Check out my ebook on quant trading where I teach you how to build profitable systematic trading strategies with Python tools, from scratch. Take a look at my new ebook on advanced trading strategies using time series analysis, machine learning and Bayesian statistics, with Python and R. A while back we discussed how to set up an Interactive Brokers demo account. Interactive Brokers is one of the main brokerages used by retail trading traders due to its relatively low minimal account balance system 10, USD and relatively straightforward API. In this article we will make use of a demo account to automate trades against the Interactive Brokers API, via Python and the IBPy plugin. I have no affiliation with Interactive Brokers. I have used them before in a professional fund context and as such am familiar with their software. Interactive Brokers is a large enterprise and as such caters to a wide-range of traders, ranging from discretionary retail to automated institutional. This has led their GUI interface, Trader Workstation TWSto possess a significant quantity of "bells and whistles". In addition to TWS there is also a lightweight component called the IB Gateway, which provides the same access to the IB servers, albeit without the extra functionality of the GUI. For our automated trading purposes we won't actually system the TWS GUI, but I think for this tutorial system is demonstrative to make use of it. It is this API that we will utilise in this tutorial to send automated orders, via IBPy. IBPy has been written to "wrap" the native Java API and make it straightforward to call from Python. The two main libraries we are interested in within IBPy are ib. The latter is higher level and makes use of functionality in the former. In the following implementation we are going to create an extremely simple example, which will simply send a single market order to buy units of Google stock, using smart order routing. The latter is designed to achieve the best price in practice, although in certain situations it can be suboptimal. However for the purposes of this tutorial it will suffice. Before we begin it is necessary to have followed the steps in the prior tutorial on setting up an Interactive Brokers account. In addition it is necessary to have a prior Python workspace so that we can install IBPywhich will allow you to tie other aspects of your code together. The tutorial on installing a Python research environment will create the necessary workspace. IBPy is a Python wrapper written around the Java-based Interactive Brokers API. It makes development of algorithmic trading systems in Python somewhat less problematic. Trading will be used as the basis for all subsequent communication with Interactive System until we consider the FIX protocol at a later date. Since IBPy is maintained on GitHub as a git repository we will need to install git. On a Ubuntu system this is trading by:. Once you have git installed you can create a subdirectory to store IBPy. On my system I have simply placed it underneath my home directory:. That completes trading installation of IBPy. The next step is to open up TWS as described in the prior tutorial. TWS Portfolio View Prior to Google Trade. The following code will demonstrate an extremely simple API-based order mechanism. The code is far from production-ready but it does demonstrate the essential functionality of the Interactive Brokers API and how to use it system order execution. The first step is to import the Contract and Order objects from the lower level ib. In addition we import the Connection and message objects from the ib. IB provides us with the capability of handling errors and server responses by a callback mechanism. The following two functions do nothing more than print out the contents of the messages returned trading the server. A more sophisticated production system would have to implement logic to ensure continual running of the system in the event of exceptional behaviour:. The following two functions wrap the creation of the Contract and Order objects, setting their trading parameters. The function docs describe each parameter individually:. The error and reply handler functions are then registered with the connection object. In a production system this must be incremented for each trade order. The next steps are to create a Contract and an Order representing a market order to buy units of Google stock. The final task is to actually place that order via the placeOrder method of the Connection object. We then disconnect from TWS:. Immediately it can be seen that the API tab opens up in Trader Trading, showing the market order to go long shares of Google:. TWS API Tab view after Google order. If we now look at the Portfolio tab we can see the Google position. You will also note a forex position in the list, which was not generated by myself! I can only assume that either the IB demo account is "shared" system some fashion due to the identical login information or IB places arbitrary orders into the account to make it appear more "realistic". If trading has any insight into trading behaviour I would be intrigued to learn more:. TWS API Portfolio view subsequent to Google system. This is the most basic form of automated execution that we could consider. In subsequent articles we are going to construct a more robust event-driven architecture system can handle realistic trading strategies. QuantStart Log In Sign Up. Learn about QuantStart Read our Books Browse the Articles List Explore the Reading List Backtest with QSTrader Query the Support Knowledge Base. Using Python, IBPy and the Interactive Brokers API to Automate Trades. By Michael Halls-Moore on February 5th, A while back we discussed how to set up an Interactive Brokers demo account. The Interactive Brokers API Interactive Brokers is a large enterprise and as such caters to a wide-range of traders, ranging from discretionary retail to automated institutional. Implementation in Python Before we begin it is necessary to have followed the steps in the prior tutorial on setting up an Interactive Brokers account. Installing IBPy System is a Python wrapper written around the Java-based Interactive Brokers API. On a Ubuntu system this is handled by: On my system I have simply placed it underneath my home directory: TWS Portfolio View Prior to Google Trade Automated Trading The trading code will demonstrate an extremely simple API-based order mechanism. Contract import Contract from ib. Order import Order from ib. A more sophisticated production system would have to implement logic to ensure continual running of the system in the event of exceptional behaviour: The function docs describe each parameter individually: We then disconnect from TWS: This will need incrementing once new orders are submitted. TWS API Tab view after System order If we now look at the Portfolio tab trading can see the Google position. If anybody has any insight into this behaviour System would be intrigued to learn more: TWS API Portfolio view subsequent to Google order This is the most basic form of automated execution that we could consider.
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