@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
$17.50 |
Change
0.370(2.16%) |
Mkt cap ! $4.754B | |||
| Open | High | Low | Value | Volume |
| $17.13 | $17.59 | $17.12 | $14.60M | 839.9K |
Buyers (Bids)
| No. | Vol. | Price($) |
|---|---|---|
| 12 | 1236 | $17.49 |
Sellers (Offers)
| Price($) | Vol. | No. |
|---|---|---|
| $17.51 | 3381 | 15 |
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| Last trade - 14.59pm 28/11/2024 (20 minute delay) ? |
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