Artificial neural systems are information processing models which mimic how the human
brain processes information. Unlike conventional, rule-based technical trading systems
popular in the 1980s, neural systems do not need Redened trading rules or “optimization”
of technical indicators to generate trading signals. Instead, through an iterative “training”
process, neural systems “learn” the underlying associations and causal relationships within
technical, as well as fundamental, data affecting a specic stock’s or commodity’s price.
Then, with a high degree of accuracy, neural systems can forecast future prices and trading
signals for that market.
Artificial neural systems are also called neural networks, neural computers, adaptive systems,
naturally intelligent systems, or neural nets. They are modeled after the structure and function
of the brain. Because they can generalize from past experience, neural systems represent a
significant advancement over rule-based trading systems, which require a knowledgeable expert
to define “if-then” trading rules to represent market dynamics.
It is practically impossible to expect that one expert can devise trading rules which
account for, and accurately reflect, volatile and rapidly changing market conditions. Inflexible,
rule based systems simply are not dynamically adaptive, despite periodic reoptimizations
of a system’s indicators.
While today’s trading systems, utilizing historical optimization procedures, risk becoming
“over-optimized” or “curve-fitted” when too many technical indicators or rules are employed,
neural systems gain in predictiveness as more data inputs are used during training. However,
it’s not as easy as it sounds.
Developing a profitable neural trading system is very much an art and not a science
that can be followed cookbookstyle. There are many serious design issues that must be
addressed when developing and training a neural trading system, if it is to be predictive,
and most importantly, profitable.
Typically, neurons within a layer do not connect to each other. Neurons between layers
communicate with one another by having specific mathematical weights (or connection
strengths) assigned to their connections.
For example, you may want to develop a trading system to predict the next day’s Treasury
Bond prices. Designing the appropriate architecture for your neural system is quite exacting,
with more than a dozen different neural designs available. One type of neural system that I
have used extensively for financial forecasting applications is known as a “feedforward”, “back
propagation” system with “supervised learning”.
Before training, the neural system has a “blank mind”. Then you provide the system with an
extensive amount of intermarket technical data related to TBonds, including various currencies,
Eurodollars, the U.S. Dollar Index, the S&P 500, as well as fundamental data such as the Fed
Funds rate. Neural systems carry the concept of “intermarket analysis” to its logical conclusion
by being able to mathematically analyze and weigh the relative impact that each input market
has on the predictiveness of the system.
These inputs must be preprocessed or “massaged using various statistical procedures,
in order to meet the system’s training requirements. Then they are paired with actual daily
prices on Treasury bonds (the desired output). It is critical that the system’s architecture,
learning method, input data, outputs, and massaging techniques are judiciously selected in
order for the system to train properly.
Learning is accomplished through a complex, mathematical, iterative process whereby the
neural system is “trained” on the input data using statistical error analysis.
During training, whenever the system’s projections are incorrect, the connection weights
between neurons are modified to minimize such errors during subsequent iterations. Each
input/output pair of data is called a fact. The system learns by having these error signals
propagate backwards through the neuronal layers to prevent the same error from happening
again each time a fact is George Lindsay’s technique applied.