148 lines
5.4 KiB
Python
148 lines
5.4 KiB
Python
import datetime
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import json
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import pandas as pd
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from yfinance import utils, const
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from yfinance.data import TickerData
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from yfinance.exceptions import YFinanceException, YFNotImplementedError
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class Fundamentals:
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def __init__(self, data: TickerData, proxy=None):
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self._data = data
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self.proxy = proxy
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self._earnings = None
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self._financials = None
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self._shares = None
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self._financials_data = None
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self._fin_data_quote = None
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self._basics_already_scraped = False
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self._financials = Financials(data)
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@property
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def financials(self) -> "Financials":
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return self._financials
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@property
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def earnings(self) -> dict:
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if self._earnings is None:
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raise YFNotImplementedError('earnings')
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return self._earnings
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@property
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def shares(self) -> pd.DataFrame:
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if self._shares is None:
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raise YFNotImplementedError('shares')
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return self._shares
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class Financials:
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def __init__(self, data: TickerData):
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self._data = data
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self._income_time_series = {}
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self._balance_sheet_time_series = {}
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self._cash_flow_time_series = {}
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def get_income_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
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res = self._income_time_series
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if freq not in res:
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res[freq] = self._fetch_time_series("income", freq, proxy)
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return res[freq]
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def get_balance_sheet_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
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res = self._balance_sheet_time_series
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if freq not in res:
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res[freq] = self._fetch_time_series("balance-sheet", freq, proxy)
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return res[freq]
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def get_cash_flow_time_series(self, freq="yearly", proxy=None) -> pd.DataFrame:
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res = self._cash_flow_time_series
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if freq not in res:
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res[freq] = self._fetch_time_series("cash-flow", freq, proxy)
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return res[freq]
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@utils.log_indent_decorator
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def _fetch_time_series(self, name, timescale, proxy=None):
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# Fetching time series preferred over scraping 'QuoteSummaryStore',
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# because it matches what Yahoo shows. But for some tickers returns nothing,
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# despite 'QuoteSummaryStore' containing valid data.
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allowed_names = ["income", "balance-sheet", "cash-flow"]
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allowed_timescales = ["yearly", "quarterly"]
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if name not in allowed_names:
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raise ValueError(f"Illegal argument: name must be one of: {allowed_names}")
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if timescale not in allowed_timescales:
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raise ValueError(f"Illegal argument: timescale must be one of: {allowed_names}")
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try:
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statement = self._create_financials_table(name, timescale, proxy)
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if statement is not None:
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return statement
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except YFinanceException as e:
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utils.get_yf_logger().error(f"{self._data.ticker}: Failed to create {name} financials table for reason: {e}")
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return pd.DataFrame()
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def _create_financials_table(self, name, timescale, proxy):
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if name == "income":
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# Yahoo stores the 'income' table internally under 'financials' key
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name = "financials"
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keys = const.fundamentals_keys[name]
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try:
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return self.get_financials_time_series(timescale, keys, proxy)
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except Exception as e:
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pass
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def get_financials_time_series(self, timescale, keys: list, proxy=None) -> pd.DataFrame:
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timescale_translation = {"yearly": "annual", "quarterly": "quarterly"}
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timescale = timescale_translation[timescale]
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# Step 2: construct url:
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ts_url_base = f"https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._data.ticker}?symbol={self._data.ticker}"
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url = ts_url_base + "&type=" + ",".join([timescale + k for k in keys])
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# Yahoo returns maximum 4 years or 5 quarters, regardless of start_dt:
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start_dt = datetime.datetime(2016, 12, 31)
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end = pd.Timestamp.utcnow().ceil("D")
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url += f"&period1={int(start_dt.timestamp())}&period2={int(end.timestamp())}"
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# Step 3: fetch and reshape data
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json_str = self._data.cache_get(url=url, proxy=proxy).text
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json_data = json.loads(json_str)
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data_raw = json_data["timeseries"]["result"]
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# data_raw = [v for v in data_raw if len(v) > 1] # Discard keys with no data
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for d in data_raw:
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del d["meta"]
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# Now reshape data into a table:
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# Step 1: get columns and index:
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timestamps = set()
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data_unpacked = {}
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for x in data_raw:
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for k in x.keys():
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if k == "timestamp":
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timestamps.update(x[k])
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else:
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data_unpacked[k] = x[k]
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timestamps = sorted(list(timestamps))
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dates = pd.to_datetime(timestamps, unit="s")
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df = pd.DataFrame(columns=dates, index=list(data_unpacked.keys()))
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for k, v in data_unpacked.items():
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if df is None:
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df = pd.DataFrame(columns=dates, index=[k])
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df.loc[k] = {pd.Timestamp(x["asOfDate"]): x["reportedValue"]["raw"] for x in v}
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df.index = df.index.str.replace("^" + timescale, "", regex=True)
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# Reorder table to match order on Yahoo website
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df = df.reindex([k for k in keys if k in df.index])
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df = df[sorted(df.columns, reverse=True)]
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return df
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