Bubble Markets and Back Testing


I saw a question on the Reddit /r/algotrading forum about back-testing and how much data one “should” use. As far as I am concerned, the only rules are “whatever works”, but within that framework, I do have some rules I follow. Bubble markets and back-testing affect one another profoundly.

When running back-tests on the strategies I build, I don’t test anything prior to January 2, 2009. I don’t go back any farther than that because of my beliefs about the market. (Remember, you don’t trade the market, you trade your beliefs about the market.) What I believe is that we’ve had 4 entirely different markets in the last 20 years, and the only one in which I can perform valid testing is the most recent.

The Internet Bubble Market: 1994-2001

The first market we had was the internet boom period – more accurately called the Internet Bubble – that started around 1994 and ended with the Dot Com Crash of early 2001. In fairness, this was merely the last 7 years of the Great Bull Market that started with Reagan and ended with the reign of Bush II. This was the time when millions of peoples thought they were genius traders because all you had to do to make money was buy some stock. This was also the heyday of the day-trading craze where everyone who could install highspeed data lines in big room could open their “day trading” company and sell seats. Any back-testing that is performed during this period is highly suspect, because the conditions that prevailed in that market simply don’t exist today.

The Real Estate Bubble Market: 2001-2008

The Dot Com Crash led to a wide-spread despair that Happy Days were never gonna Be Here Again. This was clearly unacceptable, so the Fed does what the Fed does best: goose the market. The Fed decided to slash interest rates in order to spur borrowing. That led to the Great Real Estate Bubble that topped out in October 2007 and ended with the collapse of Bear-Stearns in March 2008.

This was the period when any stock that had anything related to “Real Estate” in its name or prospectus exploded off the charts. This was also the period where “financial engineering” gained ascendancy, which also led to the gross over-valuation of financial stocks. Tech stocks were as shunned during this period as they were embraced during the Dot Com Boom. Any back-testing that covers this period is suspect, especially if  it includes real estate, finance or tech stocks. Like the Dot Com Boom period that preceded it, the conditions that existed then do not exist now.

The End of the World As We Know It Market: March 2007-March 2008

The period from March 2007-March 2008 was dominated by the collapse or  near-collapse of all the Too-Big-To-Be-Allowed-To-Fail financial institutions, the bankruptcy of General Motors, TARP and assorted bailout games, and Bernie Madoff’s $50 billion fraud. It was a time of overwhelming panic, when fundamentals were irrelevant and technicals no longer worked. I don’t consider anything that happened during that market to have any bearing on the current market.

The QE/HFT Bubble Market: March 2009-Present

When the S&P500 bottomed out at 666 in March 2009, the Fed resorted to the the only tool they had left: create money out of thin air. That was the advent of “Quantitative Easing” which utterly changed the nature of the market. Vast quantities of brand new cash came pouring into the market by way of the Fed’s largesse. Not only did fundamentals no longer matter, (most of the big financial institutions were technically insolvent during the time leading up to the QE era), not only dud technicals no longer work, but now the high-frequency traders were starting to take over the market.

Due to the advent of QE and the takeover of the markets by HFT, this market is completely unlike any that has come before it. We are now trading in a market where the dominant realities are not earnings or the promise of future earnings, but by the quantity of newly created money flooding in, and the success of competing trading algorithms. The only period that matters is this one. Don’t bother back-testing data prior to March 2009 because it is no longer relevant.

Trading Strategy for Steelcase Industries (SCS) Beats Buy & Hold

Steelcase Industries

Continuing analysis with the “Touch Moving Average” tool I built, today I report on Steelcase Industries performance. I have a trade strategy for SCS that beats Buy & Hold, albeit only by about 15%. Here’s the details:

Trade Statistics

  • Company Name: Steelcase Industries
  • Symbol: SCS
  • Market Cap: $1.94B
  • 1M Average Daily Volume: ~1M shares
  • Time Period Analyzed: 1 Jan 2009  – 11 July 2014
  • Number of trading days: 1402
  • Number of trades from this signal: 26
  • Number of winning trades: 12
  • Number of losing trades: 14
  • Average Win per share: $2.07
  • Average Loss per share: -$0.72
  • Max Win per Share traded: $3.74
  • Max Loss per share traded: -$1.54
  • Max Drawdown: 27%

Trading Strategy for Steelcase Industries

This is another iteration of my “Touch the SMA” strategy. Briefly, the strategy works like this: Wait for a touch of the SMA, then wait for a close higher than the day it touched the SMA. When that happens, BUY at the Open the next day.

In this case, the optimum strategy for SCS is to use a 34 Day Simple Moving Average as the trigger. I set a Profit Target of 127% of the opening price and a Stop-Loss Trigger of 90% of the opening price.

This is a long-only “channel” trade, which means that the expectation is that the stock trades inside a channel without significant breakouts.  This is a fairly wide channel I set here for SCS, which is why we have higher-than-normal Max Drawdown numbers.

  • The maximum number of consecutive losing trades is 4.
  • The maximum number of consecutive winning trades is also 4.
  • Average losses are about a third of the average wins, which is why this trade makes money even though only 46% of the trades are winners.
  • The expectancy of this trade is 2.45 when executed exactly according to my rules.

Results of Simulations

I ran a simulation using this strategy starting with a $10,000 account. I traded the maximum number of shares allowed for my account size, traded the signal every time it occurred since 1 January 2009, subtracted $13/00 for every account and assumed a 28% capital gains tax rate. After all that, the net account value was just under $30,000.

By comparison, using a Buy & Hold strategy for SCS, starting at the same time frame with the same starting capital resulted in a net account value of about $26,000. (I assumed a 7.5% long-term capital gains tax rate.) So, this is a marginally profitable strategy. I’m not crazy about this because I am much more conservative about my drawdowns, but I present it here for your edification because – in spite of my reservations – it still beats Buy & Hold.

Standard disclaimer: just because this trade worked in the past is absolutely no guarantee it will work in the future.

If you’d like more information about this trade or about using the analysis tool in your own trading, contact me.

Build Your Own Expectancy Calculator

In this video, I show you how to build your own expectancy calculator.

If you just plug all your trades into this calculator, it will give you a raw, unbiased measurement of your skill as a trader. If you plug into it only those trades which were executed under a particular trading system, it will give you a way to objectively measure one trading system against another.

If you’d like a copy of this spreadsheet, just sign-up for my newsletter.

[vooplayer vooid=’MjY2OTY=’ width=’543′ height=’408′]

XLP Swing Strategy

XLP: Consumer Staples ETF

Continuing with my study of stocks with the same general characteristics, this high expectancy XLP swing strategy is a Long Only trade. Average Time in Market is 28 days per trade.

Trade Statistics

  • Company Name: Consumer Staples Select SPDR ETF
  • Symbol: XLP
  • Time Period Tested: 1 Jan 2009 – 3 July 2014
  • Number of trading days: 1396
  • Number of trades from this signal: 53
  • Number of winning trades: 33
  • Number of losing trades: 20
  • Average Win per winning trade: $1.48
  • Average Loss per losing trade: -$0.65
  • Max Win per Share traded: $2.16
  • Max Loss per share traded: $0.86

Trade Overview

My goal was to select stocks which are relatively stable in price so as to minimize the opportunity for nasty surprises. I screened for stocks with a 52 Week Price Range between $10-$100, a Market Cap greater than $500M, and Average Daily Volume greater than 1M shares and which pay a dividend.

This trade is a Long Only strategy. The basic setup is to begin watching the stock after it has closed below its 25 Day Simple Moving Average (25D SMA) and then – once it closes above that price – Buy at the Open on the next day.

I set a Stop-Loss trigger of 2.5% and a profit target of 5%. Once either target was hit, I automatically closed the trade at that price. If I used a trailing stop, I could possibly improve the profitability of this trade, but I did not do any testing or calculations with that in mind.

  • The maximum number of consecutive losing trades is 5; the maximum drawdown percentage during the period tested is 10%.
  • The maximum number of consecutive winning trades is 9.
  • The expectancy of this trade is 3.72.

Results of Simulations

I ran a simulation using this strategy starting with a $10,000 account. I traded the maximum number of shares allowed for my account size, traded the signal every time it occurred since February 2009, and plowed all profits back into the trading account. By 18 June 2014, the account value was just over $31,000.

This trade has me in the market 1652 days out of 1977 possible days, (83%). I don’t like being in the market that much, but it’s hard to argue with success.

Given my selection criteria, this proves it is historically a fairly safe and predictable trade. (And of course, just because it worked in the past is absolutely no guarantee it’ll work in the future.)

Counterpoint & Conclusion

The market has been on a 5+ year bull run during the time I ran this simulation, so it would make sense that a long-only strategy would prosper. So is it my strategy that made money, or is it merely the bull market that made money? For the sake of fairness, I compared this Long Only strategy to a “Buy & Hold” strategy over the same period of time.

Over the same time period:

  • “Buy & Hold” would be worth a little less than $21,000, for a profit of $11,322
  • This Long Only system would be worth a little more than $31,000, for a profit of $21,000 over the same period
  • “Buy & Hold” had a max drawdown of 10%
  • Long Only had a max drawdown of 10%

This strategy doubles the return on the ole’ “Buy and Hold” strategy, so the result is not merely the result of market performance or general market conditions.

Send me a note if you’d like the details on this strategy.

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BBBY Long Swing Trade – Update

I have an update to the BBBY Long Swing trade I posted about previously. I spent a little time playing with different length moving averages as the trigger, and found that a 10 Day Simple Moving Average is a good deal better than the 20 Day average I used in the original strategy. The 10 Day is actually the sweet spot – much better than anything else I tested.

Here’s the Stats:

  • Starting Equity $10,000.00
  • Trades 136
  • Winners 71
  • Losers 65
  • % Winners 52%
  • Avg Win per Share per Trade $2.50
  • Avg Loss per Share per Trade  ($1.07)
  • Max Shares 1000
  • Total Win/Share $177.31
  • Total Loss/Share ($69.46)
  • Avg P/L per Trade $522.76
  • Expectancy 2.55
  • Max Consec Winners 5
  • Max Consec Losers 5
  • Max Win/Share $3.80
  • Max Loss/Share ($1.54)
  • Max Drawdown 8%
  • Days in Market 1060
  • Ending Equity $71,095.51

The negative differences between this trade and the 20 Day swing trade are:

  • This system is in the market more than the 20 Day Swing Trade – 1060 days versus 973 days
  • Average Loss is higher
  • Max Drawdown is slightly higher

All in all, I think this is an improvement on the original strategy, even though it has a slightly higher risk.

As always, if you’d like the details of this trade, drop me a note: