good to try his hand at managing money. 3
Initially he bought and sold commodities, making his bets based on fundamentals such as supply and demand. He found the experience gut wrenching, so he turned to his network of cryptographers and mathematicians for help looking at patterns: Elwyn Berlekamp and Leonard Baum, former colleagues from IDA, and Stony Brook professors Henry Laufer and James Ax. “Maybe there were some ways to predict prices statistically,” Simons said in a 2015 interview with Numberphile. “Gradually we built models.”

At their core, such models usually fall into one of two camps, trend-following or mean-reversion. Renaissance’s system had a foot in both. Its results were mixed at first: up 8.8 percent in 1988, its first year, and down 4.1 percent in 1989. But in 1990, after focusing exclusively on shorter-term trading, Medallion chalked up a 56 percent return, net of fees. “I was confident that the models would work better,” says Berlekamp, who returned to academia in 1991 and is now a professor emeritus at the University of California at Berkeley. “I didn’t think they would be as good as they were.”
Eventually the scientists went so far as to develop an in-house programming language for their models. Today, Medallion uses dozens of “strategies” that run together as one system. The code powering the fund includes several million lines, according to people familiar with the company. Various teams are responsible for specific areas of research, but in practice everybody can work on everything. There’s a meeting every Tuesday to hash out ideas.
In the early 1990s, big annual returns became the norm at Renaissance: 39.4 percent, 34 percent, 39.1 percent. Prospective investors clamored to get into Medallion, but the company didn’t pay them much heed—or coddle clients for that matter. Bonnefoy recalls dialing a Manhattan phone number to hear a recording of the monthly returns; Renaissance’s legal department doubled as unhelpful customer service representatives. (To this day the company’s website, rentec.com, looks like it dates from the Netscape era.) In 1993, Renaissance stopped accepting new money from outsiders. Fees were also ratcheted up—from 5 percent of assets and 20 percent of profits, to 5 percent and 44 percent. “They raised their fees to exorbitant levels and were still head and shoulders above everyone else,” says Bonnefoy, who, along with every other outsider, was finally booted from Medallion in 2005.
Encouraged by Medallion’s success, Simons by the mid-’90s was looking for more researchers. A résumé with Wall Street experience or even a finance background was a firm pass. “We hire people who have done good science,” Simons once said. The next surge of talent—much of which remains the core of the company today—came from a team of mathematicians at the IBM Thomas J. Watson Research Center in Yorktown Heights, N.Y., who were wrestling with speech recognition and machine translation.
In the early days of tackling these problems, computer scientists teamed with linguists and tried to code grammar. At IBM, a group including Mercer and Brown reasoned that the problems would be better solved using statistics and probabilities. (Their boss, Frederick Jelinek, liked to say, “Whenever I fire a linguist, the system gets better.”) According to scientists who worked at the research center then, the team fed reams of data into its computers. Documents from the Canadian Parliament, for instance, were available in both English and French, which none of the scientists spoke. (Mercer once disappeared for several months to type French verb conjugations into a computer, according to a source.) The data allowed them to write an algorithm that found the most likely match for the phrase Le chien est battu par Jean was “John does beat the dog.” A similar approach applied to speech recognition: Given auditory signal x, the speaker probably said the word y.
“Speech recognition and translation are the intersection of math and computer science,” says Ernie Chan, who worked at the research center in the mid-1990s and now runs quant firm QTS Capital Management. The scientists weren’t just working on academic problems; they were also developing theories and writing software to implement the solutions, he says. The group’s work eventually paved the way for Google Translate and Apple’s Siri.
Mercer and Brown went to IBM’s management in 1993 with a bold proposition, says a person who knows the two: Let them build models to manage a portion of the colossal company’s then-$28 billion pension fund. IBM balked, questioning what computational linguists would know about overseeing investments. But the duo’s fascination with financial markets was just beginning.
https://assets.bwbx.io/images/users/...0/v0/-1x-1.png
That same year, Nick Patterson, a former code breaker for British and U.S. intelligence agencies, joined Renaissance and approached acquaintances Brown and Mercer. “IBM was in serious trouble, and morale was poor, so it was something of a recruiting opportunity,” says Patterson, who worked at Renaissance until 2001 and is now a senior computational biologist researching genetics at the Broad Institute of MIT and Harvard. The two decided to join, drawn by the 50 percent pay raise. They roomed in an attic apartment in Setauket and often dined together. When the bill came, they would pull out a special calculator that could generate random numbers. Whoever produced the higher number picked up the tab.
“Renaissance was started by a couple of mathematicians,” Brown said in a 2013 conferencefor computational linguists. “They had no idea how to program. They’re people who learned how to program by reading computer manuals, and that’s not a particularly good way of learning.” He and Mercer had learned how to build large systems—with many people working on them simultaneously—which was a skill set they used to Renaissance’s advantage. Not that their new field was without challenges. “It’s all noise in finance,” he said.
More IBM veterans joined them on Long Island, including Stephen and Vincent Della Pietra, the string-theorist twins; Lalit Bahl, who had created algorithms to recognize human speech; Mukund Padmanabhan, whose specialty was digital-signal processing;David Magerman, a programmer; and Glen Whitney, who wrote software as a summer intern. “The takeaway from IBM was that the whole is greater than the sum of its parts,” says Chan. “They all worked together.”
The atmosphere at Renaissance was different than what they’d left behind. “We quickly learned that the financial world is different from IBM,” Brown said at the conference. “It’s ruthless. Either your models work better than the other guy’s, and you make money, or they don’t, and you go broke. That kind of pressure really focuses one’s attention.”
Renaissance also spent heavily collecting, sorting, and cleaning data, as well as making it accessible to its researchers. “If you have an idea, you want to test it quickly. And if you have to get the data in shape, it slows down the process tremendously,” says Patterson.
Cerebral challenges weren’t the only incentive for Renaissance’s data-hungry scientists. They also enjoyed something more intangible: a sense of family.
https://assets.bwbx.io/images/users/...Y/v0/-1x-1.jpg
James Simons, Renaissance Technologies Corp, chairman and president
Photographer: Jin Lee/Bloomberg
Simons was the benevolent father figure. No other Renaissance senior executive has possessed his people skills, those who know him and the company say, and he inspired the supernerds to stick together. “It’s an open atmosphere,” Simons said in a speech at MIT in 2010. “We make sure everyone knows what everyone else is doing, the sooner the better. That’s what stimulates people.”
When the IBM crew arrived at Renaissance, Medallion was already producing annual returns, after fees, of at least 30 percent almost exclusively from futures trading. In the early days, anomalies were easy to spot and exploit. A Renaissance scientist noted that Standard & Poor’s options and futures closing times were 15 minutes apart, a detail he turned into a profit engine for a time, one former investor says. The system was full of such aberrations, he says, and the scientists researched each of them to death. Adding them all up produced serious money—millions at first, and before long, billions.
But as financial sophistication grew and more quants plied their craft at decoding markets, the inefficiencies began disappearing. When Mercer and Brown joined they were assigned to different research areas, but it soon became apparent they were better together than apart. They fed off each other: Brown was the optimist, and Mercer the skeptic. “Peter is very creative with a lot of ideas, and Bob says, ‘I think we need to think hard about that,’ ” says Patterson. They took charge of the equities group, which people say was losing money. “It took them four years to get the system working,” says Patterson. “Jim was very patient.” The investment paid off. Today the equities group accounts for the majority of Medallion’s profits, primarily using derivatives and leverage of four to five times its capital, according to documents filed with the U.S. Department of Labor. 4
https://assets.bwbx.io/images/users/...w/v0/-1x-1.png
“You need to build a system that is layered and layered,” Simons said in a 2000 interview withInstitutional Investor, explaining some of the philosophy behind the firm and the Medallion model. “And with each new idea, you have to determine: Is this really new, or is this somehow embedded in what we’ve done already?” Once that’s determined, the team would figure out how much weighting to give it. Signals may eventually go cold over time but will usually be kept around because they can sometimes reemerge—or have unintended consequences if removed. A source says positions are held anywhere from seconds to seasons.

At the 2013 conference, Brown referenced an example they once shared with outside Medallion investors: By studying cloud cover data, they found a correlation between sunny days and rising markets from New York to Tokyo. “It turns out that when it’s cloudy in Paris, the French market is less likely to go up than when it’s sunny in Paris,” he said. It wasn’t a big moneymaker, though, because it was true only slightly more than 50 percent of the time. Brown continued: “The point is that, if there were signals that made a lot of sense that were very strong, they would have long ago been traded out. ... What we do is look for lots and lots, and we have, I don’t know, like 90 Ph.D.s in math and physics, who just sit there looking for these signals all day long. We have 10,000 processors in there that are constantly grinding away looking for signals.”
In addition to language specialists, astrophysicists have historically had an outsize impact on the system’s success, according to people familiar with the firm. These scientists excel at screening “noisy” data. String theorists have also had a major role, and the Della Pietra brothers—who reunited with their former IBM bosses to work on equities—were the first of many with that background. The identical twins, now 56, have never strayed far from each other: They took an honors science program at Columbia University as high school students; attended Princeton as undergraduates, studying physics; and received doctorates from Harvard in 1986.

“They always sat next to each other,” says Steven Strogatz, a math professor at Cornell University who remembers them as Princeton freshmen in a junior-year-level abstract algebra class. “Their talking involved a lot of arguing. It was passionate mathematical discussion, and they were always correcting the teacher or explaining something to each other.” Chan, who worked with them at IBM, remembers them screaming at each other—but never at anyone else, to whom they were kind and humble. Their twinship added another dimension, too. “They are almost telepathic,” he says.

At Renaissance, the Della Pietras have shared adjacent offices separated by an internal window to facilitate discussion. “They are creative people and very competitive with each other,” says Patterson, to whom they reported for a time.

The IBM crew focused on improving the system’s performance and efficiency. Since Renaissance’s models were short-term oriented, they spent time looking at execution costs and researching how their trades moved the markets—a particularly difficult problem to crack, according to other quants. They also ensured that the trades and profits matched what the system had intended, since a bad price or other glitch could throw off the whole operation.

https://assets.bwbx.io/images/users/...U/v0/-1x-1.png
Return sources: Labor Department, More Money Than God by Sebastian Mallaby, Investing in the Modern Age by Rachel and William Ziemba, Internal company documents, Bloomberg reporting

How much money an employee has in Medallion depends on his overall contribution to the firm—and collaboration is key to getting a bigger piece of the pie. Employees are awarded an allocation of shares they can buy. In addition, a quarter of one’s pay is deferred and invested in Medallion, where it stays for four years. Employees must also pay fees of as much as “5 and 44.”
Simons determined, almost from the beginning, that the fund’s overall size can affect performance: Too much money destroys returns. Renaissance currently caps Medallion’s assets between $9 billion and $10 billion, about twice what it was a decade ago. Profits get distributed every six months.

Thanks to Medallion, Simons—who still owns as much as 50 percent of the firm—has a net worth of $15.5 billion, according to estimates by the Bloomberg Billionaires Index. Laufer, who owns the next-largest stake (possibly as much as 25 percent), Brown, and Mercer are among other employees worth hundreds of millions of dollars.
In some ways, money, not unlike the company’s familial feel, even binds the place together. With the exception of the scientists who depart for academia or to pursue philanthropy, folks don’t leave Renaissance. Why would they? The problems are complex, the colleagues first-rate, and the paychecks huge.

As everyone became rich off Medallion, lifestyles changed. Trains to Manhattan gave way to helicopter commutes. Scientists swapped Hondas for Porsches. Fancy hobbies became normal. Simons’s cousin, Robert Lourie, who heads futures research, built an equestrian arena for his daughter, with arches so large that a bridge into New York City had to be shut down at night to facilitate their journey. Yachts also became a thing. Mercer has commissioned a succession of them, each called Sea Owl. For his part, Simons’s 222-foot Archimedes has a wood-burning fireplace. Both vessels have a propulsion system so novel that they don’t require an anchor. Always the merry ringleader, Simons planned company trips—to Bermuda, the Dominican Republic, Florida, Vermont—and encouraged employees to bring their families. Company lore is that on one of the firm’s ski trips, Simons, a longtime smoker, bought an insurance policy for a restaurant so he wouldn’t have to forgo his beloved Merits.

Money has also threatened to destroy the family atmosphere. In 2001, Renaissance hired a Russian scientist who, like many of his peers, came west after the collapse of the Soviet Union: Alexander Belopolsky. Patterson was against bringing him aboard, he says, because he had recently worked on Wall Street, where he had job-hopped. His fears proved prescient. In 2003 he and another Russian, Pavel Volfbeyn, announced they were leaving for hedge fund Millennium Partners, where they’d negotiated healthy bonuses and the right to keep a large part of their own profits. Renaissance sued them and Millennium, worried the researchers would take the firm’s secrets with them. All parties later settled out of court.

Around that time another of Renaissance’s Russian-born researchers, Alexey Kononenko, who received his Ph.D. from Penn State in 1997 and had also done a brief stint on Wall Street, was promoted within the equities group. Senior staffers ended up discussing Kononenko’s advancement during one of their regular dinners at Simons’s house. One person familiar with the situation says the scientists were just questioning why he had moved ahead of colleagues who had been there much longer, much the way an academic might complain about a younger colleague getting tenure. Other people with knowledge of the firm say Kononenko’s promotion was a significant event in Renaissance’s history and that the Russian had actually executed a power play.
Whatever the reasons for Kononenko’s advancement, the outcome has safeguarded the well from which Renaissance’s wealth flows: Medallion has averaged more than a 40 percent return, after fees, since the dinner.

When rivals and former investors are asked how Renaissance can continue to make such mind-blowing returns, the response is unanimous: They run faster than anyone else. Yet all that running hasn’t always kept them on their feet when everyone else stumbled.

In August 2007, rising mortgage defaults sent several of the largest quant hedge funds, including a $30 billion giant run by Goldman Sachs, into a tailspin. Managers at these firms were forced to cut positions, worsening the carnage. Insiders say the rout cost Medallion almost $1 billion—around one-fifth of the fund—in a matter of days. Renaissance executives, wary that continued chaos would wipe out their own fund, braced to turn down their own risk dial and begin selling positions. They were on the verge of capitulating when the market rebounded; over the remainder of the year, Medallion made up the losses and more, ending 2007 with an 85.9 percent gain. The Renaissance executives had learned an important lesson: Don’t mess with the models.

Another lesson may one day prove even more important: Beware of the damage others can cause. In a letter that same month to investors in his public institutional equities fund, Simons wrote: “While we believe we have an excellent set of predictive signals, some of these are undoubtedly shared by a number of long/short hedge funds.”
No system lasts forever, say quants. They ask how long Medallion’s magic can continue. But seven years after Simons’s retirement, the fund’s money-printing ways persist. Even in the first half of 2016, while many hedge funds struggled, it made more than 20 percent. Wealth and influence at Renaissance have grown apace.

The most important business stories of the day.
Get Bloomberg's daily newsletter.
Sign Up
Yet as successful as Renaissance has been under Brown and Mercer—who are 61 and 70, respectively—industry insiders wonder how the firm will handle its next succession. They also reserve their reverence. Take, for instance, the anecdote from an invite-only conference earlier this year. An audience member asked a panel of quant managers, “Who would be your dream hire?” After a bit of nervous laughter, one of them gave his honest answer: Jim Simons.

The Big Indices Divergence, correlation is out of the window.

The Nasdaq cracked, the S&P500 Just broke down and the DOW is Holding firm in an uptrend, go figure who leads who ?.

And they say that Bonds are in a Bubble, this is the 30-10 and the 5 Year U.S. Treasury Bonds Futures .

Saturday, December 03, 2016
The breakout stop comes to the rescue (again)
Posted by Trader Steve

In the second half of 2011, and in particular the last quarter, I was experiencing a frustrating time. The markets were volatile, and I suffered a number of trades where price initially broke out in my favour, only to sharply reverse and end up generating losses around a full -1R. This was because those trades had not been open a sufficient length of time for my trailing stop to start moving from its initial price level.

The trade that really stung was a long position in a small UK stock in November 2011. After initiating the trade, my open profits reached about +5R after only three days, yet my initial stop had not moved, based on the rules I used at the time. Then, price did a sharp about-turn, and three sessions later, I was stopped out for a full -1R loss.

After this happened, I retreated to cash and took a break to think.

The subsequent change to my own method resulted from reading David Ryan's interview in Market Wizards. Let's look at what he had to say:

"I want to buy it as soon as it goes to new highs."

"Stocks should be at a profit the first day you buy them. In fact, having a profit on the first day is one of the best indicators that you are going to make money on that trade."

So that tallied with my own beliefs as a trend follower.

Jack Schwager asked him the following question: If you buy a stock at new highs and then it pulls back into the range, at what point do you decide it was a false breakout?

Ryan's response was; "If it reenters its base, I have a rule to cut at least 50 percent of the position."

He went on:

"In some cases, it will break out and come back to the top of the base, but not reenter. That's fine, and I will stay with the stock. But if the top of the base was $20 and it breaks back to $19 3/4, I want to sell at least half of the position because the stock didn't keep on moving. Frequently, when a stock drops back into its base, it goes all the way back down to the lower end of the base. In the example, if it goes down from $21 down to $19 3/4, it will often go all the way back down to $16. Therefore, you want to cut your losses quickly."

As Ed Seykota would say, reading that was an "A-ha!" moment, and I implemented what I refer to as the 'breakout stop' into my own method. The only difference is that I am more aggressive than David Ryan in cutting the losses - I fully close the position if price falls back below the breakout level.

Using this type of stop does have its own frustrations - if price oscillates around the breakout level, you can get whipsawed around. But I can live with that.

Immediately, the average size of my losing trades began to drop. Since adopting the rule in the summer of 2012, the average loss suffered has reduced to -0.41R.

In addition, my losing positions generally are cut very quickly - normally within 24 hours of opening the trade. This means that I do not have equity tied up for long in a non-performing position.

When you are adopting a trend following method, where the typical win ratio is less than 50%, this aggressive cutting of losses will have a major positive impact on your equity when compounded over a period of time.

My latest losing trade demonstrates a breakout failure very well. Here is the chart:


https://2.bp.blogspot.com/-dxyKCzKrX...AR-17%2529.png

You can see how price was starting to form a pattern of higher highs and higher lows from the early October price low. Throughout November, price started consolidating, and volatility started contracting. This gave me a potential setup. As soon as price broke out, I entered on an intra-day basis.

As we can clearly see, this attempted price move was immediately repelled, and price fell back below the breakout level, triggering an exit the next morning. In this case, the loss suffered was -0.25R.

We can also see what happened next with price, which again mirrored Ryan's own observations. As of yesterday's close, price had fallen well below where my initial stop would have been placed (which, without the breakout stop, would have created a bigger loss), and price is now threatening to breakout in the opposite direction!

I have seen far too many charts, and suffered far too many losses, where the use of my own version of the 'breakout' stop' has helped save me money, far outstripping the losses suffered due to any whipsawing.

This type of stop matches my own beliefs about trading and breakouts in particular, and was therefore easy to adopt from a psychological point of view.

I am a breakout trader, and if price breaks out, then at worst price will re-test the breakout level, but will not fall back within the previous price range. If it does, I bail out and move on - the breakout has failed, and the reason or rationale for entering the trade no longer exists.

As a result, the vast majority of my losing trades can be viewed simply as a cost of business.