the question seems pretty easy, but I just did not find a solution yet.
I want to obtain mid prices for a certain timespan. Obviously I can calculate the mid price from bid and ask prices myself. But I wondered, whether it could be done via getTicks directly. I tried eventType = 'MID' and eventType = 'PX_MID', but both don't seem to be supported:
con <- Rblpapi::blpConnect()
Ticker <- 'AAPL US Equity'
Time <- as.POSIXct('2022-02-02 14:22:50 CET')
df <- Rblpapi::getTicks(security = Ticker,
eventType = 'MID',
startTime = Time - 5 * 60,
endTime = Time + 5 * 60)
Error in getTicks_Impl(con, security, eventType, startUTC, endUTC, setCondCodes = returnAs %in% :
Constant with value 'MID' does not exist.
The same code using eventType = 'BID' or eventType = 'ASK' or eventType = 'TRADE' works fine.
I was already looking for a list of all supported eventTypes, but I did not find anything in the documentation. Does such a list exist?
And is there a simple solution to this problem? Or is it possible, that Bloomberg does not support MID prices with Tick Data?
Thanks a lot!
I do not know how to code and I am trying to learn Pinescript but it really makes no sense to me so i googled how to set a backtest range and used some code someone else wrote but it doesn't seem to be actually testing the area i would like, it tests the entirety of the chart. I'd like to test from 1/1/2018 to present. I'm trying to do this for multiple strategies so I can better tailor them to the current market. here is wat I have for one of them and if you are willing to help with the others I would very much appreciate it!!! feel free to DM me.
//#version=5
strategy("Bollinger Bands BACKTEST", overlay=true)
source = close
length = input.int(20, minval=1)
mult = input.float(2.0, minval=0.001, maxval=50)
basis = ta.sma(source, length)
dev = mult * ta.stdev(source, length)
upper = basis + dev
lower = basis - dev
buyEntry = ta.crossover(source, lower)
sellEntry = ta.crossunder(source, upper)
if (ta.crossover(source, lower))
strategy.entry("BBandLE", strategy.long, stop=lower, oca_name="BollingerBands", oca_type=strategy.oca.cancel, comment="BBandLE")
else
strategy.cancel(id="BBandLE")
if (ta.crossunder(source, upper))
strategy.entry("BBandSE", strategy.short, stop=upper, oca_name="BollingerBands", oca_type=strategy.oca.cancel, comment="BBandSE")
else
strategy.cancel(id="BBandSE")
//plot(strategy.equity, title="equity", color=color.red, linewidth=2, style=plot.style_areabr)
// === INPUT BACKTEST RANGE ===
fromMonth = input.int(defval = 1, title = "From Month", minval = 1, maxval = 12)
fromDay = input.int(defval = 1, title = "From Day", minval = 1, maxval = 31)
fromYear = input.int(defval = 2018, title = "From Year", minval = 1970)
This is partly a theory question, and partly an implementation question. My stats is a little rusty...
I am developing a report that is attempting to determine if the difference in occurances between a reference group and a selected group are statistically significant.
So, for example, if something occurs in X of n tests for one group, is it statistically significant than if it 'normally' occurs at a rate of Y of m tests for a different (control) group.
So, my H0 is that the rate is Y of m, per the control group
h1 is that it is not the same as the control group. (ideally, I'd like to use a 1-tailed test, depending if the observed occurrence is greater or less than the control, but my current implementation is 2 tailed)
I'd be comfortable with a CI of 80%.
I've got (slightly pseudocode here):
Zscore =
VAR pControl = DIVIDE(COUNT([Control occurrences]), COUNT([Control Tests])) RETURN
VAR pTest = DIVIDE(COUNT([Test occurrences]), COUNT([Test Tests])) RETURN
VAR controlStandardError =
SQRT(
DIVIDE(
(pControl * (1-pControl)
, COUNT([Control Tests])
)
) RETURN
VAR testStandardError =
SQRT(
DIVIDE(
(pTest* (1-pTest)
, COUNT([Test Tests])
)
) RETURN
DIVIDE(
(pTest - pControl)
, SQRT(POWER(testStandardError, 2) + POWER(controlStandardError, 2)
)
I'm then calculating:
p-Value =
VAR pControl = DIVIDE(COUNT([Control occurrences]), COUNT([Control Tests])) RETURN
IF(pControl > 0,
1 - ABS(NORM.DIST(Zscore, 0, 1, TRUE)
)
I am then displaying in a table each of my non-null hypotheses and filtering the table such that p-Value is less than 0.1. (2-tailed 80%)
am I on the right track here? Or have I completely bungled the theory on this one?
Theory and example tables - Right-tailed (μ > μ₀)
DAX
ControlGroup
XControl = COUNTROWS(FILTER(ControlGroup,ControlGroup[Outcome]=1))
NControl = COUNTROWS(ControlGroup)
pControl = DIVIDE([XControl],[NControl])
TreatmentGroup
XTreatment = COUNTROWS(FILTER(TreatmentGroup,TreatmentGroup[Outcome]=1))
NTreatment = COUNTROWS(TreatmentGroup)
pTreatment = DIVIDE([XTreatment],[NTreatment])
Test Parameters
PooledProportion =
DIVIDE(
[XTreatment]+[XControl],
[NTreatment]+[NControl]
)
ZCritivalValue = NORM.S.INV(0.90)
ZValue = DIVIDE(
[pTreatment]-[pControl],
SQRT(
[PooledProportion]*(1-[PooledProportion])*((1/[NTreatment])+(1/[NControl]))
)
)
Visualization (example)
This is what I've got, but I'm getting weird results. Can you spot an error?:
#Zero Coupon Inflation Indexed Swap Data
zciisData = [(ql.Date(18,4,2020), 1.9948999881744385),
(ql.Date(18,4,2021), 1.9567999839782715),
(ql.Date(18,4,2022), 1.9566999673843384),
(ql.Date(18,4,2023), 1.9639999866485596),
(ql.Date(18,4,2024), 2.017400026321411),
(ql.Date(18,4,2025), 2.0074000358581543),
(ql.Date(18,4,2026), 2.0297999382019043),
(ql.Date(18,4,2027), 2.05430006980896),
(ql.Date(18,4,2028), 2.0873000621795654),
(ql.Date(18,4,2029), 2.1166999340057373),
(ql.Date(18,4,2031), 2.152100086212158),
(ql.Date(18,4,2034), 2.18179988861084),
(ql.Date(18,4,2039), 2.190999984741211),
(ql.Date(18,4,2044), 2.2016000747680664),
(ql.Date(18,4,2049), 2.193000078201294)]
def build_inflation_term_structure(calendar, observationDate):
dayCounter = ql.ActualActual()
yTS = build_yield_curve()
lag = 3
fixing_date = calendar.advance(observationDate,-lag, ql.Months)
convention = ql.ModifiedFollowing
cpiTS = ql.RelinkableZeroInflationTermStructureHandle()
inflationIndex = ql.USCPI(False, cpiTS)
#last observed CPI level
fixing_rate = 252.0
baseZeroRate = 1.8
inflationIndex.addFixing(fixing_date, fixing_rate)
observationLag = ql.Period(lag, ql.Months)
zeroSwapHelpers = []
for date,rate in zciisData:
nextZeroSwapHelper = ql.ZeroCouponInflationSwapHelper(rate/100,observationLag,date,calendar,
convention,dayCounter,inflationIndex)
zeroSwapHelpers = zeroSwapHelpers + [nextZeroSwapHelper]
# the derived inflation curve
derived_inflation_curve = ql.PiecewiseZeroInflation(observationDate, calendar, dayCounter, observationLag,
inflationIndex.frequency(), inflationIndex.interpolated(),
baseZeroRate, yTS, zeroSwapHelpers,
1.0e-12, ql.Linear())
cpiTS.linkTo(derived_inflation_curve)
return inflationIndex, derived_inflation_curve, cpiTS, yTS
observation_date = ql.Date(17, 4, 2019)
calendar = ql.UnitedStates()
inflationIndex, derived_inflation_curve, cpiTS, yTS = build_inflation_term_structure(calendar, observation_date)
If I plot the inflationIndex zero rates, I get this:
I've now been checking the same problem and first of all I don't think you need that function ZeroCouponInflationSwapHelper at all.
Just to let you know I've perfectly replicated OpenRiskEngine (ORE)'s inflation curve which itself is built on QuantLib and the idea is quite simple.
compute base value I(0) which is the lagged CPI (this is supposedly your value 252.0),
compute CPI(t)=I(T)=I(0)*(1+quote)^T, where T = ZCIS expiry date, t is T - 3M
I'm not sure where you took I(0) from, but since a lag is present the I(0) is not the latest available CPI value. Instead I(0)=I(2019-04-17) is interpolated value of CPI(2019-01) and CPI(2019-02).
Also to build the CPI curve you don't need any interest rate yield curve at all because the ZCIS pays at maturity T a single cash flow: 'floating' (I(T)/I(0)-1) for 'fixed' (1+quote)^T-1 cash flows. If you equate these, you can back out the 'fair' I(T) which I used above.
Assuming your value I(0)=252.0 is correct, your CPI curve would look like this:
import QuantLib as ql
import pandas as pd
fixing_rate = 252.0
observation_date = ql.Date(17, 4, 2019)
zciisData = [(ql.Date(18,4,2020), 1.9948999881744385),
(ql.Date(18,4,2021), 1.9567999839782715),
(ql.Date(18,4,2022), 1.9566999673843384),
(ql.Date(18,4,2023), 1.9639999866485596),
(ql.Date(18,4,2024), 2.017400026321411),
(ql.Date(18,4,2025), 2.0074000358581543),
(ql.Date(18,4,2026), 2.0297999382019043),
(ql.Date(18,4,2027), 2.05430006980896),
(ql.Date(18,4,2028), 2.0873000621795654),
(ql.Date(18,4,2029), 2.1166999340057373),
(ql.Date(18,4,2031), 2.152100086212158),
(ql.Date(18,4,2034), 2.18179988861084),
(ql.Date(18,4,2039), 2.190999984741211),
(ql.Date(18,4,2044), 2.2016000747680664),
(ql.Date(18,4,2049), 2.193000078201294)]
fixing_dates = []
CPI_computed = []
for tenor, quote in zciisData:
fixing_dates.append(tenor - ql.Period('3M')) # this is 'fixing date' t
pay_date = ql.ActualActual().yearFraction(observation_date, tenor) # this is year fraction of 'pay date' T
CPI_computed.append(fixing_rate * (1+quote/100)**(pay_date))
results = pd.DataFrame({'date': pd.Series(fixing_dates).apply(ql.Date.to_date), 'CPI':CPI_computed})
display(results)
results.set_index('date')['CPI'].plot();
Good afternoon,
I have a problem when displaying the contents of my table in a list. The action does not make the impression of the data in each row and do not understand why.
I have the 1076 version of Corona SDK and does not work, but with the previous IF it worked.
I hope your help.
local function onRowRender( event )
print("oooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo")
local phase = event.phase
local row = event.row
local rowGroup = event.view
local label = aux.corrigeEspeciales (rowTitles[ row.index ])
local color = 20
print ("label" .. label)
row.itemName = label
row.textObj = display.newRetinaText(rowGroup,label, 0, 0, "Verdana", 12 )
row.textObj:setTextColor( color )
row.textObj:setReferencePoint( display.CenterLeftReferencePoint )
row.textObj.x, row.textObj.y = 20, rowGroup.contentHeight * 0.5
rowGroup:insert( row.textObj )
row.arrow = display.newImage( "images/tiendarowArrow.png", false )
row.arrow.x = rowGroup.contentWidth - row.arrow.contentWidth * 2
row.arrow.y = rowGroup.contentHeight * 0.5
rowGroup:insert( row.arrow )
end
You can show text info in 2013.1076 Corona SDK version using:
local phase = event.phase
local row = event.row
local rowGroup = event.view
local label = aux.corrigeEspeciales (rowTitles[ row.index ])
local color = 20
row.itemName = label
local rowTitle = display.newText(row,label, 0, 0, "Verdana", 12 )
rowTitle.x = row.x - ( row.contentWidth * 0.5 ) + ( rowTitle.contentWidth * 0.5 )
rowTitle.y = row.contentHeight * 0.5
rowTitle:setTextColor( 0, 0, 0 )
Corona 1076 I think uses Widget 2.0 widgets. They require a different syntax for determining the row group. See this article on updating your widgets to the new syntax.
http://docs.coronalabs.com/api/library/widget/migration.html
Also there is a new public build, 1135 that contains considerable bug fixes to the widget library. I would suggest upgrading so that you have these fixes.