joelonsql

🗺🐘 1000+ PostgreSQL EXTENSIONs

This is a list of URLs to PostgreSQL EXTENSION repos, listed in alphabetical order of parent repo, with active forks listed under each parent.

⭐️ >= 10 stars
⭐️⭐️ >= 100 stars
⭐️⭐️⭐️ >= 1000 stars
Numbers of stars might not be up-to-date.

nicklatin

"""
This function takes observable macro factors (surprises) as inputs and creates macro factor mimicking portfolios (MFMPs) as outputs. It uses
a novel machine-learning approach, the Principal Components Instrumental Variables FMP Estimator,
described by Jurczenko and Teiletche (2020) in Macro Factor-Micking Portfolios:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3363598
The methodology addresses many of the common problems associated with macro factors and multifactor risk modeling and, as they show,
is superior to other common FMP approaches.
We describe the steps of the PCIV algorithm below, as well as in our Medium post:
For a more detailed explanation, see the link to the paper above.
Note that access to macroeconomic and base assets is required to estimate macro factor-mimicking portfolios. See the macro factors

nicklatin

# orthogonalization of correlated factors in a multifactor model
def orthogonalize_factors(factors_df, output_format='df'):

    """
    As described by Klein and Chow (2013) in Orthogonalized Factors and Systematic Risk Decompositions:
    https://www.sciencedirect.com/science/article/abs/pii/S1062976913000185
    They propose an optimal simultaneous orthogonal transformation of factors, following the so-called symmetric procedure
    of Schweinler and Wigner (1970) and Löwdin (1970).  The data transformation allows the identification of the underlying uncorrelated
    components of common factors without changing their correlation with the original factors. It also facilitates the systematic risk
    decomposition by disentangling the coefficient of determination (R²) based on factors' volatilities, which makes it easier to distinguish

nicklatin

# high-low spread estimator (hlse)
def hlse(ohlc_df, frequency='daily'):
    """
    Computes the high-low spread estimator, an estimate of bid-offer spreads, a measure of liquidity risk.
    See Corwin & Schultz (2011) for details: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1106193

    Parameters
    ----------
    ohlc_df: DataFrame
        DataFrame with DatetimeIndex and Open, High, Low and Close (OHLC) prices from which to compute the high-low spread estimates.

fritschy

#!/usr/bin/env bash
# 
#    Author: Markus (MawKKe) ekkwam@gmail.com
#    Date: 2018-03-19
#
#
# What?
#
#    Linux dm-crypt + dm-integrity + dm-raid (RAID1) 
#

imitronov

Problem

In Arch Linux mkinitcpio -p linux

shows

Possibly missing firmware for module: aic94xx
Possibly missing firmware for module: wd719x
Possibly missing firmware for module: xhci_pci

huntrar

Arch Linux Full-Disk Encryption Installation Guide

This guide provides instructions for an Arch Linux installation featuring full-disk encryption via LVM on LUKS and an encrypted boot partition (GRUB) for UEFI systems.

Following the main installation are further instructions to harden against Evil Maid attacks via UEFI Secure Boot custom key enrollment and self-signed kernel and bootloader.

Preface

You will find most of this information pulled from the Arch Wiki and other resources linked thereof.

Note: The system was installed on an NVMe SSD, substitute /dev/nvme0nX with /dev/sdX or your device as needed.

flipdazed

import numba as nb
import numpy as np


def impact_perm(nu, gamma, beta):
    """Returns the permenant dollar price impact per unit time

    In paper as :math:`g(\nu)`

    Args:

maks-ivanov

import numpy as np
import pandas as pd
from datetime import datetime
from fastparquet import write 



def compute_vwap(df):
    q = df['foreignNotional']
    p = df['price']

iafisher

"""
A script to automatically export bookmarks from Firefox's SQLite database.

There does not seem to be a programmatic way to get Firefox to export its bookmarks in
the conventional HTML format. However, you can access the bookmark information directly
in Firefox's internal database, which is what this script does.

Always be careful when working with the internal database! If you delete data, you will
likely not be able to recover it.