Tag Archives: stock market

Addaptron Software Releases New Stock Market Software SMT1

Addaptron Software announced a software release, SMT1 (Stock Market Tools, release 1), a new advanced software system for End-Of-Day (EOD) traders. One of new advantages is all-in-one output forecast signal. This signal (number) is the result of processing data by Artificial Intelligence (AI) Forecast Module. The set of data consists of technical indicators, waves prediction, pattern filter, and cycles extrapolation. Based on Machine learning results, AI decides how to interpret all relevant data and express the conclusion in a single number.

SMT1 is intended for EOD traders with intermediate or advanced knowledge in the Stock Market and computer software. The software consists of four major functionalities: Forecast, Backtest, Simulation, and Tracking. SMT1 is provided with User’s Manual which helps to understand the general structure of the software, connections between functional modules, and how effectively utilize all features.

The software uses EOD historical prices data as input. SMT1 includes a free Downloader that allows downloading EOD historical quotes files of selected symbols (some of most traded leveraged ETFs) from Addaptron Software server for free. Optionally, users can use own input data files. User’s Manual explains how to use own input files.

The main concept of the software is to work (i.e., predict, simulate and optimize trading performance) with the group of well-traded leveraged ETFs to maximize overall return. Each ETF has inverse counterpart and represents different industries that allows finding a potential winner every day. Although the software is suited to a specific niche, users can try to use own group of symbols.

Stock Market traders use different types of sell signal to exit position. Since exit signal cannot be reliable enough, some traders use stop loss and profit target to exit position. Addaptron Software has done numerous computer simulations to learn if adding more exit conditions can improve trading return. The research discovered that a better trading return in the long run can be achieved by using as many as four conditions for exit. This multi-trigger exit concept has been implemented in SMT1 as a new 4-Way Exit Method. This is another SMT1 advantage.

The software also includes an extra feature to record buy-sell transactions, analyze a current position, recommend the action, and measure trading performance. Since AI is able to optimize many settings parameters, the number of user-defined parameters is minimized so that users can save time.

Downloading and installing SMT1 is a very easy process and explained step-by-step on download page . All retail traders are eligible for free fully-functional version during initial 30-day period.

The example of SMT1 user interface: tab-page Simulation (back testing)
The example of SMT1 user interface: tab-page Simulation (back testing)

Prediction Combined with Simple Algorithm Provides Stable Return

Any prediction can fail but if it is combined with well-tested buy-sell rules, the result is much better. Addaptron Software provides predictions of major ETFs prices (Open, High, Low, Close) for the next day. This article describes one of the simplest algorithms to use one-day candle prediction data. The algorithm consists of four simple rules:

  1. Place Buy Limit order with predicted Low price before the stock market is opened.
  2. As soon as the Buy Limit order is completed, place Sell Limit order with predicted High price.
  3. Cancel the order if it is not completed during the first half of the stock market day.
  4. If the stock market is about to close but the order is not completed, cancel the Sell Limit order and place Sell Market order.

The following charts and tables demonstrate one of the practical examples of described above approach (symbol BOIL).

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

The chart of predicted and actual prices


Predicted and actual prices table:

The table of predicted and actual prices


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

The intraday prices


The result of using the described algorithm:

The result of using the described algorithm


The example is based on the following assumptions: 300 shares are used for trading, transaction fee is $10 so that 2 transactions (buy and sell) within day cost $20. Another example (symbol SSO) can be find here

Candlestick Patterns Decoded by Neural Network

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 Patterns Decoded by Neural Network

Useful resources:

  • 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

Neural Network vs. Cycle Analysis to Predict the Stock Market

The purpose of this research was to compare two statistical methods: one that based on Cycle Analysis, another – on a simple Neural Network. Price and volume data were used to train this particular Neural Network. These statistical forecasts were built using historical data of S&P-500 index for six months (from June 2009 to January 2010).

The charts below shows how actual 5-day performance (yellow line) differ from predicted performances by these two methods. The top half is the comparison of Neural Network prediction, bottom half – Cycle Analysis. Green bars mean buy signals, red – sell*.

Neural Network vs. Cycle Analysis to Predict the Stock Market

Three major conclusions for this particular historical period:

  1. Cycle Analysis prediction gives signals too early, Neural Network prediction – too late.
  2. In average, the prediction by Cycle Analysis showed slightly better accuracy than the one by Neural Network.
  3. It seems logical to combine these two methods to improve the accuracy.

* ) The calculations have been performed by an integrated experimental system that combined two applications: Cycle Analysis predictor SMAP-3 and Neural Network predictor NNSTP-2

S&P-500 Forecast for February 22-26, 2010

S&P-500 Forecast for February 22-26, 2010

Charts represent S&P-500 forecast for February 22-26, 2010. The calculations have been performed using Neural Network Stock Trend Predictor NNSTP-2 and Stock Market Predictor SMAP-3 (cycle analysis). A summarized prediction could be a moderate uptrend (1-2%) with flat or downtrend ending. Back-testing fails can be explained by increasing news factor or a possible reversal driven by fundamental changes.