Daily rebalancing of leveraged ETFs
Source: Ernie Chan, Algorithmic Trading: Winning Strategies and Their Rationale, Wiley, May 28, 2013, chapter 7.
Per their fund objectives, leveraged ETFs must maintain a constant daily leverage relative to their underlying index (usually 2x or 3x leverage). As Ernie Chan points out in his book Algorithmic Trading, doing so requires that the ETF sponsors buy more underlying shares on days the shares go up and sell shares on days the shares go down. The larger the gain or loss, the more shares the fund will have to buy or sell to maintain the proper leverage.
Chan proposes a trading strategy that buys (sells) leveraged ETFs in the afternoon trading session following a significant intraday gain (loss), in expectation of further buying (selling) by the leveraged ETF sponsors heading into the close.
Leveraged ETF trading strategy
I create a Moonshot strategy to test Chan’s idea using 15-min data from Interactive Brokers. The strategy calculates the return from the prior close to 2 PM and takes a long or short position when the gain or loss is more than +/-2% and holds until the close. The Pandas code is shown below:
closes = prices.loc["Close"] opens = prices.loc["Open"] # Take a cross section (xs) of prices to get a specific time's price; # the close of the 15:45 bar is the session close session_closes = closes.xs("15:45:00", level="Time") # the open of the 14:00 bar is the 14:00 price afternoon_prices = opens.xs("14:00:00", level="Time") # calculate the return from yesterday's close to 14:00 prior_closes = session_closes.shift() returns = (afternoon_prices - prior_closes) / prior_closes # Go long if up more than 2%, go short if down more than -2% long_signals = returns > 0.02 short_signals = returns < -0.02
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