In anre005/pinbasic: Fast and Stable Estimation of the Probability of Informed Trading (PIN)
General PIN Framework
In the sequential microstructure models for estimating the probability of informed trading
the exchange of equities takes place over $\daysym = 1, \dots, \totaldays$
pairwise independent trading days.
No market activities are permitted in which a risk-neutral and competitive market maker is not involved.
The market maker determines and updates the bid and ask prices utilizing the information he gathered
so far for a trading day.
Trading with the market maker is possible
at every timestamp $t$ during regular market hours starting at $\originmarket$ and ending at $\closemarket$,
i.e. $t \in \left[\originmarket,\closemarket\right]$ with finite $\closemarket$.
The beginning of official trading may vary depending on the chosen bourse $m$, i.e. the New York Stock Exchange
starts regular trading at 9:30 am, whereas the German electronic marketplace XETRA opens earlier at 9:00 am.
Likewise, the upper bound $\closemarket$ of the official trading interval may also vary according
to the marketplace under consideration.
Each trading day can reside in one of three possible states of the set $\states$.
The elements of the set $\statesabbrv$, which represent the conditions of trading days,
are no-news ($\nonews$), good-news ($\goodnews$) and bad-news ($\badnews$).
Trading days on which private information influence the market activities are called information events.
Market participants can be split in two disjoint groups, informed and uninformed traders.
Traders holding private information are solely active on information events.
In addition, they are assumed to be risk neutral and competitive.
They buy (sell) if positive (negative) signals hit the market,
which is the case on good-news (bad-news) trading days.
The contrary group of traders, the uninformed market attendees,
are active on every trading day for various reasons (diversification, liquidity reasons, $\dots$).
In general, the probability of informed trading $\pintext$ can be defined as the relation of the expected number of
transactions due to private information to the expected total number of trades,
$$
\begin{align}
\label{eq:pingeneral}
\pintext = \dfrac{\text{Expected number of information-based transactions}}{\text{Expected total number of transactions}}
\end{align}
$$
anre005/pinbasic documentation built on May 6, 2022, 4:40 a.m.
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General PIN Framework
In the sequential microstructure models for estimating the probability of informed trading the exchange of equities takes place over $\daysym = 1, \dots, \totaldays$ pairwise independent trading days. No market activities are permitted in which a risk-neutral and competitive market maker is not involved. The market maker determines and updates the bid and ask prices utilizing the information he gathered so far for a trading day. Trading with the market maker is possible at every timestamp $t$ during regular market hours starting at $\originmarket$ and ending at $\closemarket$, i.e. $t \in \left[\originmarket,\closemarket\right]$ with finite $\closemarket$. The beginning of official trading may vary depending on the chosen bourse $m$, i.e. the New York Stock Exchange starts regular trading at 9:30 am, whereas the German electronic marketplace XETRA opens earlier at 9:00 am. Likewise, the upper bound $\closemarket$ of the official trading interval may also vary according to the marketplace under consideration. Each trading day can reside in one of three possible states of the set $\states$. The elements of the set $\statesabbrv$, which represent the conditions of trading days, are no-news ($\nonews$), good-news ($\goodnews$) and bad-news ($\badnews$). Trading days on which private information influence the market activities are called information events.
Market participants can be split in two disjoint groups, informed and uninformed traders. Traders holding private information are solely active on information events. In addition, they are assumed to be risk neutral and competitive. They buy (sell) if positive (negative) signals hit the market, which is the case on good-news (bad-news) trading days. The contrary group of traders, the uninformed market attendees, are active on every trading day for various reasons (diversification, liquidity reasons, $\dots$).
In general, the probability of informed trading $\pintext$ can be defined as the relation of the expected number of transactions due to private information to the expected total number of trades, $$ \begin{align} \label{eq:pingeneral} \pintext = \dfrac{\text{Expected number of information-based transactions}}{\text{Expected total number of transactions}} \end{align} $$
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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