Developing Trading Applications with the SumZero Trading API

I have open sourced a Java trading library which I have been using to develop automated trading applications for many years.  The SumZero Trading API provides the ability to develop trading applications for the equity, futures, and currency markets, by utilizing the following sub APIs

  • Market Data API – Request real time Level 1 (NBBO) and Level 2 (Market Depth) market data
  • Broker API – Submit, execute, and monitor orders
  • Historical Data API – Request intraday and end-of-day historical market data.
  • Strategy API – Develop trading strategies to automatically place buy/sell orders based on user defined algorithms.

The library includes implementation of all of these APIs for Interactive Brokers, except for the Broker API, which also has an implementation for Quantitative Brokers.

The libraries are licensed under the MIT open source license and source code is available at:
https://github.com/rterp/SumZeroTrading

In future posts I will show how easy it is to connect to Interactive Brokers to request real-time market data and place a trade using the API.

twitter: @RobTerpilowski
twitter: @SumZeroTrading

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Automatically Sorting a JavaFX TableView

With the SortedList implementation of ObservableList, automatically sorting a JavaFX TableView nearly works out of the box. In fact, it does work out of the box as long as you are adding or removing items from the list. In these circumstances, the list and the accompanying TableView will automatically resort the data. The case where the data isn’t automatically resorted is if the data in the existing rows change, but no rows are added or removed from the list.

For example, I have a TableView displaying various price information about a list of stocks including the last price, and the percent change from yesterday’s closing price. I would like to sort the list based on percent change, from lowest to highest as the prices change in real-time.  However since the app just loads the stocks into the list once and then never adds or removes items from the list, the TableView only auto sorted on the initial load, but did not automatically sort as the prices were updated.

Fortunately the fix proved to be fairly minor and easy to implement.

To start with, the application uses a Stock POJO which contains the following properties to represent the data. An ObservableList of these POJOs will be the basis of the data that the TableView will render.

public class Stock implements Level1QuoteListener {

protected StockTicker ticker;

protected SimpleStringProperty symbol = new SimpleStringProperty("");

protected SimpleDoubleProperty bid = new SimpleDoubleProperty(0);

protected SimpleDoubleProperty ask = new SimpleDoubleProperty(0);

protected SimpleDoubleProperty last = new SimpleDoubleProperty(0);

protected SimpleDoubleProperty percentChange = new SimpleDoubleProperty(0);

protected SimpleIntegerProperty volume = new SimpleIntegerProperty(0);

protected SimpleDoubleProperty previousClose = new SimpleDoubleProperty(0);

protected SimpleDoubleProperty open = new SimpleDoubleProperty(0);

 

The first step is to implement a new Callback, tying it to the property we wish to sort in the table. When this property is updated it will trigger the table to resort itself automatically.

      Callback<Stock,Observable[]> cb =(Stock stock) -> new Observable[]{
        stock.percentChangeProperty(),
    };

 

The next step is to create a new ObservableList using the Callback above.

      ObservableList<Stock> observableList = FXCollections.observableArrayList(cb);
      observableList.addAll(stocks);

Finally the last step is to create a new SortedList using the ObservableList previously created and also passing in an implementation of a Comparator which will be used to determine how to sort the data.

      SortedList<Stock> sortedList = new SortedList<>( observableList, 
      (Stock stock1, Stock stock2) -> {
        if( stock1.getPercentChange() < stock2.getPercentChange() ) {
            return -1;
        } else if( stock1.getPercentChange() > stock2.getPercentChange() ) {
            return 1;
        } else {
            return 0;
        }
    });


    tickerTableView.setItems(sortedList);
}

That’s it. The video below illustrates the auto-sorting in action as the price of the stocks are updated in real-time.


twitter: @RobTerpilowski

Formatting Rows in a JavaFX TableView Using CSS Pseudo Classes

One strategy for formatting cells in a JavaFX TableView is to utilize CSS Pseudo classes and a TableRowFactory to select the appropriate class to style the row. Below is a screenshot of an applcation that is configured to monitor real time stock quotes. Each row displays price information for a single stock. I would like the rows to be colored based on the price change of the particular stock, with a red row representing a stock that is trading lower since the previous day, and a green row to represent a stock whose current price is higher than the previous day.

enter image description here

First the domain object which represents the Stock is below. Notice that the class uses JavaFX properties to represent the various values that are displayed in each column.

public class Stock implements Level1QuoteListener {

protected StockTicker ticker;
protected SimpleStringProperty symbol = new SimpleStringProperty();
protected SimpleDoubleProperty bid = new SimpleDoubleProperty();
protected SimpleDoubleProperty ask= new SimpleDoubleProperty();
protected SimpleDoubleProperty last = new SimpleDoubleProperty();
protected SimpleDoubleProperty percentChange = new SimpleDoubleProperty();
protected SimpleIntegerProperty volume = new SimpleIntegerProperty();
protected SimpleDoubleProperty previousClose = new SimpleDoubleProperty();
protected SimpleDoubleProperty open = new SimpleDoubleProperty();


public Stock( StockTicker ticker ) {
    this.ticker = ticker;
    setSymbol( ticker.getSymbol() );
}

public void setSymbol( String symbol ) {
    this.symbol.set(symbol);
}

public String getSymbol() {
    return symbol.get();
}

public StringProperty symbol() {
    return symbol;
}

public void setBid( double bid ) {
    this.bid.set(bid);
}

public double getBid() {
    return bid.get();
}

public SimpleDoubleProperty bid() {
    return bid;
}

public void setAsk( double ask ) {
    this.ask.set(ask);
}

public double getAsk() {
    return ask.get();
}

public DoubleProperty ask() {
    return ask;
}

public void setLast( double last ) {
    this.last.set(last);
    double change = ((last-closeProperty().get() ) / closeProperty().get() ) * 100.0;
    setPercentChange(change);

}

public double getLast() {
    return last.get();
}

public DoubleProperty last() {
    return last;
}

public void setPercentChange( double percentChange ) {
    this.percentChange.set(percentChange);
}

public double getPercentChange() {
    return percentChange.get();
}

public void setVolume( int volume ) {
    this.volume.set(volume);
}

public int getVolume() {
    return volume.get();
}

public void setPreviousClose( double close ) {
    this.previousClose.set(close);
    double change = ((last.get()-close ) / close ) * 100.0;
    setPercentChange(change);
}


public double getPreviousClose() {
    return previousClose.get();
}

public DoubleProperty previousClose() {
    return previousClose;
}

public void setOpen( double open ) {
    this.open.set(open);
}

public double getOpen() {
    return open.get();
}

public DoubleProperty openProperty() {
    return open;
}

public DoubleProperty closeProperty() {
    return previousClose;
}

public DoubleProperty percentChangeProperty() {
    return percentChange;
}

public StockTicker getTicker() {
    return ticker;
}



@Override
public void quoteRecieved(ILevel1Quote quote) {
    if( quote.getTicker().equals(ticker)) {
        BigDecimal quoteValue = quote.getValue();
        switch( quote.getType() ) {
            case ASK:
                setAsk(quoteValue.doubleValue());
                break;
            case BID:
                setBid(quoteValue.doubleValue());
                break;
            case LAST:
                setLast(quoteValue.doubleValue());
                break;
            case CLOSE:                  
                setPreviousClose(quoteValue.doubleValue());
                break;
            case OPEN:
                setOpen(quoteValue.doubleValue());
                break;
            case VOLUME:
                setVolume(quoteValue.intValue());
                break;
            default:
                break;

        }
    }
}

The first step in creating a custom format is to define the styles in the CSS stylesheet that is associated with the TableView. The CSS file below shows how the table row will be formatted. Comments below describe each item including the ‘up’ and ‘down’ Pseudo classes that are defined for the .table-row-cell class.

/* Default row background is black */
.table-row-cell {
    -fx-background-color: black;
}

/* Text in the rows will be white */
.table-row-cell .text {
       -fx-fill: white ;
}

/*
Pseudo class 'up' will change the row background
 color to green when activated 
 */
.table-row-cell:up 
{
    -fx-background-color: #007054; 
}

/*
Pseudo class 'down' will change the row background 
color to red when activated
*/
.table-row-cell:down {
    -fx-background-color: #A30029;
}

Ok, so far this seems easy enough. Now the tricky part is to activate the proper Pseudo class when the % change of the stock price is positive or negative. This can be done using a TableRowFactory in the controller class which is managing the TableView.

Below is an example controller which has a reference to a TableView object called tickerTableView. The setup() method contains logic for selecting the appropriate pseudo class based on the stock data.

public class FXMLController implements Initializable {


@FXML
private TableView tickerTableView;


 public void setup() {
 //The pseudo classes 'up' and 'down' that were defined in the css file.
    PseudoClass up = PseudoClass.getPseudoClass("up");
    PseudoClass down = PseudoClass.getPseudoClass("down");



//Set a rowFactory for the table view.
tickerTableView.setRowFactory(tableView -> {
        TableRow<Stock> row = new TableRow<>();
        ChangeListener<Number> changeListener = (obs, oldPrice, newPrice) -> {
            row.pseudoClassStateChanged(up, newPrice.doubleValue() > 0);
            row.pseudoClassStateChanged(down, newPrice.doubleValue() <= 0);
        };

        row.itemProperty().addListener((obs, previousStock, currentStock) -> {
            if (previousStock != null) {
                previousStock.percentChangeProperty().removeListener(changeListener);
            }
            if (currentStock != null) {
                currentStock.percentChangeProperty().addListener(changeListener);
                row.pseudoClassStateChanged(up, currentStock.getPercentChange() > 0);
                row.pseudoClassStateChanged(down, currentStock.getPercentChange() <= 0);
            } else {
                row.pseudoClassStateChanged(up, false);
                row.pseudoClassStateChanged(down, false);
            }
        });
        return row;
    });
}

The first step is to create 2 new PseudoClass object which will map to the Pseudo classes that were defined in the css file.

PseudoClass up = PseudoClass.getPseudoClass("up");
PseudoClass down = PseudoClass.getPseudoClass("down");

Next a RowFactory needs to be defined which knows how to select the proper PseudoClass. The setRowFactory() method on the TableView class takes a Callback object which in turn takes a TableView as an argument and returns a TableRow.

The first thing to do in the row factory is to create a new change listener. This listener will monitor the stock for a particular row. The oldPrice and newPrice are passed into the change listener. The pseudo class of the row can then be activated or deactivated by invoking the pseudoClassStateChanged() method on the TableRow and passing the appropriate PseudoClass and boolean value to indicate whether or not the class should be active.

tickerTableView.setRowFactory(tableView -> {
   TableRow<Stock> row = new TableRow<>();
   ChangeListener<Number> changeListener = (obs, oldPrice, newPrice) -> {
     row.pseudoClassStateChanged(up, newPrice.doubleValue() > 0);
     row.pseudoClassStateChanged(down, newPrice.doubleValue() <= 0);
   };

The final piece is to tie the change listener to the item in the TableRow. This is done by adding a listener on the ItemProperty of the row. Once a Stock item is added to the TableRow, the ChangeListener defined above can be bound to the Stock’s percentChangeProperty. The initial state of the classes are also set when the new item is added. If a Stock item is being removed from the TableRow, the previousStock variable will be populate with the Stock object that is being removed. At this point the change listener can be removed from the row for the old Stock before the new one is added.

row.itemProperty().addListener((obs, previousStock, currentStock) -> {
            if (previousStock != null) {
                previousStock.percentChangeProperty().removeListener(changeListener);
            }
            if (currentStock != null) {
                currentStock.percentChangeProperty().addListener(changeListener);
                //Set the initial state of the pseudo classes
                row.pseudoClassStateChanged(up, currentStock.getPercentChange() > 0);
                row.pseudoClassStateChanged(down, currentStock.getPercentChange() <= 0);
            } else {
                row.pseudoClassStateChanged(up, false);
                row.pseudoClassStateChanged(down, false);
            }
        });
        return row;
    });

The final result is displayed in the table below Rows will change between red and green automatically in real time as the price of the stock fluctuates throughout the trading day.

enter image description here

twitter: @RobTerpilowski

Article in Futures Magazine on our Commodity Trading Program

From the December 2014 issue of Futures Magazine • Subscribe!

Zoi: A match made in cyber heaven

Konstantinos (Gus) Tsahas and Robert Terpilowski had similar ideas regarding trading. These partners, who have been working on trading strategies together for nearly a decade, met of all places on an Interactive Brokers (IB) message board where they began sharing ideas (both have an engineering background) despite being on separate coasts: Tsahas in New York and Terpilowski in Seattle.

“Around 2000, Interactive Brokers offered an API and I began developing a mean reversion strategy for the equity markets,” Terpilowski says. “I was working on software to automate that. Gus actually posted something on the message board of the API group so we hooked up and started bouncing ideas off of each other and hit it off.”

Tsahas adds, “It just turned out that we were working on the same mean reversion ideas. I was further along in some aspects, Rob was [further along] in others. Collaborating helped both of us to come up with some good strategies. That was the start.”

This collaboration continued for five years with both Terpilowski and Tsahas pursuing separate careers: Terpilowski in various civil engineering positions and Tsahas running his own business installing fiber optic lines.

By 2005 they were trading a number of different strategies but running into scalability issues trading individual equities and decided to look at taking their mean reverting strategies and porting them over to futures.

“We noticed in 2005 that it was difficult to get filled in equity markets,” Tsahas says.

“The reason we chose the mean reversion strategies is we were looking at forming a CTA and felt like mean reversion was a pretty good alternative to the 70% or so of [CTAs] who were trend followers,” Terpilowski says. “That would allow people to include our strategy in their portfolio and provide a return stream that was uncorrelated to other CTAs.”

At the time Tsahas went back to school to get a financial engineering degree and entered an IB-sponsored trading contest using their methodology. “I actually came in second place using this strategy and won $50,000.”

Both would build and code the strategy. “Once we hit something that was beneficial we would share it. It was a lot of test and verify,” Tsahas says. “That is the beauty of having a second person with a different set of skills. Once I saw something that I liked or Rob saw something that he liked, we would port it over and test it ourselves.”

“That is one of the reasons we have worked so well together,” Terpilowski adds. “Being from an engineering background we basically say if it is not something that we can model and backtest it is not something that we are willing to trade.”

By 2007 they began trading proprietary money on 19 futures markets in six sectors. Their strategy attempts to capture short-term pullbacks in medium-term (two to three-week) trends.  They did not attempt to optimize their strategy on each market but rather on the entire portfolio to avoid curve-fitting.

“The inputs we use for natural gas are the same inputs that we use for the S&P 500 or gold or wheat,” Terpilowski says. “We wanted to avoid curve-fitting so we decided we are going to optimize it at the portfolio level only and we included 40 markets.”

Their strategies performed extremely well in 2007-2009 and by 2010 they began trading customer money in CTA Zoi Capital.

Winning trades typically last 1.5 days, with losers lasting 2.5 days. Zoi’s Telio program has a four-point risk management strategy. They exit the market in five days if a trade does not begin to revert back to the mean.

“We want to minimize the amount of time in the market because whenever you are in the market you are assuming risk,” Terpilowski says. “We apply stop losses whenever we open a new position but they are dynamic; if volatility is higher we want to place the stops a little further away than if volatility is less to give it more room to breathe.”

However, Zoi will reduce the position size when applying wider stops. That way they maintain their risk parameters, 2.5% on every trade, but allow trades to work. They also will exit all positions if they reach a portfolio-wide drawdown of 5% in a day. They cap margin to equity at 25%, though it typically runs around 7 to 10%.

Zoi earned solid returns: 36.12% in 2010, 25.34% in 2011 and 32.66% in 2012, trading basically for friends and family and looked to raise more assets.

In 2013 Zoi got its first institutional client when Jack Schwager provided an allocation from the ADM Investor Services Diversified Strategies Fund he was co-portfolio manager of, which is ironic as Terpilowski first got interested in technical trading after reading a Jack Schwager book.

The Telio program has produced a compound annual return of 20.36% since April 2010 with a Sharpe ratio of 1.05. It is up 6.56% in 2014 through October.

Zoi is looking to expand its program to 40 markets, which should be no problem as the strategy has already been rigorously tested. As Tsahas says, “If we can’t see it in a model, validate it and make sure it is not curve fitted, we are not going to trade it.”