Month: March 2013

A New Approach to Trading

Recently I changed the way I approach trading system development. Reading A Different Approach to Money Management is what gave me the original idea to alter my approach to trading, but my research is what pushed me over the edge.

Old Way

I approached trading system development with the goal of trying to develop a system with maximum exposure. I didn’t think of running two systems at the same time on the same pool of money (without splitting up the money) if the systems had little/no conflicting signals. Since most of the systems I tested on SPY either had either:

  • high exposure but low returns
  • high returns (after factoring for exposure) but low exposure

I was left with trying to develop a new system, or trying to trade the system over a large group of stocks (I used the Nasdaq-100 as my stock universe). While I was able to ramp up my exposure to around 70% with any system I tested, I often had to trade 5 or more stocks just to have a reasonable draw-down, which meant that commissions ate a large percent of my trading profits, considering my systems were short term in nature and I have limited capital.

Pros:

  • Only one system to manage
  • I don’t have to check for as many signals

Cons:

  • Not diversified – higher risk
  • High commissions due to being forced to trade many stocks to increase exposure
  • Can be curve fit easily since I try to develop one super-system
  • Higher model risk – there is only one model, which can fail at any given time

New Way

Now, I try to develop multiple systems that have high average profit% per trade with little/no regard for exposure. Even if the system trades only once every two or three months, I can combine many of these systems to trade at a frequency I would be trading at with my old way, but still maintain a highly profitable trading system with lower risk (assuming the systems do not have perfect correlation). While it’s too soon to draw any conclusions from live performance, the historical backtest shows dramatically improved results.

Pros

  • Higher returns
  • Lower risk
  • Lower commissions since I only have to trade one stock (I trade SPY)
  • Less risk of curve-fit – I will not be forced to include multiple filters to decrease risk/increase returns
  • Lower model risk – chances of multiple models of failing is lower than one model failing

Cons

  • Much more of a headache to manage when entering in trades EOD
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Trading Every Other Trading Day

Something odd that I found:

Test 1:

  • Buy SPY at close every other trading day
  • Sell the next day at close
  • 1/29/1993 – 3/25/2013

1-29-1993 - 3-25-2013 Trading Every Other Day

The 1990’s bull market, and the 2003-2007 market cycle are almost completely missing from the equity curve.

Test 2:

  • Buy SPY at close every other trading day
  • Sell the next day at close
  • 2/1/1993 – 3/25/2013

2-1-1993 - 3-25-2013 Trading Every Other Day

The popping of the tech bubble and the bull market since 2009 are almost completely missing from the equity curve.

Conclusion:

It’s incredibly strange how trading every other trading day can wipe out the gains/losses of any one cycle (1993-2003, 2003-2007, 2007-2013). What does this mean? Absolutely no idea.

Short Term Trend-Following within Mean-Reversion Trading

A while ago CSS Analytics had a post titled Improving Trend-Following Strategies with Counter-Trend Entries, which discusses using a mean-reversion indicator to filter out trades from a trend-following system. It got me thinking about using trend-following philosophy with mean-reversion trading systems. Mean-reversion is based off of buying dips; however, many times oscillators and other mean-reversion indicators are too quick when calling a market dip, and end up losing money the next day or so of an entered trade. To counter-act this tendency, why not use the old trend-following adage to only buy if the trend is up?

To test this, I used my favorite mean-reversion system:

  • Buy if DV2 < 50
  • Sell if DV2 > 50
  • No Shorting

with two variations. Before I go into the two variations, I will post the results of this simple mean-reversion system traded on SPY from 1/1/2000 – 1/1/2013 for comparison. All results are  frictionless:

Simple DV2 1-1-2000 - 1-1-2013

Variation One

  • Buy if DV2 < 50 AND Today’s DV2 is ABOVE Yesterday’s DV2
  • Sell if DV2 > 50
  • No Shorting

Trend Following Entry DV2 1-1-2000 - 1-1-2013

Variation Two

  • Buy if DV2 < 50 AND Today’s Close is ABOVE Yesterday’s Close
  • Sell if DV2  > 50
  • No Shorting

Trend Following Entry with Close DV2 1-1-2000 - 1-1-2013

Variation One is vastly superior to the simple DV2 strategy when one factors for exposure and maximum drawdown. Variation Two has slightly lower returns when adjusted for exposure, but the maximum drawdown is reduced by 1/3. Using trend-following techniques to filter out bad trades can vastly increase risk-adjusted returns in mean-reversion trading systems.

Mean Reversion Trading in Moderation

Simple mean-reversion trading strategies in US equities have performed poorly since 2010/2011. Mean reversion is not dead, it never will be, but it may expressed differently than previously. Mean reversion prior to 2010, existed mainly in the form of extremes. The more extreme a pullback, the higher chance for a huge reversal. To test this, I ran 10 different frictionless  tests on SPY (from Yahoo! Finance) from 1/1/2000 – 1/1/2010.

Rules:

  • Buy if the 250-day DV2 is greater than a threshold AND if it is less than the threshold + 10 (this means I will only buy if the DV2 is within a certain 10 point range).
  • Sell the next day.
  •  It is worth noting here that I have an option on in AmiBroker that prevents me from entering buy orders the same day that I enter sell orders, which will lower the overall exposure of these systems.

Here are the results:

1-1-2000 - 1-1-2010 Moderate Mean Reversion

We can clearly see that the returns are largely derived from the 1-11 bucket, meaning that extreme mean reversion was the source of returns. In the past 3 years, mean reversion exists in more moderate forms. Extreme pullbacks no longer indicate large reversals, but moderate pullbacks are more indicative of future gains. To test this, I ran the same test from 1/1/2010 – 1/1/2013:

1-1-2010 - 1-1-2013 Moderate Mean Reversion

The returns for the past three years are from the 31-41, 41-51, and 51-61 bucket. The 1-11 bucket went from a 6% CAGR to a -2% CAGR.

Here are the rules for another test I ran. This should be the result of the above rules if I allowed entries to be entered the same day as exits.

Rules:

  • Buy if the 250-day DV2 is greater than a threshold AND if it is less than the threshold + 10 (this means I will only buy if the DV2 is within a certain 10 point range).
  • Sell if the 250-day DV2 is less than a threshold OR if it is more than the threshold + 10

Results for 1/1/2000 – 1/1/2010:

Full Exposure Moderate Mean Reversion 1-1-2000 - 1-1-2010

Results for the same test for 1/1/2010 – 1/1/2013:

Full Exposure Moderate Mean Reversion 1-1-2010 - 1-1-2013

This is only a preliminary test over SPY, but it does lead to avenues for further research.