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Shows function Internals

library(BayesianFirstAid)
debugonce(bayes.t.test) # turns off debugging for function after first run
bayes.t.test(co2)

Returns hidden and unhidden environments

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library(ggplot2)
library(dplyr) # easier data wrangling 
library(viridis) # colour blind friendly palette, works in B&W also
library(Interpol.T) #  will generate a large dataset on initial load
library(lubridate) # for easy date manipulation
library(ggExtra) # because remembering ggplot theme options is beyond me
library(tidyr) 


data<- data(Trentino_hourly_T,package = "Interpol.T")

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require(quantmod)
require(ggplot2)
require(reshape2)
require(plyr)
require(scales)

# Download some Data, e.g. the CBOE VIX 
getSymbols("^VIX",src="yahoo")

# Make a dataframe

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#### VPIN calculation #########################################################
#install.packages('fasttime',repos='http://www.rforge.net/')
require(data.table); require(fasttime); require(plyr)
# Assuming TAQ data is arranged in 1 year stock csv files
stock=fread('/TAQ_data.csv'); stock=stock[,1:3,with=FALSE]
setnames(stock,colnames(stock),c('DateTime','Price','Volume'));
stock[,DateTime:=paste(paste(substr(DateTime,1,4),substr(DateTime,5,6),
    substr(DateTime,7,8),sep='-'),substr(DateTime,10,17))]
setkey(stock,DateTime);
stock=as.xts(stock[,2:3,with=FALSE],unique=FALSE,

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Installing tmux-256color for macOS

• macOS 10.15.5
• tmux 3.1b

macOS has ncurses version 5.7 which does not ship the terminfo description for tmux. There're two ways that can help you to solve this problem.

The Fast Blazing Solution

Instead of tmux-256color, use screen-256color which comes with system. Place this command into ~/.tmux.conf or ~/.config/tmux/tmux.conf(for version 3.1 and later):

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<?xml version="1.0"?>
<root>
    <item>
        <name>For Topre REALFORCE 87u Users</name>
        <devicevendordef>
            <vendorname>TopreCorporation</vendorname>
            <vendorid>0x0853</vendorid>
        </devicevendordef>
        <item>
            <name>Fix FN + F1..F12 and functional keys</name>

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Performance testing java applications - Martin Thompson

What is testing

Fast is generic, breaks down to: -- throughput -- bandwidth

-- latency -- response time

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1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

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library(dplyr)
library(tidyr)
library(purrr) # map
library(slider)

# only returns rsquared
get_coef1 <- function(data) {
    coef1 <- lm(data = data, r1 ~ r2 + r3) %>%
        coef() %>%
        .["r2"] %>%

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https://web.archive.org/web/20110219163448/http://howtohft.wordpress.com/2011/02/15/how-to-build-a-fast-limit-order-book/

The response to my first few posts has been much larger than I’d imagined and I’d like to thank everyone for the encouragement.

If you’re interested in building a trading system I recommend first reading my previous post on general ideas to keep in mind.

My first really technical post will be on how to build a limit order book, probably the single most important component of a trading system. Because the data structure chosen to represent the limit order book will be the primary source of market information for trading models, it is important to make it both absolutely correct and extremely fast.

To give some idea of the data volumes, the Nasdaq TotalView ITCH feed, which is every event in every instrument traded on the Nasdaq, can have data rates of 20+ gigabytes/day with spikes of 3 megabytes/second or more. The individual messages average about 20 bytes each so this means handling