1 / 12

Captain Carnage Asset Management: Dual Moving Average Crossover (DMAC) Trading Strategy

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

laird
Download Presentation

Captain Carnage Asset Management: Dual Moving Average Crossover (DMAC) Trading Strategy

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Captain Carnage Asset Management:Dual Moving Average Crossover (DMAC) Trading Strategy Merrill Liechty Murray Spence Lance Stover

  2. 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

  3. Short Short Short Long • Long Long

  4. 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)

  5. 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

  6. 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)

  7. In Sample Results

  8. Parameter Selection for Out of Sample Analysis We did not select the best performing MA pair of 5000 candidates to go out-of-sample!

  9. Out of Sample Results

  10. 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.

  11. 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

  12. 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

More Related