API Flow-Percentage
This chapter will cover recreating daily percentage flows for some of EPFR’s macro strategies, by retrieving data via the API.
This chapter includes recreations for,
- Flow-Percentage Country Strategy
- Multi-Asset Region Strategy
- Multi-Asset FI Strategy
API Flow-Percentage Country Strategy
The Flow-Percentage Country Strategy is an equity-based rotation strategy which ranks different countries across a customized universe of both developed and emerging markets. This approach uses the stated allocations of equity funds with a cross-border focus to calculate the percentage flow for each country.
More information about this strategy can be found under Flow-Percentage Country Strategy.
Download the R Script source for this strategy
The steps to recreating the flow-percentage country strategy are as follows,
- Collect all equity country allocations.
- Collect all cross-border equity fund flows.
- Subset to funds reporting both allocations and fund flows.
- Scale monthly country allocations against daily flow/AuM for each fund.
- Divide the scaled flow by the scaled AuM to get the flow percentage for each country.
We will be recreating daily percentage flow per country across one allocation month.
Both country allocations and fund flows use the same function epfr.get.reportid to create the report which returns data. Full documentation for the parameters of epfr.get.reportid can be in the API documentation.
More about available filters, asset classes, and return value categories call the api dictionaries available here.
API Country Allocations
Step 1 is to retrieve country allocations for all available asset classes, across one month.
alloc_month <- "202505"
date <- flow_to_api_date(yyyymm.to.day(alloc_month))
all_acs <- as.list(epfr.get.dictionary.assetclasses("CA")$data %>%
filter(grepl("Equity", name)) %>% pull(id) %>% as.list()) # ALL AVALIABLE ASSET CLASSES
report_ac <- epfr.get.reportid(dataset = "CA",
from_date = date,
to_date = date,
asset_classes = all_acs,
frequency = "Monthly",
level = 2, # FUND LEVEL
categories = list(85), # FUND ID
average_type = 1,
allocation_ids = as.list(seq(1, 187)), # ALL COUNTRIES
assets = T,
universal_filters = NULL,
equity_filters = NULL,
bond_filters = NULL,
alternative_filters = NULL)After the report ID is created, wait for report’s completion, then return the result.
report_id_ac <- report_ac$data$reportId
wait_for_completion(report_id_ac, "CA", inital_time_offset = 20, time_offset = 10)
report_result_ca <- epfr.get.reportoutput("CA", report_id_ac) ## [1] "Inital wait for report to complete, delay 20"## [1] "Report complete: 20 seconds taken"The resulting data frame will contain the allocation Date, FundID, Total Net Assets, and the funds weight towards individual countries (as a %), as well as it’s API asset class and any API filters (in this case “none”).
| Date | FundID | Total Net Assets | Australia | Brazil | China | USA | Asset Class | Filters |
|---|---|---|---|---|---|---|---|---|
| 5/31/2025 | 30470 | 53.1179 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | Europe Regional-Western Europe-CA-Equity | None |
| 5/31/2025 | 91358 | 681.5458 | 0.5058 | 0.0000 | 0.8725 | 67.2088 | Global-Global-CA-Equity | None |
| 5/31/2025 | 850 | 522.9530 | 0.0000 | 8.0940 | 29.1253 | 0.0000 | Global Emerging Markets-GEM-CA-Equity | None |
| 5/31/2025 | 98514 | 1167.1641 | 1.8673 | 0.0000 | 0.8029 | 2.5642 | Global ex-US-Global-CA-Equity | None |
| 5/31/2025 | 13005 | 864.4328 | 0.4300 | 0.4800 | 2.9600 | 70.4200 | Global-Global-CA-Equity | None |
| 5/31/2025 | 92366 | 406.3273 | 13.2687 | 0.0000 | 28.5633 | 0.0000 | Asia ex-Japan Regional-Asia Ex-Japan-CA-Equity | None |
| 5/31/2025 | 271 | 841.3444 | 7.8766 | 0.0000 | 27.5127 | 2.5021 | Asia ex-Japan Regional-Asia Ex-Japan-CA-Equity | None |
| 5/31/2025 | 92368 | 1585.2415 | 14.2611 | 0.0000 | 28.0676 | 0.0000 | Asia ex-Japan Regional-Asia Ex-Japan-CA-Equity | None |
| 5/31/2025 | 40529 | 466.0487 | 13.9533 | 0.0000 | 23.9249 | 0.0000 | Asia ex-Japan Regional-Asia Ex-Japan-CA-Equity | None |
| 5/31/2025 | 102060 | 75.6420 | 0.0000 | 4.7855 | 24.6677 | 0.0000 | Global Emerging Markets-GEM-CA-Equity | None |
Transform data such that there is a column of FundIDs and the rest of columns are the country allocations for each fund in the data frame.
curr_alloc_data <- report_result_ca %>%
select(!c(Date, Fund, `Total Net Assets`, `Asset Class`, Filters)) %>%
mutate(across(
.cols = -c(FundID),
.fns = ~ . / 100
))
print(curr_alloc_data %>% select(FundID, Australia, Brazil, China, USA) %>% top_n(10))| FundID | Australia | Brazil | China | USA |
|---|---|---|---|---|
| 10959 | 0.000000 | 0 | 0.000000 | 0.766340 |
| 78120 | 0.000000 | 0 | 0.000000 | 0.768722 |
| 3701 | 0.000000 | 0 | 0.000000 | 0.819344 |
| 88963 | 0.008400 | 0 | 0.000000 | 0.876300 |
| 50111 | 0.013707 | 0 | 0.000585 | 0.805743 |
| 15342 | 0.000000 | 0 | 0.000000 | 0.762000 |
| 90008 | 0.000000 | 0 | 0.000000 | 0.982066 |
| 13663 | 0.004851 | 0 | 0.000000 | 0.844482 |
| 5168 | 0.004182 | 0 | 0.013083 | 0.784196 |
| 39696 | 0.000000 | 0 | 0.000356 | 0.765399 |
API Cross-Border Fund Flows
Next step is to retrieve the fund flows for all cross border funds which report allocations data. To do this query for all funds which have a cross border focus then filter the dataframe for FundIDs contained in the allocations dataframe curr_alloc_data.
First, we collect the start and end flow dates for the chosen allocation month.
flow_dates <- flowdate.ex.AllocMo(alloc_month)
start_date_api <- flow_to_api_date(flow_dates[1])
end_date_api <- flow_to_api_date(flow_dates[length(flow_dates)])To get the cross border fund asset classes use the classif file, which is located in the sftp/ftp under Classifications/classif-GeoId.txt.
classif_geoid <- read in file from ftp located at Classifications/classif-GeoId.txt
xborder <- classif_geoid %>%
filter(xBord == 1) %>%
select(ApiEquityId) %>%
pull()Create the report id for the allocations data using the cross border ids. The report will return the FundIDs, flow USD and Net Assets Start for each fund over the time period.
report_ff <- epfr.get.reportid(dataset = "FF",
from_date = start_date_api,
to_date = end_date_api,
asset_classes = xborder,
frequency = "Daily",
level = 2,
categories = list(53, 3, 13), # FUND ID, FLOW USD, NET ASSETS START
average_type = NULL,
allocation_ids = NULL,
assets = NULL,
universal_filters = NULL,
equity_filters = NULL,
bond_filters = NULL,
alternative_filters = NULL)After creating the report wait for completion.
## [1] "Inital wait for report to complete, delay 30"## [1] "Report complete: 30 seconds taken"Examine the top rows of the output.
| Date | Asset Class | Filters | Flow US$ mill | Total Net Assets Start | FundID |
|---|---|---|---|---|---|
| 7/22/2025 | Asia ex-Japan Regional-Asia Ex-Japan-FF-Equity | None | -0.0002 | 50.6880 | 93036 |
| 7/22/2025 | Global-Global-FF-Equity | None | 0.0000 | 176.6451 | 77027 |
| 7/22/2025 | Global-Global-FF-Equity | None | 0.0000 | 24.6424 | 50120 |
| 7/22/2025 | Global-Global-FF-Equity | None | 0.0003 | 100.6267 | 77587 |
| 7/22/2025 | Global-Global-FF-Equity | None | 0.0580 | 180.8213 | 76996 |
| 7/22/2025 | Global-Global-FF-Equity | None | -0.2588 | 1324.8773 | 94879 |
| 7/22/2025 | Global-Global-FF-Equity | None | 0.0000 | 1.3349 | 96822 |
| 7/22/2025 | Global-Global-FF-Equity | None | -0.3366 | 70.8008 | 90098 |
| 7/22/2025 | Global-Global-FF-Equity | None | 0.0015 | 0.9452 | 91045 |
| 7/22/2025 | Global-Global-FF-Equity | None | 0.0000 | 12.0685 | 90121 |
We will only need the Date, FundID, Flow, and Total Net Assets Start to calculate the flow percentage, all other columns can be discarded.
report_result_ff <- epfr.get.reportoutput("FF", report_id_ff)
curr_flow_data <- report_result_ff %>%
select(c(Date, FundID,`Flow US$ mill`, `Total Net Assets Start`))
print(curr_flow_data %>% top_n(10))| Date | FundID | Flow US$ mill | Total Net Assets Start |
|---|---|---|---|
| 7/22/2025 | 14460 | 0.5472 | 164576.2 |
| 7/18/2025 | 14460 | 0.7913 | 163995.9 |
| 7/15/2025 | 14460 | 1.1877 | 164019.1 |
| 7/14/2025 | 14460 | 0.3411 | 163990.0 |
| 7/11/2025 | 14460 | 0.6604 | 165373.9 |
| 7/10/2025 | 14460 | 0.5022 | 165113.8 |
| 7/7/2025 | 14460 | 84.7347 | 164311.4 |
| 7/4/2025 | 14460 | 0.0000 | 164311.4 |
| 7/3/2025 | 14460 | 1.1156 | 164137.9 |
| 7/1/2025 | 14460 | 1.2995 | 163790.9 |
Calculating Flow-Percentage Country
To calculate the percentage flow to a given country, for a single day we use the formula \(\frac{100 \times \sum{(Flow \times Country Allocation)}}{\sum{(AssetsStart\times Country Allocation)}}\). We will start by summing Total Fund Flows and Total Net Assets Start scaled by Country Allocations, separately.
# SCALE NET ASSETS START
net_asset_start <- curr_alloc_data %>%
mutate(FundID = as.character(FundID)) %>%
inner_join(curr_flow_data, by="FundID") %>%
mutate(across(
.cols = -c(Date, FundID, `Total Net Assets Start`, `Flow US$ mill`),
.fns = ~.* `Total Net Assets Start`)) %>%
select(!c(FundID, `Total Net Assets Start`, `Flow US$ mill`)) %>%
group_by(Date) %>%
summarise(across(everything(), sum))
# SCALE FLOW
flow <- curr_alloc_data %>%
mutate(FundID = as.character(FundID)) %>%
inner_join(curr_flow_data, by="FundID") %>%
mutate(across(
.cols = -c(Date, FundID, `Total Net Assets Start`, `Flow US$ mill`),
.fns = ~.* `Flow US$ mill`)) %>%
select(!c(FundID, `Total Net Assets Start`, `Flow US$ mill`)) %>%
group_by(Date) %>%
summarise(across(everything(), sum))We then calculate the weighted Total Fund Flow as a percentage over the weighted Total Net Assets Start, to get the Flow-Percentage for each country for a given Flow Date.
# CALCULATE FLOW PERCENTAGE
flowpct <- net_asset_start %>%
inner_join(flow, by="Date", suffix = c("_NAS", "_flow")) %>%
mutate(across(
.cols = ends_with("_flow"),
.fns = ~. / get(sub("_flow$", "_NAS", cur_column())) * 100,
.names = "{sub('_flow$', '', .col)}_flowpct")) %>%
select(Date, ends_with("_flowpct")) %>%
mutate(Date = chrdate_to_flowdate_vec(Date)) %>%
arrange(Date) %>%
tibble::column_to_rownames(var = "Date")
names(flowpct) <- sub("_flowpct$", "", names(flowpct))
print(flowpct %>% select(Australia, Brazil, China, USA) %>% top_n(10))| Flow Date | Australia | Brazil | China | USA |
|---|---|---|---|---|
| 20250624 | 0.0281460 | 0.0106213 | 0.0154277 | 0.0301169 |
| 20250627 | 0.0707934 | 0.0562816 | 0.0651659 | 0.0248924 |
| 20250701 | 0.0489678 | 0.0847893 | 0.0717443 | 0.0721819 |
| 20250703 | 0.0388208 | 0.1395051 | 0.1612564 | 0.1207161 |
| 20250709 | 0.0745372 | 0.0352085 | 0.0383067 | 0.0153098 |
| 20250711 | 0.0600016 | 0.0091183 | 0.0220054 | 0.1102320 |
| 20250714 | 0.0280433 | -0.0441572 | -0.0221710 | 0.0231083 |
| 20250715 | 0.0454806 | 0.0269412 | 0.0539303 | 0.0207744 |
| 20250718 | 0.0370245 | 0.0363192 | 0.0768154 | 0.0484770 |
| 20250721 | 0.0336125 | 0.0130824 | 0.0592443 | 0.0280501 |
Save result locally.
API Multi-Asset Region Strategy
EPFR’s Multi-Asset Strategy, rotates between asset classes within equity and fixed-income universes. The following code recreates the equity portion of the Multi-Asset strategy using the API. To learn more about this strategy you can read Multi-Asset Strategy section of this notebook.
Download the R Script source for this strategy
For this strategy we calculate aggregate flows into 7 different equity regions. Most of these regions are a single asset class where the API would aggregate flow percentage for us, but pacific ex Japan is made up of 5 asset classes meaning flow percentage will be aggregated using the net assets start and flow USD.
| Category | Asset Class - Equity | API ID |
|---|---|---|
| AsiaXJP | asia ex japan | FF121000 |
| EurXGB | europe ex uk | FF114004 |
| Japan | japan | FF111003 |
| LatAm | latain america | FF124000 |
| UK | uk | FF114017 |
| USA | usa | FF113002 |
| PacXJP | australia | FF111001 |
| PacXJP | hong kong | FF111002 |
| PacXJP | pacific regional | FF111004 |
| PacXJP | singapore | FF111005 |
| PacXJP | new zeland | FF111006 |
dataset <- "FF"
from_date <- "01-01-2025"
to_date <- "07-28-2025" # TODAYS DATE
asset_classes <- list("FF121000", # ASIA EX JAPAN
"FF114004", # EUROPE EX GB
"FF111003", # JAPAN
"FF124000", # LATAM
"FF114017", # UK
"FF113002", # USA
"FF111001", # AUSTRALIA
"FF111002", # HONG KONG
"FF111004", # PACIFIC REGIONAL
"FF111005", # SINGAPORE
"FF111006" # NEW ZELAND
)
frequency <- "Daily"
level <- 0 # AGGREGATE
categories <- list(13, 3) # ASSETS START, FLOW USD
assets <- "true"
universal_filters <- list("U100005") # ACTIVE FUNDS
equity_filters <- NULLThe next steps for calling data from the API are to create the report, wait for completion, and then return the data.
report <- epfr.get.reportid(dataset, from_date, to_date, asset_classes, frequency, level,
categories, NULL, NULL, assets, universal_filters, equity_filters, NULL, NULL)
report_id <- report$data$reportId
wait_for_completion(report_id, "FF", 30, 30)
result <- epfr.get.reportoutput(dataset, report_id)
print(result %>% top_n(10))## [1] "Inital wait for report to complete, delay 30"## [1] "Report Incomplete time taken: 60"## [1] "Report Incomplete time taken: 90"## [1] "Report Incomplete time taken: 120"## [1] "Report Incomplete time taken: 150"## [1] "Report complete: 150 seconds taken"## Selecting by Total Net Assets Start| Date | Asset Class | Filters | Flow US$ mill | Total Net Assets Start |
|---|---|---|---|---|
| 7/28/2025 | USA-North America-FF-Equity | Active | -33.4447 | 4548326 |
| 7/25/2025 | USA-North America-FF-Equity | Active | -2316.4397 | 4534417 |
| 7/24/2025 | USA-North America-FF-Equity | Active | -63.6056 | 4529274 |
| 7/23/2025 | USA-North America-FF-Equity | Active | -522.0354 | 4502067 |
| 7/22/2025 | USA-North America-FF-Equity | Active | 1142.6047 | 4492392 |
| 7/21/2025 | USA-North America-FF-Equity | Active | -1197.8905 | 4496470 |
| 7/18/2025 | USA-North America-FF-Equity | Active | 520.7728 | 4495671 |
| 7/11/2025 | USA-North America-FF-Equity | Active | -1033.4003 | 4500242 |
| 7/10/2025 | USA-North America-FF-Equity | Active | -1202.9866 | 4492208 |
| 7/7/2025 | USA-North America-FF-Equity | Active | 109.0544 | 4487428 |
After getting the report output calculate the percent flow to each of the 7 equity regions, as \(\frac{100 \times \sum{Flow}}{\sum{AssetsStart}}\). Complete methodology is outlined in the Multi-Asset Strategy section of this notebook.
result_multiAsset_Rgn_daily <- result %>%
mutate(Ctry = recode(`Asset Class`,
"Asia Ex-Japan-FF-Equity" = "AsiaXJP",
"Europe ex-UK Regional-Western Europe-FF-Equity" = "EurXGB",
"Japan-Asia Pacific-FF-Equity" = "Japan",
"LatAm-FF-Equity" = "LatAm",
"United Kingdom-Western Europe-FF-Equity" = "UK",
"USA-North America-FF-Equity" = "USA",
"Australia-Asia Pacific-FF-Equity" = "PacXJP",
"Hong Kong Special Administrative Region of China-Asia Pacific-FF-Equity" = "PacXJP",
"Singapore-Asia Pacific-FF-Equity" = "PacXJP",
"Pacific Regional-Asia Pacific-FF-Equity" = "PacXJP",
"New Zealand-Asia Pacific-FF-Equity" = "PacXJP",
.default = `Asset Class`)
) %>%
group_by(Date, Ctry) %>%
summarise(`Flow US$ mill` = sum(`Flow US$ mill`),
`Total Net Assets Start` = sum(`Total Net Assets Start`)) %>%
mutate(floPct = 100 *`Flow US$ mill` / `Total Net Assets Start`) %>%
select(Date, Ctry, floPct) %>%
pivot_wider(names_from = Ctry, values_from = floPct) %>%
mutate(Date = as.Date(Date, format = "%m/%d/%Y")) %>%
arrange(Date)Examine the top rows of the output.
| Date | FundID | Flow US$ mill | Total Net Assets Start |
|---|---|---|---|
| 7/7/2025 | 14460 | 84.7347 | 164311.4 |
| 7/4/2025 | 14460 | 0.0000 | 164311.4 |
| 7/3/2025 | 14460 | 1.1156 | 164137.9 |
| 7/22/2025 | 14460 | 0.5472 | 164576.2 |
| 7/18/2025 | 14460 | 0.7913 | 163995.9 |
| 7/15/2025 | 14460 | 1.1877 | 164019.1 |
| 7/14/2025 | 14460 | 0.3411 | 163990.0 |
| 7/11/2025 | 14460 | 0.6604 | 165373.9 |
| 7/10/2025 | 14460 | 0.5022 | 165113.8 |
| 7/1/2025 | 14460 | 1.2995 | 163790.9 |
Save result locally.
API Multi-Asset FI Strategy
EPFR’s Multi-Asset Strategy, rotates between asset classes within equity and fixed-income universes. The following code recreates the fixed-income portion of the Multi-Asset strategy using the API. To learn more about this strategy you can read Multi-Asset Strategy section of this notebook.
Download the R Script source for this strategy
For this strategy we calculate aggregate flows into 9 fixed-income asset classes, which are listed below. We will need to use multiple filters to achieve these desired asset classes using the API. (Note that when a filter is applied to an asset class it will be applied over all data in the API call, so for this reason there are multiple API reports created.)
Each category is defined in a list with 3 elements: category name, asset class, and bond filter.
| Category | Asset Class | API ID1 | Bond Filter | API ID2 |
|---|---|---|---|---|
| GLOBEM | global EMs bond | FF223002 | ||
| HYIELD | all bonds | FF200000 | high yield | B100013 |
| FLOATS | all bonds | FF200000 | bank loan | B100016 |
| USTRIN | usa bonds | FF213002 | intermediate term | B100009 |
| USTRLT | usa bonds | FF213002 | long term | B100008 |
| USTRST | usa bonds | FF213002 | short term | B100010 |
| CASH | all money market | FF400000 | ||
| USMUNI | usa bonds | FF213002 | municipal | B100019 |
| GLOFIX | global bond | FF212000 |
dataset <- "FF"
from_date <- "01-01-2025"
to_date <- "07-28-2025" # TODAY'S DATE
frequency <- "Daily"
level <- 0
categories <- list(13, 3) # ASSETS START, FLOW USD
assets <- "true"
filters <- list(list("GLOBEM", "FF223002", NULL),
list("HYIELD", "FF200000", "B100013"),
list("FLOATS", "FF200000", "B100016"),
list("USTRIN", "FF213002", "B100009"),
list("USTRLT", "FF213002", "B100008"),
list("USTRST", "FF213002", "B100010"),
list("CASH", "FF400000", NULL),
list("USMUNI", "FF213002", "B100019"),
list("GLOFIX", "FF212000", NULL))The next steps for calling data from the API are to create the report, wait for completion, and then return the data. We will do this for all 9 fixed-income asset classes.
result_multiAsset_FI_daily <- NULL
for (i in 1:length(filters)) {
print(paste0("Calling data for: ", filters[[i]][[1]]))
report <- epfr.get.reportid(dataset,
from_date,
to_date,
list(filters[[i]][[2]]), # ASSET CLASS
frequency,
level,
categories,
NULL,
NULL,
assets,
NULL,
NULL,
if (is.null(filters[[i]][[3]])) NULL else list(filters[[i]][[3]]),
NULL) # BOND FILTER
report_id <- report$data$reportId
wait_for_completion(report_id, dataset, 20, 30)
result_catagory <- epfr.get.reportoutput(dataset = "FF", report_id = toString(report_id))
restult_catagory_clean <- result_catagory %>%
mutate(FloPct = 100 *`Flow US$ mill` / `Total Net Assets Start`,
Date = as.Date(Date, format = "%m/%d/%Y")) %>%
select(Date, FloPct) %>%
rename(!!filters[[i]][[1]] := FloPct)
if (is.null(result_multiAsset_FI_daily)) {
result_multiAsset_FI_daily <- restult_catagory_clean
} else {
result_multiAsset_FI_daily <- result_multiAsset_FI_daily %>% inner_join(restult_catagory_clean, by = "Date")
}
}## [1] "Calling data for: GLOBEM"## [1] "Inital wait for report to complete, delay 20"## [1] "Report complete: 20 seconds taken"## [1] "Calling data for: HYIELD"## [1] "Inital wait for report to complete, delay 20"## [1] "Report complete: 20 seconds taken"## [1] "Calling data for: FLOATS"## [1] "Inital wait for report to complete, delay 20"## [1] "Report complete: 20 seconds taken"## [1] "Calling data for: USTRIN"## [1] "Inital wait for report to complete, delay 20"## [1] "Report complete: 20 seconds taken"## [1] "Calling data for: USTRLT"## [1] "Inital wait for report to complete, delay 20"## [1] "Report complete: 20 seconds taken"## [1] "Calling data for: USTRST"## [1] "Inital wait for report to complete, delay 20"## [1] "Report complete: 20 seconds taken"## [1] "Calling data for: CASH"## [1] "Inital wait for report to complete, delay 20"## [1] "Report complete: 20 seconds taken"## [1] "Calling data for: USMUNI"## [1] "Inital wait for report to complete, delay 20"## [1] "Report complete: 20 seconds taken"## [1] "Calling data for: GLOFIX"## [1] "Inital wait for report to complete, delay 20"## [1] "Report complete: 20 seconds taken"Check top rows of output.
| Date | GLOBEM | HYIELD | FLOATS | USTRIN | USTRLT | USTRST | CASH | USMUNI | GLOFIX |
|---|---|---|---|---|---|---|---|---|---|
| 2019-06-05 | -0.0279737 | 0.0953499 | -0.1986875 | 0.0177541 | 0.0094597 | 0.1615458 | 0.0729610 | 0.0417629 | 0.4192535 |
| 2014-05-26 | 0.0521502 | -0.0093754 | -0.0000483 | 0.0024525 | -0.0004529 | -0.0018943 | -0.1878645 | -0.0011076 | 0.3851974 |
| 2010-04-27 | 0.3376269 | 0.1029392 | 0.2648442 | 0.0030902 | 0.1746803 | 0.0602334 | 0.0982793 | 0.0080934 | 0.3946408 |
| 2010-04-16 | 0.4127297 | 0.0711022 | 0.4498816 | -0.0104679 | 0.0531455 | 0.0845487 | -0.1359191 | 0.0055590 | 0.3806760 |
| 2010-03-03 | 0.2413263 | 0.0803211 | 0.2213384 | 0.0867212 | 0.0254124 | 0.2971411 | 0.0944945 | 0.0471712 | 0.4351618 |
| 2010-01-20 | 0.3366571 | 0.0616013 | 0.2117595 | 0.0333624 | 0.0255387 | 0.2164246 | -0.0404757 | 0.0590163 | 0.3594302 |
| 2009-10-27 | 0.3782404 | 0.0570280 | 0.0604020 | 0.0131985 | -0.0056886 | 0.2609019 | 0.1491588 | 0.0774289 | 0.3631106 |
| 2009-10-19 | 0.0045374 | 0.0594008 | -0.1519629 | 0.1209147 | 0.0139186 | 0.3042336 | 0.0739953 | 0.0844787 | 0.4147324 |
| 2009-07-16 | 0.2589983 | 0.0386666 | 0.1033427 | 0.1252718 | -0.0324792 | 0.2348748 | 0.2249244 | 0.0721367 | 0.3610647 |
| 2009-07-06 | 0.0130407 | 0.0321640 | 0.3548821 | -0.2521641 | 0.0004447 | 0.3632362 | 0.2354842 | 0.0784644 | 0.5482938 |
Save result locally.