There are a variety of instruments that can be retrieved using the API object created in Oanda like all that are traded on the platform, eg. In the following example, the start date is taken to be 3 rd Feb and the end date is assigned as 4 th Feb However, for backtesting, we need to play with more such data. Therefore the granularity is changed to one minute, which gives us more clarity. A DatetimeIndex object needs to be created. Generally, the data is saved on the disk for easy retrieval.

The important thing to note is that the data is retrieved chunk wise, and therefore has to be appended to the data frame object. Note that the granuality is set to M1, which means every minute. There is also the facility to set the Timezone of the data.

The code is executed in 1. The highlighted portion is an important step to write the data we retrieved from the Oanda API to database object and HDFStore and save it on the disk.

This is a binary storage format. Perhaps the best way to test a strategy is to have a look at its returns. In the following code, we calculate the log return of a strategy for later performance judgement. This is done in a completely vectorised fashion using numpy function. The strategy used for this backtesting has been quite popular over the years and is also part of research papers, which generate a signal based on two different trends: a shorter simple rolling mean over 5 observations read minutes while the other longer trend, over 15 observations.

While visualising the data, we come across the following pattern. The blue line is our basic data gathered from the market above, the green curve is the short term trend and the red curve represents a longer trend. Since this visualisation does not give a complete picture of the trends because of overlaps, we focus on sample observational points.

Here we can see clearly that the spot price blue curve shows an upward trend when the shorter trend breaches the longer trend from below, while a downward trend is visible when the green curve crosses the red curve from above.

Once our strategy is mimicking the market, we can generate signals to be issued to the market when certain conditions are met. In this particular case, we are going for a long-only strategy.

The green curve represents our trading strategy while the blue curve is indicative of the returns from the market. From the graph, it is clear that we are losing money in the extreme left a downward trend and also in the extreme right. The "number of trades" is calculated to understand the cost of implementing in case we are using do actual trading. For a rather short period of time, there are trades happening in this strategy. So, although the trading is not high-frequency, it is very active at the same time.

Another statistic of interest might be standard deviation SD of returns, which gives us an idea regarding the volatility for minute-bar returns. We see here that SD of the market trading strategy is quite a bit higher than the strategy devised given our trend based investments. This is also true because we have not invested for longer periods of time. With this, we can see that our strategies are performing well for the random time interval chosen from last year's data.

Therefore, our next step would be to work with streaming data and automating in real-time. These topics will be a part of our next blog post. If you want to ideate and implement Quant Strategies in Python, this blog post will help you get there.

Do you think python is preferred over other languages in Automated Trading? See Why. Return to the OANDA dashboard and click the green highlighted "Launch FXTrade Practice" link. It will bring up a Java dialog asking whether you want to run it. Click "Run" and the fxTrade Practice tool will load. OANDA fxTrade Practice screen. At this point we are ready to begin designing and coding our automated forex trading system against the OANDA API. If you have been following the event-driven backtester series for equities and ETFs that I created last year, you'll be aware of how such an event-driven trading system functions.

For those of you who are new to event-driven software , I would strongly suggest reading through the article in order to gain some insight into how they work. In essence, the entire program is executed in an infinte while loop that only terminates when the trading system is shut off. The central communication mechanism of the program is given via a queue that contains events.

The queue is constantly queried to check for new events. Once an event has been taken off the top of the queue it must be handled by an appropriate component of the program. Hence a market data feed might create TickEvent s that are placed onto the queue when a new market price arrives.

A signal-generating strategy object might create OrderEvent s that are to be sent to a brokerage. The usefulness of such a system is given by the fact that it doesn't matter what order or types of events are placed on the queue, as they will always be correctly handled by the right component within the program. 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. py you will begin generating orders, assuming you have filled in your account ID and authentication token from OANDA. 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: real , practice and sandbox. 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.

BE EXTREMELY CAREFUL! IMPORTANT: 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.

In the following we are using the practice account as given by the DOMAIN setting. We need two separate dictionaries for the domains, one each for the streaming and trading API components. I've filled the two below with dummy IDs so you will need to utilise your own, which can be accessed from the OANDA account page:. The next step is to define the events that the queue will use to help all of the individual components communicate. We need two: TickEvent and OrderEvent.

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! The strategy. py file can be found below. 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.

It's certainly not the world's greatest trading strategy, but it will be more than suitable for our OANDA brokerage API testing purposes! The next component is the execution handler. This class is tasked with acting upon OrderEvent instances and making requests to the broker in this case OANDA in a "dumb" fashion. 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. These dictionaries will then be correctly encoded partially by urllib , another Python library to be sent as an HTTP POST request to OANDAs API.

We pass the Content-Type and Authorization header parameters, which include our authentication information. Finally, we make the request and save the response:. The most complex component of the trading system is the StreamingForexPrices object, which handles the market price updates from OANDA.

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. the response code is not HTTP , then we simply return and exit. If it is successful we try to load the JSON packet returned into a Python dictionary. 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.

The goal of this file, known as trading. py , is to create two separate threads , one of which runs the pricing handler and the other which runs the trading handler.

Why do we need two separate threads? Put simply, we are executing two "separate" pieces of code, both of which are continuously running. If we were to create a non-threaded program, then the streaming socket used for the pricing updates would never ever "release" back to the main code path and hence we would never actually carry out any trading. Similarly, if we ran the trade loop see below , we would never actually return the flow path to the price streaming socket.

Hence we need multiple threads, one for each component, so that they can be carried out independently. They will both communicate to each other via the events queue. Thus we are able to run two, effectively infinite looping, code segments independently, which both communicate through the events queue. Note that the Python threading library does not produce a true multi-core multithreaded environment due to the CPython implementation of Python and the Global Interpreter Lock GIL.

If you would like to read more about multithreading on Python, please take a look at this article. Let's examine the rest of the code in detail. Firstly we import all of the necessary libraries including Queue , threading and time.

We then import all of the above code files. I personally prefer to capitalise any configuration settings, which is a habit I picked up from working with Django! After that we define the trade function, which was explained in Python-pseudocode above. An infinite while loop is carried out while True: that continuously polls from the events queue and only skips the loop if it is found empty. If an event is found then it is either a TickEvent or a OrderEvent and then the appropriate component is called to carry it out.

In this case it is either a strategy or execution handler. The loop then simply sleeps for "heartbeat" seconds in this case 0. It is well commented below, but I will summarise here. We then create the StreamingForexPrices price streaming class and then subsequently the Execution execution handler.

Both receive the necessary authentication details that are given by OANDA when creating an account. We then create the TestRandomStrategy instance. Finally we define the two threads and then start them:. To run the code you simply need to place all the files in the same directory and call the following at the terminal:. Note that to stop the code at this stage requires a hard kill of the Python process , via "Ctrl-Z" or equivalent!

I previously mentioned in the QuantStart: In Review article that I would be spending some of writing about automated forex trading. Given that I myself usually carry out research in equities and futures markets, I thought it would be fun and educational! to write about my experiences of entering the forex market in the style of a diary.

Each "diary entry" will attempt to build on all those before, but should also be relatively self-contained. In this first entry of the diary I'll be describing how to set up a new practice brokerage account with OANDA as well as how to create a basic multithreaded event-driven trading engine that can automatically execute trades in both a practice and live setting.

Last year we spent a lot of time looking at the event-driven backtester , primarily for equities and ETFs. The one I present below is geared towards forex and can be used for either paper trading or live trading.

I have written all of the following instructions for Ubuntu The only additional library used for the Python trading engine is the requests library, which is necessary for HTTP communication to the OANDA API. Since this is the first post directly about foreign exchange trading, and the code presented below can be straightforwardly adapted to a live trading environment, I would like to present the following disclaimers:.

Disclaimer: Trading foreign exchange on margin carries a high level of risk, and may not be suitable for all investors. Past performance is not indicative of future results. The high degree of leverage can work against you as well as for you. Before deciding to invest in foreign exchange you should carefully consider your investment objectives, level of experience, and risk appetite.

The possibility exists that you could sustain a loss of some or all of your initial investment and therefore you should not invest money that you cannot afford to lose. You should be aware of all the risks associated with foreign exchange trading, and seek advice from an independent financial advisor if you have any doubts.

This software is provided "as is" and any expressed or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the regents or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption however caused and on any theory of liability, whether in contract, strict liability, or tort including negligence or otherwise arising in any out of the use of this software, even if advised of the possibility of such damage.

The first question that comes to mind is "Why choose OANDA? Simply put, after a bit of Googling around for forex brokers that had APIs, I saw that OANDA had recently released a proper REST API that could easily be communicated with from nearly any language in an extremely straightforward manner.

After reading through their developer API documentation , I decided to give them a try, at least with a practice account. To be clear - I have no prior or existing relationship with OANDA and am only providing this recommendation based on my limited experience playing around with their practice API and some brief usage for market data download while employed at a fund previously. If anybody has come across any other forex brokers that also have a similarly modern API then I'd be happy to give them a look as well.

Before utilising the API it is necessary to sign up for a practice account. To do this, head to the sign-up link. You will see the following screen:. OANDA sign-up screen. You will then be able to sign in with your login credentials. Make sure to select the "fxTradePractice" tab from the sign-in screen:. OANDA sign-in screen. Once in you will need to make a note of your Account ID. It is listed underneath the black "My Funds" header next to "Primary". Mine is a 7-digit number.

In addition you will also need to generate a personal API token. To do this click "Manage API Access" underneath the "Other Actions" tab on the lower left:. OANDA dashboard. At this stage you will be able to generate an API token. You will need the key for use later, so make sure to write it down as well.

You will now want to launch the FXTrade Practice application, which will allow us to see the executed orders and our paper! If you are running a Ubuntu system you will need to install a slightly different version of Java. In particular, the Oracle version of Java 8. If you don't do this then the practice simulator will not load from the browser. I ran these commands on my system:. You will now be able to launch the practice trading environment. Return to the OANDA dashboard and click the green highlighted "Launch FXTrade Practice" link.

It will bring up a Java dialog asking whether you want to run it. Click "Run" and the fxTrade Practice tool will load. OANDA fxTrade Practice screen. At this point we are ready to begin designing and coding our automated forex trading system against the OANDA API. If you have been following the event-driven backtester series for equities and ETFs that I created last year, you'll be aware of how such an event-driven trading system functions.

For those of you who are new to event-driven software , I would strongly suggest reading through the article in order to gain some insight into how they work. In essence, the entire program is executed in an infinte while loop that only terminates when the trading system is shut off.

The central communication mechanism of the program is given via a queue that contains events. The queue is constantly queried to check for new events. Once an event has been taken off the top of the queue it must be handled by an appropriate component of the program. Hence a market data feed might create TickEvent s that are placed onto the queue when a new market price arrives.

A signal-generating strategy object might create OrderEvent s that are to be sent to a brokerage. The usefulness of such a system is given by the fact that it doesn't matter what order or types of events are placed on the queue, as they will always be correctly handled by the right component within the program.

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. py you will begin generating orders, assuming you have filled in your account ID and authentication token from OANDA.

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: real , practice and sandbox. 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.

BE EXTREMELY CAREFUL! IMPORTANT: 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.

In the following we are using the practice account as given by the DOMAIN setting. We need two separate dictionaries for the domains, one each for the streaming and trading API components. I've filled the two below with dummy IDs so you will need to utilise your own, which can be accessed from the OANDA account page:. The next step is to define the events that the queue will use to help all of the individual components communicate.

We need two: TickEvent and OrderEvent. 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! The strategy. py file can be found below. Let's work through it and see what's going on.

Firstly we import the random library and the OrderEvent object from events.

Hence we need multiple threads, one for each component, so that they can be carried out independently. 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. Hence a market data feed might create TickEvent s that are placed onto the queue when a new market price arrives. get False except Queue. I have written all of the following instructions for Ubuntu

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