org.mov.analyser
Class ANNPaperTrade

java.lang.Object
  org.mov.analyser.PaperTrade
      org.mov.analyser.ANNPaperTrade

public class ANNPaperTrade
extends PaperTrade

This class perform the paper trade analysis for the artificial neural network. A specific class has been developed, extended from PaperTrade PaperTrade, because ANNs have a complete different behaviour compared to other analysis based on Gondola language. ANNs need to be trained, and the training session needs to know how the things would be happened, if different choices have been taken day by day. For further information about the techniques used, you should find out the Cross Target technique (@author Prof. Pietro Terna). That is the technique used here to get the buy/sell signals. See http://web.econ.unito.it/terna/ct-era/ct-era.html.

The final portfolio will contain a single cash and a single share account. Cross Target method to get buy and sell signal through an ANN. The cross target method works in the following way: we make some guesses about buy and sell signals (actions) and about capital (effect of actions), the guesses are done by artificial neural network (ANN); then we train the ANN comparing what the ANN has guessed with the following values: the buy and sell signals are compared with the buy and sell signals which would be to get a capital equal to the capital guessed plus the percental increment wished; the capital signal is compared with the capital got trading with the guessed buy and sell signals. For the sake of simplicity in Merchant of Venice we've used a simplified version of CT technique. We do not use the capital as output of ANN, but we use only two outputs (the buy and sell signals). We pilot the buy and sell signals according to what happens in the future: we calculate if we gain enough in one of the next days (one from the next day trading until the window forecast day trading). We gain enough if and only if the earning percentage is higher than the user defined one, in one of the window forecast days. The core of the CT method has done in the setANNTrainingParameters method in this class.

Author:

Alberto Nacher

Nested Class Summary

Nested classes/interfaces inherited from class org.mov.analyser.PaperTrade

PaperTrade.Environment

Field Summary

Fields inherited from class org.mov.analyser.PaperTrade

buyRule, buyValue, CASH_ACCOUNT_NAME, sellRule, sellValue, SHARE_ACCOUNT_NAME, STOCKS_PER_LINES, symbolStock

Method Summary

static Portfolio	paperTrade(java.lang.String portfolioName, EODQuoteBundle quoteBundle, Variables variables, OrderCache orderCache, TradingDate startDate, TradingDate endDate, Money capital, int numberStocks, Money tradeCost, java.lang.String tradeValueBuy, java.lang.String tradeValueSell, ProgressDialog progress, Expression[] inputExpressions, ArtificialNeuralNetwork artificialNeuralNetwork) Perform paper trading using a fix number of stocks.
static Portfolio	paperTrade(java.lang.String portfolioName, EODQuoteBundle quoteBundle, Variables variables, OrderCache orderCache, TradingDate startDate, TradingDate endDate, Money capital, Money stockValue, Money tradeCost, java.lang.String tradeValueBuy, java.lang.String tradeValueSell, ProgressDialog progress, Expression[] inputExpressions, ArtificialNeuralNetwork artificialNeuralNetwork) Perform paper trading using a fixed stock value.
static void	paperTraining(java.lang.String portfolioName, EODQuoteBundle quoteBundle, Variables variables, OrderCache orderCache, TradingDate startDate, TradingDate endDate, Money capital, int numberStocks, Money tradeCost, java.lang.String tradeValueBuy, java.lang.String tradeValueSell, ProgressDialog progress, ANNTrainingPage ANNTrainingPage, Expression[] inputExpressions, ArtificialNeuralNetwork artificialNeuralNetwork) Perform training using a fix number of stocks.
static void	paperTraining(java.lang.String portfolioName, EODQuoteBundle quoteBundle, Variables variables, OrderCache orderCache, TradingDate startDate, TradingDate endDate, Money capital, Money stockValue, Money tradeCost, java.lang.String tradeValueBuy, java.lang.String tradeValueSell, ProgressDialog progress, ANNTrainingPage ANNTrainingPage, Expression[] inputExpressions, ArtificialNeuralNetwork artificialNeuralNetwork) Perform training using a fixed stock value.

Methods inherited from class org.mov.analyser.PaperTrade

buy, getCapital, getHoldingTime, getStockCapital, getTip, paperTrade, paperTrade, sell

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

paperTrade

public static Portfolio paperTrade(java.lang.String portfolioName,
                                   EODQuoteBundle quoteBundle,
                                   Variables variables,
                                   OrderCache orderCache,
                                   TradingDate startDate,
                                   TradingDate endDate,
                                   Money capital,
                                   Money stockValue,
                                   Money tradeCost,
                                   java.lang.String tradeValueBuy,
                                   java.lang.String tradeValueSell,
                                   ProgressDialog progress,
                                   Expression[] inputExpressions,
                                   ArtificialNeuralNetwork artificialNeuralNetwork)
                            throws EvaluationException

Perform paper trading using a fixed stock value.

Parameters:

portfolioName - name to call portfolio

quoteBundle - historical quote data

variables - any Gondola variables set

orderCache - cache of ordered symbols

startDate - start date of trading

endDate - last date of trading

capital - initial capital in the portfolio

stockValue - the rough value of each stock holding

tradeCost - the cost of a trade

tradeValueBuy - the value at which we want to buy

tradeValueSell - the value at which we want to sell

progress - the progress bar shown while ANN is running

inputExpressions - the input expressions of ANN

artificialNeuralNetwork - the ANN object

Returns:

the portfolio at the close of the last day's trade

Throws:

EvaluationException

paperTraining

public static void paperTraining(java.lang.String portfolioName,
                                 EODQuoteBundle quoteBundle,
                                 Variables variables,
                                 OrderCache orderCache,
                                 TradingDate startDate,
                                 TradingDate endDate,
                                 Money capital,
                                 Money stockValue,
                                 Money tradeCost,
                                 java.lang.String tradeValueBuy,
                                 java.lang.String tradeValueSell,
                                 ProgressDialog progress,
                                 ANNTrainingPage ANNTrainingPage,
                                 Expression[] inputExpressions,
                                 ArtificialNeuralNetwork artificialNeuralNetwork)
                          throws EvaluationException

Perform training using a fixed stock value.

Parameters:

portfolioName - name to call portfolio

quoteBundle - historical quote data

variables - any Gondola variables set

orderCache - cache of ordered symbols

startDate - start date of trading

endDate - last date of trading

capital - initial capital in the portfolio

stockValue - the rough value of each stock holding

tradeCost - the cost of a trade

tradeValueBuy - the value at which we want to buy

tradeValueSell - the value at which we want to sell

progress - the progress bar shown while ANN is running

ANNTrainingPage - the pointer to the training page

inputExpressions - the input expressions of ANN

artificialNeuralNetwork - the ANN object

Throws:

EvaluationException

paperTraining

public static void paperTraining(java.lang.String portfolioName,
                                 EODQuoteBundle quoteBundle,
                                 Variables variables,
                                 OrderCache orderCache,
                                 TradingDate startDate,
                                 TradingDate endDate,
                                 Money capital,
                                 int numberStocks,
                                 Money tradeCost,
                                 java.lang.String tradeValueBuy,
                                 java.lang.String tradeValueSell,
                                 ProgressDialog progress,
                                 ANNTrainingPage ANNTrainingPage,
                                 Expression[] inputExpressions,
                                 ArtificialNeuralNetwork artificialNeuralNetwork)
                          throws EvaluationException

Perform training using a fix number of stocks.

Parameters:

portfolioName - name to call portfolio

quoteBundle - historical quote data

variables - any Gondola variables set

orderCache - cache of ordered symbols

startDate - start date of trading

endDate - last date of trading

capital - initial capital in the portfolio

numberStocks - try to keep this number of stocks in the portfolio

tradeCost - the cost of a trade

tradeValueBuy - the value at which we want to buy

tradeValueSell - the value at which we want to sell

progress - the progress bar shown while ANN is running

ANNTrainingPage - the pointer to the training page

inputExpressions - the input expressions of ANN

artificialNeuralNetwork - the ANN object

Throws:

EvaluationException

paperTrade

public static Portfolio paperTrade(java.lang.String portfolioName,
                                   EODQuoteBundle quoteBundle,
                                   Variables variables,
                                   OrderCache orderCache,
                                   TradingDate startDate,
                                   TradingDate endDate,
                                   Money capital,
                                   int numberStocks,
                                   Money tradeCost,
                                   java.lang.String tradeValueBuy,
                                   java.lang.String tradeValueSell,
                                   ProgressDialog progress,
                                   Expression[] inputExpressions,
                                   ArtificialNeuralNetwork artificialNeuralNetwork)
                            throws EvaluationException

Perform paper trading using a fix number of stocks.

Parameters:

portfolioName - name to call portfolio

quoteBundle - historical quote data

variables - any Gondola variables set

orderCache - cache of ordered symbols

startDate - start date of trading

endDate - last date of trading

capital - initial capital in the portfolio

numberStocks - try to keep this number of stocks in the portfolio

tradeCost - the cost of a trade

tradeValueBuy - the value at which we want to buy

tradeValueSell - the value at which we want to sell

progress - the progress bar shown while ANN is running

inputExpressions - the input expressions of ANN

artificialNeuralNetwork - the ANN object

Returns:

the portfolio at the close of the last day's trade

Throws:

EvaluationException