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Verifying my Blockstack ID is secured with the address 14BzAGWqh8hx72DZ7J5Df71fAeWMzKrckH https://explorer.blockstack.org/address/14BzAGWqh8hx72DZ7J5Df71fAeWMzKrckH |
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############################################################### | |
# exit first profitable price over entry | |
# exit after fixed bars (max_hold) if no profitable trade | |
############################################################### | |
signal = [0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0] | |
close = [10,10,8,3,14,5,6,2,4,22,2,1,1,1,10,3,3,3,3,3,3,30] | |
first_prof = zero(signal) | |
max_hold = 6 | |
for i = 1:length(signal) # 1:length(signal)-1 if buying next bar at open |
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# Download Data Using Alphavantage | |
using HTTP | |
using StatPlots | |
res = HTTP.get("https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=AAPL&interval=1min&apikey=your_api_key&datatype=csv") | |
mycsv = readcsv(res.body) | |
x = convert(DataFrame, mycsv) | |
x = x[2:nrow(x),:] # subset remove header row | |
# Rename Columns | |
colnames = ["Date","Open","High","Low","Close","Volume"] |
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import requests | |
from pandas.io.common import urlencode | |
from pandas.tseries.frequencies import to_offset | |
ALPHAVANTAGE_API_URL = "http://www.alphavantage.co/query" | |
ALPHAVANTAGE_API_KEY_DEFAULT = "demo" | |
def _init_session(session): |
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#!/usr/bin/env python | |
""" | |
Read Lobster orderbook files to Python Pandas DataFrame | |
""" | |
import click | |
import os | |
import pandas as pd | |
from pandas.io.common import ZipFile |
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Verifying that +femtotrader is my blockchain ID. https://onename.com/femtotrader |
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# -*- coding: utf-8 -*- | |
""" | |
Created on Mon Feb 17 13:56:10 2014 | |
@author: root | |
""" | |
import urllib2 | |
opener = urllib2.build_opener() | |
from bs4 import BeautifulSoup |
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#http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html#example-applications-face-recognition-py | |
from sklearn.ensemble import RandomForestClassifier | |
from numpy import genfromtxt, savetxt | |
from sklearn import neighbors, datasets | |
from sklearn.cross_validation import train_test_split | |
from sklearn.datasets import fetch_lfw_people | |
from sklearn.grid_search import GridSearchCV | |
from sklearn.metrics import classification_report | |
from sklearn.metrics import confusion_matrix |
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############################################################## | |
## ## | |
## VaR and C-VaR computation with ETV on EURUSD ## | |
## ## | |
## by Charles-Abner Dadi ## | |
## ## | |
## <[email protected]> ## | |
## ## | |
############################################################## |
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{ | |
"metadata": { | |
"name": "" | |
}, | |
"nbformat": 3, | |
"nbformat_minor": 0, | |
"worksheets": [ | |
{ | |
"cells": [ | |
{ |
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