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Scrolling in the Linux Console (ttys)

Scroll Up: Shift + Page Up

Scroll Down: Shift + Page Down

The scrollback buffer is quite short in Linux consoles, and the scrollback buffer is not stored on system RAM, but VGA RAM. The buffer is also lost on switching ttys. There was a kernel patch to fix this, but not yet accepted.

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This was tested on a ThinkPad P70 laptop with an Intel integrated graphics and an NVIDIA GPU:

lspci | egrep 'VGA|3D'
00:02.0 VGA compatible controller: Intel Corporation Device 191b (rev 06)
01:00.0 VGA compatible controller: NVIDIA Corporation GM204GLM [Quadro M3000M] (rev a1)

A reason to use the integrated graphics for display is if installing the NVIDIA drivers causes the display to stop working properly. In my case, Ubuntu would get stuck in a login loop after installing the NVIDIA drivers. This happened regardless if I installed the drivers from the "Additional Drivers" tab in "System Settings" or the ppa:graphics-drivers/ppa in the command-line.

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# Backtest of ETF Replay Ranking Algorithm

# clear the workspace
rm(list=ls())

# step-by-step example of ETF Replay Ranking Algorithm

##### etfReplayRank Function #####
# we can use the same step-by-step approach outlined above to write a function
# to rank instruments based on ETF Replay

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# Ranking Algorithm for ETF Replay

# Rank based on 3 factors
# 1. ReturnA    (ret1)
# 2. ReturnB    (ret2)
# 3. Volatility (vol)

# Returns are ranked from high to low
# Volatility is ranked from low to high

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# 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

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# 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.

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"""
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

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"""
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.

vishalbelsare

# lazy data frame
#
# like idata.frame, but exploits lazy evaluation + macro code generation
# instead of active bindings
library(vadr)

lazy.frame <- function(df, enclos=parent.frame(), ...) UseMethod("lazy.frame")

lazy.frame.lazy.frame <- function(df, ...) df

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{
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  "name": ""
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 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
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