Your calculation 2 is the relevant metric since that is what you actually paid, and later received, both measured in your home currency EUR.
If you want to track it in Yahoo Finance, try adding two investments to your portfolio:
The 100 USD bought at day 0 at EUR 90 and
the US stock you bought at day 0 for EUR 90.
With both the USD position and the stock ...
Using @mde answer's for the average price method and developing it for Fifo method:
# avg price based PnLCalculator
self.quantity = 0
self.cost = 0.0
self.market_value = 0.0
self.r_pnl = 0.0
self.average_price = 0.0
def fill(self, pos_change, exec_price):
This is usually called the "Linking problem" or multi-period linking in Performance Attribution.
Several methods have been proposed and there is some controversy as to which is best (in fact a great deal of discussion goes on in performance journals seemingly without coming to a resolution).
For what it is worth, based on my reading of articles such as ...
The only time I've used net volume was for performance measurements of my own trading activity. Specifically, I was looking for the net volume traded 10 ms before my own execution vs 10 ms after my execution. That would indicate how ahead of my competitors I was in execution. I've never used such a metric for daily applications.
As for how to compute net ...
I would convert all data from daily to monthly using the generic total return formula you specified. Then do the attribution.
The issue is going to be with the weights of assets, sectors, and countries; you will have to make an assumption whether you use end of month weight or average weight over the month. For example, end of month weight of manager A vs ...
There is no right or wrong, just those 2 conventions are different, each one with its pros/cons.
In general what is more important is to be clear about conventions used to avoid miscommunication and mistakes.
Now if you calculate returns over an interval where the magnitudes are meant to be small then mathematically speaking the difference between raw ...
Net volume is an important indicator. It's related to the concept of informed traders of microstructure theory. And the volume indicators such as OBV are constructed from the considering the volume imbalance. The volume indicators are used together with price indicators. How much predictive power it has depends on the instruments you trade. You need to ...
there is information there indeed, you can even get tick data with bid and offer prices and volumes.
there are books on this, like the one below, haven't read any though.
A Complete Guide To Volume Price Analysis: Read the book then read the market by Anna Coulling
We have used it as a criterion for portfolio selection, for example in An Empirical Analysis of Alternative Portfolio Selection Criteria and Risk-Reward Optimisation for Long-Run Investors: An Empirical Analysis.
What we found there, however, is that reducing the downside was more important than increasing the upside.
If you use logreturns it becomes simpler:
logreturn on stock A: log(price_At+1/price_At)
logreturn on stock B: log(price_Bt+1/price_Bt)
total logreturn on pairs trade: logreturn on stock A + logreturn on stock B =
=spreadt+1 - spreadt
Now it is all consistent, thanks to the property that $\log(x ...
You add 1 to every monthly return of a given quarter, take the product of those returns, and then subtract 1.
In R (without any package): Suppose r are the monthly returns, and dt are the timestamps.
r <- rep(0.01, 12)
dt <- seq(from = as.Date("2020-1-1"),
to = as.Date("2020-12-1"),
by = "1 month")
It’s definitely possible to have negative returns but positive alpha. It just means that your portfolio had excess return in respect to this particular asset pricing theory. It may very well be possible you’d have a negative alpha in 5-factor model especially if the portfolio had a momentum rally.