freedomtowin

def is_psd(A):
    if np.array_equal(A, A.T):
        try:
            np.linalg.cholesky(A)
            return True
        except np.linalg.LinAlgError:
            return False
    else:
        return False

freedomtowin

#Correlation
import plotly
import plotly.subplots
corr_combos = ['corr_sym1=atvi_sym2=dash',
'corr_sym1=dash_sym2=dis', 'corr_sym1=dash_sym2=nvda',
'corr_sym1=dash_sym2=wmt']

# Construct a 2 x 1 Plotly figure
fig = plotly.subplots.make_subplots(rows=len(corr_combos), cols=1)
# price Line

freedomtowin

def getEMA(df,M,K,col,out):
    alpha=2./(1.+M)
    df_a = df.select(['sym',
                      'ranked_indx',
                      'time'
                      ]).alias('a').withColumnRenamed("ranked_indx", "ranked_indx_a") \
                                                             .withColumnRenamed("time", "time_a")
    df_b = df.select(['sym',
                      'ranked_indx',
                      'time',

freedomtowin

def getSMABands(df,M,K):

    df_a = df.select(['sym',
                      'ranked_indx',
                      'time']).alias('a').withColumnRenamed("ranked_indx", "ranked_indx_a") \
                                                             .withColumnRenamed("time", "time_a")
    df_b = df.select(['sym',
                      'ranked_indx',
                      'time',
                      'close',

freedomtowin

import itertools

def getCorr(df,M,sym1,sym2):
    stg=df.filter(sf.col(sym1).isNotNull()&sf.col(sym2).isNotNull())
    windowTime = Window.orderBy("time")
    stg=stg.withColumn("ranked_indx",sf.row_number().over(windowTime))
    updatedWindowIndx = Window.orderBy("ranked_indx").rangeBetween(-M+1,0)
    
    stg = stg.withColumn('corr_sym1={}_sym2={}'.format(sym1,sym2),
                                     sf.corr(sf.col(sym2),sf.col(sym1)).over(updatedWindowIndx)

freedomtowin

import boto3
import pyspark as pyspark
from pyspark import SparkContext
from pyspark.sql.session import SparkSession
import os
#you might need to set these env vars
os.environ['PYSPARK_DRIVER_PYTHON']='jupyter'
os.environ['PYSPARK_DRIVER_PYTHON_OPTS']='notebook'
os.environ['PYSPARK_PYTHON']='python'
#create a user

freedomtowin

import boto3

client = boto3.client('ecs')

cluster_name = ''
task_definition = ''

def lambda_handler(event, context):
    

freedomtowin

import json
import base64
import copy
def lambda_handler(event, context):
    output = []

    for record in event['records']:

        # Decode from base64 (Firehose records are base64 encoded)
        payload = base64.b64decode(record['data'])

freedomtowin

import sqlite3 as sq
import pandas as pd

sql_data = "3d0d7e5fb2ce288813306e4d4636395e047a3d28" #- Creates DB names SQLite
conn = sq.connect(sql_data)
cur = conn.cursor()


qry = """select datetime(message.date/1000000000 + strftime('%s','2001-01-01') ,'unixepoch','localtime') as date,
         case when is_from_me=0 then 'Friend' else 'Me' end as name,  text

freedomtowin

@tf.function
def MSE(ytrue,ypred):
    loss = tf.reduce_mean(tf.square(ypred - ytrue))
    return loss


class ARMODEL(tf.keras.Model):
    def __init__(self,shape):
        super(ARMODEL,self).__init__(name='armodel')