bjorskog

"""
Extracting various figures from Statnett

"""

import requests
import datetime
import pprint

bjorskog

# 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)

four43

#!/bin/bash
# from here: http://www.codingsteps.com/install-redis-2-6-on-amazon-ec2-linux-ami-or-centos/
# and here: https://raw.github.com/gist/257849/9f1e627e0b7dbe68882fa2b7bdb1b2b263522004/redis-server
###############################################
# To use: 
# wget https://gist.githubusercontent.com/four43/e00d01ca084c5972f229/raw/install-redis.sh
# chmod 777 install-redis.sh
# ./install-redis.sh
###############################################
echo "*****************************************"

Inpirical-Coder

# 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

svmComputeOneForecast = function(
      id,
      data,
      response,
      startPoints,
      endPoints,
      len,
      history=500,
      trace=FALSE,
      kernel="radial",

Inpirical-Coder

# 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

Inpirical-Coder

# 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)

mrbcuda

ivannp

svmComputeOneForecast = function(
      id,
      data,
      response,
      startPoints,
      endPoints,
      len,
      history=500,
      trace=FALSE,
      kernel="radial",