13 March 2025 - Written by ML

1. Overview

This report examines traded futures and options in energy on the Chicago Mercantile Exchange (CME). The analysis includes:

A tidying of the raw Excel data.
Summary statistics for numeric and categorical variables.
Contrasts between Futures and Options by product group.
Detailed insights such as top records by Open Interest for each combination of product group and cleared type.

2. Data Preparation

The raw data was obtained from the CME website (CME Group Products, 2025) as an excel file and imported into R. There it was tidied for analysis. Key steps include

renaming columns,
handling missing values, and
converting numeric fields.

# Read the Excel file and save the raw data (update file paths as necessary)
data <- read_excel("C:/Users/ml/project/R/DomEnergyR/data/raw/cme/Product_Slate_Export.xlsx", 
                   sheet = 1, skip = 3)  # Skip metadata rows

# Save the raw data for future reference
save(data, file = "C:/Users/ml/project/R/DomEnergyR/data/clean/cme/cme_product_slate_clean_20250313.RData")

# Tidy the data
tidy_data <- data %>%
  rename_with(~ gsub(" ", "_", .)) %>%        # Rename columns to snake_case
  mutate(Floor = na_if(Floor, "-")) %>%         # Replace "-" with NA in Floor
  mutate(across(c(Volume, Open_Interest), ~ parse_number(.)))  # Convert Volume and Open_Interest to numeric

# Display structure and sample rows
str(tidy_data)

## tibble [1,025 × 13] (S3: tbl_df/tbl/data.frame)
##  $ Product_Name : chr [1:1025] "Crude Oil Option" "Crude Oil Futures" "RBOB Gasoline Futures" "WTI Houston (Argus) vs. WTI Trade Month Futures" ...
##  $ Clearing     : chr [1:1025] "LO" "CL" "RB" "HTT" ...
##  $ Globex       : chr [1:1025] "LO" "CL" "RB" "HTT" ...
##  $ Floor        : chr [1:1025] NA NA NA NA ...
##  $ Clearport    : chr [1:1025] "LO" "CL" "RB" "HTT" ...
##  $ Exchange     : chr [1:1025] "NYMEX" "NYMEX" "NYMEX" "NYMEX" ...
##  $ Asset_Class  : chr [1:1025] "Energy" "Energy" "Energy" "Energy" ...
##  $ Product_Group: chr [1:1025] "Crude Oil" "Crude Oil" "Refined Products" "Crude Oil" ...
##  $ Category     : chr [1:1025] "North American" "North American" "North American" "North American" ...
##  $ Sub_Category : chr [1:1025] "Outrights" "Outrights" "Outrights" "Spreads" ...
##  $ Cleared_As   : chr [1:1025] "Options" "Futures" "Futures" "Futures" ...
##  $ Volume       : num [1:1025] 180906 636171 179282 9214 46750 ...
##  $ Open_Interest: num [1:1025] 2294536 1793310 445652 436539 426915 ...

head(tidy_data)

## # A tibble: 6 × 13
##   Product_Name              Clearing Globex Floor Clearport Exchange Asset_Class
##   <chr>                     <chr>    <chr>  <chr> <chr>     <chr>    <chr>      
## 1 Crude Oil Option          LO       LO     <NA>  LO        NYMEX    Energy     
## 2 Crude Oil Futures         CL       CL     <NA>  CL        NYMEX    Energy     
## 3 RBOB Gasoline Futures     RB       RB     <NA>  RB        NYMEX    Energy     
## 4 WTI Houston (Argus) vs. … HTT      HTT    <NA>  HTT       NYMEX    Energy     
## 5 Crude Oil Financial Cale… 7A       B7A    <NA>  7A        NYMEX    Energy     
## 6 WTI Average Price Option  AO       AAO    <NA>  AO        NYMEX    Energy     
## # ℹ 6 more variables: Product_Group <chr>, Category <chr>, Sub_Category <chr>,
## #   Cleared_As <chr>, Volume <dbl>, Open_Interest <dbl>

tail(tidy_data)

## # A tibble: 6 × 13
##   Product_Name              Clearing Globex Floor Clearport Exchange Asset_Class
##   <chr>                     <chr>    <chr>  <chr> <chr>     <chr>    <chr>      
## 1 Southern Green Canyon (A… SCB      SCB    <NA>  SCB       NYMEX    Energy     
## 2 NY Harbor ULSD Financial… FAF      FAF    <NA>  FAF       NYMEX    Energy     
## 3 Natural Gas Monday Weekl… HN4      HN4    <NA>  HN4       NYMEX    Energy     
## 4 Natural Gas Tuesday Week… IN5      IN5    <NA>  IN5       NYMEX    Energy     
## 5 Natural Gas Wednesday We… JN5      JN5    <NA>  JN5       NYMEX    Energy     
## 6 HLS (Argus) vs. WTI Trad… HLT      HLT    <NA>  HLT       NYMEX    Energy     
## # ℹ 6 more variables: Product_Group <chr>, Category <chr>, Sub_Category <chr>,
## #   Cleared_As <chr>, Volume <dbl>, Open_Interest <dbl>

3. Summary Statistics

3.1 Numeric Summary

The following summary provides basic descriptive statistics (Count, Mean, Standard Deviation, Min, Median, and Max) for numeric columns (e.g., Volume, Open Interest).

numeric_summary <- tidy_data %>%
  select(where(is.numeric)) %>%
  summarise(across(everything(), list(
    Count = ~ sum(!is.na(.)),
    Mean = ~ mean(., na.rm = TRUE),
    SD = ~ sd(., na.rm = TRUE),
    Min = ~ min(., na.rm = TRUE),
    Median = ~ median(., na.rm = TRUE),
    Max = ~ max(., na.rm = TRUE)
  ))) %>%
  pivot_longer(
    cols = everything(),
    names_to = c("Variable", "Statistic"),
    names_pattern = "(.*)_(\\w+)$"
  )

# Print numeric summary neatly
numeric_summary %>%
  kable(caption = "Numeric Summary", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE)

Numeric Summary
Variable	Statistic	value
Volume	Count	1025.000
Volume	Mean	1437.697
Volume	SD	22437.838
Volume	Min	0.000
Volume	Median	0.000
Volume	Max	636171.000
Open_Interest	Count	1025.000
Open_Interest	Mean	7820.434
Open_Interest	SD	96283.071
Open_Interest	Min	0.000
Open_Interest	Median	0.000
Open_Interest	Max	2294536.000

3.2 Categorical Summary

Below is a summary of character columns that shows the number of unique items and the top three most frequent values (with their counts) for each column.

categorical_summary <- map_dfr(names(tidy_data)[sapply(tidy_data, is.character)], function(col_name) {
  freq_table <- tidy_data %>%
    filter(!is.na(.data[[col_name]])) %>%
    count(.data[[col_name]]) %>%
    arrange(desc(n))
  
  top_n <- head(freq_table, 3)
  top_items <- paste(top_n[[col_name]], collapse = ", ")
  top_counts <- paste(top_n$n, collapse = ", ")
  
  tibble(
    Column = col_name,
    Unique_Count = n_distinct(tidy_data[[col_name]]),
    Top_Values = top_items,
    Top_Frequencies = top_counts
  )
})

categorical_summary %>%
  kable(caption = "Categorical Summary", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE)

Categorical Summary
Column	Unique_Count	Top_Values	Top_Frequencies
Product_Name	1024	PJM AEP Dayton Hub Peak 50 MW Calendar-Month LMP Option, 1% Fuel Oil Barges FOB Rdam (Platts) vs. 1% Fuel Oil Cargoes FOB NWE (Platts) BALMO Futures, 1% Fuel Oil Cargoes CIF MED (Platts) BALMO Futures	2, 1, 1
Clearing	1024	EH, 0A, 0B	2, 1, 1
Globex	1025	0A, 0B, 0C	1, 1, 1
Floor	1
Clearport	1024	EH, 0A, 0B	2, 1, 1
Exchange	2	NYMEX, CBOT	1022, 3
Asset_Class	1	Energy	1025
Product_Group	7	Refined Products, Electricity, Crude Oil	354, 295, 208
Category	16	North American, European, PJM	290, 248, 138
Sub_Category	10	Outrights, Spreads, Daily Peak	384, 215, 89
Cleared_As	2	Futures, Options	790, 235

4. Subsets

4.1 CBOT Contracts

The following subset shows records for contracts traded on the CBOT.

cbot_data <- tidy_data %>% 
  filter(Exchange == "CBOT")
cbot_data %>%
  kable(caption = "CBOT Contracts", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE)

CBOT Contracts
Product_Name	Clearing	Globex	Floor	Clearport	Exchange	Asset_Class	Product_Group	Category	Sub_Category	Cleared_As
Ethanol Forward Month Futures	71	FZE	NA	71	CBOT	Energy	Biofuels	North American	Outrights	Futures
Ethanol Futures	EH	EH	NA	EH	CBOT	Energy	Biofuels	North American	Outrights	Futures
Ethanol Options	EH	OEH	NA	EH	CBOT	Energy	Biofuels	North American	Outrights	Options

4.2 Futures and Options

We also create separate subsets for Futures and Options contracts.

futures_data <- tidy_data %>% filter(Cleared_As == "Futures")
options_data <- tidy_data %>% filter(Cleared_As == "Options")

# Display count for each subset
cat("Number of Futures Records: ", nrow(futures_data), "\n")

## Number of Futures Records:  790

cat("Number of Options Records: ", nrow(options_data), "\n")

## Number of Options Records:  235

5. Index Analysis

5.1 Category and Product

The following analyses contrast the distribution of Categories and Product Groups between Futures and Options.

# Summary for Category by Cleared_As:
category_contrast <- tidy_data %>%
  group_by(Cleared_As, Category) %>%
  summarise(Count = n(), .groups = "drop") %>%
  group_by(Cleared_As) %>%
  mutate(Percent = round(Count / sum(Count) * 100, 1)) %>%
  arrange(Cleared_As, desc(Count))

category_contrast %>%
  kable(caption = "Category Contrast by Cleared_As", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE)

Category Contrast by Cleared_As
Cleared_As	Category	Count	Percent
Futures	European	203	25.7
Futures	North American	171	21.6
Futures	PJM	91	11.5
Futures	Asian	68	8.6
Futures	ERCOT	33	4.2
Futures	NGL	33	4.2
Futures	Arb	31	3.9
Futures	ISO-NE	31	3.9
Futures	NYISO	28	3.5
Futures	MISO	27	3.4
Futures	Wet	22	2.8
Futures	Western Power	16	2.0
Futures	Canadian	14	1.8
Futures	LPG	12	1.5
Futures	Olefin	7	0.9
Futures	Resin	3	0.4
Options	North American	119	50.6
Options	PJM	47	20.0
Options	European	45	19.1
Options	Asian	8	3.4
Options	NGL	5	2.1
Options	Arb	3	1.3
Options	NYISO	3	1.3
Options	ISO-NE	2	0.9
Options	MISO	2	0.9
Options	Western Power	1	0.4

# Summary for Product_Group by Cleared_As:
product_group_contrast <- tidy_data %>%
  group_by(Cleared_As, Product_Group) %>%
  summarise(Count = n(), .groups = "drop") %>%
  group_by(Cleared_As) %>%
  mutate(Percent = round(Count / sum(Count) * 100, 1)) %>%
  arrange(Cleared_As, desc(Count))

product_group_contrast %>%
  kable(caption = "Product Group Contrast by Cleared_As", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE)

Product Group Contrast by Cleared_As
Cleared_As	Product_Group	Count	Percent
Futures	Refined Products	313	39.6
Futures	Electricity	240	30.4
Futures	Crude Oil	116	14.7
Futures	Petrochemicals	59	7.5
Futures	Biofuels	27	3.4
Futures	Freight	22	2.8
Futures	Natural Gas	13	1.6
Options	Crude Oil	92	39.1
Options	Electricity	55	23.4
Options	Refined Products	41	17.4
Options	Natural Gas	30	12.8
Options	Biofuels	12	5.1
Options	Petrochemicals	5	2.1

5.2 Index by Type

We can present product group counts side by side (Futures vs Options) for easy comparison.

product_group_side_by_side <- tidy_data %>%
  group_by(Product_Group, Cleared_As) %>%
  summarise(Count = n(), .groups = "drop") %>%
  pivot_wider(names_from = Cleared_As, values_from = Count, values_fill = 0)

product_group_side_by_side %>%
  kable(caption = "Side-by-Side Product Group Comparison", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE)

Side-by-Side Product Group Comparison
Product_Group	Futures	Options
Biofuels	27	12
Crude Oil	116	92
Electricity	240	55
Freight	22	0
Natural Gas	13	30
Petrochemicals	59	5
Refined Products	313	41

category_side_by_side <- tidy_data %>%
  group_by(Category, Cleared_As) %>%
  summarise(Count = n(), .groups = "drop") %>%
  pivot_wider(names_from = Cleared_As, values_from = Count, values_fill = 0)

# Print the side-by-side table
category_side_by_side %>%
  kable(caption = "Side-by-Side Category Group Comparison", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE)

Side-by-Side Category Group Comparison
Category	Futures	Options
Arb	31	3
Asian	68	8
Canadian	14	0
ERCOT	33	0
European	203	45
ISO-NE	31	2
LPG	12	0
MISO	27	2
NGL	33	5
NYISO	28	3
North American	171	119
Olefin	7	0
PJM	91	47
Resin	3	0
Western Power	16	1
Wet	22	0

5.3 Product Group stats

Here we summarize key numeric metrics (Volume and Open Interest) for each product group, split by Futures and Options.

futures_options_stats <- tidy_data %>%
  group_by(Product_Group, Cleared_As) %>%
  summarise(
    Count = n(),
    Mean_Volume = mean(Volume, na.rm = TRUE),
    Median_Volume = median(Volume, na.rm = TRUE),
    SD_Volume = sd(Volume, na.rm = TRUE),
    Max_Volume = max(Volume, na.rm = TRUE),
    Mean_Open_Interest = mean(Open_Interest, na.rm = TRUE),
    Median_Open_Interest = median(Open_Interest, na.rm = TRUE),
    SD_Open_Interest = sd(Open_Interest, na.rm = TRUE),
    Max_Open_Interest = max(Open_Interest, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(Product_Group, Cleared_As)

futures_options_stats %>%
  kable(caption = "Futures and Options Statistics by Product Group", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE)

Futures and Options Statistics by Product Group
Product_Group	Cleared_As	Count	Mean_Volume	SD_Volume	Max_Volume	Mean_Open_Interest	SD_Open_Interest	Max_Open_Interest
Biofuels	Futures	27	145.333333	707.55043	3680	1533.62963	6455.98072	33107
Biofuels	Options	12	0.000000	0.00000	0	378.66667	1311.73981	4544
Crude Oil	Futures	116	7068.051724	59986.94723	636171	26123.50862	174113.30243	1793310
Crude Oil	Options	92	2718.510870	19420.43170	180906	38806.32609	245822.24983	2294536
Electricity	Futures	240	0.000000	0.00000	0	0.00000	0.00000	0
Electricity	Options	55	0.000000	0.00000	0	0.00000	0.00000	0
Freight	Futures	22	0.000000	0.00000	0	19.18182	81.48306	382
Natural Gas	Futures	13	2042.230769	5245.99473	17277	606.00000	1414.44972	4840
Natural Gas	Options	30	6.733333	23.51072	105	15.20000	52.75735	271
Petrochemicals	Futures	59	190.474576	809.12195	5766	6246.62712	22928.61114	156574
Petrochemicals	Options	5	0.000000	0.00000	0	99.80000	223.15958	499
Refined Products	Futures	313	1146.418530	14117.68451	179282	3122.03834	31793.23006	445652
Refined Products	Options	41	70.731707	452.90391	2900	353.17073	2256.59859	14450

5.4 Open Interest top 3

Finally, we extract the top three records (by Open Interest) for each product group and cleared type.

top3_by_group <- tidy_data %>%
  group_by(Product_Group, Cleared_As) %>%
  slice_max(Open_Interest, n = 3, with_ties = FALSE) %>%
  ungroup() %>%
  arrange(Product_Group, Cleared_As, desc(Open_Interest))

top3_by_group %>%
  kable(caption = "Top 3 Records by Open Interest for Each Product Group and Cleared_As", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE)

Top 3 Records by Open Interest for Each Product Group and Cleared_As
Product_Name	Clearing	Globex	Floor	Clearport	Exchange	Asset_Class	Product_Group	Category	Sub_Category	Cleared_As	Volume	Open_Interest
Chicago Ethanol (Platts) Futures	CU	CU	NA	CU	NYMEX	Energy	Biofuels	North American	Outrights	Futures	3680	33107
Ethanol T2 FOB Rdam Including Duty (Platts) Futures	Z1	AZ1	NA	Z1	NYMEX	Energy	Biofuels	European	Outrights	Futures	209	7101
NY Ethanol (Platts) Futures	EZ	AEZ	NA	EZ	NYMEX	Energy	Biofuels	North American	Outrights	Futures	0	665
Chicago Ethanol (Platts) Average Price Option	CVR	CVR	NA	CVR	NYMEX	Energy	Biofuels	North American	Outrights	Options	0	4544
Ethanol T2 FOB Rdam Including Duty (Platts) Average Price Option	Z1O	Z1O	NA	Z1O	NYMEX	Energy	Biofuels	European	Outrights	Options	0	0
D4 Biodiesel RINs (OPIS) vs. D6 Ethanol RINs (OPIS) Average Price Option	R46	R46	NA	R46	NYMEX	Energy	Biofuels	North American	Spreads	Options	0	0
Crude Oil Futures	CL	CL	NA	CL	NYMEX	Energy	Crude Oil	North American	Outrights	Futures	636171	1793310
WTI Houston (Argus) vs. WTI Trade Month Futures	HTT	HTT	NA	HTT	NYMEX	Energy	Crude Oil	North American	Spreads	Futures	9214	436539
WTI Midland (Argus) vs. WTI Trade Month Futures	WTT	WTT	NA	WTT	NYMEX	Energy	Crude Oil	North American	Spreads	Futures	1385	310359
Crude Oil Option	LO	LO	NA	LO	NYMEX	Energy	Crude Oil	North American	Outrights	Options	180906	2294536
Crude Oil Financial Calendar Spread Option 1 Month	7A	B7A	NA	7A	NYMEX	Energy	Crude Oil	North American	Outrights	Options	46750	426915
WTI Average Price Option	AO	AAO	NA	AO	NYMEX	Energy	Crude Oil	North American	Outrights	Options	0	375765
PJM Western Hub Peak Calendar-Month Real-Time LMP Futures	L1	AL1	NA	L1	NYMEX	Energy	Electricity	PJM	Monthly Peak	Futures	0	0
ERCOT West 345 kV Hub Day-Ahead 5 MW Peak Calendar-Day Futures	EWW	EAW	NA	EWW	NYMEX	Energy	Electricity	ERCOT	Daily Peak	Futures	0	0
PJM PECO Zone 5 MW Peak Calendar-Month Day-Ahead LMP Futures	4N	A4N	NA	4N	NYMEX	Energy	Electricity	PJM	Monthly Peak	Futures	0	0
PJM PSEG Zone Peak Calendar-Month Day-Ahead LMP 5 MW Option	PSG	ASG	NA	PSG	NYMEX	Energy	Electricity	PJM	Monthly Peak	Options	0	0
CAISO SP15 EZ Gen Hub 5 MW Peak Calendar-Month Day-Ahead LMP Option	CSZ	CSA	NA	CSZ	NYMEX	Energy	Electricity	Western Power	Monthly Peak	Options	0	0
PJM West Hub 50 MW Same Day Option D03	J03	Z03	NA	J03	NYMEX	Energy	Electricity	PJM	Daily Peak	Options	0	0
LNG Freight US Gulf to Continent (BLNG2-174)	BG2	BG2	NA	BG2	NYMEX	Energy	Freight	Wet	Outrights	Futures	0	382
LNG Freight Australia to Japan (BLNG1-174)	BG1	BG1	NA	BG1	NYMEX	Energy	Freight	Wet	Outrights	Futures	0	40
LPG Freight Route Middle East to Japan (BLPG1) (Baltic) Futures	FLP	FLP	NA	FLP	NYMEX	Energy	Freight	Wet	Outrights	Futures	0	0
Micro Henry Hub Natural Gas Futures	MNG	MNG	NA	MNG	NYMEX	Energy	Natural Gas	North American	Outrights	Futures	17277	4840
Dutch TTF Natural Gas Calendar Month Futures	TTF	TTF	NA	TTF	NYMEX	Energy	Natural Gas	European	Outrights	Futures	9272	2128
Japan Crude Cocktail (Detailed) Futures	JCC	JCC	NA	JCC	NYMEX	Energy	Natural Gas	Asian	Outrights	Futures	0	910
Natural Gas Wednesday Weekly Financial Option - Week 2	JN2	JN2	NA	JN2	NYMEX	Energy	Natural Gas	North American	Outrights	Options	105	271
Natural Gas Wednesday Weekly Financial Option - Week 4	JN4	JN4	NA	JN4	NYMEX	Energy	Natural Gas	North American	Outrights	Options	0	100
Natural Gas Tuesday Weekly Financial Option - Week 3	IN3	IN3	NA	IN3	NYMEX	Energy	Natural Gas	North American	Outrights	Options	79	61
Mont Belvieu LDH Propane (OPIS) Futures	B0	B0	NA	B0	NYMEX	Energy	Petrochemicals	NGL	Financial	Futures	5766	156574
Mont Belvieu Ethane (OPIS) Futures	C0	AC0	NA	C0	NYMEX	Energy	Petrochemicals	NGL	Financial	Futures	950	58206
Mont Belvieu Normal Butane (OPIS) Futures	D0	AD0	NA	D0	NYMEX	Energy	Petrochemicals	NGL	Financial	Futures	2295	52233
Mont Belvieu LDH Propane (OPIS) Average Price Option	4H	A4H	NA	4H	NYMEX	Energy	Petrochemicals	NGL	Financial	Options	0	499
Mont Belvieu Ethane (OPIS) Average Price Option	4J	A4J	NA	4J	NYMEX	Energy	Petrochemicals	NGL	Financial	Options	0	0
Conway Propane (OPIS) Average Price Option	CPR	CPR	NA	CPR	NYMEX	Energy	Petrochemicals	NGL	Financial	Options	0	0
RBOB Gasoline Futures	RB	RB	NA	RB	NYMEX	Energy	Refined Products	North American	Outrights	Futures	179282	445652
NY Harbor ULSD Futures	HO	HO	NA	HO	NYMEX	Energy	Refined Products	North American	Outrights	Futures	174500	342451
Gulf Coast ULSD (Platts) Up-Down Futures	LT	LT	NA	LT	NYMEX	Energy	Refined Products	North American	Spreads	Futures	1230	34652
NY Harbor ULSD Calendar Spread Option - 1 Month	FA	FAY	NA	FA	NYMEX	Energy	Refined Products	North American	Spreads	Options	2900	14450
EIA Flat Tax On-Highway Diesel Average Price Option	DAP	DAP	NA	DAP	NYMEX	Energy	Refined Products	North American	Outrights	Options	0	30
RBOB Gasoline 1 Month Calendar Spread Option	ZA	ZAY	NA	ZA	NYMEX	Energy	Refined Products	North American	Spreads	Options	0	0

6. Futures active series

6.1 Biofuels - European

biofuels_european_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Biofuels", Category == "European") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

biofuels_european_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Biofuels, Category: European", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Biofuels, Category: European
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Ethanol T2 FOB Rdam Including Duty (Platts) Futures	Z1	Biofuels	European	209	7101
Methanol T2 FOB Rdam (ICIS) Futures	MT2	Biofuels	European	5	485
UCO fob ARA (Argus) Futures	UCD	Biofuels	European	0	30
RME Biodiesel FOB Rdam (Argus) (RED Compliant) vs. Low Sulphur Gasoil Futures	BFR	Biofuels	European	0	0
UCOME Biodiesel (RED Compliant) FOB ARA (Argus) Futures	UCR	Biofuels	European	0	0

6.2 Biofuels - North America

biofuels_northamerican_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Biofuels", Category == "North American") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

biofuels_northamerican_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Biofuels, Category: North American", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Biofuels, Category: North American
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Chicago Ethanol (Platts) Futures	CU	Biofuels	North American	3680	33107
NY Ethanol (Platts) Futures	EZ	Biofuels	North American	0	665
Denatured Fuel Ethanol Futures	EL	Biofuels	North American	20	20
Ethanol Forward Month Futures	71	Biofuels	North American	0	0
Methanol FOB Houston (Argus) Futures	MTH	Biofuels	North American	0	0

6.3 Crude Oil - European

crudeoil_european_options <- tidy_data %>%
  filter(Cleared_As == "Options", Product_Group == "Crude Oil", Category == "European") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

crudeoil_european_options %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Options in Product Group: Crude Oil, Category: European", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Options in Product Group: Crude Oil, Category: European
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Brent Crude Oil Last Day Financial Calendar Spread (1 Month) Option	9C	Crude Oil	European	0	30600
Brent Crude Oil Futures-Style Margin Option	BZO	Crude Oil	European	6000	26700
Brent Financial Average Price Option	BA	Crude Oil	European	0	360
Brent Last Day Financial European Option	BE	Crude Oil	European	0	2
Brent Last Day Financial Option	OS	Crude Oil	European	0	0

6.4 Crude Oil - North American

crudeoil_northamerican_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Crude Oil", Category == "North American") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

crudeoil_northamerican_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Crude Oil, Category: North American", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Crude Oil, Category: North American
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Crude Oil Futures	CL	Crude Oil	North American	636171	1793310
WTI Houston (Argus) vs. WTI Trade Month Futures	HTT	Crude Oil	North American	9214	436539
WTI Midland (Argus) vs. WTI Trade Month Futures	WTT	Crude Oil	North American	1385	310359
WTI Financial Futures	CS	Crude Oil	North American	619	136946
Micro WTI Crude Oil Futures	MCL	Crude Oil	North American	50639	19781

6.5 Freight - Wet

freight_wet_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Freight", Category == "Wet") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

freight_wet_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Freight, Category: Wet", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Freight, Category: Wet
Product_Name	Clearing	Product_Group	Category	Open_Interest
LNG Freight US Gulf to Continent (BLNG2-174)	BG2	Freight	Wet	382
LNG Freight Australia to Japan (BLNG1-174)	BG1	Freight	Wet	40
LPG Freight Route Middle East to Japan (BLPG1) (Baltic) Futures	FLP	Freight	Wet	0
Freight Route US Gulf to ARA (TD25) (Baltic) Futures	AEB	Freight	Wet	0
Freight Route Middle East to UK Continent (TC20) (Baltic) Futures	TF2	Freight	Wet	0

6.6 Natural Gas - North American

natgas_northamerican_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Natural Gas", Category == "North American") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

natgas_northamerican_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Natural Gas, Category: North American", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Natural Gas, Category: North American
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Micro Henry Hub Natural Gas Futures	MNG	Natural Gas	North American	17277	4840
Henry Hub Natural Gas Weekly Futures	HHW	Natural Gas	North American	0	0
Gulf Coast LNG Export Futures	LNG	Natural Gas	North American	0	0

6.7 Natural Gas - European

natgas_european_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Natural Gas", Category == "European") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

natgas_european_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Natural Gas, Category: European", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Natural Gas, Category: European
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Dutch TTF Natural Gas Calendar Month Futures	TTF	Natural Gas	European	9272	2128
Henry Hub NBP (ICIS Heren) Natural Gas Spread Futures	NYP	Natural Gas	European	0	0
Dutch TTF Natural Gas Daily Futures	TTD	Natural Gas	European	0	0
Dutch TTF Natural Gas (USD/MMBtu) (ICIS Heren) Front Month Futures	TTE	Natural Gas	European	0	0
Henry Hub TTF (ICIS Heren) Natural Gas Spread Futures	THD	Natural Gas	European	0	0

6.8 Natural Gas - Asian

natgas_asian_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Natural Gas", Category == "Asian") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

natgas_asian_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Natural Gas, Category: Asian", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Natural Gas, Category: Asian
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Japan Crude Cocktail (Detailed) Futures	JCC	Natural Gas	Asian	0	910

6.9 Petrochemicals - NGL

petrochemicals_ngl_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Petrochemicals", Category == "NGL") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

petrochemicals_ngl_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Petrochemicals, Category: NGL", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Petrochemicals, Category: NGL
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Mont Belvieu LDH Propane (OPIS) Futures	B0	Petrochemicals	NGL	5766	156574
Mont Belvieu Ethane (OPIS) Futures	C0	Petrochemicals	NGL	950	58206
Mont Belvieu Normal Butane (OPIS) Futures	D0	Petrochemicals	NGL	2295	52233
Mont Belvieu Natural Gasoline (OPIS) Futures	7Q	Petrochemicals	NGL	555	30538
Propane Non-LDH Mont Belvieu (OPIS) Futures	1R	Petrochemicals	NGL	360	29559

6.10 Petrochemicals - Olefin

petrochemicals_olefin_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Petrochemicals", Category == "Olefin") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

petrochemicals_olefin_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Petrochemicals, Category: Olefin", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Petrochemicals, Category: Olefin
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
PGP Polymer Grade Propylene (PCW) Financial Futures	PGP	Petrochemicals	Olefin	165	5025
Mont Belvieu Ethylene (OPIS PCW) Futures	MBN	Petrochemicals	Olefin	0	1453
Mont Belvieu Ethylene (OPIS PCW) BALMO Futures	MBB	Petrochemicals	Olefin	0	0
PP Polypropylene (PCW) BALMO Futures	PPW	Petrochemicals	Olefin	0	0
Mont Belvieu Spot Ethylene In-Well (OPIS PCW) Futures	MBE	Petrochemicals	Olefin	0	0

6.11 Petrochemicals - LPG

petrochemicals_lpg_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Petrochemicals", Category == "LPG") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 10)

petrochemicals_lpg_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Petrochemicals, Category: LPG", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Petrochemicals, Category: LPG
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Argus Propane Far East Index Futures	7E	Petrochemicals	LPG	214	4769
Mini European Propane CIF ARA (Argus) Futures	MPS	Petrochemicals	LPG	0	2945
European Propane CIF ARA (Argus) Futures	PS	Petrochemicals	LPG	93	1799
Argus Propane (Saudi Aramco) Futures	9N	Petrochemicals	LPG	54	1745
Mini Argus Propane Far East Index Futures	MAE	Petrochemicals	LPG	40	1010
Mini Argus Propane (Saudi Aramco) Futures	MAS	Petrochemicals	LPG	0	240
Argus Propane Far East Index BALMO Futures	22	Petrochemicals	LPG	13	46
European Propane CIF ARA (Argus) BALMO Futures	32	Petrochemicals	LPG	0	27
European Butane CIF ARA (Argus) Futures	BEF	Petrochemicals	LPG	0	0
Mini-Argus Butane (Saudi Aramco) Futures	MAA	Petrochemicals	LPG	0	0

6.12 Petrochemicals - Arb

petrochemicals_arb_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Petrochemicals", Category == "Arb") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

petrochemicals_arb_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Petrochemicals, Category: Arb", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Petrochemicals, Category: Arb
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Argus Propane Far East Index vs. European Propane CIF ARA (Argus) Futures	91	Petrochemicals	Arb	2	632
Mont Belvieu LDH Propane (OPIS) vs. Argus Propane Far East Index Futures	PMF	Petrochemicals	Arb	0	52
Mont Belvieu Non-LDH Propane (OPIS) vs. Argus Propane Far East Index Futures	PNF	Petrochemicals	Arb	0	0
Mont Belvieu LDH Propane (OPIS) vs. European Propane CIF ARA (Argus) Futures	51	Petrochemicals	Arb	0	0

6.13 Petrochemicals - Resin

petrochemicals_ngl_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Petrochemicals", Category == "Resin") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

petrochemicals_ngl_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Petrochemicals, Category: Resin", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Petrochemicals, Category: Resin
Product_Name	Clearing	Product_Group	Category
LLDPE Linear Low Density Polyethylene (PCW) Financial Futures	LPE	Petrochemicals	Resin
HDPE High Density Polyethylene (PCW) Financial Futures	HPE	Petrochemicals	Resin
PP Polypropylene (PCW) Financial Futures	PPP	Petrochemicals	Resin

6.14 Refined Products - North American

refinedprod_northamerican_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Refined Products", Category == "North American") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

refinedprod_northamerican_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Refined Products, Category: North American", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Refined Products, Category: North American
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
RBOB Gasoline Futures	RB	Refined Products	North American	179282	445652
NY Harbor ULSD Futures	HO	Refined Products	North American	174500	342451
Gulf Coast ULSD (Platts) Up-Down Futures	LT	Refined Products	North American	1230	34652
Gulf Coast CBOB Gasoline A2 (Platts) vs. RBOB Gasoline Futures	CRB	Refined Products	North American	1100	19314
Gulf Coast Jet (Platts) Up-Down Futures	ME	Refined Products	North American	250	16038

6.15 Refined Products - European

refinedprod_european_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Refined Products", Category == "European") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

refinedprod_european_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Refined Products, Category: European", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Refined Products, Category: European
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Gasoline Euro-bob Oxy NWE Barges (Argus) Crack Spread Futures	7K	Refined Products	European	150	13391
European Naphtha (Platts) Crack Spread Futures	EN	Refined Products	European	14	5023
RBOB Gasoline Brent Crack Spread Futures	RBB	Refined Products	European	0	4325
Gasoline Euro-bob Oxy NWE Barges (Argus) Futures	7H	Refined Products	European	50	3035
Micro Gasoil 0.1% Barges FOB ARA (Platts) Futures	M1B	Refined Products	European	0	2709

6.16 Refined Products - Asian

refinedprod_asian_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Refined Products", Category == "Asian") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 10)

refinedprod_asian_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Refined Products, Category: Asian", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Refined Products, Category: Asian
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Mini Singapore FOB Marine Fuel 0.5% (Platts) Futures	S5M	Refined Products	Asian	133	2445
Singapore Mogas 92 Unleaded (Platts) Brent Crack Spread Futures	1NB	Refined Products	Asian	0	1700
Japan C&F Naphtha (Platts) Brent Crack Spread Futures	JB	Refined Products	Asian	0	975
Japan C&F Naphtha (Platts) Futures	JA	Refined Products	Asian	0	841
Singapore Fuel Oil 380 cst (Platts) Futures	SE	Refined Products	Asian	3	638
Argus Propane Far East Index vs. Japan C&F Naphtha (Platts) Futures	3NA	Refined Products	Asian	6	434
Singapore FOB Marine Fuel 0.5% (Platts) Futures	S5F	Refined Products	Asian	48	418
Mini Singapore Fuel Oil 380 cst (Platts) Futures	MTS	Refined Products	Asian	30	274
Micro Singapore FOB Marine Fuel 0.5% (Platts) Futures	S5O	Refined Products	Asian	0	224
Singapore Mogas 92 Unleaded (Platts) Futures	1N	Refined Products	Asian	0	200

6.17 Refined Products - Arb

refinedprod_arb_futures <- tidy_data %>%
  filter(Cleared_As == "Futures", Product_Group == "Refined Products", Category == "Arb") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

refinedprod_arb_futures %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Refined Products, Category: Arb", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Refined Products, Category: Arb
Product_Name	Clearing	Product_Group	Category	Open_Interest
East-West Naphtha: Japan C&F vs. Cargoes CIF NWE Spread (Platts) Futures	EWN	Refined Products	Arb	305
Gulf Coast HSFO (Platts) vs. European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures	GCU	Refined Products	Arb	165
Singapore Gasoil (Platts) vs. Low Sulphur Gasoil Futures	GA	Refined Products	Arb	0
Singapore FOB Marine Fuel 0.5% (Platts) vs. European FOB Rdam Marine Fuel 0.5% Barges (Platts) BALMO Futures	SRB	Refined Products	Arb	0
USGC Marine Fuel 0.5% Barges (Platts) (mt) vs. European FOB Rdam Marine Fuel 0.5% Barges (Platts) Futures	UPM	Refined Products	Arb	0

7. Options active series

7.1 Biofuels - European

biofuels_european_options <- tidy_data %>%
  filter(Cleared_As == "Options", Product_Group == "Biofuels", Category == "European") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

biofuels_european_options %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Futures in Product Group: Biofuels, Category: European", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Futures in Product Group: Biofuels, Category: European
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Ethanol T2 FOB Rdam Including Duty (Platts) Average Price Option	Z1O	Biofuels	European	0	0

7.2 Biofuels - North America

biofuels_northamerican_options <- tidy_data %>%
  filter(Cleared_As == "Options", Product_Group == "Biofuels", Category == "North American") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

biofuels_northamerican_options %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Options in Product Group: Biofuels, Category: North American", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Options in Product Group: Biofuels, Category: North American
Product_Name	Clearing	Product_Group	Category	Open_Interest
Chicago Ethanol (Platts) Average Price Option	CVR	Biofuels	North American	4544
D4 Biodiesel RINs (OPIS) vs. D6 Ethanol RINs (OPIS) Average Price Option	R46	Biofuels	North American	0
NY Ethanol (Platts) Average Price Option	NVP	Biofuels	North American	0
Chicago Ethanol (Platts) Calendar Spread Option - Three Month	CE3	Biofuels	North American	0
Ethanol Options	EH	Biofuels	North American	0

7.3 Crude Oil - European

crudeoil_european_options <- tidy_data %>%
  filter(Cleared_As == "Options", Product_Group == "Crude Oil", Category == "European") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

crudeoil_european_options %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Options in Product Group: Crude Oil, Category: European", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Options in Product Group: Crude Oil, Category: European
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Brent Crude Oil Last Day Financial Calendar Spread (1 Month) Option	9C	Crude Oil	European	0	30600
Brent Crude Oil Futures-Style Margin Option	BZO	Crude Oil	European	6000	26700
Brent Financial Average Price Option	BA	Crude Oil	European	0	360
Brent Last Day Financial European Option	BE	Crude Oil	European	0	2
Brent Last Day Financial Option	OS	Crude Oil	European	0	0

7.4 Crude Oil - North American

crudeoil_northamerican_options <- tidy_data %>%
  filter(Cleared_As == "Options", Product_Group == "Crude Oil", Category == "North American") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

crudeoil_northamerican_options %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Options in Product Group: Crude Oil, Category: North American", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Options in Product Group: Crude Oil, Category: North American
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Crude Oil Option	LO	Crude Oil	North American	180906	2294536
Crude Oil Financial Calendar Spread Option 1 Month	7A	Crude Oil	North American	46750	426915
WTI Average Price Option	AO	Crude Oil	North American	0	375765
WTI Crude Oil 1 Month Calendar Spread Option	WA	Crude Oil	North American	0	160125
Light Sweet Crude Oil European Financial Option	LC	Crude Oil	North American	0	77446

7.5 Freight - Wet

7.6 Natural Gas - North American

natgas_northamerican_options <- tidy_data %>%
  filter(Cleared_As == "Options", Product_Group == "Natural Gas", Category == "North American") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

natgas_northamerican_options %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Options in Product Group: Natural Gas, Category: North American", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Options in Product Group: Natural Gas, Category: North American
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
Natural Gas Wednesday Weekly Financial Option - Week 2	JN2	Natural Gas	North American	105	271
Natural Gas Wednesday Weekly Financial Option - Week 4	JN4	Natural Gas	North American	0	100
Natural Gas Tuesday Weekly Financial Option - Week 3	IN3	Natural Gas	North American	79	61
Natural Gas Monday Weekly Financial Option - Week 3	HN3	Natural Gas	North American	2	22
Natural Gas Wednesday Weekly Financial Option - Week 3	JN3	Natural Gas	North American	2	1

7.7 Natural Gas - European

natgas_european_options <- tidy_data %>%
  filter(Cleared_As == "Options", Product_Group == "Natural Gas", Category == "European") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

natgas_european_options %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Options in Product Group: Natural Gas, Category: European", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Options in Product Group: Natural Gas, Category: European
Product_Name	Clearing	Product_Group	Category
Dutch TTF Natural Gas Futures-Style Margined Calendar Month Option	TFO	Natural Gas	European
UK NBP Natural Gas Calendar Month Option	UKO	Natural Gas	European
Dutch TTF Natural Gas Calendar Month Option	TTO	Natural Gas	European
UK NBP Natural Gas Futures-Style Margined Calendar Month Option	UFO	Natural Gas	European

7.8 Natural Gas - Asian

7.9 Petrochemicals - NGL

petrochemicals_ngl_options <- tidy_data %>%
  filter(Cleared_As == "Options", Product_Group == "Petrochemicals", Category == "NGL") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

petrochemicals_ngl_options %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Options in Product Group: Petrochemicals, Category: NGL", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Options in Product Group: Petrochemicals, Category: NGL
Product_Name	Clearing	Product_Group	Category	Open_Interest
Mont Belvieu LDH Propane (OPIS) Average Price Option	4H	Petrochemicals	NGL	499
Mont Belvieu Ethane (OPIS) Average Price Option	4J	Petrochemicals	NGL	0
Conway Propane (OPIS) Average Price Option	CPR	Petrochemicals	NGL	0
Mont Belvieu Normal Butane (OPIS) Average Price Option	4K	Petrochemicals	NGL	0
Mont Belvieu Natural Gasoline (OPIS) Average Price Option	4I	Petrochemicals	NGL	0

7.10 Petrochemicals - Olefin

7.11 Petrochemicals - LPG

7.12 Petrochemicals - Arb

7.13 Petrochemicals - Resin

7.14 Refined Products - North American

refinedprod_northamerican_options <- tidy_data %>%
  filter(Cleared_As == "Options", Product_Group == "Refined Products", Category == "North American") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

refinedprod_northamerican_options %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Options in Product Group: Refined Products, Category: North American", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Options in Product Group: Refined Products, Category: North American
Product_Name	Clearing	Product_Group	Category	Volume	Open_Interest
NY Harbor ULSD Calendar Spread Option - 1 Month	FA	Refined Products	North American	2900	14450
EIA Flat Tax On-Highway Diesel Average Price Option	DAP	Refined Products	North American	0	30
RBOB Gasoline 1 Month Calendar Spread Option	ZA	Refined Products	North American	0	0
NY Harbor ULSD Calendar Spread Option - 6 Month	FM	Refined Products	North American	0	0
NY Harbor ULSD Calendar Spread Option - 3 Month	FC	Refined Products	North American	0	0

7.15 Refined Products - European

refinedprod_european_options <- tidy_data %>%
  filter(Cleared_As == "Options", Product_Group == "Refined Products", Category == "European") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 5)

refinedprod_european_options %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Options in Product Group: Refined Products, Category: European", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Options in Product Group: Refined Products, Category: European
Product_Name	Clearing	Product_Group	Category
European Jet Kerosene Cargoes CIF NWE (Platts) Average Price Option	30	Refined Products	European
European Low Sulphur Gasoil Calendar (6 month) Spread Option	GXM	Refined Products	European
RBOB Gasoline Brent Crack Spread Average Price Option	RBC	Refined Products	European
European Low Sulphur Gasoil Calendar (2 month) Spread Option	GXB	Refined Products	European
European Low Sulphur Gasoil Calendar (3 month) Spread Option	GXC	Refined Products	European

7.16 Refined Products - Asian

refinedprod_asian_options <- tidy_data %>%
  filter(Cleared_As == "Options", Product_Group == "Refined Products", Category == "Asian") %>%
  arrange(desc(Open_Interest)) %>%
  slice_head(n = 10)

refinedprod_asian_options %>%
  select(Product_Name, Clearing, Product_Group, Category, Volume, Open_Interest) %>%
  kable(caption = "Top 5 Options in Product Group: Refined Products, Category: Asian", format = "html") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                full_width = FALSE)

Top 5 Options in Product Group: Refined Products, Category: Asian
Product_Name	Clearing	Product_Group	Category
Singapore Gasoil (Platts) Average Price Option	M2	Refined Products	Asian
Singapore Fuel Oil 380 cst (Platts) Average Price Option	8H	Refined Products	Asian
Japan C&F Naphtha (Platts) Average Price Option	JA5	Refined Products	Asian
Singapore Jet Kerosene (Platts) Average Price Option	N2	Refined Products	Asian
Singapore Fuel Oil 380cst (Platts) Brent Crack Spread (1000mt) Average Price Option	SCO	Refined Products	Asian
Singapore Fuel Oil 180 cst (Platts) Average Price Option	C5	Refined Products	Asian
Singapore Mogas 92 Unleaded (Platts) Average Price Option	1N5	Refined Products	Asian

7.17 Refined Products - Arb

8. Conclusion

This analysis provides a comprehensive overview of CME energy futures and options, with insights into: - The overall distribution of numeric and categorical data. - Differences between Futures and Options contracts. - Detailed product group comparisons including key metrics such as Volume and Open Interest. - Identification of the top active records within each product group.

Further analyses could include time series trends or deeper dives into specific categories to inform trading strategies.

References

CME group products. (2025). https://www.cmegroup.com/markets/products.html#sortDirection=desc&sortField=oi&groups=7&subGroups=13,9,10,11,12,15,16,17,18&cat=12,17,8,9,1,2,3,4,5,6,7,10,11,13,14,15,16&subCat=7,16,9,10,11,12,13,14,1,2,3,4,5,6,18,19,20,15,17.