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# julia language 
using Plots
# n = degrees
n = 360
sin_out = fill(NaN,n)
cos_out = fill(NaN,n)
for i = 1:size(sin_out,1)
    # julia function - sind,cosd = sin/cos in degrees
    sin_out[i] = sind(i)
    cos_out[i] = cosd(i)

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"""
```
Relative strength index
rsi(x::Array{T}; n::Int64=14)::Array{Float64}
Indicator weights the average gain and loss correctly not averaging over 0 values. Accounts for NaN in the calculation
using NaNMath; nm=NaNMath
```
"""
function rsi(x::AbstractArray{T}; n::Int64=14)::Array{Float64} where {T<:Real}
    dims = size(x,1)

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// Implements a dictionary's functionality

#include <ctype.h>
#include <stdbool.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <strings.h>
#include <math.h>

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function collatz(n::Int64)::Int64
    while(n != 1)
        # base case 
        if n == 1
            break
        end
        # if even
        if (n % 2 == 0)
            n = Int64(n /2)
        elseif (n % 2 != 0)

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# import python modules
import os
import pandas as pd

# directory name from which
# we are going to extract our files with its size
path = "C:/Users/andre/Downloads/OneDrive_1_4-5-2022"

# Get list of all files only in the given directory
fun = lambda x: os.path.isfile(os.path.join(path, x))

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using FredApi, DataFrames, Dates, Statistics, Plots, TimeSeries

# load FRED oil data
key = "your_key"
set_api_key(key)

# full sample
crude_oil = get_symbols("WTISPLC")
price = values(crude_oil)

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using DataFrames, Plots, CSV, Dates, Statistics

# Load CDC NHCS covid weekly data
nhcs = CSV.read("C:/Users/andrew.bannerman/Desktop/Julia/scripts/Provisional_COVID-19_Deaths_by_Week__Sex__and_Age.csv", DataFrame, header=true, delim=",")

#rename columns
colnames = ["Data_as_of", "State", "MMWR_Week", "End_Week", "Sex", "Age_Group", "Total_Deaths", "COVID19_Deaths"]
rename!(nhcs, colnames)

# Subset data only for all sexes

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# GHCN data
using CSV
using DataFrames
using Dates
using Statistics
using Plots

# Data
# USCHN
# https://cdiac.ess-dive.lbl.gov/epubs/ndp/ushcn/ushcn.html#:~:text=The%20United%20States%20Historical%20Climatology%20Network%20%28USHCN%29%20is,observing%20stations%20across%20the%2048%20contiguous%20United%20States.

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# Data: https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc%3AC00861

######################################################################################################
# load .dly data file
# load as 1x string per row
# split the string per the readme .txt element / column position (static)
# export as .csv
######################################################################################################
# read all files in directory
all_dly_files = cd(readdir, "C:/Users/andrew.bannerman/Desktop/Julia/scripts/GHCN data/ghcnd_all/")

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using CSV, DataFrames, Plots

# load data
data = CSV.read("D:/Model Book/pattern recognition/data_store/MOXC.csv",header=true)

# Prepare data for OHLC plot
n = size(data,1)
high_p = Float64.(data.high)
low_p =  Float64.(data.low)
open_p = Float64.(data.open)