# Tag Info

## Hot answers tagged charts

9

SciChart I used SciChart and was happy with it. This is extremely rich charting library ($500 per licence; \$1000 with source code). Infragistics A premium UI library. I haven't tried Infragistics charts particularly, but I'm sure they are as much perfect as their other controls (grids, ribbons etc). MS Chart Extended WPF Toolkit SciChart vs ...

4

I know of two sites that promise to do that but I haven't used either of them so I cannot tell you how reliable they are: http://thepatternsite.com/chartpatterns.html http://www.priceactionlab.com/Manual/manual.html The excellent blog systematic investor has some articles about how to do it in R: http://systematicinvestor.wordpress.com/2012/01/13/time-...

3

Sounds a little bit similar to variance ratio - the seminal paper of which was Lo, MacKinlay 1988, however that deals with variance, not differences of extrema. That you find "risk" narrows over time is odd, given that $Var[x + y] = Var[x] + Var[y]$ for independent variables $x \& y$, and one could look at days as being independent, and similarly ...

3

Definitely check out SciChart, which is a commercial WPF stock chart control built with financial & scientific users in mind. There is a review posted here and here. SciChart supports Candlestick, OHLC Line, Step-Line Mountain, Column Scatter Band series (High Low Fill) Annotations such as line, arrow, trendline, custom buy or sell markers Composite ...

3

I have played around with those a bit and my results were mixed. Bollinger bands essentially show you the price relative to rolling window volatility. One interpretation is that if the current price leaves the Bollinger bands, a trend or movement emerges (of course depending on your time frame as with all technical indicators) in that direction. The ...

2

def bbands(price, length=30, numsd=2): """ returns average, upper band, and lower band""" ave = pd.stats.moments.rolling_mean(price,length) sd = pd.stats.moments.rolling_std(price,length) upband = ave + (sd*numsd) dnband = ave - (sd*numsd) return np.round(ave,3), np.round(upband,3), np.round(dnband,3) sp['ave'], sp['upper'], sp['...

2

If you look at longer time returns (monthly or weekly as compared to daily) then these can be seen as the sum of daily (log-)returns: $$r^w = \sum r_1^d + \cdots r_5^d.$$ It is in general not true that the $r_i$ are iid because they are not independent. If they were then $r_i^2$ would be iid too and we know that volatility clusters. Even without ...

2

John Bollinger, the developer of Bollinger Bands, provides descriptions of methods he suggests for using his bands on his website BBands.com. They can be found under Four Methods in the support area. Bollinger Bands are most effective when used with other indicators for confirmation, and are very powerful for mean reversion and for price breakouts. On the ...

2

I answered @Anilca's question in SO (and the answer was accepted) I summarize my answer with the working solution: public class Aroon : IndicatorCalculatorBase { public override List<OhlcSample> OhlcList { get; set; } private readonly int _period; public int Period { get { return _period; } } public Aroon(int period)...

1

You can try Macroaxis. Although it does not let you define your custom patterns but it can plot and recognize more than 300 technical indicator patterns on historical data. You can see example of pattern recognition for google at http://www.macroaxis.com/invest/Pattern-Recognition/GOOG Disclaimer: I work for Macroaxis

1

If you really just want the charts, you can get this on the TD Ameritrade platform. You do need a funded account, so it is not exactly "free" but there aren't any fees associated with it. In their desktop client, you can select "On Demand" mode. This gives you the ability to rewind to earlier points in history for sim trading purposes. Just rewind back ...

1

I think that the approach suggested by @Alex is pretty standard and I have seen such charts before but for a less orthodox approach that may clean up the graphics a bit, you might consider the final cumulative return as a segmented bar or pie chart (though I might discourage pie chart). See this example of a segmented bar for what I'm talking about. Each bar ...

1

I would make a cumulative return chart, using a different color for each sector. Each line starts at 1, and each successive point is found by multiplying the previous point by (1+SectorReturn) for that sector. The horizontal axis shows dates. By looking at the lines on this chart you get a visual feel for what sectors performed best overall and also the ...

1

Granularity Display granularity: You should choose granularity based on screen size constraint. Most screens support about 100 pixels per inch and can display 2000 discrete pixels in the horizontal axis. Let's say you store 2000 minutes of data. It will be impossible for a regular monitor screen to resolve the difference if you displayed them as points in ...

1

Try to plot the rolling mean against your quotes for SP and see if it makes sense. Although you line of code to compute the rolling mean is correct, there might be something wrong in the data that you pass as input.

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The moving average in your platform is most likely based on closing prices of whatever time frame you're visualizing. Daily data would using daily closing prices; weekly data would use weekly closing prices; 5 minute data would use 5 minute closing prices, etc. Therefore, as you change time frame the data used to calculate the moving average changes. Your ...

1

The period is the time between two ticks. It means you have to work with the last 3 periods, each periods have a lenght of 1/14 of a day. So you are working with a time windows of 3/14 of a day.

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I own Gigasoft, if researching financial WPF charting, see our 7 meg demo download. Fast to download, uninstalls easily with no issues, and demonstrate our robustness, speed, user code, and rendering quality: link to financial .net wpf charting. Date time handling, mult-axes, and extensive annotations are key to good financial use and we've spent years ...

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