A backtest of the golden cross

goldencross.jl

# this is the data set that will be used
# note: Cl is simply OHLC["Close"]

julia> OHLC
16103x6 TimeArray{Float64,2} 1950-01-03 to 2013-12-31

             Open     High     Low      Close    Volume          Adj Close
1950-01-03 | 16.66    16.66    16.66    16.66    1.26e6          16.66
1950-01-04 | 16.85    16.85    16.85    16.85    1.89e6          16.85
1950-01-05 | 16.93    16.93    16.93    16.93    2.55e6          16.93
1950-01-06 | 16.98    16.98    16.98    16.98    2.01e6          16.98
⋮
2013-12-26 | 1834.96  1842.84  1834.96  1842.02  1.98227e9       1842.02
2013-12-27 | 1842.97  1844.89  1839.81  1841.4   2.05292e9       1841.4
2013-12-30 | 1841.47  1842.47  1838.77  1841.07  0.0             1841.07
2013-12-31 | 1842.61  1849.44  1842.41  1848.36  2.31284e9       1848.36

# Generating a blotter in three lines of code

julia> signal = (sma(Cl,50) .> sma(Cl,200));

julia> spx    = FinancialTimeSeries(OHLC, "SPX");

julia> goldencross = Blotter(signal, spx)
65x2 Blotter 1950-10-19T23:59:59 UTC to 2012-02-01T23:59:59 UTC

                          Qty     Fill
1950-10-19T23:59:59 UTC | 100     20.02
1953-05-12T23:59:59 UTC | -100    24.74
1953-12-22T23:59:59 UTC | 100     24.76
1956-10-29T23:59:59 UTC | -100    46.4
⋮
2010-07-06T23:59:59 UTC | -100    1028.09
2010-10-25T23:59:59 UTC | 100     1184.74
2011-08-15T23:59:59 UTC | -100    1178.86
2012-02-01T23:59:59 UTC | 100     1312.45

# trade statistics are a breeze ....

julia> trades = tradearray(goldencross, spx);

julia> pnls = float([(t.close-t.open) for t in trades]).*100;

julia> pos = pnls[pnls.>0];

julia> neg = pnls[pnls.<0];

julia> profitfactor = sum(pos)/abs(sum(neg))
27.427328391878795 

Better to generalize pnls

float([(t.close-t.open)*t.quantity for t in trades])