Learn more about OnlineTechnicalIndicators usage
Feeding a technical analysis indicator one observation at a time
A technical indicator can be feeded using
fit!function.It's feeded one observation at a time.
Showing sample data (close prices)
Some sample data are provided for testing purpose.
julia> using OnlineTechnicalIndicators
julia> using OnlineTechnicalIndicators.SampleData: CLOSE_TMPL, V_OHLCV
julia> CLOSE_TMPL
50-element Vector{Float64}:
10.5
9.78
10.46
10.51
⋮
10.15
10.3
10.59
10.23
10.0Calculate SMA (simple moving average)
julia> ind = SMA{Float64}(period = 3) # this is a SISO (single input / single output) indicator
SMA: n=0 | value=missing
julia> for p in CLOSE_TMPL
fit!(ind, p)
println(value(ind))
end
missing
missing
10.246666666666668
10.250000000000002
10.50666666666667
10.593333333333335
10.476666666666668
⋮
9.283333333333339
9.886666666666672
10.346666666666671
10.373333333333338
10.273333333333339Calculate BB (Bollinger bands)
julia> ind = BB{Float64}(period = 3) # this is a SIMO (single input / multiple output) indicator
for p in CLOSE_TMPL
fit!(ind, p)
println(value(ind))
end
missing
missing
OnlineTechnicalIndicators.BBVal{Float64}(9.585892709687261, 10.246666666666668, 10.907440623646075)
OnlineTechnicalIndicators.BBVal{Float64}(9.584067070444279, 10.250000000000002, 10.915932929555725)
OnlineTechnicalIndicators.BBVal{Float64}(10.433030926552087, 10.50666666666667, 10.580302406781252)
⋮
OnlineTechnicalIndicators.BBVal{Float64}(7.923987085233826, 9.283333333333339, 10.642679581432851)
OnlineTechnicalIndicators.BBVal{Float64}(8.921909932792502, 9.886666666666672, 10.851423400540842)
OnlineTechnicalIndicators.BBVal{Float64}(9.981396599151932, 10.346666666666671, 10.71193673418141)
OnlineTechnicalIndicators.BBVal{Float64}(10.061635473931714, 10.373333333333338, 10.685031192734963)
OnlineTechnicalIndicators.BBVal{Float64}(9.787718030627357, 10.273333333333339, 10.758948636039321)Showing sample data (OHLCV data)
julia> V_OHLCV # fields are open/high/low/close/volume/time
50-element Vector{OHLCV{Missing, Float64, Float64}}:
OHLCV{Missing, Float64, Float64}(10.81, 11.02, 9.9, 10.5, 55.03, missing)
OHLCV{Missing, Float64, Float64}(10.58, 10.74, 9.78, 9.78, 117.86, missing)
OHLCV{Missing, Float64, Float64}(10.07, 10.65, 9.5, 10.46, 301.04, missing)
OHLCV{Missing, Float64, Float64}(10.58, 11.05, 10.47, 10.51, 157.94, missing)
⋮
OHLCV{Missing, Float64, Float64}(9.3, 10.5, 9.26, 10.15, 255.3, missing)
OHLCV{Missing, Float64, Float64}(10.23, 10.3, 10.0, 10.3, 111.55, missing)
OHLCV{Missing, Float64, Float64}(10.29, 10.86, 10.19, 10.59, 108.27, missing)
OHLCV{Missing, Float64, Float64}(10.77, 10.77, 10.15, 10.23, 48.29, missing)
OHLCV{Missing, Float64, Float64}(10.28, 10.39, 9.62, 10.0, 81.66, missing)Calculate ATR (Average true range)
julia> ind = ATR{OHLCV}(period = 3) # this is a MISO (multi input / single output) indicator
ATR: n=0 | value=missing
julia> for candle in V_OHLCV
fit!(ind, candle)
println(value(ind))
end
missing
missing
1.0766666666666669
0.9144444444444445
0.7562962962962961
⋮
0.898122497312842
0.6987483315418949
0.6891655543612633
0.6661103695741752
0.700740246382784Calculate Stoch (Stochastic)
julia> ind = Stoch{OHLCV{Missing,Float64,Float64}}(period = 3) # this is a MIMO indicator
Stoch: n=0 | value=missing
julia> for candle in V_OHLCV
fit!(ind, candle)
println(value(ind))
end
OnlineTechnicalIndicators.StochVal{Float64}(53.57142857142858, missing)
OnlineTechnicalIndicators.StochVal{Float64}(0.0, missing)
OnlineTechnicalIndicators.StochVal{Float64}(63.15789473684218, 38.90977443609025)
OnlineTechnicalIndicators.StochVal{Float64}(65.1612903225806, 42.77306168647426)
OnlineTechnicalIndicators.StochVal{Float64}(67.74193548387099, 65.35370684776458)
⋮
OnlineTechnicalIndicators.StochVal{Float64}(83.17307692307695, 54.98661936768733)
OnlineTechnicalIndicators.StochVal{Float64}(90.38461538461543, 83.17307692307693)
OnlineTechnicalIndicators.StochVal{Float64}(83.12500000000001, 85.56089743589745)
OnlineTechnicalIndicators.StochVal{Float64}(26.744186046511697, 66.75126714370903)
OnlineTechnicalIndicators.StochVal{Float64}(30.645161290322637, 46.83811577894477)Feeding a technical analysis indicator with a compatible Tables.jl table such as TSFrame
A technical analysis indicator can also accept a Tables.jl compatible table (TSFrame) as input parameter.
Showing sample data (OHLCV data)
julia> using MarketData
julia> using TSFrames
julia> using Random
julia> Random.seed!(1234) # to have reproductible results (so won't be really random)
julia> ta = random_ohlcv() # should return a TimeSeries.TimeArray
julia> ts = TSFrame(ta) # converts a TimeSeries.TimeArray to TSFrames.TSFrame
500×5 TSFrame with DateTime Index
Index Open High Low Close Volume
DateTime Float64 Float64 Float64 Float64 Float64
──────────────────────────────────────────────────────────────────
2020-01-01T00:00:00 326.75 334.03 326.18 333.16 83.6
2020-01-01T01:00:00 333.29 334.6 330.01 330.3 45.9
2020-01-01T02:00:00 330.79 336.7 329.99 334.0 71.2
2020-01-01T03:00:00 334.83 339.79 334.83 338.39 97.1
2020-01-01T04:00:00 338.36 339.09 331.22 331.22 79.1
⋮ ⋮ ⋮ ⋮ ⋮ ⋮
2020-01-21T15:00:00 353.2 360.62 349.99 358.86 59.0
2020-01-21T16:00:00 358.81 364.03 354.5 364.03 4.2
2020-01-21T17:00:00 363.06 367.52 362.31 362.31 90.0
2020-01-21T18:00:00 362.03 364.81 360.4 363.3 45.6
2020-01-21T19:00:00 362.35 363.23 358.28 361.52 19.8Simple Moving Average (SMA) of close prices
julia> SMA(ts; period = 3)
500×1 TSFrame with DateTime Index
Index SMA
DateTime Float64?
──────────────────────────────────
2020-01-01T00:00:00 missing
2020-01-01T01:00:00 missing
2020-01-01T02:00:00 332.487
2020-01-01T03:00:00 334.23
2020-01-01T04:00:00 334.537
⋮ ⋮
2020-01-21T15:00:00 352.087
2020-01-21T16:00:00 358.41
2020-01-21T17:00:00 361.733
2020-01-21T18:00:00 363.213
2020-01-21T19:00:00 362.377Simple Moving Average (SMA) of open prices
julia> SMA(ts; period = 3, default = :Open)
500×1 TSFrame with DateTime Index
Index SMA
DateTime Float64?
──────────────────────────────────
2020-01-01T00:00:00 missing
2020-01-01T01:00:00 missing
2020-01-01T02:00:00 330.277
2020-01-01T03:00:00 332.97
2020-01-01T04:00:00 334.66
⋮ ⋮
2020-01-21T15:00:00 346.72
2020-01-21T16:00:00 352.293
2020-01-21T17:00:00 358.357
2020-01-21T18:00:00 361.3
2020-01-21T19:00:00 362.48Calculate BB (Bollinger bands)
julia> BB(ts; period = 3)
500×3 TSFrame with DateTime Index
Index BB_lower BB_central BB_upper
DateTime Float64? Float64? Float64?
────────────────────────────────────────────────────────────
2020-01-01T00:00:00 missing missing missing
2020-01-01T01:00:00 missing missing missing
2020-01-01T02:00:00 329.319 332.487 335.654
2020-01-01T03:00:00 327.617 334.23 340.843
2020-01-01T04:00:00 328.633 334.537 340.44
⋮ ⋮ ⋮ ⋮
2020-01-21T15:00:00 340.813 352.087 363.36
2020-01-21T16:00:00 348.844 358.41 367.976
2020-01-21T17:00:00 357.434 361.733 366.033
2020-01-21T18:00:00 361.804 363.213 364.623
2020-01-21T19:00:00 360.92 362.377 363.833Calculate ATR (Average true range)
julia> ATR(ts; period = 3)
500×1 TSFrame with DateTime Index
Index ATR
DateTime Float64?
────────────────────────────────────
2020-01-01T00:00:00 missing
2020-01-01T01:00:00 missing
2020-01-01T02:00:00 6.38333
2020-01-01T03:00:00 6.18556
2020-01-01T04:00:00 6.74704
⋮ ⋮
2020-01-21T15:00:00 8.53068
2020-01-21T16:00:00 8.86378
2020-01-21T17:00:00 7.64586
2020-01-21T18:00:00 6.56724
2020-01-21T19:00:00 6.05149Calculate Stoch (Stochastic)
julia> Stoch(ts; period = 3)
500×2 TSFrame with DateTime Index
Index Stoch_k Stoch_d
DateTime Float64 Float64?
───────────────────────────────────────────────
2020-01-01T00:00:00 88.9172 missing
2020-01-01T01:00:00 48.9311 missing
2020-01-01T02:00:00 74.3346 70.7276
2020-01-01T03:00:00 85.7143 69.66
2020-01-01T04:00:00 12.551 57.5333
⋮ ⋮ ⋮
2020-01-21T15:00:00 91.4272 93.9504
2020-01-21T16:00:00 100.0 97.1424
2020-01-21T17:00:00 70.2795 87.2356
2020-01-21T18:00:00 67.5883 79.2893
2020-01-21T19:00:00 35.0649 57.6443