Pavel Paramonov multidis

## julia_short_if.jl
# instead of
if <cond> <statement> end
# could use
<cond> && <statement>
# which could be read as: must have <cond> to do <statement>

# similarly
if ! <cond> <statement> end
<cond> || <statement>
# read: either <cond> is met or do <statement> (e.g. throw error)

## julia_symcas.jl
## first verify python-sympy Ubuntu package is installed,
##  and Pkg.add("SymPy") in Julia
using Gadfly # before SymPy to have automatic plotting as below
using SymPy

# declare symbols: one of several ways
#  see https://github.com/jverzani/SymPy.jl
x, a, b, c = @syms x a b c
typeof(x)
f(x) = a*exp(-(x - b)^2/c)

## juliarc.jl
# if using custom places for keeping Julia modules
push!(LOAD_PATH, "/home/path/tothatplace")

# defining new env variables
ENV["NEWVAR_NAME"] = "/home/path/newvar"

## qcqp_convex.jl
## QCQP problem in a convex sub-domain

a = 77.5625
F = [   351.0     26255.2          -6647.07         1282.81
 26255.2         2.23819e6  -780203.0         80689.2
 -6647.07  -780203.0              1.25858e6  151221.0
  1282.81    80689.2         151221.0        979604.0]
V = [375.77,11564.5,18360.6,3891.82]

# unconstrained min

## qcqp_jump.jl
## QCQP problem in a convex sub-domain

a = 77.5625
F = [   351.0     26255.2          -6647.07         1282.81
 26255.2         2.23819e6  -780203.0         80689.2
 -6647.07  -780203.0              1.25858e6  151221.0
  1282.81    80689.2         151221.0        979604.0]
V = [375.77,11564.5,18360.6,3891.82]

# unconstrained min

## julia_to_R_RCall.jl
## RCall data exchange between Julia and R
using DataFrames
using RCall

# example DataFrame
df = DataFrame(coln1 = 1:4, coln2 = ["M", "F", "F", "M"], coln3 = [false, true, false, true], coln4 = int8([1, 0, 1, 1]))

# R environment in a session started from Julia
g = globalEnv
reval(rparse("dfls <- NULL"))

## 00_tuftehand_quantmod.md

      
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              Last active
              August 29, 2015 14:14
            
              
                Tufte-style handouts with knitr: quantmod chart options.
              
          
    Tufte handouts

Tufterhandout package. Knit to html in RStudio, initial example.
Problem

Some charts in quantmod draw multiple chunks. Knitr returns error when it can not find all of the corresponding files. This is of course a wider problem than quantmod.
Solution


## julia_reactive.jl
## http://julialang.org/Reactive.jl/

using Reactive

x = Input(0)
xsquared = lift(a -> a*a, Int, x)
push!(x,5)
xsquared.value

a = Input(randn(25,3))

## reactive_sigdiff.jl
## signal difference
using Reactive

s = Input(0.0)

function difference(prev, x)
    prev_diff, prev_val = prev
    # x becomes prev_val in the next call
    return (x-prev_val, x)
end

## reactive_concurrency_async.jl
## concurrent signal updates
using Reactive

# trading system case: target and actual position
pact = Input(0)
buy = Input(false)

fptarg(b::Bool) = b ? 10 : 0
ptarg = lift(fptarg, buy)
	# instead of
	if <cond> <statement> end
	# could use
	<cond> && <statement>
	# which could be read as: must have <cond> to do <statement>

	# similarly
	if ! <cond> <statement> end
	<cond> \|\| <statement>
	# read: either <cond> is met or do <statement> (e.g. throw error)
	## first verify python-sympy Ubuntu package is installed,
	## and Pkg.add("SymPy") in Julia
	using Gadfly # before SymPy to have automatic plotting as below
	using SymPy

	# declare symbols: one of several ways
	# see https://github.com/jverzani/SymPy.jl
	x, a, b, c = @syms x a b c
	typeof(x)
	f(x) = a*exp(-(x - b)^2/c)
	# if using custom places for keeping Julia modules
	push!(LOAD_PATH, "/home/path/tothatplace")

	# defining new env variables
	ENV["NEWVAR_NAME"] = "/home/path/newvar"
	## QCQP problem in a convex sub-domain

	a = 77.5625
	F = [ 351.0 26255.2 -6647.07 1282.81
	26255.2 2.23819e6 -780203.0 80689.2
	-6647.07 -780203.0 1.25858e6 151221.0
	1282.81 80689.2 151221.0 979604.0]
	V = [375.77,11564.5,18360.6,3891.82]

	# unconstrained min
	## RCall data exchange between Julia and R
	using DataFrames
	using RCall

	# example DataFrame
	df = DataFrame(coln1 = 1:4, coln2 = ["M", "F", "F", "M"], coln3 = [false, true, false, true], coln4 = int8([1, 0, 1, 1]))

	# R environment in a session started from Julia
	g = globalEnv
	reval(rparse("dfls <- NULL"))
	## http://julialang.org/Reactive.jl/

	using Reactive

	x = Input(0)
	xsquared = lift(a -> a*a, Int, x)
	push!(x,5)
	xsquared.value

	a = Input(randn(25,3))
	## signal difference
	using Reactive

	s = Input(0.0)

	function difference(prev, x)
	prev_diff, prev_val = prev
	# x becomes prev_val in the next call
	return (x-prev_val, x)
	end
	## concurrent signal updates
	using Reactive

	# trading system case: target and actual position
	pact = Input(0)
	buy = Input(false)

	fptarg(b::Bool) = b ? 10 : 0
	ptarg = lift(fptarg, buy)