In our case we use and also br MACD Moving

In our case we use =12 and also =26.
MACD Moving Average Convergence/Divergence (MACD) is an oscillator that should reveal changes in the strength, direction, momentum, and duration of a trend in a stock׳s price. The simplest version of MACD is the difference between two moving averages, one over a shorter selective and one over a longer period . Further insight can be obtained by using a third moving average of the itself over a period , called \"signal line\" . When increases and crosses the signal line, it is a bullish signal; when it decreases and crosses the signal line, it is a bearish signal. In our case we use =12, =26 and =9.
ROI Return on Investment (ROI) is one way of considering profits in relation to capital invested. Usually it is the ratio between return and invested capital. In our case, we use the average return over the last days, denoted by , and the current closing value. In our case we use =10, 20 and 30.
RSI Relative Strength index (RSI) is a momentum oscillator that compares the magnitude of recent gains and losses over a specified time period to measure speed and change of price movements of a security. By defining the upward change as if and 0 otherwise, and the downward change as if and 0 otherwise, the relative strength is the average of the last n upward changes divided the average of the last n downward changes. Then, RSI is computed as follows In our case we use =10, 14 and 30.
STOCHRSI Stochastic oscillators attempt to predict price turning points by comparing the closing price of a security to its price range. This concept can be applied to the RSI itself, obtaining the Stochastic RSI (SRSI). By computing the RSI range from its minimum in the last n periods and its maximum in the last n periods , the SRSI is defined as follows. In our case we use n = 10, 14 and 30.
ATR Average True Range (ATR) measures the degree of price volatility. The rage of a price is simply defined as , the True Range (TR) extends it to yesterday׳s closing price if it was outside of today׳s range: Now, by denoting with the exponential moving average of a generic over the last n periods, we have that ATR is the exponential moving average of the TR: In our case we use n=14.
ADX By defining the upmove as and the downmove as , Now, recalling that denotes the exponential moving average of over the last n periods, we compute ADX is finally computed as follows, with denoting the absolute value: In our case we use =14.
Williams %R Williams %R is an oscillator that analyzes whether a stock or commodity market is trading near the high or the low, or somewhere in between, of its recent trading range. In our case we use n=14. Range: Negative complementation ranges between −100 and 0. A value of −100 means that the close today was the lowest low of the past n days, and 0 means that today׳s close was the highest high of the past n days.
CCI Commodity Channel Index (CCI) is used to identify cyclical trends not only in commodities, but also equities and currencies. Define the Typical Price (TP) as follows. By computing the simple average over the last n periods of the typical price and its standard deviation, CCI is defined as follows. In our case we use =20.
UO Ultimate Oscillator (UO) uses buying or selling \"pressure\", represented by where the daily closing price falls within the daily true range. The Buying Pressure (BP) and the True Range (TR) are computed as follows. Then, the total buying pressure over the past days is computed as follows. Such a total buying pressure is computed for short, intermediate and long time intervals, and the UO is: In our case we use =7, =14 and =28.
Class The problem of data classification is the attribution of labels to records according to a criterion automatically learned from a training set, that is a set of records that already have a class. Classification is a very important data mining task (see also [5]), and many classifications algorithms are today available (e.g., [6]). We assign the class to each record, so that any portion of the dataset can be used as training set. After this learning phase, the classification algorithm will be able to predict the class for the rest of the records. The accuracy of such a prediction can be computed by comparing it with the real class, which is the one given in the dataset.