Captain Carnage Asset Management:Dual Moving Average Crossover

(DMAC) Trading Strategy

Merrill Liechty

Murray Spence

Lance Stover

Dual Moving Average Crossover Trading Strategy

• Definitions:– STMA Short term moving average– LTMA Long term moving average

• Economic Rationale & Strategy– Technical Momentum-based approach– Value “Mean-reversion” approach

EUR:USD Price and Simple Moving Average Comparison

1.175

1.177

1.179

1.181

1.183

1.185

1.187

1.189

0:00

0:50

1:40

2:30

3:20

4:10

5:00

5:50

6:40

7:20

8:10

9:00

9:50

10:4

0

11:3

0

12:2

0

13:1

0

14:0

0

14:5

0

15:4

0

16:3

0

17:1

5

18:0

5

18:5

5

Pri

ce

(E

UR

)

Exchange Rate(EUR:USD)

Short Term SMA(5 day)

Long Term SMA(10 day)

January 4, 1999

• Long

Short

Short Short

Long

Long

Data

• High frequency foreign exchange data– Currency rate: EUR:USD

– 5 minute intervals

– January 03, 1999 to February 06, 2002

– 328K + observations

– Removed “stale” weekend data

• Hold out Sample – Approximately 37 weeks of data (75,000 observations)

Methodology

– Calculate crossover points– Determine Buy /Sell trading signals– Calculate metrics for each pair of moving

averages:• Profit (with and without slippage)• # Wins / # Losses• Average Win / Loss• Max/Min Portfolio Value• % Time Below Initial Investment

Software

• Challenges– Data set too large for Excel 64,000 row limit

– Needed stronger computational power to consider nearly 5000 DMAC combinations

• Selected C++ as development platform– Considered all combinations of 10 unit SMA intervals up

to 1000 (e.g. 10,50; 220,740)

In Sample ResultsTrial Run TR1 TR2 TR3

Description of Trial RunIn Sample or Out of Sample? In Sample In Sample In SampleSlippage or No Slippage? No Slippage Slippage SlippageTechnical of Value-Based Trigger Selection? Technical Technical Value

Num of Double Moving Average Crossover (DMAC) CombinationsSpan of Short Term Moving Average 10 - 980 10 - 980 10 - 980Span of Long Term Moving Average 20 - 990 20 - 990 20 - 990Num of DMAC Combinations Considered 4950 4950 4950Num of DMAC Combinations with Executed Trades 4851 4851 4851

DMAC Profitability MetricsNum of Profitable DMAC Combinations 4475 1717 738Num of Unprofitable DMAC Combinations 376 3134 4113% Profitable 92% 35% 15%% Unprofitable 8% 65% 85%

DMAC Total Return MetricsAverage Total Return 8.6% -4.0% -11.4%Standard Deviation of Return 6.0% 10.5% 15.1%Maximum Return 27.1% 14.7% 20.8%Mininum Return -7.4% -94.1% -95.4%

Parameter Selection for Out of Sample Analysis

We did not select the best performing MA pair of 5000 candidates to go out-of-sample!

Frequency of STMA Appearance In Most Profitable In Sample DMAC Combinations

0

5

10

15

20

25

10 30 50 70 90 110

130

150

170

190

STMA

# A

pp

eara

nce

s

Frequency of LTMA Appearance In Most Profitable In Sample DMAC Combinations

0

2

4

6

8

660

680

700

720

740

760

780

800

820

840

860

880

900

LTMA

# A

pp

eara

nce

s

Out of Sample ResultsTrial Run TR2 TR4

Description of Trial RunIn Sample or Out of Sample? In Sample Out of SampleSlippage or No Slippage? Slippage SlippageTechnical of Value-Based Trigger Selection? Technical Technical

Num of Double Moving Average Crossover (DMAC) CombinationsSpan of Short Term Moving Average 10 - 980 50 - 130 *Span of Long Term Moving Average 20 - 990 740 - 810 *Num of DMAC Combinations Considered 4950 72Num of DMAC Combinations with Executed Trades 4851 72

DMAC Profitability MetricsNum of Profitable DMAC Combinations 1717 64Num of Unprofitable DMAC Combinations 3134 8% Profitable 35% 89%% Unprofitable 65% 11%

DMAC Total Return MetricsAverage Total Return -4.0% 2.5% **Standard Deviation of Return 10.5% 2.4%Maximum Return 14.7% 7.8%Mininum Return -94.1% -2.7%

* Out-of-Sample Recommedations derived from Trial Run 2** Annualized return for average Out of Sample return would be:

Conclusions

– In Sample• DMAC provides consistent profits when slippage is

not considered.• Slippage makes a “blind” DMAC strategy

unprofitable.• Technical approach outperforms value approach.

– Out of Sample• Selecting “intelligent” DMAC parameters yields

small but consistent profits with low risk.

Potential Issues

• Data– Quality of data– Need to examine multiple currencies

• Methodology– Parameter selection and “over-optimization”

• Risks– More than standard deviation– Consider “filter approach” to reduce whipsaws

Next Steps

• Capture more profit through better timing strategies – Identify “trending” versus trading periods– Consider hidden Markov statistical modeling

• Selection of asset classes (currencies, securities, futures)

• Catastrophic event analysis