give bar chart with top SAAS companies (not msft, google) - give 2 charts - 1 with their stock prices, noramlised 24 months. And another chart with their PE ratio. give top 10 saas companies by market cap.
give bar chart with top SAAS companies (not msft, google) - give 2 charts - 1 with their stock prices, noramlised 24 months. And another chart with their PE ratio. give top 10 saas companies by market cap.
Calling: exa_web_search
- post_search_concerns="I need to identify the top SaaS companies by market cap to select a list for analysis. The generic request requires identifying industry leaders.", queries=["top SaaS companies by market cap 2026", "list of top SaaS companies by market capitalization excluding Microsoft and Google"]
Calling: load
- tickers=["ORCL", "PLTR", "SAP", "CRM", "SHOP", "INTU", "PANW", "CRWD", "ADBE", "NOW"], metrics=["close", "trailing_pe"], start_date="2024-06-01"
Calling: compute
- dataframe_ids=[77], code="df_77['first_close'] = df_77.groupby('ticker')['close'].transform('first')\ndf_77['normalized_price'] = (df_77['close'] / df_77['first_close']) * 100\nresult = df_77"
DataFrame 79
OK result_79 (5000 rows)
Calling: show_chart
- chart_type="line", title="Normalized Stock Performance (Last 24 Months)", dataframe_id=79, series=["normalized_price"]
Blob 12
DataFrame 79
OK result_79 (5000 rows)
Calling: show_chart
- dataframe_id=78, title="PE Ratio of Top SaaS Companies", chart_type="bar", series=["trailing_pe"], x="ticker"
Blob 13
DataFrame 78
OK 10stocks_overview (10 rows)
1. Remove palantit and add something else instead. 2. For PE ratio - also can you give 2 year timeline and a lilne chart (dont normalise PE ratio line charT)
Calling: load
- start_date="2024-06-01", tickers=["ORCL", "SNPS", "SAP", "CRM", "SHOP", "INTU", "PANW", "CRWD", "ADBE", "NOW"], metrics=["close"]
DataFrame 80
OK 10stocks_price_095443_62bebf (5000 rows)
Calling: load
- metrics=["eps_reported"], tickers=["ORCL", "SNPS", "SAP", "CRM", "SHOP", "INTU", "PANW", "CRWD", "ADBE", "NOW"], start_date="2024-06-01"
DataFrame 81
OK 10stocks_fundamentals (1098 rows)
Calling: compute
- code="# Stock Price Normalization\ndf_80['first_close'] = df_80.groupby('ticker')['close'].transform('first')\ndf_80['normalized_price'] = (df_80['close'] / df_80['first_close']) * 100\n\n# PE Ratio Calculation\n# Convert reported_date to datetime if necessary (it's often object/str in DataFrames)\ndf_81['reported_date'] = pd.to_datetime(df_81['reported_date'])\ndf_80['date'] = pd.to_datetime(df_80['date'])\n\n# Merge prices with EPS\n# Sort both by date for merge_asof\nprices_sorted = df_80.sort_values('date')\neps_sorted = df_81.dropna(subset=['reported_date']).sort_values('reported_date')\n\nmerged = pd.merge_asof(prices_sorted, eps_sorted, \n left_on='date', right_on='reported_date', \n by='ticker', direction='backward')\n\n# Calculate PE (Price / EPS)\nmerged['pe_ratio'] = merged['close'] / merged['eps_reported']\n\n# Filter to keep only the last 24 months (2024-06-01 onwards is already loaded)\nresult = merged", dataframe_ids=[80, 81]
DataFrame 82
OK result_82 (5000 rows)
Calling: show_chart
- x="date_x", chart_type="line", series=["normalized_price"], title="Normalized Stock Performance (24 Months)", timeframe="2Y", dataframe_id=82
Blob 14
DataFrame 82
OK result_82 (5000 rows)
Calling: show_chart
- x="date_x", series=["pe_ratio"], dataframe_id=82, title="PE Ratio (24 Months)", chart_type="line", timeframe="2Y"
Blob 15
DataFrame 82
OK result_82 (5000 rows)
Something seems wrong - the sudden tops and increases seem like we are not dealing with splits etc correctly otherwise its not possible. What was the raw data you used?
[Response aborted]
im refering to the PE chart
Calling: load
- metrics=["eps_reported", "close", "shares_outstanding"], tickers=["ORCL", "SNPS", "SAP", "CRM", "SHOP", "INTU", "PANW", "CRWD", "ADBE", "NOW"], start_date="2024-06-01"
Calling: compute
- dataframe_ids=[83, 85], code="# Let's inspect the eps data to see why the PE ratios spiked.\n# Many of the spikes in the previous chart were due to low earnings in certain quarters \n# (denominator effect). A 4-quarter rolling sum (TTM) is the standard for normalizing PE.\n\n# Sort EPS by date to do a rolling sum\ndf_85['reported_date'] = pd.to_datetime(df_85['reported_date'])\ndf_85 = df_85.sort_values(['ticker', 'reported_date'])\n\n# Calculate TTM EPS: Rolling sum of 4 quarters\ndf_85['ttm_eps'] = df_85.groupby('ticker')['eps_reported'].rolling(window=4).sum().reset_index(0, drop=True)\n\n# Merge back with prices.\ndf_83['date'] = pd.to_datetime(df_83['date'])\ndf_85['reported_date'] = pd.to_datetime(df_85['reported_date'])\n\n# Merge price and TTM EPS\nmerged = pd.merge_asof(df_83.sort_values('date'), \n df_85.dropna(subset=['reported_date']).sort_values('reported_date'),\n left_on='date', right_on='reported_date', \n by='ticker', direction='backward')\n\n# Calculate TTM PE\nmerged['ttm_pe'] = merged['close'] / merged['ttm_eps']\n\n# Cap the PE at a reasonable value (e.g., 500) to avoid extreme outliers if earnings are near zero\nmerged['ttm_pe'] = merged['ttm_pe'].clip(0, 500)\n\nresult = merged"
DataFrame 86
OK result_86 (5000 rows)
Calling: show_chart
- timeframe="2Y", title="Normalized Stock Performance (24 Months)", dataframe_id=86, series=["normalized_price"], x="date_x", chart_type="line"
Error: Unknown series column(s): normalized_price
Calling: compute
- dataframe_ids=[86], code="# Add back normalized price to the merged dataframe\ndf_86['first_close'] = df_86.groupby('ticker')['close'].transform('first')\ndf_86['normalized_price'] = (df_86['close'] / df_86['first_close']) * 100\nresult = df_86"
DataFrame 87
OK result_87 (5000 rows)
Calling: show_chart
- x="date_x", chart_type="line", title="Normalized Stock Performance (24 Months)", dataframe_id=87, series=["normalized_price"], timeframe="2Y"
Blob 16
DataFrame 87
OK result_87 (5000 rows)
Calling: show_chart
- chart_type="line", timeframe="2Y", series=["ttm_pe"], dataframe_id=87, title="PE Ratio (Last 24 Months)", x="date_x"
Blob 17
DataFrame 87
OK result_87 (5000 rows)