Spaces:
Sleeping
Sleeping
File size: 107,134 Bytes
7a364d2 5c32400 7a364d2 5c32400 7a364d2 5c32400 7a364d2 5c32400 7a364d2 5c32400 7a364d2 d365913 7a364d2 5c32400 7a364d2 5c32400 7a364d2 5c32400 7a364d2 5c32400 7a364d2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 |
import streamlit as st
import datetime, requests, pandas as pd, numpy as np
import plotly.graph_objects as go
import os
# ----- Global Configuration -----
st.set_page_config(page_title="Valuation Metrics", layout="wide")
st.title("Valuation Metrics")
st.write(
"This tool tracks and visualizes valuation multiples across time. "
"It includes trailing and forward ratios like P/E, EV/EBITDA, P/S, and others. "
"Data is sourced in real-time from reported financials and analyst forecasts. "
"Use the charts to compare historical trends, assess relative valuation, "
"and flag outliers or shifts in market expectations."
)
API_KEY = os.getenv("FMP_API_KEY")
# ----- Sidebar (includes Page Selector at the top) -----
with st.sidebar:
st.title("Parameters")
page = st.radio("Select Metric Page",
["P/E & PEG", "EV/EBITDA", "EV/EBIT", "P/S Ratio", "P/B Ratio"],
help="Choose the valuation metric page.")
with st.expander("Data Inputs", expanded=True):
ticker = st.text_input("Ticker", "MSFT", help="Enter ticker symbol (e.g. MSFT).")
years_back = st.number_input("Years / Quarters Back", min_value=1, max_value=50, value=10, help="Number of years / quarters of historical data.")
default_start = datetime.date.today() - datetime.timedelta(days=years_back*365)
#start_date = st.date_input("Start Date", default_start, help="Select start date for analysis.")
default_end = datetime.date.today() + datetime.timedelta(days=1)
#end_date = st.date_input("End Date", default_end, help="End date (today +1 day).")
with st.expander("General Settings", expanded=True):
forecast_type = st.selectbox("Forecast Type", ["annual", "quarter"], help="Select forecast frequency.")
run_analysis = st.button("Run Analysis")
# ----- Caching helper (only using spinner, no prints) -----
@st.cache_data(show_spinner=True)
def fetch_data(url):
response = requests.get(url)
response.raise_for_status()
return response.json()
# =============================================================================
# Page 1 – P/E & PEG
# =============================================================================
def pe_peg_page():
#st.markdown("---")
st.header("P/E & PEG Ratio")
st.write(
"Displays trailing and forward P/E ratios, plus PEG metrics derived from analyst EPS forecasts. "
"P/E shows how much investors are paying per unit of earnings. "
"PEG adjusts that by expected EPS growth to give a valuation-per-growth view. "
"Use the sidebar to adjust ticker, forecast frequency, and history length. "
"PEG filters control for negative growth and extreme outliers."
)
st.info(
"Chart legend items can be clicked to toggle series on/off. "
"Hover to inspect exact values. Zoom or pan to focus on specific periods."
)
with st.expander("Methodology", expanded=False):
st.markdown("#### Methodology: P/E and PEG")
st.markdown("##### 1. Trailing P/E")
st.markdown("Calculated using actual historical earnings.")
st.latex(r"\text{Trailing EPS}_t = \sum_{i=0}^{3} \text{EPS}_{t - i}")
st.latex(r"\text{Trailing P/E}_t = \frac{\text{Stock Price}_t}{\text{Trailing EPS}_t}")
st.markdown("**Notes**")
st.markdown("- High P/E → market pricing in growth or quality.")
st.markdown("- Low P/E → may reflect pessimism or undervaluation.")
st.markdown("- Near-zero or negative EPS inflates or invalidates the ratio.")
st.markdown("---")
st.markdown("##### 2. Forward P/E")
st.markdown("Based on analyst EPS forecasts.")
st.latex(r"\text{Forward EPS}_t^{(X)} = \sum_{i=1}^{4} \text{Forecast EPS}_{t+i}^{(X)}")
st.latex(r"\text{Forward P/E}_t^{(X)} = \frac{\text{Stock Price}_t}{\text{Forward EPS}_t^{(X)}}")
st.markdown("**Notes**")
st.markdown("- Lower forward P/E → priced attractively vs expected earnings.")
st.markdown("- Higher forward P/E → premium pricing or stable outlook.")
st.markdown("- Sensitive to forecast quality.")
st.markdown("---")
st.markdown("##### 3. EPS Growth")
st.markdown("Used to normalize valuation.")
st.latex(r"\text{EPS Growth}_t^{(X)} = \frac{\text{Forward EPS}_t^{(X)}}{\text{Trailing EPS}_t} - 1")
st.latex(r"\text{EPS Growth (Trailing)}_t = \frac{\text{Trailing EPS}_t}{\text{Trailing EPS}_{t - s}} - 1")
st.markdown("\\(s = 4\\) for quarters, \\(s = 1\\) for annual.")
st.markdown("**Warnings**")
st.markdown("- Near-zero EPS inflates growth.")
st.markdown("- Negative trailing EPS makes growth and PEG unusable.")
st.markdown("- Large growth swings distort PEG.")
st.markdown("---")
st.markdown("##### 4. PEG Ratio")
st.markdown("PEG ratio adjusts P/E valuation by growth rate to normalize across companies or periods.")
st.latex(r"\text{PEG}_t^{(X)} = \frac{\text{Forward P/E}_t^{(X)}}{\text{EPS Growth}_t^{(X)} \times 100}")
st.latex(r"\text{Trailing PEG}_t = \frac{\text{Trailing P/E}_t}{\text{EPS Growth (Trailing)}_t \times 100}")
st.markdown("**Interpretation**")
st.markdown("- PEG ≈ 1 → priced in line with growth.")
st.markdown("- PEG < 1 → undervalued vs growth.")
st.markdown("- PEG > 1 → premium pricing.")
st.markdown("**Issues**")
st.markdown("- Near-zero growth → unstable PEG.")
st.markdown("- Negative growth → PEG undefined.")
st.markdown("- Small EPS → unreliable denominator.")
st.markdown("---")
st.markdown("##### 5. Filtering")
st.markdown("- PEG excluded if growth ≤ 0 (unless `INCLUDE_NEGATIVE_PEGS=True`).")
st.markdown("- PEG dropped if \\(|\text{PEG}| > \text{MAX_ABS_PEG}\\).")
st.markdown("- Filters reduce noise and false signals.")
st.markdown("---")
st.markdown("##### 6. How to Read the Outputs")
st.markdown("**Trailing and Forward P/E**")
st.markdown("P/E ratios show how much investors are paying for each unit of earnings.")
st.markdown("- **Trailing P/E** uses actual earnings. Reflects historical profitability.")
st.markdown("- **Forward P/E** uses forecast earnings. Reflects market expectations.")
st.markdown("**How to read the relationship:**")
st.markdown("- **Trailing P/E high, Forward P/E lower** → Analysts expect strong earnings growth. Valuation may look rich today but justified by growth ahead.")
st.markdown("- **Trailing P/E low, Forward P/E even lower** → Possibly undervalued. But check if earnings quality or expectations are weak.")
st.markdown("- **Trailing P/E rising, Forward P/E rising** → Market is pricing in higher growth, but expectations may be getting stretched.")
st.markdown("- **Trailing P/E stable, Forward P/E rising** → Market anticipates improvement, but evidence isn't in earnings yet. This can signal a speculative rebound or recovery play.")
st.markdown("- **Trailing P/E rising, Forward P/E falling** → Analysts expect growth to cool. Watch for slowing fundamentals or sentiment shift.")
st.markdown(" **PEG Ratios**")
st.markdown("**PEG ≈ 1** → Often viewed as fair value for the growth you're buying. Works best when inputs are stable.")
st.markdown("**PEG < 1** → May indicate undervaluation relative to growth. Could be a buying opportunity. But also: could reflect skepticism around forecasts (e.g. biotech, early-stage).")
st.markdown("**PEG > 1** → Paying more than 1x growth rate. Common in stable, brand-heavy, or high-moat businesses. Not always overvalued — could reflect quality, consistency, or low-risk profile.")
# Sidebar parameters
with st.sidebar.expander("P/E & PEG Parameters", expanded=True):
include_negative_pegs = st.checkbox("Include Negative PEGs", value=False,
help="Check to include negative PEGs in the analysis.")
max_abs_peg = st.number_input("Max Absolute PEG", value=10,
help="Filter out PEGs with |PEG| above this value.")
# Initialize session state result if not present
if "pepeg_result" not in st.session_state:
st.session_state.pepeg_result = None
# Run analysis if triggered
if run_analysis:
with st.spinner("Running P/E & PEG analysis..."):
LIMIT = years_back * (4 if forecast_type == "quarter" else 1)
TICKER = ticker.upper()
if forecast_type == "annual":
analyst_period = "annual"
income_url = f"https://financialmodelingprep.com/api/v3/income-statement/{TICKER}?period=annual&limit={LIMIT}&apikey={API_KEY}"
ev_url = f"https://financialmodelingprep.com/api/v3/enterprise-values/{TICKER}?period=annual&limit={LIMIT}&apikey={API_KEY}"
else:
analyst_period = "quarter"
income_url = f"https://financialmodelingprep.com/api/v3/income-statement/{TICKER}?period=quarter&limit={LIMIT}&apikey={API_KEY}"
ev_url = f"https://financialmodelingprep.com/api/v3/enterprise-values/{TICKER}?period=quarter&limit={LIMIT}&apikey={API_KEY}"
analyst_url = f"https://financialmodelingprep.com/api/v3/analyst-estimates/{TICKER}?period={analyst_period}&apikey={API_KEY}"
quote_url = f"https://financialmodelingprep.com/api/v3/quote/{TICKER}?apikey={API_KEY}"
# Helper functions
def local_fetch(url):
return fetch_data(url)
def get_income_data():
data = local_fetch(income_url)
if not data:
st.error("Income statement data is empty.")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True, ignore_index=True)
if 'eps' not in df.columns:
st.error("Field 'eps' not found in income statement data.")
return None
df['Trailing_EPS'] = df['eps'].rolling(window=4).sum() if forecast_type == "quarter" else df['eps']
df.dropna(subset=['Trailing_EPS'], inplace=True)
return df
def get_ev_data():
data = local_fetch(ev_url)
if not data:
st.error("EV data is empty.")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True)
if 'stockPrice' not in df.columns:
st.error("Field 'stockPrice' missing in EV data.")
return None
return df[['date', 'stockPrice']]
def extend_ev_today(df_ev):
q_data = local_fetch(quote_url)
if q_data:
if 'price' not in q_data[0]:
st.error("Field 'price' missing in quote data.")
else:
current_price = q_data[0]['price']
today = pd.to_datetime("today").normalize()
df_today = pd.DataFrame({"date": [today], "stockPrice": [current_price]})
df_ev = pd.concat([df_ev, df_today], ignore_index=True)
df_ev.sort_values("date", inplace=True)
else:
st.warning("Could not fetch today's quote.")
return df_ev
def get_analyst_data():
data = local_fetch(analyst_url)
if not data:
st.error("Analyst estimates data is empty.")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values("date", inplace=True, ignore_index=True)
for col in ['estimatedEpsLow', 'estimatedEpsAvg', 'estimatedEpsHigh']:
if col not in df.columns:
st.error(f"Field '{col}' not found in analyst data.")
return None
df.rename(columns={
"estimatedEpsLow": "Forecast_EPS_Low",
"estimatedEpsAvg": "Forecast_EPS_Avg",
"estimatedEpsHigh": "Forecast_EPS_High"
}, inplace=True)
return df
def get_future_eps(date_val, df_analyst):
future = df_analyst[df_analyst["date"] > date_val].sort_values("date")
if forecast_type == "quarter":
future = future.head(4)
low_list = future["Forecast_EPS_Low"].tolist()
avg_list = future["Forecast_EPS_Avg"].tolist()
high_list = future["Forecast_EPS_High"].tolist()
while len(low_list) < 4: low_list.append(np.nan)
while len(avg_list) < 4: avg_list.append(np.nan)
while len(high_list) < 4: high_list.append(np.nan)
return low_list, avg_list, high_list
else:
if len(future) >= 1:
row0 = future.iloc[0]
return ([row0["Forecast_EPS_Low"]] + [np.nan] * 3,
[row0["Forecast_EPS_Avg"]] + [np.nan] * 3,
[row0["Forecast_EPS_High"]] + [np.nan] * 3)
else:
return ([np.nan] * 4, [np.nan] * 4, [np.nan] * 4)
# Data processing
df_income = get_income_data()
df_ev = get_ev_data()
if df_income is None or df_ev is None:
return
df_ev = extend_ev_today(df_ev)
df_trailing = pd.merge(
df_income[["date", "eps", "Trailing_EPS"]],
df_ev[["date", "stockPrice"]],
on="date", how="inner"
)
df_trailing["Trailing_PE"] = df_trailing["stockPrice"] / df_trailing["Trailing_EPS"]
df_analyst = get_analyst_data()
if df_analyst is None:
return
earliest_date = min(df_income["date"].min(), df_ev["date"].min())
df_analyst = df_analyst[df_analyst["date"] >= earliest_date].copy()
col_names = ["EPSLow_1", "EPSLow_2", "EPSLow_3", "EPSLow_4",
"EPSAvg_1", "EPSAvg_2", "EPSAvg_3", "EPSAvg_4",
"EPSHigh_1", "EPSHigh_2", "EPSHigh_3", "EPSHigh_4"]
for c in col_names:
df_ev[c] = np.nan
for i in range(len(df_ev)):
d = df_ev.loc[i, "date"]
low_list, avg_list, high_list = get_future_eps(d, df_analyst)
df_ev.at[i, "EPSLow_1"] = low_list[0]
df_ev.at[i, "EPSLow_2"] = low_list[1]
df_ev.at[i, "EPSLow_3"] = low_list[2]
df_ev.at[i, "EPSLow_4"] = low_list[3]
df_ev.at[i, "EPSAvg_1"] = avg_list[0]
df_ev.at[i, "EPSAvg_2"] = avg_list[1]
df_ev.at[i, "EPSAvg_3"] = avg_list[2]
df_ev.at[i, "EPSAvg_4"] = avg_list[3]
df_ev.at[i, "EPSHigh_1"] = high_list[0]
df_ev.at[i, "EPSHigh_2"] = high_list[1]
df_ev.at[i, "EPSHigh_3"] = high_list[2]
df_ev.at[i, "EPSHigh_4"] = high_list[3]
df_ev["ForwardTTM_Low"] = df_ev[["EPSLow_1", "EPSLow_2", "EPSLow_3", "EPSLow_4"]].sum(axis=1, min_count=1)
df_ev["ForwardTTM_Avg"] = df_ev[["EPSAvg_1", "EPSAvg_2", "EPSAvg_3", "EPSAvg_4"]].sum(axis=1, min_count=1)
df_ev["ForwardTTM_High"] = df_ev[["EPSHigh_1", "EPSHigh_2", "EPSHigh_3", "EPSHigh_4"]].sum(axis=1, min_count=1)
df_ev["Forward_PE_Low"] = df_ev.apply(lambda row: row["stockPrice"] / row["ForwardTTM_Low"]
if pd.notna(row["ForwardTTM_Low"]) and row["ForwardTTM_Low"] > 0 else np.nan, axis=1)
df_ev["Forward_PE_Avg"] = df_ev.apply(lambda row: row["stockPrice"] / row["ForwardTTM_Avg"]
if pd.notna(row["ForwardTTM_Avg"]) and row["ForwardTTM_Avg"] > 0 else np.nan, axis=1)
df_ev["Forward_PE_High"] = df_ev.apply(lambda row: row["stockPrice"] / row["ForwardTTM_High"]
if pd.notna(row["ForwardTTM_High"]) and row["ForwardTTM_High"] > 0 else np.nan, axis=1)
df_final = pd.merge(df_trailing, df_ev, on="date", how="outer", suffixes=("_trail", "_fwd"))
df_final.sort_values("date", inplace=True, ignore_index=True)
if "stockPrice_trail" in df_final.columns and "stockPrice_fwd" in df_final.columns:
df_final["stockPrice"] = df_final["stockPrice_trail"].fillna(df_final["stockPrice_fwd"])
df_final.drop(columns=["stockPrice_trail", "stockPrice_fwd"], inplace=True)
date_set = set(df_income["date"]).union(df_trailing["date"]).union(df_ev["date"])
df_final = df_final[df_final["date"].isin(date_set)].copy()
df_final["EPSGrowth_Low"] = np.where((df_final["Trailing_EPS"] > 0) & df_final["Trailing_EPS"].notna(),
(df_final["ForwardTTM_Low"] / df_final["Trailing_EPS"]) - 1, np.nan)
df_final["EPSGrowth_Avg"] = np.where((df_final["Trailing_EPS"] > 0) & df_final["Trailing_EPS"].notna(),
(df_final["ForwardTTM_Avg"] / df_final["Trailing_EPS"]) - 1, np.nan)
df_final["EPSGrowth_High"] = np.where((df_final["Trailing_EPS"] > 0) & df_final["Trailing_EPS"].notna(),
(df_final["ForwardTTM_High"] / df_final["Trailing_EPS"]) - 1, np.nan)
if include_negative_pegs:
df_final["PEG_Low"] = df_final["Forward_PE_Low"] / (df_final["EPSGrowth_Low"] * 100)
df_final["PEG_Avg"] = df_final["Forward_PE_Avg"] / (df_final["EPSGrowth_Avg"] * 100)
df_final["PEG_High"] = df_final["Forward_PE_High"] / (df_final["EPSGrowth_High"] * 100)
else:
df_final["PEG_Low"] = np.where(df_final["EPSGrowth_Low"] > 0,
df_final["Forward_PE_Low"] / (df_final["EPSGrowth_Low"] * 100), np.nan)
df_final["PEG_Avg"] = np.where(df_final["EPSGrowth_Avg"] > 0,
df_final["Forward_PE_Avg"] / (df_final["EPSGrowth_Avg"] * 100), np.nan)
df_final["PEG_High"] = np.where(df_final["EPSGrowth_High"] > 0,
df_final["Forward_PE_High"] / (df_final["EPSGrowth_High"] * 100), np.nan)
shift_val = 4 if forecast_type == "quarter" else 1
df_final["EPSGrowth_Trailing"] = np.where(df_final["Trailing_EPS"].notna() &
(df_final["Trailing_EPS"].shift(shift_val) > 0),
(df_final["Trailing_EPS"] / df_final["Trailing_EPS"].shift(shift_val)) - 1, np.nan)
if include_negative_pegs:
df_final["Trailing_PEG"] = df_final["Trailing_PE"] / (df_final["EPSGrowth_Trailing"] * 100)
else:
df_final["Trailing_PEG"] = np.where(df_final["EPSGrowth_Trailing"] > 0,
df_final["Trailing_PE"] / (df_final["EPSGrowth_Trailing"] * 100), np.nan)
def filter_extreme_peg(x):
if pd.isna(x):
return np.nan
return x if abs(x) <= max_abs_peg else np.nan
df_final["PEG_Low"] = df_final["PEG_Low"].apply(filter_extreme_peg)
df_final["PEG_Avg"] = df_final["PEG_Avg"].apply(filter_extreme_peg)
df_final["PEG_High"] = df_final["PEG_High"].apply(filter_extreme_peg)
df_final["Trailing_PEG"] = df_final["Trailing_PEG"].apply(filter_extreme_peg)
# --- Chart 1: Trailing vs Forward P/E with double y-axes ---
fig1 = go.Figure()
fig1.add_trace(go.Scatter(x=df_final["date"], y=df_final["Trailing_PE"],
mode="lines+markers", name="Trailing P/E", line=dict(width=2), yaxis="y1"))
fig1.add_trace(go.Scatter(x=df_final["date"], y=df_final["Forward_PE_Low"],
mode="lines+markers", name="Forward P/E (Low)", line=dict(width=1), yaxis="y1"))
fig1.add_trace(go.Scatter(x=df_final["date"], y=df_final["Forward_PE_Avg"],
mode="lines+markers", name="Forward P/E (Avg)", line=dict(width=1), yaxis="y1"))
fig1.add_trace(go.Scatter(x=df_final["date"], y=df_final["Forward_PE_High"],
mode="lines+markers", name="Forward P/E (High)", line=dict(width=1), yaxis="y1"))
start_date_str = df_final["date"].min().strftime("%Y-%m-%d")
end_date_str = df_final["date"].max().strftime("%Y-%m-%d")
daily_url = f"https://financialmodelingprep.com/api/v3/historical-price-full/{TICKER}?from={start_date_str}&to={end_date_str}&serietype=line&apikey={API_KEY}"
daily_data = local_fetch(daily_url)
df_daily = pd.DataFrame(daily_data.get("historical", []))
if not df_daily.empty:
df_daily["date"] = pd.to_datetime(df_daily["date"])
df_daily.sort_values("date", inplace=True)
fig1.add_trace(go.Scatter(x=df_daily["date"], y=df_daily["close"],
mode="lines", name="Daily Stock Price", line=dict(width=1), opacity=0.2, yaxis="y2"))
if forecast_type == "quarter":
fig1.update_xaxes(tickformat="%Y-%m", dtick="M3")
else:
fig1.update_xaxes(tickformat="%Y", dtick="M12")
fig1.update_layout(
title=f"{TICKER} Trailing vs Forward P/E (Low/Avg/High) with Daily Stock ({forecast_type.capitalize()} freq)",
xaxis=dict(title="Date"),
yaxis=dict(title="P/E Ratio", side="left"),
yaxis2=dict(title="Stock Price", overlaying="y", side="right"),
template="plotly_dark", legend=dict(x=0.02, y=0.98)
)
# --- Chart 2: PEG Ratios with double y-axes ---
fig2 = go.Figure()
fig2.add_trace(go.Scatter(x=df_final["date"], y=df_final["PEG_Low"],
mode="lines+markers", name="Forward PEG (Low)", line=dict(width=1), yaxis="y1"))
fig2.add_trace(go.Scatter(x=df_final["date"], y=df_final["PEG_Avg"],
mode="lines+markers", name="Forward PEG (Avg)", line=dict(width=1), yaxis="y1"))
fig2.add_trace(go.Scatter(x=df_final["date"], y=df_final["PEG_High"],
mode="lines+markers", name="Forward PEG (High)", line=dict(width=1), yaxis="y1"))
fig2.add_trace(go.Scatter(x=df_final["date"], y=df_final["Trailing_PEG"],
mode="lines+markers", name="Trailing PEG", line=dict(width=1), yaxis="y1"))
if not df_daily.empty:
fig2.add_trace(go.Scatter(x=df_daily["date"], y=df_daily["close"],
mode="lines", name="Daily Stock Price", line=dict(width=1), opacity=0.2, yaxis="y2"))
if forecast_type == "quarter":
fig2.update_xaxes(tickformat="%Y-%m", dtick="M3")
else:
fig2.update_xaxes(tickformat="%Y", dtick="M12")
fig2.update_layout(
title=f"{TICKER} PEG Ratios vs Stock Price ({forecast_type.capitalize()} freq)",
xaxis=dict(title="Date"),
yaxis=dict(title="PEG Ratio", side="left"),
yaxis2=dict(title="Stock Price", overlaying="y", side="right"),
template="plotly_dark", legend=dict(x=0.02, y=0.98)
)
# Build the dynamic interpretation string (as per your original raw code)
ticker_var = TICKER
period_var = forecast_type.capitalize()
latest_full = df_final.dropna(subset=["Trailing_PE", "Forward_PE_Avg", "PEG_Avg"]).iloc[-1]
latest_date = latest_full["date"].strftime("%Y-%m-%d")
trailing_pe = latest_full["Trailing_PE"]
forward_pe = latest_full["Forward_PE_Avg"]
peg = latest_full["PEG_Avg"]
growth = latest_full["EPSGrowth_Avg"] * 100
interp_text = f"""--- {ticker_var} Valuation Interpretation ({period_var} data as of {latest_date}) ---
Trailing P/E: {trailing_pe:.2f}
Forward P/E (Avg): {forward_pe:.2f}
EPS Growth (Avg): {growth:.2f}%
Forward PEG (Avg): {peg:.2f}
--- P/E Relationship ---
"""
if forward_pe < trailing_pe and growth > 0:
interp_text += f"Forward P/E is lower than trailing → {ticker_var} is priced for EPS growth under {period_var} expectations.\n"
elif forward_pe > trailing_pe and growth > 0:
interp_text += f"Forward P/E is higher than trailing → {ticker_var} pricing reflects optimism on a future rebound ({period_var} view).\n"
elif forward_pe > trailing_pe and growth <= 0:
interp_text += f"Forward P/E exceeds trailing despite low or negative growth → expectations for {ticker_var} may be decoupled from fundamentals.\n"
else:
interp_text += f"P/E levels are close → {ticker_var} may be priced for flat or stable performance ({period_var} view).\n"
interp_text += "\n--- PEG Ratio Interpretation ---\n"
if np.isnan(peg) or peg > max_abs_peg:
interp_text += f"PEG ratio unavailable or filtered (e.g., due to unstable or extreme inputs in {period_var} data).\n"
elif peg < 0:
interp_text += f"PEG is negative → EPS growth forecast for {ticker_var} is negative in the {period_var} window.\n"
elif peg < 0.8:
interp_text += f"PEG < 0.8 → {ticker_var} is priced lower relative to expected growth in {period_var} forecasts.\n"
elif 0.8 <= peg <= 1.2:
interp_text += f"PEG ≈ 1 → Valuation of {ticker_var} aligns proportionally with its forecast growth ({period_var} data).\n"
elif peg > 1.2 and peg <= 2:
interp_text += f"PEG > 1 → Market may value {ticker_var}'s consistency, margins, or quality beyond pure growth ({period_var} horizon).\n"
else:
interp_text += f"PEG > 2 → Price may reflect attributes outside EPS growth (e.g., defensive profile or brand value).\n"
interp_text += "\n--- Additional practical context ---\n"
if growth < 5:
interp_text += f"EPS growth < 5% → PEG becomes more sensitive to input shifts under {period_var} conditions.\n"
if growth < 0:
interp_text += f"EPS growth is negative → PEG loses interpretability in {period_var} data.\n"
if trailing_pe < 10 and peg < 1:
interp_text += f"Low trailing P/E and PEG → {ticker_var} may be seen as attractively priced relative to growth.\n"
if forward_pe > 25 and peg > 2:
interp_text += f"High forward P/E and PEG → Valuation assumptions for {ticker_var} may be stretched in {period_var} forecast.\n"
if 10 < forward_pe < 20 and peg < 1:
interp_text += f"{ticker_var} has moderate forward P/E and sub-1 PEG → Pricing appears efficient on a growth-adjusted basis.\n"
latest_fwd_row = df_final[df_final["Forward_PE_Avg"].notna()].iloc[-1]
latest_fwd_date = latest_fwd_row["date"].strftime("%Y-%m-%d")
latest_fwd_pe = latest_fwd_row["Forward_PE_Avg"]
if latest_fwd_date != latest_date:
interp_text += f"\nNote: Latest forward P/E ({latest_fwd_pe:.2f}) is from {latest_fwd_date} — a more recent forecast-only update.\n"
if latest_fwd_pe < trailing_pe:
interp_text += f"As of {latest_fwd_date}, {ticker_var} has a forward P/E lower than historical — forward sentiment remains constructive.\n"
elif latest_fwd_pe > trailing_pe:
interp_text += f"As of {latest_fwd_date}, {ticker_var} has a forward P/E above trailing — signals possible rebound expectations.\n"
else:
interp_text += f"As of {latest_fwd_date}, {ticker_var} forward P/E equals trailing — market sees little near-term earnings reversion.\n"
interp_text += f"\n[Summary] {ticker_var} ({period_var}): Trailing P/E = {trailing_pe:.2f}, Forward P/E = {forward_pe:.2f}, EPS Growth = {growth:.2f}%, PEG = {peg:.2f}"
st.session_state.pepeg_result = {
"df_final": df_final,
"fig1": fig1,
"fig2": fig2,
"interpretation": interp_text
}
st.success("P/E & PEG analysis complete.")
# Only display results if the analysis has been run
if st.session_state.pepeg_result is not None:
# Display the two charts
st.plotly_chart(st.session_state.pepeg_result["fig1"], use_container_width=True)
st.plotly_chart(st.session_state.pepeg_result["fig2"], use_container_width=True)
# Single Dynamic Interpretation expander
with st.expander("Dynamic Interpretation", expanded=False):
st.text(st.session_state.pepeg_result["interpretation"])
# Display final DataFrame
st.dataframe(st.session_state.pepeg_result["df_final"])
# =============================================================================
# Page 2 – EV/EBITDA
# =============================================================================
def ev_ebitda_page():
#st.markdown("---")
st.header("EV/EBITDA Ratio")
st.write(
"Shows trailing and forward EV/EBITDA based on reported results and analyst EBITDA forecasts. "
"EV/EBITDA measures valuation relative to operating earnings, independent of capital structure. "
"Lower values suggest the stock is priced lower per unit of EBITDA; higher values imply premium pricing."
)
st.info(
"Chart legend items can be clicked to toggle series on/off. "
"Hover to inspect exact values. Zoom or pan to focus on specific periods."
)
with st.expander("Methodology", expanded=False):
st.markdown("#### Methodology: EV/EBITDA Ratios")
st.markdown("##### 1. Trailing EV/EBITDA")
st.markdown("Trailing EV/EBITDA is calculated using historical TTM (trailing twelve-month) EBITDA and reported enterprise value:")
st.markdown("###### Formula")
st.latex(r"\text{TTM EBITDA}_t = \sum_{i=0}^{3} \text{EBITDA}_{t - i}")
st.markdown("Then:")
st.latex(r"\text{Trailing EV/EBITDA}_t = \frac{\text{Enterprise Value}_t}{\text{TTM EBITDA}_t}")
st.markdown("###### Interpretation")
st.markdown("- Measures how expensive the company is relative to actual operating earnings.")
st.markdown("- Lower values → potentially cheaper valuation.")
st.markdown("- Higher values → may reflect growth expectations, quality, or overvaluation.")
st.markdown("Unlike P/E, this metric includes debt and ignores non-cash items. It works better across firms with different capital structures.")
st.markdown("---")
st.markdown("##### 2. Forward EV/EBITDA")
st.markdown("Forward EV/EBITDA uses analyst forecasts for EBITDA to project valuation:")
st.markdown("###### Formula")
st.latex(r"\text{Forward EBITDA}^{(X)}_t = \sum_{i=1}^{4} \text{Forecast EBITDA}_{t + i}^{(X)} \quad \text{where } X \in \{\text{Low},\,\text{Avg},\,\text{High}\}")
st.markdown("Then:")
st.latex(r"\text{Forward EV/EBITDA}^{(X)}_t = \frac{\text{Enterprise Value}_t}{\text{Forward EBITDA}^{(X)}_t}")
st.markdown("###### Interpretation")
st.markdown("- Projects how valuation looks against future operating earnings.")
st.markdown("- Lower forward EV/EBITDA may indicate market is undervaluing future EBITDA.")
st.markdown("- Higher values may reflect rich expectations or optimism about margin expansion.")
st.markdown("Forward estimates depend on forecast quality. Optimism or outdated revisions can distort the ratio.")
st.markdown("---")
st.markdown("##### 3. Enterprise Value Context")
st.markdown("EV includes:")
st.latex(r"EV = \text{Market Cap} + \text{Total Debt} - \text{Cash and Equivalents}")
st.markdown("This makes it capital-structure neutral. More stable than market cap alone when companies hold debt or cash.")
st.markdown("---")
st.markdown("##### 4. Summary: How to Use the Outputs")
st.markdown("###### Trailing vs Forward EV/EBITDA")
st.markdown("- **Trailing EV/EBITDA high, forward low** → Market expects earnings to rebound. Could be a turnaround signal or too optimistic.")
st.markdown("- **Trailing low, forward even lower** → Could suggest undervaluation — or deteriorating forecast quality.")
st.markdown("- **Both rising** → Market pricing in growth, but check if EBITDA forecasts are keeping up.")
st.markdown("- **Trailing stable, forward rising** → Margin compression or growth downgrade is expected.")
st.markdown("Always cross-check this against margins, cash flows, and capex to avoid false positives.")
# (No additional sidebar parameters are needed here)
if "ev_ebitda_result" not in st.session_state:
st.session_state.ev_ebitda_result = None
if run_analysis:
with st.spinner("Running EV/EBITDA analysis..."):
LIMIT = years_back * (4 if forecast_type == "quarter" else 1)
TICKER = ticker.upper()
if forecast_type == "annual":
period_str = "annual"
income_url = f"https://financialmodelingprep.com/api/v3/income-statement/{TICKER}?period=annual&limit={LIMIT}&apikey={API_KEY}"
ev_url = f"https://financialmodelingprep.com/api/v3/enterprise-values/{TICKER}?period=annual&limit={LIMIT}&apikey={API_KEY}"
else:
period_str = "quarter"
income_url = f"https://financialmodelingprep.com/api/v3/income-statement/{TICKER}?period=quarter&limit={LIMIT}&apikey={API_KEY}"
ev_url = f"https://financialmodelingprep.com/api/v3/enterprise-values/{TICKER}?period=quarter&limit={LIMIT}&apikey={API_KEY}"
analyst_url = f"https://financialmodelingprep.com/api/v3/analyst-estimates/{TICKER}?period={period_str}&apikey={API_KEY}"
quote_url = f"https://financialmodelingprep.com/api/v3/quote/{TICKER}?apikey={API_KEY}"
def local_fetch(url):
return fetch_data(url)
def get_income_data():
data = local_fetch(income_url)
if not data:
st.error("Income statement data is empty.")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True)
if 'ebitda' not in df.columns:
st.error("Field 'ebitda' not found in income statement.")
return None
df.rename(columns={'ebitda': 'EBITDA_raw'}, inplace=True)
df['TTM_EBITDA'] = df['EBITDA_raw'].rolling(4).sum() if forecast_type == "quarter" else df['EBITDA_raw']
df.dropna(subset=['TTM_EBITDA'], inplace=True)
return df
def get_ev_data():
data = local_fetch(ev_url)
if not data:
st.error("EV data is empty.")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True)
if 'enterpriseValue' not in df.columns:
st.error("Field 'enterpriseValue' missing in EV data.")
return None
return df[['date', 'enterpriseValue']]
def extend_ev_today(df_ev):
qdata = local_fetch(quote_url)
if qdata:
today_value = qdata[0].get('enterpriseValue', None)
today = pd.to_datetime('today').normalize()
df_today = pd.DataFrame({'date': [today], 'enterpriseValue': [today_value]})
else:
df_today = pd.DataFrame({'date': [pd.to_datetime('today').normalize()], 'enterpriseValue': [None]})
df_ev = pd.concat([df_ev, df_today], ignore_index=True).sort_values('date')
return df_ev
def get_analyst_data():
data = local_fetch(analyst_url)
if not data:
st.error("Analyst estimates data is empty.")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True)
for col in ['estimatedEbitdaLow', 'estimatedEbitdaAvg', 'estimatedEbitdaHigh']:
if col not in df.columns:
st.error(f"Field '{col}' missing in analyst data.")
return None
df.rename(columns={'estimatedEbitdaLow': 'Forecast_EBITDA_Low',
'estimatedEbitdaAvg': 'Forecast_EBITDA_Avg',
'estimatedEbitdaHigh': 'Forecast_EBITDA_High'}, inplace=True)
return df
def forecast_ebitda_rows(d, df_analyst):
future = df_analyst[df_analyst['date'] > d].sort_values('date')
if forecast_type == "quarter":
future = future.head(4)
else:
future = future.head(1)
if future.empty:
return [], [], []
lows = future['Forecast_EBITDA_Low'].tolist()
avgs = future['Forecast_EBITDA_Avg'].tolist()
highs = future['Forecast_EBITDA_High'].tolist()
while len(lows) < 4: lows.append(np.nan)
while len(avgs) < 4: avgs.append(np.nan)
while len(highs) < 4: highs.append(np.nan)
return lows, avgs, highs
df_income = get_income_data()
df_ev = get_ev_data()
if df_income is None or df_ev is None:
return
df_ev = extend_ev_today(df_ev)
df_trailing = pd.merge(df_income[['date', 'EBITDA_raw', 'TTM_EBITDA']],
df_ev, on='date', how='outer')
df_trailing['Trailing_EV_EBITDA'] = df_trailing.apply(
lambda row: row['enterpriseValue'] / row['TTM_EBITDA'] if pd.notna(row['enterpriseValue']) and row['TTM_EBITDA'] != 0 else np.nan,
axis=1
)
df_analyst = get_analyst_data()
if df_analyst is None:
return
# Add forecast EBITDA columns into df_ev
for c in ['EBITDALow_1','EBITDALow_2','EBITDALow_3','EBITDALow_4',
'EBITDAAvg_1','EBITDAAvg_2','EBITDAAvg_3','EBITDAAvg_4',
'EBITDAHigh_1','EBITDAHigh_2','EBITDAHigh_3','EBITDAHigh_4']:
df_ev[c] = np.nan
for i in range(len(df_ev)):
d = df_ev.loc[i, 'date']
lows, avgs, highs = forecast_ebitda_rows(d, df_analyst)
df_ev.at[i, 'EBITDALow_1'] = lows[0]
df_ev.at[i, 'EBITDALow_2'] = lows[1]
df_ev.at[i, 'EBITDALow_3'] = lows[2]
df_ev.at[i, 'EBITDALow_4'] = lows[3]
df_ev.at[i, 'EBITDAAvg_1'] = avgs[0]
df_ev.at[i, 'EBITDAAvg_2'] = avgs[1]
df_ev.at[i, 'EBITDAAvg_3'] = avgs[2]
df_ev.at[i, 'EBITDAAvg_4'] = avgs[3]
df_ev.at[i, 'EBITDAHigh_1'] = highs[0]
df_ev.at[i, 'EBITDAHigh_2'] = highs[1]
df_ev.at[i, 'EBITDAHigh_3'] = highs[2]
df_ev.at[i, 'EBITDAHigh_4'] = highs[3]
df_ev['ForwardTTM_Low'] = df_ev[['EBITDALow_1','EBITDALow_2','EBITDALow_3','EBITDALow_4']].sum(axis=1, min_count=1)
df_ev['ForwardTTM_Avg'] = df_ev[['EBITDAAvg_1','EBITDAAvg_2','EBITDAAvg_3','EBITDAAvg_4']].sum(axis=1, min_count=1)
df_ev['ForwardTTM_High'] = df_ev[['EBITDAHigh_1','EBITDAHigh_2','EBITDAHigh_3','EBITDAHigh_4']].sum(axis=1, min_count=1)
df_ev['Forward_EV_EBITDA_Low'] = df_ev.apply(lambda row: row['enterpriseValue'] / row['ForwardTTM_Low']
if pd.notna(row['enterpriseValue']) and pd.notna(row['ForwardTTM_Low']) and row['ForwardTTM_Low'] != 0 else np.nan,
axis=1)
df_ev['Forward_EV_EBITDA_Avg'] = df_ev.apply(lambda row: row['enterpriseValue'] / row['ForwardTTM_Avg']
if pd.notna(row['enterpriseValue']) and pd.notna(row['ForwardTTM_Avg']) and row['ForwardTTM_Avg'] != 0 else np.nan,
axis=1)
df_ev['Forward_EV_EBITDA_High'] = df_ev.apply(lambda row: row['enterpriseValue'] / row['ForwardTTM_High']
if pd.notna(row['enterpriseValue']) and pd.notna(row['ForwardTTM_High']) and row['ForwardTTM_High'] != 0 else np.nan,
axis=1)
df_final = pd.merge(df_trailing, df_ev, on='date', how='outer', suffixes=('_trailing','_fwd'))
df_final.sort_values('date', inplace=True)
if 'enterpriseValue_trailing' in df_final.columns and 'enterpriseValue_fwd' in df_final.columns:
df_final['enterpriseValue'] = df_final['enterpriseValue_trailing'].fillna(df_final['enterpriseValue_fwd'])
df_final.drop(columns=['enterpriseValue_trailing','enterpriseValue_fwd'], inplace=True)
date_set = set(df_income['date']) | set(df_trailing['date']) | set(df_ev['date'])
df_final = df_final[df_final['date'].isin(date_set)].copy()
start_date_str = df_final['date'].min().strftime('%Y-%m-%d')
end_date_str = df_final['date'].max().strftime('%Y-%m-%d')
daily_url = f"https://financialmodelingprep.com/api/v3/historical-price-full/{TICKER}?from={start_date_str}&to={end_date_str}&serietype=line&apikey={API_KEY}"
daily_data = local_fetch(daily_url)
df_daily = pd.DataFrame(daily_data.get('historical', []))
if not df_daily.empty:
df_daily['date'] = pd.to_datetime(df_daily['date'])
df_daily.sort_values('date', inplace=True)
# Build chart using double y-axes
fig = go.Figure()
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['Trailing_EV_EBITDA'],
mode='lines+markers', name='Trailing EV/EBITDA', line=dict(width=2), yaxis="y1"))
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['Forward_EV_EBITDA_Low'],
mode='lines+markers', name='Forward EV/EBITDA (Low)', line=dict(width=1), yaxis="y1"))
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['Forward_EV_EBITDA_Avg'],
mode='lines+markers', name='Forward EV/EBITDA (Avg)', line=dict(width=1), yaxis="y1"))
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['Forward_EV_EBITDA_High'],
mode='lines+markers', name='Forward EV/EBITDA (High)', line=dict(width=1), yaxis="y1"))
if not df_daily.empty:
fig.add_trace(go.Scatter(x=df_daily['date'], y=df_daily['close'],
mode='lines', name='Daily Stock Price', line=dict(width=1), opacity=0.2, yaxis="y2"))
if forecast_type == "quarter":
fig.update_xaxes(tickformat="%Y-%m", dtick="M3")
else:
fig.update_xaxes(tickformat="%Y", dtick="M12")
fig.update_layout(
title=f"{TICKER} EV/EBITDA (Trailing & Forward Low/Avg/High) with Daily Stock ({forecast_type.capitalize()} freq)",
xaxis=dict(title='Date'),
yaxis=dict(title='EV/EBITDA Ratio', side="left"),
yaxis2=dict(title='Stock Price', overlaying="y", side="right"),
template='plotly_dark', legend=dict(x=0.02, y=0.98)
)
# Build dynamic interpretation string using the raw interpretation block provided
# (Assumes df_final has at least one valid row for Forward_EV_EBITDA_Avg)
latest_row = df_final[df_final[['Trailing_EV_EBITDA', 'Forward_EV_EBITDA_Avg']].notna().all(axis=1)].iloc[-1]
latest_date = latest_row['date'].strftime('%Y-%m-%d')
trailing_val = latest_row['Trailing_EV_EBITDA']
forward_avg = latest_row['Forward_EV_EBITDA_Avg']
forward_low = latest_row['Forward_EV_EBITDA_Low']
forward_high = latest_row['Forward_EV_EBITDA_High']
interp_text = f"""--- {TICKER} EV/EBITDA Interpretation ({period_str.capitalize()} data as of {latest_date}) ---
Trailing EV/EBITDA: {trailing_val:.2f}
Forward EV/EBITDA (Avg): {forward_avg:.2f}
Forward EV/EBITDA Range: [{forward_low:.2f}, {forward_high:.2f}]
-- RELATIVE LEVEL: Forward vs Trailing --
"""
if forward_avg < trailing_val:
interp_text += f"Forward EV/EBITDA is lower than trailing → the market may be anticipating higher EBITDA in future {forecast_type.lower()}s.\n"
interp_text += "This dynamic can reflect confidence in operational leverage or growth in contribution margin.\n"
interp_text += "Lower forward multiples in this case suggest valuation compresses if EBITDA targets are hit.\n"
elif forward_avg > trailing_val:
interp_text += f"Forward EV/EBITDA is higher than trailing → future EBITDA is expected to be flat or weaker relative to today.\n"
interp_text += "This can imply valuation uplift is not supported by near-term EBITDA growth.\n"
interp_text += "Investors could be paying more today based on optionality, strategic value, or stability expectations.\n"
else:
interp_text += f"Forward and trailing EV/EBITDA are roughly equal → no material change expected in operating performance.\n"
interp_text += "This tends to show a steady-state assumption by the market.\n"
interp_text += "\n-- ABSOLUTE LEVEL: Forward EV/EBITDA (Valuation framing) --\n"
if forward_avg < 8:
interp_text += f"Forward EV/EBITDA < 8 → {TICKER} appears inexpensive on forward {forecast_type.lower()} performance.\n"
interp_text += "At these levels, the valuation multiple is often associated with value segments, cyclicals, or uncertainty.\n"
elif 8 <= forward_avg <= 14:
interp_text += f"Forward EV/EBITDA in 8–14 range → common for mature operators with predictable margin structures.\n"
interp_text += "This range typically reflects healthy but not speculative expectations.\n"
else:
interp_text += f"Forward EV/EBITDA > 14 → valuation implies elevated expectations.\n"
interp_text += "Market may be assigning a premium for stability, brand strength, network effects, or strategic factors.\n"
interp_text += "Alternatively, high EV/EBITDA with soft forecasts can point to stretched pricing.\n"
interp_text += "\n-- DISPERSION: Forecast Range --\n"
if pd.notna(forward_low) and pd.notna(forward_high):
spread = forward_high - forward_low
if spread > 5:
interp_text += f"Forecast range is wide ({spread:.2f} multiple points) → dispersion in EBITDA outlook is elevated.\n"
interp_text += "This can come from disagreement in assumptions around margin normalization, revenue trajectory, or cost inflation.\n"
interp_text += "High dispersion tends to reduce confidence in the forward signal and makes the valuation more sensitive to sentiment.\n"
elif spread < 2:
interp_text += f"Forecast range is tight ({spread:.2f} points) → analysts generally agree on expected operating results.\n"
interp_text += "Lower uncertainty in forecasts may support cleaner valuation signals and tighter trading multiples.\n"
interp_text += "\n-- EDGE CASE: Forward-only data --\n"
forward_only_row = df_final[df_final['Forward_EV_EBITDA_Avg'].notna()].iloc[-1]
forward_only_date = forward_only_row['date'].strftime('%Y-%m-%d')
forward_only_val = forward_only_row['Forward_EV_EBITDA_Avg']
if forward_only_date != latest_date:
interp_text += f"Note: Most recent forward-only EV/EBITDA observation is from {forward_only_date}, value = {forward_only_val:.2f}\n"
if forward_only_val < trailing_val:
interp_text += "This still reflects a discount relative to trailing EBITDA multiple, assuming EBITDA growth materializes.\n"
elif forward_only_val > trailing_val:
interp_text += "Forward-only multiple is elevated → could indicate lower expected EBITDA for the next forecast period.\n"
interp_text += "Might also be due to a higher EV figure if the price moved ahead of earnings revisions.\n"
interp_text += f"\n[Summary] {TICKER} ({period_str.capitalize()}): Trailing EV/EBITDA = {trailing_val:.2f}, Forward EV/EBITDA (Avg) = {forward_avg:.2f}, Range = [{forward_low:.2f}, {forward_high:.2f}]"
st.session_state.ev_ebitda_result = {
"df_final": df_final,
"fig": fig,
"interpretation": interp_text
}
st.success("EV/EBITDA analysis complete.")
if st.session_state.ev_ebitda_result is not None:
# Display the chart
st.plotly_chart(st.session_state.ev_ebitda_result["fig"], use_container_width=True)
# Single Dynamic Interpretation expander
with st.expander("Dynamic Interpretation", expanded=False):
st.text(st.session_state.ev_ebitda_result["interpretation"])
# Display final DataFrame
st.dataframe(st.session_state.ev_ebitda_result["df_final"])
# =============================================================================
# Page 3 – P/B Ratio
# =============================================================================
def pb_ratio_page():
#st.markdown("---")
st.header("P/B Ratio")
st.write(
"This page computes the Price-to-Book (P/B) Ratio and Book Value per Share (BVPS). "
"Use it to assess valuation versus the balance sheet. "
"Best combined with profitability metrics like ROE or net margins for context."
)
st.info(
"Chart legend items can be clicked to toggle series on/off. "
"Hover to inspect exact values. Zoom or pan to focus on specific periods."
)
# Methodology expander
with st.expander("Methodology", expanded=False):
st.markdown("#### Methodology: Price-to-Book (P/B) Ratio")
st.markdown("This chart tracks valuation trends versus underlying book value over time.")
st.markdown("##### 1. Book Value Per Share (BVPS)")
st.markdown("Book value per share is computed using total equity and shares outstanding.")
st.markdown("###### Formula")
st.latex(r"\text{BVPS}_t = \frac{\text{Total Equity}_t}{\text{Number of Shares}_t}")
st.markdown("- Total equity is sourced from the latest balance sheet as of each date.")
st.markdown("- Number of shares is aligned to the same date (or as-of matched).")
st.markdown("###### Interpretation")
st.markdown("- Measures the per-share value of net assets.")
st.markdown("- Rising BVPS → equity base is growing.")
st.markdown("- Flat or declining BVPS → dilution, losses, or stagnant balance sheet.")
st.markdown("---")
st.markdown("##### 2. Price-to-Book Ratio (P/B)")
st.markdown("The P/B ratio is calculated as:")
st.latex(r"\text{P/B Ratio}_t = \frac{\text{Stock Price}_t}{\text{BVPS}_t}")
st.markdown("###### Interpretation")
st.markdown("- P/B < 1 → stock trades below net asset value. Could imply undervaluation or distress.")
st.markdown("- P/B ≈ 1 → market is valuing the business near its net asset base.")
st.markdown("- P/B > 1 → market sees value beyond assets (e.g. brand, IP, growth).")
st.markdown("---")
st.markdown("##### 3. Relationship Between Inputs")
st.markdown("- **Rising price, flat BVPS** → P/B increases. Market is bidding the stock up without balance sheet growth. May signal rerating or momentum.")
st.markdown("- **Flat price, rising BVPS** → P/B decreases. Business value is compounding, but price isn't reflecting it yet.")
st.markdown("- **Both rising proportionally** → P/B stays stable. Valuation keeps pace with book value growth.")
st.markdown("- **BVPS growing faster than price** → P/B compresses. Could indicate improving fundamentals not yet priced in.")
st.markdown("- **Price rising faster than BVPS** → P/B expands. May reflect sentiment shift or expectations of better returns on equity.")
st.markdown("---")
st.markdown("##### 4. Practical Flags")
st.markdown("- **BVPS near zero or negative** → P/B ratio becomes meaningless. Avoid interpreting in these cases.")
st.markdown("- **Large jumps in equity or share count** → Check for corporate actions like buybacks, dilution, capital raises, or restatements.")
st.markdown("- **Stock price spike with flat BVPS** → P/B expands due to sentiment or speculative moves. Validate with fundamentals.")
st.markdown("- **Price drop with flat BVPS** → P/B compresses. Market is de-rating the stock despite unchanged book value.")
st.markdown("- **Sudden P/B swings** → Can signal data issues, corporate events, or anomalies in equity reporting.")
st.markdown("---")
st.markdown("##### 5. Use Cases")
st.markdown("- Most relevant in financials, cyclicals, or capital-intensive firms.")
st.markdown("- Less useful for asset-light sectors (e.g. software, media).")
st.markdown("- Combine with ROE and margin metrics to assess valuation versus quality.")
# (No extra sidebar expander for parameters is needed.)
if "pb_result" not in st.session_state:
st.session_state.pb_result = None
if run_analysis:
with st.spinner("Running P/B Ratio analysis..."):
LIMIT = years_back * (4 if forecast_type == "quarter" else 1)
TICKER = ticker.upper()
if forecast_type == "annual":
bs_url = f"https://financialmodelingprep.com/api/v3/balance-sheet-statement/{TICKER}?period=annual&limit={LIMIT}&apikey={API_KEY}"
ev_url = f"https://financialmodelingprep.com/api/v3/enterprise-values/{TICKER}?period=annual&limit={LIMIT}&apikey={API_KEY}"
else:
bs_url = f"https://financialmodelingprep.com/api/v3/balance-sheet-statement/{TICKER}?period=quarter&limit={LIMIT}&apikey={API_KEY}"
ev_url = f"https://financialmodelingprep.com/api/v3/enterprise-values/{TICKER}?period=quarter&limit={LIMIT}&apikey={API_KEY}"
quote_url = f"https://financialmodelingprep.com/api/v3/quote/{TICKER}?apikey={API_KEY}"
def local_fetch(url):
return fetch_data(url)
def get_balance_sheet():
data = local_fetch(bs_url)
if not data:
st.error("Balance sheet data empty.")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True, ignore_index=True)
eq_field = 'totalStockholdersEquity' if 'totalStockholdersEquity' in df.columns else (
'totalEquity' if 'totalEquity' in df.columns else None)
if not eq_field:
st.error("No equity field found in balance sheet data.")
return None
df.rename(columns={eq_field: 'Total_Equity'}, inplace=True)
return df
def get_ev_data():
data = local_fetch(ev_url)
if not data:
st.error("EV data empty.")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True)
for col in ['stockPrice', 'numberOfShares']:
if col not in df.columns:
st.error(f"No '{col}' in EV data.")
return None
return df[['date','stockPrice','numberOfShares']]
def extend_ev_today(df_ev):
q_data = local_fetch(quote_url)
if q_data:
daily_price = q_data[0].get('price', None)
shares = q_data[0].get('sharesOutstanding', q_data[0].get('numberOfShares', None))
today = pd.to_datetime('today').normalize()
df_today = pd.DataFrame({'date': [today], 'stockPrice': [daily_price], 'numberOfShares': [shares]})
else:
df_today = pd.DataFrame({'date': [pd.to_datetime('today').normalize()],
'stockPrice': [None],
'numberOfShares': [None]})
df_ev = pd.concat([df_ev, df_today], ignore_index=True).sort_values('date')
return df_ev
def merge_equity_ev(df_bs, df_ev):
df_bs_sorted = df_bs.sort_values('date')
df_ev_sorted = df_ev.sort_values('date')
df_merged = pd.merge_asof(df_ev_sorted, df_bs_sorted[['date', 'Total_Equity']], on='date', direction='backward')
return df_merged
df_bs = get_balance_sheet()
df_ev = get_ev_data()
if df_bs is None or df_ev is None:
return
df_ev = extend_ev_today(df_ev)
df_merged = merge_equity_ev(df_bs, df_ev)
df_merged['Book_Value_Per_Share'] = df_merged['Total_Equity'] / df_merged['numberOfShares']
df_merged['PB_Ratio'] = df_merged['stockPrice'] / df_merged['Book_Value_Per_Share']
date_set = set(df_bs['date']) | set(df_ev['date'])
df_final = df_merged[df_merged['date'].isin(date_set)].copy()
start_date_str = df_final['date'].min().strftime('%Y-%m-%d')
end_date_str = df_final['date'].max().strftime('%Y-%m-%d')
daily_url = f"https://financialmodelingprep.com/api/v3/historical-price-full/{TICKER}?from={start_date_str}&to={end_date_str}&serietype=line&apikey={API_KEY}"
daily_data = local_fetch(daily_url)
df_daily = pd.DataFrame(daily_data.get('historical', []))
if not df_daily.empty:
df_daily['date'] = pd.to_datetime(df_daily['date'])
df_daily.sort_values('date', inplace=True)
fig = go.Figure()
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['PB_Ratio'],
mode='lines+markers', name='P/B Ratio', line=dict(width=2), yaxis="y1"))
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['Book_Value_Per_Share'],
mode='lines+markers', name='Book Value Per Share', line=dict(width=1), opacity=0.3, yaxis="y2"))
if not df_daily.empty:
fig.add_trace(go.Scatter(x=df_daily['date'], y=df_daily['close'],
mode='lines', name='Daily Stock Price', line=dict(width=1), opacity=0.2, yaxis="y2"))
else:
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['stockPrice'],
mode='lines', name=f"{forecast_type.capitalize()} Stock Price", line=dict(width=1), opacity=0.3, yaxis="y2"))
if forecast_type=="quarter":
fig.update_xaxes(tickformat="%Y-%m", dtick="M3")
else:
fig.update_xaxes(tickformat="%Y", dtick="M12")
fig.update_layout(
title=f"{TICKER} Price-to-Book (P/B) Ratio with Book Value/Share ({forecast_type.capitalize()} data)",
xaxis=dict(title="Date"),
yaxis=dict(title="P/B Ratio", side="left"),
yaxis2=dict(title="Book Value/Share & Stock Price", overlaying="y", side="right"),
template="plotly_dark", legend=dict(x=0.02, y=0.98)
)
interpretation = f"""--- {TICKER} Price-to-Book Analysis ({forecast_type.capitalize()} data as of {df_final['date'].iloc[-1].strftime('%Y-%m-%d')}) ---
P/B Ratio: {df_final['PB_Ratio'].iloc[-1]:.2f}
Book Value per Share: {df_final['Book_Value_Per_Share'].iloc[-1]:,.2f}
Stock Price: {df_final['stockPrice'].iloc[-1]:,.2f}
--- Absolute level analysis ---
{"P/B < 1 → " + TICKER + " is trading below book value." if df_final['PB_Ratio'].iloc[-1] < 1 else "P/B between 1–2 → " + TICKER + " is priced modestly above book value." if 1 <= df_final['PB_Ratio'].iloc[-1] <= 2 else "P/B > 2 → " + TICKER + " trades well above its net asset value."}
--- Temporal pattern analysis ---
{"P/B has increased recently." if (df_final['PB_Ratio'].tail(4).iloc[-1] - df_final['PB_Ratio'].tail(4).iloc[0]) > 0.2 else "P/B has declined recently." if (df_final['PB_Ratio'].tail(4).iloc[-1] - df_final['PB_Ratio'].tail(4).iloc[0]) < -0.2 else "P/B has remained relatively stable."}
[Summary] {TICKER} ({forecast_type.capitalize()}): Stock trades at {df_final['PB_Ratio'].iloc[-1]:.2f}x book value.
"""
st.session_state.pb_result = {"df_final": df_final, "fig": fig, "interpretation": interpretation}
st.success("P/B Ratio analysis complete.")
if st.session_state.pb_result is not None:
st.plotly_chart(st.session_state.pb_result["fig"], use_container_width=True)
# Single Dynamic Interpretation expander
with st.expander("Dynamic Interpretation", expanded=False):
st.text(st.session_state.pb_result["interpretation"])
# Display final DataFrame and chart
st.dataframe(st.session_state.pb_result["df_final"], use_container_width=True)
# =============================================================================
# Page 4 – P/S Ratio
# =============================================================================
def ps_ratio_page():
#st.markdown("---")
st.header("P/S Ratio")
st.write(
"This page calculates trailing and forward Price-to-Sales (P/S) ratios. "
"Use it to compare valuation against actual and forecast revenue levels. "
"Especially useful when earnings are distorted or unavailable."
)
st.info(
"Chart legend items can be clicked to toggle series on/off. "
"Hover to inspect exact values. Zoom or pan to focus on specific periods."
)
with st.expander("Methodology", expanded=False):
st.markdown("#### Methodology: Price-to-Sales (P/S) Ratio")
st.markdown("This chart visualizes market valuation relative to top-line performance over time.")
st.markdown("##### 1. Trailing Revenue and Market Cap")
st.markdown("Revenue is taken as either annual (if `FORECAST_TYPE = 'annual'`) or as trailing four quarters (TTM) for quarterly data.")
st.markdown("###### Formula")
st.latex(r"TTM\,Revenue_t = \sum_{i=0}^{3} Revenue_{t-i}")
st.markdown("Market cap is computed as:")
st.latex(r"Market\,Cap_t = Stock\,Price_t \times Shares\,Outstanding_t")
st.markdown("---")
st.markdown("##### 2. Trailing P/S Ratio")
st.markdown("###### Formula")
st.latex(r"Trailing\,P/S_t = \frac{Market\,Cap_t}{TTM\,Revenue_t}")
st.markdown("###### Interpretation")
st.markdown("- Shows how much the market is paying per unit of revenue.")
st.markdown("- Higher P/S → pricing in strong growth, margins, or defensibility.")
st.markdown("- Lower P/S → cheaper relative to revenue, could reflect uncertainty or weaker outlook.")
st.markdown("---")
st.markdown("##### 3. Forward P/S Ratio")
st.markdown("Forecasted revenues (low, average, high) are summed over the next 4 quarters:")
st.latex(r"Forward\,Revenue_t^{(X)} = \sum_{i=1}^{4} Forecast\,Revenue_{t+i}^{(X)}")
st.markdown("Then:")
st.latex(r"Forward\,P/S_t^{(X)} = \frac{Market\,Cap_t}{Forward\,Revenue_t^{(X)}}")
st.markdown("###### Interpretation")
st.markdown("- Lower forward P/S → lower valuation against expected revenue.")
st.markdown("- Higher forward P/S → may reflect baked-in optimism or strong sentiment.")
st.markdown("---")
st.markdown("##### 4. Practical Interpretation")
st.markdown("- **Stock price rising, revenue flat** → P/S increases. This may reflect bullish sentiment or rerating, even without business growth.")
st.markdown("- **Revenue growing, price flat** → P/S compresses. Valuation gets cheaper. Could be overlooked improvement or lagging market recognition.")
st.markdown("- **Both price and revenue rising** → P/S holds steady. Implies market is rewarding growth proportionally.")
st.markdown("- **P/S rising while revenue is flat or falling** → Suggests a speculative move. Check if it's based on expectations or hype.")
st.markdown("- **P/S falling while revenue is stable or rising** → Could point to derating, skepticism, or risk-off sentiment.")
st.markdown("- **Volatile P/S with stable inputs** → Look for restatements, share count errors, or stale data.")
st.markdown("---")
st.markdown("##### 5. Use Cases and Caveats")
st.markdown("- More stable than P/E for early-stage or low-margin firms.")
st.markdown("- Useful in SaaS, recurring-revenue, and growth sectors.")
st.markdown("- On its own, does not reflect margins, profitability, or capital efficiency.")
st.markdown("###### Key Flags")
st.markdown("- High P/S with weak margins → could signal overvaluation.")
st.markdown("- Low P/S in recurring-revenue models → may point to undervaluation.")
st.markdown("- Sharp drops in revenue forecasts → spikes in forward P/S.")
st.markdown("- Near-zero forecast revenue → forward P/S becomes unreliable.")
# No additional sidebar parameters are used for this page.
if "ps_result" not in st.session_state:
st.session_state.ps_result = None
if run_analysis:
with st.spinner("Running P/S Ratio analysis..."):
LIMIT = years_back * (4 if forecast_type == "quarter" else 1)
TICKER = ticker.upper()
if forecast_type == "annual":
period_str = "annual"
income_url = f"https://financialmodelingprep.com/api/v3/income-statement/{TICKER}?period=annual&limit={LIMIT}&apikey={API_KEY}"
ev_url = f"https://financialmodelingprep.com/api/v3/enterprise-values/{TICKER}?period=annual&limit={LIMIT}&apikey={API_KEY}"
else:
period_str = "quarter"
income_url = f"https://financialmodelingprep.com/api/v3/income-statement/{TICKER}?period=quarter&limit={LIMIT}&apikey={API_KEY}"
ev_url = f"https://financialmodelingprep.com/api/v3/enterprise-values/{TICKER}?period=quarter&limit={LIMIT}&apikey={API_KEY}"
analyst_url = f"https://financialmodelingprep.com/api/v3/analyst-estimates/{TICKER}?period={period_str}&apikey={API_KEY}"
quote_url = f"https://financialmodelingprep.com/api/v3/quote/{TICKER}?apikey={API_KEY}"
def local_fetch(url):
return fetch_data(url)
def get_income_data():
data = local_fetch(income_url)
if not data:
st.error("Income statement data empty.")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True)
if 'revenue' not in df.columns:
st.error("Revenue field missing in income statement data.")
return None
df.rename(columns={'revenue': 'Revenue_raw'}, inplace=True)
df['TTM_Revenue'] = df['Revenue_raw'].rolling(4).sum() if forecast_type == "quarter" else df['Revenue_raw']
df.dropna(subset=['TTM_Revenue'], inplace=True)
return df
def get_ev_data():
data = local_fetch(ev_url)
if not data:
st.error("EV data empty.")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True)
for col in ['stockPrice', 'numberOfShares']:
if col not in df.columns:
st.error(f"Field '{col}' missing in EV data.")
return None
return df[['date', 'stockPrice', 'numberOfShares']]
def extend_ev_today(df_ev):
qdata = local_fetch(quote_url)
if qdata:
if 'price' not in qdata[0]:
st.error("Price field missing in quote data.")
today_price = qdata[0]['price']
if 'sharesOutstanding' in qdata[0]:
today_shares = qdata[0]['sharesOutstanding']
elif 'numberOfShares' in qdata[0]:
today_shares = qdata[0]['numberOfShares']
else:
today_shares = None
now = pd.to_datetime('today').normalize()
df_today = pd.DataFrame({'date': [now],
'stockPrice': [today_price],
'numberOfShares': [today_shares]})
df_ev = pd.concat([df_ev, df_today], ignore_index=True)
df_ev.sort_values('date', inplace=True)
else:
st.warning("Quote data not fetched.")
return df_ev
def get_analyst_data():
data = local_fetch(analyst_url)
if not data:
st.error("Analyst data empty.")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True)
for col in ['estimatedRevenueLow', 'estimatedRevenueAvg', 'estimatedRevenueHigh']:
if col not in df.columns:
st.error(f"Field '{col}' missing in analyst data.")
return None
df.rename(columns={
'estimatedRevenueLow': 'Forecast_Revenue_Low',
'estimatedRevenueAvg': 'Forecast_Revenue_Avg',
'estimatedRevenueHigh': 'Forecast_Revenue_High'
}, inplace=True)
return df
def get_forecast_rows(d, df_analyst):
future = df_analyst[df_analyst['date'] > d].sort_values('date')
if forecast_type == "quarter":
future = future.head(4)
else:
future = future.head(1)
if future.empty:
return [], [], []
low_list = future['Forecast_Revenue_Low'].tolist()
avg_list = future['Forecast_Revenue_Avg'].tolist()
high_list = future['Forecast_Revenue_High'].tolist()
while len(low_list) < 4: low_list.append(np.nan)
while len(avg_list) < 4: avg_list.append(np.nan)
while len(high_list) < 4: high_list.append(np.nan)
return low_list, avg_list, high_list
df_income = get_income_data()
df_ev = get_ev_data()
if df_income is None or df_ev is None:
return
df_ev = extend_ev_today(df_ev)
df_trailing = pd.merge(df_income[['date', 'Revenue_raw', 'TTM_Revenue']],
df_ev[['date', 'stockPrice', 'numberOfShares']], on='date', how='inner')
df_trailing['Trailing_PS'] = (df_trailing['stockPrice'] * df_trailing['numberOfShares']) / df_trailing['TTM_Revenue']
df_analyst = get_analyst_data()
if df_analyst is None:
return
for c in ['RevenueLow_1','RevenueLow_2','RevenueLow_3','RevenueLow_4',
'RevenueAvg_1','RevenueAvg_2','RevenueAvg_3','RevenueAvg_4',
'RevenueHigh_1','RevenueHigh_2','RevenueHigh_3','RevenueHigh_4']:
df_ev[c] = np.nan
for i in range(len(df_ev)):
d = df_ev.loc[i, 'date']
lows, avgs, highs = get_forecast_rows(d, df_analyst)
df_ev.at[i, 'RevenueLow_1'] = lows[0]
df_ev.at[i, 'RevenueLow_2'] = lows[1]
df_ev.at[i, 'RevenueLow_3'] = lows[2]
df_ev.at[i, 'RevenueLow_4'] = lows[3]
df_ev.at[i, 'RevenueAvg_1'] = avgs[0]
df_ev.at[i, 'RevenueAvg_2'] = avgs[1]
df_ev.at[i, 'RevenueAvg_3'] = avgs[2]
df_ev.at[i, 'RevenueAvg_4'] = avgs[3]
df_ev.at[i, 'RevenueHigh_1'] = highs[0]
df_ev.at[i, 'RevenueHigh_2'] = highs[1]
df_ev.at[i, 'RevenueHigh_3'] = highs[2]
df_ev.at[i, 'RevenueHigh_4'] = highs[3]
df_ev['ForwardTTM_Low'] = df_ev[['RevenueLow_1','RevenueLow_2','RevenueLow_3','RevenueLow_4']].sum(axis=1, min_count=1)
df_ev['ForwardTTM_Avg'] = df_ev[['RevenueAvg_1','RevenueAvg_2','RevenueAvg_3','RevenueAvg_4']].sum(axis=1, min_count=1)
df_ev['ForwardTTM_High'] = df_ev[['RevenueHigh_1','RevenueHigh_2','RevenueHigh_3','RevenueHigh_4']].sum(axis=1, min_count=1)
df_ev['Forward_PS_Low'] = df_ev.apply(lambda row: (row['stockPrice'] * row['numberOfShares']) / row['ForwardTTM_Low']
if pd.notna(row['stockPrice']) and pd.notna(row['numberOfShares']) and pd.notna(row['ForwardTTM_Low']) and row['ForwardTTM_Low'] > 0 else np.nan, axis=1)
df_ev['Forward_PS_Avg'] = df_ev.apply(lambda row: (row['stockPrice'] * row['numberOfShares']) / row['ForwardTTM_Avg']
if pd.notna(row['stockPrice']) and pd.notna(row['numberOfShares']) and pd.notna(row['ForwardTTM_Avg']) and row['ForwardTTM_Avg'] > 0 else np.nan, axis=1)
df_ev['Forward_PS_High'] = df_ev.apply(lambda row: (row['stockPrice'] * row['numberOfShares']) / row['ForwardTTM_High']
if pd.notna(row['stockPrice']) and pd.notna(row['numberOfShares']) and pd.notna(row['ForwardTTM_High']) and row['ForwardTTM_High'] > 0 else np.nan, axis=1)
df_final = pd.merge(df_trailing, df_ev, on='date', how='outer', suffixes=('_trail', '_fwd'))
df_final.sort_values('date', inplace=True)
if 'stockPrice_trail' in df_final.columns and 'stockPrice_fwd' in df_final.columns:
df_final['stockPrice'] = df_final['stockPrice_trail'].fillna(df_final['stockPrice_fwd'])
df_final.drop(columns=['stockPrice_trail','stockPrice_fwd'], inplace=True)
date_set = set(df_income['date']) | set(df_trailing['date']) | set(df_ev['date'])
df_final = df_final[df_final['date'].isin(date_set)].copy()
start_date_str = df_final['date'].min().strftime('%Y-%m-%d')
end_date_str = df_final['date'].max().strftime('%Y-%m-%d')
daily_url = f"https://financialmodelingprep.com/api/v3/historical-price-full/{TICKER}?from={start_date_str}&to={end_date_str}&serietype=line&apikey={API_KEY}"
daily_data = local_fetch(daily_url)
df_daily = pd.DataFrame(daily_data.get('historical', []))
if not df_daily.empty:
df_daily['date'] = pd.to_datetime(df_daily['date'])
df_daily.sort_values('date', inplace=True)
fig = go.Figure()
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['Trailing_PS'],
mode='lines+markers', name='Trailing P/S', line=dict(width=2), yaxis="y1"))
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['Forward_PS_Low'],
mode='lines+markers', name='Forward P/S (Low)', line=dict(width=1), yaxis="y1"))
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['Forward_PS_Avg'],
mode='lines+markers', name='Forward P/S (Avg)', line=dict(width=1), yaxis="y1"))
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['Forward_PS_High'],
mode='lines+markers', name='Forward P/S (High)', line=dict(width=1), yaxis="y1"))
if not df_daily.empty:
fig.add_trace(go.Scatter(x=df_daily['date'], y=df_daily['close'],
mode='lines', name='Daily Stock Price', line=dict(width=1), opacity=0.2, yaxis="y2"))
if forecast_type=="quarter":
fig.update_xaxes(tickformat="%Y-%m", dtick="M3")
else:
fig.update_xaxes(tickformat="%Y", dtick="M12")
fig.update_layout(
title=f"{TICKER} Trailing vs Forward P/S (Low/Avg/High) + Daily Stock ({forecast_type.capitalize()} freq)",
xaxis=dict(title="Date"),
yaxis=dict(title="P/S Ratio", side="left"),
yaxis2=dict(title="Stock Price (Daily)", overlaying="y", side="right"),
template="plotly_dark", legend=dict(x=0.02, y=0.98)
)
# Dynamic Interpretation string (including all elements from your original code)
# Filter for valid rows
df_valid = df_final[
df_final[['Trailing_PS', 'Forward_PS_Low', 'Forward_PS_Avg', 'Forward_PS_High']].notna().any(axis=1) &
df_final['date'].notna()
]
if not df_valid.empty:
latest_row = df_valid.iloc[-1]
latest_date_obj = latest_row['date']
latest_date_str = latest_date_obj.strftime('%Y-%m-%d')
ps_trailing = latest_row['Trailing_PS']
ps_fwd_low = latest_row['Forward_PS_Low']
ps_fwd_avg = latest_row['Forward_PS_Avg']
ps_fwd_high = latest_row['Forward_PS_High']
interp_text = f"""--- Latest Combined P/S Interpretation for {TICKER} as of {latest_date_str} ({forecast_type}) ---
Trailing P/S: {ps_trailing:.2f}
Forward P/S (Avg): {ps_fwd_avg:.2f}
Forward P/S Range: [{ps_fwd_low:.2f} – {ps_fwd_high:.2f}] (Spread: {(ps_fwd_high-ps_fwd_low):.2f})
"""
if ps_trailing < 3:
interp_text += "- Trailing P/S is relatively low. Market isn't pricing sales at a large premium.\n"
elif ps_trailing > 10:
interp_text += "- Trailing P/S is high. Investors are paying a significant multiple on historical revenue.\n"
else:
interp_text += "- Trailing P/S is in a moderate range. Valuation relative to past revenue is balanced.\n"
if ps_fwd_avg < 3:
interp_text += "- Forward P/S is modest. Market expects revenue to grow into the current valuation.\n"
elif ps_fwd_avg > 10:
interp_text += "- Forward P/S is high. Expectations may be aggressive relative to upcoming sales.\n"
else:
interp_text += "- Forward P/S (Avg) is in-line with historical norms.\n"
if pd.notna(ps_fwd_low) and pd.notna(ps_fwd_high):
spread = ps_fwd_high - ps_fwd_low
interp_text += f"Forward P/S Range: {ps_fwd_low:.2f} – {ps_fwd_high:.2f} (Spread: {spread:.2f})\n"
if spread > 2:
interp_text += "- Analyst dispersion on forward sales is wide. Potential uncertainty in top-line forecasts.\n"
else:
interp_text += "- Tight range in forecasts. Suggests consistency in expected growth.\n"
else:
interp_text = "--- No valid combined P/S data available for interpretation. ---\n"
# Extra interpretation block for today's forward-only row
df_today_row = df_final[
(df_final['date'] == pd.to_datetime('today').normalize()) &
df_final[['Forward_PS_Low', 'Forward_PS_Avg', 'Forward_PS_High']].notna().any(axis=1)
]
if not df_today_row.empty:
row_today = df_today_row.iloc[0]
fwd_only_date = row_today['date'].strftime('%Y-%m-%d')
fwd_low = row_today['Forward_PS_Low']
fwd_avg = row_today['Forward_PS_Avg']
fwd_high = row_today['Forward_PS_High']
extra_text = f"""--- Forward-Only P/S Snapshot for {TICKER} as of {fwd_only_date} ({forecast_type}) ---
Forward P/S (Avg): {fwd_avg:.2f}
Forward P/S Range: {fwd_low:.2f} – {fwd_high:.2f} (Spread: {(fwd_high-fwd_low):.2f})
"""
interp_text += "\n" + extra_text
interp_text += f"\n[Summary] {TICKER} ({forecast_type}): Trailing P/S = {ps_trailing:.2f}"
st.session_state.ps_result = {"df_final": df_final, "fig": fig, "interpretation": interp_text}
st.success("P/S Ratio analysis complete.")
if st.session_state.ps_result is not None:
# Single Methodology expander
st.plotly_chart(st.session_state.ps_result["fig"], use_container_width=True)
# Single Dynamic Interpretation expander
with st.expander("Dynamic Interpretation", expanded=False):
st.text(st.session_state.ps_result["interpretation"])
st.dataframe(st.session_state.ps_result["df_final"])
# =============================================================================
# Page 5 – EV/EBIT
# =============================================================================
def ev_ebit_page():
#st.markdown("---")
st.header("EV/EBIT Ratio")
st.write(
"This page computes trailing and forward EV/EBIT ratios. "
"The ratio measures how the market is valuing a company relative to its operating earnings. "
"Trailing EBIT is based on reported figures. Forward EBIT comes from analyst forecasts. "
"Used to compare valuation across time or versus peers, especially in capital-intensive sectors."
)
st.info(
"Chart legend items can be clicked to toggle series on/off. "
"Hover to inspect exact values. Zoom or pan to focus on specific periods."
)
with st.expander("Methodology", expanded=False):
st.markdown("### Methodology: EV/EBIT Ratio")
st.markdown(
"This chart tracks valuation relative to operating profit using both historical and forecast inputs. "
"Helps assess how market expectations evolve over time."
)
st.markdown("#### 1. EBIT: Operating Profit as Earnings Base")
st.markdown(
"EBIT is taken from the income statement (`ebit` or `operatingIncome`). "
"Trailing values are summed over the last 4 quarters to form TTM EBIT."
)
st.markdown("##### Formula (quarterly)")
st.latex(r"TTM\,EBIT_t = \sum_{i=0}^{3} EBIT_{t-i}")
st.markdown("---")
st.markdown("#### 2. Enterprise Value (EV)")
st.markdown("EV reflects market capitalization plus net debt:")
st.latex(r"EV_t = Market\,Cap_t + Total\,Debt_t - Cash_t")
st.markdown("---")
st.markdown("#### 3. Trailing EV/EBIT Ratio")
st.markdown("##### Formula")
st.latex(r"Trailing\,EV/EBIT_t = \frac{EV_t}{TTM\,EBIT_t}")
st.markdown("##### Interpretation")
st.markdown(
"- High EV/EBIT → stock is expensive relative to operating earnings. "
"May reflect strong earnings visibility, brand value, or perceived defensibility."
)
st.markdown(
"- Low EV/EBIT → stock appears cheaper. Could signal undervaluation, uncertainty, or operational issues."
)
st.markdown(
"- EV/EBIT < 10 is often flagged as cheap; > 20 may suggest the market is pricing in growth or quality premiums."
)
st.markdown("- Always consider sector context — norms vary widely across industries.")
st.markdown("---")
st.markdown("#### 4. Forward EV/EBIT Ratio")
st.markdown("Forecast EBIT is aggregated from analyst estimates.")
st.markdown("##### Formula")
st.latex(r"Forward\,EBIT^{(X)}_t = \sum_{i=1}^{4} Forecast\,EBIT_{t+i}^{(X)}")
st.markdown("Then:")
st.latex(r"Forward\,EV/EBIT^{(X)}_t = \frac{EV_t}{Forward\,EBIT^{(X)}_t}")
st.markdown("---")
st.markdown("#### 5. Interpretation Guidelines")
st.markdown(
"- EV/EBIT measures market valuation relative to core earnings.\n"
"- Lower values → possibly underpriced, but confirm EBIT quality.\n"
"- Higher values → may reflect confidence in sustained profitability or structural advantages."
)
st.markdown("- Use forward values to gauge if market is pricing in improvement or deterioration.")
st.markdown("---")
st.markdown("#### 6. Practical Behavior")
st.markdown(
"- Track changes in EV/EBIT alongside forecast dispersion.\n"
"- Sharp drops in the ratio with flat EV could signal improved forecasts.\n"
"- Sudden spikes with no change in EBIT → valuation expansion or sentiment shift.\n"
"- Pair with margin trends to check if EBIT growth is sustainable."
)
st.markdown("---")
st.markdown("#### 7. Usage Tips")
st.markdown(
"- Use when net income includes distortions (e.g. taxes, one-offs).\n"
"- Works well in capital-heavy sectors or where leverage is significant.\n"
"- Combine with return on capital to check if valuation is justified.\n"
"- Be cautious comparing across firms with different debt loads or capex cycles."
)
# No extra sidebar parameters for this page.
if "ev_ebit_result" not in st.session_state:
st.session_state.ev_ebit_result = None
if run_analysis:
with st.spinner("Running EV/EBIT analysis..."):
LIMIT = years_back * (4 if forecast_type == "quarter" else 1)
TICKER = ticker.upper()
if forecast_type == "quarter":
period_str = "quarter"
income_url = f"https://financialmodelingprep.com/api/v3/income-statement/{TICKER}?period=quarter&limit={LIMIT}&apikey={API_KEY}"
ev_url = f"https://financialmodelingprep.com/api/v3/enterprise-values/{TICKER}?period=quarter&limit={LIMIT}&apikey={API_KEY}"
analyst_url = f"https://financialmodelingprep.com/api/v3/analyst-estimates/{TICKER}?period=quarter&apikey={API_KEY}"
else:
period_str = "annual"
income_url = f"https://financialmodelingprep.com/api/v3/income-statement/{TICKER}?period=annual&limit={LIMIT}&apikey={API_KEY}"
ev_url = f"https://financialmodelingprep.com/api/v3/enterprise-values/{TICKER}?period=annual&limit={LIMIT}&apikey={API_KEY}"
analyst_url = f"https://financialmodelingprep.com/api/v3/analyst-estimates/{TICKER}?period=annual&apikey={API_KEY}"
quote_url = f"https://financialmodelingprep.com/api/v3/quote/{TICKER}?apikey={API_KEY}"
def local_fetch(url):
return fetch_data(url)
def get_income_data():
data = local_fetch(income_url)
if not data:
st.error("Income statement data is empty!")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True)
if 'ebit' in df.columns:
ebit_field = 'ebit'
elif 'operatingIncome' in df.columns:
ebit_field = 'operatingIncome'
else:
st.error("Neither 'ebit' nor 'operatingIncome' found in income statement.")
return None
df.rename(columns={ebit_field: 'EBIT_raw'}, inplace=True)
df['TTM_EBIT'] = df['EBIT_raw'].rolling(4).sum() if forecast_type == "quarter" else df['EBIT_raw']
df.dropna(subset=['TTM_EBIT'], inplace=True)
return df
def get_ev_data():
data = local_fetch(ev_url)
if not data:
st.error("EV data is empty.")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True)
if 'enterpriseValue' not in df.columns:
st.error("Field 'enterpriseValue' missing in EV data.")
return None
return df[['date', 'enterpriseValue']]
def extend_ev_today(df_ev):
qdata = local_fetch(quote_url)
if qdata:
ev_today = qdata[0].get('enterpriseValue', None)
now = pd.to_datetime('today').normalize()
df_today = pd.DataFrame({'date': [now], 'enterpriseValue': [ev_today]})
df_ev = pd.concat([df_ev, df_today], ignore_index=True)
df_ev.sort_values('date', inplace=True)
else:
st.warning("Quote data not available.")
return df_ev
def get_analyst_data():
data = local_fetch(analyst_url)
if not data:
st.error("Analyst estimates data is empty.")
return None
df = pd.DataFrame(data)
df['date'] = pd.to_datetime(df['date'])
df.sort_values('date', inplace=True)
for col in ['estimatedEbitLow', 'estimatedEbitAvg', 'estimatedEbitHigh']:
if col not in df.columns:
st.error(f"Field '{col}' missing in analyst data for EBIT.")
return None
df.rename(columns={'estimatedEbitLow': 'Forecast_EBIT_Low',
'estimatedEbitAvg': 'Forecast_EBIT_Avg',
'estimatedEbitHigh': 'Forecast_EBIT_High'}, inplace=True)
return df
def get_future_ebit(date_val, df_analyst):
future = df_analyst[df_analyst['date'] > date_val].sort_values('date')
if forecast_type == "quarter":
future = future.head(4)
else:
future = future.head(1)
if future.empty:
return [], [], []
lows = future['Forecast_EBIT_Low'].tolist()
avgs = future['Forecast_EBIT_Avg'].tolist()
highs = future['Forecast_EBIT_High'].tolist()
while len(lows) < 4: lows.append(np.nan)
while len(avgs) < 4: avgs.append(np.nan)
while len(highs) < 4: highs.append(np.nan)
return lows, avgs, highs
df_income = get_income_data()
df_ev = get_ev_data()
if df_income is None or df_ev is None:
return
df_ev = extend_ev_today(df_ev)
df_trailing = pd.merge(df_income[['date', 'EBIT_raw', 'TTM_EBIT']],
df_ev, on='date', how='inner')
df_trailing['Trailing_EV_EBIT'] = df_trailing.apply(
lambda row: row['enterpriseValue'] / row['TTM_EBIT']
if pd.notna(row['enterpriseValue']) and pd.notna(row['TTM_EBIT']) and row['TTM_EBIT'] != 0 else np.nan,
axis=1
)
df_analyst = get_analyst_data()
if df_analyst is None:
return
# Add forecast EBIT columns to df_ev
for c in ['EBITLow_1', 'EBITLow_2', 'EBITLow_3', 'EBITLow_4',
'EBITAvg_1', 'EBITAvg_2', 'EBITAvg_3', 'EBITAvg_4',
'EBITHigh_1', 'EBITHigh_2', 'EBITHigh_3', 'EBITHigh_4']:
df_ev[c] = np.nan
for i in range(len(df_ev)):
d = df_ev.loc[i, 'date']
lows, avgs, highs = get_future_ebit(d, df_analyst)
df_ev.at[i, 'EBITLow_1'] = lows[0]
df_ev.at[i, 'EBITLow_2'] = lows[1]
df_ev.at[i, 'EBITLow_3'] = lows[2]
df_ev.at[i, 'EBITLow_4'] = lows[3]
df_ev.at[i, 'EBITAvg_1'] = avgs[0]
df_ev.at[i, 'EBITAvg_2'] = avgs[1]
df_ev.at[i, 'EBITAvg_3'] = avgs[2]
df_ev.at[i, 'EBITAvg_4'] = avgs[3]
df_ev.at[i, 'EBITHigh_1'] = highs[0]
df_ev.at[i, 'EBITHigh_2'] = highs[1]
df_ev.at[i, 'EBITHigh_3'] = highs[2]
df_ev.at[i, 'EBITHigh_4'] = highs[3]
df_ev['ForwardTTM_Low'] = df_ev[['EBITLow_1', 'EBITLow_2', 'EBITLow_3', 'EBITLow_4']].sum(axis=1, min_count=1)
df_ev['ForwardTTM_Avg'] = df_ev[['EBITAvg_1', 'EBITAvg_2', 'EBITAvg_3', 'EBITAvg_4']].sum(axis=1, min_count=1)
df_ev['ForwardTTM_High'] = df_ev[['EBITHigh_1', 'EBITHigh_2', 'EBITHigh_3', 'EBITHigh_4']].sum(axis=1, min_count=1)
df_ev['Forward_EV_EBIT_Low'] = df_ev.apply(lambda row: row['enterpriseValue'] / row['ForwardTTM_Low']
if pd.notna(row['enterpriseValue']) and pd.notna(row['ForwardTTM_Low']) and row['ForwardTTM_Low'] > 0 else np.nan,
axis=1)
df_ev['Forward_EV_EBIT_Avg'] = df_ev.apply(lambda row: row['enterpriseValue'] / row['ForwardTTM_Avg']
if pd.notna(row['enterpriseValue']) and pd.notna(row['ForwardTTM_Avg']) and row['ForwardTTM_Avg'] > 0 else np.nan,
axis=1)
df_ev['Forward_EV_EBIT_High'] = df_ev.apply(lambda row: row['enterpriseValue'] / row['ForwardTTM_High']
if pd.notna(row['enterpriseValue']) and pd.notna(row['ForwardTTM_High']) and row['ForwardTTM_High'] > 0 else np.nan,
axis=1)
df_final = pd.merge(df_trailing, df_ev, on='date', how='outer', suffixes=('_trailing', '_fwd'))
df_final.sort_values('date', inplace=True)
if 'enterpriseValue_trailing' in df_final.columns and 'enterpriseValue_fwd' in df_final.columns:
df_final['enterpriseValue'] = df_final['enterpriseValue_trailing'].fillna(df_final['enterpriseValue_fwd'])
df_final.drop(columns=['enterpriseValue_trailing', 'enterpriseValue_fwd'], inplace=True)
date_set = set(df_income['date']) | set(df_trailing['date']) | set(df_ev['date'])
df_final = df_final[df_final['date'].isin(date_set)].copy()
start_date_str = df_final['date'].min().strftime('%Y-%m-%d')
end_date_str = df_final['date'].max().strftime('%Y-%m-%d')
daily_url = f"https://financialmodelingprep.com/api/v3/historical-price-full/{TICKER}?from={start_date_str}&to={end_date_str}&serietype=line&apikey={API_KEY}"
daily_data = local_fetch(daily_url)
df_daily = pd.DataFrame(daily_data.get('historical', []))
if not df_daily.empty:
df_daily['date'] = pd.to_datetime(df_daily['date'])
df_daily.sort_values('date', inplace=True)
fig = go.Figure()
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['Trailing_EV_EBIT'],
mode='lines+markers', name='Trailing EV/EBIT', line=dict(width=2), yaxis="y1"))
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['Forward_EV_EBIT_Low'],
mode='lines+markers', name='Forward EV/EBIT (Low)', line=dict(width=1), yaxis="y1"))
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['Forward_EV_EBIT_Avg'],
mode='lines+markers', name='Forward EV/EBIT (Avg)', line=dict(width=1), yaxis="y1"))
fig.add_trace(go.Scatter(x=df_final['date'], y=df_final['Forward_EV_EBIT_High'],
mode='lines+markers', name='Forward EV/EBIT (High)', line=dict(width=1), yaxis="y1"))
if not df_daily.empty:
fig.add_trace(go.Scatter(x=df_daily['date'], y=df_daily['close'],
mode='lines', name='Daily Stock Price', line=dict(width=1), opacity=0.2, yaxis="y2"))
if forecast_type == "quarter":
fig.update_xaxes(tickformat="%Y-%m", dtick="M3")
else:
fig.update_xaxes(tickformat="%Y", dtick="M12")
fig.update_layout(
title=f"{TICKER} EV/EBIT (Trailing & Forward Low/Avg/High) + Daily Stock ({'Quarterly' if forecast_type=='quarter' else 'Annual'})",
xaxis=dict(title="Date"),
yaxis=dict(title="EV/EBIT Ratio", side="left"),
yaxis2=dict(title="Stock Price (Daily)", overlaying="y", side="right"),
template="plotly_dark", legend=dict(x=0.02, y=0.98)
)
# Build dynamic interpretation string using the provided interpretation block
interp_parts = []
# Filter valid rows for EV/EBIT interpretation
df_latest_valid = df_final[
df_final[['Trailing_EV_EBIT', 'Forward_EV_EBIT_Low', 'Forward_EV_EBIT_Avg', 'Forward_EV_EBIT_High']].notna().any(axis=1) &
df_final['date'].notna()
]
if not df_latest_valid.empty:
latest_row = df_latest_valid.iloc[-1]
latest_date = latest_row['date'].strftime('%Y-%m-%d')
trailing = latest_row['Trailing_EV_EBIT']
fwd_low = latest_row['Forward_EV_EBIT_Low']
fwd_avg = latest_row['Forward_EV_EBIT_Avg']
fwd_high = latest_row['Forward_EV_EBIT_High']
interp_parts.append(f"--- EV/EBIT Interpretation for {TICKER} on {latest_date} ({forecast_type.capitalize()}) ---")
if pd.notna(trailing):
interp_parts.append(f"Trailing EV/EBIT: {trailing:.2f}")
if trailing < 8:
interp_parts.append(f"- EV/EBIT is low → {TICKER} may be priced conservatively relative to trailing EBIT.")
elif trailing > 20:
interp_parts.append("- EV/EBIT is elevated → market might be pricing in strong margin durability or strategic optionality.")
else:
interp_parts.append("- EV/EBIT falls in a typical range → stable trailing profitability is reflected in current pricing.")
if pd.notna(fwd_avg):
interp_parts.append(f"Forward EV/EBIT (Avg): {fwd_avg:.2f}")
if fwd_avg < 10:
interp_parts.append(f"- Forecast EBIT implies reasonable forward valuation for {TICKER}.")
elif fwd_avg > 20:
interp_parts.append("- Forward EV/EBIT is high → price may reflect expected growth, margin upside, or non-operating asset value.")
else:
interp_parts.append("- Market valuation appears aligned with forecast EBIT expectations.")
if pd.notna(fwd_low) and pd.notna(fwd_high):
spread = fwd_high - fwd_low
interp_parts.append(f"Forward EV/EBIT Range: {fwd_low:.2f} – {fwd_high:.2f} (Spread: {spread:.2f})")
if spread > 5:
interp_parts.append("- Forecast dispersion is high. Analyst expectations around EBIT vary significantly.")
else:
interp_parts.append("- Forecasts are consistent. Market may have strong consensus around earnings trajectory.")
else:
interp_parts.append("--- No valid combined EV/EBIT data available for interpretation. ---")
# Extra forward-only snapshot for today
df_today_row = df_final[
(df_final['date'] == pd.to_datetime('today').normalize()) &
df_final[['Forward_EV_EBIT_Low', 'Forward_EV_EBIT_Avg', 'Forward_EV_EBIT_High']].notna().any(axis=1)
]
if not df_today_row.empty:
row_today = df_today_row.iloc[0]
today_date = row_today['date'].strftime('%Y-%m-%d')
fwd_low_today = row_today['Forward_EV_EBIT_Low']
fwd_avg_today = row_today['Forward_EV_EBIT_Avg']
fwd_high_today = row_today['Forward_EV_EBIT_High']
interp_parts.append(f"\n--- Forward EV/EBIT Snapshot for {TICKER} on {today_date} ---")
if pd.notna(fwd_avg_today):
interp_parts.append(f"Forward EV/EBIT (Avg): {fwd_avg_today:.2f}")
if fwd_avg_today < 10:
interp_parts.append("- Latest valuation reflects modest EBIT expectations.")
elif fwd_avg_today > 20:
interp_parts.append("- High multiple suggests the market may be leaning into positive revisions or optionality.")
else:
interp_parts.append("- Forward valuation appears neutral.")
if pd.notna(fwd_low_today) and pd.notna(fwd_high_today):
spread_today = fwd_high_today - fwd_low_today
interp_parts.append(f"Range: {fwd_low_today:.2f} – {fwd_high_today:.2f} (Spread: {spread_today:.2f})")
if spread_today > 5:
interp_parts.append("- Wide range in estimates implies uncertainty or debate around operating leverage.")
else:
interp_parts.append("- Estimates are tightly grouped. Market outlook is more aligned.")
# Final summary line
if df_latest_valid.empty:
summary_line = "[Summary] No valid EV/EBIT data available."
else:
summary_line = f"[Summary] {TICKER} ({period_str.capitalize()}): Trailing EV/EBIT = {trailing:.2f}, Forward EV/EBIT (Avg) = {fwd_avg:.2f}"
interp_parts.append("\n" + summary_line)
interpretation = "\n".join(interp_parts)
st.session_state.ev_ebit_result = {
"df_final": df_final,
"fig": fig,
"interpretation": interpretation
}
st.success("EV/EBIT analysis complete.")
if st.session_state.ev_ebit_result is not None:
st.plotly_chart(st.session_state.ev_ebit_result["fig"], use_container_width=True)
with st.expander("Dynamic Interpretation", expanded=False):
st.text(st.session_state.ev_ebit_result["interpretation"])
st.dataframe(st.session_state.ev_ebit_result["df_final"])
# =============================================================================
# Main: Call the selected page function
# =============================================================================
if page == "P/E & PEG":
with st.container(border=True):
pe_peg_page()
elif page == "EV/EBITDA":
with st.container(border=True):
ev_ebitda_page()
elif page == "P/B Ratio":
with st.container(border=True):
pb_ratio_page()
elif page == "P/S Ratio":
with st.container(border=True):
ps_ratio_page()
elif page == "EV/EBIT":
with st.container(border=True):
ev_ebit_page()
# Hide default Streamlit style
st.markdown(
"""
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
""",
unsafe_allow_html=True
)
|