Quote from TSGannGalt:
Does that mean it's my turn?
Anyways...
I figured that bwolinksy may ask for the MAE and MFE so I added 2 columns for the OpenDD / OpenRun-up (I usually don't follow the "fill efficiency" but I figured it can help bwolinsky...) for each trade.
Format is tab-delimited:
Actual PL - OP/DD - OP/RU
That's right, Gann, it is your turn.
Profit
Percentiles Smallest
1% -.1 -.1
5% -.09 -.1
10% -.08 -.1 Obs 50832
25% -.04 -.1 Sum of Wgt. 50832
50% .03 Mean .0251957
Largest Std. Dev. .0725205
75% .09 .15
90% .13 .15 Variance .0052592
95% .14 .15 Skewness -.003583
99% .15 .15 Kurtosis 1.802538
MAE
Percentiles Smallest
1% -.12 -.18
5% -.1 -.17
10% -.09 -.17 Obs 50832
25% -.05 -.16 Sum of Wgt. 50832
50% .01 Mean .0093378
Largest Std. Dev. .0737714
75% .07 .15
90% .11 .15 Variance .0054422
95% .12 .15 Skewness -.0117744
99% .14 .15 Kurtosis 1.878039
MFE
Percentiles Smallest
1% -.09 -.1
5% -.07 -.1
10% -.06 -.1 Obs 50832
25% -.02 -.1 Sum of Wgt. 50832
50% .04 Mean .0409791
Largest Std. Dev. .0736008
75% .1 .21
90% .14 .23 Variance .0054171
95% .15 .27 Skewness -.0018808
99% .17 .32 Kurtosis 1.872599
I'm having trouble understanding what I'm looking at. Your description goes:
Actual PL - OP/DD - OP/RU
I'm missing why there would be negative values for your OP/RU and positive values for your OP/DD? Can you try to explain that? I've always thought of MAE and MFE as hard values where MAE would be given always below 0, and MFE always above 0. Perhaps there are some data manipulations I need to do to understand what you're calculating?
I'm thinking about replacing any OP/DD>0 just as 0, and any OP/RU as 0 as well. If this was not your intention, perhaps you are showing slippage this way, where there was never any MAE or MFE, possibly?
The data seem a bit more reasonable when I make that manipulation:
Profit
Percentiles Smallest
1% -.1 -.1
5% -.09 -.1
10% -.08 -.1 Obs 50832
25% -.04 -.1 Sum of Wgt. 50832
50% .03 Mean .0251957
Largest Std. Dev. .0725205
75% .09 .15
90% .13 .15 Variance .0052592
95% .14 .15 Skewness -.003583
99% .15 .15 Kurtosis 1.802538
MAE
Percentiles Smallest
1% -.12 -.18
5% -.1 -.17
10% -.09 -.17 Obs 50832
25% -.05 -.16 Sum of Wgt. 50832
50% 0 Mean -.0272466
Largest Std. Dev. .0379016
75% 0 0
90% 0 0 Variance .0014365
95% 0 0 Skewness -1.122019
99% 0 0 Kurtosis 2.921274
MFE
Percentiles Smallest
1% 0 0
5% 0 0
10% 0 0 Obs 50832
25% 0 0 Sum of Wgt. 50832
50% .04 Mean .0555341
Largest Std. Dev. .0561523
75% .1 .21
90% .14 .23 Variance .0031531
95% .15 .27 Skewness .5384886
99% .17 .32 Kurtosis 1.908338
From this, I'm going to try to construct a histogram, and find an optimal profit target and stop only. I may only find a stop, but if these values have already been optimized it is not likely that I could find better fits for the data.
My goodness, where are my manners? You've been waiting a long time, Gann. So I'll analyze just the profit distribution from the summary statistics below:
Profit
Percentiles Smallest
1% -.1 -.1
5% -.09 -.1
10% -.08 -.1 Obs 50832
25% -.04 -.1 Sum of Wgt. 50832
50% .03 Mean .0251957
Largest Std. Dev. .0725205
75% .09 .15
90% .13 .15 Variance .0052592
95% .14 .15 Skewness -.003583
99% .15 .15 Kurtosis 1.802538
(BTW you have one hellaciously large dataset. When constructing new values, I get this message:
no room to add more variables due to width
An attempt was made to add a variable that would have increased the memory required to store an observation beyond what is currently
possible. You have the following alternatives:
1. Store existing variables more efficiently; see help compress.
2. Drop some variables or observations; see help drop. (Think of Stata's data area as the area of a rectangle; Stata can trade off
width and length.)
3. Increase the amount of memory allocated to the data area using the set memory command; see help memory.
)
So I guess I'm going to have to use only the description, I guess as we were always doing, neither of the three options enabled me to manipulate this data beyond the changes I had made.
Profit
Percentiles Smallest
1% -.1 -.1
5% -.09 -.1
10% -.08 -.1 Obs 50832
25% -.04 -.1 Sum of Wgt. 50832
50% .03 Mean .0251957
Largest Std. Dev. .0725205
75% .09 .15
90% .13 .15 Variance .0052592
95% .14 .15 Skewness -.003583
99% .15 .15 Kurtosis 1.802538
I'm sure first you think I'm going single you out with the negative skewness of -0.003583. This I don't believe is significantly different than 0, so there's actually no skewness to address. The kurtosis is quite peaked, and I noticed your average profit being 0.0251957 or 2.51957%, which is excellent for a 50832 trade dataset. I think we'll see large profits from you in the future. So 95% of the trades approximately fall between 2.519+/-7.25%*1.96= a confidence interval of 16 3/4's percent and minus 11 3/4's percent. That does show me where the skewness is, and appears that your losses, while slightly less, have a greater impact on your profits. With a win percent rate of 0.580146364=58%, for a large dataset and given the large average profit, I would say this is a very good system. Kudos, but I don't think you needed me to tell you that, and I do appreciate your participation. My next post, I will try to see if there's some way to optimize this data, but I think it may be optimized already. If it is, please tell me, and I won't try to.