by Don K. Mak

The book is organized in fourteen chapters.

Chapter 1 describes why the book is written. This book aims to
analyze the equipment that professional traders used, and attempt to
distinguish the tools from the junk.

Chapter 2 presents the latest development of scientific
investigation in the financial market. A new field, called Econophysics,
has cropped up. It involves the application of the principles of Physics to
the study of financial markets. One of the areas concerns the
development of a theoretical model to explain some of the properties of
the stochastic dynamics of stock prices. There exist also growing
evidences that the market is non-random, as supported by new statistical
tests. In any case, market crashes have been considered to be nonrandom
events. What the signatures are before a crash and how a crash
can be forecasted will be described.

Chapter 3 analyzes the trending indicators used by traders. The
trending indicators are actually low pass filters. The amplitude and
phase response of one of the most popular indicators, the exponential
moving average, is characterized using spectrum analysis. Other low
pass filters, the Butterworth and the sine functions are also looked into.
In addition, an adaptive exponential moving average, whose parameter is
a function of frequency, is introduced.

Chapter 4 modified the exponential moving average such that
new designs would have less phase or time lag than the original one. It
also pointed out that the "Zero-lag" exponential moving average recently
designed by a trader does not live up to its claim.

Chapter 5 describes causal wavelet filters, which are actually
band-pass filters with a zero phase lag at a certain frequency. The
Mexican Hat Wavelet is used as an example. Calculation of the
frequency where the zero phase lag occurs is shown. Furthermore, it is
demonstrated how a series of causal wavelet filters with different
frequency ranges can be constructed. This tool will allow the traders to
monitor the long-term, mid-term and short-term market movements.

Chapter 6 introduces a trigonometric approach to find out the
instantaneous frequency of a time series using four or five data points.
The wave velocity and acceleration are then deduced. The method is
then applied to theoretical data as well as real financial data.

Chapter 7 explains the relationship between the real and
imaginary part of the frequency response function of a causal system,
H(co). Given only the phase of a system, a method is implemented to
deduce H(co). Several examples are given. The phase or time response
of a system or indicator is important for a trader tracking the market
movements. The method would allow them to predetermine the phase,
and work backward to find out what the system is like.

Chapter 8 depicts several newly created causal high-pass filters.
The filters are compared to the conventional momentum indicator
currently popular with traders. Much less phase lags are achieved with
the new filters.

Chapter 9 describes in detail the advantages and limitations of a
new technique called skipped convolution. Skipped convolution, applied
to any indicator, can alert traders of a trading opportunity earlier.
However, it also generates more noise. A skipped exponential moving
average would be used as an example. Furthermore, the relationship
between skipped convolution and downsampled signal is illustrated.

Chapter 10 analyzes and dissects some of the popular trading
tactics employed by traders, in order to differentiate the truths from the
myths. It explains the meaning behind divergence of momentum (or
velocity) from price. It unravels the significance of the MACD (Moving
Average Convergence-Divergence) line and MACD-Histogram, but
downplays the importance of the MACD-Histogram divergence.

Before putting up a trade, traders would look at charts of
different timeframes to track the long-term and short-term movements of
the market. The advantages and disadvantages of a long-term timeframe
are pointed out in Chapter 11. This chapter also discusses how a trading
plan should be put together. The popular Triple Screen Trading System
is used as one of the examples.

The market is assumed to be random in Chapters 12 and 13.
This modeling is good as a first approximation, and renders the
application of probability theory to money management techniques
practiced by traders. Chapter 12 discusses the profitability of the market
at any moment in time. Chapter 13 derives and computes how traders
can optimize their gain by moving the stop-loss.

The final chapter, Chapter 14, discusses the reality of financial
market trading. It takes years of hard work and training to be a
successful trader. In addition, the trader needs to update himself of
current technology and methodology in order to keep ahead of the game.