Usage
Feeding a portfolio analytics one observation at a time
A portfolio analytics object can be feeded using
fit!function.It's feeded one observation at a time.
Showing sample data
Some sample data are provided for testing purpose.
julia> using OnlinePortfolioAnalytics
julia> using OnlinePortfolioAnalytics.SampleData: dates, TSLA, NFLX, MSFT, weights
julia> dates
Dates.Date("2020-12-31"):Dates.Month(1):Dates.Date("2021-12-31")
julia> TSLA
13-element Vector{Float64}:
235.22
264.51
225.16
222.64
236.48
208.4
226.56
229.06
245.24
258.49
371.33
381.58
352.26
julia> NFLX
13-element Vector{Float64}:
540.73
532.39
538.85
521.66
513.47
502.81
528.21
517.57
569.19
610.34
690.31
641.9
602.44
julia> MSFT
13-element Vector{Float64}:
222.42
231.96
232.38
235.77
252.18
249.68
270.9
284.91
301.88
281.92
331.62
330.59
336.32
julia> weights
3-element Vector{Float64}:
0.4
0.4
0.2Calculate returns (from TSLA prices)
julia> stat = SimpleAssetReturn{Float64}()
SimpleAssetReturn: n=0 | value=missing
julia> fit!(stat, 235.22)
SimpleAssetReturn: n=1 | value=missing
julia> fit!(stat, 264.51)
SimpleAssetReturn: n=2 | value=0.124522
julia> value(stat)
0.12452172434316806Calculate returns (natural log method)
julia> stat = LogAssetReturn{Float64}()
LogAssetReturn: n=0 | value=missing
julia> fit!(stat, 235.22)
LogAssetReturn: n=1 | value=missing
julia> fit!(stat, 264.51)
LogAssetReturn: n=2 | value=0.117358Others
See API usage
Portfolio analytics with Tables.jl compatible sources
Here is an example showing how to use OnlinePortfolioAnalytics with Tables.jl compatible sources.
Sample data
julia> using TSFrames
julia> prices_ts = TSFrame([TSLA NFLX MSFT], dates, colnames = [:TSLA, :NFLX, :MSFT])
13×3 TSFrame with Date Index
Index TSLA NFLX MSFT
Date Float64 Float64 Float64
───────────────────────────────────────
2020-12-31 235.22 540.73 222.42
2021-01-31 264.51 532.39 231.96
2021-02-28 225.16 538.85 232.38
2021-03-31 222.64 521.66 235.77
2021-04-30 236.48 513.47 252.18
2021-05-31 208.4 502.81 249.68
2021-06-30 226.56 528.21 270.9
2021-07-31 229.06 517.57 284.91
2021-08-31 245.24 569.19 301.88
2021-09-30 258.49 610.34 281.92
2021-10-31 371.33 690.31 331.62
2021-11-30 381.58 641.9 330.59
2021-12-31 352.26 602.44 336.3Returns
julia> returns = SimpleAssetReturn(prices_ts)
13×3 TSFrame with Date Index
Index TSLA MSFT NFLX
Date Float64? Float64? Float64?
────────────────────────────────────────────────────────────────
2020-12-31 missing missing missing
2021-01-31 0.124522 0.0428918 -0.0154236
2021-02-28 -0.148766 0.00181066 0.012134
2021-03-31 -0.011192 0.0145882 -0.0319013
2021-04-30 0.0621631 0.0696017 -0.0156999
2021-05-31 -0.118742 -0.00991355 -0.0207607
2021-06-30 0.0871401 0.0849888 0.0505161
2021-07-31 0.0110346 0.0517165 -0.0201435
2021-08-31 0.0706365 0.0595627 0.0997353
2021-09-30 0.0540287 -0.066119 0.0722957
2021-10-31 0.436535 0.176291 0.131025
2021-11-30 0.0276035 -0.00310596 -0.0701279
2021-12-31 -0.0768384 0.0173326 -0.0614737Remove missing from returns
julia> using DataFrames
julia> returns = dropmissing(returns.coredata) |> TSFrame
12×3 TSFrame with Date Index
Index TSLA MSFT NFLX
Date Float64 Float64 Float64
─────────────────────────────────────────────────
2021-01-31 0.124522 0.0428918 -0.0154236
2021-02-28 -0.148766 0.00181066 0.012134
2021-03-31 -0.011192 0.0145882 -0.0319013
2021-04-30 0.0621631 0.0696017 -0.0156999
2021-05-31 -0.118742 -0.00991355 -0.0207607
2021-06-30 0.0871401 0.0849888 0.0505161
2021-07-31 0.0110346 0.0517165 -0.0201435
2021-08-31 0.0706365 0.0595627 0.0997353
2021-09-30 0.0540287 -0.066119 0.0722957
2021-10-31 0.436535 0.176291 0.131025
2021-11-30 0.0276035 -0.00310596 -0.0701279
2021-12-31 -0.0768384 0.0173326 -0.0614737Calculate standard deviation of returns
julia> stddev = StdDev(returns)
12×3 TSFrame with Date Index
Index TSLA MSFT NFLX
Date Float64 Float64 Float64
─────────────────────────────────────────────
2021-01-31 1.0 1.0 1.0
2021-02-28 0.193243 0.0290488 0.0194861
2021-03-31 0.136645 0.0210239 0.0222487
2021-04-30 0.117541 0.0302594 0.0182741
2021-05-31 0.116225 0.0322773 0.016229
2021-06-30 0.11253 0.0381779 0.0301918
2021-07-31 0.102823 0.0354894 0.0282681
2021-08-31 0.0983386 0.0338512 0.0456038
2021-09-30 0.0931719 0.047328 0.0478437
2021-10-31 0.159765 0.064804 0.0582396
2021-11-30 0.15182 0.0630003 0.0624165
2021-12-31 0.149608 0.0603753 0.0637211Calculate arithmetic mean returns
julia> amr = ArithmeticMeanReturn(returns)
12×3 TSFrame with Date Index
Index TSLA MSFT NFLX
Date Float64 Float64 Float64
──────────────────────────────────────────────────
2021-01-31 0.124522 0.0428918 -0.0154236
2021-02-28 -0.012122 0.0223512 -0.00164482
2021-03-31 -0.011812 0.0197636 -0.0117303
2021-04-30 0.00668179 0.0322231 -0.0127227
2021-05-31 -0.0184029 0.0237958 -0.0143303
2021-06-30 -0.000812376 0.0339946 -0.00352257
2021-07-31 0.00088005 0.0365263 -0.00589699
2021-08-31 0.00959961 0.0394058 0.00730705
2021-09-30 0.0145362 0.0276809 0.014528
2021-10-31 0.0567361 0.0425419 0.0261777
2021-11-30 0.0540877 0.0383921 0.0174227
2021-12-31 0.0431772 0.0366371 0.010848Calculate geometric mean returns
julia> gmr = GeometricMeanReturn(returns)
12×3 TSFrame with Date Index
Index TSLA MSFT NFLX
Date Float64 Float64 Float64
─────────────────────────────────────────────────
2021-01-31 0.124522 0.0428918 -0.0154236
2021-02-28 -0.0216179 0.0221449 -0.0017399
2021-03-31 -0.0181549 0.0196197 -0.0118967
2021-04-30 0.00133649 0.0318919 -0.0128489
2021-05-31 -0.0239216 0.0233919 -0.0144363
2021-06-30 -0.0062324 0.0334097 -0.00389675
2021-07-31 -0.00378386 0.0360053 -0.0062341
2021-08-31 0.00522813 0.0389211 0.00643238
2021-09-30 0.0105369 0.0266895 0.013546
2021-10-31 0.0467153 0.0407506 0.0247227
2021-11-30 0.0449633 0.0366852 0.0157142
2021-12-31 0.0342267 0.0350585 0.00904634Calculate asset log returns from prices
julia> log_returns = LogAssetReturn(prices_ts)
13×3 TSFrame with Date Index
Index TSLA MSFT NFLX
Date Float64? Float64? Float64?
────────────────────────────────────────────────────────────────
2020-12-31 missing missing missing
2021-01-31 0.117358 0.0419975 -0.0155438
2021-02-28 -0.161068 0.00180902 0.0120609
2021-03-31 -0.0112551 0.0144828 -0.0324212
2021-04-30 0.0603075 0.0672864 -0.0158244
2021-05-31 -0.126404 -0.00996302 -0.0209792
2021-06-30 0.0835505 0.0815697 0.0492816
2021-07-31 0.0109742 0.0504236 -0.0203492
2021-08-31 0.0682533 0.0578562 0.0950695
2021-09-30 0.0526197 -0.0684062 0.0698019
2021-10-31 0.362234 0.162366 0.123125
2021-11-30 0.0272294 -0.0031108 -0.0727082
2021-12-31 -0.079951 0.0171842 -0.0634445Calculate cumulative returns
julia> cum_returns = CumulativeReturn(returns)
12×3 TSFrame with Date Index
Index TSLA MSFT NFLX
Date Float64 Float64 Float64
─────────────────────────────────────────
2021-01-31 1.12452 1.04289 0.984576
2021-02-28 0.957232 1.04478 0.996523
2021-03-31 0.946518 1.06002 0.964733
2021-04-30 1.00536 1.1338 0.949587
2021-05-31 0.885979 1.12256 0.929873
2021-06-30 0.963183 1.21797 0.976846
2021-07-31 0.973812 1.28095 0.957169
2021-08-31 1.0426 1.35725 1.05263
2021-09-30 1.09893 1.26751 1.12873
2021-10-31 1.57865 1.49096 1.27663
2021-11-30 1.62223 1.48633 1.1871
2021-12-31 1.49758 1.51209 1.11412Calculate Drawdowns
julia> dd = DrawDowns(returns)
12×3 TSFrame with Date Index
Index TSLA MSFT NFLX
Date Float64 Float64 Float64
─────────────────────────────────────────────────
2021-01-31 0.0 0.0 0.0
2021-02-28 -0.148766 0.0 0.0
2021-03-31 -0.158293 0.0 -0.0319013
2021-04-30 -0.10597 0.0 -0.0471003
2021-05-31 -0.212128 -0.00991355 -0.0668832
2021-06-30 -0.143473 0.0 -0.0197458
2021-07-31 -0.134021 0.0 -0.0394915
2021-08-31 -0.0728517 0.0 0.0
2021-09-30 -0.0227591 -0.066119 0.0
2021-10-31 0.0 0.0 0.0
2021-11-30 0.0 -0.00310596 -0.0701279
2021-12-31 -0.0768384 0.0 -0.127291Calculate Drawdowns (Arithmetic method)
julia> arith_dd = ArithmeticDrawDowns(returns)
12×3 TSFrame with Date Index
Index TSLA MSFT NFLX
Date Float64 Float64 Float64
─────────────────────────────────────────────────
2021-01-31 0.0 0.0 0.0
2021-02-28 -0.132292 0.0 0.0
2021-03-31 -0.142245 0.0 -0.0320066
2021-04-30 -0.0869655 0.0 -0.0477583
2021-05-31 -0.192558 -0.00878166 -0.0685875
2021-06-30 -0.115068 0.0 -0.0179047
2021-07-31 -0.105255 0.0 -0.0381146
2021-08-31 -0.0424401 0.0 0.0
2021-09-30 0.0 -0.0502712 0.0
2021-10-31 0.0 0.0 0.0
2021-11-30 0.0 -0.00217898 -0.0555787
2021-12-31 -0.0481756 0.0 -0.104299Calculate statistical moments of returns
julia> moments = AssetReturnMoments(returns)
12×3 TSFrame with Date Index
Index TSLA MSFT NFLX
Date Any Any Any
─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
2021-01-31 (mean = 0.124522, std = NaN, ske… (mean = 0.0428918, std = NaN, sk… (mean = -0.0154236, std = NaN, s…
2021-02-28 (mean = -0.012122, std = 0.19324… (mean = 0.0223512, std = 0.02904… (mean = -0.00164482, std = 0.019…
2021-03-31 (mean = -0.011812, std = 0.13664… (mean = 0.0197636, std = 0.02102… (mean = -0.0117303, std = 0.0222…
2021-04-30 (mean = 0.00668179, std = 0.1175… (mean = 0.0322231, std = 0.03025… (mean = -0.0127227, std = 0.0182…
2021-05-31 (mean = -0.0184029, std = 0.1162… (mean = 0.0237958, std = 0.03227… (mean = -0.0143303, std = 0.0162…
2021-06-30 (mean = -0.000812376, std = 0.11… (mean = 0.0339946, std = 0.03817… (mean = -0.00352257, std = 0.030…
2021-07-31 (mean = 0.00088005, std = 0.1028… (mean = 0.0365263, std = 0.03548… (mean = -0.00589699, std = 0.028…
2021-08-31 (mean = 0.00959961, std = 0.0983… (mean = 0.0394058, std = 0.03385… (mean = 0.00730705, std = 0.0456…
2021-09-30 (mean = 0.0145362, std = 0.09317… (mean = 0.0276809, std = 0.04732… (mean = 0.014528, std = 0.047843…
2021-10-31 (mean = 0.0567361, std = 0.15976… (mean = 0.0425419, std = 0.06480… (mean = 0.0261777, std = 0.05823…
2021-11-30 (mean = 0.0540877, std = 0.15182… (mean = 0.0383921, std = 0.06300… (mean = 0.0174227, std = 0.06241…
2021-12-31 (mean = 0.0431772, std = 0.14960… (mean = 0.0366371, std = 0.06037… (mean = 0.010848, std = 0.063721…Calculate Sharpe ratio (from returns)
julia> sharpe = Sharpe(returns, period = 1)
12×3 TSFrame with Date Index
Index TSLA MSFT NFLX
Date Float64 Float64 Float64
────────────────────────────────────────────────
2021-01-31 0.124522 0.0428918 -0.0154236
2021-02-28 -0.062729 0.769438 -0.0844096
2021-03-31 -0.086443 0.940052 -0.527235
2021-04-30 0.0568464 1.0649 -0.696214
2021-05-31 -0.158339 0.73723 -0.883007
2021-06-30 -0.00721917 0.890425 -0.116673
2021-07-31 0.00855887 1.02922 -0.208609
2021-08-31 0.0976179 1.16409 0.160229
2021-09-30 0.156015 0.584873 0.303656
2021-10-31 0.355123 0.65647 0.449484
2021-11-30 0.356261 0.609396 0.279136
2021-12-31 0.288602 0.606824 0.170242Calculate Sortino ratio (from returns)
julia> sortino = Sortino(returns)
12×3 TSFrame with Date Index
Index TSLA MSFT NFLX
Date Float64 Float64 Float64
───────────────────────────────────────────────
2021-01-31 1.97672 0.680887 -0.244842
2021-02-28 -0.19243 0.354815 -0.0261106
2021-03-31 -1.92754 0.313737 -15.9819
2021-04-30 1.09037 0.511526 -21.4069
2021-05-31 -4.03861 0.377746 -29.527
2021-06-30 -0.17828 0.539648 -7.25811
2021-07-31 0.193132 0.579837 -14.0099
2021-08-31 2.10669 0.625548 17.3599
2021-09-30 3.19005 11.0565 34.5153
2021-10-31 12.4511 16.9924 62.1925
2021-11-30 11.8698 17.6228 13.163
2021-12-31 11.4992 16.8173 7.56288