kevincdurand1

$FolderPath = Get-ChildItem -Directory -Path "C:\temp\" -Recurse -Force
$Output = @()
ForEach ($Folder in $FolderPath) {
    $Acl = Get-Acl -Path $Folder.FullName
    ForEach ($Access in $Acl.Access) {
$Properties = [ordered]@{'Folder Name'=$Folder.FullName;'Group/User'=$Access.IdentityReference;'Permissions'=$Access.FileSystemRights;'Inherited'=$Access.IsInherited}
$Output += New-Object -TypeName PSObject -Property $Properties            
}
}
$Output | Out-GridView

kevincdurand1

# https://www.kaggle.com/dimitreoliveira/deep-learning-for-time-series-forecasting

def series_to_supervised(data, window=1, lag=1, dropnan=True):
    cols, names = list(), list()
    # Input sequence (t-n, ... t-1)
    for i in range(window, 0, -1):
        cols.append(data.shift(i))
        names += [('%s(t-%d)' % (col, i)) for col in data.columns]
    # Current timestep (t=0)
    cols.append(data)

kevincdurand1

// http://channel9.msdn.com/Events/Build/2013/2-401
// http://www.quaetrix.com/Build2013.html

using System;

// For 2013 Microsoft Build Conference attendees
// June 25-28, 2013
// San Francisco, CA
//
// This is source for a C# console application.

kevincdurand1

from pandas import DataFrame
from pandas import concat
 
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
	"""
	Frame a time series as a supervised learning dataset.
	Arguments:
		data: Sequence of observations as a list or NumPy array.
		n_in: Number of lag observations as input (X).
		n_out: Number of observations as output (y).

kevincdurand1

from pandas import Series
from statsmodels.tsa.stattools import adfuller
def test_stationarity(timeseries, windowroll = 12, cutoff = 0.05):    
    #Determing rolling statistics
    rolmean = pd.rolling_mean(timeseries, window=windowroll)
    rolstd = pd.rolling_std(timeseries, window=windowroll)
    #Plot rolling statistics:
    orig = plt.plot(timeseries, color='blue',label='Original')
    mean = plt.plot(rolmean, color='red', label='Rolling Mean')
    std = plt.plot(rolstd, color='black', label = 'Rolling Std')

kevincdurand1

  	 SELECT [DateTime], [Close]
,(log([Close]) - log(LAG ([Close],1) OVER (ORDER BY [DateTime])))*100 AS LogReturn
  FROM [EURUSD_Daily_198001020000_201805180000_Quant]

kevincdurand1

 SELECT [DateTime]
,([Close] - LAG ([Close],1) OVER (ORDER BY [DateTime]))/LAG ([Close],1) OVER (ORDER BY [DateTime])*100 AS pct_change_close_1
,([Close] - LAG ([Close],2) OVER (ORDER BY [DateTime]))/LAG ([Close],2) OVER (ORDER BY [DateTime])*100 AS pct_change_close_2
,([Close] - LAG ([Close],3) OVER (ORDER BY [DateTime]))/LAG ([Close],3) OVER (ORDER BY [DateTime])*100 AS pct_change_close_3
,([Close] - LAG ([Close],4) OVER (ORDER BY [DateTime]))/LAG ([Close],4) OVER (ORDER BY [DateTime])*100 AS pct_change_close_4
,([Close] - LAG ([Close],5) OVER (ORDER BY [DateTime]))/LAG ([Close],5) OVER (ORDER BY [DateTime])*100 AS pct_change_close_5
,([Close] - LAG ([Close],6) OVER (ORDER BY [DateTime]))/LAG ([Close],6) OVER (ORDER BY [DateTime])*100 AS pct_change_close_6
,([Close] - LAG ([Close],7) OVER (ORDER BY [DateTime]))/LAG ([Close],7) OVER (ORDER BY [DateTime])*100 AS pct_change_close_7
,([Close] - LAG ([Close],8) OVER (ORDER BY [DateTime]))/LAG ([Close],8) OVER (ORDER BY [DateTime])*100 AS pct_change_close_8
,([Cl

kevincdurand1

from flask import *
app = Flask(__name__)
@app.route('/post', methods = ['POST'])
def post():
    json = request.json
    encoded = request.form
    cp1_ = (encoded.get("cp1"))
    cp2_ = (encoded.get("cp2"))
    cp3_ = (encoded.get("cp3"))
    cp4_ = (encoded.get("cp4"))

kevincdurand1

 SELECT [DateTime],[Close]
,([Close] - LAG ([Close],1) OVER (ORDER BY [DateTime]))/LAG ([Close],1) OVER (ORDER BY [DateTime])*100 AS PCT_CHANGE_CLOSE
  FROM [dbsec].[dbo].[EURUSDM1FiboMA];

kevincdurand1

select * 
,CASE WHEN ROW_NUMBER() OVER (ORDER BY [Datetime]) > 1 THEN SUM([Close]) OVER (ORDER BY [Datetime] ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) END/2 as MA2
,CASE WHEN ROW_NUMBER() OVER (ORDER BY [Datetime]) > 2 THEN SUM([Close]) OVER (ORDER BY [Datetime] ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) END/3 as MA3
,CASE WHEN ROW_NUMBER() OVER (ORDER BY [Datetime]) > 4 THEN SUM([Close]) OVER (ORDER BY [Datetime] ROWS BETWEEN 4 PRECEDING AND CURRENT ROW) END/4 as MA5
,CASE WHEN ROW_NUMBER() OVER (ORDER BY [Datetime]) > 7 THEN SUM([Close]) OVER (ORDER BY [Datetime] ROWS BETWEEN 7 PRECEDING AND CURRENT ROW) END/8 as MA8
,CASE WHEN ROW_NUMBER() OVER (ORDER BY [Datetime]) > 12 THEN SUM([Close]) OVER (ORDER BY [Datetime] ROWS BETWEEN 12 PRECEDING AND CURRENT ROW) END/13 as MA13
,CASE WHEN ROW_NUMBER() OVER (ORDER BY [Datetime]) > 20 THEN SUM([Close]) OVER (ORDER BY [Datetime] ROWS BETWEEN 20 PRECEDING AND CURRENT ROW) END/21 as MA21
,CASE WHEN ROW_NUMBER() OVER (ORDER BY [Datetime]) > 33 THEN SUM([Close]) OVER (OR