December 7, 2016

Using recent returns for Mean Reversion

In most of my mean reversion posts, I use RSI(2) to determine if a stock has sold off. In this post, I will explore how to use a stock’s recent return to determine if it has sold off. This will be done in way to normalize the return between low and high volatile stocks. This basic strategy has only two setup rules.

Rate of Change

We will be using Rate of Change (ROC) of the closing price. The formula is:

ROC(length) = 100*(today’s close / the close length days ago – 1)

Here is recent data on the S&P-500 index

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A ROC(3) which is the 3 day rate of change, would be

ROC(3) = 100 *(today’s close / the close 3 days ago – 1) and for 11/25/2016 it would be

ROC(3) = 100 *(2213.35/2198.18 – 1) = .69%

Potential Issues with ROC

One could simply have a buy rule that ROC be less than -10% to determine if a stock has sold off. The potential problem with this is that for a high volatile stock this may not be a very big sell off. But for a stock with low volatility this may rarely happen. How do we normalize this so we get signals that make sense for both low and high volatile stocks?

PercentRank

What we want to know is how does the current ROC(n) compare to the past ROC(n) of the stock. In particular I want to know where does the current ROC(n) rank over all the ROC(n)s over the last year.

For AmiBroker users, I am using the PercentRank formula. This is how AmiBroker defines it

Returns percent rank (0…100) of the current element within all elements over the specified range.

 

A value of 100 indicates that the current element is the highest for the given lookback range, while a value of 0 indicates that the current value is the lowest for the given lookback range.

Walking through an example should make it clearer. Example of PercentRank of ROC(3) over the last 30 days using the S&P-500.

161207a

In the “ROC(3)” column we have all the 3 day returns for the last 30 days. Next we want to know what is the PercentRank for the return on 11/25/2016. For each of the last 30 days we enter a “1” under the “Count?” column if that day’s return is less than or equal to the return on 11/25/2016 of .69%. Next we add up the values over the last 30 days. Here we have 20. We then take this total and divide it by the look back period of 30. PercentRank = 100*(20/30)=66.67%. This tells us the 3 day return on 11/25 is better than 66% of the returns over the last 30 days.

Since we are looking for a sell off and I want to compare to over the last year, we want PercentRank over the last 252 days of the ROC(3) to be less than 15. Clear as mud? If this does not make sense, ask questions in the comments below.

The Strategy

Setup

  • Stock is member of the Russell 1000 index
  • The price of the stock is greater than $1. Want to avoid very low priced stocks
  • The 21 day moving average of Close time Volume is greater than $10 million. Want to make sure there is liquidity.
  • The close is above the 100 day moving average.
  • The 252 day PercentRank of the 2 day returns is below 5

Buy

  • Enter at a limit price today at 1/2 of ATR(10) below previous close
  • 12.5% of portfolio in each position
  • Only enter orders so if they all fill will not have over 8 open positions

Sell

  • 2 period RSI greater than 40
  • Exit on next open

 

Nothing fancy here. Simple setup, entry and exit rules.

Results

Dates: 1/1/2006 to 10/31/2016

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(click image to enlarge)

These are some decent looking numbers given the rules. Unfortunately like a lot of stock mean reversion strategies, the last couple of years have not been that great.

Spreadsheet

Fill the form below to get the spreadsheet with lots of more information. This includes other values for parameters ,yearly breakdown, worst 5 drawdowns and more statistics.

Final Thoughts

Is this strategy good enough to trade as-is? Not really. The spreadsheet has variations with better returns. One can take these ideas and improve on them. I wanted to show another way of measuring a sell off.

Backtesting platform used: AmiBroker. Data provider:Norgate Data (referral link)

1/4/2017 Additional research on this strategy, N-Day exits with Mean Reversion.

Good Quant Trading,

Fill in for free spreadsheet:

spreadsheeticon

 
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scott - December 7, 2016 Reply

Just a typo, on this line

ROC(3) = 100 *(2213.35-2198.18 – 1) = .69%

the first minus sign should be a division, not a subtraction, correct?

    Cesar Alvarez - December 7, 2016 Reply

    Good catch on the mistake. I corrected it above

MP - December 7, 2016 Reply

One interesting column to add to the spreadsheet would be pct. of entry orders that were filled. Also, maybe include a “Factor” of 0 for each strat, to know what the stats look like if entering via limit orders priced at prev. close price?

Thanks for the research.

MP - December 7, 2016 Reply

You mean the Russell 1000 Index, I assume?

David - December 12, 2016 Reply

Great research! When do you sell? At close, next day open?

    Cesar Alvarez - December 12, 2016 Reply

    On the next open. I added that to the rules above.

Ellis - December 20, 2016 Reply

Hi Cesar,

Here is this strategy.

Cheers,

Ellis

SetOption( “ExtraColumnsLocation”, 1 );
SetOption ( “CommissionMode”, 2 ); // $ per trade
SetOption( “CommissionAmount”, 1); //$1.00 per trade
SetOption( “InitialEquity”, 40000 );
MaxPos = 4; //Optimize(“MaxPos”,4,2,20,2);
SetPositionSize(100/MaxPos,spsPercentOfEquity);
SetOption( “MaxOpenPositions”, MaxPos );
SetBacktestMode( backtestRegularRawMulti );
SetTradeDelays( 0, 0, 0, 0 );
SetBacktestMode(backtestRegular);

/////////////////////RSI Criteria////////////////////////////////////
RSINum = 2; //Optimize(“RSINum”,2,2,10,1);
RSIBuy = 5; //Optimize(“RSIBuy”,12,5,12,1);
RSISell = 40; // Optimize(“RSISell”,50,30,90,5);
RSIShort = 95; //Optimize(“RSIShort”,95,35,93,10);
RSICover = 50; //Optimize(“RSICover”,50,30,70,10);

///////PercentRank Criteria; PCRK = Percent Rank//////////
PCRKLB = Optimize(“PCRKLB”,250,50,500,50); //Days for PCRK to Look Back
PCRKTh = Optimize(“PCRKTh”,25,5,30,5); /////Threshold for PCRK
PCRK1 = PercentRank(RSI(RSINum),PCRKLB); ////PCRK based on RSI
PCRK2 = PercentRank(ROC(C,2),PCRKLB); ////PCRK based on ROC

///////////////////BUY AND SELL CRITERIA//////////////////////////////////
AvDolVol = EMA(C*V,20);
//Cond1 = C>1 AND AvDolVol>10000000; //10 Million shares Cesar’s original price and AvDolVol
Cond1 = C>10 AND AvDolVol>100000000; //I raised both tenfold; Prefer bigger cap stocks
Cond2 = C>EMA(C,100);
Cond3 = PCRK1 < PCRKTh;
Cond4 = Cond1 AND Cond2 AND Cond3; ///This is used for Exploration after the day's data are collected; generates tomorrow's orders

Buy = Cond1 AND Cond2 AND Ref(PCRK1,-1)Ref(C,-1); //Yesterday the PCRK was below threshhold and today the Open is > than yesterday’s close
//Buy = Cond1 AND Cond2 AND Ref(PCRK2,-1)Ref(C,-1); //Use this for ROC instead of RSI
BuyPrice = O; ////////////Place a BuyStop on the open if it is at or above yesterday’s close

Sell = C>Ref(C,-1); ////////SELL ON ANY UP DAY AND ALLOW SAME DAY OPEN AND CLOSE IN SETTINGS////////
//Sell = RSI(RSINum)>RSISell; ///ALTERNATIVE METHOD FOR CLOSING TRADES/////////////////
SellPrice = C;

////////////EXPLORATION AFTER TODAY’S DATA ARE DOWNLOADED. GENERATES BUY CANDIDATES FOR TOMORROW’S OPEN///////////////////
Filter = Cond4;
AddColumn(C,”Close”,1.2);
AddColumn(RSI(2),”RSI2″,1.2);
AddColumn(ROC(2),”ROC2″,1.2);
AddColumn((40000/MaxPos)/C,”Num Shares”,1.0);
AddColumn(PCRK1,”PCRK1″,1.2);
AddColumn(PCRK2,”PCRK2″,1.2);
AddColumn(Ref(C,-1),”C-1″,1.2); ///Yesterday’s Close
AddColumn(AvDolVol,”AvDolVol”,1.0);

kefa - March 11, 2017 Reply

How do you model the limit order entries? Using end of day data you have no idea in what order your stocks will get filled.

    Cesar Alvarez - March 11, 2017 Reply

    I can assume if the the low of the day is below my limit price that I will get filled. In actual trading I have found this to be true.

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