Stocks financials

docs/source/index.rst

@@ -13,6 +13,7 @@ This documentation is for the Python client only. For details about the response
   Quotes
   Reference
   Trades
+  vX
   Models
 
 

docs/source/vX.rst

@@ -0,0 +1,10 @@
+.. _vX_header:
+
+vX
+==========
+
+======================
+List stock financials
+======================
+.. automethod:: polygon.rest.VXClient.list_stock_financials
+

polygon/rest/__init__.py

@@ -10,6 +10,7 @@
     ConditionsClient,
     ExchangesClient,
 )
+from .vx import VXClient
 
 
 class RESTClient(
@@ -24,4 +25,6 @@ class RESTClient(
     ConditionsClient,
     ExchangesClient,
 ):
-    pass
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.vx = VXClient(*args, **kwargs)

polygon/rest/models/__init__.py

@@ -9,3 +9,4 @@
 from .conditions import *
 from .exchanges import *
 from .snapshot import *
+from .financials import *

polygon/rest/models/financials.py

@@ -0,0 +1,330 @@
+from typing import Optional, Dict
+from dataclasses import dataclass
+
+
+@dataclass
+class DataPoint:
+    "An individual financial data point."
+    formula: Optional[str] = None
+    label: Optional[str] = None
+    order: Optional[int] = None
+    unit: Optional[str] = None
+    value: Optional[int] = None
+    xpath: Optional[str] = None
+
+    @staticmethod
+    def from_dict(d):
+        return DataPoint(**d)
+
+
+@dataclass
+class ExchangeGainsLosses:
+    "Contains exchange gains losses data for a cash flow statement."
+    formula: Optional[str] = None
+    label: Optional[str] = None
+    order: Optional[int] = None
+    unit: Optional[str] = None
+    value: Optional[int] = None
+    xpath: Optional[str] = None
+
+    @staticmethod
+    def from_dict(d):
+        return ExchangeGainsLosses(**d)
+
+
+@dataclass
+class NetCashFlow:
+    "Contains net cash flow data for a cash flow statement."
+    formula: Optional[str] = None
+    label: Optional[str] = None
+    order: Optional[int] = None
+    unit: Optional[str] = None
+    value: Optional[int] = None
+    xpath: Optional[str] = None
+
+    @staticmethod
+    def from_dict(d):
+        return NetCashFlow(**d)
+
+
+@dataclass
+class NetCashFlowFromFinancingActivities:
+    "Contains net cash flow from financing activities data for a cash flow statement."
+    formula: Optional[str] = None
+    label: Optional[str] = None
+    order: Optional[int] = None
+    unit: Optional[str] = None
+    value: Optional[int] = None
+    xpath: Optional[str] = None
+
+    @staticmethod
+    def from_dict(d):
+        return NetCashFlowFromFinancingActivities(**d)
+
+
+@dataclass
+class CashFlowStatement:
+    "Contains cash flow statement data."
+    exchange_gains_losses: Optional[ExchangeGainsLosses] = None
+    net_cash_flow: Optional[NetCashFlow] = None
+    net_cash_flow_from_financing_activities: Optional[
+        NetCashFlowFromFinancingActivities
+    ] = None
+
+    @staticmethod
+    def from_dict(d):
+        return CashFlowStatement(
+            exchange_gains_losses=None
+            if "exchange_gains_losses" not in d
+            else [ExchangeGainsLosses.from_dict(d["exchange_gains_losses"])],
+            net_cash_flow=None
+            if "net_cash_flow" not in d
+            else [NetCashFlow.from_dict(d["net_cash_flow"])],
+            net_cash_flow_from_financing_activities=None
+            if "net_cash_flow_from_financing_activities" not in d
+            else [
+                NetCashFlowFromFinancingActivities.from_dict(
+                    d["net_cash_flow_from_financing_activities"]
+                )
+            ],
+        )
+
+
+@dataclass
+class ComprehensiveIncomeLoss:
+    "Contains comprehensive income loss data for comprehensive income."
+    formula: Optional[str] = None
+    label: Optional[str] = None
+    order: Optional[int] = None
+    unit: Optional[str] = None
+    value: Optional[int] = None
+    xpath: Optional[str] = None
+
+    @staticmethod
+    def from_dict(d):
+        return ComprehensiveIncomeLoss(**d)
+
+
+@dataclass
+class ComprehensiveIncomeLossAttributableToParent:
+    "Contains comprehensive income loss attributable to parent data for comprehensive income."
+    formula: Optional[str] = None
+    label: Optional[str] = None
+    order: Optional[int] = None
+    unit: Optional[str] = None
+    value: Optional[int] = None
+    xpath: Optional[str] = None
+
+    @staticmethod
+    def from_dict(d):
+        return ComprehensiveIncomeLossAttributableToParent(**d)
+
+
+@dataclass
+class OtherComprehensiveIncomeLoss:
+    "Contains other comprehensive income loss data for comprehensive income."
+    formula: Optional[str] = None
+    label: Optional[str] = None
+    order: Optional[int] = None
+    unit: Optional[str] = None
+    value: Optional[int] = None
+    xpath: Optional[str] = None
+
+    @staticmethod
+    def from_dict(d):
+        return OtherComprehensiveIncomeLoss(**d)
+
+
+@dataclass
+class ComprehensiveIncome:
+    "Contains comprehensive income data."
+    comprehensive_income_loss: Optional[ComprehensiveIncomeLoss] = None
+    comprehensive_income_loss_attributable_to_parent: Optional[
+        ComprehensiveIncomeLossAttributableToParent
+    ] = None
+    other_comprehensive_income_loss: Optional[OtherComprehensiveIncomeLoss] = None
+
+    @staticmethod
+    def from_dict(d):
+        return ComprehensiveIncome(
+            comprehensive_income_loss=None
+            if "comprehensive_income_loss" not in d
+            else [ComprehensiveIncomeLoss.from_dict(d["comprehensive_income_loss"])],
+            comprehensive_income_loss_attributable_to_parent=None
+            if "comprehensive_income_loss_attributable_to_parent" not in d
+            else [
+                ComprehensiveIncomeLossAttributableToParent.from_dict(
+                    d["comprehensive_income_loss_attributable_to_parent"]
+                )
+            ],
+            other_comprehensive_income_loss=None
+            if "other_comprehensive_income_loss" not in d
+            else [
+                OtherComprehensiveIncomeLoss.from_dict(
+                    d["other_comprehensive_income_loss"]
+                )
+            ],
+        )
+
+
+@dataclass
+class BasicEarningsPerShare:
+    "Contains basic earning per share data for an income statement."
+    formula: Optional[str] = None
+    label: Optional[str] = None
+    order: Optional[int] = None
+    unit: Optional[str] = None
+    value: Optional[int] = None
+    xpath: Optional[str] = None
+
+    @staticmethod
+    def from_dict(d):
+        return BasicEarningsPerShare(**d)
+
+
+@dataclass
+class CostOfRevenue:
+    "Contains cost of revenue data for an income statement."
+    formula: Optional[str] = None
+    label: Optional[str] = None
+    order: Optional[int] = None
+    unit: Optional[str] = None
+    value: Optional[int] = None
+    xpath: Optional[str] = None
+
+    @staticmethod
+    def from_dict(d):
+        return CostOfRevenue(**d)
+
+
+@dataclass
+class GrossProfit:
+    "Contains gross profit data for an income statement."
+    formula: Optional[str] = None
+    label: Optional[str] = None
+    order: Optional[int] = None
+    unit: Optional[str] = None
+    value: Optional[int] = None
+    xpath: Optional[str] = None
+
+    @staticmethod
+    def from_dict(d):
+        return GrossProfit(**d)
+
+
+@dataclass
+class OperatingExpenses:
+    "Contains operating expenses data for an income statement."
+    formula: Optional[str] = None
+    label: Optional[str] = None
+    order: Optional[int] = None
+    unit: Optional[str] = None
+    value: Optional[int] = None
+    xpath: Optional[str] = None
+
+    @staticmethod
+    def from_dict(d):
+        return OperatingExpenses(**d)
+
+
+@dataclass
+class Revenues:
+    "Contains revenues data for an income statement."
+    formula: Optional[str] = None
+    label: Optional[str] = None
+    order: Optional[int] = None
+    unit: Optional[str] = None
+    value: Optional[int] = None
+    xpath: Optional[str] = None
+
+    @staticmethod
+    def from_dict(d):
+        return Revenues(**d)
+
+
+@dataclass
+class IncomeStatement:
+    "Contains income statement data."
+    basic_earnings_per_share: Optional[BasicEarningsPerShare] = None
+    cost_of_revenue: Optional[CostOfRevenue] = None
+    gross_profit: Optional[GrossProfit] = None
+    operating_expenses: Optional[OperatingExpenses] = None
+    revenues: Optional[Revenues] = None
+
+    @staticmethod
+    def from_dict(d):
+        return IncomeStatement(
+            basic_earnings_per_share=None
+            if "basic_earnings_per_share" not in d
+            else [BasicEarningsPerShare.from_dict(d["basic_earnings_per_share"])],
+            cost_of_revenue=None
+            if "cost_of_revenue" not in d
+            else [CostOfRevenue.from_dict(d["cost_of_revenue"])],
+            gross_profit=None
+            if "gross_profit" not in d
+            else [GrossProfit.from_dict(d["gross_profit"])],
+            operating_expenses=None
+            if "operating_expenses" not in d
+            else [OperatingExpenses.from_dict(d["operating_expenses"])],
+            revenues=None
+            if "revenues" not in d
+            else [Revenues.from_dict(d["revenues"])],
+        )
+
+
+@dataclass
+class Financials:
+    "Contains financial data."
+    balance_sheet: Optional[Dict[str, DataPoint]] = None
+    cash_flow_statement: Optional[CashFlowStatement] = None
+    comprehensive_income: Optional[ComprehensiveIncome] = None
+    income_statement: Optional[IncomeStatement] = None
+
+    @staticmethod
+    def from_dict(d):
+        return Financials(
+            balance_sheet=None
+            if "balance_sheet" not in d
+            else {k: DataPoint.from_dict(v) for (k, v) in d["balance_sheet"].items()},
+            cash_flow_statement=None
+            if "cash_flow_statement" not in d
+            else [CashFlowStatement.from_dict(d["cash_flow_statement"])],
+            comprehensive_income=None
+            if "comprehensive_income" not in d
+            else [ComprehensiveIncome.from_dict(d["comprehensive_income"])],
+            income_statement=None
+            if "income_statement" not in d
+            else [IncomeStatement.from_dict(d["income_statement"])],
+        )
+
+
+@dataclass
+class StockFinancial:
+    "StockFinancial contains historical financial data for a stock ticker."
+    cik: Optional[str] = None
+    company_name: Optional[str] = None
+    end_date: Optional[str] = None
+    filing_date: Optional[str] = None
+    financials: Optional[Financials] = None
+    fiscal_period: Optional[str] = None
+    fiscal_year: Optional[str] = None
+    source_filing_file_url: Optional[str] = None
+    source_filing_url: Optional[str] = None
+    start_date: Optional[str] = None
+
+    @staticmethod
+    def from_dict(d):
+        return StockFinancial(
+            cik=d.get("cik", None),
+            company_name=d.get("company_name", None),
+            end_date=d.get("end_date", None),
+            filing_date=d.get("filing_date", None),
+            financials=None
+            if "financials" not in d
+            else [Financials.from_dict(d["financials"])],
+            fiscal_period=d.get("fiscal_period", None),
+            fiscal_year=d.get("fiscal_year", None),
+            source_filing_file_url=d.get("source_filing_file_url", None),
+            source_filing_url=d.get("source_filing_url", None),
+            start_date=d.get("start_date", None),
+        )

polygon/rest/models/shared.py

@@ -71,3 +71,8 @@ class SnapshotMarketType(Enum):
     STOCKS = "stocks"
     FOREX = "forex"
     CRYPTO = "crypto"
+
+
+class Timeframe(Enum):
+    ANNUAL = "annual"
+    QUARTERLY = "quarterly"