Calling: search_ticker - query="Salesforce"

Calling: load - tickers=["CRM"], metrics=["total_revenue", "net_income", "operating_cashflow", "cashflow_from_investment", "capital_expenditures", "payments_for_repurchase_of_common_stock", "dividend_payout", "long_term_debt", "short_long_term_debt_total", "cash_and_cash_equivalents_at_carrying_value", "short_term_investments", "long_term_investments"] DataFrame 249 OK crm_financial_statements (25 rows)

Calling: compute - code="\ndf = df_249.copy()\ndf['date'] = pd.to_datetime(df['date'])\nfor col in df.columns[3:]:\n df[col] = pd.to_numeric(df[col], errors='coerce')\n\nq = df[df['period_type'] == 'quarterly'].sort_values('date').reset_index(drop=True)\n\nttm = q.tail(4).agg({\n 'total_revenue': 'sum',\n 'net_income': 'sum',\n 'operating_cashflow': 'sum',\n 'cashflow_from_investment': 'sum',\n 'capital_expenditures': 'sum',\n 'dividend_payout': 'sum',\n}).to_frame(name='TTM').T\nttm['as_of'] = 'Apr 26 TTM'\nttm = ttm[['as_of', 'total_revenue', 'net_income', 'operating_cashflow', 'cashflow_from_investment', 'capital_expenditures', 'dividend_payout']]\n\na = df[df['period_type'] == 'annual'].sort_values('date').reset_index(drop=True)\na = a[a['date'] >= '2022-01-31'].copy()\na['fiscal_year'] = a['date'].dt.year - 1\na['fiscal_year'] = a['fiscal_year'].astype(str) + '/' + a['fiscal_year'].add(1).astype(str).str[-2:]\n\ninv_rows = a[['fiscal_year', 'cashflow_from_investment', 'capital_expenditures', 'dividend_payout']].rename(columns={\n 'cashflow_from_investment': 'Investing CF',\n 'capital_expenditures': 'CapEx',\n 'dividend_payout': 'Dividends Paid',\n})\n\nannual_summary = a[['fiscal_year', 'total_revenue', 'net_income', 'operating_cashflow', 'cashflow_from_investment']].rename(columns={\n 'total_revenue': 'Revenue',\n 'net_income': 'Net Income',\n 'operating_cashflow': 'Operating CF',\n 'cashflow_from_investment': 'Investing CF',\n})\n\nlatest_q = q.iloc[-1]\ndebt = float(latest_q['short_long_term_debt_total'])\ncash = float(latest_q['cash_and_cash_equivalents_at_carrying_value'])\nsti = float(latest_q['short_term_investments'])\nlti = float(latest_q['long_term_investments'])\nnet_debt = debt - cash - sti\n\nnd = pd.DataFrame([{\n 'Metric': ['Short+Long Term Debt', 'Cash & Equivalents', 'Short-Term Investments', 'Long-Term Investments', 'Net Debt (debt - cash - ST inv)'],\n 'Value ($B)': [debt/1e9, cash/1e9, sti/1e9, lti/1e9, net_debt/1e9]\n}])\n\nprint('=== ANNUAL SUMMARY (FY) ===')\nfor _, r in annual_summary.iterrows():\n print(f\"{r['fiscal_year']}: Rev=${r['Revenue']/1e9:.2f}B NI=${r['Net Income']/1e9:.2f}B OpCF=${r['Operating CF']/1e9:.2f}B InvCF=${r['Investing CF']/1e9:.2f}B\")\n\nprint('\\n=== CASH FLOW LINE ITEMS (Investing detail) ===')\nfor _, r in inv_rows.iterrows():\n print(f\"{r['fiscal_year']}: InvCF=${r['Investing CF']/1e9:.2f}B CapEx=${r['CapEx']/1e9:.2f}B Div=${r['Dividends Paid']/1e9:.2f}B\")\n\nprint('\\n=== TTM (Q2FY26 = Apr 26) ===')\nprint(ttm.to_string(index=False))\n\nprint('\\n=== NET DEBT (as of Apr 30, 2026) ===')\nprint(nd.to_string(index=False))\n\nresult = annual_summary\n", dataframe_ids=[249], name="CRM summary" DataFrame 250 OK CRM summary (26 rows)

Calling: load - tickers=["CRM"], metrics=["adjusted_close_daily"], start_date="2021-02-01", end_date="2026-06-12" DataFrame 251 OK crm_price (1344 rows)

Calling: load - tickers=["CRM"], metrics=["net_income", "common_stock_shares_outstanding"]

Calling: compute - code="\nprice = df_251.copy()\nprice['date'] = pd.to_datetime(price['date'])\nprice = price.sort_values('date')\n\nfs = df_252.copy()\nfs['date'] = pd.to_datetime(fs['date'])\nfs['net_income'] = pd.to_numeric(fs['net_income'], errors='coerce')\nfs['common_stock_shares_outstanding'] = pd.to_numeric(fs['common_stock_shares_outstanding'], errors='coerce')\n\nq = fs[fs['period_type'] == 'quarterly'].sort_values('date').reset_index(drop=True)\nq['ttm_ni'] = q['net_income'].rolling(4).sum()\nq['eps'] = q['net_income'] / q['common_stock_shares_outstanding']\nq['ttm_eps'] = q['ttm_ni'] / q['common_stock_shares_outstanding']\nq = q.dropna(subset=['ttm_eps'])\n\nmerged = pd.merge_asof(\n price.sort_values('date'),\n q[['date', 'ttm_eps']].sort_values('date'),\n on='date', direction='backward'\n)\nmerged = merged.dropna(subset=['ttm_eps'])\nmerged['pe'] = merged['adjusted_close_daily'] / merged['ttm_eps']\n\nmonthly = merged.set_index('date').resample('ME').last().dropna(subset=['pe']).reset_index()\nresult = monthly[['date', 'adjusted_close_daily', 'ttm_eps', 'pe']].rename(columns={\n 'date': 'Date',\n 'adjusted_close_daily': 'Price ($)',\n 'ttm_eps': 'TTM EPS ($)',\n 'pe': 'PE (TTM)',\n})\nresult = result[result['Date'] >= '2021-12-31']\n", dataframe_ids=[251, 252], name="CRM PE monthly" DataFrame 253 OK CRM PE monthly (50 rows)

Calling: show_chart - dataframe_id=253, x="Date", series=["PE (TTM)"], chart_type="line", title="Salesforce (CRM) — Trailing PE Ratio" Blob 108 DataFrame 253 OK CRM PE monthly (50 rows)

Calling: show_chart - dataframe_id=253, x="Date", series=["Price ($)"], chart_type="line", title="Salesforce (CRM) — Adjusted Close Price"

Calling: show - dataframe_id=253, rows=50