Addaptron Software released a new Stock Market Forecast Tools SMFT-2. It is an integrated advanced system that is the next generation improved software based on older SMFT-1 version and a few recent development projects.
Most prediction modules provided with back-test calculation to estimate the accuracy of forecast within the recent performance periods. Additionally, the back-testing computations play an important role if more than one method is used. It allows estimating a weight of each method in a composed result; the weights that are proportional to the ability of the methods to predict the price.
SMFT-2 currently includes five major modules:
- TA Predictor – prediction for day or week period based on technical analysis, pattern recognition and Neural Networks (generates composite result). Back-analysis models optimization and batch calculation for comparative analysis included.
- Waves – Elliott Wave model: back-test optimization, up to 10 waves forecast.
- Cycles – prediction based on cycle analysis.
- Week day – search for maximum performance using price behavior depending on week day. It allows discovering the best entry/exit days of week; batch calculation included.
- Month day – search for maximum performance using price behavior depending on month day. It allows discovering the best entry/exit days of month; batch calculation included.
The implemented methods are statistically proven and widely used. All modules share the same EOD (end-of-day) input data. The software is provided with a free Downloader that allows downloading EOD historical quotes files from the Internet for free. A fully-functional software SMFT-2 during initial 30-day period is free. The software and associated documentation are delivered via download links over the Internet. For technical requirements, installation instruction, and download link, visit SMFT-2 download page.
Candlestick patterns can signal a trend continuation or reversal. Many traders and investors know how to benefit from candlesticks analysis. As a rule, a simple chart analysis is used that requires analysts to remember and correctly interpret, at least, typical candlestick patterns. However, the number of possible different candlestick shapes and their combinations in a row can be huge. Besides, some patterns interpretations might be considered as contradictory or doubtful.
Fortunately, statistical methods combined with computer power can be a good solution to make the candlestick patterns recognition works less time-consuming and more effective. These days, Neural Network (NN) can help to automate a candlestick patterns recognition task. NN should be properly trained in order to be able to predict the next candlestick parameters with the highest probability. One of the obvious problems of implementing a candlestick pattern NN predicting system is a formalization of inputs.
Numerous tests show many possibilities of improving NN candlestick patterns recognition systems. For example, output result can be composed from selected optimized calculations based on different historical periods. Also there are many different ways to formalize the shapes and relative positions of candlesticks. As well as, as back- and forward-testing show, it is reasonable to re-train NN for each particular type of shares and latest historical periods to make the forecast more accurate.
Optimal Solution. There is an automated tool FTA-2 (free use of fully-functional version for one month). It has module which enables using Neural Network to recognize typical candlestick patterns and predict future prices (open, high, low, close). This module predicts only one next candlestick but the prediction can be successfully used for different widths of candlestick, i.e., the number of trading days in one candlestick. The module has been enhanced to calculate result that is composed from different historical periods that allows making the forecast more accurate. Also it can perform comparative forecast analysis for many symbols.
- Candlestick basics – major signals
- Neural Network basics – introduction
- The computer program which enables using Neural Network to recognize typical candles patterns and predict future prices – software FTA-2
The chart shows a probable behavior of S&P-500 index for the next week, May 23 – 27, 2011. The forecast has been calculated by pattern recognition system. It predicts some fluctuations with a slight uptrend.
The chart shows S&P-500 index possible behavior for February 7 – 11, 2011. The forecast is a fluctuation with a small downtrend. The prediction has been calculated using the pattern recognition feature of Investment Analyzer InvAn-4.
Chart shows S&P-500 index forecast for December 13-17, 2010. The forecast is a downtrend. The prediction has been performed using Investment Analyzer InvAn-4 (pattern recognition forecast).