R语言实现数据框对冲头寸移除与净额结算开发需求

Got it, let's work through how to clean your dataframe by removing those offsetting boxed positions in R. Here's a step-by-step solution using dplyr (one of the most common packages for data manipulation):

First, let's clarify the approach #

We need to identify groups of rows where:

• Name, Strike, Maturity, and Nominal are identical
• The group contains both "Call" and "Put" positions, with matching counts (so they fully offset each other)

Then we'll remove all rows from these fully hedged groups.

Example code with sample data #

First, let's create a sample dataframe to test with (matches the scenario you described):

# Sample dataframe with hedged and non-hedged positions
df <- data.frame(
  Name = c("Amazon", "Amazon", "Apple", "Google", "Google", "Google"),
  Strike = c(150, 150, 200, 300, 300, 300),
  Maturity = as.Date(c("2024-12-31", "2024-12-31", "2024-06-30", "2024-09-30", "2024-09-30", "2024-09-30")),
  Nominal = c(1000, 1000, 500, 2000, 2000, 1500),
  Type = c("Call", "Put", "Call", "Call", "Put", "Call"),
  stringsAsFactors = FALSE
)

Now, let's identify and remove the hedged positions:

library(dplyr)

# Step 1: Identify fully hedged groups
hedged_groups <- df %>%
  group_by(Name, Strike, Maturity, Nominal) %>%
  summarise(
    has_call = any(Type == "Call"),
    has_put = any(Type == "Put"),
    call_count = sum(Type == "Call"),
    put_count = sum(Type == "Put"),
    .groups = "drop"
  ) %>%
  # Filter groups where Call and Put counts match perfectly (full hedge)
  filter(has_call & has_put & call_count == put_count)

# Step 2: Remove all rows from these hedged groups
cleaned_df <- df %>%
  anti_join(hedged_groups, by = c("Name", "Strike", "Maturity", "Nominal"))

What this does: #

• The first part groups your data by the key identifiers, then checks if each group has both Call and Put positions with equal counts. These are your fully offset "boxed positions".
• anti_join removes any rows from the original dataframe that match these hedged groups, leaving only non-hedged positions.

Bonus: Netting for partial hedges #

If you also need to handle partial hedges (e.g., 3 Calls and 2 Puts for the same key, so net 1 Call remains), here's how to do netting:

netted_df <- df %>%
  # Group by key and Type, sum nominal amounts (adjust if your Nominal is per-row)
  group_by(Name, Strike, Maturity, Nominal, Type) %>%
  summarise(total = sum(Nominal), .groups = "drop") %>%
  # Reshape to compare Call and Put totals
  pivot_wider(names_from = Type, values_from = total, values_fill = 0) %>%
  # Calculate net positions
  mutate(
    net = Call - Put,
    final_type = ifelse(net > 0, "Call", ifelse(net < 0, "Put", NA)),
    final_nominal = abs(net)
  ) %>%
  # Remove fully hedged positions (net = 0)
  filter(!is.na(final_type)) %>%
  # Clean up columns to match original structure
  select(Name, Strike, Maturity, final_nominal, final_type) %>%
  rename(Nominal = final_nominal, Type = final_type)

This version calculates the net position for each group and keeps only the non-zero net positions, which is useful if you don't want to remove partial hedges entirely.

内容的提问来源于stack exchange，提问作者user8453031