Calling: search_ticker - query="Alphabet Inc"

Calling: load - tickers=["GOOGL"], metrics=["cash_and_cash_equivalents_at_carrying_value", "operating_cashflow", "capital_expenditures", "cashflow_from_financing", "cashflow_from_investment"] DataFrame 188 OK googl_fundamentals (25 rows)

Calling: compute - code="result = df_188[df_188['period_type'] == 'annual'].sort_values('date')", dataframe_ids=[188] DataFrame 189 OK result_189 (5 rows)

Calling: compute - dataframe_ids=[], code="data = {\n 'Category': ['Cash (2024)', 'Operating Cash Flow', 'Investing Cash Flow', 'Financing Cash Flow', 'Cash (2025)'],\n 'Value': [23466000000.0, 164713000000.0, -120291000000.0, -37388000000.0, 30708000000.0]\n}\nresult = pd.DataFrame(data)" Error: dataframe_ids is required

Calling: compute - code="data = {\n 'Category': ['Cash (2024)', 'Operating Cash Flow', 'Investing Cash Flow', 'Financing Cash Flow', 'Cash (2025)'],\n 'Value': [23466000000.0, 164713000000.0, -120291000000.0, -37388000000.0, 30708000000.0]\n}\nresult = pd.DataFrame(data)", dataframe_ids=[189] DataFrame 190 OK result_190 (5 rows)

Calling: show_chart - chart_type="bar", series=["Value"], title="Alphabet Cash Bridge (2024-2025)", x="Category", dataframe_id=190 Blob 88 DataFrame 190 OK result_190 (5 rows)

Calling: show - dataframe_id=190 Blob 89 DataFrame 190 OK result_190 (5 rows)

Calling: search_sec_compressed_filing - ticker="GOOGL", year="2025", search_patterns=["Cash flows from operating activities", "Cash flows from investing activities", "Cash flows from financing activities", "Net cash provided by operating activities", "Net cash used in investing activities", "Net cash used in financing activities", "Purchases of property and equipment", "Repurchase of common stock"]

Calling: load - tickers=["GOOGL"], metrics=["cash_and_cash_equivalents_at_carrying_value", "operating_cashflow", "capital_expenditures", "cashflow_from_financing", "cashflow_from_investment"] DataFrame 191 OK googl_fundamentals_101757_409f6d (25 rows)

Calling: compute - dataframe_ids=[191], code="df_q = df_191[df_191['period_type'] == 'quarterly'].sort_values('date')\n\n# Function to get TTM for a given end date (inclusive)\ndef get_ttm(end_date, df):\n # Get 4 quarters ending on or before end_date\n subset = df[df['date'] <= end_date].tail(4)\n if len(subset) < 4: return None\n \n # Sum of flows\n ttm = subset[['operating_cashflow', 'capital_expenditures', 'cashflow_from_financing', 'cashflow_from_investment']].sum()\n \n # Beginning Cash (Cash at the start of the 4-quarter window)\n # The start date is the date of the quarter before the one we are in, or 3 quarters prior.\n # Actually, the change in cash is roughly OCF + Capex + Financing + Investment\n # Let's verify by just using the cash at the start and end.\n start_cash = subset.iloc[0]['cash_and_cash_equivalents_at_carrying_value'] # This isn't quite right. \n # The cash at the end of the previous period (before the window) is the starting point.\n # The last quarter's ending cash is the final point.\n \n end_cash = subset.iloc[-1]['cash_and_cash_equivalents_at_carrying_value']\n # We need the cash from the quarter *before* the first one in the subset to be precise, \n # but for simplicity, let's just grab the dates.\n \n return ttm\n\n# Calculate for TTM 26 (ending 2026-03-31)\nttm_26 = get_ttm('2026-03-31', df_q)\n# Calculate for TTM 25 (ending 2025-03-31)\nttm_25 = get_ttm('2025-03-31', df_q)\n\nresult = pd.DataFrame({'TTM 26': ttm_26, 'TTM 25': ttm_25}).T\n" DataFrame 192 OK result_192 (2 rows)

Calling: compute - code="# Get explicit cash values\ndef get_cash(date, df):\n return df[df['date'] == date]['cash_and_cash_equivalents_at_carrying_value'].values[0]\n\n# TTM 26: \n# Period: 2025-06-30 to 2026-03-31\n# Start Cash (Cash at 2025-03-31)\n# End Cash (Cash at 2026-03-31)\nbeg_cash_26 = get_cash('2025-03-31', df_q)\nend_cash_26 = get_cash('2026-03-31', df_q)\n\n# TTM 25:\n# Period: 2024-06-30 to 2025-03-31\n# Start Cash (Cash at 2024-03-31)\n# End Cash (Cash at 2025-03-31)\nbeg_cash_25 = get_cash('2024-03-31', df_q)\nend_cash_25 = get_cash('2025-03-31', df_q)\n\n# Compile results\ndata = {\n 'Metric': ['Beginning Cash', 'Operating Cash Flow', 'Investing Cash Flow', 'Financing Cash Flow', 'Ending Cash'],\n 'TTM 26': [beg_cash_26, 174353000000.0, -167486000000.0, 7890000000.0, end_cash_26],\n 'TTM 25': [beg_cash_25, 132601000000.0, -53166000000.0, -80220000000.0, end_cash_25]\n}\n\nresult = pd.DataFrame(data)\n", dataframe_ids=[191] Error: Python error:

Calling: compute - code="# Check available dates\nresult = df_191[['date', 'cash_and_cash_equivalents_at_carrying_value']].sort_values('date')\n", dataframe_ids=[191] DataFrame 193 OK result_193 (25 rows)

Calling: compute - dataframe_ids=[191], code="# Get the last few rows to see the latest dates\nresult = df_191.tail(10)\n" DataFrame 194 OK result_194 (10 rows)

Calling: compute - dataframe_ids=[191], code="# Filter quarterly data\ndf_q = df_191[df_191['period_type'] == 'quarterly']\n\n# TTM 26: Q2 25, Q3 25, Q4 25, Q1 26\nttm_26_dates = ['2025-06-30', '2025-09-30', '2025-12-31', '2026-03-31']\nttm_26_df = df_q[df_q['date'].isin(ttm_26_dates)]\nttm_26_flows = ttm_26_df[['operating_cashflow', 'capital_expenditures', 'cashflow_from_financing', 'cashflow_from_investment']].sum()\n\n# Cash at start of TTM 26 (End of Q1 25)\nbeg_cash_26 = df_q[df_q['date'] == '2025-03-31']['cash_and_cash_equivalents_at_carrying_value'].values[0]\n# Cash at end of TTM 26 (End of Q1 26)\nend_cash_26 = df_q[df_q['date'] == '2026-03-31']['cash_and_cash_equivalents_at_carrying_value'].values[0]\n\n# TTM 25: Q2 24, Q3 24, Q4 24, Q1 25\nttm_25_dates = ['2024-06-30', '2024-09-30', '2024-12-31', '2025-03-31']\nttm_25_df = df_q[df_q['date'].isin(ttm_25_dates)]\nttm_25_flows = ttm_25_df[['operating_cashflow', 'capital_expenditures', 'cashflow_from_financing', 'cashflow_from_investment']].sum()\n\n# Cash at start of TTM 25 (End of Q1 24)\nbeg_cash_25 = df_q[df_q['date'] == '2024-03-31']['cash_and_cash_equivalents_at_carrying_value'].values[0]\n# Cash at end of TTM 25 (End of Q1 25)\nend_cash_25 = df_q[df_q['date'] == '2025-03-31']['cash_and_cash_equivalents_at_carrying_value'].values[0]\n\n# Create result dataframe\ndata = {\n 'Category': ['Beginning Cash', 'Operating Cash Flow', 'Capital Expenditures', 'Investing Cash Flow (Net)', 'Financing Cash Flow (Net)', 'Ending Cash'],\n 'TTM 25': [beg_cash_25, ttm_25_flows['operating_cashflow'], ttm_25_flows['capital_expenditures'], ttm_25_flows['cashflow_from_investment'], ttm_25_flows['cashflow_from_financing'], end_cash_25],\n 'TTM 26': [beg_cash_26, ttm_26_flows['operating_cashflow'], ttm_26_flows['capital_expenditures'], ttm_26_flows['cashflow_from_investment'], ttm_26_flows['cashflow_from_financing'], end_cash_26]\n}\n\nresult = pd.DataFrame(data)\n" DataFrame 195 OK result_195 (6 rows)

Calling: search_sec_compressed_filing - search_patterns=["Net cash provided by operating activities", "Purchases of property and equipment", "Purchases of marketable securities", "Maturities and sales of marketable securities", "Repurchases of stock", "Dividend payments", "Proceeds from issuance of debt, net of costs", "Repayments of debt", "Effect of exchange rate changes on cash and cash equivalents"], year="2023", ticker="GOOGL"

Calling: compute - dataframe_ids=[191], code="# Filter quarterly data\ndf_q = df_191[df_191['period_type'] == 'quarterly'].sort_values('date')\n\ndef get_ttm_data(end_date_q):\n # Get 4 quarters ending on end_date_q\n # Find index of end_date_q\n idx = df_q[df_q['date'] == end_date_q].index[0]\n \n # Get 4 quarters\n # This assumes data is ordered and complete, which might be risky.\n # Let's get the 4 rows ending at idx\n # Need to be careful about non-consecutive quarters.\n # Safe way: get all rows <= end_date_q and take last 4\n subset = df_q[df_q['date'] <= end_date_q].tail(4)\n \n flows = subset[['operating_cashflow', 'capital_expenditures', 'cashflow_from_financing', 'cashflow_from_investment']].sum()\n \n # Beginning Cash (at the start of the 4-quarter window)\n # The 4 quarters are: Q(n-3), Q(n-2), Q(n-1), Q(n)\n # We want the cash balance at the end of Q(n-4) which is the start of Q(n-3)\n # Get the row before the subset.\n first_date = subset.iloc[0]['date']\n # Get the row before this\n prev_row = df_q[df_q['date'] < first_date].tail(1)\n if not prev_row.empty:\n beg_cash = prev_row.iloc[0]['cash_and_cash_equivalents_at_carrying_value']\n else:\n beg_cash = subset.iloc[0]['cash_and_cash_equivalents_at_carrying_value'] # fallback\n \n end_cash = subset.iloc[-1]['cash_and_cash_equivalents_at_carrying_value']\n \n return flows, beg_cash, end_cash\n\n# Calculate TTMs\nttm_dates = ['2026-03-31', '2025-03-31', '2024-03-31', '2023-03-31']\ndata_rows = []\n\nfor d in ttm_dates:\n flows, beg, end = get_ttm_data(d)\n data_rows.append({\n 'Period': f'TTM {d[:4]}',\n 'Beginning Cash': beg,\n 'Operating Cash Flow': flows['operating_cashflow'],\n 'CapEx': flows['capital_expenditures'],\n 'Investing (Net)': flows['cashflow_from_investment'],\n 'Financing (Net)': flows['cashflow_from_financing'],\n 'Ending Cash': end\n })\n\nresult = pd.DataFrame(data_rows).set_index('Period').T\n" DataFrame 196 OK result_196 (6 rows)

Calling: search_sec_compressed_filing - year="2022", search_patterns=["Net cash provided by operating activities", "Purchases of property and equipment", "Purchases of marketable securities", "Maturities and sales of marketable securities", "Repurchases of stock", "Dividend payments", "Proceeds from issuance of debt, net of costs", "Repayments of debt", "Effect of exchange rate changes on cash and cash equivalents"], ticker="GOOGL"