Jasen Mackie jaymon0703

## HRP.py
# Python 3 code

import matplotlib.pyplot as mpl
import scipy.cluster.hierarchy as sch,random
import numpy as np
import pandas as pd

# Now, take corMat and covMat generated from R, input into Marcos' Python, and check output is the same...
col_list = ["IGV.Close", "TLT.Close", "IAU.Close", "IYR.Close"]
corr = pd.read_csv("corMat_23072021.csv", usecols=col_list)

## HRP.R
# R code

corMat <- read.csv("corMat_23072021.csv")[,-1]
covMat <- read.csv("covMat_23072021.csv")[,-1]

clustOrder <- hclust(dist(corMat), method = 'single')$order
clustOrder

# Plot Dendogram for some visualization of the order
clust <- hclust(dist(corMat), method = 'single')

## slack-notify-build.yml
name: R-CMD-check

on:
  - push
  - pull_request

jobs:
  build:
    runs-on: ubuntu-latest
    env:

## EDA_logistic_torch_DL.Rmd
---
title: "Jane_Street_Kaggle"
author: "Jasen Mackie"
date: "18/12/2020"
output: html_document
---

```{r setup, include=FALSE}
require(data.table)
require(ggplot2)

## macd_sig_yahoo.R
# Simple MACD signal strategy
#
# blotter v 0.10.0
# quantstrat v 0.9.1739
# FinancialInstrument v 1.2.0
# Rblpapi v 0.3.4
#
#
# Author: Jasen
###############################################################################

## quantstrat_test.ipynb

      
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                jaymon0703
                / quantstrat_test.ipynb
            
            
              Created
              May 30, 2020 01:15
            
              
                Running an R package's (quantstrat) demo in a Jupyter notebook using a Python3 Kernel
              
          
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## Bitcoin.cpp
#include "math.h"
#include "stdio.h"

double AttackerSuccessProbability(double q, int z)
{
 double p = 1.0 - q;
 double lambda = z * (q / p);
 double sum = 1.0;
 int i;
 int k;

## CSCV3.Rmd
---
title: A replication of the Practical Application section in 'The Probability of Backtest
  Overfitting' - Bailey et al.
output:
    html_notebook:
  # html_document:
  #   df_print: paged
---

In their paper "The Probability of Backtest

## random_strategy_builder.R
require(blotter)

# Remove portfolio and account data if run previously
try(rm("portfolio.txnsim_rnorm_port","account.txnsim_rnorm_acct",pos=.blotter), silent = TRUE)

# load the example data
currency("USD")
stock("GSPC",currency="USD",multiplier=1)
getSymbols('^GSPC', src='yahoo', index.class=c("POSIXt","POSIXct"),from='1998-01-01')

## profit.hurdle.R
###############################################################################
# R (http://r-project.org/) Quantitative Strategy Model Framework
#
# Copyright (c) 2009-2017
# Peter Carl, Dirk Eddelbuettel, Brian G. Peterson, Jeffrey Ryan, and Joshua Ulrich
#
# This library is distributed under the terms of the GNU Public License (GPL)
# for full details see the file COPYING
#
# $Id$
	# Python 3 code

	import matplotlib.pyplot as mpl
	import scipy.cluster.hierarchy as sch,random
	import numpy as np
	import pandas as pd

	# Now, take corMat and covMat generated from R, input into Marcos' Python, and check output is the same...
	col_list = ["IGV.Close", "TLT.Close", "IAU.Close", "IYR.Close"]
	corr = pd.read_csv("corMat_23072021.csv", usecols=col_list)
	# R code

	corMat <- read.csv("corMat_23072021.csv")[,-1]
	covMat <- read.csv("covMat_23072021.csv")[,-1]

	clustOrder <- hclust(dist(corMat), method = 'single')$order
	clustOrder

	# Plot Dendogram for some visualization of the order
	clust <- hclust(dist(corMat), method = 'single')
	name: R-CMD-check

	on:
	- push
	- pull_request

	jobs:
	build:
	runs-on: ubuntu-latest
	env:
	---
	title: "Jane_Street_Kaggle"
	author: "Jasen Mackie"
	date: "18/12/2020"
	output: html_document
	---

	```{r setup, include=FALSE}
	require(data.table)
	require(ggplot2)
	# Simple MACD signal strategy
	#
	# blotter v 0.10.0
	# quantstrat v 0.9.1739
	# FinancialInstrument v 1.2.0
	# Rblpapi v 0.3.4
	#
	#
	# Author: Jasen
	###############################################################################
	#include "math.h"
	#include "stdio.h"

	double AttackerSuccessProbability(double q, int z)
	{
	double p = 1.0 - q;
	double lambda = z * (q / p);
	double sum = 1.0;
	int i;
	int k;
	---
	title: A replication of the Practical Application section in 'The Probability of Backtest
	Overfitting' - Bailey et al.
	output:
	html_notebook:
	# html_document:
	# df_print: paged
	---

	In their paper "The Probability of Backtest
	require(blotter)

	# Remove portfolio and account data if run previously
	try(rm("portfolio.txnsim_rnorm_port","account.txnsim_rnorm_acct",pos=.blotter), silent = TRUE)

	# load the example data
	currency("USD")
	stock("GSPC",currency="USD",multiplier=1)
	getSymbols('^GSPC', src='yahoo', index.class=c("POSIXt","POSIXct"),from='1998-01-01')
	###############################################################################
	# R (http://r-project.org/) Quantitative Strategy Model Framework
	#
	# Copyright (c) 2009-2017
	# Peter Carl, Dirk Eddelbuettel, Brian G. Peterson, Jeffrey Ryan, and Joshua Ulrich
	#
	# This library is distributed under the terms of the GNU Public License (GPL)
	# for full details see the file COPYING
	#
	# $Id$