Adaptive Moving Averages, Regime Changes, and Overfitting Risk

# How to adapt a Moving Average period to market conditions?


# How to adapt a Moving Average period to market conditions?












I would like to know if there is some way to adapt the period of a moving average to market conditions like for instance the stop loss can be adapted to market conditions using the average true range. Thanks!

EDIT: As suggested, I add some references and context to my question.

I have a trend following system that in the test period 2009 - 2014, with one hour candles, no parameters to optimize, EUR/USD produces these figures:

```
Max drawdown        -48$ 13% (MAE -74$ 20%)
Number of trades    165 (28/year, 1/week, 1/day)
Percent winning     31%
Annual return       139%
Profit factor       1.99 (PRR 1.56)
Sharpe ratio        0.92
Kelly criterion     0.55
R2 coefficient      0.647
Ulcer index         9.4%
```

However the same system in 2003 - 2008 makes:

```
    Max drawdown        -131$ 158% (MAE -154$ 185%)
    Number of trades    212 (36/year, 1/week, 1/day)
    Percent winning     19%
    Annual return       14%
    Profit factor       1.15 (PRR 0.90)
    Sharpe ratio        0.27
    Kelly criterion     0.39
    R2 coefficient      0.110
    Ulcer index         68.2%
```

So I tried to look for an adaptive moving average that adapts to market conditions, I read among others these two references:

http://www.mesasoftware.com/papers/MAMA.pdf (just in case it goes down) https://books.google.com.uy/books?id=_KjOT1b9bfUC&pg=PA113&lpg=PA113#v=onepage&q&f=false

I have tried to reproduce the method of the first link mith mixed results, I tested with EUR/USD and candles of 8 hours this code, sorry for the length, I hope you bear with me:

```
vars Price = series(price());           
Stop = 2*ATR(100);  
MAMA(Price,0.05,0.5);
vars MAMAs = series(rMAMA);
vars FAMAs = series(rFAMA);

if( crossUnder(FAMAs,MAMAs) ){
    reverseShort(1);//if the Following Adaptive Moving Average crosses under the Mother of Adaptive Moving Averages then I enter long, closing previous short if any
} else if( crossOver(FAMAs,MAMAs) ) {
    reverseLong(1);
}
plot("price",Price[0],MAIN|LINE,BLACK);
plot("Mama",MAMAs,LINE,RED);
plot("Fama",FAMAs,LINE,BLUE);
```

Testing that in 2009 - 2014 gave

```
Max drawdown        -157$ 64% (MAE -159$ 65%)
Number of trades    392 (68/year, 2/week, 1/day)
Percent winning     62%
Annual return       35%
Profit factor       1.15 (PRR 1.00)
Sharpe ratio        0.52
Kelly criterion     0.73
R2 coefficient      0.002
Ulcer index         21.2%
```

But in 2003 - 2008 it gives

```
Max drawdown        -255$ -1396% (MAE -255$ -1397%)
Number of trades    383 (66/year, 2/week, 1/day)
Percent winning     60%
Annual return       -2%
Profit factor       0.99 (PRR 0.85)
Sharpe ratio        -0.02
Kelly criterion     -0.05
R2 coefficient      0.013
Ulcer index         38.7%
```

However if I test in 2003 - 2008 with 4 hour candles the results are

```
Max drawdown        -145$ 67% (MAE -146$ 67%)
Number of trades    790 (134/year, 3/week, 1/day)
Percent winning     61%
Annual return       32%
Profit factor       1.10 (PRR 0.99)
Sharpe ratio        0.50
Kelly criterion     0.71
R2 coefficient      0.415
Ulcer index         23.0%
```

So, even this 'adaptive' moving average needed to be adapted to a different market by adjusting the duration of candles, but I don't know whether this is curve fitting. Is there a way to detect this change in market conditions between 2003 - 2008 and 2009 - 2014 in this test?

## Answer by radvan (score -1)

https://quant.stackexchange.com/a/18772

As it was already mentioned. Try to read about Kalman filter and Markow Switching models. I have even seen some academic papers where authors tried to define MA length based on MSM or KF. Try to google it ...