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| #!/usr/bin/env python | |
| # Building high-frequency trading signals in Python with Databento and sklearn | |
| # | |
| # This is a simple example that demonstrates how to build high-frequency trading signals in Python, | |
| # using order book and market depth data from [Databento](https://databento.com) together with | |
| # machine learning models from [sklearn](https://scikit-learn.org/). | |
| import databento as db | |
| import numpy as np |
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| // answer to https://quant.stackexchange.com/q/55088/35 | |
| // compile with c++ pcap_udp.cpp -lpcap | |
| #include <iostream> | |
| #include <pcap/pcap.h> | |
| #include <netinet/if_ether.h> | |
| #include <netinet/ip.h> | |
| #include <netinet/udp.h> |
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| import re | |
| import math | |
| import operator | |
| import logging | |
| from collections import defaultdict, Counter | |
| import numpy as np | |
| class Tokenizer: | |
| def __init__(self, stop_words, ): |
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| #!/usr/bin/env python | |
| """ | |
| Clone all public and private repositories from a GitHub user or organization. | |
| Copyright (c) 2018 Yuriy Guts | |
| usage: github-clone-all.py [-h] [--auth-user AUTH_USER] | |
| [--auth-password AUTH_PASSWORD] [--clone-user USER] | |
| [--clone-org ORG] |
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| EasyLanguage Code To TesT The Predictability Of An Event | |
| Vars: | |
| Event(false), | |
| FuturePrice(0), | |
| I(0), | |
| CG(0), | |
| Denom(0); | |
| Arrays: | |
| PredictBin[100](0); |
$ sudo npm install -g hexo-cli
$ hexo -v
hexo-cli: 0.1.9
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| # this script has been tested and worked in a freshly installed Ubuntu 16.04 and 16.10 | |
| # it assumes that you are running airflow in a private netowrk and no need to be worry about outside access | |
| # if that's not the case, the lines for PostgreSQL and Redis in this script need to be updated accordingly | |
| # run as root | |
| sudo su | |
| # initial system updates and installs | |
| apt-get update && apt-get upgrade -y && apt-get autoremove && apt-get autoclean | |
| apt-get -y install build-essential binutils gcc make git htop nethogs tmux |
adapted from the article "Crawling anonymously with Tor in Python" by S. Acharya, Nov 2, 2013.
The most common use-case is to be able to hide one's identity using TOR or being able to change identities programmatically, for example when you are crawling a website like Google and you don’t want to be rate-limited or blocked via IP address.
Install Tor.
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