Machine learning in trading: theory, models, practice and algo-trading - page 264
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Sorry, I got distracted...
Here is the data https://drop.me/aGE2kB
I did not make any edits because I had no time, while the glass is only a delta, some days with gaps, but as for the tests will do
I do not know if these lectures will be useful to someone, but for general development is probably not bad, and just interesting
time series forecasting:
https://www.youtube.com/watch?v=u433nrxdf5k
feature transformation:
https://www.youtube.com/watch?v=U1QYrGj6Ztg
At the end of the video about feature transformation the lecturer mentions an interesting dimensionality reduction method which can be used for example to evaluate class separability, this method(t-SNE) is considered more progressive than PCA and deserves attention
I compared how downsampling methods are divided
And indeed the method compares favorably with others.
The data and code can be taken from this articlehttp://biostat-r.blogspot.com/2016/05/pca-mds-t-sne.html
method packages : tsne, Rtsne
The last one is fast and written in C++
I haven't run it on market data yet...
Dr.Trader Remember you said you don't know how scale() works? I found out ))
# аналог
(x - mean(x)) / sd(x)
I do not know if these lectures will be useful to someone, but for general development is probably not bad, and just interesting
time series forecasting:
https://www.youtube.com/watch?v=u433nrxdf5k
feature transformation:
https://www.youtube.com/watch?v=U1QYrGj6Ztg
I see, this all explains why I wanted the result in 0...1, but got it in some different bounds for each column.
Sorry, I got distracted...
Here is the data https://drop.me/aGE2kB
I did not make any edits because I had no time, while the glass is only a delta, some days with gaps, but as for the tests will do
I do not know if these lectures will be useful to someone, but for general development is probably not bad, and just interesting
time series forecasting:
https://www.youtube.com/watch?v=u433nrxdf5k
Yes, there are interesting points, like check by residuals and program selection of features.
But it's strange that the person first says "validation is absolutely necessary" and then "if part of the story interferes with model fitting, just cut it off" .
Hi all!
1) About t-SNE: it didn't work with market data.
2) I found a package with already implemented candlestick patterns, you can install it like this:
I would like to play around with it but it so happens that I have not seriously encountered xts data, how do I translate my quotes into the right format?
my data
X.DATE. X.TIME. X.OPEN. X.HIGH. X.LOW. X.CLOSE. X.VOL.
385327 20170117 204000 115420 115440 115400 115400 314
385328 20170117 204500 115400 115440 115370 115410 559
385329 20170117 205000 115410 115440 115380 115420 475
385330 20170117 205500 115410 115510 115360 115470 1745
385331 20170117 210000 115470 115490 115430 115440 607
385332 20170117 210500 115440 115490 115420 115470 453
[1] "data.frame"
I need xts format
RIH7.Open RIH7.High RIH7.Low RIH7.Close RIH7.Volume
2017-01-30 10:00:00 119060 119060 118480 118620 12191
2017-01-30 10:05:00 118610 118620 118260 118320 13219
2017-01-30 10:10:00 118320 118470 118230 118250 8519
2017-01-30 10:15:00 118240 118260 118080 118120 11010
2017-01-30 10:20:00 118110 118160 117930 117980 8108
2017-01-30 10:25:00 117980 118100 117910 118020 5544
[1] "xts" "zoo"
Hi all!
1) About t-SNE: it didn't work with market data.
2) I found a package with already implemented candlestick patterns, you can install it like this:
I would like to play around with it but it so happens that I have not seriously encountered xts data, how do I translate my quotes into the right format?
my data
X.DATE. X.TIME. X.OPEN. X.HIGH. X.LOW. X.CLOSE. X.VOL.
385327 20170117 204000 115420 115440 115400 115400 314
385328 20170117 204500 115400 115440 115370 115410 559
385329 20170117 205000 115410 115440 115380 115420 475
385330 20170117 205500 115410 115510 115360 115470 1745
385331 20170117 210000 115470 115490 115430 115440 607
385332 20170117 210500 115440 115490 115420 115470 453
[1] "data.frame"
I need xts format
RIH7.Open RIH7.High RIH7.Low RIH7.Close RIH7.Volume
2017-01-30 10:00:00 119060 119060 118480 118620 12191
2017-01-30 10:05:00 118610 118620 118260 118320 13219
2017-01-30 10:10:00 118320 118470 118230 118250 8519
2017-01-30 10:15:00 118240 118260 118080 118120 11010
2017-01-30 10:20:00 118110 118160 117930 117980 8108
2017-01-30 10:25:00 117980 118100 117910 118020 5544
[1] "xts" "zoo"