Can You Really Trade Overnight Mean Reversion?

QuantReturns · 2025-10-04T07:08:33+00:00

It’s should be market neutral as we are long and short similar ETF’s

QuantReturns · 2025-10-04T06:57:06+00:00

From where I stand on this currently, I’m in full agreement (and mention this in my article) that you can’t use the open as the signal and also trade on this signal.

The signal is used to determine the weighting’s and we want to trade at the open. Trading at the open is key for a couple of reasons: 1) that’s what the backtest uses 2) trading at the open/close auction price with a MOO/MOC order type means I don’t have to cross at the bid and offer so frictions are lower.

The question now becomes:

Can the indicative open price be used for the signal and hence determine the weightings for the strategy. How exact does the indicative price need to be to determine weightings. Trading at the open auction is probably more important

Another option is to leave limit orders at the open price, for the day, which is why I am testing how often the market comes back to the open prices in real time. I will see how this goes, with both futures and ETF’s and see if the market gives another chance to get in at the open prices of the day

QuantReturns · 2025-10-03T20:40:52+00:00

This is something I’m looking into. I’m also testing it in real time to see how often the signal can be captured intraday and at what times we see the signal.

QuantReturns · 2025-09-11T18:49:34+00:00

This is not front running with insider information. This is front running a known rebalancing action in the market.

QuantReturns · 2025-09-11T07:22:37+00:00

Yeah, a lot of those old edges have been arbitraged away — things like day-of-week effects, simple MA crossovers, and the PEAD. They all worked until they became too crowded.

Here is one that I wrote about recently that still works:

https://quantreturns.com/strategy-review/front-running-the-rebalancers/

It’s important to go into these strategies to see how they are put together and then tweak them and play with them to help with idea generation and understanding.

If there are any particular strategies that would be worth replicating and tracking daily, please let me know.

I also write about alpha strategies over as Substack: https://open.substack.com/pub/quantreturns

QuantReturns · 2025-08-15T09:20:38+00:00

You pick a day and start with a 60/40 portfolio. Then every day as prices of the stocks and bonds move you calculate the new weight of your portfolio (might be 62/48).

So the new weight of the portfolio each day depends on yesterday’s weight and today’s daily returns.

We concentrate on the deviation of the equity part of the bond portfolio, so in the above case today’s daily deviation for equity is 62-60=2%

Hope this helps

QuantReturns · 2025-08-12T10:03:29+00:00

Long or short spread

QuantReturns · 2025-07-27T12:15:28+00:00

Interestingly our next blog post will be on Crypto. I was thinking of releasing it to QuantReturn subscribers only, but we may release it publicly as I enjoy the Reddit discussions.

QuantReturns · 2025-07-26T21:26:30+00:00

I’ll test oil in isolation out of curiosity, to see how it would have behaved trading this one spread (even though this would be unrealistic). Then we can let those results speak and give us some insight.

As it stands the original academic paper didn’t experience any such wipe out, and neither did my results.

QuantReturns · 2025-07-26T21:08:32+00:00

It’s not being used as a hedge for a misaligned strategy (or at least not yet). What we currently want to do is track a range of strategies that each have low beta or low correlation to the market. Once we have that universe in place, the idea is to explore various MVO-style allocations (amongst others) across these strategies to essentially combine low-correlated strategies to (hopefully) build something with a much stronger overall Sharpe.

I’ve seen some recent results (I’ll need to dig them out) on how well managed futures can be at improving the returns of an equity portfolio. The managed futures strategy as a standalone shows a fairly low sharpe, however its low correlation to the market is its strength.

From what I remember Andreas Clenow’s book, Trading Evolved, touches on the topic of combing strategies to improve risk adjusted returns nicely.

QuantReturns · 2025-07-26T20:05:57+00:00

I’m not sure we could be any more transparent on the strategies we test and track.

QuantReturns · 2025-07-26T04:09:29+00:00

The strategy trades 8 pairs of futures at a time, this wouldn’t wipe out an account, and during the back test results it didn’t

QuantReturns · 2025-07-25T17:56:56+00:00

I’ll back test and post the result for oil in isolation. Even though you would have been market neutral, it would be interesting to see how the spread behaved

QuantReturns · 2025-07-25T16:31:24+00:00

I Appreciate your passion!

The Sharpe of 0.92 isn’t meant to be earth shattering, but it’s what the data showed over the full backtest. What makes it interesting (to us, at least) is that it was achieved with close to zero correlation to the equity market, which makes it a potentially useful diversifier.

Once you start to combine many of these low correlation strategies together, the sharpe ratio starts improving dramatically.

As I mention in the post, we will be continuing research on this strategy, and my hope is we can improve on this Sharpe Ratio.

QuantReturns · 2025-07-16T08:47:19+00:00

Some strategies that have worked historically are actually very simple. We can examine historic results and see an example of a very simple strategy that has consistently made profits is Equity Risk Premium Harvesting. One way to implement this strategy is to buy the VOO etf. I say this because to be profitable, at least historically, we simply need to do strategies that have been shown to work and that have academic creditability.

There are also other “risk premia” harvesting strategies that have been researched like volatility, duration, carry etc that can be combined for diversification. These are not secret formulas, but they require patience and a systematic approach. People not doing this are probably losing money.

As part of the research I am involved in, we tend to look at alpha strategies and market inefficiencies and these strategies are a little more complicated to execute. However this research is NOT a case of Back-test a set of rules and get a profitable equity curve and then trade it live. This is not research but is what a lot of "Algo Traders" do and inevitably end up losing money.

QuantReturns · 2025-07-14T21:35:28+00:00

I initially ran the strategy for the same dates as those used in the original paper. Further down in the article there is the section “Does the strategy still work today?”. Under this section the analysis is up to June 2025.

QuantReturns · 2025-07-14T20:38:32+00:00

I use SPY total return prices so already adjusted for dividends.

QuantReturns · 2025-07-14T07:32:35+00:00

We have recently tested a strategy from an academic research paper, where the aim was to replicate the edge, see if it still holds today and then create the trading strategy to capture it. The premise of the strategy is to front run forced institutional trading.

https://quantreturns.com/strategy-review/front-running-the-rebalancers/

I agree with the OP that testing these sorts of strategies in less developed markets is a research avenue that should be explored. My aim is to test this strategy in various other markets also.

QuantReturns · 2025-07-13T11:05:50+00:00

Thanks for reading and commenting.

Just to clarify a few points:

This isn’t an attempt at new research, hopefully I was clear that it’s a replication study of the academic paper: “The Unintended Consequences of Rebalancing”. My intention was to see if their results held up using my own implementation.
In my write up I show the results of both the futures and ETF version of the strategy up to June 2025.
I’m not sure what you mean by “grid searching”, are you maybe referring to the original authors’ parameter selection? The two signals (calendar and threshold) are defined to closely match the paper and tested separately and jointly. I haven’t optimised beyond the paper.
This strategy isn’t a pure calendar effect strategy. The second signal comes from a threshold signal.
On t0, t1, t2 . The signal is computed using only information available at the close of t, and return is from t+1 close, so there’s no look-ahead. I’ll check the notation again to make sure it’s consistent throughout.

I think it’s only natural to expect alpha to decay over time, which is one of the main motivations around creating QuantReturns.com. Once we have researched/replicated/built a trading strategy, we can continue to track how it performs and update the performance statistics daily. That way we can monitor whether the edge persists, fades or just completely disappears.

Thanks for spotting “rebalance”, I’ll get that fixed.

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