ETMA_The Evolution

Acceptable performance robustness Parameters for Strategy Development as per Kevin Davey.

Intermarket Divergence – A Robust Method for Signal Generation

Many markets are interrelated. These interrelationships can offer predictive capabilities for many markets. The study of these interrelationships is called intermarket analysis. In this article I will briefly explain a robust method for generating robust signals for a wide range of markets. I will also offer a free TradeStation tool to help you explore intermarket relationships.
Standard correlations between markets are not useful if our goal is to either predict future prices or generate profitable signals because current correlation does not tell us anything about future prices. A methodology we originally developed in the mid 1990s called intermarket divergence allows us to gauge the predictive power of an intermarket relationship and produce 100% objective signals. During the past 17 years we have used this methodology to develop trading systems which have produced robust and reliable trading signals even 17 years after the models were originally developed without any re-optimization. Other methodologies of processing intermarket relationships to develop trading signals might perform as well during in-sample periods, but do not perform as well during walk forward periods and during real trading.

A widely known intermarket relationship is the one between the S&P 500 and the 30 year Treasury bond. Bond prices generally are positively correlated with the S&P 500 (while yields are negatively correlated), although this is not always true, bonds should generally lead stocks at turning points. Another important fact is that one of the best trades you can make in the S&P 500 is when 30 year Treasury bond diverge from the S&P 500; for example, when (a) bonds are rising and the S&P 500 is falling, buy the S&P 500 and (b) bonds are falling and the S&P 500 is rising, sell S&P 500. Although this relationship has broken down over the past few years, its long term existence is of historical importance to the science of intermarket analysis.
Simple But Powerful Method of Market Predictions
We will use classic mechanical methods for trading intermarket relationships, applying them to 30 year Treasury bond using a concept called “intermarket divergence,” (first coined in 1998) which is when a traded market moves in an opposite direction to what is expected. For example, if we trade the S&P 500, 30 year Treasury bond rising and the S&P 500 falling would be divergence since these are positively correlated. If we were trading the 30 year Treasury bond, both bonds and gold rising would classify as divergence since they are negatively correlated. We will define an uptrend as when prices are above a moving average and a downtrend as when they are below the moving average. Now we can predict with some reliability the future direction of bonds, stocks, gold, crude and even currencies using this simple intermarket divergence model. Pseudo code for this basic model is as follows:

Price relative to a simple moving average
Let InterInd = Close of Intermarket - Average (Close of Intermarket,N) Let MarkInd = Close Traded Market - Average (Close of Traded Market,M)

Positive correlation
If InterInd > 0 and MarkInd < 0 then buy at next bars open If InterInd < 0 and MarkInd > 0 then sell at next bars open

Negative correlation
If InterInd < 0 and MarkInd < 0 then at buy at next bars open If InterInd > 0 and MarkInd > 0 then sell at next bars open

This simple concept represented above has proven to be a robust methodology for predicting future price action using intermarket analysis. In 1998, I published a simple intermarket based system for trading 30 year Treasury bond futures. This model used ‘The NYSE Utility Average (NNA)’, which was a basket of Utility stocks. The NNA was discontinued in 2004. Another utility index which also worked fairly well was the Philadelphia Electrical Utility index which was used as a replacement for NNA in our research. Back in 1998, when I did the original research and article, both indexes worked similarly, but NNA had a longer price history than UTY did. The original analysis using NNA was done as follows. We used a positive correlated intermarket divergence model and a moving average of eight days for 30 year Treasury bond and 18 days for NNA. We tested over the period Jan 1, 1988 to Dec 31, 1997. We did not deduct anything for slippage and commission. My original published results were as follows:

Net profit: $111,293.00
Trades: 126
Win %: 60%
Average trade: $883.38
Drawdown: $-8,582.00
Profit factor: 2.83

Now let us see how UTY worked during this same period using the original set of parameters used with NNA. This set of parameters was suboptimal for UTY but we are using the NNA set of parameters for consistency to show the robustness of our model.

Total Net Profit: $83,557.98
Total # of trades: 141
Percent Profitable: 58.87%
Avg. Trade (win & loss): $592.61
Max intraday drawdown: ($11,722.50)
Profit Factor: 2.03

Out-of-Sample Results
Let us study just the out-of-sample period with a first trade after 01/01/1998 to 10/25/2011.

Total Net Profit: $129,166.32
Total # of trades: 257
Percent Profitable: 61.87%
Avg. Trade (win & loss): $502.59
Max intraday drawdown: ($26,133.36)
Profit Factor: 1.67

We can see these out-of-sample results are very similar to the results over the whole period and the average trade differs by less than 20% between the in-sample and out-of-sample period. Let us look at the year by year out-of-sample results (see Table I).

We have seen that intermarket divergence is a powerful concept. When an intermarket divergence occurs we stay in that position until an opposite divergence occurs. One question is, “Why does this divergence concept work?” Also, what is interesting is that my research shows that the zero crossing is significant, we cannot improve the results of intermarket divergence by using a non-zero threshold. It is my belief that this concept works as an arbitrage play. Since we do not know the relative equilibrium between the traded market and underlying market, for example in the case of Treasury bonds and UTY, divergence is the only confirmed mispricing; we have in terms of a reliable arbitrage play. We know that this cannot be the most efficient signal. We can see by studying our Treasury bond trades that some trades are early; for others we give back large percentage of open profits and sometime large winning trades can become losers, even though intermarket divergence still produces outstanding results.

Here, we have a very profitable trade but we gave back almost all of the profit and then the market moved back in the direction of the trade. This shows a problem with intermarket divergence namely “Reversal Strategy” which is always in the market. There are other cases including (a) a winning trade ending up as a losing one and (b) trades which never become profitable. Despite these issues our results are amazing. One solution to this problem is to build a finite state machine which covers all possible states of the intermarket relationship during the process of going from ‘long to short’ or ‘short to long’. My research has shown that this state map of all possibilities is the key in greatly improving the performance of these simple divergence models. We can also create a state map which will allow us to combine multiple intermarkets against a market we are trading. Correlation and forward correlations analysis between markets can also be used to filter and improve these models. Sometimes correlation analysis can make the long term out-of-sample performance less robust if it is not integrated carefully. Hence, it is important to do the surface analysis discussed earlier to make sure that the correlation relationships we are looking at are robust and stationary.

Conclusion
Intermarket divergence is not something which just works on the bond market. It works on a broad range of markets from bonds, to stock groups, to currencies; even markets like gold, crude, live cattle and copper.
Intermarket analysis is an exciting area of market production. New methodologies of representing these relationships will help not only classic trading system development but also using advance technologies as for example using a finite state model can allow machine learning methods to easily see patterns which can be used to build more reliable models.
Below you will find a TradeStation ELD download you can use to explore possible intramarket relationships between two symbols. Included is a PDF manual demonstrating how to use the software.

— by Murray Ruggiero of Using EasyLanguage.

Free Intramarket Divergence TradeStation Tool

1. Intermarket Divergence WorkSpace (TradeStation WorkSpace)
2. Intermarket Divergence Source (TradeStation ELD)
3. Intermarket Divergence Source (Text File)
4. Intermarket Divergence Manual (PDF documentation)
5. Here is the Spreadsheet containing the performance of the S&P and Bond system described in this article.

A video demonstration has also been created. Click the image below…

This article was created based upon the paper, Intermarket Divergence, which was published within Computational Intelligence for Financial Engineering & Economics (CIFEr), 2012 IEEE Conference on March of 2012.

Improving The Summing Strategy
APRIL 4, 2016 5:00 AM

In a recent series of articles by Kevin Davey, a method of combining multiple trading strategies into one single strategy was presented. Kevin’s concept is a great way to tackle the issues of trading multiple systems on the same instrument. Kevin’s technique is to create a “summing” strategy where there is one single buy or sell signal and the number of contracts are based upon which strategy has been triggered. Traditionally, I code systems together into one single strategy where the system will have multiple entry and exit points. My technique has its own unique set of problems and I think Kevin’s “summing” concept is better. If you’ve not read Kevin’s articles you can find the links below.

1. Trading Multiple Strategies With The Same Instrument – Part 1
2. Trading Multiple Strategies With The Same Instrument – Part 2
3. Trading Multiple Strategies With The Same Instrument – Part 3

For this article I wanted to take a look at Kevin’s example a bit more closely and see if we can improve upon it. This is going to be a technical article on programming where we’ll take a concept and attempt to maximize it for flexibility and readability. So fair warning! If you’re not into programming this article may bore the hell out of you.
Kevin may have very well implemented very similar changes to his own personal strategies, but if not hopefully Kevin and you, the reader of this article, will find the modifications helpful.
For this article I’m going to start with the code provided from Kevin’s third article, Trading Multiple Strategies With The Same Instrument – Part 3. This code includes the summation strategy which contains two very simple strategies. The first strategy is a momentum-based strategy while the second strategy is a trend-based strategy.

As a reminder the strategies look something like this:

Momentum Strategy
If close>close[5] then buy next bar at market;
If close<close[5] then sellshort next bar at market;
setstoploss(1000);

Trend Strategy
If Close > average(Close,20) then buy next bar at market;
If Close < average(Close,20) then sellshort next bar at market;
setstoploss(1000);

Convert Each Strategy To Function

In the original article series the final summing strategy embeds the code for both strategies in-line. This is fine for simple concepts but often I like to move functionality into a function.
Functions are blocks of code which are contained within their own file. Traditionally, you can’t put a strategy within a function because buy/sell signals don’t work within this structure. However, because the actual buy/sell signals have been stripped from the two strategies, both our momentum and trend following strategy can be easily converted into a function.
Here is the Momentum strategy converted into a function called, KJD_Momentum_Strategy_Example:

As you can see, this function contains all the code for the momentum strategy example. The function returns the “FinalPosition” which is the current position of the strategy. This function can now be called within the main summation strategy, as seen below:
http://systemtradersuccess.com/wp-co...ntumCall_1.png
So this single line of code can now be placed within the main summation strategy. Why would we do this? First, it cleans up the code for the main summation strategy. The main summation code only deals with determining the number of contracts and placing the trades. In our example, the trade logic for the momentum strategy is contained within its own file. This increases the readability of all the code. Granted, our example momentum strategy is very simple but this technique would be even more important with a more complicated strategies.

Second, this provides more flexibility in regards to code reuse. Maybe we trade both of these two strategies (momentum and trend) together on the oil futures but also discover they work great on the Euro futures. Great, we simply make a copy of our summation strategy that can trade it on the Euro. But could we not do that with the original code? Yes, but what happens if you want to make a slight change on the momentum strategy due to a recently discovered bug? Or maybe you wanted to add a new feature to the momentum strategy? Well, you would have to change the code in two locations. One for the oil futures and again for the euro futures.
If you have your strategies as functions there is only one place to change the code and all summation strategies that utilize that function will be using the modified code. This saves you time and possible headaches!

Make Parameters Dynamic

Our next modification is an obvious next choice. Our momentum strategy that we placed within a function has particular values which can be viewed as an input parameter. For our momentum strategy we have a look-back value and a stop loss value. Let’s convert this into an input for the function. We do this by declaring two input values. See the code below. Notice that I removed in StopLoss$ and Lookback variables and moved them to the “Input:” section of the code.

We then must pass in the stop loss value and look-back period when we call the function within our summation strategy:
http://systemtradersuccess.com/wp-co...umStrategy.png
The obvious benefit here is we can now use the momentum function within different summation strategies yet use unique stop loss values and look-back periods. We use the look-back value of five for trading oil futures but discover that using 15 on the euro futures works even better. We’re still using the same momentum code for each summation strategy, but we now have the ability to modify the look-back period for each time we use it.

The other benefit here is we can optimize over these values within the summation strategy.

Conclusion

So there you have it. A way to improve the summation strategy for clarity, code reuse, and maintenance. As Kevin pointed out in his original series, try to build your strategies with the summation concept in mind so down the road if you wish to combine your strategies together, it will be much easier to do. I would also like to add, build your strategies as functions and you’ll be on your way to building more flexible and maintainable code.

Downloads

Download All Files For TradeStation (ELD)
Download TradeStation WorkSpace (TSW)
Download the two strategy functions (Text)
Download the main summation strategy (Text)

The Anatomy Of A Breakout Automated Trading Strategy: The Concept

By being an automated full-time trader for 7 years, I came pretty early in my trading career to the conclusion that a well-constructed trading breakout model is by far the best way to aim for stable returns in automated trading. During all those years, Ive been experimenting with many different approaches most probably with all you know or can imagine. But from my experience, breakout models are timeless and very universal. In the following three articles Id like to share some of my concepts and the most crucial components behind them. A Breakout Automated Trading System (ATS) modeI use: 4 crucial components There is no reason for looking for (over) complexity in automated trading. In fact, over the years []
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The Anatomy Of A Breakout Automated Trading Strategy: The Components

In the previous part of this 3-part article, I explained the concept of my breakout ATS model that Ive been successfully using for about seven years. Weve learned about four crucial components of the model: POI, distance, time filter, and a regular filter. In this part, well explore each filter more in detail. POI When it comes to scouting for the best Point Of Initiation (POI), you must be as creative as possible. Your POI can be basically anything. When I started constructing my first breakout ATS strategies, I used pretty simple methods of getting a POI. It usually was: Yesterdays Close Todays Open Todays Low (for longs) Todays High (for shorts) Lowest O/H/L/C X-days back Highest O/H/L/C X-days back []
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The Anatomy Of A Breakout Automated Trading Strategy: Markets, Timeframes, Exits, Strategies

In the previous two parts, we went through the anatomy of the breakout ATS. I explained all the components of the model Ive been successfully using for many years, and I went into each component more in detail. In this final part, I would like to show you some examples of what you can realistically expect from my breakout model and Ill also add some more information which are quite important. Markets, Timeframes, Exits One of the usual questions is what markets and timeframes should be the model applied to. I personally trade only futures markets and Ive found that this model can be applied to any market. Most of my breakout ATSs have been developed for index markets, however, []
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http://www.landofcash.net/?utm_campa...tor+properties
https://drive.google.com/file/d/1vFV...gJfXYDBGr/edit
https://fx1.net/strategy-quant-reports.php



StrategyQuant Output System Modeling Examples, could be Exported to Ninja and Meta4 .

Success story - a "perfect" forex portfolio
by Mark Fric

This article will be about a StrategyQuant success story. The title is a little exaggeration, of course there is no such thing as perfect portfolio, but in this article I'll present one that is close :-)

I was contacted recently by two traders who have very good results building and live trading portfolio of strategies generated by StrategyQuant.
Their results can be used as an inspiration and showcase how it is possible to generate very nice looking portfolio of strategies in StrategyQuant.
Nice not only in sense of great equity chart on historical data, but with continued consistent results also in live trading on real (not demo) accounts.

Portfolio equity

Note that they continue to improve and manage the portfolio. It started with less than 20 strategies, currently it trades around 30 strategies.

Their portfolio consists of around 30 strategies right now, trading on multiple symbols on multiple timeframes ranging from 15 minutes to 4 hours.

The last part of the equity is after starting on live account, results in SQ match the results on their live account very closely.
Note very small drawdowns and consistent growth almost all the time.

Myfxbook - trading on live account (since February 2015)

The portfolio is traded live since August 2014, I started
trading it (for tracking purposes) on a live account in February 2015.

http://widgets.myfxbook.com/widgets/1188718/mini.jpg

This also shows the power of portfolio - I believe that it is possible to achieve this kind of stability only by a bigger portfolio of strategies.
No single strategy (if it is not curve fitted) would be able to have such consistent returns.

So one thing to learn is that in order to achieve stability in earnings we have to think in terms of portfolios, not searching for one perfect strategy.
The portfolio itself should consist of different, non-correlated strategies that trade at different times, possibly on different instruments / timeframes, with different entry and exit conditions.

Note
as far as I now those traders are not active on our forum. They also clearly expressed that they have NO INTEREST in:

1. selling/exchanging their strategies
2. teaching or discussing their specific process
3. managing external money

This Is How To Boost Emotional Resilience: 10 Research-Backed Secrets

Here's how to increase your emotional resilience:

1. Foster optimism: Don't be in denial. See the world clearly but believe in your abilities.
2. Face your fears: Hiding from fear makes it worse. Face it and you overcome it.
3. Have a moral compass: A strong feeling of right and wrong tells us we must when we feel we can't.
4. Practice spirituality: Be a part of a group that has strong beliefs.
5. Give and receive social support: Tapping on the wall of your cell can keep you going.
6. Imitate resilient role models: Or have people you know you donot want to be.
7. Physical fitness: Exercise adapts your body to stress.
8. Be a lifelong learner: Keep your brain sharp and it will give you solutions when you need them most.
9. Have a number of ways to cope: Be like Navy SEALs and Special Forces operators -- and laugh.
10. Have meaning in your life: Don't just do a job; have a calling and a purpose.

Mmmmmmm. Very interesting Bass. Looks like we need to pile up on non-correlated systems. :-)

Strategy Testing, Violating These Steps Will Damage Your Account
DECEMBER 30, 2013

One of the most rewarding experiences for a TradeStation trader is to pick up a performance report that proves their great strategy idea is indeed a profitable strategy. Strategy testing done properly, as is outlined in this article, can verify the efficacy of your trading strategy and give you confidence to start trading it. But be forewarned, strategy testing done improperly can lead you toward financial destruction.

Strategy testing done incorrectly can result in false hope in a losing strategy. A trader recently shared his experience of getting great results from strategy testing his idea, but after trading it live in the market, he was losing money every day. He was baffled about what he did wrong. Having gotten excellent results on his back-testing performance report, he wondered why his promising strategy was draining his trading account. The problem was he violated several of the proper steps necessary for reliable strategy testing.

With the knowledge of how to get an accurate performance report you will be able to trust your strategy in live trading and protect your trading account. In order to properly test a strategy, there are 5 main steps that are vital to follow; configure TradeStation, in-sample data testing, out-of-sample data testing, live forward testing on the simulator account, and real live trading execution.

Step 1: Configure TradeStation
Before you begin testing your data, you must configure TradeStation so that the data it pulls onto your performance report will be accurate. Follow these 3 critical items to properly configure TradeStation.

(a) In your TradeStation platform menu, go to format symbol and give TradeStation a starting and ending date to test. This historical date range is called the in-sample data. Do not include the most recent six months in this in-sample data. The most recent six months is called the out-of-sample data, and it will be used later during your out-of-sample data testing step.

(b) Next, in your TradeStation platform menu, go to format strategy and select properties for all. Now select the general tab and enter the commissions and slippage (be as realistic as possible, or estimate too high if you are not sure). If this step is skipped, then the strategy testing performance report will be meaningless. If this is not done you might have a good looking performance report equity curve, but as soon as you enter the commissions and slippage figures the equity curve can reverse into an underwater equity curve.

(c) The last configuration step is under properties for all under the general tab. Look in the bottom left section called strategy testing resolution. Check the look-inside-bar back-testing option and then select the smallest time frame available for your chart style to make the strategy testing more closely resemble live data. When strategy testing, TradeStation uses the open, high, low, and closing data, thus the larger the time frame bar, the more distorted the strategy testing performance report can be. This look-inside bar back-testing option will make the computer do a lot more strategy testing calculations. This may really slow down your performance report generation, so please be patient. For an accurate performance report you must use the look-inside bar back-testing option.

These configuration steps are critical to getting an accurate performance report, so be sure this is completed precisely before continuing. Once TradeStation has been configured correctly, you can begin testing your strategy.

Step 2: In-Sample Data Testing (also called Back testing)

You are now ready to start testing your strategy idea. We will begin with testing the in-sample data that you set up for testing during the configuration steps. Begin with bringing up a TradeStation performance report. Right now I have a performance report in front of me that I will refer to, but you will be looking at your own performance report to analyze your own numbers. This is what we will be referring to in the steps below. There are 7 sub-steps to in-sample data testing, as follows:

First, look at how many trades the strategy made. To reduce strategy testing errors, where error is defined by [ error = 1 / Square Root (Number Trades In Test) ], you want at least 400 trades to reduce the margin for error to 5% in your strategy testing results. At 100 trades you have a 10% margin for error. The greater the number of inputs in your strategy that you optimize, the greater the number of trades you need to keep from over optimizing your strategy.

Also look at how many times the strategy traded on average per day. The more often a strategy trades the more profit it can generate.

In the performance report that I am looking at, it traded 397 trades in the last 3 1/2 months, averaging 5.3 trades per day.

Second, look at the Average Trade Amount. It needs to be large enough that slow order fills and/or larger than normal slippage does not kill the profitability of the strategy.

In my report the Average Trade Amount is $162.32. The commissions and slippage amount as defined in the set up steps is already subtracted in this performance report.

65% of the time this strategy trades 1 contract.
35% of the time this strategy trades 3 contracts.
10% of the time this strategy trades 5 contracts.

Third, look to see if the Profit Factor and Ratio Average Win-Average Loss are both above 1.5 and the percentage of winning trades around 45% or better
This strategy had a Profit Factor of 1.83.

This strategy had a Ratio Average Win-Average Loss of 2.28 (2.28 means breakeven is around 28% Percent Winning Trades)

On this strategy the Percent Winning Trades was 44.58%.

Fourth, look at the trade list page and assess the profit run ups and draw downs column. Notice how many trades made money and how much money they made before the trade exit occurred. Looking at what amount of money was made in relation to the profit run up and draw down, you want to know if managing the trades could generate more profits. The example used here shows that a good percentage of trades made much higher profits than where the automated exit points occurred.

Fifth, look at the three draw down (DD) numbers. I like to see the largest number at 15% or less of the Total Net Profit and the Max DD at 5% or less of the Total Net Profit (these numbers tell about the draw down risk level during your trades).

Total Profit $64,440
Peak to Valley DD $8,960 is 13% of Total Profit
Close to Close DD $7,120 is 11% of Total Profit
Max DD $3,420 5% of Total Profit

Sixth, Looking at the Largest Losing Trade on the report, I like to see 5% or less of the Total Net Profit. In my report the Largest Losing Trade that occurred was $2,580 which is 4% of Total Net Profit.

Seventh, I review the length of time in the average trade. Does the average time in a trade comply with the golden rule of trading; cut your losses quickly and let your profits run? You will also want to see if the strategy is built using only profit exits (no real stop loss exits). It might have a nice looking report, but it could show a messed up ratio between average bars per winning trade verses average bars per losing trade if there are no stop loss exits. Here are my average bars:

Average bars per winning trade 7.24
Average bars per losing trade 3.51 bars

This strategy complies with the golden rule of trading. Notice how it cuts losses quickly, at an average of 3.51 bars, and lets the profits run for an average of 7.24 bars.
So what does this all mean? It means this strategy has passed the historical strategy testing phase of strategy testing.

Step 3: Out-of-Sample Data (also called Walk Forward Testing)

Once you have tested your in-sample data and have determined that your strategy is worthy of continued testing, you can now test your strategy against the out-of-sample data. If you have not yet tested your in-sample data, do that before proceeding.

To test the out-of-sample data we use the most recent 6 months of data available that you reserved in step 1(a). In step 1(b) of this article, we talked about configuring TradeStation and covered entering commissions and slippage and using the look-inside-bar back-testing option, which must be used to run any performance report used in your strategy testing. Be sure you have configured TradeStation correctly before moving on.

Go into format symbol and change the date range to include ONLY the out-of-sample data date range that was NOT used during the strategy testing on in-sample data. This is referred to as testing on the out-of-sample data.

Begin with bringing up a TradeStation performance report on the out-of-sample data and review all the items that we discussed in step 2 above on this out-of-sample performance report. The closer it performs to the Step 2 in-sample data performance report, the more robust the strategy is. This suggests that the results were not from curve fitting and you have a good chance of having a viable strategy. This out-of-sample date range test is much more important than the strategy testing step on the in-sample-data for finding a successful strategy. It is a good idea to test multiple different out-of-sample date ranges, which is called Walk Forward Analysis.

Robustness: Perry J. Kaufman stated, Practically speaking, a robust trading strategy is one that produces consistently good results across a broad set of parameter (input) values applied to many different markets tested for many years.

If the strategy fails during this out-of-sample data test, do NOT optimize using your reserved out-of-sample data. This would defeat this vitally important step in strategy development. You can go back to your strategy and fix it, or else drop it and develop a new strategy idea.

One caveat if your strategy is capitalizing on a certain market condition, like the current volatility, and then you out-of-sample data test a non-volatile date range, it may not perform well, However in our next phase of testing, Live Forward Testing, it could prove to be successful since we are still in a volatile market. You must understand why your strategy works, under what market conditions it performs well, and in what market conditions it does not perform well.

Now that you have tested your out-of-sample data and your strategy is promising, you are ready to live forward test your strategy on the simulator account.

At this point you have configured TradeStation so your performance report will be accurate, you have tested your in-sample data and your out-of-sample data and your strategy still looks great. Now you are ready to live forward test your strategy on the simulator account.

Step 4: Live Forward Testing on the Simulator Account

During your live forward testing on the simulator, you want to verify that the live data feed entries and exits are similar to historical entries and exits. After you have made live data trades for a day, save the live trade list. Now reload this same chart so the strategy recalculates based on the historical for this same day. Record the historical trade list and compare the live entries and exits to the historical entries and exits. Are they the same or at least similar? Do you understand the differences and the impact your Live Data test says about your strategy?

Only by monitoring the program daily can performance be seen under real live market conditions. Continue Live Forward Testing on the simulator until you are totally comfortable that your strategy works on live data. Real time results will often be less profitable than your historic results. The key question is does the real time testing show that you have a profitable strategy that is worth trading?

Step 5: Real Live Trading Execution

Once you have done your due diligence and are comfortable with your strategy results on the simulator, you are ready to trade live. Since you are trading a brand new strategy with real money, start with a significantly reduced position sizing risk of 1/4 of 1% of your account equity at risk per your stop loss point per trade. Continue trading with minimal risk until you have verified everything is working properly within your new strategy during live order executions.

Once your strategy is making money in the live market, slowly over time begin to increase your position sizing risk. Move your risk upward from 1/4 of 1% toward 1%. Continue trading at 1% risk until you have several weeks to months of consistent trading performance. If you want to be aggressive and use more, you can continue to increase toward the maximum 3% of your account equity at risk per trade.

If you follow the 5 steps as outlined in this article, you will now be able to confidently strategy test any strategy idea that you have. Keep this article for reference so the next time you are inspired with a great idea, you will be able to prove it out, protect your TradeStation trading account, and have confidence in live trading your strategy.

Yes, You Can Time the Market. How It Works, And Why
MARCH 21, 2016

One of the most commonly cited maxims is that market timing is impossible. In fact, empirical evidence makes a compelling case that market timing is feasible and can yield substantial economic benefits. Whats more, we even understand why it works. For the typical portfolio investor, applying simple techniques to adjust their market exposure can prevent substantial losses during market downturns.

The Background From Empirical and Theoretical Research

For the last 50 years, since the work of Paul Samuelson, the prevailing view among economists has been that markets are (mostly) efficient and they follow a random walk. Empirical evidence to the contrary was mostly regarded as anomalous and/or unimportant economically. Over time, however, evidence has accumulated that market effects may persist that are exploitable. The famous 1992 paper published by Fama and French, for example, identified important economic effects in stock returns due to size and value factors, while Cahart (1997) demonstrated the important incremental effect of momentum. The combined four-factor Cahart model explains around 50% of the variation in stock returns, but leaves a large proportion that cannot be accounted for.

Other empirical studies have provided evidence that stock returns are predictable at various frequencies. Important examples include work by Brock, Lakonishok and LeBaron (1992), Pesaran and Timmermann (1995) and Lo, Mamaysky and Wang (2000), who provide further evidence using a range of technical indicators with wide popularity among traders showing that this adds value even at the individual stock level over and above the performance of a stock index. The research in these and other papers tends to be exceptional in terms of both quality and comprehensiveness, as one might expect from academics risking their reputations in taking on established theory. The appendix of test results to the Pesaran and Timmermann study, for example, is so lengthy that is available only in CD-ROM format.

A more recent example is the work of Paskalis Glabadanidis, in a 2012 paper entitled, Market Timing with Moving Averages. Glabadanidis examines a simple moving average strategy that, he finds, produces economically and statistically significant alphas of 10% to 15% per year, after transaction costs, and which are largely insensitive to the four Cahart factors.

Glabadanidis reports evidence regarding the profitability of the MA strategy in seven international stock markets. The performance of the MA strategies also holds for more than 18,000 individual stocks. He finds that:

The substantial market timing ability of the MA strategy appears to be the main driver of the abnormal returns.

An Illustration of a Simple Marketing Timing Strategy in SPY
It is impossible to do justice to Glabadanidiss research in a brief article and the interested reader is recommended to review the paper in full. However, we can illustrate the essence of the idea using the SPY ETF as an example.
A 24-period moving average of the monthly price series over the period from 1993 to 2016 is plotted in red in the chart below.
http://systemtradersuccess.com/wp-co.../03/Fig1-2.png

The moving average indicator is used to time the market using the following simple rule:
if Pt >= MAt invest in SPY in month t+1
if Pt < MAt invest in T-bills in month t+1
In other words, we invest or remain invested in SPY when the monthly closing price of the ETF lies at or above the 24-month moving average, otherwise we switch our investment to T-Bills.
The process of switching our investment will naturally incur transaction costs and these are included in the net monthly returns.
The outcome of the strategy in terms of compound growth is compared to the original long-only SPY investment in the following chart.
http://systemtradersuccess.com/wp-co.../03/Fig2-1.png
The marketing timing strategy outperforms the long-only ETF, with a CAGR of 16.16% vs. 14.75% (net of transaction costs), largely due to its avoidance of the major market sell-offs in 2000-2003 and 2008-2009.
But the improvement isnt limited to a 141bp improvement in annual compound returns. The chart below compares the distributions of monthly returns in the SPY ETF and market timing strategy.
http://systemtradersuccess.com/wp-co.../03/Fig3-1.png
It is clear that, in addition to a higher average monthly return, the market timing strategy has lower dispersion in the distribution in returns. This leads to a significantly higher information ratio for the strategy compared to the long-only ETF. Nor is that all: the market timing strategy has both higher skewness and kurtosis, both desirable features.
http://systemtradersuccess.com/wp-co.../03/Fig4-1.png

These results are entirely consistent with Glabadanidiss research. He finds that the performance of the market timing strategy is robust to different lags of the moving average and in subperiods, while investor sentiment, liquidity risks, business cycles, up and down markets, and the default spread cannot fully account for its performance. The strategy works just as well with randomly generated returns and bootstrapped returns as it does for the more than 18,000 stocks in the study.
A follow-up study by the author applying the same methodology to a universe of 20 REIT indices and 274 individual REITs reaches largely similar conclusions.

Why Marketing Timing Works
For many investors, empirical evidence compelling though it may be is not enough to make market timing a credible strategy, absent some kind of fundamental explanation of why it works. Unusually, in the case of the simple moving average strategy, such explanation is possible.

It was Cox, Ross, and Rubinstein who in 1979 developed the binomial model as a numerical method for pricing options. The methodology relies on the concept of option replication, in which one constructs a portfolio comprising holdings of the underlying stock and bonds to produce the same cash flows as the option at every point in time (the proportion of stock to hold is given by the option delta). Since the replicating portfolio produces the same cash flows as the option, it must have the same value, and since one knows the price of the stock and bond at each point in time one can therefore price the option. For those interested in the detail, Wikipedia gives a detailed explanation of the technique.

We can apply the concept of option replication to construct something very close to the MA market timing strategy, as follows. Consider what happens when the ETF falls below the moving average level. In that case we convert the ETF portfolio to cash and use the proceeds to acquire T-Bills. An equivalent outcome would be achieved by continuing to hold our long ETF position and acquiring a put option to hedge it. The combination of a long ETF position, and a 1-month put option with delta of -1, would provide the same riskless payoff as the market timing strategy, i.e. the return on 30-day T-Bills. An option in which the strike price is based on the average price of the underlying is known as an Arithmetic Asian option. Hence, when we apply the MA timing strategy we are effectively constructing a dynamic portfolio that replicates the payoff of an Arithmetic Asian protective put option struck as (just above) the moving average level.

Market Timing Alpha and The Cost of Hedging
None of these explanations is particularly contentious the theory behind option replication through dynamic hedging is well understood and it provides a largely complete understanding of the way the MA market timing strategy works, one that should satisfy those who are otherwise unpersuaded by arguments purely from empirical research.

There is one aspect of the foregoing description that remains a puzzle, however. An option is a valuable financial instrument and the owner of a protective put of the kind described can expect to pay a price amounting to tens or perhaps hundreds of basis points. Of course, in the market timing strategy we are not purchasing a put option per se, but creating one synthetically through dynamic replication. The cost of creating this synthetic equivalent comprises the transaction costs incurred as we liquidate and re-assemble our portfolio from month to month, in the form of bid/ask spread and commissions. According to efficient market theory, one should be indifferent as to whether one purchases the option at a fair market price or constructs it synthetically through replication the cost should be equivalent in either case. And yet in empirical tests the cost of the synthetic protective put falls far short of what one would expect to pay for an equivalent option instrument. This is, in fact, the source of the alpha in the market timing strategy.

According to efficient market theory one might expect to pay something of the order of 140 basis points a year in transaction costs the difference between the CAGR of the market timing strategy and the SPY ETF in order to construct the protective put. Yet, we find that no such costs are incurred.

Now, it might be argued that there is a hidden cost not revealed in our simple study of a market timing strategy applied to a single underlying ETF, which is the potential costs that could be incurred if the ETF repeatedly crosses and re-crosses the level of the moving average, month after month. In those circumstances the transaction costs would be much higher than indicated here. The fact that, in a single example, such costs that dont arise do not detract in any way from the potential for such a scenario to play out. Therefore, the argument goes, the actual costs from the strategy are likely to prove much higher over time, or when implemented for a large number of stocks.

All well and good, but this is precisely the scenario that Glabadanidiss research addresses, by examining the outcomes, not only for tens of thousands of stocks, but also using a large number of scenarios generated from random and/or bootstrapped returns. If the explanation offered did indeed account for the hidden costs of hedging, it would have been evident in the research findings.

Instead, Glabadanidis concludes:
This switching strategy does not involve any heavy trading when implemented with break-even transaction costs, suggesting that it will be actionable even for small investors.

Implications For Current Market Conditions
As at the time of writing, in mid-February 2016, the price of the SPY ETF remains just above the 24-month moving average level. Consequently the market timing strategy implies that one should continue to hold the market portfolio for the time being, although that could change very shortly, given recent market action.

Conclusion
The empirical evidence that market timing strategies produce significant alphas is difficult to challenge. Furthermore, we have reached an understanding of why they work, from an application of widely accepted option replication theory. It appears that using a simple moving average to time market entries and exits is approximately equivalent to hedging a portfolio with a protective Arithmetic Asian put option.

What remains to be answered is why the cost of constructing put protection synthetically is so low. At the current time, research indicates that market timing strategies consequently are able to generate alphas of 10% to 15% per annum.

Contributed by Jonathan Kinlay, Head of Quantitative Research and Trading at Systematic Strategies see www.jonathankinlay.com

Nice Bass. I am going to file this away as ammunition against those 'danged' EMH fundamentalists :-)

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Testing new Systems, not entirely finished yet but working on it ( I've got to get rid of that DD for the First 30 Trades).

Woooo!

Nice spike!

You are not wrong. Bass is getting aggressive.

PS Bass, what timeframe were those 300+ trades over?

And to think, they did not even give us the good stuff. The good stuff is never published, so this is very very encouraging to system traders.

I do that too Nick. If I smell a parabolic trend I may start checking stuff every 15min( instead of every hour) and will add on every position in profit.

Thanks J., keep in mind that this is not a finished Job yet, the TF is marked over the Equity Curve C. its H4 or 240 Min, the Equity curve tested is In and Out of Sample Data 80% and 20%, 10 Years In sample and 2 Out, still a long way to be viable .

The strategies is 1 Mean Reversion and 1 Trend Following tested with clean tick data which is tested for errors.

Yes, I have dabbled with this as well. My ongoing mission is to find out as near as possible when to switch from trending to ranging. I know that I will most likely never solve this, but it will be a nice little puzzle as I age.

Cheers Bass. I was blind temporarily. That's a great result mate. I will cross my fingers for you in finding a solution to a blend that resolves those first 30 trades to plug the gap. :-)