# Trading Hub Price Analysis

Compare prices across trading hubs to identify arbitrage opportunities and congestion patterns.

```python
import pandas as pd

# Fetch one week of hourly average prices for major ERCOT hubs
df = client.get_dataset(
    "ercot_lmp_by_settlement_point",
    start="2026-01-01",
    end="2026-01-08",
    filter_column="location",
    filter_value="HB_HOUSTON,HB_NORTH,HB_WEST,HB_SOUTH",
    filter_operator="in",
    columns=["interval_start_utc", "location", "lmp"],
    resample="1 hour",
    resample_function="mean"
)

# Pivot to get locations as columns
df_pivot = df.pivot(index='interval_start_utc', columns='location', values='lmp')

# Calculate price spreads
df_pivot['Houston_North'] = df_pivot['HB_HOUSTON'] - df_pivot['HB_NORTH']
df_pivot['Houston_West'] = df_pivot['HB_HOUSTON'] - df_pivot['HB_WEST']

# Summary statistics
print("=== Hub Price Spread Analysis ===")
print(f"\nHouston-North Spread:")
print(f"  Mean: ${df_pivot['Houston_North'].mean():.2f}/MWh")
print(f"  Max: ${df_pivot['Houston_North'].max():.2f}/MWh")
print(f"  Min: ${df_pivot['Houston_North'].min():.2f}/MWh")
print(f"  Std Dev: ${df_pivot['Houston_North'].std():.2f}/MWh")

print(f"\nHouston-West Spread:")
print(f"  Mean: ${df_pivot['Houston_West'].mean():.2f}/MWh")
print(f"  Max: ${df_pivot['Houston_West'].max():.2f}/MWh")

# Find hours with largest spreads (potential congestion)
print(f"\n=== Top 5 Hours with Largest Houston-North Spread ===")
top_spreads = df_pivot.nlargest(5, 'Houston_North')[['HB_HOUSTON', 'HB_NORTH', 'Houston_North']]
print(top_spreads)
```


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