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Intraday Momentum with Leveraged ETFs

Posted October 25, 2019 at 9:52 am
QuantRocket LLC


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

Visit QuantRocket website to read the full article and to download the code:

Any trading symbols displayed are for illustrative purposes only and are not intended to portray recommendations.

Disclosure: Interactive Brokers

Information posted on IBKR Campus that is provided by third-parties does NOT constitute a recommendation that you should contract for the services of that third party. Third-party participants who contribute to IBKR Campus are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.

This material is from QuantRocket LLC and is being posted with its permission. The views expressed in this material are solely those of the author and/or QuantRocket LLC and Interactive Brokers is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to buy or sell any security. It should not be construed as research or investment advice or a recommendation to buy, sell or hold any security or commodity. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.

Disclosure: Margin Trading

Trading on margin is only for experienced investors with high risk tolerance. You may lose more than your initial investment. For additional information regarding margin loan rates, see

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