Calling: load - tickers=["NVDA"], metrics=["total_revenue"] DataFrame 124 OK nvda_fundamentals (101 rows)

Calling: compute - dataframe_ids=[124], code="result = df_124[df_124['period_type'] == 'annual'].sort_values('date', ascending=False).head(5).sort_values('date')" DataFrame 125 OK result_125 (5 rows)

Calling: show - dataframe_id=125 Blob 54 DataFrame 125 OK result_125 (5 rows)

Calling: show_chart - series=["total_revenue"], dataframe_id=125, title="NVIDIA Annual Revenue (Last 5 Years)" Blob 55 DataFrame 125 OK result_125 (5 rows)

Calling: search_ticker - query="AMD"

Calling: load - metrics=["total_revenue"], tickers=["NVDA", "AMD"] DataFrame 126 OK nvda_amd_fundamentals (202 rows)

Calling: compute - code="# Filter annual data\nannual = df_126[df_126['period_type'] == 'annual']\n\n# Ensure date is datetime\nannual['date'] = pd.to_datetime(annual['date'])\n\n# Get the most recent 5 fiscal years for each ticker\n# We want to make sure we have data for both.\n# Let's get the latest 5 years based on the year of the date.\nresult = annual[annual['date'].dt.year >= 2022].sort_values(['ticker', 'date'])\n", dataframe_ids=[126] DataFrame 127 OK result_127 (9 rows)

Calling: show - dataframe_id=127 Blob 56 DataFrame 127 OK result_127 (9 rows)

Calling: show_chart - series=["total_revenue"], title="Annual Revenue Comparison: NVDA vs. AMD", dataframe_id=127 Blob 57 DataFrame 127 OK result_127 (9 rows)