This Gist: http://is.gd/dokkudjango
DigitalOcean: https://www.digitalocean.com/
Dokku: https://github.com/progrium/dokku
Dokku-Postgres: https://github.com/Kloadut/dokku-pg-plugin
cschanhniem
/ README.md
Created
December 7, 2022 11:41
— forked from mjhea0/README.md
Installing Dokku and Deploying a Django App
cschanhniem
/ AWQL to Spreadsheets
Created
June 28, 2020 15:24
— forked from siliconvallaeys/AWQL to Spreadsheets
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| /* Put Google Ads Data in Google Spreadsheet | |
| * ----------------------------------------- | |
| * | |
| * Script by Optmyzr.com | |
| * | |
| * v3 (20190702) | |
| * - updated to API v201809 | |
| * v2 (20180810) | |
| * - updated to newer reporting version in ads API | |
| * |
cschanhniem
/ black-crypto-swans.ipynb
Created
May 30, 2020 18:01
— forked from gjlr2000/black-crypto-swans.ipynb
Black Crypto-Swans.ipynb
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cschanhniem
/ meanreversiontutorial_4_azur.ipynb
Created
May 30, 2020 18:00
— forked from gjlr2000/meanreversiontutorial_4_azur.ipynb
MeanReversionTutorial_4_azur.ipynb
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cschanhniem
/ fiatccy-mean-reversion-json-public.ipynb
Created
May 30, 2020 17:59
— forked from gjlr2000/fiatccy-mean-reversion-json-public.ipynb
FiatCCY mean reversion JSON public.ipynb
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cschanhniem
/ meanreversiontutorial_2_j.ipynb
Created
May 30, 2020 17:57
— forked from gjlr2000/meanreversiontutorial_2_j.ipynb
MeanReversionTutorial_2_j.ipynb
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cschanhniem
/ dollarbar_generator.py
Created
February 23, 2020 18:22
— forked from GerardBCN/dollarbar_generator.py
Dollar bar generator
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| import numpy as np | |
| # expects a numpy array with trades | |
| # each trade is composed of: [time, price, quantity] | |
| def generate_dollarbars(trades, frequency=1000): | |
| times = trades[:,0] | |
| prices = trades[:,1] | |
| volumes = trades[:,2] | |
| ans = np.zeros(shape=(len(prices), 6)) | |
| candle_counter = 0 |
cschanhniem
/ volumebar_generator.py
Created
February 23, 2020 18:22
— forked from GerardBCN/volumebar_generator.py
Volume bar generator
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| import numpy as np | |
| # expects a numpy array with trades | |
| # each trade is composed of: [time, price, quantity] | |
| def generate_volumebars(trades, frequency=10): | |
| times = trades[:,0] | |
| prices = trades[:,1] | |
| volumes = trades[:,2] | |
| ans = np.zeros(shape=(len(prices), 6)) | |
| candle_counter = 0 |