As far as I know there is no library. With some other researchers, we implemented this 20 years ago in scheme (yes, it was long ago, when Lisp, and not python, was the language of AI).
Our methodology (that was really fast), was the following
you need a time scale, one week for instance
mark all the local minima and local maxima at the time scale
now you ...
Pricing of vanillas is basically interpolation of existing (or past) quotes.
It is easier to interpolate in implied volatility space , than in price space.
Reasons are we need to interpolate in multidimensional space (maturity, strike,forward, etc) and satisfy non-arbitrage conditions.
Using Black-scholes formula is convenient mapping which would also ...
If you want to compare quotes across markets or over time it can be useful to use fixed points: eg the 110%/90% points to compute skew or the +/-25 delta points for risk-reversal. You can't rely on quotes existing at exactly those points so you would want to interpolate.
It looks like there is no impact on the volume of transactions depending on the day of the week if we refer to this chart: https://www.quora.com/What-is-the-average-total-number-of-trades-daily-on-NASDAQ#:~:text=What%20is%20the%20average%20total%20number%20of%20trades%20daily%20on%20NASDAQ%3F,-ad%20b&text=http%3A%2F%2Fwww.nasdaqtrader.com,daily%2C%...
Aspects that I presently see are:
1. The higher the liquidity, the better.
2. A contract with the smaller currency equivalent is better, if everything
else is the same - this makes a finer position sizing possible (relevant
for not so big portfolios). So prefer mini instruments over normal
3. Taking an instrument in the own ('home') ...
If a corporate bond is less liquid / harder to source (e.g. it was issued years ago and most people who hold it now intend to hold it to maturity; or there just isn't a lot outstanding) then, ceteris paribus, the bid-ask spread is likely to be wider than comparable bonds; the spread on top of treasury yield is usually wider (to compensate for the risk of ...
I’m by no means an “expert”, though I’ve spent a fair amount of time studying this and writing quant software.
There are three important starting places to study this question, in this order:
1 dark pools ( see https://squeezemetrics.com/monitor/dix )
40% to 60% of large trades are now done in dark pools.
2 the “closing auction” at 4pm
3 the “on balance ...
Let me try to answer. I have worked at an Algo-trading firm that trades equities and have seen how trades are executed at the order book level. Let's say the price of the stock is 100 (last traded price). Let's say the order book is as follows:
Bids: Bid1 = 99 (size = 10,000), Bid2 = 98 (size = 20,000), Bid3 = 97 (size = 25,000), Bid4 = 96 (size = 30,000), ...