First, let us examine some general characteristics of the data. It captures all trades transacted on FXCM occurring in 2017, time stamped in milliseconds, and with their trade prices and signed trade sizes. The sign of a trade is positive if it is the result of a buy market order, and negative if it is the result of a sell. If we take the absolute value of these trade sizes and sum them over hourly intervals, we obtain the usual hourly volumes (click to enlarge) aggregated over the 1 year data set:
It is not surprising that the highest volume occurs between 16:00-17:00 London time, as 16:00 is when the benchmark rate (the "fix") is determined. The secondary peak at 9:00-10:00 is of course the start of the business day in London.
Next, I compute the daily total order flow of EURUSD (with the end of day at New York's midnight), and I establish a histogram of the last 20 days' daily order flow. I then determine the average next-day return of each daily order flow quintile. (I.e. I bin a next-day return based on which quintile the prior day's order flow fell into, and then take the average of the returns in each bin.) The result is satisfying:
(One may be tempted to also regress future returns against past order flows, but the result is statistically insignificant. Apparently only the top and bottom quintiles of order flow are predictive. This situation is actually quite common in finance, which is why linear regression isn't used more often in trading strategies.)
Finally, one more sanity check before backtesting. I want to see if the buy trades (trades resulting from buy market orders) are filled above the bid price, and the sell trades are filled below the ask price. Here is the plot for one day (times are in New York):
We can see that by and large, the relationship between trade and quote prices is satisfied. We can't really expect that this relationship holds 100%, due to rare occasions that the quote has moved in the sub-millisecond after the trade occurred and the change is reported as synchronous with the trade, or when there is a delay in the reporting of either a trade or a quote change.
So now we are ready to construct a simple trading strategy that uses order flow as a predictor. We can simply buy EURUSD at the end of day when the daily flow is in the top quintile among its last 20 days' values, and hold for one day, and short it when it is in the bottom quintile. Since our daily flow was measured at midnight New York time, we also define the end of day at that time. (Similar results are obtained if we use London or Zurich's midnight, which suggests we can stagger our positions.) In my backtest, I have subtracted 0.20 bps commissions (based on Interactive Brokers), and I assume I buy at the ask and sell at the bid using market orders. The equity curve is shown below:
The CAGR is 13.7%, with a Sharpe ratio of 1.6. Not bad for a single factor model!
Acknowledgement: I thank Zachary David for his review and comments on an earlier draft of this post, and of course FXCM for providing their data for this research.
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