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A recent article from Reuters titled “Economic optimism lifts US copper to 13-month peak” provides an example of the leap of faith many are taking when they observe rising copper prices and conclude that global economic prospects are brighter. The article included a lot of bullet points, but most related to matters unrelated and unconnected to economic activity.

Some of the discussion related to charting. Terms such as “double bottom neckline” and “resistance level” may have meaning among charting traders, but those terms should be confined to discussions relating to where people betting/gambling/speculating on the copper price see the price heading. In other words speculators who are long copper because of a chart pattern shouldn’t lead a journalist or sub editor to frame the article around economic matters. The predictions being made based on “double bottom neckline” are unrelated to whether or not economic optimism exists and are unrelated to economic data. For clarity I am not saying a trader can’t profit from these things, but merely that they are what they are, and what they are is independent of economic fundamentals.

Another bullet point related to housing sales. The assumption is presumably that, as a so-called bellwether metal, a rise in housing sales means a rise in construction, which means a rise in copper consumption, which means an expectation of a rise in demand. Below is a chart of US copper consumption this decade. For reasons unclear to me consumption has been falling all decade. The International Copper Study Group (ICSG) has forecast consumption in the USA, Europe and Japan to be down approximately 17% this year.

copper2

US copper consumption

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An interesting article over on SeekingAlpha recently “Baltic Dry Index’s fall misleads investors.” The author pointed out that while the BDI is down, there is also a surplus of ships, so that the BDI weakness is not entirely due to lower levels of trade. Among the discussions that followed was a comment/question about the seasonality of imports and exports. I collected data from the Port of Los Angeles and trade the Census Bureau. The chart below shows US exports of goods (not services) plotted against port activity at Los Angeles. Not surprisingly a good correlation but I’m not sure if you can glean any seasonal trends from the data (and the data pre-2000 is much the same).

US goods exported and outward container TEUs

US goods exported and outward container TEUs

The import data correlation is not quite as good as the export data but it is tempting to conclude that some sort of seasonal pattern exists with import lows earlier in the year. Incidentally for a very clear example of a seasonal trade pattern have a look at imports from China.

US goods imported and inward container TEUs

US goods imported and inward container TEUs

So part of the rebound in import activity could be viewed as a seasonal pattern. We can assume that the decline in imports reflects the fall in consumer demand and/or a fall in anticipated consumer demand by importers.

Pretty much every metric you can find collapsed (overshot?) early in 09. The data shows an increase in containers being moved since the numbers collapsed, and an increase in trade in (declining) dollar terms, but also shows that we are some ways off from a true recovery.

We hear a lot about the BRIC countries. The plot below — world crude steel production this decade as of end of August — pretty much sums up why it is all about China, the “C” in BRIC, when it comes to iron ore and metallurgical coal demand. The plot shows the enormous growth in Chinese steel production this decade, compared to the low growth in the other three BRIC group and the rest of the world.

world crude steel production

world crude steel production

Iron ore producers took a hit in pricing negotiations this year–admittedly from high price levels in 2008. The pick up in Chinese steel production this year is good news for iron and coal producers for 2010 pricing if this recovery in Chinese steel production is sustainable.

Given the weakness (freefall?) of the US Dollar I decided to make another series of LME data, this one with Trade Weight Index corrected pricing. The Fed Trade Weight Index (TWI) is published weekly whereas the LME trades daily and is closed on certain holidays. So I wanted to create a list of the TWI for each day of LME trading this decade. There may be easier ways (?) but this is the approach I took:

1. Convert all dates to absolute time.
2. The first LME trading day this decade was on 4th January 2000. Remove TWI data prior to that:

time = AbsoluteTime[{2000, 1, 4, 0, 0, 0.}];
TWIData = DeleteCases[TWIData, x_ /; AbsoluteTime[First@x] < time];

3. Collect the LME trading days for each weekly TWI datum. I figured the easiest way to do this was to use BinLists. Each week has 604800 seconds so this will be the bin width.

bins = BinLists[LMEdates, {start, finish, steps}];

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Lynas

Lynas dropped 24% in their first day of trading since the proposed hook up with Chinese co. CNMC was blocked by the Australian FIRB. Down further on Friday closing at 65 cents after trading at 90 before the FIRB stepped in. Could be a long term buy if it retraces back to levels we saw prior to CNMC showing interest. You’d be buying in the hope or expectation that prices for rare earths will increase (substantially) because currently it seems risky given the predicted cash cost versus predicted revenues (i.e. current rare earth prices) equation.

Introduction

The WWW is rich with sources of useful data, some of which are available directly, others require registration and subsequent login. I want to discuss how independent investors, without access to Bloomberg, Reuters and other expensive data sources, can streamline their work-flow by automating data access and processing. You’re busy—you want to analyse your data, not spend all your time collecting it and processing it.

I’m going to start by writing about retrieving the data once you have established regular and reliable sources, then discuss processing and presentation. Those stages will all be done by Mathematica, sometimes with a little help from wget during the retrieval stage. The final stage is automation. I run Mac OS X so that stage will describe how to tie everything together in one automated flow. The objective is to set all this happening to a timer and wake up each morning (assuming the data of interest is daily) and have a chart, or several charts, with your data, presented in a design you prefer, ready for you in your email inbox.

Data format

The formats you are likely to want to be acquiring are HTML pages, Excel or CSV files, or zipped files—typically zipped Excel or CSV files. One way to process data available from the web in Excel, or other formats, is to download the data and then import it into Mathematica. You’ll want to be using Mathematica’s Import function. Some additional documentation can be found here.

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According to Reuters, London Metals Exchange (LME) tin traders are describing the market as “disorderly” and saying that prices do not reflect reality. Apparently a single entity owns 90% of long warrants. My reaction is that while having one entity controlling the long side is obviously a distortion, what is causing other metals to be similarly distorted in so far as prices being positively correlated with stockpiles?

Below is a Wildebeest Correlation Plot—a plot of the rolling correlation between prices and stockpiles—of LME tin as of the end of September:

Tin WCI plot

Tin WCI plot

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