dalamar66

import os
import io
import subprocess
import pandas as pd
import numpy as np
from sklearn import tree
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
from sklearn.tree import export_graphviz
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier

dalamar66

dalamar66

library(quantmod)
getSymbols(c("GDP", "CMDEBT"), src="FRED")
plot(cumsum(na.omit(diff(GDP) - diff(CMDEBT))))

dalamar66

# Some code to asses an SVM with a two-dimensional feature space:
# trend (price - simple moving average) and relative strength index.
# The code is an adaptation of the code found in the following linkedin post:
# "Trading the RSI using a Support Vector Machine"
# "https://www.linkedin.com/pulse/article/20141103165037-172934333-trading-the-rsi-using-a-support-vector-machine"

# Settings
sma.window = 50 # Number of observations in simple moving average.
rsi.window = 3  # Number of observation in relative strength index (RSI)

dalamar66

""" 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,:])

dalamar66

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

dalamar66

# Short scripts for testing three different sentiment classifiers on tweets,
# acquiring the tweets used for testing,
# calculating systems' precision, recall and F-measures.


require(RCurl)   # For downloading file from a given URL.
require(twitteR) # Used for the 'twitter' class.
require(sentiment) # For bayes and voter classifiers.
source("sent140.R")  # Used for the Sentiment 140 API. Can be downloaded from here:
# https://github.com/okugami79/sentiment140/blob/master/R/sentiment.r

dalamar66

# Code to fetch news streams from 5 live sources, process the streams and text
# and apply a simple sentiment scoring algorigthm.
#
# A writeup of the analysis can be found here:
# https://www.linkedin.com/pulse/article/20141109035942-34768479-r-sentiment-scoring-hsbc-w-harvard-general-inquirer

# Define the packages we want to load:
packs = c(
  "tm",                         # Text mining
  "tm.plugin.webmining",        # Web-source plugin for text mining

dalamar66

# Some code to asses an SVM with a two-dimensional feature space:
# trend (price - simple moving average) and relative strength index.
# The code is an adaptation of the code found in the following linkedin post:
# "Trading the RSI using a Support Vector Machine"
# "https://www.linkedin.com/pulse/article/20141103165037-172934333-trading-the-rsi-using-a-support-vector-machine"

# Settings
sma.window = 50 # Number of observations in simple moving average.
rsi.window = 3  # Number of observation in relative strength index (RSI)

dalamar66

findBestArma = function(
   xx,  
   minOrder=c(0,0),
   maxOrder=c(5,5),
   trace=FALSE )
{
   bestAic = 1e9
   len = NROW( xx ) 
   for( p in minOrder[1]:maxOrder[1] ) for( q in minOrder[2]:maxOrder[2] )
   {