- AUD - RANGE
- GBP - DOWN
- USD - RANGE
- EUR - RANGE
- JPY - DOWN
- CHF - DOWN
May 2007 Archives
Sub ETF_TREND() ' ' LinReg Macro ' Macro recorded 3/8/2007 by Thomas Ott ' 'Clear Data Columns("G:Q").Select Selection.ClearContents 'Calc ETF Trends Range("G1").Select ActiveCell.FormulaR1C1 = "8 Week" Range("H1").Select ActiveCell.FormulaR1C1 = "13 Week" Range("I1").Select ActiveCell.FormulaR1C1 = "26 Week" Range("G9").Select ActiveCell.FormulaR1C1 = "=SLOPE(R[-7]C[-2]:RC[-2],R[-7]C[-6]:RC[-6])" Selection.AutoFill Destination:=Range("G9:G54"), Type:=xlFillDefault Range("G9:G54").Select Range("H14").Select ActiveCell.FormulaR1C1 = "=SLOPE(R[-12]C[-3]:RC[-3],R[-12]C[-7]:RC[-7])" Selection.AutoFill Destination:=Range("H14:H54"), Type:=xlFillDefault Range("H14:H54").Select Range("I27").Select ActiveCell.FormulaR1C1 = "=SLOPE(R[-25]C[-4]:RC[-4],R[-25]C[-8]:RC[-8])" Selection.AutoFill Destination:=Range("I27:I54"), Type:=xlFillDefault Range("I27:I54").Select ' Format Columns Range("G9").Select Selection.FormatConditions.Delete Selection.FormatConditions.Add Type:=xlCellValue, Operator:=xlLess, _ Formula1:="0" Selection.FormatConditions(1).Font.ColorIndex = 3 Selection.FormatConditions.Add Type:=xlCellValue, Operator:=xlGreater, _ Formula1:="0" Selection.FormatConditions(2).Font.ColorIndex = 50 Selection.Copy Range("G9:I54").Select Selection.PasteSpecial Paste:=xlPasteFormats, Operation:=xlNone, _ SkipBlanks:=False, Transpose:=False Application.CutCopyMode = False Selection.NumberFormat = "0.000000" Selection.NumberFormat = "0.00000" Selection.NumberFormat = "0.0000" Selection.NumberFormat = "0.000" ' Percent Change Function Range("J1").Select ActiveCell.FormulaR1C1 = "% Change" Range("J2").Select ActiveWindow.SmallScroll Down:=18 Range("J53").Select ActiveCell.FormulaR1C1 = "=(RC[-5]-R[-51]C[-5])/R[-51]C[-5]" ActiveWindow.SmallScroll Down:=6 Selection.Style = "Percent" Selection.NumberFormat = "0.0%" Selection.NumberFormat = "0.00%" Selection.AutoFill Destination:=Range("J53:J54"), Type:=xlFillDefault Range("J53:J54").Select End SubStep 3: Save the file and then activate the macro by clicking Run. You should see that the macro created four new columns and color coded the slopes. It should look something like this XLS: GSPC ETF Trend Example 2 Step 4: This step is optional but I highly recommend you do this. You should build a chart from that 8, 13, and 26 week slopes. This will help you identify the peaks and valleys in the ETF's (or index's) trend. See our last XLS example: GSPC ETF Trend Example 3 There you have it! A very simple and fun way for you to build a basic ETF trend system. Please feel free to modify the macro, or add to it as you see fit. If you have any questions or comments, please feel free to contact me.
Remember, its wise to always question your results. [tags]AI, NeuralNets, FOREX, Currency, Dollar, Euro, Franc, Pound, Money[/tags]
- Â AUD - UP
- GBP - STRONG UP
- USD - STRONG DOWN
- EUR - STRONG UP
- JPY - DOWN
- CHF - RANGE
[tags]Futures, Corn, Ethanol, BioFuel, Agriculture[/tags][The Agriculture Index is] An index of agricultural commodity contracts, including Wheat, Red Wheat, Corn, Soybeans, Cotton, Sugar, Coffee, Cocoa, and Orange Juice. Compiled by Goldman Sachs [via traderlog].
Punctuated equilibrium (also called punctuated equilibria) is a theory in evolutionary biology, which states that most sexually reproducing species will show little change for most of their geological history. When phenotypic evolution occurs, it is localized in rare events of branching speciation (called cladogenesis), and occurs relatively quickly compared to the species' full and stable duration on earth. [via wikipedia]I understand that some of these theories have changed over the years but its premise stayed with me for years. Can the upset of punctuated equilibrium (PE) or something similar explain the sudden emergence or death of trends? Although we like to believe in market equilibrium (PE?) and slow evolution of prices when new fundamentals occur, I'm a firm believer that the markets themselves are not always seeking equilibrium. Sentiment and fundamentals drive a trend and then the trend in turn drives the sentiment and fundamentals of that market. Trends become reinforcing and suck more and more capital into them until they crash. Then the crash becomes self reinforcing as the sentiment and fundamentals change and a new trend emerges downward. Where's the equilibrium in that? What truly interests me in trend following is the moment a financial asteroid hits the trend. What are the events or sudden changes in the financial environment that will allow some trends to die and cause others to evolve? Was it single event or several smaller events together that killed a trend or caused financial havoc? The first example that comes to mind was the Russian domestic debt default in the late 90's that led to Long Term Capital Management (LTCM)'s sudden demise. I know I don't have all the answers, all I have is an interesting brain tease, and an interesting biological theory that I'm trying to superimpose on existing trends, hoping to uncover future financial asteroids.
- Data Loading Preferences
- Model Writing Preferences
- Performance Preferences
- Run the experiment
Tip: You can skip this step but I highly advise that you don't. You can create breakpoints at any step in the experiment if you choose but its more valuable during the data loading stage.Model Saving Preferences Scroll down to the ModelWriter operator and click on it. You'll see only one field that will allow you select the path location to save your model. Click on it > select your data directory > type "gold_final.mod" and hit enter. Done! Performance Preferences Now we reach the final step, the setting of the performance preferences. Scroll down to the Performance Evaluator operator and click on it. You should see several fields available with check boxes. Scroll down and check the absolute error, relative error, correlation, square correlation, accuracy, and classification error boxes. Make sure the field with the pull down menu is set at correlation. Refer to the image below for the setup. You're done now. Let's run the experiment! Run the Experiment This is the best part, all your hard work is about to pay off! Find the "play" button and click it! The experiment should load your data in flash and then reach the breakpoint we discussed about. The experiment will automatically switch to the results screen which should look like this: This is where the fun in data analysis begins! This results screen (only if you used the breakpoint) will tell you what the model sees as your output variable (label column). If its not GC Trend, then press the stop button and go back to the ExcelExampleSource operator and check your preferences. Take a moment and click on the "plot view" option. Here you can create scatter plots, self organizing maps, or historgrams to your heart's content. Take a moment and create a scatter plot, choose whatever you want for the X, Y, and Point Colors. YALE should automatically create a plot for you with several dots. These dots are from your id_column preference, in this case the date. Remember we added in the data visualization operator? Doing this allows us to click on anyone of those scatter points and find out more about that data point. Adding this operator lets you determine that data composition of outliers and or specific information about a data point of your choosing. When you're all done, you'll have to resume the experiment. Click on the resume button. Now the experiment will create the model and determine its performance. This step could take a few minutes, depending on the size of your data. While you're waiting, take a moment to subscribe to my RSS feed (shameless plug). When the experiment finishes you should see the information in the results tab be replaced with the following screen: I'm not going to discuss the importance of the statistical measures here but I will tell you that in building a classification model, like this, a high correlation is good. The correlation can be positive or negative and the closer it is to 1 (or -1) the better. Congratulations! You've finished your first YALE experiment and build your first model! In Lesson V, I will show you how to build a prediction experiment and we'll finally predict some current trends for Gold. As always, if you have any questions regarding this lesson or the topics covered so far, please leave a comment or email me.
I wanted to take a moment to recognize C++ Trader and Blogger Jacks, two quant/programming related blogs, Iâ€™ve stumbled across in the last few days. I like them so much, I added them to my blogroll. They post topics range from algorithmic trading systems to modeling for market inefficiencies. I feel so inspired by their blogs that I want become a member of their quant club, if they let me in. I promise to bring my own pocket protector and chips.
C++ Trader went to so far and gave Neural Market Trends a shout out about my YALE Tutorial, which made me realize I havenâ€™t posted Lesson IV (I promise to post it tonight). We exchanged comments and plan on exchanging ideas in the future to hopefully find each others mistakes. :)
[tags]AI, NeuralNets, Algorithmic, Trading, Development, Programming[/tags]
SF - Range JY - UP EU - Strong UP DX - Strong Down BP - Strong Up AD - UpWhatever is happening (or not happening) with the Yen, we'll keep watching it and bide our time. [tags]Currency, NeuralNet, AI, Investing, Trading[/tags]
In his cubicle overlooking the trading floor, Kearns, 44, consults with Lehman Brothers traders as Ph.D.s tap away at secret software. The programs they're writing are designed to sift through billions of trades and spot subtle patterns in world markets. Kearns, a computer scientist who has a doctorate from Harvard University, says the code is part of a dream he's been chasing for more than two decades: to imbue computers with artificial intelligence, or AI.That's precisely the strength of an AI model, the ability to find and learn subtle patterns and help you find an emerging (or ending) trend.
Financial service companies have already begun to deploy basic machine-learning programs, Kearns says. Such programs typically work in reverse to solve problems and learn from mistakes.
Like every move a player makes in a game of chess, every trade changes the potential outcome, Kearns says. Machine-learning algorithms are designed to examine possible scenarios at every point along the way, from beginning to middle to end, and figure out the best choice at each moment. [By Jason Kelly]I firmly believe that data mining, AI, and machine learning trading will accelerate over the years. Who knows, maybe my little model will move markets one day! :) [tags]AI, NeuralNet, Models, Quantitative, Analysis, Trading[/tags]
I bought GE for my retirement account because of its dividend payout and my uncanny ability to buy GE products without even knowing it.
When it comes to picking stocks for long term appreciation, I like to use good old fundamental analysis. That doesn't mean I ignore technical factors that affect its price.
Silly me, I bought GE at a recent high but I can be patient. Why? Well I'm testing out a new Fundamental Analytic Neural Model that's telling me GE should be trading around $43.00.
The same model is telling me that NE should be trading at $64. :)
[tags]GE, Lightbulbs, NeuralNets, FundamentalAnalysis, TechnicalAnalysis, Stocks[/tags]
- AUD - UP
- BP - Strong Up
- USD - Strong DOWN
- EUR - Strong UP
- JPY - UP (this is suspect)
- CHF - RANGE