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Homepage > Future Plans
Future Plans
We haven't taken StockWave as far as we can, and certainly not as
far as we want to go; these are our plans for next 12 months or so (-
note that there won't be a StockWave 2.0 for at least a year).
There are a number of things we are thinking of adding to our
system -
Realtime Data Feeds
With StockWave you get streaming intra-day data for free, with no
subscriptions, but because we rely on free sources, this data has a
(small) delay on it. Streaming realtime data is expensive and will
remain so, it is highly unlikely that anyone will ever give it away
for free - in the financial world it is the culture that one must "pay
through the nose for everything", so this is unlikely to change.
For some users delayed data is unacceptable - we quite understand
this; currently, with some coaxing and the crafting of an appropriate
configuration file we can actually take prices from an ASCII, i.e.
simple text file, which may be an adequate solution if your current
datafeed can export data in such a way.
But again, you may need more than this, and wish for direct support
for e.g. eSignal, Reuters DataLink, Bloomberg, MarketEye, etc.
Update April 2007 - we are going to add the OpenTick datafeed.
Mac and Linux Users
StockWave is a Windows-only program, but we would like to support Mac
and Linux users; although StockWave may work under some emulation
programs, a true cross-platform solution, e.g. using Qt, would be
preferrable.
The introduction of the new version of Windows - Vista, adds
further complication to the matter.
Update April 2007 - it seems we don't need to bother with this -
BootCamp from Apple and the WINE project for Linux are compatible with StockWave.
Company Fundamentals Database
We haven't really touched on the matter of fundamentals so far - not
because we don't think its important - we don't believe in throwing
away any potentially useful information just because it doesn't fit in
with some preconceived notions about what matters and what doesn't -
so although from moment to moment, it is the share price itself, and
the current market sentiment from recent news events that seems
to have the most effect on the evolution of the share price, we do
believe fundamentals can give us something, if we approach them in the
right way.
Currently investors look at key ratios of fundamentals to decide
whether a company is 'good' or 'bad' with respect to some pre-defined
stock-picking criterion, buy the shares if they seem cheap, and then
hold-on to them for the long-term, arguing that eventually the
company's good fundamentals will be reflected in the share price.
But individually, key ratios may not mean very much (- even when
not being subject to gross and fraudulent manipulation) - to get
anything from fundamentals we have to look at them collectively for a
sample of companies. The fashionable buzzword to use in this context
is data-mining - there are a
mature variety of algorithms for doing this (- clustering, k-means,
self-organizing maps, decision trees, maximum entropy).
What this approach gives you is the ability to identify, should
they exist, the 'signals', i.e. defining characteristics of good and
bad companies; with such a filter one can then go on to classify
hitherto little-known companies to see if they have the right-stuff,
whatever it happens to be. (Note that this 'right stuff' may be a lot
more complicated than anything which can be reasonably described by
classical stock-picking rules.)
We know exactly what we want to do here - we have a good design,
but the main problem lies in gathering the data to begin with. Again,
as you might expect, selling investors access to proprietary databases
of company fundamentals is a lucrative business (- see e.g. Hemscott,
Dun&Bradstreet) - you cannot get such data for free, but what you
can do is to gather it from open sources which are common and easily
accessible on the internet. The problem lies in taking unstructured,
often very messy, raw data downloaded from web sources and process it
into the kind of structured formats which you need for a 'proper'
database; although a level of automation is possible, some human
intervention is often required, which is very slow. Since we need at
least several hundred, and possibly more likely several thousand
company data files for the database to be viable, you can imagine the
problems; the database also needs to be updated, at least every
quarter.
Once gathered into a proper database and allied with our
data-mining algorithms, we can potentially do a great deal with our
data, still, finding genuinely useful information can be tricky; the
fact is that interacting with databases currently is, well, ... a bit
rubbish, and although there
are some very expensive visualisation packages which allow you to
graphically 'browse' your data, mostly these do little more than the
usual tedious barcharts/piecharts/scatterplots etc, or they let you do
some fairly baffling and rather pointless things like plotting
arbitrary quantities along the xyz axes and representing other
various data by having coloured shapes and so forth; on the other hand
we are developing a novel and informative visualisation which uses
advanced graphics to uncover hidden relationships within the company
data.
Improved Neural Networks for Time
Series Prediction
Neural nets are great at learning from data, but finding a suitable
net can be difficult, and if you want to find the very best for the
job, almost impossible. StockWave includes networks with 3 different
architectures, each of which can be further parametrised by the number
of nodes and/or hidden layers they use.
Having a choice of the common architectures hopefully covers most
of the bases for our practical application, but it is entirely
possible that there is some wild and wacky architecture, completely
different from the usual types which is best for your problem;
actually discovering this specific configuration is less likely than
finding a specific grain of sand on Bondi beach.
Building your own specialised, custom-job neural networks to solve
a specific problem is tedious in the extreme - if you want to try it
out, then download the Stuttgart Neural Network Simulator and play
around with it - see how long your patience lasts.
One approach to the structural optimisation problem is to revert to
genetic algorithms - the classic solution to "global search when we
don't have a clue"; a prototype neurogenetic analyzer has been
developed but is much too slow for the mass market - you really need a
multiprocessor system for it to become viable. It is interesting to
note that the most fruitful efforts in doing this kind of thing have
relied on customised hardware, mostly clusters of FPGA-based systems
(- i.e. racks of specialised computer chips) - and before you ask, the
answer is NO - you cannot buy this kind of kit down at PC World for
£399. (Or at 10 or 100 times this price - the Pentagon might
object!)
We have another approach to this problem we call the SSONN - the
structurally self-optimising neural network, but this is at a very
early stage of development and cannot be guaranteed to bear fruit.
(It's interesting, though
...)
HFS
This is a variation of the Bayesian Correlator analyzer, another
timeseries technique.
Natural Language Processing
Having news analysis alongside the usual charting tools is quite
unusual in a stockmarket program; news articles are aggregated into
news events and assignments made of their MFI values. The MFI scale is
a classification system for news events; the scores are assigned by
comparing a candidates event against a list of known types - the final
assignments of values is done by various ad hoc algorithms based
around pattern matching on keywords and keyphrases. Having an
accurate, by which we really mean 'good enough', MFI score allows us
to examine share price response to news, and to do so quantitatively.
Because the MFI assignment algorithm doesn't really 'read the news' it
sometimes gets things wrong; what we need to do is to add a genuine
natural language parsing engine which can more accurately judge the
senses of the words in an article, and also account for the kind thing
humans find easy, but computers hard, i.e. resolving ambiguity.
NLP techniques can also be used to sharpen up the Web Agent; when
we go deep-digging for specific information about a company we start
with the search engine query results - mostly we can find what we
want, but sometimes due to the nature of search engines, we can miss
out on high relevance content due to keyword-rich commercial spam.
Search engines are all about keywords and commercial webmasters have
become very skilled at producing pages which rank highly on these
engines (- some might say it is killing the art of writing); the
engines don't have much understanding of related concepts and of
context - although some seem to have added a thesaurus to their
algorithms and use clustering to relate things together, it doesn't
seem that good, and it makes things slower. NLP can help to sharpen
our heuristics for what we think is relevant to our queries thus
helping to home-in on specific issues, for example, legal cases where
a company is the plaintiff; a naive search engine result would throw
up a lot of cases where the company was the defender, or documents
relating to legal cases where the company was mentioned in passing but
not involved in that particular case, plus a whole lot of other stuff
which was irrelevant.
News Event Modelling
When you start reading the news archives you begin to get a feel for
the kinds of things that happen in the financial world - the range and
character of financial discourse, how one thing follows another, and
so on. Common events of note include - takeovers, mergers,
resignations, lawsuits, strikes, new products, share buybacks,
currency devaluations, interest rate changes, terrorism, wars, natural
disaster, defaults - these are all the kind of 'surprises' which can
cause jumps in the share price. What is more, there are often
interrelationships between events, sometimes strongly causal, and
sometimes loose, probabilistic; sometimes not at all - in the
financial world, that is to say, a subset of the human world, things
do not normally happen in a vacuum, they follow on from previous
events e.g. if profits are bad the share price might tank, then there
is a shareholders revolt at the AGM, at which the CEO gets forced out,
and then the share price goes back up again, or when the share price
was low after the poor results, a hostile bid was launched by a
competitor, which was referred to the competition regulators,
simulaneously as the Bank of England raised interest rates ...
Our goal is to create a probabilistic event transition model
directly relating to real-world news events which is calibrated
specifically for our current company of interest, i.e. a detailed
model for the kinds of news events we have referred to as
'trendbreakers' elsewhere in our documentation.
In the field of risk management it has long been acknowledged that
there are two types of randomness to deal with - lets just call them
'nice' and 'nasty'; the nasty variety is what causes big jumps in the
share price and leads to the 'fat tail' phenomenon (- or leptokurtosis if you want to use
the correct jargon); this is the type of phenomenon which gives
financial managers fits and sleepness nights as they try to hedge
their portfolios while generating acceptable returns. Quants try to
deal with the nasty randomness using variations of their mathematical
models - 'jump diffusion', 'Levy processes' - but these models are crude.
Data Fusion
Data fusion is the act of taking together different data, of possibly
different types, origins and formats and squeezing it all together to
produce a collective, overall, inference; you use all the data to
produce a single number which tells you what you want to know, and
consequently, the right thing to do.
When you have a system which has share price timeseries, news event
archives and company fundamentals, obviously you would like to somehow
aggregate all this data together and express it as a unified belief
about the future share price; we have developed two approaches to
achieving this, but of course we cannot get started until we have all
the elements in place.
Currently in the field of financial data analysis when something
happens that we don't know how to deal with within the methodology of
our model we label it as an 'externality'/ 'outlier'/'exogeneous', ...
then proceed to ignore it! But this isn't good enough - we need to
take all the data, even different types of data, process it and
express it as a single probabilistic prediction - a heatmap with all
relevant information factored in to it.
Scripting Language
Typical usage of StockWave leads to some quite repetitive tasks - we
can setup a batch job system for automating these. But we can go a
step further than this and introduce a full-featured (i.e.
Turing-complete) 'proper' programming language.
With this facility we can create 'cyber-traders' which can, e.g.
mine the stocks database for interesting stock picks, do analyses for
these stocks, select trading opportunities, make trades and monitor
the outcomes; furthermore we can incorporate learning into this
stock-trading 'meta-algorithm', so that our automated trader can
improve his own performance over time.
In view of this idea it is fascinating to look forward at the
possible future of the market system itself; it is quite likely that
in less than 10 years time there will be no traders, no analysts and
no fund managers anymore, simply a collection of supercomputer AI
systems run by the central banks, investment banks and hedge funds all
trading against each other at super high frequencies. (You can bet
that a lot of money is being spent by these big players on
super-secret behind-the-scenes technology to give themselves whatever
small trading advantage that can make them money; just don't expect
them to tell you about it!)
If such a situation ever arises it will be interesting to see what
happens - markets could become completely 'efficient', or we could see
a total financial meltdown, followed by economic collapse; Adam Smith
or Karl Marx - take your pick!
If you are interested in any of our ideas, in any way, contact us -
we'd like to hear from you. We can always do with help from suitably
skilled individuals - we've got a lot of work to do!
Update Nov 2007 : After considering various options we have decided
to use Python as the programming interface for StockWave; a StockWave API
module will be created which can be imported into the Python system. Note
that Python is a full-featured programming language, with extensive
libraries and a very easy syntax - it can be downloaded for free, as can
high quality programming tools like Eclipse; we believe this will give users
who feel the need to develop their own custom systems, the maximum flexibility
possible. Apologies to those who had hoped we might provide an interface with
Visual Basic, EasyLanguage, C# or something else - trust us, Python is better than
any of these. Learn Python!
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