X Zhou (@11.0) vs D Boyer (@1.02)

Our Prediction:

D Boyer will win

X Zhou – D Boyer Match Prediction | 03-10-2019 01:00

It is a common indicator in stock analysis. The weight number is a fixed value equal to . The standard MACD is the 12-day EMA subtracted by the 26-day EMA, which is also called the DIF. Similar to the MACD, the MACD histogram is an oscillator that fluctuates above and below the zero line. The MACD histogram, which was developed by T. The analysis process of the cross and deviation strategy of DIF and DEA includes the following three steps. The number of the MACD histogram is usually called the MACD bar or OSC. Aspray in 1986, measures the signed distance between the MACD and its signal line calculated using the 9-day EMA of the MACD, which is called the DEA. The construction formula is as follows: where , , and . MACD evolved from the exponential moving average (EMA), which was proposed by Gerald Appel in the 1970s.

The HVIX in this paper is the change index of the volatility in the past days. It reflects the panic of the market to a certain extent; thus, it is also called the panic index. In this study, the weight is based on the historical volatility. The above process is expressed by the code shown in Algorithm 1. It is expected that the accuracy and stability of MACD can be improved. It is similar to the market volatility index VIX used by the Chicago options exchange. The validity and sensitivity of MACD have a strong relationship with the choice of parameters. Different investors choose different parameters to achieve the best return for different stocks. The construction formula is as follows:Here, the weight changes over time; HVIX is the change index of the historical volatility of a stock. We present an empirical study in Section 5. The essence of a good technical indicator is a smooth trading strategy; i.e., the constructed index must be a stationary process.

Thea D. Tlsty, PhD

The proposed model was tested with three different stocks and it generated the high prediction accuracy for all the cases. Therefore, the improved model has higher maneuverability in securities investment and allows investors to capture every buy-and-sell points in the market. For both the buy-and-sell strategy and the buy-and-hold strategy, the empirical results indicated that the proposed model can make more precise predictions than the traditional model. As indicated by Tables 1, 2, and 3, we buy-and-sell stock based on improved MACD; then we found all the accuracy is higher than that before the improvement. Test shows that it is stable; however, in the ever-changing market, an abnormal situation can cause incalculable losses to investors. In future research, we will investigate other factors for the model by constantly updating the data and the training model to obtain a better prediction effect. In addition, while a smoothing index is used to construct the MACD index and the impact of the past price declines exponentially, the MACD-HVIX does not have this property. Although the MACD-HVIX index is improved compared with the MACD index, the stationarity of the MACD-HVIX index is difficult prove theoretically.

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The toxicophore fingerprint was calculated basedon substructurematching from SMARTS queries proposed in ref (37) originally as potentialindicators of AMES mutagenicity (available as Supporting Information). The toxicophore substructure matching,molecular properties, and pharmacophore calculations were obtainedusing the RDkit cheminformatics toolkit. A complete list of calculatedproperties can be found in the Supporting Information(Table S1). Six nonexclusive pharmacophore classes are considered(i.e., an atom can belong to more than one class): hydrophobic, aromatic,hydrogen acceptor, hydrogen donor, positive ionizable, and negativeionizable.


Such a predictive capability may be an essential computational toolfor property optimization and to guide screening initiatives. An intuitive graph representationof a compound can be achieved by representing atoms as nodes and theircovalent bonds as edges. Substructure matching, implemented for instance as a toxicophore search,37 frequent subgraph mining,38 and graph kernels,39 are examplesof approaches for extracting patterns from these graphs. Togetherwith experimental data on particular properties of interest (e.g.,ADMET properties), these descriptors can then be used as evidenceto train highly accurate predictive models via machine learning methods. This simple representation can be decoratedwith labels denoting, for instance, physicochemical properties ofatom and bonds, from which structural patterns could be prospected. Graph modeling is anintuitive and well established mathematical representation of chemicalentities, from which different descriptors encompassing both moleculestructure and chemistry can be extracted.

A freely accessible web server(http://structure.bioc.cam.ac.uk/pkcsm), which retainsno information submitted to it, provides an integrated platform torapidly evaluate pharmacokinetic and toxicity properties. Drug development has a high attritionrate, with poor pharmacokineticand safety properties a significant hurdle. We have developed a novel approach(pkCSM) which uses graph-based signatures to develop predictive modelsof central ADMET properties for drug development. Computational approachesmay help minimize these risks. pkCSM performs aswell or better than current methods.

The MACD histogram shown in Figure 7 indicates the buy-and-sell points; we should buy the stock at a buy point on days 391, 1,071, 1,181, 1,326, and 1,481, and sell the stock at a sell point on days 791, 881, and 911. The prediction situation is shown in Table 2. Then, we compare the prediction accuracy between the two indicators. Using the -dggf- stock data chosen in Section 4, we first investigate the buy or sell points for both the indicators with the buy-and-hold strategy applied for 5 d.

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Next, using the -payh- stock data chosen in Section 4, we investigate the buy or sell points for both the indicators with the buy-and-hold strategy applied for 10d. The prediction situation is shown in Table 3. The MACD histogram in Figure 10 indicates the buy-and-sell points. Then, we compare the prediction accuracy between the two indicators. We should buy the stock at a buy point on day 901, and sell the stock at a sell point on days 621, 1,971, 2,071, 2,291, 2,431, and 2,661.

The body indicates the opening and closing prices, and the wick indicates the highest and lowest traded prices of a stock during the time interval represented. For a green body, the opening price is at the top, and the closing price is at the bottom. For a red body, the opening price is at the bottom, and the closing price is at the top. The area between the opening and the closing prices is called the body, and price excursions above or below the real body are called the wick. Candlesticks are usually composed of a red and green body, as well as an upper wick and a lower wick. Figure 2 shows the candlestick chart and MACD histogram. In the candlestick chart, the blue line represents the 12-d EMA, and the red line represents the 26-d EMA.