What would you need:
- Postgres 9.3, 9.4, 9.5, 9.6 or 10 with cstore_fdw extention (https://github.com/citusdata/cstore_fdw)
- Docker 1.12.6 or higher
- Docker Compose
- Linux machine
Hardware requirements
What would you need:
Hardware requirements
import alpaca_trade_api as tradeapi | |
api = tradeapi.REST(key_id=<your key id>,secret_key=<your secret key>) | |
class positionHandler: | |
def __init__(self,startingBalance=10000,liveTrading=False): | |
self.cashBalance = startingBalance | |
self.livePositions = {} # Dictionary of currently open positions | |
self.openOrders = [] # List of open orders | |
self.positionHistory = [] # List of items [Symbol, new position size] |
/* | |
NB: erste Zeile je die Bipartitionen, zweite Zeile die Konfidenzen dazu (gerundet auf zwei Nachkommastellen). | |
*/ | |
false,false,false,false,false,true | |
[ 0.00, 0.00, 0.00, 0.00, 0.00, 1.00, ] | |
true,false,false,false,false,false | |
[ 1.00, 0.00, 0.00, 0.00, 0.00, 0.00, ] | |
false,false,false,false,false,true |
package put.mlc.examples.pcc; | |
import mulan.classifier.MultiLabelOutput; | |
import mulan.data.MultiLabelInstances; | |
import put.mlc.classifiers.pcc.PCC; | |
import put.mlc.classifiers.pcc.inference.Inference; | |
import put.mlc.classifiers.pcc.inference.montecarlo.FMeasureMaximizerInference; | |
import put.mlc.classifiers.pcc.inference.ExhaustiveInference; | |
import weka.classifiers.functions.Logistic; | |
import weka.core.Instances; |
############################################################ | |
## Using Genetic Algorithms in Quantitative Trading | |
## | |
## thertrader@gmail.com - Mar 2014 | |
############################################################ | |
library(PerformanceAnalytics) | |
library(rgenoud) | |
library(quantmod) | |
library(TTR) |
#!/bin/sh | |
case "$1" in | |
bundlerextension) | |
jupyter-bundlerextension ${@:2} | |
;; | |
console) | |
jupyter-console ${@:2} | |
;; | |
kernelspec) |
<?xml version="1.0" encoding="UTF-8"?> | |
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd"> | |
<plist version="1.0"> | |
<dict> | |
<key>Label</key> | |
<string>homebrew.mxcl.kwm</string> | |
<key>ProgramArguments</key> | |
<array> | |
<string>/usr/local/opt/kwm/kwm</string> | |
</array> |
function _common_section | |
printf $c1 | |
printf $argv[1] | |
printf $c0 | |
printf ":" | |
printf $c2 | |
printf $argv[2] | |
printf $argv[3] | |
printf $c0 | |
printf ", " |
{-# LANGUAGE OverloadedStrings #-} | |
module Main where | |
{- | |
This is the main entry point for the multi label correlation coefficient | |
microservice which listens to RPC calls for applications which need to work | |
with computed data about the label correlation coefficients, i.e. data viz | |
in dashboards and such. The data is fetched from a MySQL database. | |
-} |