Julia 0.7

.travis.yml

@@ -3,7 +3,8 @@ language: julia
 os:
   - linux
 julia:
-  - 0.6
+  - 0.7
+  - 1.0
   - nightly
 matrix:
     allow_failures:

README.md

@@ -37,7 +37,8 @@ The key indicator used in this strategy is John Ehlers's MESA Adaptive Moving Av
 This strategy simply goes long when the *MAMA* crosses over the *FAMA*, and goes short when the *FAMA* crosses over the *MAMA*. Below is an implementation that shows how to set default arguments to the `Indicators.mama` function and run a simple backtest using those parameters, and also define specified ranges over which we might like to see how the strategy behaves under different parameter sets.
 
 ```julia
-using Strategems, Temporal, Indicators, Base.Dates
+using Strategems, Temporal, Indicators
+using Dates
 
 # define universe and gather data
 assets = ["CHRIS/CME_CL1", "CHRIS/CME_RB1"]

REQUIRE

@@ -1,3 +1,3 @@
-julia 0.6
+julia 0.7
 Temporal
 Indicators

examples/faber.jl

@@ -1,5 +1,6 @@
-using Strategems, Temporal, Indicators, Base.Dates
-using Base.Test
+using Strategems, Temporal, Indicators
+using Dates
+using Test
 
 # define universe and gather data
 assets = ["EOD/AAPL", "EOD/MCD", "EOD/JPM", "EOD/MMM", "EOD/XOM"]

examples/mama.jl

@@ -1,5 +1,6 @@
-using Strategems, Temporal, Indicators, Base.Dates
-using Base.Test
+using Strategems, Temporal, Indicators
+using Dates
+using Test
 
 # define universe and gather data
 assets = ["CHRIS/CME_CL1", "CHRIS/CME_RB1"]

src/Strategems.jl

@@ -1,7 +1,7 @@
 VERSION >= v"0.6-" && __precompile__(true)
 
 module Strategems
-using Base.Dates
+using Dates
 using Temporal
 using Indicators 
 

src/indicator.jl

@@ -12,7 +12,7 @@ function calculate(indicator::Indicator, input::TS)::TS
     return indicator.fun(input; generate_dict(indicator.paramset)...)
 end
 
-# function calculate!(indicator::Indicator, input::TS)::Void
+# function calculate!(indicator::Indicator, input::TS)::Nothing
 #     indicator.data = calculate(indicator, input)
 #     return nothing
 # end

src/order.jl

@@ -27,11 +27,11 @@ end
 #TODO: add logic whereby the order logic is altered by the type `T` of qty
 # (if T<:Int, order that many *shares*, else if T<:Float64, interpret qty as a fraction of the portfolio at time t)
 
-liquidate{T<:Real}(qty::T) = qty
+(liquidate(qty::T)::T) where {T<:Real} = qty
 
-long{T<:Real}(qty::T)::T = qty
-buy{T<:Real}(qty::T)::T = qty
+(long(qty::T)::T) where {T<:Real} = qty
+(buy(qty::T)::T) where {T<:Real} = qty
 
-short{T<:Real}(qty::T)::T = qty
-sell{T<:Real}(qty::T)::T = qty
+(short(qty::T)::T) where {T<:Real} = qty
+(sell(qty::T)::T) where {T<:Real} = qty
 

src/paramset.jl

@@ -27,7 +27,7 @@ function get_n_runs(ps::ParameterSet)::Int
 end
 
 function get_param_combos(ps::ParameterSet; n_runs::Int=get_n_runs(ps))::Matrix
-    combos = Matrix{Any}(n_runs, ps.n_args)
+    combos = Matrix{Any}(undef, n_runs, ps.n_args)
     P = 1
     for j in 1:ps.n_args
         n_vals = length(ps.arg_ranges[j])
@@ -38,7 +38,7 @@ function get_param_combos(ps::ParameterSet; n_runs::Int=get_n_runs(ps))::Matrix
             last_row = first_row + n_vals*step_by - 1
             rows = first_row:step_by:last_row
             arg_val = ps.arg_ranges[j][i]
-            combos[rows,j] = arg_val
+            combos[rows,j] .= arg_val
         end
         P *= n_vals
     end

src/results.jl

@@ -8,7 +8,7 @@ mutable struct Results
     optimization::Matrix{Float64}
     function Results(trades::Dict{String,TS}=Dict{String,TS}(),
                      backtest::Dict{String,TS{Float64}}=Dict{String,TS{Float64}}(),
-                     optimization::Matrix{Float64}=Matrix{Float64}(0,0))
+                     optimization::Matrix{Float64}=Matrix{Float64}(undef,0,0))
         return new(trades, backtest, optimization)
     end
 end

src/rule.jl

@@ -16,7 +16,7 @@ macro rule(logic::Expr, args...)
     trigger = :($(logic.args[2]))
     #action = :($(logic.args[3])$((args...)))
     action = :($(logic.args[3]))
-    args = :($(args...))
+    args = :($(args))
     return esc(:(Rule($trigger, $action, $args)))
 end
 

src/strategy.jl

@@ -1,3 +1,5 @@
+using Random
+
 #=
 Type definition and methods containing the overarching backtesting object fueling the engine
 =#
@@ -32,7 +34,7 @@ function generate_trades(strat::Strategy; verbose::Bool=true)::Dict{String,TS}
     return all_trades
 end
 
-function generate_trades!(strat::Strategy; args...)::Void
+function generate_trades!(strat::Strategy; args...)::Nothing
     strat.results.trades = generate_trades(strat; args...)
     return nothing
 end
@@ -81,7 +83,7 @@ function backtest(strat::Strategy; px_trade::Symbol=:Open, px_close::Symbol=:Set
     return result
 end
 
-function backtest!(strat::Strategy; args...)::Void
+function backtest!(strat::Strategy; args...)::Nothing
     strat.results.backtest = backtest(strat; args...)
     return nothing
 end
@@ -91,7 +93,7 @@ Base.copy(strat::Strategy) = Strategy(strat.universe, strat.indicator, strat.rul
 # define matrix row iterator protocol
 # this allows us to `enumerate(EachRow(M))`
 # thereby getting the count of the iteration as well as the row
-immutable EachRow{T<:AbstractMatrix}
+struct EachRow{T<:AbstractMatrix}
     A::T
 end
 Base.start(::EachRow) = 1
@@ -106,7 +108,7 @@ function optimize(strat::Strategy; samples::Int=0, seed::Int=0, verbose::Bool=tr
     n_runs = get_n_runs(strat.indicator.paramset)
     idx_samples::Vector{Int} = collect(1:n_runs)
     if samples > 0
-        srand(seed)
+        Random.seed!(seed)
         idx_samples = rand(idx_samples, samples)
     else
         samples = n_runs
@@ -126,12 +128,12 @@ function optimize(strat::Strategy; samples::Int=0, seed::Int=0, verbose::Bool=tr
 end
 
 # TODO: implement function to edit results member of strat in place
-function optimize!(strat::Strategy; samples::Int=0, seed::Int=0, verbose::Bool=true, summary_fun::Function=cum_pnl, args...)::Void
+function optimize!(strat::Strategy; samples::Int=0, seed::Int=0, verbose::Bool=true, summary_fun::Function=cum_pnl, args...)::Nothing
     n_runs = get_n_runs(strat.indicator.paramset)
     idx_samples::Vector{Int} = collect(1:n_runs)
     if samples > 0
         if seed >= 0
-            srand(seed)
+            Random.seed!(seed)
         end
         idx_samples = rand(idx_samples, samples)
     else

src/universe.jl

@@ -16,7 +16,7 @@ mutable struct Universe
     data::Dict{String,TS}
     from::TimeType
     thru::TimeType
-    function Universe(assets::Vector{String}, from::TimeType=Base.Dates.Date(0), thru::TimeType=Base.Dates.today())
+    function Universe(assets::Vector{String}, from::TimeType=Dates.Date(0), thru::TimeType=Dates.today())
         @assert assets == unique(assets)
         # tickers = guess_tickers(assets)
         data = Dict{String,TS}()
@@ -28,9 +28,9 @@ mutable struct Universe
 end
 
 #TODO: ensure type compatibility across variables (specifically with regard to TimeTypes)
-function gather!(universe::Universe; source::Function=Temporal.quandl, verbose::Bool=true)::Void
-    t0 = Vector{Base.Dates.Date}()
-    tN = Vector{Base.Dates.Date}()
+function gather!(universe::Universe; source::Function=Temporal.quandl, verbose::Bool=true)::Nothing
+    t0 = Vector{Dates.Date}()
+    tN = Vector{Dates.Date}()
     @inbounds for asset in universe.assets
         verbose ? print("Sourcing data for asset $asset...") : nothing
         indata = source(asset)

test/runtests.jl

@@ -1,5 +1,7 @@
-using Strategems, Temporal, Indicators, Base.Dates
-using Base.Test
+using Strategems, Temporal, Indicators
+using Dates
+using Test
+using Pkg
 
 # define universe and gather data
 assets = ["Corn"]
@@ -22,7 +24,7 @@ assets = ["Corn"]
         #     end
         # end
         # gather!(universe, source=datasource)
-        gather!(universe, source=(asset)->Temporal.tsread("$(Pkg.dir("Temporal"))/data/$asset.csv"))
+        gather!(universe, source=(asset)->Temporal.tsread(joinpath(dirname(pathof(Temporal)), "..", "data/$asset.csv")))
         @test length(setdiff(assets, collect(keys(universe.data)))) == 0
     end
 end