Stock Forecast Methods

Stock Market Trading and Investing

New Stock Market Prediction Software SMFT-2 Released

Addaptron Software announced the release of Stock Market Forecast Tools SMFT-2. It is a new integrated system, the next generation of the software that intended to replace older SMFT-1 version. As a result of a few recent development projects, the new version is based on new advanced methods, provides more accurate prediction, and is easier to use.

Prediction modules is built with back-test calculation to estimate the accuracy of forecast within the recent performance periods. Additionally, the back-testing computations is important if more than one method is used. It allows estimating the weight of each method in a composed result; the weights that are proportional to the ability of the methods to predict the price.

New Stock Market Prediction Software SMFT-2

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 is free during initial 30-day period. 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.

Advertisements

March 4, 2015 Posted by | Stock Market Forecast, Stock Market Software | , , , , , , , , , , , , | Leave a comment

New Improved Version of SMFT-1 Released

Addaptron Software released a new version of Stock Market Forecast Tools SMFT-1. The software includes several improvements: models optimization, more different input file formats, and optional free Downloader. SMFT-1 is an integrated system that includes three major software modules: FTA-2 – a modified version of InvAn-4 that is a comprehensive tool used by serious investors for many years, the most popular software program SMAP-3 for stock market cycles analysis and forecast, and Neural Network Stock Trend Predictor NNSTP-2.

New Improved Version of SMFT-1 Released

FTA-2 itself consists of six major modules:

  • “Technical analysis” – more than 50 popular technical indicators; chart analysis; indicators (each separately or all) used as input for Neural Network (NN) to build 10-day price forecast. The forecasts from all indicators result into a single forecast – each forecast added with the weight proportionally to the current ability of the indicator to predict prices.
  • “Waves” – Elliott Wave NN forecast.
  • “Candles” – candlestick pattern NN forecast model.
  • “Pattern recognition” – pattern-recognition filter and predictor.
  • “Correlation” – correlation analysis tool to perform analysis and evaluate the future trend using a mutually-correlated pair (or in opposite correlation) with time shift.
  • “Comprehensive 3-month fundamental-technical ratings model” – analyzing-predicting model that is based on key fundamental ratios and technical parameters reflecting a company-stock state and dynamics.

SMAP-3 is able not only to extract basic cycles of the stock market (indexes, sectors, or well-traded shares) but also to predict an optimal timing to buy or sell stocks. Its calculation mainly based on extracting basic cyclical functions with different periods, amplitudes, and phases from historical quote curve. Additionally, SMAP-3 enables finding optimal timing to buy/sell by analyzing months of year, days of month, and days of week (the calculation is based on statistical analysis).

NNSTP-2 is to help stock traders in predicting stock prices for short terms. It predicts future share prices or their percentage changes (can be chosen in settings menu) using Fuzzy Neural Network (FNN). It operates automatically when creating the FNN, training it, and mapping to classify a new input vector.

April 27, 2013 Posted by | Stock Market Software | , , , , , , , , , | Leave a comment

The Power of Using Multi-model Forecast in Stock Market

The Power of Using Multi-model Forecast in Stock MarketA multi-model forecast provides a significant improvement over the best individual forecast. It can be explained by existence of many different independent factors contributing to the error in each forecast which distributed around actual value.

Today, airplanes are equipped with a few different altimeters – barometric altimeter, radar altimeter, and GPS. Not to mention, pilots also use a visual estimation of altitude. Why do we need to use so many measurement tools? The first reason is that any of them can fail. There is another reason why multiple measurements are used everywhere – it is accuracy. For example, many different methods are used in weather forecasting to improve the accuracy of forecast.

The phenomenon of multiple forecast improvement can be compared with Expert Method. This method can be illustrated by the following. As example, an experimentalist shows a pen and asks a group of several people to write down their estimate of the length. Then he collects notes and calculates the average number – normally it is almost a precise result. Why it works? Because everyone makes errors in different directions so that averaging self-compensates erroneous deviations.

Concerning the stock market forecast, as experiments show, a multi-model forecast provides a significant (10-25%) improvement over the best individual forecast. Also tests show the advantage of using information even from multiple forecasts of different quality. That is because there are many different independent factors contributing to the error in each forecast and the results from different models are normally distributed around actual value.

Evidently, the methods should be different by their nature. Traditionally, fundamental factors and technical analysis are the major stock market tools. Within technical analysis, there are several different models: technical indicators, chart pattern analysis, Elliott Wave theory, cycle analysis, candlesticks model, trend lines analysis, regression models, etc. Most of these methods are statistically proven and widely used that often create self-fulfilling results.

As a rule, learning and also correctly using many of technical analysis methods may require a lot of time, especially, in a modern dynamic trading environment. Fortunately, the forecast methods combined with computer power have become a good solution to make the works less time-consuming and more effective. Except different linear and non-linear solvers and analytical methods, these days, Neural Network (NN) can help to automate a lot of computational tasks. A properly trained NN may enable predictions to the highest accuracy.

However, implementing NN application can be difficult for non-experts. Besides, one of the big obstacles of implementing NN predicting system is a formalization of inputs. Luckily, there are some software tools that already well-developed and do not require a deep technical understanding. These tools are optimized for each method and users might not notice even all computational power behind the buttons, windows, and charts.

Useful resources:

March 12, 2012 Posted by | Stock Market Forecast, Stock Market Software | , , , , , , , | Leave a comment

Elliott Wave: Extracting Extremes and Predicting next One by Neural Network

Article first published as Elliott Wave: Extracting Extremes and Predicting next One by Neural Network on Technorati.

Improved Elliott Wave model can be successfully used as an additional input for making investing decisions in modern market conditions

The Elliott Wave idea is to use in stock market forecast. It is based on a crowd psychology that changes between optimistic and pessimistic trends creating patterns that can be fitted to reoccurred sequences. To use waves for prediction the assumption is made that waves are developing in the sequence of Fibonacci, harmonic, or fractal ratios. So that each wave has a programmed position and characterized by a particular direction and duration with extreme as a reversal point.

In fact, the model has been used by stock market analysts for almost a century. Although it looks very attractive due to its strict formalism and deterministic outcome, its predictive power is weak because of a few reasons. Firstly, the predictive values are dependent on waves that were counted – determining where first and next wave start can be subjective. Secondly, according to Efficient Market Hypothesis, using an exact Elliott Wave model by many traders could lead to the disappearance of the patterns they anticipate. And finally, nowadays one’s trading success based on predictions is rather a chance game in a modern market with its irrational behavior.

The purpose of this research was to explore if Elliott Wave principle can be used these days in stock market forecast. To eliminate the subjectivity in counting waves, Neural Network (NN) was used to analyze and predict waves. Also instead of assuming that waves obey only the sequence of Fibonacci, harmonic, or fractal ratios, a more general approach used – the software processes all extracted waves. Besides, employing NN enables identifying both the price and date of extremes. The first experiment has been done using an artificial data set. The data consist of two sinusoidal functions with different periods. The second group of experiments has been done on real market data.

The main conclusion is that the Elliott Wave idea can be used in predicting stock market. Although it does not generate always accurate and consistent forecasts, its result can be successfully used as an additional input for making a trading or investing decision in modern market conditions.

Elliott Wave: Extracting Extremes and Predicting next One by Neural Network

Charts and calculation results by SMFT-1 (TA-1 sub-system Waves module interface)

October 12, 2011 Posted by | Stock Market Forecast | , , , , , , | Leave a comment