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<title>ChartMill Articles for Traders</title>
<link>http://www.chartmill.com/feed</link>
<description><![CDATA[Articles for Stock Market Traders]]></description>
<image><title>ChartMill Articles for Traders</title>
<link>http://www.chartmill.com/feed</link>
<url>http://www.rightbrainsolution.com/images/logo.gif</url>
</image>
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<title>some indicators added</title>
<link>http://www.chartmill.com/documentation.php?t=some indicators added</link>
<pubDate>Sun, 24 Feb 2013 00:00:00 +0100</pubDate>
<description><![CDATA[The following indicators where added to the charts:

PPO(Price Procent Oscillator)
Larry Williams' WVAD indicator
Moving Linear Regression Indicator&nbsp;

All of these were members requests ... feel free to request missing indicators!
Enjoy.
&nbsp;]]></description>
</item>
<item>
<title>MACD and Gap filters added to screener</title>
<link>http://www.chartmill.com/documentation.php?t=MACD and Gap filters added to screener</link>
<pubDate>Sun, 27 Jan 2013 00:00:00 +0100</pubDate>
<description><![CDATA[The screener now allows screening for up- and down gaps. These filters can be found under 'signal'. Check out an example here.
Also filters to the daily and weekly values of the MACD indicator have been added. Find an example here.]]></description>
</item>
<item>
<title>How to login</title>
<link>http://www.chartmill.com/documentation.php?t=How to login</link>
<pubDate>Mon, 07 Jan 2013 00:00:00 +0100</pubDate>
<description><![CDATA[
iframe
{
border: 1px solid black;
}


How to login

]]></description>
</item>
<item>
<title>How to create a watchlist</title>
<link>http://www.chartmill.com/documentation.php?t=How to create a watchlist</link>
<pubDate>Mon, 07 Jan 2013 00:00:00 +0100</pubDate>
<description><![CDATA[
iframe
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border: 1px solid black;
}

How to create a watchlist


]]></description>
</item>
<item>
<title>How to create and save chart settings</title>
<link>http://www.chartmill.com/documentation.php?t=How to create and save chart settings</link>
<pubDate>Mon, 07 Jan 2013 00:00:00 +0100</pubDate>
<description><![CDATA[
iframe
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border: 1px solid black;
}


How to save chart settings?



]]></description>
</item>
<item>
<title>How to create and save a new stock screen</title>
<link>http://www.chartmill.com/documentation.php?t=How to create and save a new stock screen</link>
<pubDate>Mon, 07 Jan 2013 00:00:00 +0100</pubDate>
<description><![CDATA[
iframe
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border: 1px solid black;
}


How to create and save a new screen?

]]></description>
</item>
<item>
<title>Screener and charts working together</title>
<link>http://www.chartmill.com/documentation.php?t=Screener and charts working together</link>
<pubDate>Mon, 07 Jan 2013 00:00:00 +0100</pubDate>
<description><![CDATA[
iframe
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border: 1px solid black;
}


Screener and Charts working together ...

]]></description>
</item>
<item>
<title>Using the position sizing module</title>
<link>http://www.chartmill.com/documentation.php?t=Using the position sizing module</link>
<pubDate>Mon, 07 Jan 2013 00:00:00 +0100</pubDate>
<description><![CDATA[
iframe
{
border: 1px solid black;
}


Position Sizing Tool


]]></description>
</item>
<item>
<title>Annotations in charts and screener</title>
<link>http://www.chartmill.com/documentation.php?t=Annotations in charts and screener</link>
<pubDate>Mon, 07 Jan 2013 00:00:00 +0100</pubDate>
<description><![CDATA[
iframe
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border: 1px solid black;
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Annotations in Chartmill

]]></description>
</item>
<item>
<title>How to use Chartmill</title>
<link>http://www.chartmill.com/documentation.php?t=How to use Chartmill</link>
<pubDate>Sun, 06 Jan 2013 00:00:00 +0100</pubDate>
<description><![CDATA[
iframe
{
border: 1px solid black;
}


How to login



How to create a watchlist



How to save chart settings?



How to create and save a new screen?



Screener and Charts working together ...



Annotations in Chartmill



Position Sizing Tool


]]></description>
</item>
<item>
<title>New Annotations and Position Sizing Tool</title>
<link>http://www.chartmill.com/documentation.php?t=New Annotations and Position Sizing Tool</link>
<pubDate>Sun, 06 Jan 2013 00:00:00 +0100</pubDate>
<description><![CDATA[New annotations and position sizing tools have been added to the basic charts and the stockscreener. Documentation can be found here.]]></description>
</item>
<item>
<title>Documentation on breadth indicators added.</title>
<link>http://www.chartmill.com/documentation.php?t=Documentation on breadth indicators added.</link>
<pubDate>Wed, 07 Nov 2012 00:00:00 +0100</pubDate>
<description><![CDATA[Find it here.]]></description>
</item>
<item>
<title>Buy Dont Hold Market Model added</title>
<link>http://www.chartmill.com/documentation.php?t=Buy Dont Hold Market Model added</link>
<pubDate>Mon, 05 Nov 2012 00:00:00 +0100</pubDate>
<description><![CDATA[The market model from the book 'Buy Don't Hold' has been added to the site. You can find it here.]]></description>
</item>
<item>
<title>RSI(2) and RSI(14) added to screener</title>
<link>http://www.chartmill.com/documentation.php?t=RSI(2) and RSI(14) added to screener</link>
<pubDate>Fri, 19 Oct 2012 00:00:00 +0200</pubDate>
<description><![CDATA[The screener allows filtering based on RSI(14) or RSI(2) values now.
Enjoy!]]></description>
</item>
<item>
<title>Screen for 10 or 20 day highs and lows</title>
<link>http://www.chartmill.com/documentation.php?t=Screen for 10 or 20 day highs and lows</link>
<pubDate>Wed, 10 Oct 2012 00:00:00 +0200</pubDate>
<description><![CDATA[The screener has some new 'signals' added:

new 10 day high today.
new 10 day low today.
new 20 day high today.
new 20 day low today.

This allows screening for stocks making new high or lows in a shorter timeframe than the classic 52 week high or low.
&nbsp;]]></description>
</item>
<item>
<title>True Strength Index Indicator added</title>
<link>http://www.chartmill.com/documentation.php?t=True Strength Index Indicator added</link>
<pubDate>Sun, 30 Sep 2012 00:00:00 +0200</pubDate>
<description><![CDATA[We added the True Strength Index to our list of indicators. These indicator is also known as the&nbsp;Ergodic" indicator.&nbsp;
Enjoy]]></description>
</item>
<item>
<title>Store up to 200 items in your watchlists</title>
<link>http://www.chartmill.com/documentation.php?t=Store up to 200 items in your watchlists</link>
<pubDate>Thu, 27 Sep 2012 00:00:00 +0200</pubDate>
<description><![CDATA[The maximum amount of items in watchlists was increased to 200.
Learn more about watchlists here.]]></description>
</item>
<item>
<title>Performance from open in the screener</title>
<link>http://www.chartmill.com/documentation.php?t=Performance from open in the screener</link>
<pubDate>Mon, 17 Sep 2012 00:00:00 +0200</pubDate>
<description><![CDATA[The 'performance' filter in the screener allows filtering by performance from the open today. Sometimes a stock is losing compared to the previous close, but still making a strong move from the open. The new filters allow finding such stocks.
Click here for an example.
&nbsp;
&nbsp;]]></description>
</item>
<item>
<title>New Homepage</title>
<link>http://www.chartmill.com/documentation.php?t=New Homepage</link>
<pubDate>Sun, 16 Sep 2012 00:00:00 +0200</pubDate>
<description><![CDATA[We updated our homepage.
The new homepage provides lots of screener usage examples.
Enjoy!]]></description>
</item>
<item>
<title>Integrated Stocktwits and Twitter</title>
<link>http://www.chartmill.com/documentation.php?t=Integrated Stocktwits and Twitter</link>
<pubDate>Sun, 02 Sep 2012 00:00:00 +0200</pubDate>
<description><![CDATA[More information can be found here.]]></description>
</item>
<item>
<title>Bull and Bear Flag Detection</title>
<link>http://www.chartmill.com/documentation.php?t=Bull and Bear Flag Detection</link>
<pubDate>Sun, 19 Aug 2012 00:00:00 +0200</pubDate>
<description><![CDATA[Chartmill has support for finding stocks that show a bull or bear flag pattern. The bull or bear flag can also be shown on the chart by adding the 'Chartmill Flag' overlay indicator.
Bull and Bear Flags in the screener
In the stockscreener, &nbsp;in the 'signal' field&nbsp;on the 'general tab', one can filter on the stocks showing a flag pattern by selecting one of the flag related filters:
&nbsp;

Once selected, the screener will display a list of stocks showing a 'bull flag', 'bear flag', 'weekly bull flag' or 'weekly bear flag'.
Bull and Bear Flags on the Charts.
To visualize the bull or bear flag on the chart, select the 'Chartmill Flag' overlay indicator. The flag will be shown on the chart. For example:

Examples.
Bull flags in stocks with average volume above 500K.
Bear flags in stocks with average volume above 500K.

Weekly Bull flags in stocks with average volume above 500K.
Weekly Bear flags in stocks with average volume above 500K.

&nbsp;]]></description>
</item>
<item>
<title>Flag Detection Support</title>
<link>http://www.chartmill.com/documentation.php?t=Flag Detection Support</link>
<pubDate>Sun, 19 Aug 2012 00:00:00 +0200</pubDate>
<description><![CDATA[Chartill now supports bull and bear flag detection. See here for more info.]]></description>
</item>
<item>
<title>Chartmill Channels featured in Stocks &amp; Commodities</title>
<link>http://www.chartmill.com/documentation.php?t=Chartmill Channels featured in Stocks &amp; Commodities</link>
<pubDate>Tue, 12 Jun 2012 00:00:00 +0200</pubDate>
<description />
</item>
<item>
<title>Quotes View: view pre-and after market quotes.</title>
<link>http://www.chartmill.com/documentation.php?t=Quotes View: view pre-and after market quotes.</link>
<pubDate>Fri, 08 Jun 2012 00:00:00 +0200</pubDate>
<description><![CDATA[A new view was added to the basic charts and screener tools: the 'quotes' view. The view shows the last quote, as well as the pre- or after market last quote. It also shows some basic data like the relative strength, the short and longer term trend and performance numbers over multiple periods. &nbsp;
An example can be seen here.]]></description>
</item>
<item>
<title>Several Extensions to Screener</title>
<link>http://www.chartmill.com/documentation.php?t=Several Extensions to Screener</link>
<pubDate>Fri, 08 Jun 2012 00:00:00 +0200</pubDate>
<description><![CDATA[Several extensions were made to the&nbsp;stock screener:

Scan for short and longer term trends. For example: stocks with a minimum average volume of 500K where both short and long term trend are positive.
Scan for the current day Effective Volume. For example: stock being bought by large players today.
Scan for todays Top Gainers and Top Losers (= Signal). For example: top gainers today.
Scan for pre- or after market Movers (= Signal). For example: after market gainers today.

Enjoy!]]></description>
</item>
<item>
<title>Unification of Basic Charts and Screener</title>
<link>http://www.chartmill.com/documentation.php?t=Unification of Basic Charts and Screener</link>
<pubDate>Fri, 08 Jun 2012 00:00:00 +0200</pubDate>
<description><![CDATA[The 'basic charts' and the 'screener' have been unified. The exact same chart settings are now available in the screener and all views are shared between the 2 tools. Comparen to the previous versions there are a number of advantages to this:

Sorting of results is available now in the basic charts.
Filtering of tickers is available now in the basic charts.
Other views ( table, quickcharts, ...) are now also available in the basic charts.
The charts-view, which is the default in the basic charts is now also available in the screener.
In the screener, you can load your saved chart settings.
In the screener, you can load your watchlists and do further filtering or sorting on the elements in the list.
... and of course, both tools have a consistant interface now, which should improve usability.

Enjoy!]]></description>
</item>
<item>
<title>Better,Faster,Longer Intraday Charts</title>
<link>http://www.chartmill.com/documentation.php?t=Better,Faster,Longer Intraday Charts</link>
<pubDate>Sun, 27 May 2012 00:00:00 +0200</pubDate>
<description><![CDATA[Various improvements have been made to our intraday charts in the last months:

Intraday data is available during the trading day now for US stocks. The data is maximum 15 minutes delayed.&nbsp;
Indicators derived from Intraday data, such as effective volume, are also available during the trading day.
More intraday data history is available for intraday charts: up to 5 months of data history can be used on a chart.
New intraday timeframes have been added. There is now support for 1,2,5,10,15,30 minute charts, as well as hourly, 2 and 4 hourly charts.
The intraday charts are loading a lot faster now than before.

Enjoy!&nbsp;]]></description>
</item>
<item>
<title>Chart Type: Cumulative data</title>
<link>http://www.chartmill.com/documentation.php?t=Chart Type: Cumulative data</link>
<pubDate>Sun, 20 May 2012 00:00:00 +0200</pubDate>
<description><![CDATA[The basic charts allows you to view data 'cumulated'. The cumulative view can be obtained by selecting 'Cumulative' as chart type ( where the default is 'Candles').
This feature was added mainly to be used with the new breadth indices. For example: the 'NYSE advance - decline' index ($ADDN) shows for each day the difference between the number of advancing issues and the number of declining issues. When you show this data in cumulated mode, as can be seen here, &nbsp;you get the 'Advance Decline Line' ( or ADL ). The Advance decline line is described by investopedia as:
"A technical indicator that plots changes in the value of the advance-decline index&nbsp;over a&nbsp;certain time period.&nbsp;Each&nbsp;point on the chart is calculated by taking the difference between the number of advancing/declining issues and&nbsp;adding the result to the previous period's value" ( link ).
]]></description>
</item>
<item>
<title>Breadth Indices added.</title>
<link>http://www.chartmill.com/documentation.php?t=Breadth Indices added.</link>
<pubDate>Sat, 19 May 2012 00:00:00 +0200</pubDate>
<description><![CDATA[Lots of breadth indices were added. An overview can be found here.]]></description>
</item>
<item>
<title>Tim  Bourquin (Trader Interviews)</title>
<link>http://www.chartmill.com/documentation.php?t=Tim  Bourquin (Trader Interviews)</link>
<pubDate>Sat, 11 Feb 2012 00:00:00 +0100</pubDate>
<description><![CDATA[Learn from Real Traders

Tim Bourquin is the co-founder of both the Online Trading Expo (now
Traders Expo) and the Forex Trading Expo. While a police officer with 
LAPD, Tim was trading the stock and currency markets by morning and 
arresting criminals by night. When he went looking for a convention 
for traders to learn more about how other traders were approaching the 
markets, he couldn't find any. 
 
So in 1999, along with a business partner, he started an annual 
convention and tradeshow for online traders and investors. Those 
events continue to be the premier events for active retail traders 
with shows in New York, Las Vegas and Los Angeles. 
 
After speaking with countless traders throughout the past 14 years as 
a trader himself, Tim realized that the best way to learn how to make 
money trading was to ask those who were already doing it every day. 
Tim set out to find the best in the business and ask them exactly how 
they made their money. Some people talked to him and others refused, 
but through persistence, he was slowly able to interview hundreds of 
traders about their strategies. In 2006 Tim founded 
TraderInterviews.com, an online media site featuring those frank 
discussions. 
 
Each week Tim interviews successful full-time traders and asks them 
tough questions about the strategies they employ, the software they 
use, and how they became confident in the markets. 
 
Watch the video where he talks about going from cop to trader here: 
TraderInterviews.com]]></description>
</item>
<item>
<title>Begin To Trade with ChartMill</title>
<link>http://www.chartmill.com/documentation.php?t=Begin To Trade with ChartMill</link>
<pubDate>Sat, 11 Feb 2012 00:00:00 +0100</pubDate>
<description><![CDATA[ChartMill Documentation Center for Traders
Welcome at the ChartMill Documentation Center for Traders.  Here you can find documentation on the supported indicators and screens.
Please subscribe to our newsletter for regular updates.
Enjoy! The ChartMill Team.
Newsletters

Very First Chartmill Newsletter!
How To Scan For Mean Reversion Setups
]]></description>
</item>
<item>
<title>Pascal Willain</title>
<link>http://www.chartmill.com/documentation.php?t=Pascal Willain</link>
<pubDate>Sat, 11 Feb 2012 00:00:00 +0100</pubDate>
<description><![CDATA[Biography will be added soon.]]></description>
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<item>
<title>Pradeep Bonde (Stockbee.biz)</title>
<link>http://www.chartmill.com/documentation.php?t=Pradeep Bonde (Stockbee.biz)</link>
<pubDate>Mon, 06 Feb 2012 00:00:00 +0100</pubDate>
<description><![CDATA[Methods Trump Markets
Pradeep Bonde is the owner, teacher and motivator behind the trading website stockbee.biz
Pradeep, also called Easyguru by his members, trades, develops and explains various easy-to-understand and clear cut systems based on market anomalies and stock life cycle profiling. Before becoming a professional full time trader, Pradeep worked for over 15 years in India and USA for companies like Lintas, DHL Wordwide Express and Blue Dart India.

A Free Goldmine of Trading Articles
Pradeep offers some great free content on his blog stockbee.blogspot.com
We recommend interested traders to read the massive amount of articles before jumping into the markets.




7 Concepts that can make you a better trader.
How to develop Trading Expertise?
The Little Secret to Trading Succes
What it Takes to Become a Good Trader



Do you have a burning desire to be profitable?



]]></description>
</item>
<item>
<title>My Chartmill</title>
<link>http://www.chartmill.com/documentation.php?t=My Chartmill</link>
<pubDate>Wed, 01 Feb 2012 00:00:00 +0100</pubDate>
<description><![CDATA[Managing Watchlists

Registered users can:


Save and load custom chart configurations.
Save and load custom screener configurations.
Create and manage watchlists.
Add and remove items to watchlists.
set custom defaults for the charts and screener applications.


Saving and loading chart/screener settings.

Both the chart and screener application have a save icon in the top right corner. When this icon is clicked, the user will be prompted for a name to save the current settings.  Once these settings are saved, they can be loaded again by selecting the saved configuration by name from the 'load settings' dropdown menu.

Creating and managing watchlists.
Both below and above the screener or charts output the following dialog is availabe for managing watchlists:

Each item on the page has a select box associated with it. The user should select a number of items and then he or she can select a watchlist to which the items should be added to.&nbsp;
On the right hand site of the dialog there is an icon that allows creating a new empty whatchlists. The icons next to the new watchlist icon allow selecting all items or clearing the selection.

Managing watchlists and settings.
The 'my account' page allows managing watchlists and saved settings configurations. The page present an overview of all watchlists and saved configurations. This page allows:

removing items from a watchlist.
renaming a watchlist.
opening a watchlist.
removing the complete watchlist.
renaming saved chart or screener settings.
removing saved chart or screener settings.
opening saved chart or screener settings.
setting default chart or screener settings.


The 'default settings' are the settings that will be loaded automatically when a user enters the charting or screener application.

Getting started.
To use these features you must first&nbsp;register. After registration you will get an email containing a link that will activate your account. Once your account is activated you can&nbsp;login&nbsp;and start playing.]]></description>
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<item>
<title>Monest Value Indicator (backtesting)</title>
<link>http://www.chartmill.com/documentation.php?t=Monest Value Indicator (backtesting)</link>
<pubDate>Tue, 31 Jan 2012 00:00:00 +0100</pubDate>
<description><![CDATA[A New Breed of Oscillator

In the first article of this series, we developed an oscillator that wasn&rsquo;t prone to stickiness in the overbought and oversold regions and doesn&rsquo;t have the lag that oscillators have which are built on moving averages. In this article, as in the previous one, we&rsquo;re studying it&rsquo;s usage and usefulness.

Tekst: Dirk Vandycke

The Monest Value Indicator (MVI) is a short term oscillator trying to capture overbought and oversold setups. As a consequence of how it&rsquo;s built, by a statistical normalization procedure, it&rsquo;s unlike most other well known oscillators. For one thing, it doesn&rsquo;t have the stickiness that keep other range bound oscillators in overbought and oversold zones while a strong trend develops. But also the lag of moving average based oscillators isn&rsquo;t an issue with the MVI. What&rsquo;s more, having no parameters in its equation, it is totally objective by definition. This still leaves us with a necessity for an objective interpretation. Let&rsquo;s find out if our indicator is of any real and statistical significant usefulness.

Recap
Anything less than -8 (two standard deviations) is considered oversold or short term undervalued, while overbought and short term overvalued is indicated by any value greater than 8. As this might give us too few signals in back tests we can lower limits to anything outside of the [-7,+7] interval to increase our number of samples, if necessary.

In the previous article we proved that buying at undervaluation might aid any long trade staying largely out of initial loss before takeoff. Likewise, short trades can be more successful, not needlessly getting stopped out, when entered in the presence of an overvalued MVI. In figure 3 different pure entry systems (i.e. based on non technical entries) are compared with entry on undervaluation, during a bull market. The undervalued MVI entry clearly seems to make a difference.

This sets stage for our MVI as an add-on to existing trading systems or existing indicators. Since no indicator is a complete trading system in its own right. In fact, the minute most indicators make it to the back tests, results seem to get disappointing very often very fast. For the record, no set of indicators is a complete system either. Successful trading needs careful risk management and consistent money management discipline. However in this article we&rsquo;re going to look into the effect of the MVI when added to other indicators or entry systems. The Monest Value indicator as a catalyst.

Pattern filter
First we want to assess the value of the MVI indicator as a pattern filter. To this purpose we use an objectively defined pattern and see if we can pimp it with our freshly discovered oscillator. As an objectively defined pattern we chose a key reversal bar, defined as a bar opening below the previous bar&rsquo;s close but closing above the previous bar&rsquo;s high. In our back test we round up all those key reversal days and have a look at the average profit for each day forward after such a bar.

Figure 4 shows an average profit of about 3%, 30 days after a key reversal day was taken. After 30 days the effect of a key reversal day seems to wear off. In the first 5 to 7 days the chart shows an average loss never amounting more than 1%.

When the key reversal days get filtered with a Monest Value indication on top, allowing only those key reversal day entries to be taken when they were accompanied by an MVI less than -4, the number of valid entry signals drops with about 50%.

However, the average profit on 30 days, almost doubles. This can also be seen in the chart of figure 4. Of course adding an additional criteria to the signals taken, can never change anything about the wearing off. After about one month, the signal effect fades away. What&rsquo;s more is that the small (average) adversity in the beginning of a key reversal day trade, seems to be halve in length (only about 3 days instead of up to 7).

System filter
A key reversal day is just a pattern. We see patterns everywhere. We&rsquo;re evolutionary shaped to do this. In evolutionary terms, it pays off to assume a tiger where there isn&rsquo;t one (false positive). That shaping happened a really long time ago, because even a horse is scared of garden hose (assuming it&rsquo;s a snake). So one of our common ancestors already must have developed this trait. However, it takes far more than a pattern to have a complete trading system. The pattern on which to enter a trade might well be of lesser importance.

But if entries can be fine tuned by adding our MVI as an additional filter, it might certainly be a good idea to put the idea to the test of adding an MVI filter to a complete trading system. We backtested a really easy, but totally objectively defined, trend following system that only can enter a trade when the 25 bar simple moving average is above the 75 bar simple moving average. Trades are entered when prices break above the highest high of the previous five bars. That is actually called a 5 period Donchian Channel breakout.

Again, we wanted to see what happens, on average, with price, up to 50 days after entry. The result is shown in figure 5. And they&rsquo;re far from impressive. It takes the average trade about 30 days to become, after all, only marginally profitable.

Next we superpose the trend following trading system with an MVI &lt; -8 filter, meaning only those 5 bar Donchian Channels breakouts are taken when the Monest Value Indicator has a value below -8, a sign of short term temporary undervaluation. Of course the moving averages requirement also still holds.

The result is quite impressive. First, the average trade has far less initial drawdown, both in terms of duration as well as in terms of size. The maximum drawdown is about half the original drawdown, while the days the average trade is in losing territory are minimized to only about 5 to 6 days (from almost 30 in the original, not enhanced, system). Secondly, the average trade has an overall much clearer trend. And finally, compared 50 days upon entry, the average trade for the enhanced trend following trade system has up to 5 times more profit.

Conclusion
In our search towards a better oscillator that produces sharper and more objective signals with least lag, we build the Monest Value Indicator based on the concept of context. Short term valuation perception being mainly lead by most recent prices, we used statistical normalization to capture an objective interpretation of the idea. However, the distribution in bull and bear markets will be skewed from perfect normal, meaning that under- and overvaluation, now fixed at -8 and +8, could be calibrated onto the real distribution. So, in a bull market, undervalued probably will have a slightly higher threshold than -8. Likewise, in a bear market, overvaluation perhaps could be calibrated a little lower. But as far as different financial instrument were studied (futures, commodities, equities, &hellip;) there were no family specific, nor product individual differences. So a certain stock (of a certain company) has not a different value distribution, nor a specific one.

We conducted three back test experiments. One experiment was aimed at proving the standalone quality of the Monest Value Indicator in its own right. The results from this experiment are repeated in figure 1. We compared buying undervaluation with buying at random, buying overvaluation, buying on a dollar cost averaging basis and a combination of random entry with undervaluation. An experiment which made more than a nice case for the quality of our new bred oscillator.

In a second and third experiment we tried to answer the question if the Monest Value Indicator oscillator could act a catalyst to enhance both pattern performance and system performances. And though two experiments might be little to make a general case, they seem very promising, at least justifying further research on our new oscillator.

To that purpose this indicator is freely available at www.chartmill.com, both on the charts as well as in its screener. So it&rsquo;s possible to add screening constraints on the MVI value (greater of smaller than a certain level), giving, for instance, only undervalued equities and ETF&rsquo;s in your custom screen based on other criteria. So you can add the Monest Value Indicator easily to any classic customized scan you are used to. Making the use of the MVI as a catalyst a piece of cake while trimming down long scan result lists.

Of course any additional research data to the advantage or disadvantage of this oscillator, as well as any question, remarks or suggestions are welcome on the homepage of this website or with dirk @ monest.net. More information about building dynamic indicators can also be found in the book 'Dynamic Trading Indicators'.


Figure 3: Different entry strategies compared to an undervalued MVI entry system.
Buying while short term undervalued (MVI&lt;-7) seems to pay off against other entry strategies. All systems are compared to a random entry system.

Figure 4: The Monest Value Indicator as a Pattern add-on
Effect on the average return up to 50 days after a key reversal day when only the key reversals are taken that are accompanied by an MVI&lt;-8. This demonstrates the Monest Value Indicator as a pattern catalyst. 


Figure 5: The Monest Value Indicator as a trade system booster
Effect on the return of a trend following trade system when only entries are taken when the Monest Value Indicator shows undervaluation.

This article was originally published in the January 2012 issue of Traders' Magazine. You can download the pdf here]]></description>
</item>
<item>
<title>Chartmill Accounts</title>
<link>http://www.chartmill.com/documentation.php?t=Chartmill Accounts</link>
<pubDate>Wed, 25 Jan 2012 00:00:00 +0100</pubDate>
<description><![CDATA[Make watchlists and save screeners!

Chartmill.com proudly presents: 'my chartmill'.]]></description>
</item>
<item>
<title>Using Monest Value Indicator</title>
<link>http://www.chartmill.com/documentation.php?t=Using Monest Value Indicator</link>
<pubDate>Sat, 31 Dec 2011 00:00:00 +0100</pubDate>
<description><![CDATA[A New Breed of Oscillator

In the previous article we developed an oscillator that wasn&rsquo;t prone to stickiness in the overbought and oversold regions and doesn&rsquo;t have the lag that oscillators have which are built on moving averages. Next up, we&rsquo;re going to study it&rsquo;s usage and usefulness.


Tekst: Dirk Vandycke

The Monest Value Indicator (MVI) is a short term oscillator trying to capture overbought and oversold setups. As a consequence of how it&rsquo;s built, by a statistical normalization procedure, it&rsquo;s unlike most other well known oscillators. For one thing, it doesn&rsquo;t have the stickiness that keep other range bound oscillators in overbought and oversold zones while a strong trend develops. But also the lag of moving average based oscillators isn&rsquo;t an issue with the MVI. What&rsquo;s more, having no parameters in its equation, it is totally objective by definition. This still leaves us with a necessity for an objective interpretation. Let&rsquo;s find out if our indicator is of any real and statistical significant usefulness.

Value consensus
In the previous article we introduced the Monest Value Indicator (MVI) and how it is constructed. Due to its normalizing nature it can be used in the same way for any time series.

Any value between -4 and +4 indicates a strong short term consensus regarding current pricing and fair value. Between 4 and 8 we have slight overvaluation and anything higher than 8 indicates plain overpricing. It&rsquo;s very rare to see values much higher than 8 with this indicator, even though this not a range bound oscillator. Given that value for the MVI is rarely much higher than 9, this would indicate major overvaluation with respect to short term consensus.

Likewise we define minor undervaluation in regard to short term consensus between -4 and -8 and plain undervaluation below -8. Anything lower than -9 would be considered major undervaluation.

These levels are not written in stone, but nevertheless they are deducted from the standard deviation intervals we have in the statistical&nbsp; normalization process.

For the purpose of finding the advantages of the MVI, we&rsquo;ll consider the violations of 8 and -8 of primary interest to us. In other words, we&rsquo;ll look into what happens if we focus on just those overvaluation and undervaluation signals. Anything still further from zero would give us far to less signals to obtain a meaningful sample space.

Right, right away
Do you remember a trade where at first you have this small adverse movement against you, before the trade finally takes off? I think every trader knows what I mean. In fact, when you open a position, costs are made, putting the position in the red from the very beginning. If you can picture that, than the trade where you get stopped out before it materializes isn&rsquo;t hard to imagine either, isn&rsquo;t it? Wouldn&rsquo;t it just be cool if we could do something about this?

Look at figure 1. It&rsquo;s an example of how a setup accompanied by a short term overvaluation (MVI&gt;4) can be postponed until the MVI drops below -8. What we did is, of course after the facts, see how the trade would have turned out if it would have been entered on the highest MVI value of the week (5.53) in comparison to what would have happened if we bought at the lowest MVI value that week (-8.43).The effect is a far lesser adverse excursion of the trade after its initialization. What&rsquo;s more, the postponed entry can make the difference in getting stopped out and loosing the setup out of sight, and having a very successful trade, riding the subsequent trend in the case of the postponed entry. This is just an example of course. But It&rsquo;s a start to see if the idea of just taking the undervalued signals of a system has merit. At least, the idea is sound, because if you only take setups that are undervalued, you might have a far better profit/loss ratio on the trade. And we just might have better entries leaving us with less initial adverse recursion before trade takes off. Which, in turn, might even lead to less losers because less setups are taken. We only take those setups where an undervaluation is present or turns up soon enough after the setup signal. And because of the less countermovement, fewer trades might get stopped out.

So, what if we opened long positions only on moments of short term undervaluation and, likewise, short positions only when short term consensus of value is overrated. MVI to the rescue. To prove the possible added value of our indicator here, we pull out our Monte Carlo simulator and look at a whole lot of random entries where the MVI went below -8 within the next week (remember our MVI is a short term value indicator). One could point out the fact we only take the random entries where the MVI goes below -8 within the next week, as a problem. What with all the random picks where the MVI doesn&rsquo;t go below -8 in the five days that follow. Well we don&rsquo;t have to take those setups. At this point we don&rsquo;t want to prove that we have a better system with the MVI constraint added. We&rsquo;ll get into that next. For the moment, we just want to see what happens with our initial adverse recursion of trades when we wait for undervaluation, or, if that doesn&rsquo;t happen, don&rsquo;t take the trades at all.

Figure 2 displays an average trade (of thousands of random entries) in a few months of a strong bull market where an undervaluation (MVI&lt;-8) followed within the next week. In the same figure one can see the difference if we just wait for the MVI to go below -8 before taking an entry. The difference is clear. The random entry has an initial drawdown of up to 10%. On the position, that is. Remember that, through position sizing, one might invest 20% of one&rsquo;s portfolio while taking only 1% risk on that portfolio. Which, in turn, might stand for a 10% loss on the position (but only 1% on the portfolio). Nevertheless, there&rsquo;s hardly any drawdown left when the random entries are postponed until the MVI goes below -8 (or the setup gets canceled if it doesn&rsquo;t go below -8 in the next week). This simulation didn&rsquo;t take costs into account. But the point for using our MVI is clearly made. I won&rsquo;t go into a similar simulation for a bear market, using &nbsp;short positions. Results were indicative of a similar effect taking place.

Of course this is based on what we see for an average trade. It doesn&rsquo;t say anything about what could happen with any specific trade taken.

Let&rsquo;s dig deeper.
With the promising results from our simple preliminary test, I wanted to dig deeper into the possible benefits of the Monest Value Indicator (MVI). So I came up with the following test setup.

Let&rsquo;s see what our average trade would look like under different entry strategies. One of which will be our undervalued filter based on an MVI value below -7. In contrast, as the opposite entry extreme, we have an overvalued entry strategy, buying only when the MVI is greater than 7. Our third entry strategy is a dollar cost averaging one, buying every first day of each month. Of course for the purpose of comparison, we still have our average random entry trade and also our random entry trade only made when the MVI is below -7. Hence we compare 5 strategies to a mere random entry strategy. Again the test is run in a pure bull market.

All six strategies were compared on a 50 day basis after each entry. The result of which can be seen in figure 3.
The entry systems had, on average, the following MVI value on entry:


Random Entry: -0.01
Undervalued: -7.70
Overvalued: 7.53
Dollar Cost Averaging: -0.84
Undervalued Random Entry -8.09


The period tested being a bull market, still doesn&rsquo;t seem to help our overvalued entry system, which is barely able to make money with several large periods in the red zone. The dollar cost averaging entry system performs a lot better, even better than the random entries system. Exactly what could be expected, because dollar cost averaging benefits from buying more shares at lower prices and less shares at higher prices. The best performing system is, as we hoped, the entry system buying only on undervaluation. It performs best over almost the entire period.

Combining undervaluation with random entry (taking only the random entries when the MVI is below -7), does seem to improve the random entry system. However the equity curve becomes more volatile. Which could be the reason it losses from merely random entry by a big performance drop at the end.

The overall conclusion is that buying on undervaluation, as indicated by the Monest Value Indicator, results in a better performance and far less time of the trade being spent in the red.

Conclusion
If time is money, I would add that &lsquo;timing is money&rsquo;. There&rsquo;s evidence supporting the idea that our Monest Value Indicator (MVI for short) has true added value not only in measuring short term valuation consensus, but also might be a valuable system add-on to lower the frequency of losers by lowering the frequency of trades, dumping the ones with a poor profit/loss ratio. It seems as if the initial drawdown on trades can be kept smaller, meaning fewer positions are getting stopped out because of initial adverse recursion. In our final article on this new indicator we&rsquo;ll go into its effect as a possible trade system enhancer (taking only the undervalued setups).

As a reminder. This indicator is available at www.chartmill.com, both on the charts as well as in its screener. So it&rsquo;s possible to add screening constraints on the MVI value (greater of smaller than a certain level), giving, for instance, only undervalued equities and ETF&rsquo;s in your custom screen based on other criteria.

&nbsp;


Figure 1: An overvalued (or at least not undervalued) versus an undervalued buy.

What happens when we buy a setup not on the signal but on the first undervaluation (MVI&lt;-8) after that. In this chart two entries are compared, coinciding with the highest and lowest MVI value for that week.


Figure 2: Profit chart of an average random buy versus a postponed random buy.

Comparison of an average random buy (during a bull market) and what happens if the buy is postponed until a MVI undervaluation (&lt;-8) is recorded or, if that doesn&rsquo;t happen within a week, the setup is aborted.


Figure 3: Different entry strategies compared to an undervalued MVI entry system.

Buying while short term undervalued (MVI&lt;-7) seems to pay off against other entry strategies. All systems are compared to a random entry system

This article was originally published in the December 2011 issue of Traders' Magazine. You can download the pdf here]]></description>
</item>
<item>
<title>Webreview Traders Magazine (Chartmill)</title>
<link>http://www.chartmill.com/documentation.php?t=Webreview Traders Magazine (Chartmill)</link>
<pubDate>Sat, 15 Oct 2011 00:00:00 +0200</pubDate>
<description><![CDATA[Review of CM in TM


The website www.chartmill.com is a Technical Analysis website created by traders for traders. Its main features are charting applications, a stock screener and a sector analysis tool. Chartmill supports most of the classical technical analysis indicators along with some state-of &ndash;the&ndash;art indicators and concepts like Pocket Pivots, Effective Volume, Relative Strength and Anchored VWAPs (MIDAS curves). 

First, we'll discuss some of these concepts. After that, we'll have a look at the screener and charting applications. 

Relative strength

Relative Strength is available in different forms at chartmill.com. Two relative strength related indicators are available in the charts and screener: 


Mansfield Relative Strength: this compares the performance of the stock to the S&amp;P500 index. This form of relative strength is mostly known from Stan Weinsteins Classic &ldquo;Secrets for Profiting in Bull and Bear Markets'. 

Dorsey Relative Strength: this indicator also compares the performance of the stock to the S&amp;P500, but in a slightly different manner. This form of Relative Strength was used in &ldquo;Point and Figure Charting&rdquo; by Thomas Dorsey. 



Besides these two indicators, there is also the Relative Strength Ranking Number. Here, each stock gets assigned a number between 0 and 100, indicating the percentage of stocks that is outperformed by this stock. For example: a stock with a Relative Strength Ranking number of 92 outperformed 92% of all stocks in the last year. 

This latter form is highly usefull in the screener where one can filter stocks based on their ranking number. 

Strong Stocks

The screener supports the concept of 'strong stocks', which are stocks with a high relative strength. But there&rsquo;s more. Not all stocks with a high relative strength ranking are also 'strong stocks'. Relative strength looks at the performance over the past year. Some stocks just made a huge jump at some point because of a fundamental event. This jump gives them a high relative strength value. However, one is not interested in those stocks, but in stocks that show a nice and steady trend over the year, not stocks that are flat, jump 300% higher and then flatten out again. Strong Stocks filters the nicest steady trends in the market and does a terrific job in mimicking IBD stock lists. 

Effective Volume

Effective Volume is an indicator that was introduced in the book 'Value In Time' by Pascal Willain. The indicator looks at the intraday minute data to find out what large players are doing in a certain stock. It is able to discover accumulation or distribution by large players in the analyzed stock. 

Effective Volume is used in several ways on chartmill.com: 


To analyze accumulation and distribution in individual stocks in the charting applications. 
To filter stocks under heavy accumulation or distribution by large players in the screener. Especially those situations where a divergence between accumulation or distribution is measured against the performance of the stock, are of interest. 

To measure the accumulation or distribution of all stocks in a certain sector or industry. This gives us a nice market breadth indicator. 


More 'not so common indicators' ... 

The charts and screener support some other indicators that are not commonly found in other charting applications: 


Support and Resistance : support and resistance trend lines will automatically be drawn on the chart when this indicator is selected. Also the screener allows screening for stocks near their trend lines or about to break them. 

Monest Channels : an indicator developed by www.monest.net. Monest channels draw a horizontal channel that is optimized for both width and length: The Monest Channel determines the longest and narrowest channel possible on a chart. This is very useful for finding stocks that trade in a channel, like for instance required for Weinstein and CANSLIM setups. 

Monest Value Indicator : another indicator by www.monest.net that determines whether a stock is cheap or expensive within its short term context. Contrasting most other oscillators it hasn&rsquo;t got lag or stickiness in overbought/oversold zones while a trend marches on. 

Pocket pivots : pocket pivots were recently introduced by Chris Kacher &amp; Gil Morales in their book &ldquo;Trade Like an O&rsquo;Neil Disciple&rdquo;. They basically find moves under high volume in stocks. The screener supports screening for stocks that had one or more pocket pivot recently. 


StockScreener
The most impressive feature of www.chartmill.com is definitely the stock screener. The concepts listed above are all supported by the screener. Figure 1 shows a screenshot of the screener.  


 

The screener has a number of tabs ( marked 'A' in screenshot ) that group a set of settings ( 'B' in the screenshot). There is: 


A General tab. Here general filters such as the market, sector or industry can be applied. A very interesting field on this tab is 'signal'. 'Signal' has a list of signals that are very useful starting points. Examples are: 'near new high', 'strong stocks' or 'pocket pivot today'.  

The Technical tab allows for lots of technical filters like price performance, relative strength, accumulation or distribution and many other technical indicators.  

The 'Monest Channels' tab allows filtering based on the properties of the Monest Channels. Here one can filter on the width or length of the channel, but also the 'strength' which is the number of times it was visited by the price in the past. The strength of a channel also determines how strongly the channel acts as support or resistance. 

The 'Support and Resistance' tab allows filtering based on the properties of the support &amp; resistance lines on the chart. This allows you to search for stocks that are near important support or resistance levels.  



Below the filters, you can select how you want to see your results. ('C' in the screenshot ). 


View: allows you to select between a table, quickcharts, a detailed technical view or a CSV based ticker list.  
Order by' allows for sorting results. You can for instance sort by 3 month performance , relative strength, or one of many other sorting options.  


All views of the screener will present overlay charts of the results so that you can immediately inspect   lots of them. You can fully configure the charts that will be shown in the overlay with the chart settings ( 'D' in the screenshot). You can even include indicators. 

Screening Shortcuts 
The chartmill.com homepage offers an easy entry to the screener and contains some handy shortcuts to get started. Traders can start from the shortcut and can then further refine the filters based on their needs. Figure 2 shows these shortcuts. 



Charting 

Chartmill.com has 2 charting modules: the Basic Charts and the Advanced Charts. Both allow charts in different formats and timeframes with technical indicators and user annotation.

A nice feature of both basic and advanced charts is that you can view multiple charts at a time. If you enter a comma separated list of tickers, all your charts will be shown. 

The Advanced charts are more interactive. The advanced charts allow right-clicking on the chart to add anchored indicators on a certain candle. Examples of anchored indicators are stops or vwap&rsquo;s and will be explained in a minute. Another nice feature of the advanced charts is the position sizing tool, which will also be explained next.

Position Sizing Tool

As every trader should know, the most important aspect of trading is position sizing: you determine where you get out if the trade fails and based on your entry and exit you can determine how many shares you can buy given your account size and the risk you allow for this trade. The position sizing tool does these calculations for you. 


The picture above shows the position sizing tool. It shows a trade on AMZN. The desired entry and exit are filled in, which can be done by right-clicking a chart at the desired entry en exit level. The tool requires the available capital and the % risk one wants to take for the trade. With these numbers   filled in, the results are shown in the tool. 

In the example, the tool calculates that 72 shares can be bought. If the trades is stopped out, the trader will lose 1,5% of his account or 748.48 dollar. Note that the tool also warns that the total investment is rather big ( over 25% of total port size ) and that the stop is positioned at less than 1 ATR from the entry price, which is too close. 

Anchored VWAPS and Stops 
An input to for instance an ATR-stop on a chart is the day of the entry. The stop should calculate the maximum price from that day onwards and should subtract n times the ATR value from that. The starting point is called the 'anchor point'. 

Anchored VWAPS and Stops are supported by both charting applications, but the advanced charts allow you to interactively select the candle where the VWAP or Stop should be anchored, i.e. calculated from.  

Sector Analysis 
The site also has a sector analysis tool which allows sorting sectors and industries based on their recent performance,   the average accumulation or distribution, the number of new highs, the amount of pocket pivots or many more sector properties. 
When a certain sector is found interesting, clicking it opens the screener with the individual stocks from the selected sector.    

Feedback 

Finally, chartmill.com also listens to traders and its homepage contains a feedback form. All feedback and suggestions are welcome and are taken into account. ]]></description>
</item>
<item>
<title>Some indicators added to the charts</title>
<link>http://www.chartmill.com/documentation.php?t=Some indicators added to the charts</link>
<pubDate>Sat, 15 Oct 2011 00:00:00 +0200</pubDate>
<description><![CDATA[William R, Stochastics, ... added
The following indicators were added to the charts:

William %R
Fast Stochastic
Slow Stochastic
Full Stochastic

Enjoy!

Examples


Fast Stochastic Indicator






Slow Stochastic Indicator





]]></description>
</item>
<item>
<title>Chartmill featured in the October 2011 issue of Traders Magazine</title>
<link>http://www.chartmill.com/documentation.php?t=Chartmill featured in the October 2011 issue of Traders Magazine</link>
<pubDate>Wed, 05 Oct 2011 00:00:00 +0200</pubDate>
<description><![CDATA[CM in TM
Our chartmill website was subject to a webreview by Traders' Magazine, in its October 2011 issue.


]]></description>
</item>
<item>
<title>Candlestick patterns added to screener.</title>
<link>http://www.chartmill.com/documentation.php?t=Candlestick patterns added to screener.</link>
<pubDate>Sat, 10 Sep 2011 00:00:00 +0200</pubDate>
<description><![CDATA[Scan for Specific Candlestick Patterns

The stockscreener&nbsp;now supports several popular candlestick patterns. See 'candlestick' on the technical tab.



]]></description>
</item>
<item>
<title>Bigger charts in Basic Charts</title>
<link>http://www.chartmill.com/documentation.php?t=Bigger charts in Basic Charts</link>
<pubDate>Sun, 04 Sep 2011 00:00:00 +0200</pubDate>
<description><![CDATA[Big, Bigger, Biggest!

The basic charts now support charts up to 1200 pixels wide.  The width parameter also has some 'px++' settings that will produce charts width a bigger height than the default.
Enjoy!

How to get Bigger Charts?

Just go to our basic charts and choose 1200px in the list


Big Stock Charts in Chartmill
]]></description>
</item>
<item>
<title>Monest Value Indicator (explanation)</title>
<link>http://www.chartmill.com/documentation.php?t=Monest Value Indicator (explanation)</link>
<pubDate>Wed, 31 Aug 2011 00:00:00 +0200</pubDate>
<description><![CDATA[A New Breed of Oscillator

Oscillators claim to bring a universal way of short term valuation of any financial asset, pointing out overvalued situations, also called overbought prices, and moments of undervaluation, often called the oversold area. Classical oscillators do have their merits, but they all seem to struggle with the same ever-recurring disadvantages, which we&rsquo;ll address in this articles series.

Tekst: Dirk Vandycke

Oscillators
Anyone using technical analysis is familiar with the concept of an oscillator. An oscillator tries to capture a short term valuation of its underlying series. Almost all classical oscillators like RSI, MACD, Stochastic, &hellip; fall into two categories. Range compression oscillators (like RSI) basically try to squeeze a price chart into a fixed range like [0, 100] or [-1,+1], while smoothing oscillators (like MACD) use moving averages to get rid of noise. None of these classical oscillators in technical analysis though, interprets the relative value of a stock, which accounts for almost all of their shortcomings.

Range compression oscillators, for one thing, just get you the same information you see when you squint your eyes looking at the original price chart. They also have a stickiness problem, meaning that as they try to fit trends of any length in to the same narrow range, trends more often than not get compressed in the small oversold or overbought zones. This giving extended overbought or oversold signals, while the trend just marches on, making those signals as good as useless. Moving averages being their primary building blocks, smoothing oscillators have the same lagging(&deg;) problems any good old fashioned moving average has.

To worsen things, they all need parameters which leaves them open to a lot of subjectivity both in their usage as well as their interpretation. This also puts traders backtesting these oscillators in harm&rsquo;s way, as they might fall victim to curve fitting. This lack of transparency is seen in the myriad ways they are used, while in fact they&rsquo;re often nothing more than a small, distorted, version of the original price chart.

Well, that wasn&rsquo;t so nice a description of one of the most popular families of indicators in technical analysis, now was it? This leaves us with the simple question: can we cope with the lag, stickiness and subjectivity, to come up with a better oscillator. The answer is yes and the solution to this problem of building such an oscillator lies in the statistics of what short term value really is.

Lag is the effect by which the oscillator turns after price does, just like moving averages do.

Universal Value
Value, and its relation to price, is a matter of future price gain. Future prices will emerge from what other people will do after your order gets filled. Any transaction is an agreement over current price with a disagreement over future prices. Or as Buffett puts it: price is what you pay, value is what you get. If value for us will be determined by future transactions, it can&rsquo;t be known the moment we put in our order. Only afterwards will it become clear as the position starts showing us a profit or a loss.

Though future long term value may be estimated by fundamental analysis, short term future value depends mostly on the perception of those people closely watching the most recent price action. That is, people just having their order filled or wanting to put one in. Consensus and our perception of value, after all, originates from comparing thing to each other. And previous prices are the closest thing to compare price with, both in time and in place (on a chart).

So we must start building our oscillator on the premise of people changing their perception as prices change. First and foremost, when a higher candle is established with regards to the previous one, the perception of people close to the action, will be that the stock got more expensive. Now if it keeps going up, perception will change to too expensive en gamblers fallacy will kick in, making people believe the change of a down period gets higher the more up periods they see.

Now suppose the stock got more expensive. Our oscillator should show a higher value. But what if, for the next 2 periods price stayed at this new higher level? Your first thought may be that the oscillator should stick to its level. In fact that&rsquo;s what many existing oscillators do. Perception though will shift to that of less expensive the longer prices stay at that higher level. So if a stock goes from 9 to 10 from one period to the next, it becomes more expensive and an good oscillator should peak. But as it stays 10, the oscillator should start to drop, as 10 becomes consensus, rather than expensive.

Just remember, we&rsquo;re talking short term here, a few days tops. But as we will see it&rsquo;s on this shorter time frame one can get really good entries to hop on board of a trend in a longer time frame.

Monest Value Indicator
So we want a short term consensus about price to put price changes in perspective to that consensus. To obtain that goal we&rsquo;ll use an idea from statistics where a normalization process is used to obtain a standardized distribution. Here&rsquo;s why. First of all, absolute prices don&rsquo;t mean a thing. We have to look at their relation to recent prices. Secondly, different markets have different volatility. A price change of 1 implies a lot more volatility with a 5 dollar stock, then it does in case of a 50 dollar stock. Furthermore price series on financial markets don&rsquo;t show normal distribution. So we have to look for a characteristic that is normally distributed if we want to use statistical analysis (&dagger;). Finally, we want our valuation model to behave as an oscillator but without the lag, the stickiness and without the subjectivity in both definition as well as interpretation.

First we establish a 5 day consensus as the 5 period moving average of the midrange of each period, i.e. (high+low)/2. Next we&rsquo;ll offset all OHLC data to this 5 day consensus line. We do this by subtracting the consenus value from the open, high, low and close, giving us an new open, high, low and close with the same relative position but, this time, around a straight zero consensus line. Picture it this way: imagine we pull both ends of the consensus line, which is meandering through prices (figure 2-1), and stretch it to a straight line, while all candles keep their relative position to this line (figure 2-2). Finally we&rsquo;ll divide all consensus adapted OHLC data by a 5 day moving average of the true range, divided by 5, to account for volatility (figure 2-3). Figure 2 shows this process in two steps. From the orginal candlestick chart (1) to the standardization in two steps (2 and 3). The close on the result of the final step is the eventual Monest Value Indicator (MVI).

One of the faulty assumptions for instance Bollinger Bands are based on.

True Range
The true range mentioned above is nothing more than the daily range, adjusted for gaps. It&rsquo;s the difference between the true high and the true low. The true high being the largest of the current bar&rsquo;s high and the previous bar&rsquo;s close. The true low, in the same way, is calculated as the lowest of the current bar&rsquo;s low and the previous bar&rsquo;s close. It comes down to the original true range of Welles Wilder, but formulated a bit easier (I think). The volatility used in the standardization process is the 5 day simple moving average of this true range. More commonly called the 5 bar average true range.

Charts
In figure 1 you can see the Monest Value Indicator next to the classic oscillators MACD and RSI. Rounded rectangles and circles show false positives, i.e. false or dubious signals. Rectangles show good signals (true positives). As a first exhibit it seems as if the Monest Value indicator has more accurate and sharper, i.e. clearer signals. Figure 2 shows the construction of the MVI in two steps. First standardizing towards consensus (flattening the chart), next normalizing the chart for dispersion.

On www.chartmill.com the Monest Value Indicator is integration in both basic as well as advanced charts. The screener also makes it possible to scan for low and high values of the MVI. As we&rsquo;ll see in the next article, this indicator has a true universal meaning of short term under- and overvaluation.


Figure 1: The Monest Value Indicator, a new breed of oscillator.

The Monest Value Indicator as opposed to popular oscillators as the RSI and MACD. Rounded rectangles and circles show false positives, i.e. false or dubious signals. Rectangles show good signals (true positives). As a first exhibit it seems as if the Monest Value indicator has more accurate and sharper, i.e. clearer signals.



Figure 2: Construction of the Monest Value Indicator
The Monest Value Indicator is produced by statistical standardization. The consensus line meandering through the original candles (1), is leveled to a straight line (2), each candles keeping its relative distance to this line. In the final step (3), all data points are divided by a short term volatility measure. The close of the normalized candles then result in the Monest Value Indicator.

This article was originally published in the August 2011 issue of Traders' Magazine. You can download the pdf here.]]></description>
</item>
<item>
<title>Monest Trend Indicator and Overlay</title>
<link>http://www.chartmill.com/documentation.php?t=Monest Trend Indicator and Overlay</link>
<pubDate>Mon, 01 Aug 2011 00:00:00 +0200</pubDate>
<description><![CDATA[What is the market doing?
The Monest Trend Indicator indicates whether a trend is positive, negative or neutral.
The indicator is available in our charts as overlay or as a standalone indicator:


Monest Trend Indicator
Monest Trend Overlay


The overlay will be colored green when the trend is positive, red when it is negative or grey when it is neutral.
The standalone indicator has a value 1 for a positive trend, -1 for a negative trend or 0 for a neutral trend. The same color code is used in the standalone indicator.
The indicator is meant to be used in the Weekly timeframe and measures the long term trend. It can also be used in the daily timeframe to indicate shorter term trends.
The distinction between positive, negative or neutral is made by comparing the price to the position of the 30 week exponential moving average (30 EMA):


If the low of the week is above a rising 30 EMA and the 30 EMA has been rising for at least 3 weeks: the trend is positive.
If the high of the week is below a declining 30 EMA and the 30 EMA has been declining for at least 3 weeks: the trend is negative.
Otherwise the trend is neutral.


Although the trend indicator has a fairly simple formula for determining a trend, the example will show that the results are quite pleasing. We will have a look at the S&amp;P 500 over the last 10 years by looking at the chart of SPY:

On the chart you see:


Until around August 2000 the trend is mainly positive
From August 2000 till round March 2003 the trend is mostly negative. It does return to neutral territory a couple of times, but never becomes positive.
From 2003 till late 2007 the trend remains mostly postive. It did fall into the neutral or even negative area during the more heavy corrections.
From 2008 till March 2009 the market is almost fully in the negative area.
After March 2009 we are again in a positive area.
]]></description>
</item>
<item>
<title>Price Near SMA x filters added</title>
<link>http://www.chartmill.com/documentation.php?t=Price Near SMA x filters added</link>
<pubDate>Sat, 30 Jul 2011 00:00:00 +0200</pubDate>
<description><![CDATA[Looking for stocks near their 10,20 or 50MA?

In the screener, some filters were added to the 'SMA' fields in the technical tab: 'Price Near SMA x' was added for the 20,50,100 and 200 SMA. 'Near' is defined as within a 5% range of the SMA value.

For example:
'Price Near SMA 50' will select all stocks where the current close is maximum 5% higher or lower than the value of the 50 day Simple Moving Average.

Example 1: Looking for stocks near their 50 MA

Go to the screener and look in the Technical Tab for SMA1. Choose "Near 50 MA"


]]></description>
</item>
<item>
<title>Charting: Distribution Days Counter</title>
<link>http://www.chartmill.com/documentation.php?t=Charting: Distribution Days Counter</link>
<pubDate>Wed, 23 Feb 2011 00:00:00 +0100</pubDate>
<description><![CDATA[2 distribution days related indicators are available in the charts:

Distribution Days: an overlay indicating a distribution day
Distribution Day Count: an indicator counting the number of distribution days in a moving window of n days ( where n is the parameter, the value is 20 by default)

&nbsp;
&nbsp;]]></description>
</item>
<item>
<title>Sector Analysis</title>
<link>http://www.chartmill.com/documentation.php?t=Sector Analysis</link>
<pubDate>Sun, 07 Nov 2010 00:00:00 +0100</pubDate>
<description><![CDATA[A new sector analysis tool was added to the site. It can be found here.]]></description>
</item>
<item>
<title>On Relative Strength</title>
<link>http://www.chartmill.com/documentation.php?t=On Relative Strength</link>
<pubDate>Sat, 30 Oct 2010 00:00:00 +0200</pubDate>
<description><![CDATA[The Strongest of the Strongest

Relative Strength(RS) is a concept that is heavily used on the Chartmill.com site. We use different flavors of Relative Strength. This article is meant to clarify the different kinds of relative strength that exist and are used at Chartmill.com.

Lets start by clearing one potential confusion. The popular RSI (Relative Strength Indicator) has nothing to do with the kind of relative strength we will discuss here. The RSI is an indicator that can be calculated completely from the historical data of one single stock. The RSI compares average profit to average loss of the same stock over a certain period.

This immediately brings us to a key point of the Relative Strength we will discuss: The Relative Strength of a stock always involves a comparison with another stock or other stocks. It expresses the performance of a stock relative to another one or relative to a whole set of stocks. The &lsquo;other stock&rsquo; is typically a market or sector index.

We will discuss to kinds of Relative Strength:

A stock is compared with the market or sector index. This measures how the stock is doing compared to the market or sector.
A stock is compared to a set of other stocks. The set of other stocks can be the complete set of stocks we know or for instance all stocks that belong to the same sector or industry.


For the first kind of Relative Strength we have indicators availabe in our charts. We will look at Dorsey &amp; Mansfield Relative Strength.
The second kind of Relative Strength is used in our sector analysis tool and in our screener. Here, the Relative Strength is a single number between 0 and 100. This number expresses how much % of the other stocks are outperformed by the stock we are looking at: a stock with a relative strength of 80 outperforms 80% of the other stocks in the market or group.

1 Relative Strength Indicators
Relative Strength (Dorsey)

This indicator is available on our charts. This form or Relative Strength was described and used in the book &ldquo;Point &amp; Figure Charting&rdquo; by Thomas Dorsey. Dorsey was using XO-charts, but one can also interprete it on a regular chart.

The formula for calculating the indicator is quite simple:

RSD= ( close / close_index ) * 100

The value of the indicator is calculated each day by dividing the price of the security by the price of the index. It is multiplied by 100 just to have some numbers that are more or less &lsquo;reasonable&rsquo;, but this doesn&rsquo;t impact the behavior of the indicator.

It is easy to see that if this indicator rises the stock is outperforming the market and if it declines, the stock underperforms the market. The rising or declining is independent of the actual rise or decline of the stock itself. The Relative Strength only expresses how the stock performs relative to the market. It is perfectly possible for a rising stock to have a declining relative strength: if the market rises harder, the division will lead to smaller numbers each day.

Currently, you can not choose the index that is used to compare to. We always compare with the S&amp;P 500 index and we use the ETF with ticker &lsquo;SPY&rsquo; to do this. ( As a consequence: if you look at the Dorsey Relative Strength for SPY, it will always be 100 )

So what can we do with this indicator? Have a look at the following chart:





First of all, you can just look at whether a stock is out- or underperforming the market. If its Relative Strength is rising, it is outperforming the market. ( As we see on this chart ).
In Dorseys book, this form of Relative Strength is used to look for Relative Strength breakouts. As on the chart, you can draw support &amp; resistance lines on the RS indicator chart. A breakout of the RS confirms that a stock starts outperforming the market. On our chart, we see that somewhere in the beginning of august we have both a price and a RS breakout. The price and volume breakouts are perhaps enough to act upon, but when this is accompanied by a RS breakout, you have confirmation that the stocks also outperforms the market.
The same thing is applicable to sectors. You can analyse which sectors gain relative strength in the market. Similarly, you can also compare stocks to their sector index to see which stocks are strong in a certain group.


Relative Strength (Mansfield)
This variant of Relative Strength was heavily used in the book &ldquo;Secrets For Profiting in Bull and Bear Markets&rsquo; by Stan Weinstein. Weinstein was using the indicator mainly on weekly charts. In our charts, the default value for the parameter is 52, which is also meant to be used in the weekly timeframe. The meaning of this parameter will become clear shortly.

The formulae of this indicator is a bit more difficult than the regular Dorsey RS, but it is worth understanding:
RSM = (( RSD(today) /sma(RSD(today), n)) - 1 ) * 100
Where:

RSD = Relative Strength (Dorsey)  ( see above &hellip;)
SMA = Simple moving average over n days.

So, the Dorsey Relative Strength value is divided by its own n-day moving average and then one is subtracted. If we look at the daily timeframe and use 200 for n, this means:

We get 0 if the RSD is exactly equal to its 200 day moving average.
We get a negative number if the RSD is below its 200 day moving average.
We get a positive number if the RSD is above its 200 day moving average.

Most Technical analysists are familiar with the stock price being above or below a certain Simple Moving Average. With Mansfields RS, the same applies: if the relative strength is above its moving average, positive values are seen, if it is below, we see negative values.

Usage:



As with the regular ( Dorsey ) RS, you can use Mansfield RS to examine if a stock performs better than the market. Only the rising or decling of the indicator matters for this.
Stan Weinstein used the inidcator only on weekly charts with 52 as the parameter value for n. He insisted that break outs out of a base had to go together with rising relative strength. The Mansfield RS needed to be rising an close to or above 0.
This indicator has the advantage over the Dorsey RS that the values are below or above the 0 line. This allows us to screen for stocks with a MRS value above 0. When we see that the MRS is far above 0 and has been above 0 for some time, we have found a stock that outperforms the market heavily. We can buy this kind of stock when dips occur. We can use the &lsquo;Monest Value Indicator&rsquo; to seach for dips.
The chart shown above is SOITEC. We see that the Mansfield Relative Strength went above 0 somewhere end of march 2009. The Monest Value Indicator is shown to indicate where the buying oppurtunities were.



Relative Strength by Ranking
A second kind of relative strength is obtained when stock performance is compared with the performance of all other stocks and then ranking the stocks. This approach leads to a single number that expresses how much percent of the other stocks are outperformed by this stock: a stock with a Relative Strength of 88 outperforms 88% of all other stocks.

Relative Strength
At Chartmill.com, we assign a number for &lsquo;Relative Strength(MRS)&rsquo; to each stock in our database. We calculate this number by comparing the performance of each stock in our database over the last year to the performance of all other stocks in the same period.

We do not just take the yearly performance directly but we divide the year into 4 quarters and add the performances of each quarter. Because we think the more recent performance is more important when you are looking for strong stocks, we add the performance of the last quarter twice. This means that the last quarter has more weigth in our performance metric:

So:
Metric = pQ1 + pQ2 + pQ3 + 2 x pQ4  ( where pQ1 means: absolute performance in Q1, e.g +12% )
When we have done this for each stock in our database, we will rank the stocks according to the metric. After ranking them, we can assign a value between 0 and 100 for each stock. The stock with the highest value for the metric will get value 100 because it outperforms all other stocks ( = 100% ) in our database. The second one only outperforms 7999 of 8000 stocks, so it will get a Relative Strength number of 99.99.
Our screener allows to filter on Relative Strength:



On the &lsquo;Technical&rsquo; tab, you can select the &lsquo;Relative Strength&rsquo; filter and choose to view only stocks with a minimum or maximum Relative Strength (MRS)&rsquo; number. This will show you only the strongest or weakest stocks in the entire database.
We also allow sorting by Relative Strength for each filter or combination of filters you select. This allows for instance to rank all ETFs or all Euronext stocks according to Relative Strength.

]]></description>
</item>
<item>
<title>Effective Volume (explanation)</title>
<link>http://www.chartmill.com/documentation.php?t=Effective Volume (explanation)</link>
<pubDate>Sat, 30 Oct 2010 00:00:00 +0200</pubDate>
<description><![CDATA[Are the large players accumulating?
Chartmills&nbsp;charts, stockscreener&nbsp;and sector analysis tool&nbsp;support&nbsp;Effective Volume.&nbsp;Effective Volume was described in the book&nbsp;&lsquo;Value in Time&lsquo; by Pascal Willain. Pascal validated and approved our implementation.

For a thorough understanding of Effective Volume and its usage, we advise reading the book. More information can also be found on Pascal Willains website.

Effective Volume basically analyses the intraday volume on the minute level to determine what small and large players are doing in terms of accumulation and distribution. Interesting situations occur if the Large Effective Volume trend diverges from the price trend.

In our charts, the following Effective Volume indicators are present:


Effective Volume: S/L Players: shows the action of Small and Large players.
Effective Volume: S/L/A Players: shows the action of Small, Large and All players.
Effective Volume: All Players: shows the action of All platers
Effective Volume: Large Players: shows the action of Large players.


The screener&nbsp;allows you to filter on the properties of the Large Players Effective Volume. On the technical tab, there is a field named 'LEV' ( Large Effective Volume ) which can be filtered based on its 10 and 20 daily moving average. ( E.g: value above rising 10 DMA ).
Effective Ratio
In his book, Pascal defines the Effective Ratio as the total Effective Volume by Large Players divided by the total volume. This results in the percentage of volume that was accumulated or distributed by large players in a specific stock.&nbsp;
We use the effective ratio in our screener:


The technical tab has a field named acc/dist. Here you can filter on the 3 or 5 average daily effective ratio. If you select for instance '3DER &gt; 5%', it will filter all stocks where the Daily Effective Ratio was over 5% on average in the last 3 days. 
Stocks can also be sorted according to their 3 or 5 average daily effective ratio percentage. If you sort stocks on a descending 5DER, you will get stocks showing the strongest accumulation in the last 5 days on top.


The 3 and 5 Daily Effective Ratio are also used in our&nbsp;sector analysis tool:


The tool has 3DER and 5DER columns. This shows the average 3 or 5 Daily Effective Ratio of all stocks in the industry or sector.
The tools allows sorting the sectors by 3 or 5 DER. Sorting them will show you which sectors or industries show the strongest accumulation or distribution by large players.&nbsp;
]]></description>
</item>
<item>
<title>Monest Channels (explanation)</title>
<link>http://www.chartmill.com/documentation.php?t=Monest Channels (explanation)</link>
<pubDate>Sat, 30 Oct 2010 00:00:00 +0200</pubDate>
<description><![CDATA[Adaptive and Optimized Donchian Channels

Channels are at the heart and soul of technical analysis, from as early as its conception. However, up to this day, they come with a lot of subjectivity. This implies that it&rsquo;s hard to implement them algorithmically. Yet, computers and automation might have been the single most important driver in the wide spread adoption of the technical analysis discipline. This article will show how to objectify and optimize the calculation of horizontal channels and, hence, the support and resistance lines they&rsquo;re made up of. 
Text: Dirk Vandycke
Ranges rock
Ranges are quite important in the analysis of charts and the automation of it. They give birth to the timecompression needed for new trends to develop and for existing trends to turn. Even the most well known systems like William O&rsquo;Neil&rsquo;s CANSLIM approach, Weinstein&rsquo;s stage analysis and a lot of trend following strategies depend on them to make decisions concerning the possible start of a trend, phase or stage. Consolidation, also known as a base, from where an existing trend continues its run, is another example where ranges emerge.

The detection of ranges is where horizontal channels come into the picture. One of the oldest and perhaps most popular ways of 
describing horizontal channels is by using the definition provided to us by Richard Davoud Donchian 

Donchian Channels
Perhaps Donchian&rsquo;s channels, named after himself, are even better known as an x-bar high/low. 
But they&rsquo;re merely the implementation of the mathematical minimum (min) and maximum (max) functions. 
What these channels stand for, is basically  the looking back over a certain period and calculate the lowest low in this interval, 
for a support line, or the highest high for a resistance line. Figure 1 shows an upper Donchian channel line (resistance) with a look back period of 
36 days and a lower Donchian channel line (support) with a window of 20 days. When you go back in time, starting at the right side of the chart, the highest 
high over the past 36 days was 3.29, while the lowest low over the past 20 days was 2.26. These lines are annotated on the chart as D36 and D20.

Perhaps, by now, you already noticed the fact that the most recent bar isn&rsquo;t included to calculate the highest high or the lowest low, because that would mean that our channel lines never could be violated, as, for instance, a higher high for the current bar than the highest high over the look back period, would implicate that our current high would be< the upper Donchian channel, by definition. Idem ditto for lower Donchian channel lines.
Furthermore, one could redefine Donchian lines using the close. Our D20 from the example, by this definition, would then become the lowest close over de past 20 bars, while our D36 would stand for the highest close over the past 36 days.
There&rsquo;s really not much to all this. In fact, Donchian channels are so simple, one might think there&rsquo;s nothing to add at this point. These channels should do be able to help us detect horizontal channels in an fully automated way. But they don&rsquo;t.


F1 Donchian Channels

Donchian Channels are amongst the oldest and most basic channels in technical analysis. They are also known as x-bar high/low channels. But they are not completely objectively defined as, in this example, a 36 day upper Donchian Channel can be replaced with a 100 day upper Donchian Channel at the same price.

Not so fast
The truth is, there&rsquo;s a lot of subjectivity left with Donchian channels, as with many indicators in technical analysis. The question hereby arises what the look-back-period should be. Do we take a 50 bar high or a 60 bar high? Answering this question based on back test results, imposes the danger of curve-fitting on your system.
Secondly, they&rsquo;re static, meaning they don&rsquo;t account for any context adaption, such as absolute versus relative prices or, to a lesser degree, volatility. A channel that&rsquo;s $10 in width in a $10 stock has not the same importance as the same $10 channel in an $100 stock. Even if you would think about expressing channel-width in percentage to stock price, this wouldn&rsquo;t account for volatility. Defining width in terms of volatility (like ATR units) would be far better. Still, the issue about adaptivity and subjectivity stands, because of the look back period that has to be chosen arbitrarily and up front. So which look back period would you choose?

Monest Channels
Meet Monest Channels, relieving us from the disadvantages of Donchian Channels. As you will see they are totally objective in both their definition and their implementation, since they don&rsquo;t need any parameters and they adapt themselves to the context of price, time and volatility.

What are they all about? Looking at figure 1 again, you can see that D36 is the same line as D100. What this means is that our highest high over the past 36 days, happens to be the same highest high over the past 37 days. But also the same highest high over the past 38 days. In fact, we have to go back, up to a 100 days, before we find yet a new higher high. So if we would have arbitrarily chosen a look back period of 36 or higher but anywhere less than 100, we wouldn&rsquo;t have optimal usage of our channels when interpreting them without a chart. For a 36 day look back period we could have gotten an extra 64 days in length, without widening the channels.
So let&rsquo;s go over this again for a moment as we outline a few important thoughts. First of all, we need to stop focusing merely on the look back window of Donchian Channel lines, which only accounts for the horizontal dimension. Instead we should calculate the largest possible Donchian Channel line in length for a given price, price representing the vertical dimension. We need to stop thinking of horizontal channels as having only one dimension, as we probably get tricked into this exactly because of the horizontal nature of the separate channel lines. A horizontal line may not have a height in itself, it sure has a vertical coordinate on a chart, which makes for a width of the channel as the difference between the heights of both channel lines the channel is made up of.
Secondly there are opposing forces at work, since the wider we make a channel, i.e. the range of historical prices it captures, the longer its channel lines will get. Look at it this way; if you expand the lines enough, eventually all historical prices will lie between them. Length and width of any channel are, as a matter of fact, functional interdependent of each other. Length is inversely reciprocal to width.

Of course for a channel to be useful it can&rsquo;t be too wide. On the other hand the longer it gets the stronger a breakout signal may be. A too wide a channel might not be as useful, since price already moved substantially before reaching one of the channel lines, giving late warning to any outbreak. A too short a channel isn&rsquo;t meaningful as the longer a channel gets the more people come to recognize it and use it and thus the more meaningful it becomes as an analysis instrument. So, how can we have both maximum length and minimum width, or, as that&rsquo;s not possible, a optimal trade-off?

Implementation
The answer to this question is given to us by classic high school math to solve max/min problems through the use of derivatives. There&rsquo;s one problem though, we don&rsquo;t know the function describing a price chart and, as a consequence, neither do we know the functional description of the relationship between width and height of any channels.  We&rsquo;ll probably never know. Luckily, there&rsquo;s a simple trick to circumvent this problem as we can use discrete derivatives. Now, don&rsquo;t you stop reading. This sounds a lot more complex than it really is.

The first derivative of a function gives us the speed at which that function is ascending or descending. Translated to our price charts, this simply means we are going to look at how much longer the maximum Donchian Channels line gets as we increase its height by the smallest increment possible (i.e. a tick &ndash; being most often one cent). So to calculate the upper Monest Channel line we start by measuring the length that prices stay beneath one cent above our most recent close. Next we calculate the same length for a price one cent higher. The extra length we get is the first derivative at those price levels. It basically comes down to how much bars our channel line gets longer if we widen our channel with one cent to the upside. The same goes for the lower Monest Channel line while decrementing each time with one cent.  By doing this incrementally for every cent, we can get the optimum as the biggest increase in length we gain for any cent we widened the channel line during the iteration.

Let&rsquo;s go back to our example from figure 1, as figure 2 charts this gain in length for every increase in width by one cent. What this chart is telling us, is how much longer the Monest Channels become at every distance in price from the last close.

Look at the chart. You&rsquo;ll see that the length of the upper Monest Channel line (annotated as UMC), jumps from 32 days to 101 days if we increase its height from 11 cents to 12 cents above the last close. Since the last close was 3.04, this means pulling up the channel line from 3.15 to 3.16 increasing it by 69 days in length. The biggest increase we get for a one cent increase in height.

The lower Monest Channel line is shown as LMC. This optimum is found the same way. Lowering the channel line from 1.94 to 1.93 gets us the largest increase in length, going from 79 to 106 days, an increase of 27 days. If we look further, the LMC length goes of the chart if we lower it to 1.54, increasing the length with 254 days for a total of 361 days. This kind of increase indicates a very important low, in this case even the all time low.


F2 Optimal Donchian Channels
If we look for the maximum Donchian Channel length we can get at every price offset from the close of the most recent bar, we can spot the largest increase in length. That&rsquo;s the place where we can find the optimal balance between width and length of the Monest Channels.


F3 Monest Channels
Pickup of the chart setup in figure 1, now with Monest Channels added. In this implementation the channels use the highest close for the upper Monest Channel line and the lowest close for the lower Monest Channel line.

Usage
Monest Channels are easy to implement and freely available at tools.monest.net where you not only can visualize them on charts for most American and European stocks. It&rsquo;s also possible to scan over the whole stock universe in a myriad of ways, looking for Monest Channel based on their length, width, strength and breakouts. You can even make use of almost all other classic technical analysis tools, while searching.

Monest Channels&rsquo; first application is, of course, the detection of bases or ranges from where consolidated trends can lift off again or from where new trends can arise. The channels provide also one more way traders can place stops systematically as the channels, by the specifics of their definition, will coincide with swing points and find significant intermediate tops and bottoms.

A careful reader might argue there&rsquo;s still the question of how far one must go back searching the maximum gain in length for an additional increase in width by one cent. In math theory the answer would be to go as far as possible. In our context, this would be as far as historical data is available. However from a practical standpoint the implementation on tools.monest.net cuts of its search as soon as channel lines drift away more than 20% from the last close. We&rsquo;re not concerned with length, nor width, in isolation. We&rsquo;re mainly interested in their balance. That is why the scanner let&rsquo;s you specify both dimensions without discarding the relevance to the other, thereby using the advantage of every &lsquo;free&rsquo; length that arbitrarily chosen Donchian Channel would ride past.

Conclusion
Monest Channels free us of any parameters left to the discretion of the trader. They take in consideration both length and width of the channel, thus optimizing the balance between both dimensions. Their inception creates the possibility to automatically and objectively detect the same horizontal channels and support and resistance lines traders in different timeframes are drawn to, as time compression reaches a climax, leading to a breakout. Of course every chart will have his upper and lower Monest Band. In a secure trend this will lead to one of the bands being quite lengthy while the other being very short. Though this might seem a disadvantage at first it can lead to the detection of trends. Further research, nevertheless, will have to point this out.

Richard Davoud Donchian is mostly know for his pioneering work in commodity futures money management and is considered one of the founders of trend following investment and trading ideas. As suxh, even the very successful trend following systems of Richard Dennis and his, by now famous, Turtle Traders, are based on Donchian&rsquo;s work.

This article was originally published in the August 2011 issue of Traders' Magazine. You can download the pdf here]]></description>
</item>
<item>
<title>Position Sizing (Tool)</title>
<link>http://www.chartmill.com/documentation.php?t=Position Sizing (Tool)</link>
<pubDate>Sat, 30 Oct 2010 00:00:00 +0200</pubDate>
<description><![CDATA[How Much I Should Buy?


Our Position Sizing Tool is a powerful trading tool that allows investors to take their trading decisions to a higher level by calculating some very important moneymanagement rules. In this article we will discuss the concepts by a virtual trading setup so you can take full advantage of the postion sizing instrument.
Let&rsquo;s say that we are interested in long position in Dell Inc ( based on a technical or/and fundamental analysis). First, we take a look at the daily chart to define our entry point.




Let&rsquo;s assume we want to buy the stock when price goes higher than the last candle shown on the chart. So we put in a buystop at 15.75$. The initial stoploss is set at 14.90$ (once our buystop is triggered).

So now we have defined our entryprice and an initial stoploss. Despite the fact that the entryprice and stoplosslevel - based on our technical analysis - are critical parameters in a trading strategy it is definitely not enough to qualify the specific setup as a valid one. Unfortunately a lot of traders (and not only beginners) are convinced that their homework is done at this stage.

There is however one very important component left which is an inherent part in the search for a profitable trading plan : money management or position sizing. Actually, it is the most important part in each and every tradingstrategy! And that&rsquo;s where our Position Sizing Tool comes in.





This powerful piece of software contains some state of the art moneymanagement filters that are calculated automatically. We only need to  feed the calculator with some basic information regarding our setup position.

Ticker: First we specify the ticker of the stock in which we want to take a position in, since we use Dell Inc. in this virtual trading setup, we fill in &lsquo;DELL&rsquo; in the white box next to &lsquo;Ticker&lsquo;.

Capital: In order to obtain reliable information we also need to fill in our total available trading capital. This is the total amount of our trading account. So next to &lsquo;Capital&lsquo; we specify the amount of our personal trading account, in this example 10000$.

Entry Price: In the white box next to &lsquo;Entry Price&lsquo; we fill in the price that we are willing to pay for the shares. For this &lsquo;DELL&rsquo; setup our buystop is set at 15.75$.
Exit Price: Next to &lsquo;Exit price&lsquo; we fill in the pricelevel at which we will sell the shares to preserve our trading capital from greater losses. So this is actually the initial stoploss price. In our example we will take our loss if price drops to 14.90$.

Note : filling in the entry and exit price in the position sizing tool is also possible by right clicking on the chart of the stock in which you want to take position in. Two options are available then : &lsquo;entry price goes here&rsquo; or &lsquo;exit price goes here&rsquo;.

Rounding: standard this box is set at 1. In this case the exact amount of shares that is specified stays the same. If however the number is set at 5 the tool wil automaticaly change the amount of shares to a multiple number of five. In this example the number of shares was initially calculated as 152 so the rounded number (multiple of five) would be 150.

% Risk: this percentage represents the total percentage of risk one is willing to take with each single setup in relation to the total availabe risk capital. The exact percentage is different from one trader to another but is usualy set from a minimum of 0,25% to 3.00% (max). In the example above the percentage is set at 1.50%. So if price is running against us after we get filled and our long position is stopped out, the total loss shall not be more than 1.50%  (or 150$) of our trading account (10000$).

Ticker: : the broker fee one has to pay in order to obtain the shares. It is strongly recommended to explore different brokers. Commission costs can (and will) be a very decisive element in profitability, especially for smaller trading accounts!

% tax: the tax fee on the transaction (if applicable). In Belgium there is a tax fee of 0,17% on a transaction so that&rsquo;s the percentage number we will use in that box.

With the above information the Position Sizing Tool is ready to calculate some very specific money management elements that are extremely valuable in order to qualify our trading setup.

Generating numbers and percentages
Total Risk : this value represents the total amount of money that we lose if our initial stoploss is hit. In the case of our &lsquo;DELL&rsquo;long setup the potential loss is limited to 149.12 euro. Important: the total loss is also taking into account the commission and tax fees. Due to our %risk level that we&rsquo;ve speficied in the fields above (1,50%) the total loss may not exceed 150 $ which is 1,5% of our intial trading capital (10.000$). In case you&rsquo;re wondering why the  percentage column is showing a %risk of 1.49% instead of 1.50% : There is a very simple and logical reason for that. Since it is not possible to buy  a portion of a share the total risk will - in most cases - fluctuate just above or beneath the 1.50% level. Because we have specified our %risk as 1.50 max the tool will always calculate the closest level possible beneath that maximum.

Risk Per Share: This is the difference between the buy and stopprice for one share. For our  DELL setup the risk per share would normally be 0.85 (buystop at 15.75 minus stoploss at 14.90). The reason that the result is higher in this example (0.98) has to do with the fact that the risk per share is revalued by taking into account the commission and tax fees. The difference between buy- and stoplosslevel (0.98) is exactly 6.24% of the shareprice that we are willing to pay for the shares.

Number of Shares :With the above information (total risk and risk per share) the number of shares we have to buy is automatically calculated by the tool. In the case of dell we have to put in a buystop order for 152 shares.

Investment : This is the total amount of money we have to pay to obtain the number of shares calculated by the tool. Again, commission and tax fees included. In our example the total investment is 2413,92$ or 24.14% of our total trading account.

Breakeven Price: This parameter is closely related to the broker commission and tax costs. Many traders tend to underestimate this kind of costs but this part of the position sizing tool clearly shows that we really have to keep an eye on it. In fact, the breakeven price tells us how much the stock has to rise before we can recover our broker and tax fees. In this setup DELL has to gain 0.84% before we can get breakeven. Of course, the greater the investment the smaller our costpercentage becomes.

ATR : This is the average true range of the stock on a daily base. It determines a stock&rsquo;s volatility over a given period. That is, the tendency of a security to move, in either direction. The Position Sizing Tool uses a 22 day period to calculate the ATR.

The value returned by the average true range is simply an indication as to how much a stock has moved either up or down on average over the defined period (22 days). High values indicate that prices are changing a large amount during the day. Low values indicate that prices are staying relatively constant.

In our setup example, the ATR value is 0.30 which represents 1.90% of the stock price we are willing to pay (15.75$).

Stop Distance:The stop distance is the difference between our entryprice and the stoplossprice. In this example the stop distance is 0.85 (entryprice minus stoplossprice) . Next to the stop distance in absolute numbers, the stop is also calculated in ATR value. Since the ATR of Dell Inc. is currently 0.30 it means that our stoploss has an ATR size of approximately 2.85 (0.85/0.30).

Liquidity: This number shows the average volume over a period of 20 days. The percentage in the third column indicates how much days are needed to cover the number of shares we want to buy.

Green and red circles
Besides the fact that the position sizing tool gives us very valuable information about our future position it even goes further by interpreting those numbers and percentages! This way, almost every single item gets checked and earns a true of false status which is visualized by a green or red circle behind every line.



In the example shown alongside we&rsquo;ve changed a few things, our trading capital is reduced to 5000 $ and our stoploss is set at a greater distance from the entry price (13.50 in stead of 14.90). All other settings are the same. By changing just these two settings, the tool automatic changes the results in the lower white part of the tool. As we can see the Risk Per Share rises to 17.42% and the Breakeven Price also rises tot 3.17%. Those two items have now -  instead of a green - a red circle.

A green circle is ok, a red circle means that for that specific parameter attention is needed by the trader. In other words, a red circle is a clear warning that the setup can be too risky for the specified trading account.

Total Risk: red circle if the total risk of the setup is greater than 2.50% of the trading account.
Risk Per Share : red circle if the risk per share is greater than 15% of the share price.
Number of Shares : red circle if the desired number of shares is more than 10% of the average daily volume (average over 20 days).
Investment: red circle if the investment of the setup becomes more than 25% of the total trading capital.
Breakeven Price: red circle if the breakeven price is more than 2,50% higher (lower in case of a short position) than the entry price.
ATR:red circle not applicable, the value and percentage are just informative
Stop Distance: red circle if the stop distance is less than two times the ATR value.
Liquidity: red circle not applicable, the value and percentage are just informative.
***Update 25/08/2010***




The position calculator in the advanced charts as of now is also equiped with dynamic position sizing.
Right click on a chart to add your entry, exit and profit target. Profit statistics are displayed in the output table of the calculator as well as the suggested risk.

You get dynamic position sizing by using the suggested % risk as % risk in the % risk input field (second from the top on the right).
The higher your profit loss ratio the more % risk can be engaged, giving expectancy a turbo effect.

It&rsquo;s up to you now!
We are convinced that using this position sizing tool in addition to your own trading strategy will give you an edge in the market.
Why? Because you will become more aware of the risk that is inherently involved in trading stocks and therefore your trading results will improve dramaticly.
So it&rsquo;s up to you to take advantage of this tool and getting the hang out of it. Once you gained some experience in it you soon discover the benefits.
The tool itself can be found in the advanced charts. Have fun!]]></description>
</item>
<item>
<title>Pocket Pivots (explanation and usage)</title>
<link>http://www.chartmill.com/documentation.php?t=Pocket Pivots (explanation and usage)</link>
<pubDate>Sat, 30 Oct 2010 00:00:00 +0200</pubDate>
<description><![CDATA[Find stocks before they make there big moves
Pocket Pivots are described in the publication of &lsquo;Trade Like an O&rsquo;Neil Disciple&rsquo;, the book of Dr. Chris Kacher and Gil Morales, and are available in the ChartMill Screener and Charts.
In the charts, the 'Pocket Pivot Overlay Indicator' takes two parameters:


 The length of the SMA from which the Pocket Pivot should originate. Default for this parameter is 10.
 The maximum offset allowed from this SMA in percent. Default value is 2.5, meaning that the current open should be within 2.5% of the 10 SMA.



Pocket Pivots are highlighted on the chart with a blue dot below the candle showing the pocket pivot. Please note, there&rsquo;s no exact definition of Pocket Pivots, but there is an exact definition for the volume signature: The volume should be higher than the largest down volume of the last 10 trading days.

In our screener, screening for Pocket Pivots can be done in 2 places:


 The signal field in the general tab has an entry 'Pocket Pivot Today'. This shows all stocks that have a Pocket Pivot in the current or last trading day.
 The Technical tab has a 'Pocket Pivot' field where one can screen for stocks showing at least 1, 2 or 3 pocket pivots in the last 5 trading days.


Pocket Pivoys should be viewed with regard to the context in which they appear. Here are some of the guidelines&nbsp; from Dr. K and Gil Morales:


 As with base breakouts, proper pocket pivots should emerge within or out of constructive basing patterns.
 The stock&rsquo;s fundamentals should be strong, ie, excellent earnings, sales,pretax margins, ROE, strong leader in its space, etc.
 The day&rsquo;s volume should be larger than the highest down volume day over&nbsp;the prior 10 days.
 If the pocket pivot occurs in an uptrend after the stock has broken out,&nbsp; it should act constructively around its 10dma. It can undercut its 10dma as&nbsp; long as it shows resilience by showing volume that is greater than the&nbsp; highest down volume day over the prior 10 days.
 Pocket pivots sometimes coincide with base breakouts or with gap ups.&nbsp;This can be thought of as added upside power should this occur.
 Do not buy pocket pivots if the overall chart formation is in a&nbsp;multi-month downtrend (5 months or longer). It is best to wait for the&nbsp;rounding part of the base to form before buying.
 Do not buy pocket pivots if the stock is under a critical moving average&nbsp;such as the 50dma or 200dma. If well under its 50dma, and getting support&nbsp;near the 200dma, it can be bought provided the base is constructive.
 Do not buy pocket pivots if the stock formed a &lsquo;V&rsquo; where it sells off&nbsp;hard down through the 10dma or 50dma then shoots straight back up in a &lsquo;V&rsquo;&nbsp;formation. Such formations are failure prone.
 Avoid buying pocket pivots that occur after wedging patterns.
 Some pocket pivots may occur after the stock is extended from the base.&nbsp;If the pivot occurs right near its 10dma, it can be bought, otherwise it is&nbsp;extended and should be avoided. Give the 10dma the chance to catch up to the&nbsp;stock, where the stock would consolidate for a few days, before buying such&nbsp;a pocket pivot.

]]></description>
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<item>
<title>Charting: Anchored VWAPs</title>
<link>http://www.chartmill.com/documentation.php?t=Charting: Anchored VWAPs</link>
<pubDate>Tue, 05 Oct 2010 00:00:00 +0200</pubDate>
<description><![CDATA[In the advanced charts, you can add an anchored VWAP from a candle of choice by right-clicking the candle in the chart and select 'add red/green/blue anchored VWAP here'.]]></description>
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<item>
<title>Monest Trend indicator and Overlay added to Our Charts</title>
<link>http://www.chartmill.com/documentation.php?t=Monest Trend indicator and Overlay added to Our Charts</link>
<pubDate>Sun, 03 Oct 2010 00:00:00 +0200</pubDate>
<description><![CDATA[A trend indicator and overlay was added to our charts. An article explaining the indicator can be found in the documentation section or&nbsp;here.]]></description>
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<item>
<title>Screening: Find leaders using Pocket Pivots and Effective Volume</title>
<link>http://www.chartmill.com/documentation.php?t=Screening: Find leaders using Pocket Pivots and Effective Volume</link>
<pubDate>Fri, 17 Sep 2010 00:00:00 +0200</pubDate>
<description><![CDATA[Our screener contains filters related to &lsquo;Effective Volume&rsquo; and &lsquo;Pocket Pivots&rsquo;. Both filters can be found on the &lsquo;technical&rsquo; tab: &lsquo;LEV&rsquo; and&nbsp;&rsquo;Pocket Pivots&rsquo;, where LEV stands for Large Effective Volume.
For example:

select &lsquo;Strong Stocks&lsquo; in the&nbsp;Signal&nbsp;field in the general tab, this will give you a list of market leaders.
select &lsquo;50 SMA &gt; 1M&lsquo; in the&nbsp;Average Volume&nbsp;field in the technical tab, this filters out illiquid stocks
select &lsquo;Above Rising 10 SMA&lsquo; in the&nbsp;LEV&nbsp;field in the technical tab. This shows stocks where the Large Effective Volume is above its rising 10 SMA.
select &lsquo;At least 1 PP in last 5 days&lsquo; in the&nbsp;Pocket Pivot&nbsp;field in the technical tab. This shows a list of stocks having at least 1 pocket pivot point in the last 5 trading days.

Now you have a list of liquid market leaders, with a pocket pivot point and Effective Volume support.
A direct link to the screen ...
Of course, many other combinations of filters are possible.
&nbsp;
&nbsp;]]></description>
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<item>
<title>Find Stocks Before They Breakout: Pocket Pivots</title>
<link>http://www.chartmill.com/documentation.php?t=Find Stocks Before They Breakout: Pocket Pivots</link>
<pubDate>Mon, 13 Sep 2010 00:00:00 +0200</pubDate>
<description><![CDATA[Find stocks before they breakout
Following the publication of &lsquo;Trade Like an O&rsquo;Neil Disciple&rsquo;, the book of Dr. Chris Kacher and Gil Morales, a working version of their Pocket Pivots were added to our charts. Documentation of Pocket Pivots can be found here.]]></description>
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<item>
<title>P/L Ratio Added to Position Sizing Calculator</title>
<link>http://www.chartmill.com/documentation.php?t=P/L Ratio Added to Position Sizing Calculator</link>
<pubDate>Sun, 15 Aug 2010 00:00:00 +0200</pubDate>
<description><![CDATA[Dynamic Position Sizing Available!
The position calculator in the advanced charts as of now is also equiped with dynamic position sizing. Right click on a chart to add your entry, exit and profit target. Profit statistics are displayed in the output table of the calculator as well as the suggested risk.

You get dynamic position sizing by using the suggested % risk as % risk in the % risk input field (second from the top on the right.  The higher your profit loss ratio the more % risk can be engaged, giving expectancy a turbo effect.




Dynamic Position Sizing on VPHM.  A higher Risk/Reward, gives a higher Risk Percentage
]]></description>
</item>
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<title>Charting: Accumulated Volume indicator</title>
<link>http://www.chartmill.com/documentation.php?t=Charting: Accumulated Volume indicator</link>
<pubDate>Thu, 12 Aug 2010 00:00:00 +0200</pubDate>
<description><![CDATA[An indicator called &lsquo;Accumulated Volume&rsquo; is available in our charts. It displays the total volume over an interval and uses two parameters:

Start of the interval, measured in number of candles from the right side of the chart (thus pinpointing the left side of the interval).
Length of the interval as a number of candles.

The value of the indicator gives the total volume over the defined interval. The visual representation is a histogram but doesn&rsquo;t show anything meaningful. Just the volume accumulating over the interval from left to right.
The accumulated volume over an interval can be valuable in measuring the length (in number of shares handled) during an accumulation phase or channel such as the Monest Channels who seek the optimum between as long and small a channel as possible.

Example of Accumulated Volume



Example of Accumulated Volume oer a period of 17 days in Jazz
]]></description>
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<item>
<title>2010-07-26: Effective Volume Indicators added.</title>
<link>http://www.chartmill.com/documentation.php?t=2010-07-26: Effective Volume Indicators added.</link>
<pubDate>Mon, 26 Jul 2010 00:00:00 +0200</pubDate>
<description><![CDATA[Added
Effective Volume indicators have been added to the charts. Effective Volume was described in the book&nbsp;&lsquo;Value in Time&lsquo; by Pascal Willain. A more elaborate article will follow, but EV basically analyses the intraday volume on the minute level to determine what small and large players are doing. Interesting situations occur if the Large Effective Volume trend diverges from the price trend.
Example of the Effective Volume Indicator

Example of the effective volume indicator on Lulu
]]></description>
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<item>
<title>Equity Curve Control</title>
<link>http://www.chartmill.com/documentation.php?t=Equity Curve Control</link>
<pubDate>Wed, 31 Mar 2010 00:00:00 +0200</pubDate>
<description><![CDATA[Second Order Technical Analysis

Though technical analysis thrives on the premise that price and volume are the ultimate synthesis of all market information digested by all market participants, it is equally important for a trader to see the bigger picture of his performance in the long run. Handling each trade right is of course very important. But the weight of each trade on performance diminishes the more trades are taken. This article series eventually goes beyond evaluating that performance and shows how it even can be controlled to a certain degree, by focusing on that bigger picture.


Every person or institution  trading, or investing in, financial markets, uses a system. Or even many different  systems. Some systems are totally automated. Others are almost fully  discretionary. In which case we’d rather call it a strategy (what’s in a name †).  Some people wouldn’t even be able to begin describing their system, when asked.  But even someone just chasing hot stock tips, actually is using a system.


Some put heavy weight on up  front analysis of the system they use, or are about to use. Putting in a lot of  intelligence, rules and adaptability. Going as far as optimizing parameters,  while back testing and fine-tuning them before putting them to the final test of  forward testing. Others don’t even have a clue what system they are using, as  they might well be switching around different systems without even knowing.  Then again, mixing systems is, again, a system on its own.
Every system has input it’s  using, usually based on some form of analysis of raw data, fundamental and/or  technical, to produce output in the form of when to buy or sell how much of  whatever financial product, a.k.a orders.

Danger ahead
The main problem most traders  and investors are struggling with, is the fact that systems go in and out of  synch with the market. This problem is most often recognized in a far more  familiar way of searching for a system that withstands the test of time. The  holy grail system that keeps on working in every kind of market, from commodities  over stocks to forex, but also through every type of market, volatile  sideways markets as well as steady and quiet bull markets. You name it. Oh and  while we’re at it, let’s aim for a reliability of at least 80% winners.
How do we know this is a  problem until this very day?  Because new  ways of analyzing market data are kept being invented. Timeframes are still  getting smaller in search for an edge while satisfying mainly the illusion of  control. Software to build systems gets more and more powerful (and complex),  implying it also continues to be sold. Why bother writing more features and new  software, if it’s not going to earn you money. This frequently ends in what is  called ‘feature-bloat’ in software slang. Creating a situation where 80% of its  users only use 20% of its functionality.
How do we know such a one-size-fits  all system, really is a myth (hence the reference to the holy grail). Simply  because of plain old system dynamics. Such a system would be adopted by more  and more users, without anyone dropping out of a system that keeps working. In  the end this would lead to system instability, ultimately ending the financial system  as we know it. In reality just the supposed holy grail system stops working, or  at least deteriorates. As long as there are financial markets, they are the living  prove such a system cannot and does not exist.
The real danger in search for  such a holy grail system is that systems, in the process, more often than not  get curve fitted on the test data they see by adding more and more constraints.  Systems thus get more complex while trying to make them more robust. Ironically  enough, the added complexity is the force pulling us away from robustness.  Again simple system dynamics explains why. The biggest part of the output of a  system (here the robustness of a trading system begin build) can be reduced to  a minor part of its input (here the rules and complexity). Even world famous  strategies exist who got entangled in rules that don’t seem to change their  robustness or even their profitability. Dead weight rules, after all.
The proof is in the pudding. Take  a very famous system like CANSLIM. It has concepts, like its ‘cup-with-handle’,  which couldn’t possibly be defined in a objective, algorithmic way. Which in  turn means, if it can’t be defined in an objective, algorithmic way, it cannot  be tested nor can it be measured. Hence its impact on the system as a whole  can’t possibly be known. But when one drops the concept of a cup-with-handle from  the system completely, the system keeps working, showing no measurable long  term difference. This in turn makes the case for unnecessary system complexity  keeping a strategy form reaching its full potential. Perhaps in performance but  certainly in terms of percentage of time it’s working (i.e. making money).
In fact on www.chartmill.com, one of the developers  succeeded to match the IBD100 list, which is based, among other things, on  fundamental data, by more than 80%, just by using technical information like  relative strength. Moreover, the real IBD 100 seems to lag the synthetic IBD100,  without using any fundamental information at all.
The second most dangerous  consequence of not finding a robust system, if not curve fitting a strategy  announced dead, is traders starting system hopping. They declare a system’s  dead, loose interest and start looking for another one. The truth is a once  working system (ok if it’s not merely based on a technical arbitrage) never  dies. It merely runs in and out of synch with the market. How do we know  that for sure, you ask? You guessed it.  System dynamics. The better a system works, the better and more it gets  adopted. The more it gets used, the more signals get anticipated sooner while also  closing the window of opportunity more quickly, resulting in less profitability  because not every anticipated signal turns out to actually be a signal. In the  end performance starts declining and people leave the strategy. This is exactly  where the seed is planted for the system’s performance to improve again. When figuratively  nobody uses it anymore, chances are it’s going to start working again. A system  based on simple rules extracted from the logic of market dynamics, has no  reason whatsoever to stop working permanently. Just as there always will be  sunshine after rain and rain after sunny periods, there will be times a system  is in synch and out of synch with the market. We can’t just throw away those  diamonds in the rough. Perhaps we should merely exchange them temporarily with  systems that are working at the very moment, only to reuse them as soon as they  start working again, perhaps pushing the system(s) they got exchanged for, in  turn, to the background. This implies we keep monitoring systems that currently  are out of synch (and thus not traded live). 
  A very know example of this is  trend following. This strategy gets declared dead in every sideways market,  though it keeps coming back letting people reap lots of profits when trends  reappear, time and again, decade after decade.
How can we avoid these dangers  while still asserting ourselves that a system is working. Or better yet, how  good it is working at any moment in time. In the end, all we need to know is  when to use a system, when to stop using it, and when to start re-using it. We  don’t have to abandon a system, we just have to put it on hold when it gets out  of synch with the market. Chances are the system will start working again in  the future. How do we know when? Simply by keeping it monitored while it’s  hibernating. 
  It should be clear by now that  the answer isn’t in changing or tweaking the system. How do we know how good a  system or strategy is working? By looking at its output. Does it make money?

Side dish
 I mentioned system dynamics a  few times. System dynamics is the behavior of a (complex) system over time. It  tries to relate the output of a system to its input, without knowing the exact  internals of the system itself. Hence the connotation of a ‘black box’. Strange  enough, the ‘black box’ description, seems the exact reason for a lot of  skeptics towards objective, algorithmic system trading. Those opposed to the  idea that you can study a system without knowing much about its inner workings,  however, seem to use the same thing they call an unpredictable black box, as a  crystal ball.
What makes using system  dynamics different from other approaches to studying complex systems is the use  of feedback loops and stocks and flows. These elements help describe how even seemingly simple  systems display baffling nonlinearity. So far for fundamental analysis,  and the many strategies based on the assumption of linearity and implied  predictability, handled with extrapolation. Take the NAIC method as an example,  here.

Consider figure 1, where we  have a financial market depicted as a black box (a blue one really J). There are a lot  of fundamental and economical powers steering the system. At the output we have  moving prices and volume data (ok and bid/ask, open interest, and so on …).  Now, while there’s a lot of discussion between proponents of fundamental  analysis and technical analysis worshippers, they are in fact doing the same  thing: crunching numbers. Only on both sides of the same spectrum. Fundamental  analysis is measuring the input in the hope to learn about the system and to  predict it’s reaction to the measured input. Perhaps that’s where the illusion  of control originates. Technical analysists, on the other hand, measure the  output of the system and react to it. But in their reaction, by placing orders,  they also influence the system, which is represented by the feedback loop in  figure 1. To cope with this feedback loop, which we also don’t understand, nor are  we able to quantify it, we need to add the feedback loop to our definition off  the system. As is superposed in figure 2.

Conclusion
We cannot improve a black box,  by tinkering with the way we measure its reaction (output) to a stimulus  (input) and curve fitting our instruments. Neither can we afford to measure the  output as a means to reaction, without regards to the feedback of this same  output back into the system. People react to prices, reacting to people,  reacting to prices, reacting …

If we cannot know how the  system is defined, simply because of limited analytic resources as opposed to  the complexity of the system, the only thing we can do is measure its reaction  in output to a certain input and make a controller to dampen the system’s  effects and the effects of the input on the output. But that will get us most  of the original degrees of freedom without the analytics complexity

Coming  up next
In the next article of this  series, we’re going to explore the concept of equity curve analysis. Starting  by tracking the performance of a system rather than tweaking the system to  change its performance. This will keep our systems from complexity and the danger  of getting curve fitted.  But it also will keep us from  hopping from one system to another, loosing track of good systems that stopped  working temporarily.


Figure 1: Financial markets as seen from a system dynamics viewpoint. Fundamental analysis measures the input of the system to predict its output. Technical analysis (at least the non predictive, objective, kind) looks at the output as to decide what to do. 


Figure 2: To cope with the internal feedback loop, where the output (prices) influence the system,  we have to include the feedback loop into our definition of the system (black box), as we do not exactly know the exact impact of this feedback in relation to the system output. 

† In this article system is also used as a synonym for strategy. The  whole of decisions used and/or necessary to actually trade.]]></description>
</item>
<item>
<title>Chartmill Stop Strategies</title>
<link>http://www.chartmill.com/documentation.php?t=Chartmill Stop Strategies</link>
<pubDate>Mon, 08 Mar 2010 00:00:00 +0100</pubDate>
<description><![CDATA[Several short and longs stops are available in the charts:

Chandelier exit: the stop price is positioned at n ATR below the maximum closing price since the entry in case of a long entry. &lsquo;n&rsquo; is a configurable parameter. In the case of a short entry, the stop price is positioned at n ATR above the minimum closing price since the entry.
5bp/7bp Fractal exit: the stop price is moved up or down every time a 5 or a 7 &lsquo;bar pattern&rsquo; is discovered. This is also called a &lsquo;fractal stop&rsquo;. A 5 bar pattern is a pattern where the middle candle shows a low below the lows of the 2 candles to its right and left. So the stop is moved at swing points in the trend.

The one but last parameter of these indicators is the candle where the trade was started. In the advanced charts you can attach the stop to the entry place by right-clicking and selecting the stop from the menu: it will automatically attach to the candle where the mouse was positioned at the time of the click.
The last parameter for the stops is an initial level for the stop. Once the moving stop value becomes higher than the initial stop, it will take over. This parameter is 0 by default and is optional. In the advanced charts the initial value will also be picked from the mouse location when adding the indicator by right-click.

Examples of Average True Range Stops and Fractal Stops


Average True Range of 3xATR for last 30 days, calculated on MSFT





Fractal Stop (5 day pattern) calculated on 50 days of action on MSFT
]]></description>
</item>
<item>
<title>Stocktwits and twitter integration</title>
<link>http://www.chartmill.com/documentation.php?t=Stocktwits and twitter integration</link>
<pubDate>Thu, 01 Jan 1970 01:00:00 +0100</pubDate>
<description><![CDATA[
Share the link!
If you are looking at an interesting set of stocks in the basic charts, or you found a nice screener configuration, you can easily share this with your friends on twitter or stocktwits by using the 'tweet' or 'share on stocktwits' buttons in the basic charts and screener applications. These buttons will share the current page that you are looking at, so your friends will be able to look at the exact same page as you are looking at.
View the stocktwits stream next to the charts.
In the screener and basic charts, you can use the 'stocktwits view' to see the stocktwits stream next to the charts of the stocks you're looking at.&nbsp;Example ....

]]></description>
</item>
<item>
<title>Finding Strong Stocks Using Chartmills Relative Strength Screener.</title>
<link>http://www.chartmill.com/documentation.php?t=Finding Strong Stocks Using Chartmills Relative Strength Screener.</link>
<pubDate>Thu, 01 Jan 1970 01:00:00 +0100</pubDate>
<description><![CDATA[Why High Relative Strength?
When people are looking to buy the next hot stock, they often don’t search in the right places.  It’s a common misunderstanding that low- priced stocks always make the biggest moves.  This is because people have a tendency to think that those equities have more chance of doubling than higher priced ones.  Psychology, dear Watson.  Some traders therefore put all their eggs in risky, low priced stock baskets with little liquidity:  a very dangerous position to be in.  When a bull move is over they often notice that their stocks never gained much compared to the stocks of those that concentrate their energy and money solely on the strongest names.  (note: Trading in low- priced instruments can be very profitable but you have to really really specialize in it)

To find strong stocks that are (much more) likely to double or triple with less risk, I use a concept called Relative Strength.  Relative Strength simply means that you are looking for stocks that outperform the general market.  When a stock continues to show strength compared to the rest of the market, it has a higher chance of showing good and longer- lasting rallies.  The general thinking behind this, is that big  (institutional) money always flows to the strongest names with the best fundamentals.  Such people are not interested in taking huge risks on low-priced, ill-liquid (my little joke) stocks with their massive Hedge-Funds-Money-Bazookas.

So why bother taking the risk to invest in low-priced stocks, if you can also make very decent money with the more liquid stocks (which often have excellent fundamentals behind them)?

How To Find Strong Stocks?

Go to the StockScreener Tab.


First of all: click on the no-tab icon to get a full overview of the screener.

  

Choose Strong Stocks in the Signal List.
  
  


Look for stocks that have an Average Volume of 50 SMA &gt; 500 K.
    


Look for Stocks that are priced at least 5$
  




You'll probably ask yourself what this  “Strong Stocks” scan does exactly  ... well, it’s a specially designed ChartMill Stock Screen to find equities that have a High Relative Strength and that don’t jump around a lot (for example if a stock is up 50% on earnings, it has a high relative strength - but we don't want that).  We are looking for stocks that have steady price performance over weeks and months.
Our scan gives a total of 54 results, which can be sorted on Relative Strength.  
Just click on the column in the screener.


Stocks that are closest to 100 are the ones with the Highest Relative Strength.
Drilling Down!

After that, you can easily move your mouse over the tickers to have a peek. It's a very good idea to do this every day while developing an entry and exit strategy that fits you. You can either enter stocks on retracement or use a break-out strategy.


Good luck!
Michel Lahaye
Co-Founder of ChartMill.
Passionate Stock Trader
(We don't recommend stocks. This article is for educational purposes only.)
]]></description>
</item>
<item>
<title>CMMB: Chartmill Market Breadth Indicators</title>
<link>http://www.chartmill.com/documentation.php?t=CMMB: Chartmill Market Breadth Indicators</link>
<pubDate>Thu, 01 Jan 1970 01:00:00 +0100</pubDate>
<description><![CDATA[In the charts, some indicators are labelled 'CMMB: xxx'. &nbsp;'CMMB' stands for 'ChartMill Market Breadth' and&nbsp;these indicators are market breadth indicators. These indicators do not depend on the specific ticker that is shown on the chart, but contain information from multiple stocks in the marketplace. The following indicators are available:

CMMB: Moving Window Money Flow
CMMB: Cumulative Money Flow
CMMB: Nasdaq Advance - Decline Line
CMMB: Nasdaq McClellan Oscillator
CMMB: Nasdaq Summation Index
CMMB: AAII Bullish Sentiment
CMMB: AAII Bearish Sentiment
CMMB: AAII All Sentiment
CMMB: BDH Market Model

Each of these indicators will be discussed in more detail below;
CMMB: Moving Window Money Flow
This indicator is based on the Large Effective Volume of a large set of US stocks. All stocks with an average volume of at least 1 million participate in the indicator. This means that a little bit over 1000 stocks participate in the indicator.
Each minute, chartmill records the addition of the 'price * LEV' of the selected stocks. In this formula:

'Price' is the current price of the security, this is expressed in dollars.
'LEV' is the Large Effective Volume for that minute. This is the total amount of stocks (volume) that was accumulated or distributed by Large Players based on the Effective Volume calculation rules. This component is negative when large players distribute and positive when they accumulate.

So for each security, the actual amount of dollars accumulated or distributed by large players is recorded every minute. The sum over all securities gives an indication of the activity of large players in the market at every point in time.&nbsp;
Typically, on a daily basis, accumulation or distribution up to 2 billion dollars by large players can be considered 'normal'. When accumulation or distribution exceeds 2 billion, large players are heavily distributing or accumulating.
The indicator has 2 parameters:

The size of the window. By default this is 20. The indicator will add the values of the bars for a window as specified by the parameter. In the daily timeframe, when the value is 20, the current value will be the sum of the total accumulation or distribution over 20 days. The value is shown by the black line on the chart. When the value is positive, this means there was net accumulation by large players in the last 20 days.
The moving average of the black line: by default the value is 20. The red line shows the moving average of the black line.

A chart with the indicator can be found&nbsp;here.
CMMB: Cumulative Money Flow
The input for the Cumulative Money Flow indicator is exactly the same as for the Moving Window Money Flow. Only this version accumulates the total from the start of the data.&nbsp;
The downside of the moving window version if that it can be difficult to interprete: When the line declines it does not necessarily mean that there was distribution on the last day. It just means that there was less accumulation than on the day that dropped out of the moving window.
The Cumulative indicator avoids this issue: if the line goes down it means that there was distribution.
This indicator is also very interesting in the intraday timeframe: it shows the current activities of large players in the market. When the market goes down and large players accumulate, this may indicate that further downside is limited. Of course, as with any other indicator, there are never guarantees.
A chart with the indicator can be found&nbsp;here.
CMMB: Nasdaq Advance - Decline Line
This indicator uses exactly the same data as the&nbsp;$ADDQ&nbsp;ticker. The indicator is the cumulative view of the Nasdaq advance - decline data.&nbsp;
CMMB: Nasdaq McClellan Oscillator
The McClellan Nasdaq Oscillator is a well known breadth indicator that used the advance - decline data as input.
CMMB: Nasdaq Summation Index
The Nasdaq Summation Index is also a popular breadth indicator. This is the cumulative value of the Nasdaq McClellan Oscillator.
CMMB: AAII Bullish Sentiment
AAII&nbsp;does a weekly survey and publishes weekly bullish, bearish and neutral investor sentiment. This indicator shows the Bullish Sentiment.&nbsp;
CMMB: AAII Bearish Sentiment
AAII&nbsp;does a weekly survey and publishes weekly bullish, bearish and neutral investor sentiment. This indicator shows the bearish sentiment.
CMMB: AAII All Sentiment
AAII&nbsp;does a weekly survey and publishes weekly bullish, bearish and neutral investor sentiment.&nbsp;This indicator shows the bullish, bearish and neutral sentiment.
CMMB: BDH Market Model
Historical data from the 'buy dont hold' market model. More information on the model can be found here.
&nbsp;]]></description>
</item>
<item>
<title>Chartmill Homepage</title>
<link>http://www.chartmill.com/documentation.php?t=Chartmill Homepage</link>
<pubDate>Thu, 01 Jan 1970 01:00:00 +0100</pubDate>
<description><![CDATA[The top of the chartmill homepage displays the following items:

An intrady chart of SPY with the Cumulative Market Money Flow line.
Market Breadth Statistics
Current Market Volume Information.

Intraday SPY chart.
SPY is the S&amp;P500 ETF. The chart also displays the intraday Cumulative Market Money Flow line. The Market Money Flow Line displays the total dollar amount accumulated or distributed by large players in the market. Some more information on the indicator can be found here.
The same chart showing the actual value of the line can be found here.
Market Breadth Statistics
The bubble chart in the middle shows several market breadth statistics. The size of a bubble is determined by the percentage of stocks that meet certain conditions. The color of the bubble indicates whether this is positive or negative. The following items are displayed:

The number of advancing stocks today.
The number of declining stocks today.
The number of stocks above their 20 SMA.
The number of stocks above their 50 SMA.
The number of stocks above their 100 SMA.&nbsp;
The number of stocks above their 200 SMA.
The number of stocks making a new high today.
The number of stocks making a new low today.
The number of stocks advancing 4% or more today.
The number of stocks declining 4% or more today.
The number of stocks showing a pocket pivot today.

Current Market Volume Information.
The bar chart at the right shows the current market volume compared to the average volume over 5 or 20 days at the same time in the day. &nbsp;]]></description>
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