Automated FX Trading Strategy Using Macro News Events

Introduction

This paper describes the implementation of an automated quantitative FX trading strategy based on macro news data provided by RavenPack.  RavenPack sources news from a variety of sources from which it produces an array of analytics (including sentiment, relevance and novelty) in real-time, and which are available historically.

We undertook the research and implemented the strategy in the Deltix QuantOffice research platform, a purpose-built C# development studio with embedded math, statistics and data libraries.

Our thesis was: does the arrival of macroeconomic news from the world’s largest economies bring additional volatility to the market? The historical data set used is described below:

  •      News Data from 1 March 2012 till 1 August 2012:

–       More than 1.1 million messages

–       Used subset of macro-economic news  for US (287,000 records), Germany (7,800), EU (3,700) and Japan (14,400).

  •       Market Data from 1 March 2012 till 1 August 2012:

–       Three currency pairs: EURUSD, USDJPY, EURJPY (bid/ask quotes)

–       Approx 100 million market data messages.

  •       News data was filtered by the following news types:

–       consumer-price-index

–       producer-price-index

–       unemployment

–       retail-sales

–       gross-domestic-product

–       durable-goods

–       interest-rate

–       consumer-confidence

–       home-sales-existing, home-sales-new

–       current-account, current-account-surplus, current-account-deficit

–       consumer-spending

–       jobless-claims

–       inflation

–       trade-balance, trade-balance-deficit, trade-balance-surplus

  •    Extra filters applied were:            

–       news relevance: RELEVANCE = 100 (maximum relevance)

–       news novelty: ENS = 100 (maximum novelty) 

Testing the Thesis

As a measure of volatility, we calculated the annualized standard deviation of log returns inside 5 minute windows of 10-sec bars (i.e. 30 bars). We also calculated variance ratio[i]:

               VR = HILO (N) / ( ATR(N) * SQRT(N))

where

               N = 30; HILO (N) is high/low price range and ATR(N) is average true range over the N bars period

All statistics were calculated for 5 minutes before the time of the news release time and for 5 minutes after. For example, for US announcements scheduled for 8:30am, the time intervals were 8:25am to 8:30am and 8:30am to 8:35am. The results, pertaining to US economic news data, are show below:

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Trading Strategy

It is clear from the results above that there is a significant change in short-term volatility of FX rates after the announcement of economic data. The next step in our research was to design and test a trading strategy which utilizes this observation.

The strategy defines breakout buy/sell levels in the five minute interval preceding the scheduled event. Upon receiving the news event, the strategy creates a long position if the price exceeds the buy level, and creates a short position if the market moves below the sell level. The strategy then closes positions five minutes after receiving the news event.

In back-testing, order execution simulation was done using the relatively conservative Best Bid Offer mode: buy at best ask price; sell at best bid price. The lot size for all trades was $100,000.

Results

automated3

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[i] Variance Ratio statistics introduced by Lo and MacKinlay (1988):

VR close to 1 indicates that market is in a random walk regime;

VR > 1 indicates that market is in a trending regime (with positive autocorrelation of price returns);

VR < 1 indicates that market is in a mean reversion regime (with negative autocorrelation of price returns).

 

 

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The Deltix Quantitative Research Team