ldhmm.read_csv_by_symbol

This is a helper utility to read sample csv file into data frame.
The main use for external users is to read the option data
since it has a different format than other price timeseries data.

data

Hidden Markov Model (HMM) based on symmetric lambda distribution
framework is implemented for the study of return time-series in the financial
market. Major features in the S&P500 index, such as regime identification,
volatility clustering, and anti-correlation between return and volatility,
can be extracted from HMM cleanly. Univariate symmetric lambda distribution
is essentially a location-scale family of exponential power distribution.
Such distribution is suitable for describing highly leptokurtic time series
obtained from the financial market. It provides a theoretically solid foundation
to explore such data where the normal distribution is not adequate. The HMM
implementation follows closely the book: "Hidden Markov Models for Time Series",
by Zucchini, MacDonald, Langrock (2016).

Stephen Lihn

ldhmm

Hidden Markov Model for Financial Time-Series Based on Lambda
Distribution

ldhmm.read_csv_by_symbol function

<dl><dt>symbol</dt>
<dd>Character for the symbol of the time series. Default: dji</dd>
<dt>extdata_dir</dt>
<dd>optionally specify user's own extdata folder</dd></dl>

Arguments

Author

Read csv file of sample data — ldhmm.read_csv_by_symbol

<dl>

<dt>symbol</dt>
<dd>Character for the symbol of the time series. Default: dji</dd>


<dt>extdata_dir</dt>
<dd>optionally specify user's own extdata folder</dd>

</dl>

ldhmm.read_csv_by_symbol: Read csv file of sample data

Description

Usage

Value

Arguments

Author

Examples