Stock Forecast Methods

Stock Market Trading and Investing

How to Use One-day Candle Prediction

Addaptron Software provides prices prediction of major ETFs for the next day. This article describes one of the simplest algorithms to use prediction data. The algorithm consists of four simple rules:

  1. Buy Limit order with predicted Low price is placed before market open.
  2. The order is canceled if it is not completed during the first half of the market day.
  3. Once order is completed, Sell Limit order is placed with predicted High price.
  4. If Sell order is not completed until almost closing market, it is canceled and Sell Market order is executed just before closing stock market.

The following charts and tables demonstrate one of the practical examples of described above approach.

Predicted and actual prices chart (dark green and red filled candles – predicted, light green and red line candles – actual):

Predicted and actual prices chart</a> (dark green and red filled candles – predicted, light green and red line candles – actual)


Predicted and actual prices table:

Predicted and actual prices table


Intraday performance chart (actual prices) for the same period:

Intraday performance chart


The result of using the described algorithm. The example is based on the following assumptions: 100 shares are used for trading, transaction fee is $10 so that 2 transactions (buy and sell) within day cost $20:

Example of using the described algorithm


Another example (symbol BOIL) can be find here

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November 9, 2014 Posted by | Stock Market Forecast, Stock Market Software | , , , | 1 Comment

Enabling Candlestick Forecast by Neural Network

A new 2012.02 version of Fundamental-Technical Analyzer FTA-2 has been released. Now it has a new module which enables using Neural Network to process past candlestick patterns and predict the future candlestick, i.e., its Open, High, Low, and Close prices. A candlestick can consists of one or many trading days. The module calculates result composed from different historical periods that allows making the forecast more accurate. Also it can perform comparative forecast analysis for many symbols.

Enabling Candlesticks Forecast by Neural Network

More details can be found on FTA-2 software webpage, as well as, in User’s Manual that is available from the main menu after the installation of FTA-2. To try the software for free, go to download page and follow the instruction.

About Candlestick Forecast Model

The idea of using the chart with candlesticks (or candles) for predicting market prices is very old. Two centuries ago, Japanese rice trader found that the candlesticks pattern chart could be used as a tool to predict future prices in a free market with a natural demand-supply balance. The method was improved later by others and today it is successfully used by many traders and investors in the stock market.

A candlestick is presented using high, low, opening, and closing prices during a certain trading period, for example, trading day. A regular candlestick figure consists of Real Body, Upper Shadow, and Lower Shadow. The number of candlesticks that is normally used for predicting can range within 1..12. Evidently, the number of different combinations of several candlesticks in a row can be big. Some believe that there are only 12 major candlestick patterns, others consider this number is 70 or even more. Anyway, in case of chart analysis, it is necessary to remember at least major patterns and process many charts in order to make forecast successful.

Apparently, statistical methods combined with computer power can be a good solution to make the candlestick patterns recognition work less time-consuming and more effective. For example, Neural Network (NN) can help to automate a candlestick patterns recognition task. NN should be properly trained in order to be able to recognize and predict further movements. One of the obvious problems of implementing a candlestick pattern NN predicting system is a formalization of inputs, i.e., the way how to express each candlestick shape and relative position of all candlesticks in numerical values.

Useful resources:

February 6, 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

It Is Time to Try a New Forecast-simulation Software

New Stock Market SoftwareA new decision support software system for stock market traders has been release. It is called Trading Forecast Weekly TFW-1. It provides weekly EOD forecasts for 20 selected ETFs by cloud computing. TFW-1 allows choosing best trading opportunities by comparative analysis, assessing different strategies using back-test simulation, finding the best configuration of algorithms among 64 possible combinations and optimizing triggering parameters, as well as, monitoring results by stand-alone computing.

The simulations and statistical analysis show that systems based on predicted entry-exit signals generate a better profit than random-entry trading systems. TFW-1 enables forecasts to increase a trading profit. The calculations are performed on server-side by a robust artificial intelligence system. Since sometimes predictions can fail, to preserve capital in a volatile market, the software enables simulating different risk management approaches. TFW-1 simulation allows testing generated predictions combined with buy-sell signals.

It is known that depending on the character of particular trading shares and current stock market condition, some ideas can work better than others. To optimize strategy for each particular case, the software enables testing a given idea and finding automatically the best set of algorithms. It is especially important for exit points to minimize losses and ultimately to maximize an overall profit. Some algorithms have customizable parameters that can also be optimized. All optimizations can be done either automatically by scanning 64 possible logical combinations and adjusting numerical parameters or manually.

TFW-1 allows monitoring the simulated or actual completed transactions, reflecting total trading activity, and evaluating the success of trading in overall by stand-alone computing. It enables working with many separate data files that is convenient in case of managing multiple assets and keeping the archives of older activities. You are welcome to download and use for free (during the first 30-day period) a fully-functional version of TFW-1.

October 6, 2011 Posted by | Stock Market Forecast, Stock Market Software | , , , , , , , , , , , , , | Leave a comment

New Trading Decision Support Systems Group on LinkedIn

New Trading Decision Support Systems group on LinkedInThe new group Trading Decision Support Systems is intended to be a resource for individual/institutional traders/investors and software developers in stock market area to share ideas, initiate and participate discussions, benefit from the collective intelligence, and to expand network. It will be primarily focused on such topics as:

  • Trading EOD and intraday different asset classes: trading tips, strategies, why, how, and results.
  • Trading systems: algorithms, methods, technologies, human factor, and statistics.
  • Software tools to support traders decisions: forecast methods, simulations, back-testing, and optimization.
  • Technical Analysis: indicators and chart patterns.
  • Fundamental Analysis: financial ratios and predictive models.
  • News: analysis and formalization by converting to measurable variables to automate systems with contributing news factor.
  • Numerical methods, data processing, artificial intelligence, and modeling in stock market areas.

Many things remain unchangeable in a trading world – supply-demand price balance, greed-fear driven mistakes, as well as, ability to think, make right decisions, and find the best solutions. When once winning approaches, strategies, or methods failed, many traders are prone to analyze the reasons why it happened. Then they create new approaches and develop new successful systems. If systems are automated, it is easy and fast to test them, collect and analyze back-testing and live statistics, and then make necessary improvements. That is why it is important to implement the best ideas in software applications that can be also used by others.

The computational technologies are changing. Systems empowered by Artificial Intelligence have self-learning abilities that enable them to adapt to market changes. One of the purposes of this group is to bring together the developers of decision support software and traders-users for mutual benefits: the developers get more ideas about their products’ improvements and make a better progress in developing software for traders, the users arise issues relating to their needs and wants. Hopefully everyone will find something useful participating in this group.

You are welcome to join this newly created networking group. Be the first to start a relevant discussion, promote your product or service. Please join Trading Decision Support Systems group on LinkedIn!

June 22, 2011 Posted by | Stock Market Forecast, Stock Market Software | , , , , , , , , , , , , , , , , , , | 1 Comment