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import datetime
import pandas as pd
from ib_insync import *

pd.set_option('display.max_rows', None)

ib = IB()
ib.connect('127.0.0.1', 7496, clientId=15)

contract = Stock('SPY', 'SMART', 'USD')

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"""
A bare bones examples of optimizing a black-box function (f) using
Natural Evolution Strategies (NES), where the parameter distribution is a 
gaussian of fixed standard deviation.
"""

import numpy as np
np.random.seed(0)

# the function we want to optimize

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pragma solidity >=0.4.22 <0.7.0;

/**
 * @title Storage
 * @dev Store & retreive value in a variable
 */
contract Storage {

    uint256 number;

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Machine Learning and AI for Finance

Advances in Financial Machine Learning, de Prado: http://www.quantresearch.org/
Machine Learning for Asset Managers, de Prado
Machine Learning for Factor Investing, Guida: http://www.mlfactor.com/
Big Data and Machine Learning in Quantitative Investment, Guida
Artificial Intelligence in Finance, Hilpisch: https://home.tpq.io/

Trading Analytics and Algorithms

Python for Finance 2d ed, Hilpisch
Derivatives Analytics with Python, Hilpisch

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#CODE

#Generate root password
import random, string
password = ''.join(random.choice(string.ascii_letters + string.digits) for i in range(20))

#Download ngrok
! wget -q -c -nc https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
! unzip -qq -n ngrok-stable-linux-amd64.zip
#Setup sshd

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### Kaggle Days SF: Hackathon submission (8th place) 

# I used the latest version of H2O (3.24.0.1)
# Latest stable always here: http://h2o-release.s3.amazonaws.com/h2o/latest_stable.html
# H2O 3.24.0.1: http://h2o-release.s3.amazonaws.com/h2o/rel-yates/1/index.html
# If you are a Python user, you can use the demo Python code available on the H2O AutoML User Guide 
# instead: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html

# Unfortunately it was a private competition, so the data is not publicly available! 

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#Background

For each type of analysis think about:

• What problem does it solve, and for whom?
• How is it being solved today?
• How can it beneficially affect business?
• What are the data inputs and where do they come from?
• What are the outputs and how are they consumed- (online algorithm, a static report, etc)
• Is this a revenue leakage ("saves us money") or a revenue growth ("makes us money") problem?

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""" From:  http://danielhnyk.cz/predicting-sequences-vectors-keras-using-rnn-lstm/ """
from keras.models import Sequential  
from keras.layers.core import TimeDistributedDense, Activation, Dropout  
from keras.layers.recurrent import GRU
import numpy as np

def _load_data(data, steps = 40):  
    docX, docY = [], []
    for i in range(0, data.shape[0]/steps-1):
        docX.append(data[i*steps:(i+1)*steps,:])

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import pandas as pd  
from random import random

flow = (list(range(1,10,1)) + list(range(10,1,-1)))*1000  
pdata = pd.DataFrame({"a":flow, "b":flow})  
pdata.b = pdata.b.shift(9)  
data = pdata.iloc[10:] * random()  # some noise  

import numpy as np