@Disagree
I've built a few backtest models. Some with very impressive returns until retail brokerage fees were included.
With brokerage, in a ranging or choppy market, many near break-even trades became losers which severely impacted the CAGR.
I the end I found best results using highly responsive sell triggers (Like Parabolic SARs or 4/9 MA) and less responsive buy triggers such as displaced price oscillators.
Have you use volatility filters or included brokerage in your calculations?
FLT - Machine Learning Experiment, page-5
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Last
$17.00 |
Change
0.020(0.12%) |
Mkt cap ! $4.754B | |||
| Open | High | Low | Value | Volume |
| $17.01 | $17.18 | $16.98 | $18.14M | 1.024M |
Buyers (Bids)
| No. | Vol. | Price($) |
|---|---|---|
| 3 | 6735 | $17.00 |
Sellers (Offers)
| Price($) | Vol. | No. |
|---|---|---|
| $17.01 | 75 | 1 |
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