May 2007 Archives

This just in!  There have been some flips in the Currency Trend neural net models:

• AUD - RANGE
• GBP - DOWN
• USD - RANGE
• EUR - RANGE
• JPY - DOWN
• CHF - DOWN

[tags]Neuralnets, AI, Currency, FOREX, Trading, Timing[/tags]

As part of the development for my S&P500 Volatility Timing Model, I test several data sets and build several tweaked models. What I'm interested in in is finding the "outliers", also known as the financial asteroids. These represent great opportunities for me because I usually buy either mutual funds, ETF's, or stocks in our 401k when they happen. In the case of the May 10, 2007 selloff, we were hit by a small financial meteor. Take a look at the posted pic, you'll can see a green dot which is "out there" from the regular cluster of data points. That was the financial meteor, the outlier! However, based on my model it was a small buying opportunity, nothing to get overly excited about. [tags]Finance, Quantitative, YALE, AI, NeuralNets, Trading, Volatility, Timing[/tags]

Today I wanted to share with you a part of the algorithmic back end to my ETF Trend System. Note, I said "part", I'm not giving away all my secrets. It's written completely in Excel, incredibly simple, and is a macro that you can import. The system works by using something called linear regression slope. What's that? The easiest way to understand what linear regression slope is, is to think back to your basic statistics class. Linear regression is the "best fit" line between a bunch of data points. A line is defined by the formula: y = mx+b, where y is your data point's position on the y-axis, m is the slope, x is your data point's position on the x-axis, and b is the slope intercept. What this ETF Trend following system does is place a "best fit" line across several price data points (8, 13, and 26 weeks) and then calculate the slope of the line. If the slope is positive, you have an upward trending ETF. Conversely, if the slope is negative then you have a downward tending slope. As the ETF trades in the markets, the price goes up, down, and sometimes consolidates inside a trend. When that happens the linear regression slope begins to "flatten" out, meaning the slope becomes more horizontal. When combined with two or more periods, like an 8, 13, and 26 week period, you can see the overall short-term, medium-term, and long-term trends in a particular ETF. This makes for a great indicator that warns you of either a change in trend or a dip buying opportunity. Ready to try it out for yourself? Just follow these easy steps and you'll be ETF Trend following in no time. First you have to make sure you have Excel 2003 or a later version installed and access to ETF data. Step 1: Get two years of ETF data. You'll need your favorite ETF and two years of weekly closing data. Make sure you include the date, open, high, low, and closing prices. You can cheat, and follow along with my example by downloading this XLS: GSPC ETF Trend Example In the example contained in this lesson, I use the S&P500 weekly data but you can substitute that with any ETF or index you'd like to follow Step 2: Copy the macro code below and paste it into your Excel Visual Basic Editor. You can find this editor by going to Tools > Macros > Visual Basic Editor.

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 Sub

Step 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.

This is week two of my positions still being trapped near the all time highs in the EURUSD. I had to lighten my load last week and close two more positions after the EURUSD's plunge triggered two buy orders. I try not to have more than 4 open positions in one currency at a time and all of them remain in negative territory. Right now my average price for my position is $1.35682 and the currency pair is trading at $1.35350. You might be wondering, why go against all trading wisdom and hold a loser. I wonder that myself but I trust my neural net models. They tell me to continue to be long. Why trust my neural net models? Good question! I trust my models because they've been right time and time again based on two observational methods: back-testing and real time observation. I'd agree with you 100% if you argued that back-testing isn't as good as forward-testing, but my models are built on older data, correlated to a very high degree, and seem to be working well in the real-time environment. Time will tell if the model is right. :) [tags]Quantitative, Finance, NeuralNets, Euro, USD, Currency, FOREX, Trading[/tags]

I fixed my Yen model and its showing the corrected trend signals.  Goes to show you, one small toggle or incorrect label designation in your prediction model and your signals are wrong.  Good thing I only had a few million at stake! :)  It's wise to always question your results. :)

•  AUD - UP
• GBP - STRONG UP
• USD - STRONG DOWN
• EUR - STRONG UP
• JPY - DOWN
• CHF - RANGE

Remember, its wise to always question your results. [tags]AI, NeuralNets, FOREX, Currency, Dollar, Euro, Franc, Pound, Money[/tags]

I’m busy working on my S&P500 Volatility Model tonight and I’m slowly inching toward a better correlation. Despite my headway, it still needs a lot of work to be statistically significant. Right now its close to 75% correlated but I still haven’t figured out how I want to rank the volatility events. I did create a nice image of how the data looks after its modeled. The x-axis is the rate of price change in the S&500 closing price and the y-axis is the volatility event (1 being severe and 0 being safe). If you squint at the image you can see a possible “U” shape outline, which means that this model could be explained by a second order function (parabola). The next image, a chart from my Excel spreadsheet, shows the parabolic function of the modeled data. Well, back to work! [tags]S&P500, Volatility, Models, NeuralNet, Timing, Quantitative, Finance[/tags]

I worked in my vegetable garden for most of the weekend and with every weed I plucked, the words "Ethanol" and "bio-fuel" kept popping in my head. I know that everywhere I turn, I see and hear about how Ethanol is going to save us from oil dependence and global warming. Despite my skepticism about Ethanol, I am curious to know what set of this new trend in the Agriculture index? Exactly what happened at the end of 2001? Was it demand from China? Or a sudden fascination with Cocoa? Inquiring minds want to know!

[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].

[tags]Futures, Corn, Ethanol, BioFuel, Agriculture[/tags]

I've been following the China iShares, FXI, for a long time now and I noticed that Maoxian posted about it nearing its all time high. This reminded me of the time when the talk was that FXI might be overextended or possibly reaching a top. At the time, it sure looked that way. I even posted on my old site saying that the trend might be over but the more I thought about it, the more I wondered if my initial call was wrong. Something didn't seem right to me so I built a classification model to analyze FXI's trend. I was surprised when the model remained long throughout the selloff. You can see the charts I posted on January 31, 2007 and April 6, 2007 on my old site. I even reiterated FXI's UP trend on one of my first blog entries here. As of today, the trend still remains UP. [tags]FXI, China, Investing, Trading, Timing, NeuralNet, Classification[/tags]

When I was first introduced to data mining and modeling, I felt like I had found the goose that laid the golden egg. I thought, erroneously, that I could create a predictive model that would be able to tell me what the closing price would be for a specific asset. I successfully modeled the S&P500 Spiders (SPY) and was able to predict the daily closing price within a 3% price range. I soon learned that this only worked well when the market was trending in a one direction. If the market turned on a dime, as it usually does, the model would fall apart. So I scratched that pipe dream and focused on identifying macro trends instead. Having successfully modeled currency, stock, and future trends, I decided to start fooling around with market timing. I’m a firm believer that market timing is critical to financial success and here’s why. When I was in MBA school, I had written an independent research paper about Hedge Funds and the various trading strategies they use. One strategy I discovered was a volatility based strategy that would invest money during times of extreme market volatility. I analyzed three fictional portfolios to see if the volatility based strategy was superior to a buy and hold, and dollar cost averaging strategy. I used the $VIX as my volatility indicator and assumed each investor would buy into the S&P500. The results shocked me! I don’t remember the exact percentages anymore (I’ll try to dig out the paper and post it) but a buy and hold investor would get a 14% return (not bad), a dollar cost averaging investor would get a 20% return (even better), and a volatility based investor would return over 100% over the same time period. Damn! Then I read “The (Mis)Behavior of Markets” and realized that its easier, and smarter, to model volatility instead of prices. If I were able to forecast and determine the magnitude of volatility for a future event, I would be ready to take profits or buy in. Now that would be truly profitable! So I began working on a S&P500 Volatility Timing Model, which is in testing phase right now. Its not perfect and it has few bugs in it (only 66% correlated) but here’s a snapshot of volatility vs. the S&P500 over the last three months. See anything that could make you money? Note: 1 is very volatile and anything below 0 is low volatility. Have a good weekend all! [tags]SP500, Markets, Timing, Volatility, NeuralNet, Forecasting, Investing[/tags]

People do dumb things at the heights of trends, like issuing the homebuilders ETF XHB. Shortly after its debut, XHB began to sell off as the negative sentiment snapped the RE buying trend. As I mentioned yesterday, sentiment and fundamentals affect trend and in turn the trend affects sentiment and fundamentals. I believe the pain for XHB and real estate isn't over but I wouldn't be a buyer here. Some people with their limit orders at $29 might just get XHB at the price they want, if they wait a bit longer. [tags]Trends, XHB, RealEstate, Bubble, Crash, ETF[/tags]

I'm still trapped in my EURUSD positions as this currency bounces around $1.3528, my average position is at $1.3609. My models still show an UP trend in the Euro and a DOWN trend in the USD so I'll have to sit tight and wait it out. [tags]NeuralNet, Currency, FOREX, Trading, Euro, Dollar[/tags]

Several years ago, when I was living in New Mexico, I had a girlfriend who used read very esoteric books about cutting edge theories on biology, evolution, and astronomy. We were talking about "what-if" scenarios one day and the conversation drifted to some called "punctuated equilibrium." It was explained to me, at the time, that species evolve slowly in an environment that's in equilibrium. A sudden leap in a species evolutionary development happens when a catastrophic internal or external event occurs. The example she used was the asteroid wiping out the dinosaurs theory. The dinosaurs lived and evolved in a relatively state of equilibrium until an asteroid killed them suddenly and allowed mammals to evolve rapidly. Wikipedia defines it as:

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.

In my old blog, Digital Breakfast, I posted a few times about my desire to build an Automated Trading System (ATS) using Excel. I figured I'd build it in Excel since I know that software the best and conveniently enough, Interactive Brokers offers an API for Excel. Today, I located my old real time ETF Trend Signal system, which is based on statistical performance measures to generate trend signals, and decided to begin additional back end development on it. I want to explore some ideas I learned over the past few days from C++ Trader and Christian about option neutral trading and even asset mispricings (arbitrage). The more I think about it, what I really want to do is to build an algorithmic trading system first and then an automated trading system. So step one, build an ATS and then step two, build an ATS. :) [tags]ATS, Algorithms, Trading, System, Excel, Trends, Quantitative, Finance[/tags]

The Spiders have been on a nice trend upward, thanks to a lot of money chasing too few good assets. One day, when the liquidity dries up, I expect the party to come to an end. In the meantime, I'll ride the wave up and make some money. :)

Welcome back! In this lesson we will set our preferences to make sure everything loads in correctly before we create the model. By now, you should've read and built the experiment framework, as described in Lesson III. If you haven't, then what I'll post here might not make a lot of sense. We'll cover 4 items in this lesson:

1. Data Loading Preferences
2. Model Writing Preferences
3. Performance Preferences
4. Run the experiment

Data Loading Preferences Before you run the experiment for the first time, you have tell it where to find your data. If you click on the ExcelExampleSource operator, you'll see the following preferences and toggle box. I highlighted the important preferences with red dots in the above image so to avoid confusion. First select your data spreadsheet (Gold Final Input.xls) from your file folders, click on the "first_row_as_names", enter the number 9 into the label_column field, and enter the number 1 into the id_column field. It's important that you get this step right. What you are doing is telling the experiment where your output variable is located and what your reference id is. The label_column field is the Excel column number of your output variable and the id_column field should be your date column number. Remember this because you'll have to fill these fields in for your other experiments. Next, you should create a breakpoint in the experiment which is nothing more than a pause in the experiment's run. We're doing this because we want the experiment to pause right after its loaded in the data. Why do we do this? By creating a breakpoint at this point in the experiment, you can inspect the loaded in data and make sure the experiment is reading in your output variable correctly.

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, I’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 haven’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]

There's something to be said about buying winners. Winners continue to win, and in the case of stocks, they keep making new highs. I know that AAPL is a favorite holding of Howard and his hedge fund's strategy of finding and investing select stocks making new 52 week all time highs is a smart. The trick is to buy the right company making a the high. It's safe to say that Apple is the right company, its emerged as an innovative powerhouse once again. Steve Jobs and company sure have been busy, first the iPod (and iTunes), a new generation of desktop computers running on Intel chips, and now the iPhone (out soon). People are loving it and buying like crazy. APPL made another 52 week high yesterday. Good company + Good Products + Strong trends = Strong Price Appreciation. [tags]Apple, AAPL, Trends, Stocks, Trading, Investing[/tags]

I'm pretty sure a short-term top is in for EURUSD and I'm trapped up there. :) I have four positions open at an average price of $1.36132 and with EURUSD trading around $1.35764, I have a long way to go before I see profits again. Still my neural trend models say to remain long so and I will be adding to this position on severe drips (around $1.3500 and then $1.34700). Its too early to tell if the Australian Dollar's trend is resuming or if its still consolidating, either way the trend model retains an UP signal for this currency. I don't regret selling my two AUDUSD positions but I'm mad that I didn't wait for the mini rally yesterday. Lesson learned, I should always trust my trend model and buy on dips, not on new highs. I have to re-examine my Yen model because it gives me a lot of UP signals in a continuing downward trend. I find it awfully suspect but then again the market forces could be there to make the Yen pop. A stronger Yen will surely rein in the carry trade and help reduce the global liquidity glut. That will in turn firm up our mortgage interest rates push more sub-prime borrowers into bankruptcy or foreclosure. Oh what a tangled web we weave, when first we practise to deceive! - Sir Walter Scott [tags]Currency, NeuralNet, Models, AI, Forex, Trading, Euro, Dollar, Australian[/tags]

Today was a USEC day for me. First I saw it profiled on WallStrip and then it showed up on Google Finance's top % gainers list. It sure looks like a healthy trending stock as it rises from the lower left corner of the chart to that magical upper right hand corner. It doesn't take a rocket scientist to figure out that the world needs more energy everyday and with all the fervor about fossil feuls and greenhouse gases, nuclear could be a viable option. The only drawback to nuclear energy is that pesky radioactive waste. So the question you have to ask yourself, do you want to glow or sizzle? [tags]USU, Nuclear, Energy, Trend, Stocks, Investing, Trading[/tags]

Good Morning! Here are the latest currency trends from my model. You'll notice that today's signals are the same as last week. A note of caution, I do find the Yen signal a bit suspect because the chart shows a further price decline but on the other hand the model could be warning us of an impending trend change.

SF - Range JY - UP EU - Strong UP DX - Strong Down BP - Strong Up AD - Up

Whatever is happening (or not happening) with the Yen, we'll keep watching it and bide our time. [tags]Currency, NeuralNet, AI, Investing, Trading[/tags]

I was curious to see what the blog posting trends were for these three assets and some other topics of interest. It seems like all three are on a steady upward trend, that's probably because oil and gold are rising in price and all the real estate bloggers are posting "I told you so." Britney spiked in late Feb, probably because of her head shaving event, Angelina is cruising along. I'm still waiting for Mr. Ugly to spike. Maybe if he shaved his head and adopted a second cat his ratings might increase. :)

I heard this first on Bloomberg Radio and then found the article. It's about the ever increasing use of data mining and AI in the financial markets.

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]

This week was a true test of my new currency trading plan and neural net models. My models have remained long the Euro and short the Dollar despite the strengthening in the USD. Unfortunately I had to sell three positions this week, two at 1% loss each and one as a scratch. I completely closed my AUDUSD position because the price action makes me think that the trend could be stalling. I say this because the economic reports coming from Australia are indicating a slowing inflation rate. Lower inflation usually means no further rate hikes and no further currency appreciation. I guess I could wait for my neural net model to catch up but sometimes you have to trade what you see and read. Next, I lightened up my load in EURUSD by selling one position at another 1% loss but entered another position right afterthe Non-farm Payroll report came out. So far my aggregate EURUSD position remains a net negative as we head into the weekend. Good thing I’m working in my garden this weekend! [Tags]Euro, Currency, Trading, Positions, Dollar[/tags]

Noble Engineering popped up on my fundamental screen last week and I've been watching it since then. Using Yahoo's stock screener, I created a special scan that only looks for key drivers of stock appreciation. Can you guess how I figured out these key drivers? I bought NE for my wife's account yesterday, so it better do damn good or I'm in trouble! :) [tags]DataMining, NeuralNet, NE, Drilling, Oil, Fundamental[/tags]

Google has been on fire since its IPO a few years back. I was suffering from post Nasdaq bubble syndrome and thought, "no way is GOOG going $400!" Boy was I wrong! What can I say? Google is the new Microsoft.

Good Morning! I had to lighten my load yesterday and sell one position in AUDUSD. It was down about 1% and I decided take a loss. Since then, both pairs (EURUSD and AUDUSD) have been languishing at there intraweek lows. I think currency traders are awaiting the Non Farm Payrolls out tomorrow to decide whether to buy or sell the USD. The EUR and AUD have no where to go but up if yesterday's ADP report was a hint, but I'll have to wait till tomorrow and continue to nurture my remaining 5 positions. [tags]Currencies, Euro, Dollar, Forex[/tags]

Could the trend party in IYR be over? Maybe. A lot of information can be gleaned from observing a price chart, mostly technical information. A lot of traders/investors forget that fundamental and sentiment information also drives prices up or down. [tags]IYR, RealEstate, ETF, Trend[/tags]

Sometimes trends have intra-trend reversals (which are great selling or buying opportunities) and then quickly resume their trends. Motorola tried to break free from their down trend, and almost succeeded with the introduction of their wildly popular Razr phone (I have one). With no new innovative product followup, MOT resumed its downward trend. I wonder if Icahn can really help? [tags]Motorola, MOT, Stocks, Trends, Charts[/tags]

Good Morning! There was further sell off in the EURUSD and AUDUSD pair last night as the USD gained strength. It's just follow through from the better than expected ISM report yesterday. I believe these market machinations will be short lived and expect the pairs to reverse and head back up in short order. In the meantime, I'm holding 6 positions in these pairs (4 in EURUSD and 2 in AUDUSD). Right now I'm down 48 and 18 pips respectively, with my largest single loss at 75 pips in the EURUSD pair. It's times like these I must remain true to my trading plan and stick it out. I ran my neural nets this morning and they still indicate UP trends for the EUR and AUD, so I'll have to be patient. The worse case scenario is that I slowly scale out of losing positions. :) [tags]Currencies, Forex, Euro, Dollar, Trading, Wealth[/tags]

Good Morning! Today's currency trends are as follows:

• AUD - UP
• BP - Strong Up
• USD - Strong DOWN
• EUR - Strong UP
• JPY - UP (this is suspect)
• CHF - RANGE

I'm considering sending out a morning email with my daily currency trend signals. What do you think? Good idea? Overtime I hope add other asset classes like Oil, Gold, and Silver. Don't forget to enter my Predict the S&P500, Win $100 contest! [tags]Currencies, Forex, NeuralNets, Trends, Signals[/tags]