R2G Slippage Study

Models
1

A Comparative Study of Slippage Rates of Five R2G Ports from Different Model Designers

Purpose:

To assess the slippage rates of 5 R2G ports and compare them to their variable slippage rate benchmark as determined by P123.

Method:

The author currently maintains an active portfolio of 5 R2G ports:

[i]Quantonomics 20 Small Cap Gems

master100

Keating’s’s Extreme 5

TWY 5 Stocks Liquid Microcap $350m

1st Next Generation 5 Stocks Liquid Earnings Surprise[/i].

All trades took place with the Canadian online trade platform Questrade from February 23, 2014 to June 2, 2014, approximately a three-month period. When the Monday morning trade signals arrive by email from P123 all sell orders are entered at market before the opening bell for the trading week. If there was cash on hand, some buy orders were entered at market before and the rest at market shortly after the opening bell. The occasional exception occurred when there was no bid/ask before opening in which case a limit order with slippage of 0.5% was submitted and usually filled quickly. Trade size is capped at 5% of the average volume of the prior 30 days; except for master100 about 1 in 12 buy trades hit the cap, master100 never hit the cap. Sell trades were not capped.

There was a large bias for placing buy orders for Quantonomics before the opening bell and a small one for master100.

Only trade orders that initiate positions or close positions are included, no re-balances are assessed.

My understanding is the P123 standard for determining trade price for the purpose of calculating port performance during this test period is the average of the opening day (usually Monday) high and low price, plus variable slippage. P123 assigns a certain percentage of slippage to a particular port via the variable slippage look-up table found in the R2G user’s manual. Subscribers can determine this number by looking up the Liquidity number found in the Trading tab, then finding the slippage rate for this number in the look-up table.

For example, if a port has an imputed slippage of 0.5%, and if a stock has a high of 20 and a low of 10 on Monday, it is presumed to have traded at 15.075 if it is a buy order and 14.925 if it is a sell order, for the purpose of calculating performance by P123.

Results:

The table below shows the average out-of-sample slippage rates for these 5 ports, sell and buy orders are separated. The P123 variable slippage rate for each port is also shown. The number of trades are in brackets. A negative number or a positive number smaller than the variable slippage number connotes a better trade result than variable slippage.

________________________________Buy Slippage (# trades)_________Sell slippage (# trades)_______________Variable slippage benchmark

Quant____________________________-0.44% (60)___________________-0.46% (47)_______________________________0.75

master100_________________________0.10% (39)___________________-0.29% (32)_______________________________0.25

Ext5______________________________0.03% (22)___________________0.12% (18)________________________________0.75

TWY_____________________________-0.58% (9)____________________-1.53% (6)_________________________________0.75

1st NxtG__________________________-0.05% (23)___________________0.44% (18)________________________________0.5

All ports achieved results comparable to or better than variable slippage, for both buy and sell trades.

Discussion:

In the author’s hands, using market orders around the time of the opening bell, slippage rates matched and often outperformed the imputed variable slippage rate. This is admittedly a small study, but the results are encouraging because of the uniform out-performance by 5 different ports. TWY performed particularly well, it is notable that it has the fewest trades/lowest turnover. Quantonomics also performed well, the bias towards buy orders before opening bell may explain some of this.

Conclusion:

It is possible for subs to trade R2G ports and achieve slippage rates as good as or better than the P123 variable slippage rate benchmark.

2

Rallan,
You mention that trade size was limited to 5% of 30 day volume. How do you manage that?
Thanks

3

Thank you!!!

4

Thanks for the post.

When trading a very small % of the stocks average daily volume it’s just impossible to beat limit and market orders at the open. So for people that are able to trade that way the “variable” setting is likely overly conservative.

For people that trade a larger % of daily volume though it isn’t possible to use limit or market orders, and trades need to be spaced out through the day. But I argue that even for those people variable slippage is still a fairly conservative estimate if care and attention are given to order execution.

Anyway, in my opinion the variable slippage setting is generally too conservative, but without knowing how much money people are trading or what type of order methods they’re using it’s a pretty reasonable algorithm. When I look at sims and R2G (especially high turnover models) I feel like I have a margin of safety, because I know I can get better fill prices than the simulations do over the long run.

5

I organize my portfolio of R2G ports on a spreadsheet. Of the data I enter Monday morning before opening one piece is the 30-day average volume, taken from finviz.com.

The spreadsheet first calculates: 1) the re-allocation of capital to distribute my capital among the 5 ports equally 2) the number of shares to buy for each port, for both re-balances and initial positions, with the amount of capital assigned. It also calculates the number of shares that represent 5% of the average 30-day volume, compares this to the number of shares it calculated for maintaining the balance, then gives me the lower of the two values as the amount to buy. This is my attempt to not distort the market for the day by placing over-large orders.

My approach with sell orders is to “rush for the exit” as I need to raise capital quickly to fill the buy recommendations, so there is no cap on sells. But they always represent 5% or less of the average volume anyway.

Assuming a steady growth of capital with R2G my long-term strategy is to add R2G ports when I hit the 5% cap too frequently. Currently, with the overlap of positions among some of the ports, I manage about 32 to 38 positions with this spreadsheet.

Randy

6

A study I did earlier of one small cap/microcap port showed the stock price often took off at opening and just kept getting higher during the day, on average, so I opted to trade at the open.

7

Thanks for the really good info.

8

Thanks. Good information about slippage. Also was thinking that is how I might trade if I give up my window trades. Now I am more convinced that I should look into that when the time comes.

9

Impressive study, I made a link to your thread directly on the R2G portfolio so others can view it as well. Thanks !

10

I read your updated News section in the Small Gems Description section. The slippage is actually negative 0.45%, not positive, so my out-of-sample performance is really 1.2% = 0.75 - (- 0.45%) better than that imputed by P123, over the study period. You may wish to edit this.

Because of the relatively large # of holdings in this port, individual positions in Small Gems are the smallest in my portfolio of R2G ports, meaning smaller trades and this may also account for better slippage results

11

Rallan,

It is more than 6 months since your last update on R2G Slippage Study.
Please, update us the recent market slippage studies.

What is the best way to trade on Mon day for micro/small cap.
(1) Market Order
(2) Limit with
Market open order, Market Open + 0.5%, Limit on Fri day close + 0.5%

Thanks
Kumar