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)
I have a following data table:
I need to create a column called FINAL VALUE with certain rules in the power query editor:
The FINAL VALUE is created based on the COUNTRY
If VALUE is greater than 100, then the previous value will be taken
if the previous values (no previous value such as 202001) are all greater than 100, then 100
So the final table should look like:
I sort COUNTRY and DATE, and then I try to apply loop in M Query. As I am not familiar with the function in M query, I have hard time figuring out. I appreciate any help. Thank you.
I believe you are better of in DAX. You can create a new column like below:
Final Value =
var curCountry = TableCountry[Country]
var curDate = TableCountry[Date]
var prevDate = CALCULATE(MAX(TableCountry[Date]), FILTER(TableCountry, curDate >= TableCountry[Date] && curCountry = TableCountry[Country] && TableCountry[Value] <=100))
return LOOKUPVALUE(TableCountry[Value], TableCountry[Country], curCountry, TableCountry[Date], prevDate, 100)
I am using dygraphs for R and I opened the following issue on GitHub the other day, however, I have not yet received an answer. Therefore, I am hoping someone in here will be able to answer my question.
I want to know if it is possible to show all the values of the prediction interval in the legend, i.e. , lower, actual, upper, without having them as three separate plain dySeries? I like the look of the shading that the upper/lower bars bring, but I would also like to be able to hover over a point and see all the values for that particular point, not just the middle one. If such a function does not exists, is there an easy workaround, maybe with fillGraph = TRUE or something?
library(dygraphs)
hw <- HoltWinters(ldeaths)
p <- predict(hw, n.ahead = 72, prediction.interval = TRUE)
dygraph(p, main = "Predicted Lung Deaths (UK)") %>%
dySeries(c("lwr", "fit", "upr"), label = "Deaths")
The preceding code is the example from the web page, which is similar to my problem. I simply want to see the lwr and upr values in the legend when hovering.
So I found a workaround for anybody looking for something similar.
library(dygraphs)
hw <- HoltWinters(ldeaths)
p <- predict(hw, n.ahead = 72, prediction.interval = TRUE)
max <- p[,2]
min <- p[,3]
p <- ts.union(p, max, min)
dygraph(p, main = "Predicted Lung Deaths (UK)") %>%
dySeries(c("p.lwr", "p.fit", "p.upr"), label = "Deaths") %>%
dySeries("max", label = "Max", pointSize = 0.01, strokeWidth = 0.001) %>%
dySeries("min", label = "Max", pointSize = 0.01, strokeWidth = 0.001)
Obviously, this can be modified to suit your needs (e.g. color of the points etc.) The main idea in this method is simply to create two new columns containing the same information that is used in the bands, and then to make the lines to these too small to see.
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();
While trying to calculate Year over Year variance (unsuccessfully for 2 days now), I get the following error message.
EARLIER/EARLIEST refers to an earlier row context which doesn't exist.
YOY Variance = var PreviousYearPrinBal = CALCULATE(SUM(Deals[Principal Balance]),FILTER(ALL(Deals[Close Date].[Year]),Deals[Close Date].[Year] = EARLIER(Deals[Close Date].[Year])))
return
if(PreviousYearPrinBal = BLANK(), BLANK(), Deals[PrincipalBalance] - PreviousYearPrinBal)
In a different SO question, there is a different approach which gives me the following error:
A column specified in the call to function 'SAMEPERIODLASTYEAR' is not of type DATE. This is not supported.
yoy = CALCULATE([PrincipalBalance], SAMEPERIODLASTYEAR(Deals[Close Date].[Year]))
While I have some idea of what these mean, I do not have an idea of how to fix them. Here is my table.
And Here is what I expect as the result.
I've tried posting this question in Power BI community but haven't received an answer yet. Calculate Year over Year Variance.
ACTUAL DATA SAMPLE:
1) Created Year and Year Difference Column (Calculated Column)
Year = YEAR(Table1[Date])
Year Difference = Table1[Year] - Min(Table1[Year])
2) Created the Variance (Measure)
Variance =
Var current_YearDifference = SELECTEDVALUE(Table1[Year Difference])
Var Current_PrincipalBalance = CALCULATE(SUM(Table1[Principal Balance]),FILTER(ALL(Table1), Table1[Year Difference] = (current_YearDifference)))
Var Previous_PrincipalBalance = CALCULATE(SUM(Table1[Principal Balance]),FILTER(ALL(Table1), Table1[Year Difference] = (current_YearDifference - 1)))
Return if(current_YearDifference <> 0, (Current_PrincipalBalance - Previous_PrincipalBalance), 0)
3) Finally Created the Variance in terms of Percentage (Measure),
Variance in terms of Percentage =
Var current_YearDifference = SELECTEDVALUE(Table1[Year Difference])
Var Current_PrincipalBalance = CALCULATE(SUM(Table1[Principal Balance]),FILTER(ALL(Table1), Table1[Year Difference] = (current_YearDifference)))
Var Previous_PrincipalBalance = CALCULATE(SUM(Table1[Principal Balance]),FILTER(ALL(Table1), Table1[Year Difference] = (current_YearDifference - 1)))
Return if(current_YearDifference <> 0, ((Current_PrincipalBalance - Previous_PrincipalBalance) / Previous_PrincipalBalance), 0)
My Final Output
The Principal Balance has the function SUM selected on the Values Pane of the Output Table, where as the Year is Don't Summarize.
My Best Practice
Always Use Vars when creating Complex Measures to simplify the
formula.
Then return only a part of the Measure to check if the output is as expected.
Kindly let me know, if it helps or not.