CFD & FOREX TRADING PLATFORMS

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At the bottom of the article is the complete listing of all source code files. Contact us for more information. Your capital is at risk. These patterns are updated every 15 minutes. Put simply, we are executing two "separate" pieces of code, both of which are continuously running.

A history of transparency

Forex Trading Diary #1 - Automated Forex Trading with the OANDA API By QuantStart Team I previously mentioned in the QuantStart: In Review article that I would be spending some of writing about automated forex trading.

Optional Age of the pattern in seconds to search for. Leverage trading is high risk and not suitable for all. You could lose all of your deposited funds. Articles are for general information purposes only and are not investment advice or a solution to buy or sell any investment product. Opinions are those of the authors and not necessarily those of OANDA, its officers, or its directors. Examples shown are for illustrative purposes only and may not reflect current prices or offers from OANDA.

Accounts Get accounts for a user Get account information. Orders Get orders for an account Create a new order Get information for an order Modify an existing order Close an order. Trades Get a list of open trades Get information on a specific trade Modify an existing trade Close an open trade. Positions Get a list of all open positions Get the position for an instrument Close an existing position. Transaction History Get transaction history Get information for a transaction Get full account history Pagination Transaction types and a sub-set of corresponding parameters.

Calendar Returns up to 1 year worth of economic calendar information relevant to an instrument. Input Query Parameters instrument: Optional Name of the instrument to retrieve calendar data for. All tradable instruments are supported. If this is left out, information for all tradable instruments will be returned. Required Period of time in seconds to retrieve calendar data for.

Values not in the following list will be automatically adjusted to the nearest valid value. A positive period is used for data in the past and a negative period for upcoming events. The title of the event. Time of the event, returned as a unix timestamp. This field describes the data found in the forecast , previous , actual and market fields. Some possible values are: This is the currency that is affected by the news event. Shows the value of the previous release of the same event.

The actual value, this is only available after the event has taken place. The market expectation of what the value was. Required Name of the instrument to retrieve historical position ratios for. In addition different parts of the program can be run in separate threads , meaning that there is never any waiting for any particular component before processing any other.

This is extremely useful in algorithmic trading situations where market data feed handlers and strategy signal generators have vastly different performance characteristics. As we stated above the code runs in an infinite loop. Firstly, the queue is polled to retrieve a new event. If the queue is empty, then the loop simply restarts after a short sleep period known as the "heartbeat".

If an event is found its type is assessed and then the relevant module either the strategy or the execution handler is called upon to handle the event and possibly generate new ones that go back onto the queue. We will now discuss the implementation of the code in detail. At the bottom of the article is the complete listing of all source code files.

If you place them in the same directory and run python trading. It is bad practice to store passwords or authentication keys within a codebase as you can never predict who will eventually be allowed access to a project.

In a production system we would store these credentials as environment variables with the system and then query these "envvars" each time the code is redeployed. This ensures that passwords and auth tokens are never stored in a version control system. However, since we are solely interested in building a "toy" trading system, and are not concerned with production details in this article, we will instead separate these auth tokens into a settings file.

In the following settings. Each sub dictionary contains three separate API endpoints: The sandbox API is purely for testing code and for checking that there are no errors or bugs. It does not have the uptime guarantees of the real or practice APIs. The practice API, in essence, provides the ability to paper trade. That is, it provides all of the features of the real API on a simulated practice account.

The real API is just that - it is live trading! If you use that endpoint in your code, it will trade against your live account balance. When trading against the practice API remember that an important transaction cost, that of market impact , is not considered.

Since no trades are actually being placed into the environment this cost must be accounted for in another way elsewhere using a market impact model if you wish to realistically assess performance. We need two separate dictionaries for the domains, one each for the streaming and trading API components. The next step is to define the events that the queue will use to help all of the individual components communicate.

The second is used to transmit orders to the execution handler and thus contains the instrument, the number of units to trade, the order type "market" or "limit" and the "side" i. To future-proof our events code we are going to create a base class called Event and have all events inherit from this. The code is provided below in events. The next class we are going to create will handle the trading strategy.

Clearly this is a ridiculous "strategy"! However, it is fantastic for testing purposes because it is straightforward to code and understand. In future diary entries we will be replacing this with something significantly more exciting that will hopefully turn a profit!

Let's work through it and see what's going on. Firstly we import the random library and the OrderEvent object from events. We need the random lib in order to select a random buy or sell order. We need OrderEvent as this is how the strategy object will send orders to the events queue, which will later be executed by the execution handler.

It then creates a ticks counter that is used to tell how many TickEvent instances it has seen. It then checks to see if the count is divisible by 5 and then randomly buys or sells, with a market order, the specified number of units.

The next component is the execution handler. That is, there is no risk management or potfolio construction overlay. The execution handler will simply execute any order that it has been given. We must pass all of the authentication information to the Execution class, including the "domain" practice, real or sandbox , the access token and account ID.

We then create a secure connection with httplib , one of Pythons built in libraries. The method requires an event as a parameter. It then constructs two dictionaries - the headers and the params. We pass the Content-Type and Authorization header parameters, which include our authentication information. Finally, we make the request and save the response:. There are two methods: The first method uses the Python requests library to connect to a streaming socket with the appropriate headers and parameters.

Note the following line:. If the response is not successful i. We now have all of the major components in place. The final step is to wrap up everything we have written so far into a "main" program. You could lose all of your deposited funds. Articles are for general information purposes only and are not investment advice or a solution to buy or sell any investment product. Opinions are those of the authors and not necessarily those of OANDA, its officers, or its directors. Examples shown are for illustrative purposes only and may not reflect current prices or offers from OANDA.

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