Technical Analysis Module In Stock NeuroMaster.

Here's you can find basic definitions of technical indicators which are used in Stock NeuroMaster. This TA module does not take effect on forecasting core, but, included for making your own analysis, and for supporting your decision of following BUY/SELL signal generated by Neural Network.

MACD

MACD, which stands for Moving Average Convergence / Divergence, is a technical analysis indicator created by Gerard Appel in the 1960s. It shows the difference between a fast and slow exponential moving average (EMA) of closing prices. Fast means a short-period average, and slow means a long period one, the standard periods are 12 and 26 days,


A signal or trigger line is then formed by smoothing this with a further EMA. The standard period for this is 9 days.


The difference between the MACD and the signal line is often calculated and shown not as a line, but a solid block histogram style. This construction was made by Thomas Aspray in 1986. The calculation is simply
histogram = MACD − signal

The set of periods for the averages, often written as say 12,26,9, can be varied. Appel and others have experimented with various combinations.

Elder Ray Bear Power

A technical indicator developed by Alexander Elder that measures the amount of buying and selling pressure in the market. This indicator consists of two separate indicators known as "bull power" and "bear power". These figures allow a trader to determine the position of the price relative to a certain exponential moving average (EMA).

Linear Regression Oscillator

Linear regression is a statistical tool used to predict future values from past values. In the case of security prices, it is commonly used to determine when prices are overextended.

A Linear Regression trendline uses the least squares method to plot a straight line through prices so as to minimize the distances between the prices and the resulting trendline.
Linear regression is widely used in biological and behavioural sciences to describe relationships between variables. It ranks as one of the most important tools used in these disciplines. For example, early evidence relating cigarette smoking to mortality came from studies employing regression. Researchers usually include several variables in their regression analysis in an effort to remove factors that might produce spurious correlations. For the cigarette smoking example, researchers might include socio-economic status in addition to smoking to ensure that any observed effect of smoking on mortality is not due to some effect of education or income. However, it is never possible to include all possible confounding variables in a study employing regression. For the smoking example, a hypothetical gene might increase mortality and also cause people to smoke more. For this reason, randomized experiments are considered to be more trustworthy than a regression analysis.

MAVEs

A moving average, in finance and especially in technical analysis, is one of a family of similar statistical techniques used to analyze time series data.

A moving average series can be calculated for any time series, but is most often applied to stock prices, returns or trading volumes. Moving averages are used to smooth out short-term fluctuations, thus highlighting longer-term trends or cycles. The threshold between short-term and long-term depends on the application, and the parameters of the moving average will be set accordingly.

Mathematically, each of these moving averages is an example of a convolution. These averages are also similar to the low-pass filters used in signal processing.
A simple moving average (SMA) is the unweighted mean of the previous n data points. For example, a 10-day simple moving average of closing price is the mean of the previous 10 days' closing prices.
In technical analysis there are various popular values for n, like 10 days, 40 days, or 200 days. The period selected depends on the kind of movement one is concentrating on, such as short, intermediate, or long term. In any case moving average levels are interpreted as support in a rising market, or resistance in a falling market.

In all cases a moving average lags behind the latest price action, simply from the nature of its smoothing. An SMA can lag to an undesirable extent, and can be influenced too much by old prices dropping out of the average. This is addressed by giving extra weight to recent prices, as in the WMA and EMA below.

One characteristic of the SMA is that if the data has a periodic fluctuation, then applying an SMA of that period will eliminate that variation (the average always containing one complete cycle). But a perfectly regular cycle is rarely encountered in economics or finance.
An exponential moving average (EMA), sometimes also called an exponentially weighted moving average (EWMA), applies weighting factors which decrease exponentially. The weighting for each day decreases by a factor, or percentage, on the one before it.

Commodity Channel Index

The Commodity Channel Index (CCI) is an oscillator originally developed by Donald Lambert. He introduced this new oscillator in an article published in the October 1980 issue of Commodities magazine (now known as Futures magazine).

The CCI is calculated as the difference between the price of a commodity and its simple moving average, divided by the normal deviation of the price. The index is usually scaled by a factor of 1/0.015 to provide more readable numbers. Therefore, it is essentially a MACD, normalized the deviation.

Since its introduction, the indicator has grown in popularity and is now a very common tool for traders to identify cyclical trends not only in commodities but also equities and currencies.

The CCI can be adjusted to the timeframe of the market traded on by changing the averaging period.
Like most oscillators in technical analysis, the CCI was developed to determine overbought and oversold levels. Although its value is based on closing prices, the indicator really measures the variation from the mean of the price over (N) periods, and thus a better classification for it would be momentum oscillators.

The Commodity Channel Index is often used for detecting divergences from price trends as an overbought/oversold indicator, and to draw patterns on it and trade those patterns. In this respect, it is similar to bollinger bands, but is presented as an indicator rather than as overbought/oversold levels.

The CCI typically oscillates above and below a zero line. Normal oscillations will occur within the range of +100 and -100. Readings above +100 imply an overbought condition, while readings below -100 imply an oversold condition. As with other overbought/oversold indicators, this means that there is a large probability that the price will correct to more representative levels.

Relative strength index

The Relative Strength Index (RSI) is a technical analysis oscillator showing price strength by comparing upward and downward close-to-close movements.

The RSI is popular because it is relatively easy to interpret. It was developed by J. Welles Wilder and published in Commodities magazine (now called Futures magazine) in June 1978, and in his New Concepts in Technical Trading Systems the same year.

Note that the term relative strength also refers to the strength of a security in relation to the overall market or to its sector. For instance XYZ might rise 2% when the rest of the market rises 1%. This is sometimes called relative strength comparative to avoid confusion. It's unrelated to the Relative Strength Index described here.
Wilder recommended a smoothing period of N=14. This is by his reckoning of EMA smoothing (see the EMA article on that), ie. α=1/14.

Wilder considered a security overbought if it reached the 70 level, meaning that the speculator should consider selling. Or conversely oversold at the 30 level. The principle is that when there's a high proportion of daily movement in one direction it suggests an extreme, and prices are likely to reverse. Levels 80 and 20 are also used, or may be varied according to market conditions (eg. a bull market may have an upward bias).

Large surges and drops in securities will affect RSI, but it could just be a false buy or sell. The RSI is best used as a complement with other technical analysis indicators.

Stochastic oscillator

The stochastic oscillator is a technical analysis oscillator (or two oscillators) showing the latest closing price in relation to the trading range of the past N days. This concept is unrelated to a stochastic in mathematics or statistics.

These stochastics were described by George Lane, in the 1950s. It's unclear whether the idea was original to him, he may have learnt of it from a previous employer Ralph Dysant, and indeed he in turn from earlier traders.

Two oscillator lines are calculated, called %K and %D, each ranging from 0 to 100. %K is the closing price within the past N-days trading range, ranging from 0 when the latest close is a new N-day low, up to 100 for a new N-day high.
%D is a 3-day simple moving average of %K.
The usual "N" is 14 days, ie. a fortnight's worth of past data, but this can be varied. Levels near the extremes 100 and 0, for either %K or %D, indicate strength or weakness (respectively) with prices making or approaching new N-day highs or lows.

Levels above 80 and below 20 can be interpreted as overbought or oversold, but not on their own, only with other factors. Lane recommended waiting for a return back through those thresholds, ie. when the oscillator goes above 80, wait for it to fall below 80 before selling; or vice versa on going below 20 wait for a rise back above 20 before buying; which in effect means waiting for a bit of a reversal. Or alternately levels 80 and 20 might be traded when some other technical indicator suggests a non-trending market.

%D acts as a trigger or signal line for %K. A buy signal is given when %K crosses up through %D, or a sell signal when it crosses down through %D. Such crossovers can occur too often, and to avoid repeated whipsaws one can wait for crossovers occurring together with an overbought/oversold pullback, or only after a peak or trough in the %D line.

Some traders consider the basic %K and %D too volatile, giving too many signals and too many whipsaws. This is addressed by forming "slow" stochastics. %K values are first smoothed by a 3-day simple moving average, and then the %D formed by a further 3-day SMA on that. This "slowed" %K is the same as the "fast" %D, but it's easiest just to think of the slow form as first inserting an extra smoothing.

%K is the same as Williams %R, though on a scale 0 to 100 instead of -100 to 0, but the terminology for the two are kept separate.

Bollinger bands

Bollinger Bands is a technical analysis tool invented by John Bollinger in the 1980s. They evolved from the concept of trading bands, and can be used to measure the relative highness or lowness of price.

Bollinger Bands consist of:
a middle band being a N-period simple moving average
an upper band at K times a N-period standard deviation above the middle band
a lower band at K times a N-period standard deviation below the middle band

Typical values for N and K are 20 and 2, respectively.
The bands cannot, as some have supposed, be used to make reliable statements regarding what fraction of an equity's prices will lie within a certain distance of the mean value. This is because an individual equity's price does not obey known distribution functions (see stochastic process). For example, if the bands for plus or minus two standard deviations are computed, it is wrong to suppose that ~95% of an equity's closing prices will, on average, lie within the bollinger bands. That would require, among other things, that the prices be normally distributed, which they are generally not. It would further require that the true standard deviation be known. The standard deviation calculated as above, however, is only an uncertain estimate of the true standard deviation. Furthermore, it should be realized that the "standard deviations" of stock prices for finite time periods are not fixed parameters as required to apply classical statistical theory, but instead are variables in constant flux depending on price volatility. Nevertheless, the bands have proved useful in the technical analysis of stock prices. The bands give a reliable visual picture of a stock's price volatility. No particular significance, however, should be attached to a price touching the upper or lower band, as Bollinger himself has pointed out. These occurrences should be considered in relation to other factors before making investment decisions.

It is of interest to note that faulty interpretation of a price touching or breaching a band based on incorrect statistical assumptions has become so widespread that some traders now use these events alone as trading signals and by so doing may have unwittingly injected significance into these band-touching events that would otherwise be absent.

When the bands lie close together a period of low volatility in stock price is indicated. When they are far apart a period of high volatility in price is indicated. When the bands have only a slight slope and lie approximately parallel for an extended time the price of a stock will be found to oscillate up and down between the bands as though in a channel. When this behavior is found to regularly repeat in conjunction with a fairly steady broad market, a traders may, with some validity, use a touch or near touch of the upper or lower band as an indication that a stock's price is nearing the limit of its trading range and therefore a price reversal is probable.

Quadrant Lines

Quadrant Lines are a series of horizontal lines that divide the highest and lowest values (usually prices) into four equal sections.
Quadrant Lines are primarily intended to aid in the visual inspection of price movements. They help you see the highest, lowest, and average price during a specified period.
An interesting technique is to display a Linear Regression trendline and Quadrant Lines. This combination displays the highest, lowest, and average price, as well as the average slope of the prices.

Williams %R

Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low of the past N days (for a given N). It was developed by trader and author Larry Williams and is normally used just in the stock market.

The oscillator is on a negative scale, from -100 (lowest) up to 0 (highest). Such a scale is a little unusual and is sometimes found altered (by adding 100), but needn't cause any confusion. A value of -100 is the close today at the lowest low of the past N days, and 0 is a close today at the highest high of the past N days.

Williams used a 10 trading day period and considered values below -80 as oversold and above -20 as overbought. But they were not to be traded directly, instead his rule to buy an oversold was
%R reaches -100%.
Five trading days pass since -100% was last reached
%R rises above -95% or -85%.

or conversely to sell an overbought condition
%R reaches 0%.
Five trading days pass since 0% was last reached
%R falls below -5% or -15%.

The timeframe can be changed for either more sensitive or smoother results. The more sensitive you make it, though, the more false signals you will get. The "close-position within a range" in the %R indicator is the same as the %K stochastic oscillator, on a different scale.