File size: 29,892 Bytes
8741a2b 7346e8f 8741a2b 7346e8f 8741a2b be8b821 8741a2b d3a0b5f 968e18a d3a0b5f 8741a2b d3a0b5f 8741a2b d3a0b5f 8741a2b d3a0b5f |
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 |
import os
import requests
import pandas as pd
import plotly.graph_objects as go
import streamlit as st
st.set_page_config(layout="wide")
# you need to sign up at FMP API to get the API key, then put it in the respective environment variable
apikey = os.environ["FMP_API_KEY"]
def parse_json(url):
resp = requests.get(url)
resp.raise_for_status()
return pd.DataFrame(resp.json())
def fmt(val):
if abs(val) >= 1e9: return f"${val/1e9:.1f}B"
if abs(val) >= 1e6: return f"${val/1e6:.1f}M"
if abs(val) >= 1e3: return f"${val/1e3:.0f}K"
return f"${val:.0f}"
def draw_balance_sankey(balance_sheet, symbol, height, font_size):
# define all Sankey flows (left is source, right is target)
flows = [
# Current Assets
("Cash and Cash Equivalents", "Total Current Assets", balance_sheet["cashAndCashEquivalents"]),
("Short-Term Investments", "Total Current Assets", balance_sheet["shortTermInvestments"]),
("Net Receivables", "Total Current Assets", balance_sheet["netReceivables"]),
("Inventory", "Total Current Assets", balance_sheet["inventory"]),
("Prepaids", "Total Current Assets", balance_sheet["prepaids"]),
# NOTE: Other Current Assets may sometimes overlap with some of the above lines
("Other Current Assets", "Total Current Assets", balance_sheet["otherCurrentAssets"]),
# Non-Current Assets
("Property, Plant and Equipment, Net", "Total Non-Current Assets", balance_sheet["propertyPlantEquipmentNet"]),
("Goodwill", "Total Non-Current Assets", balance_sheet["goodwill"]),
("Intangible Assets", "Total Non-Current Assets", balance_sheet["intangibleAssets"]),
("Long-Term Investments", "Total Non-Current Assets", balance_sheet["longTermInvestments"]),
("Tax Assets", "Total Non-Current Assets", balance_sheet["taxAssets"]),
("Other Non-Current Assets", "Total Non-Current Assets", balance_sheet["otherNonCurrentAssets"]),
# ... to Total Assets
("Total Current Assets", "Total Assets", balance_sheet["totalCurrentAssets"]),
("Total Non-Current Assets", "Total Assets", balance_sheet["totalNonCurrentAssets"]),
# Total Assets to ...
("Total Assets", "Total Liabilities", balance_sheet["totalLiabilities"]),
("Total Assets", "Total Stockholders' Equity", balance_sheet["totalStockholdersEquity"]),
# Current Liabilities
("Total Liabilities", "Total Current Liabilities", balance_sheet["totalCurrentLiabilities"]),
("Total Current Liabilities", "Tax Payables", balance_sheet["taxPayables"]),
("Total Current Liabilities", "Short-Term Debt", balance_sheet["shortTermDebt"]),
("Total Current Liabilities", "Capital Lease Obligations (Current)", balance_sheet["capitalLeaseObligationsCurrent"]),
("Total Current Liabilities", "Deferred Revenue (Current)", balance_sheet["deferredRevenue"]),
("Total Current Liabilities", "Other Current Liabilities", balance_sheet["otherCurrentLiabilities"]),
("Total Current Liabilities", "Accounts Payable", balance_sheet["accountPayables"]),
("Total Current Liabilities", "Other Payables", balance_sheet["otherPayables"]),
("Total Current Liabilities", "Accrued Expenses", balance_sheet["accruedExpenses"]),
# Non-Current Liabilities
("Total Liabilities", "Total Non-Current Liabilities", balance_sheet["totalNonCurrentLiabilities"]),
("Total Non-Current Liabilities", "Long-Term Debt", balance_sheet["longTermDebt"]),
("Total Non-Current Liabilities", "Capital Lease Obligations (Non-Current)", balance_sheet["capitalLeaseObligationsNonCurrent"]),
("Total Non-Current Liabilities", "Deferred Revenue (Non-Current)", balance_sheet["deferredRevenueNonCurrent"]),
("Total Non-Current Liabilities", "Deferred Tax Liabilities (Non-Current)", balance_sheet["deferredTaxLiabilitiesNonCurrent"]),
("Total Non-Current Liabilities", "Other Non-Current Liabilities", balance_sheet["otherNonCurrentLiabilities"]),
# Equity
("Total Stockholders' Equity", "Common Stock", balance_sheet["commonStock"]),
("Total Stockholders' Equity", "Retained Earnings", balance_sheet["retainedEarnings"]),
("Total Stockholders' Equity", "Accumulated Other Comprehensive Income (Loss)", balance_sheet["accumulatedOtherComprehensiveIncomeLoss"]),
("Total Stockholders' Equity", "Additional Paid-In Capital", balance_sheet["additionalPaidInCapital"]),
("Total Stockholders' Equity", "Other Stockholders' Equity", balance_sheet["otherTotalStockholdersEquity"]),
]
# need to adjust flow to make negative values easier to read
adjusted_flows = []
for src, tgt, val in flows:
if val >= 0:
# positive: keep direction, color green
adjusted_flows.append((src, tgt, val, 'rgba(50,200,50,0.6)'))
else:
# negative: reverse direction, color red
adjusted_flows.append((tgt, src, -val, 'rgba(200,50,50,0.6)'))
# for labelling later, first we store the source and target names
labels = []
for src, tgt, _ in flows:
if src not in labels: labels.append(src)
if tgt not in labels: labels.append(tgt)
# map label to its actual balance‐sheet value for annotation
node_values = {
"Cash and Cash Equivalents": balance_sheet["cashAndCashEquivalents"],
"Short-Term Investments": balance_sheet["shortTermInvestments"],
"Net Receivables": balance_sheet["netReceivables"],
"Inventory": balance_sheet["inventory"],
"Prepaids": balance_sheet["prepaids"],
"Other Current Assets": balance_sheet["otherCurrentAssets"],
"Total Current Assets": balance_sheet["totalCurrentAssets"],
"Property, Plant and Equipment, Net": balance_sheet["propertyPlantEquipmentNet"],
"Goodwill": balance_sheet["goodwill"],
"Intangible Assets": balance_sheet["intangibleAssets"],
"Long-Term Investments": balance_sheet["longTermInvestments"],
"Tax Assets": balance_sheet["taxAssets"],
"Other Non-Current Assets": balance_sheet["otherNonCurrentAssets"],
"Total Non-Current Assets": balance_sheet["totalNonCurrentAssets"],
"Total Assets": balance_sheet["totalAssets"],
"Total Liabilities": balance_sheet["totalLiabilities"],
"Total Current Liabilities": balance_sheet["totalCurrentLiabilities"],
"Tax Payables": balance_sheet["taxPayables"],
"Short-Term Debt": balance_sheet["shortTermDebt"],
"Capital Lease Obligations (Current)": balance_sheet["capitalLeaseObligationsCurrent"],
"Deferred Revenue (Current)": balance_sheet["deferredRevenue"],
"Other Current Liabilities": balance_sheet["otherCurrentLiabilities"],
"Accounts Payable": balance_sheet["accountPayables"],
"Other Payables": balance_sheet["otherPayables"],
"Accrued Expenses": balance_sheet["accruedExpenses"],
"Total Non-Current Liabilities": balance_sheet["totalNonCurrentLiabilities"],
"Long-Term Debt": balance_sheet["longTermDebt"],
"Capital Lease Obligations (Non-Current)": balance_sheet["capitalLeaseObligationsNonCurrent"],
"Deferred Revenue (Non-Current)": balance_sheet["deferredRevenueNonCurrent"],
"Deferred Tax Liabilities (Non-Current)": balance_sheet["deferredTaxLiabilitiesNonCurrent"],
"Other Non-Current Liabilities": balance_sheet["otherNonCurrentLiabilities"],
"Total Stockholders' Equity": balance_sheet["totalStockholdersEquity"],
"Common Stock": balance_sheet["commonStock"],
"Retained Earnings": balance_sheet["retainedEarnings"],
"Accumulated Other Comprehensive Income (Loss)": balance_sheet["accumulatedOtherComprehensiveIncomeLoss"],
"Additional Paid-In Capital": balance_sheet["additionalPaidInCapital"],
"Other Stockholders' Equity": balance_sheet["otherTotalStockholdersEquity"],
}
# for formatting, annotate labels with $ amounts and take care of billions, millions, thousands
def fmt(val):
if abs(val) >= 1e9: return f"${val/1e9:.1f}B"
if abs(val) >= 1e6: return f"${val/1e6:.1f}M"
if abs(val) >= 1e3: return f"${val/1e3:.0f}K"
return f"${val:.0f}"
# put the sorce and target values in labels
labels = []
for s, t, _, _ in adjusted_flows:
if s not in labels: labels.append(s)
if t not in labels: labels.append(t)
idx = {label:i for i,label in enumerate(labels)}
source = [ idx[s] for s, t, _, _ in adjusted_flows ] # index of sources for sankey input
target = [ idx[t] for s, t, _, _ in adjusted_flows ] # index of target for sankey input
value = [ v for _, _, v, _ in adjusted_flows ]
colors = [ c for _, _, _, c in adjusted_flows ]
label_with_values = []
for label in labels:
val = node_values[label]
base = label.replace(" (Current)", "")\
.replace(" (Non-Current)", "") # saves some printing space
if val < 0:
base += " [NEGATIVE]" # just to make negatives more obvious in the label
label_with_values.append(f"{base} ({fmt(val)})")
fig = go.Figure(go.Sankey(
arrangement="snap",
node = dict(label=label_with_values, pad=15, thickness=20),
link = dict(source=source, target=target, value=value, color=colors)
))
fig.update_layout(
title_text=f"Balance Sheet Sankey — {symbol}",
height=height,
font_size=font_size
)
return fig
def draw_income_sankey(income_statement, symbol, height, font_size):
flows = [
# Revenue─
("Revenue", "Cost of Revenue", income_statement["costOfRevenue"]),
("Revenue", "Gross Profit", income_statement["grossProfit"]),
# Gross Profit
("Gross Profit", "Operating Income", income_statement["operatingIncome"]),
("Gross Profit", "Operating Expenses", income_statement["operatingExpenses"]),
# Operating Expenses
("Operating Expenses", "Research & Development Expenses", income_statement["researchAndDevelopmentExpenses"]),
# ("Operating Expenses", "General & Administrative Expenses", income_statement["generalAndAdministrativeExpenses"]), # already in SG&A
# ("Operating Expenses", "Selling & Marketing Expenses", income_statement["sellingAndMarketingExpenses"]), # already in SG&A
("Operating Expenses", "SG&A Expenses", income_statement["sellingGeneralAndAdministrativeExpenses"]),
("Operating Expenses", "Other Operating Expenses", income_statement["otherExpenses"]),
# Pretax Income
("Pretax Income", "Income Tax Expense", income_statement["incomeTaxExpense"]),
("Pretax Income", "Net Income", income_statement["netIncome"]),
("Pretax Income", "Interest Expense", income_statement["interestExpense"]),
# this value is recorded as negative in API, but we do not need to reverse the flow like in balance sheet
# because it decreases the pretax income so we put it together at the same side with all the tax expenses
("Pretax Income", "Non-Operating Income Excl. Interest", -income_statement["nonOperatingIncomeExcludingInterest"]),
("Pretax Income", "Total Other Income & Expenses Net", income_statement["totalOtherIncomeExpensesNet"]),
("Pretax Income", "Other Adjustments to Net Income", income_statement["otherAdjustmentsToNetIncome"]),
# Other Income that goes into Pretax Income
("Operating Income", "Pretax Income", income_statement["operatingIncome"]),
("Net Interest Income", "Pretax Income", income_statement["netInterestIncome"]),
("Interest Income", "Pretax Income", income_statement["interestIncome"]),
]
# need to adjust flow to make negative values easier to read
adjusted_flows = []
for src, tgt, val in flows:
if val >= 0:
# positive: keep direction, color green
adjusted_flows.append((src, tgt, val, 'rgba(50,200,50,0.6)'))
else:
# negative: reverse direction, color red
adjusted_flows.append((tgt, src, -val, 'rgba(200,50,50,0.6)'))
# for labelling later, first we store the source and target names
labels = []
for src, tgt, _ in flows:
if src not in labels: labels.append(src)
if tgt not in labels: labels.append(tgt)
# map label to its actual balance‐sheet value for annotation
node_values = {
"Revenue": income_statement["revenue"],
"Cost of Revenue": income_statement["costOfRevenue"],
"Gross Profit": income_statement["grossProfit"],
"Operating Income": income_statement["operatingIncome"],
"Operating Expenses": income_statement["operatingExpenses"],
"Research & Development Expenses": income_statement["researchAndDevelopmentExpenses"],
#"General & Administrative Expenses": income_statement["generalAndAdministrativeExpenses"], # already in SG&A
#"Selling & Marketing Expenses": income_statement["sellingAndMarketingExpenses"], # already in SG&A
"SG&A Expenses": income_statement["sellingGeneralAndAdministrativeExpenses"],
"Other Operating Expenses": income_statement["otherExpenses"],
"Net Interest Income": income_statement["netInterestIncome"],
"Interest Income": income_statement["interestIncome"],
"Interest Expense": income_statement["interestExpense"],
"Non-Operating Income Excl. Interest":-income_statement["nonOperatingIncomeExcludingInterest"],
"Total Other Income & Expenses Net": income_statement["totalOtherIncomeExpensesNet"],
"Pretax Income": income_statement["incomeBeforeTax"],
"Income Tax Expense": income_statement["incomeTaxExpense"],
"Net Income": income_statement["netIncome"],
"Other Adjustments to Net Income": income_statement["otherAdjustmentsToNetIncome"],
"Bottom Line Net Income": income_statement["bottomLineNetIncome"],
}
# for formatting, annotate labels with $ amounts and take care of billions, millions, thousands
def fmt(val):
if abs(val) >= 1e9: return f"${val/1e9:.1f}B"
if abs(val) >= 1e6: return f"${val/1e6:.1f}M"
if abs(val) >= 1e3: return f"${val/1e3:.0f}K"
return f"${val:.0f}"
# put the sorce and target values in labels
labels = []
for s, t, _, _ in adjusted_flows:
if s not in labels: labels.append(s)
if t not in labels: labels.append(t)
idx = {label:i for i,label in enumerate(labels)}
source = [ idx[s] for s, t, _, _ in adjusted_flows ] # index of sources for sankey input
target = [ idx[t] for s, t, _, _ in adjusted_flows ] # index of target for sankey input
value = [ v for _, _, v, _ in adjusted_flows ]
colors = [ c for _, _, _, c in adjusted_flows ]
label_with_values = []
for label in labels:
val = node_values[label]
base = label.replace(" (Current)", "")\
.replace(" (Non-Current)", "") # saves some printing space
if val < 0:
base += " [NEGATIVE]" # just to make negatives more obvious in the label
label_with_values.append(f"{base} ({fmt(val)})")
fig = go.Figure(go.Sankey(
arrangement="snap",
node = dict(label=label_with_values, pad=15, thickness=20),
link = dict(source=source, target=target, value=value, color=colors)
))
fig.update_layout(
title_text=f"Income Statement Sankey — {symbol}",
height=height,
font_size=font_size
)
return fig
def draw_cashflow_sankey(cash_flow, symbol, height, font_size):
flows = [
# Operating Activities (Inflow)
("Net Income", "Operating Activities", cash_flow["netIncome"]),
("Depreciation & Amortization", "Operating Activities", cash_flow["depreciationAndAmortization"]),
("Deferred Income Tax", "Operating Activities", cash_flow["deferredIncomeTax"]),
("Stock-Based Compensation", "Operating Activities", cash_flow["stockBasedCompensation"]),
("Change in Working Capital", "Operating Activities", cash_flow["changeInWorkingCapital"]),
("Accounts Receivables Δ", "Operating Activities", cash_flow["accountsReceivables"]),
("Inventory Δ", "Operating Activities", cash_flow["inventory"]),
("Accounts Payable Δ", "Operating Activities", cash_flow["accountsPayables"]),
#("Other Working Capital Δ", "Operating Activities", cash_flow["otherWorkingCapital"]), # overlap with some lines
("Other Non-Cash Items", "Operating Activities", cash_flow["otherNonCashItems"]),
("Operating Activities", "Net Cash Inflow (Operating)", cash_flow["netCashProvidedByOperatingActivities"]),
# Investing Activities (Outflow)
("Investments in PP&E", "Investing Activities", cash_flow["investmentsInPropertyPlantAndEquipment"]),
("Acquisitions, Net", "Investing Activities", cash_flow["acquisitionsNet"]),
("Purchases of Investments", "Investing Activities", cash_flow["purchasesOfInvestments"]),
("Sales/Maturities of Investments", "Investing Activities", cash_flow["salesMaturitiesOfInvestments"]),
("Other Investing Activities", "Investing Activities", cash_flow["otherInvestingActivities"]),
("Investing Activities", "Net Cash Outflow (Investing)", cash_flow["netCashProvidedByInvestingActivities"]),
# Financing Activities (Outflow)
("Net Debt Issuance", "Financing Activities", cash_flow["netDebtIssuance"]),
#("Long-Term Net Debt Issuance", "Financing Activities", cash_flow["longTermNetDebtIssuance"]), # already under net debt
#("Short-Term Net Debt Issuance", "Financing Activities", cash_flow["shortTermNetDebtIssuance"]), # already under net debt
#("Net Stock Issuance", "Financing Activities", cash_flow["netStockIssuance"]),
("Net Common Stock Issuance", "Financing Activities", cash_flow["netCommonStockIssuance"]),
#("Common Stock Issuance", "Financing Activities", cash_flow["commonStockIssuance"]), # already under net common stock issuance
#("Common Stock Repurchased", "Financing Activities", cash_flow["commonStockRepurchased"]), # already under net common stock issuance
("Net Preferred Stock Issuance", "Financing Activities", cash_flow["netPreferredStockIssuance"]),
("Net Dividends Paid", "Financing Activities", cash_flow["netDividendsPaid"]),
#("Common Dividends Paid", "Financing Activities", cash_flow["commonDividendsPaid"]), # already under net dividends paid
#("Preferred Dividends Paid", "Financing Activities", cash_flow["preferredDividendsPaid"]), # already under net dividends paid
("Other Financing Activities", "Financing Activities", cash_flow["otherFinancingActivities"]),
("Financing Activities", "Net Cash Outflow (Financing)", cash_flow["netCashProvidedByFinancingActivities"]),
# Combine In and Out Flows
("Net Cash Inflow (Operating)", "Net Change in Cash", cash_flow["netCashProvidedByOperatingActivities"]),
("Net Cash Outflow (Investing)", "Net Change in Cash", cash_flow["netCashProvidedByInvestingActivities"]),
("Net Cash Outflow (Financing)", "Net Change in Cash", cash_flow["netCashProvidedByFinancingActivities"]),
("Effect of Forex on Cash", "Net Change in Cash", cash_flow["effectOfForexChangesOnCash"]),
# Beginning & Change → Ending Balance
("Cash at Beginning of Period", "Cash at End of Period", cash_flow["cashAtBeginningOfPeriod"]),
("Net Change in Cash", "Cash at End of Period", cash_flow["netChangeInCash"]),
]
adjusted_flows = []
for src, tgt, val in flows:
if val >= 0:
adjusted_flows.append((src, tgt, val, 'rgba(50,200,50,0.6)'))
else:
# reverse direction for readability
adjusted_flows.append((tgt, src, -val, 'rgba(200,50,50,0.6)'))
labels = []
for s, t, _, _ in adjusted_flows:
if s not in labels: labels.append(s)
if t not in labels: labels.append(t)
node_values = { lbl: cash_flow[
{
"Net Income": "netIncome",
"Depreciation & Amortization": "depreciationAndAmortization",
"Deferred Income Tax": "deferredIncomeTax",
"Stock-Based Compensation": "stockBasedCompensation",
"Change in Working Capital": "changeInWorkingCapital",
"Accounts Receivables Δ": "accountsReceivables",
"Inventory Δ": "inventory",
"Accounts Payable Δ": "accountsPayables",
"Other Working Capital Δ": "otherWorkingCapital",
"Other Non-Cash Items": "otherNonCashItems",
"Operating Activities": "netCashProvidedByOperatingActivities",
"Investments in PP&E": "investmentsInPropertyPlantAndEquipment",
"Acquisitions, Net": "acquisitionsNet",
"Purchases of Investments": "purchasesOfInvestments",
"Sales/Maturities of Investments": "salesMaturitiesOfInvestments",
"Other Investing Activities": "otherInvestingActivities",
"Investing Activities": "netCashProvidedByInvestingActivities",
"Net Debt Issuance": "netDebtIssuance",
"Long-Term Net Debt Issuance": "longTermNetDebtIssuance",
"Short-Term Net Debt Issuance": "shortTermNetDebtIssuance",
"Net Stock Issuance": "netStockIssuance",
"Net Common Stock Issuance": "netCommonStockIssuance",
"Common Stock Issuance": "commonStockIssuance",
"Common Stock Repurchased": "commonStockRepurchased",
"Net Preferred Stock Issuance": "netPreferredStockIssuance",
"Net Dividends Paid": "netDividendsPaid",
"Common Dividends Paid": "commonDividendsPaid",
"Preferred Dividends Paid": "preferredDividendsPaid",
"Other Financing Activities": "otherFinancingActivities",
"Financing Activities": "netCashProvidedByFinancingActivities",
"Net Cash Inflow (Operating)": "netCashProvidedByOperatingActivities",
"Net Cash Outflow (Investing)": "netCashProvidedByInvestingActivities",
"Net Cash Outflow (Financing)": "netCashProvidedByFinancingActivities",
"Effect of Forex on Cash": "effectOfForexChangesOnCash",
"Net Change in Cash": "netChangeInCash",
"Cash at Beginning of Period": "cashAtBeginningOfPeriod",
"Cash at End of Period": "cashAtEndOfPeriod",
}[lbl]
] for lbl in labels }
def fmt(val):
if abs(val) >= 1e9: return f"${val/1e9:.1f}B"
if abs(val) >= 1e6: return f"${val/1e6:.1f}M"
if abs(val) >= 1e3: return f"${val/1e3:.0f}K"
return f"${val:.0f}"
idx = { lbl:i for i,lbl in enumerate(labels) }
source = [ idx[s] for s, t, _, _ in adjusted_flows ]
target = [ idx[t] for s, t, _, _ in adjusted_flows ]
value = [ v for _, _, v, _ in adjusted_flows ]
colors = [ c for _, _, _, c in adjusted_flows ]
label_with_values = []
for lbl in labels:
val = node_values[lbl]
base = lbl
if val < 0: base += " [NEGATIVE]"
label_with_values.append(f"{base} ({fmt(val)})")
fig = go.Figure(go.Sankey(
arrangement="snap",
node = dict(label=label_with_values, pad=15, thickness=20),
link = dict(source=source, target=target, value=value, color=colors)
))
fig.update_layout(
title_text=f"Cash Flow Statement Sankey — {symbol}",
height=height,
font_size=font_size
)
return fig
st.title("Financial Sankeys")
symbol = st.sidebar.text_input("Ticker symbol", "AMZN").upper()
# sidebar controls
bs_height = st.sidebar.slider("Balance Sheet height", 500, 1500, 800)
bs_font = st.sidebar.slider("Balance Sheet font size", 5, 15, 10)
is_height = st.sidebar.slider("Income Statement height", 500, 1500, 600)
is_font = st.sidebar.slider("Income Statement font size", 5, 15, 10)
cf_height = st.sidebar.slider("Cash Flow Statement height", 500, 1500, 800)
cf_font = st.sidebar.slider("Cash Flow Statement font size", 5, 15, 10)
# where the data came from
st.sidebar.markdown("## [Financial Modeling Prep API](https://site.financialmodelingprep.com/?utm_source=medium&utm_medium=medium&utm_campaign=damian8)\
\n\nFinancial statements are obtained from the FinancialModelingPrep API, feel free to sign up\
[here](https://site.financialmodelingprep.com/?utm_source=medium&utm_medium=medium&utm_campaign=damian8)\
if you wish.")
if symbol:
# Balance Sheet
st.header(f"Balance Sheet — {symbol}")
try:
df_bs = parse_json(f"https://financialmodelingprep.com/stable/balance-sheet-statement?symbol={symbol}&apikey={apikey}")
balance_sheet = df_bs.iloc[0]
fig_bs = draw_balance_sankey(balance_sheet, symbol.upper(), bs_height, bs_font)
st.plotly_chart(fig_bs, use_container_width=True)
except Exception as e:
st.error(f"{e}")
# Income Statement
st.header(f"Income Statement — {symbol}")
try:
df_is = parse_json(f"https://financialmodelingprep.com/stable/income-statement?symbol={symbol}&apikey={apikey}")
income_statement = df_is.iloc[0]
fig_is = draw_income_sankey(income_statement, symbol.upper(), is_height, is_font)
st.plotly_chart(fig_is, use_container_width=True)
except Exception as e:
st.error(f"{e}")
# Cash Flow Statement
st.header(f"Cash Flow Statement — {symbol}")
try:
df_cf = parse_json(f"https://financialmodelingprep.com/stable/cash-flow-statement?symbol={symbol}&apikey={apikey}")
cash_flow = df_cf.iloc[0]
fig_cf = draw_cashflow_sankey(cash_flow, symbol.upper(), cf_height, cf_font)
st.plotly_chart(fig_cf, use_container_width=True)
except Exception as e:
st.error(f"{e}")
|