ewma | Exponentially Weighted Moving Average algorithms for Go | Machine Learning library
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kandi X-RAY | ewma Summary
This repo provides Exponentially Weighted Moving Average algorithms, or EWMAs for short, based on our Quantifying Abnormal Behavior talk.
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QUESTION
What is the best method for calculating the EWMA returns for every column of my time series? Above all columns we have the returns from Today - 260d (-1Year) until Today -1d.
**The returns are calculated by the division of the close prices between days.
I was using the following function:
...ANSWER
Answered 2021-Sep-21 at 18:01You can use map_df from the purrr package to do it in one line.
QUESTION
I have this simple dataset of a stock price, where Col1 is for Dates, Col2 is for Returns (Close Price D / Close Price D-1, the same as the pct_change in Python) and Col3 for the Ewma Volatility. I'm working with projections and were ploting the Returns vs Ewma Volatility, but reading some articles I realized that I need to plot the Daily Volatility x Ewma Volatility so I can see the predictions more clearly
So I wanted to see a graph like these, but in Red I have Returns and instead of it I need the red line to be Volatility calculated by normal methods (simple Vol). In black is the EwmaVol calculated by the MTS::EWMAvol package.
Here's the data:
...ANSWER
Answered 2021-Sep-13 at 18:12library(tidyverse)
library(MTS)
library(ggpubr)
#> Loading required package: magrittr
#>
#> Attaching package: 'magrittr'
#> The following object is masked from 'package:purrr':
#>
#> set_names
#> The following object is masked from 'package:tidyr':
#>
#> extract
#> Loading required package: magrittr
#>
#> Attaching package: 'magrittr'
#> The following object is masked from 'package:purrr':
#>
#> set_names
#> The following object is masked from 'package:tidyr':
#>
#> extract
data <- structure(list(
date = structure(c(
18513, 18514, 18516, 18519,
18520, 18521, 18522, 18523, 18526, 18527, 18528, 18529, 18530,
18533, 18534, 18535, 18536, 18537, 18540, 18541, 18542, 18543,
18544, 18547, 18548, 18549, 18550, 18551, 18554, 18555, 18556,
18557, 18558, 18561, 18562, 18563, 18564, 18565, 18568, 18569,
18570, 18571, 18572, 18575, 18576, 18577, 18578, 18579, 18582,
18583, 18584, 18585, 18586, 18589, 18590, 18591, 18592, 18593,
18596, 18597, 18598, 18599, 18600, 18603, 18604, 18605, 18606,
18607, 18610, 18611, 18612, 18613, 18614, 18617, 18618, 18619,
18620, 18621, 18624, 18625, 18626, 18627, 18628, 18631, 18632,
18633, 18634, 18635, 18638, 18639, 18640, 18641, 18642, 18645,
18646, 18647, 18648, 18649, 18652, 18653, 18654, 18655, 18656,
18659, 18660, 18661, 18662, 18663, 18666, 18667, 18668, 18669,
18670, 18673, 18674, 18675, 18676, 18677, 18680, 18681, 18682,
18683, 18684, 18687, 18688, 18689, 18690, 18691, 18694, 18695,
18696, 18697, 18698, 18701, 18702, 18703, 18704, 18705, 18708,
18709, 18710, 18711, 18712, 18715, 18716, 18717, 18718, 18719,
18722, 18723, 18724, 18725, 18726, 18729, 18730, 18731, 18732,
18733, 18736, 18737, 18738, 18739, 18740, 18743, 18744, 18745,
18746, 18747, 18750, 18751, 18752, 18753, 18754, 18757, 18758,
18759, 18760, 18761, 18764, 18765, 18766, 18767, 18768, 18771,
18772, 18773, 18774, 18775, 18778, 18779, 18780, 18781, 18782,
18785, 18786, 18787, 18788, 18789, 18792, 18793, 18794, 18795,
18796, 18799, 18800, 18801, 18802, 18803, 18806, 18807, 18808,
18809, 18810, 18813, 18814, 18815, 18816, 18817, 18820, 18821,
18822, 18823, 18824, 18827, 18828, 18829, 18830, 18831, 18834,
18835, 18836, 18837, 18838, 18841, 18842, 18843, 18844, 18845,
18848, 18849, 18850, 18851, 18852, 18855, 18856, 18857, 18858,
18859, 18862, 18863, 18864, 18865, 18866, 18869, 18870, 18871,
18872, 18873, 18876, 18877, 18878, 18879, 18880
), class = "Date"),
return = c(
-0.344318028823296, 0.269214666620058, 0.126435486075415,
0.190402598580308, 0.118204959190486, -0.411914395032645,
-0.280554281566071, 0.0134834445697551, -0.209400032450252,
0.440220250108175, -0.299538435031037, 0.0790056559320964,
0.368012578536158, 0.213210937684974, -0.119491426933381,
0.324232635217204, 0.33565827603383, -0.284052393900706,
0.0174981257069227, -0.293140728783266, 0.262857810110247,
0.25815207221046, 0.234032193845141, 0, 0.47229978173055,
0.16344672539841, 0.0254415401713886, 0.0782307185721609,
-0.502295230104942, 0.322732032393595, 0.125213641008163,
0.0260011812318895, -0.0119807619653632, -0.442636109202831,
0.119360281355763, -0.935912609987246, 0.503025377994561,
-0.515851084169158, 0, 0.566675653173945, 0.54537601383754,
0.288514339206156, -0.384940437925295, 0.423464532950096,
-0.360198194766125, 0.34916380193169, -0.488427517439975,
0.751803563456712, 0.0407270958491847, -0.308511236722092,
-0.356669697629545, -0.00433655513272652, 0.25103546278182,
0.330904413577973, 0.215960242799815, 0.00310717943959164,
-0.0202688039084646, 0.148748985331507, -0.35100173325186,
0.114510581854206, 0.148599263370308, -0.24952519697232,
0.0901961472235016, -0.184463114050532, 0.293294243386703,
-0.167917218613252, -0.264913772913978, 0.21209802725542,
0.257358936163117, 0.218459709282958, 0.320686532500207,
-0.042363642590243, -0.157974967460515, 0.0326488873406457,
0.155724946337242, 0.308194493705213, -0.00373113226282772,
0, 0.388011313498459, -0.160799203187683, -0.0364451647751474,
-0.00787981636365025, 0, 0.00347476577235568, 0.0958786936178273,
-0.494900147494504, 0.698924415442, 0.573739010391536, -0.0244258685250395,
-0.118236771455138, -0.0497255705517377, -0.194646421800621,
0.0626373850623438, 0.161831284061245, 0.349877415042077,
0.244794751129892, 0.106084615995957, 0.0496011476778498,
-0.0155409324898941, -0.248181207363339, -0.565019988759,
0.713402006899163, -0.492682269522014, 0.560742579750566,
0.0933300580868543, 0.406010324025276, 0.498072877122919,
0.183140575863927, 0.211708797400874, 0.0467600239312594,
-0.26047696162337, 0.156025603919635, 0.0579917193297143,
0, 0, 0.279910939036699, 0.0361670915156291, -0.0308432601603755,
-0.303210761690193, -0.0332159735063931, 0.239412585331418,
-0.440324459780256, -0.0677518990798903, 0.693820654046051,
0.106956183681477, -0.0265983858612053, -0.566418194735492,
0.758734670629567, -0.461816077286941, 0.220311418745246,
-0.212421535066087, 0.11649266375866, -0.0816306818701426,
0.14720395503046, 0.0998096394235824, 0.125421489047231,
-0.80404235252332, -0.0571527697596768, -0.0198909048934907,
-0.384393817169225, -0.00512169253872485, 0.39762002446679,
0.376723339854912, -0.00691542822410588, -0.0596391805140118,
0.0496648004967207, 0.365778349002057, 0, 0.290193208620667,
-0.0756981954953341, 0.303622329534577, 0.132066617966757,
0.483747928454163, 0.206842677360989, 0.000809787937924961,
-0.0932400960462393, 0.153919143128314, -0.0367657873301875,
-0.279532278711733, -0.201551916340671, 0, -0.434262146566655,
0.516522716400583, -0.085490199662681, 0.00110354252617679,
-0.0718299839560837, 0.0607082870466307, -0.0272938411585472,
0.18260643173571, -0.137250457849561, -0.0490415885207028,
-0.00981171552565897, 0.226374538622723, -0.125646625601663,
-0.114097545274073, -0.662064537293731, 0.441816451091909,
0.272870771417137, 0.0287418778864461, -0.153691128743901,
-0.0535034017089503, 0.183233994720022, 0.0485946620325522,
0.178109740301375, 0.0246408691518447, 0.105609201872649,
-0.0371879979512152, 0.074575753280852, 0.193587247420517,
-0.143096141476954, 0.0120547889261328, 0, 0.0789650925019282,
-0.00102809637148435, -0.0231060990016233, -0.00602180433261802,
0.0252638098895634, 0.133415121207804, 0.129518349212272,
-0.24291680503609, -0.0358833918191526, 0.00388487206410979,
0.140270275560446, 0.209234313518833, 0.0691442382855439,
-0.0656206987662583, 0.0382006909145182, 0.0527534442678976,
0.0712377999932313, 0.105814832605434, 0.199474140948777,
0.0367215633770196, 0.471059947866816, 0.182962161591784,
0.00686636091130052, 0.331666913038535, -0.211401586120729,
0.0150297692120097, 0.294266409263838, 0.0305328433574382,
-0.107694099922229, 0.0348020405913332, -0.28173634642339,
-0.200294650252061, 0.27169010217408, 0.085643621458738,
0.22240270432515, 0.0986854063924764, -0.306275163317938,
-0.342494037770066, -0.0816072482913978, 0.0236860357074207,
-0.249044731611009, 0.0228069475130009, 0.275178781456765,
-0.339227965049474, 0.105235656491434, 0.0344585485449368,
0.102195427143629, -0.255540645170535, 0.00219929895942133,
0.0611859110733352, -0.162852193753338, -0.413395221687142,
-0.245350759376831, 0.0139098467565878, 0.384223744149518,
0.229978347693158, 0.0890188167461016, 0.17247352519955,
0.107792570447737, -0.312370635130228, 0.29430590173766,
-0.167286040723373, -0.275329695257916, -0.127324776340247,
-0.219446501196637, 0.152123649809292, 0.183579633672417,
0, -0.170514041270507, 0.0995481496508168, 0
)
), class = "data.frame", row.names = c(
NA,
-263L
))
sigma <-
data %>%
column_to_rownames("date") %>%
EWMAvol() %>%
pluck("Sigma.t") %>%
as.numeric()
data["sigma"] <- sigma
data %>% as_tibble()
#> # A tibble: 263 x 3
#> date return sigma
#>
#> 1 2020-09-08 -0.344 0.0710
#> 2 2020-09-09 0.269 0.0739
#> 3 2020-09-11 0.126 0.0732
#> 4 2020-09-14 0.190 0.0706
#> 5 2020-09-15 0.118 0.0687
#> 6 2020-09-16 -0.412 0.0663
#> 7 2020-09-17 -0.281 0.0716
#> 8 2020-09-18 0.0135 0.0726
#> 9 2020-09-21 -0.209 0.0697
#> 10 2020-09-22 0.440 0.0693
#> # … with 253 more rows
data %>%
ggplot(aes(return, sigma)) +
geom_point() +
stat_smooth(method = "lm") +
stat_cor()
#> `geom_smooth()` using formula 'y ~ x'
QUESTION
I have the following dataframe and subsequent EWMA function:
...ANSWER
Answered 2021-May-28 at 14:13You can use ewm and set min_periods in rolling to 1:
QUESTION
At some point during 35k endurance load test of my java web app which fetches static data from Elasticsearch, I am start getting following elasticsearch exception:
...ANSWER
Answered 2020-Nov-21 at 06:05For such a small size, you are using 5 primary shards, which I feel, due to your ES version 6.X(default was 5), and you never changed it, but In short having high number of primary shards for small index, has severe performance penalty, please refer very similar use-case(I was also having 5 PS 😀) which I covered in my blog.
As you already mentioned that your index size will not grow significantly in future, I would suggest to have 1 primary shard and 4 replica shards
- 1 Primary shard means for a single search, only one thread and one request will be created in Elasticsearch, this will provide better utilisation of resources.
- As you have 5 data nodes, having 4 replica means shards are properly distributed on each data node, so your throughput and performance will be optimal.
After this change, measure the performance, and I am sure after this, you can again reduce the search queue size to 1k, as you know having high queue size is just delaying the problem and not addressing the issue at hand.
Coming to your search slow log, I feel you have very high threshold, for query phase 1 seconds for a query is really high for user-facing application, try to lower it ~100ms and not down those queries and optimize them further.
QUESTION
I have a elasticsearch cluster with 6 nodes. The heapsize is set as 50GB.(I know less than 32 is what is recommended but this was already set to 50Gb for some reason which I don't know). Now I am seeing a lot of rejections from search thread_pool.
...ANSWER
Answered 2020-Oct-26 at 08:10search thread_pool has an associated queue on which search worker threads work on and its default capacity is 1000(note its per node) which is consumed in your case, please refer thread_pools in ES for more info.
Now, queue capacity can be exhausted due to one of the below reasons:
- High search requests rate on your ES node, which your node might not be able to keep up with.
- Some slow queries which are taking a lot more time to process, and causing other requests to wait in the search queue, and ultimately leading to rejection.
- Your nodes and shards are properly balanced in your cluster, this can cause few nodes to receive a high number of search queries.
You should look at the search slow-logs to filter the costly queries during the issue and follow opster's tips to improve search speed.
And if its legitimate increase in search traffic, you should scale your cluster to handle more search requests.
Regarding young heap logs, if it's not taking more time and under 50-70ms its not that bigger concern, and you should focus on fixing the search rejection issue which might solve or lower the heap issue and in any case you should have Max heap size lower than 32 GB for better performance.
QUESTION
I am trying to port this line of code from Python 2.7/Pandas 0.17.0 to Python 3.7/Pandas 1.1.2:
python 2.7 / pandas 0.17.0
returnVar = pd.ewma(varSeries, span = varSpan)
python 3.7 / pandas 1.1.2.
returnVar = varSeries.ewm(span = varSpan)
In the legacy code, the return type is pandas.core.series.Series whereas in the migrated code the return type is pandas.core.window.ewm.ExponentialMovingWindow
How do I fix this so that I get the exact same return value and type?
python 2.7 / pandas 0.17.0
ANSWER
Answered 2020-Sep-30 at 17:53Thanks to Erfan.
The ported code should be:
QUESTION
I have to calculate an EWMA correlation and create a plot with plotly() of the data frame df.dataCorrelation, which is defined as:
ANSWER
Answered 2020-Sep-10 at 12:19if a plotly alternative is acceptable, this works
EDIT (ugly quickfix)
QUESTION
I am running an ES node on a 8 cores/16G RAM Qbox server. I am doing some indexing and search operations. indexing(max 5/second), search(max 1e4/second). My index has around 2M records an 1.2G of data. Search query takes around 300ms median. I am puzzled on why the server can not handle even such a low traffic.
Bellow is my mapping and the query I am sending: Mapping:
...ANSWER
Answered 2020-Aug-19 at 11:12You says :
Search query takes around 300ms median. I am puzzled on why the server can not handle even such a low traffic.
I guess you feel that 300ms a too long. It does not mean that the server don't handle traffic, just that query is a little slow.
At first how many shard your index have The goal for a shard size should be around 50Gb. so if you have more than 1 shard and 1 replica for this 1.2GB index, it's to much.
If you can plan a maintenance on this index, you should reindex with a proper number of shards and do a forcemerge. about your mapping, not a lot of change to do.
Usually, ids are used only for strict equality, so we used to map it as keyword, not integer (user_id, id, channel_id, address_id, etc...) but it will not really have a sensitive impact on perfs
If you use a recent Elasticsearch, and don't use score, when you reindex you can think about index sorting on order_at and created_at fields. It should help saving time.
https://www.elastic.co/guide/en/elasticsearch/reference/master/index-modules-index-sorting.html
On the query itself:
You should considere to use filters.
Filters don't influence score and are cachable at elastic and OS side.
In your use case, the whole index can stay in RAM. That can make a huge difference.
Your should have the same behaviour with something like this (must be tested):
QUESTION
I'm struggling to understand the behavior of the arguments in the below scan function. I understand the EWMA calc and have made an Excel worksheet to match in an attempt to try to understand but the kdb syntax is throwing me off in terms of what (and when) is x,y and z. I've referenced Q for Mortals, books and https://code.kx.com/q/ref/over/ and I do understand whats going on in the simpler examples provided.
I understand the EWMA formula based on the Excel calc but how is that translated into the function below?
x = constant, y= passed in values (but also appears to be prior result?) and z= (prev period?)
ANSWER
Answered 2020-Aug-19 at 05:410N! is useful in these cases for determining variables passed. Simply add to start of function to display variable in console. EG. to show what z is being passed in as each run:
QUESTION
I have a dataframe that looks like this:
...ANSWER
Answered 2020-Aug-10 at 13:41First, I created the data frame; changed Date to type datetime; and made Date the index:
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