This is a simple trading strategy that provides some core mean-reverting properties. It involves the following:. The average in this case, is calculated by a linear regression curve because a simple moving average is often a lagging indicator and becomes a big problem with long look-back periods.

Playing around with the look-back period can provide some interesting results, try it out! Thoughts and suggestions are always welcome. More on the strategy can be found here. The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.

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Can you rewrite this so we can backtest it against individual stocks rather than the whole market that would be appreciated!

I also fixed where the lower bollinger band was missing. Doesn't this function run every minuet in a full backtest? Wouldn't that cause the check to happen every 20min as opposed to 20 days? Thanks for mentioning that, I hadn't thought about how it would work in minutely data as I only tested it in daily data, but here's a way to test it in minutely data as well. I'm unaccustomed to reading Python code, so I may be missing something, but where is the "exit position" command in your code?

I see you buying shares when you're below the lower threshold and selling when you're above the upper, but I don't see you exiting anywhere in the middle.

I ask because, in the header, you say that positions are exited when the price crosses the moving average. Again, new to Python, so I could be very wrong. Unlike the futures market, the long side of stock markets work quite differently than the short side, at least that is what I have seen.

It is probably because we humans react differently to greed and to fear.

Mean Reversion

The short sides are quick steep drops lasting for short periods, while the long side is more mean reverting trading strategies climbs and lasts paris livestock market report. Based on that, the mean reversions need different parameters to work on both short and long sides.

I love to see the exchange make cash fast illegally ideas and generosity of the able coders here. Mean reverting trading strategies my knowledge, linregress 1 returns the intercept of the linregress line cbot trading days [0] would return the slopemore here And you're right about the exit position, there is none for now, will get on that soon.

And yes, there is a bit of leverage used here although as to how much would depend on the order amount.

Ah yes, you're right about linregress. From a statistical point of view, that is a very strange choice on their part. Are you able to run the strategy without any leverage, so we could get an idea of what the returns would be in that situation. I ask because I've played with similar strategies that gave nowhere near the same performance as yours, but they've been unleveraged, so I want to make sure I'm making a fair comparison.

Still working on the leverage, but I've incorporated exit positions into the algorithm and the returns are very different.

How to build mean reversion trading systems and examples

If you'd like to find out more forex advisory service reviews leverage there's a Quantopian thread here as paris livestock market report. The current exit position is whenever the price crosses the mean, and I think there'd be a better exit position than that especially with the 20 day lookback period on that.

If you have any suggestions on that, please last gasp for stock options wsj free to post.

Quantitative Trading: Mean reversion, momentum, and volatility term structure

The latest backtest I've uploaded doesn't use leverage so you could use that as a good way to compare your tests goodbody stockbrokers ballsbridge park mine. Here's a way to adapt it to minutely data which works! Marco, sorry newbie here It seems closer to https: Did you post in the wrong thread?

Can you outline any new changes you made When I cloned and run your algorithm, I got the remington model 710 parts list warning I created this algorithm before 'history ' was released. The last version that I have here uses history to query for past data, feel free to use this one instead.

Hi Seong, Well I feel that if rather than buying when close price crosses lower Bollinger for the first time, you should buy it once close price resurfaces and equals the lower Bollinger and similarly free stock market ticker windows 7 shorting also.

Have you ever heard of overfitting? Walk-forward testing among other things are needed! Can anyone help me change this algo to something smaller? What's up with that? How can i earn money from home in india without investment I'm a total noob.

The problem here is probably related to your order, being way too large. What's happening is that you are buying and selling lots of shares which makes your strategy unreasonable. For a good ressource on order types, try:. Sorry, something went wrong. Try again or contact us by sending feedback. Point72 is a family office. Point72 does not seek, solicit or accept investors that are not eligible family clients. This is not intended to be a testimonial and the reader should not construe it as such.

Investor Relations Allocations Research Datasets Notebooks Algorithms Community Forums Events Contest Learn Getting Started Tutorials Lectures Workshops Help FAQ API Reference Contact Support Log In Sign Up. Hello everyone, This is a simple trading strategy that provides some core mean-reverting properties.

It involves the following: There was an error loading this backtest. Backtest from to with initial capital. Overall Metrics Returns Alpha Beta Sharpe Sortino Volatility Max Drawdown Total Returns. Returns 1 Month 3 Month 6 Month 12 Month. Alpha 1 Month 3 Month 6 Month 12 Month. Beta 1 Month 3 Month 6 Month 12 Month. Sharpe 1 Month 3 Month 6 Month 12 Month. Sortino 1 Month 3 Month 6 Month 12 Month. Volatility 1 Month 3 Month 6 Month 12 Month.

Max Drawdown 1 Month 3 Month 6 Month 12 Month. We have migrated this algorithm to work with a new version of the Quantopian API. The code is different than the original version, but the investment rationale of the algorithm has not changed.

We've put everything you need to know here on one page. Create, register and name a pipeline in initialize. This backtest was created using an older version of the backtester. Please re-run this backtest to see results using the latest backtester. Learn more about the recent changes. There was a runtime error. Sorry for the inconvenience. Try using the built-in debugger to analyze your code.

If you would like help, send us an email. Seong, this is an fascinating algo. PR, Thanks for mentioning that, I hadn't thought about how it would work in minutely data as I only tested it in daily data, but here's a way to test it in minutely data as well. Also, are you using leverage here? Thank you everyone for sharing. If you have any suggestions on that, please feel free to post Thanks, Seong.

Bcf, The latest backtest I've uploaded doesn't use leverage so you could use that as a good way to compare your tests to mine. Hi Seong, When I cloned and run your algorithm, I got the following warning Hi Nyan, I created this algorithm before 'history ' was released.

So if you wanted the past 20 days of trading data you would do: Enter sid here to use the algo with a single stock context. Returns true if it's 1: Hi Slgja, The strategy was published on October 2nd, ! Hey Frank, The problem here is probably related to your order, being way too large. For a good ressource on order types, try: Please sign in or join Quantopian to post a reply.

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Momentum vs. Mean Reversion - The Big Picture

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