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Fully caffeinated for your safety

brian piercy bjpcjp

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Fully caffeinated for your safety
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@bjpcjp
bjpcjp / d3js-bullet.html
Created February 10, 2024 17:25
D3.js - bullet chart
<!DOCTYPE html>
<meta charset="utf-8">
<style>
body {
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
margin: auto;
padding-top: 40px;
position: relative;
width: 800px;
}
@bjpcjp
bjpcjp / shell-best-practices.sh
Last active July 31, 2024 13:12
shell script best practices template
#!/usr/bin/env bash
# source: https://sharats.me/posts/shell-script-best-practices/
set -o errexit
set -o nounset
set -o pipefail
if [[ "${TRACE-0}" == "1" ]]; then
set -o xtrace
fi
@bjpcjp
bjpcjp / my first svg
Created July 6, 2022 19:50
<svg width="10" height="10">
<rect x="0" y="0" width="10" height="10" fill="blue" />
</svg>
@bjpcjp
bjpcjp / pocket_json_prodmgmt_50_20220607
Created June 7, 2022 16:49
{"status"=>1, "complete"=>1, "list"=>{"3492948982"=>{"item_id"=>"3492948982", "resolved_id"=>"3492948982", "given_url"=>"https://www.practicalecommerce.com/tag/video-tools", "given_title"=>"", "favorite"=>"0", "status"=>"0", "time_added"=>"1638225235", "time_updated"=>"1654562695", "time_read"=>"0", "time_favorited"=>"0", "sort_id"=>0, "resolved_title"=>"Video Tools", "resolved_url"=>"https://www.practicalecommerce.com/tag/video-tools", "excerpt"=>"Video offers merchants a range of ways to engage customers and prospects. Here is a list of new and updated tools from video platforms and social media applications. There are tools for promoting products and producing shoppable videos and as well as for editing, live-streaming, and monetizing.", "is_article"=>"1", "is_index"=>"0", "has_video"=>"0", "has_image"=>"0", "word_count"=>"55", "lang"=>"en", "tags"=>{"ecommerce"=>{"item_id"=>"3492948982", "tag"=>"ecommerce"}, "prodmgmt"=>{"item_id"=>"3492948982", "tag"=>"prodmgmt"}, "tools"=>{"item_id"=>"3492948982", "tag"
@bjpcjp
bjpcjp / trading-investment-metrics.py
Last active July 31, 2024 13:17 — forked from bfan1256/metrics.py
5 Performance Metrics for Trading Algorithms and Investment Portfolios
from blankly import Alpaca, CoinbasePro # supports stocks, crypto, and forex
import numpy as np
from math import sqrt
def cagr(start_value: float, end_value: float, years: int):
return (end_value / start_value) ** (1.0 / years) - 1
def sharpe(account_values: np.array, risk_free_rate, annualize_coefficient):
diff = np.diff(account_values, 1) / account_values[1:] # this gets our pct_return in the array
@bjpcjp
bjpcjp / approximate_nearest_neighbors.py
Created April 1, 2021 19:38
https://scikit-learn.org example - approximate_nearest_neighbors.py
# Author: Tom Dupre la Tour
#
# License: BSD 3 clause
import time
import sys
try:
import annoy
except ImportError:
print("The package 'annoy' is required to run this example.")
@bjpcjp
bjpcjp / prodmgmt-art-of-profitability-booknotes.md
Last active May 14, 2019 21:07

The Art of Profitability

Original notes by James Clear

  • Do the math yourself. Too many people take numbers from unreliable sources.

  • There are 4 levels of learning: Awareness, Awkwardness, Application, Assimilation

  • Customer-Solution Profit: Know your customers incredibly well and create a solution specifically for them.

@bjpcjp
bjpcjp / dash-hello-world.py
Created February 20, 2019 02:10
$python app.py -- launches basic example of Dash web app framework. View results on localhost port# 8050.
import dash
import dash_core_components as dcc
import dash_html_components as html
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div(children=[
html.H1(children='Hello Dash'),
@bjpcjp
bjpcjp / housingScrape.py
Created February 8, 2018 22:57 — forked from theriley106/housingScrape.py
Scraping Valid Addresses from all US ZipCodes
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
import requests
import bs4
import zipcode
import threading
import re
import json
import time
@bjpcjp
bjpcjp / main.r
Created February 8, 2016 02:46
Build your own neural network classifier in R (source: http://junma5.weebly.com/data-blog)
# How to build your own NN classifier in r
# source: http://www.r-bloggers.com/build-your-own-neural-network-classifier-in-r/
# reference: http://junma5.weebly.com/data-blog/build-your-own-neural-network-classifier-in-r
# project:
# 1) build simple NN with 2 fully-connected layers
# 2) use NN to classify a dataset of 4-class 2D images & visualize decision boundary.
# 3) train NN with MNIST dataset
# ref: stanford CS23 source: http://cs231n.github.io/