171 lines
6.6 KiB
Python
171 lines
6.6 KiB
Python
import datetime
|
|
import json
|
|
import warnings
|
|
|
|
import pandas as pd
|
|
|
|
from yfinance import utils, const
|
|
from yfinance.data import YfData
|
|
from yfinance.exceptions import YFException, YFNotImplementedError
|
|
|
|
class Fundamentals:
|
|
|
|
def __init__(self, data: YfData, symbol: str, proxy=const._SENTINEL_):
|
|
if proxy is not const._SENTINEL_:
|
|
utils.print_once("YF deprecation warning: set proxy via new config function: yf.set_config(proxy=proxy)")
|
|
data._set_proxy(proxy)
|
|
|
|
self._data = data
|
|
self._symbol = symbol
|
|
|
|
self._earnings = None
|
|
self._financials = None
|
|
self._shares = None
|
|
|
|
self._financials_data = None
|
|
self._fin_data_quote = None
|
|
self._basics_already_scraped = False
|
|
self._financials = Financials(data, symbol)
|
|
|
|
@property
|
|
def financials(self) -> "Financials":
|
|
return self._financials
|
|
|
|
@property
|
|
def earnings(self) -> dict:
|
|
warnings.warn("'Ticker.earnings' is deprecated as not available via API. Look for \"Net Income\" in Ticker.income_stmt.", DeprecationWarning)
|
|
return None
|
|
|
|
@property
|
|
def shares(self) -> pd.DataFrame:
|
|
if self._shares is None:
|
|
raise YFNotImplementedError('shares')
|
|
return self._shares
|
|
|
|
|
|
class Financials:
|
|
def __init__(self, data: YfData, symbol: str):
|
|
self._data = data
|
|
self._symbol = symbol
|
|
self._income_time_series = {}
|
|
self._balance_sheet_time_series = {}
|
|
self._cash_flow_time_series = {}
|
|
|
|
def get_income_time_series(self, freq="yearly", proxy=const._SENTINEL_) -> pd.DataFrame:
|
|
if proxy is not const._SENTINEL_:
|
|
utils.print_once("YF deprecation warning: set proxy via new config function: yf.set_config(proxy=proxy)")
|
|
self._data._set_proxy(proxy)
|
|
|
|
res = self._income_time_series
|
|
if freq not in res:
|
|
res[freq] = self._fetch_time_series("income", freq)
|
|
return res[freq]
|
|
|
|
def get_balance_sheet_time_series(self, freq="yearly", proxy=const._SENTINEL_) -> pd.DataFrame:
|
|
if proxy is not const._SENTINEL_:
|
|
utils.print_once("YF deprecation warning: set proxy via new config function: yf.set_config(proxy=proxy)")
|
|
self._data._set_proxy(proxy)
|
|
|
|
res = self._balance_sheet_time_series
|
|
if freq not in res:
|
|
res[freq] = self._fetch_time_series("balance-sheet", freq)
|
|
return res[freq]
|
|
|
|
def get_cash_flow_time_series(self, freq="yearly", proxy=const._SENTINEL_) -> pd.DataFrame:
|
|
if proxy is not const._SENTINEL_:
|
|
utils.print_once("YF deprecation warning: set proxy via new config function: yf.set_config(proxy=proxy)")
|
|
self._data._set_proxy(proxy)
|
|
|
|
res = self._cash_flow_time_series
|
|
if freq not in res:
|
|
res[freq] = self._fetch_time_series("cash-flow", freq)
|
|
return res[freq]
|
|
|
|
@utils.log_indent_decorator
|
|
def _fetch_time_series(self, name, timescale):
|
|
# Fetching time series preferred over scraping 'QuoteSummaryStore',
|
|
# because it matches what Yahoo shows. But for some tickers returns nothing,
|
|
# despite 'QuoteSummaryStore' containing valid data.
|
|
|
|
allowed_names = ["income", "balance-sheet", "cash-flow"]
|
|
allowed_timescales = ["yearly", "quarterly", "trailing"]
|
|
|
|
if name not in allowed_names:
|
|
raise ValueError(f"Illegal argument: name must be one of: {allowed_names}")
|
|
if timescale not in allowed_timescales:
|
|
raise ValueError(f"Illegal argument: timescale must be one of: {allowed_timescales}")
|
|
if timescale == "trailing" and name not in ('income', 'cash-flow'):
|
|
raise ValueError("Illegal argument: frequency 'trailing'" +
|
|
" only available for cash-flow or income data.")
|
|
|
|
try:
|
|
statement = self._create_financials_table(name, timescale)
|
|
|
|
if statement is not None:
|
|
return statement
|
|
except YFException as e:
|
|
utils.get_yf_logger().error(f"{self._symbol}: Failed to create {name} financials table for reason: {e}")
|
|
return pd.DataFrame()
|
|
|
|
def _create_financials_table(self, name, timescale):
|
|
if name == "income":
|
|
# Yahoo stores the 'income' table internally under 'financials' key
|
|
name = "financials"
|
|
|
|
keys = const.fundamentals_keys[name]
|
|
|
|
try:
|
|
return self._get_financials_time_series(timescale, keys)
|
|
except Exception:
|
|
pass
|
|
|
|
def _get_financials_time_series(self, timescale, keys: list) -> pd.DataFrame:
|
|
timescale_translation = {"yearly": "annual", "quarterly": "quarterly", "trailing": "trailing"}
|
|
timescale = timescale_translation[timescale]
|
|
|
|
# Step 2: construct url:
|
|
ts_url_base = f"https://query2.finance.yahoo.com/ws/fundamentals-timeseries/v1/finance/timeseries/{self._symbol}?symbol={self._symbol}"
|
|
url = ts_url_base + "&type=" + ",".join([timescale + k for k in keys])
|
|
# Yahoo returns maximum 4 years or 5 quarters, regardless of start_dt:
|
|
start_dt = datetime.datetime(2016, 12, 31)
|
|
end = pd.Timestamp.utcnow().ceil("D")
|
|
url += f"&period1={int(start_dt.timestamp())}&period2={int(end.timestamp())}"
|
|
|
|
# Step 3: fetch and reshape data
|
|
json_str = self._data.cache_get(url=url).text
|
|
json_data = json.loads(json_str)
|
|
data_raw = json_data["timeseries"]["result"]
|
|
# data_raw = [v for v in data_raw if len(v) > 1] # Discard keys with no data
|
|
for d in data_raw:
|
|
del d["meta"]
|
|
|
|
# Now reshape data into a table:
|
|
# Step 1: get columns and index:
|
|
timestamps = set()
|
|
data_unpacked = {}
|
|
for x in data_raw:
|
|
for k in x.keys():
|
|
if k == "timestamp":
|
|
timestamps.update(x[k])
|
|
else:
|
|
data_unpacked[k] = x[k]
|
|
timestamps = sorted(list(timestamps))
|
|
dates = pd.to_datetime(timestamps, unit="s")
|
|
df = pd.DataFrame(columns=dates, index=list(data_unpacked.keys()))
|
|
for k, v in data_unpacked.items():
|
|
if df is None:
|
|
df = pd.DataFrame(columns=dates, index=[k])
|
|
df.loc[k] = {pd.Timestamp(x["asOfDate"]): x["reportedValue"]["raw"] for x in v}
|
|
|
|
df.index = df.index.str.replace("^" + timescale, "", regex=True)
|
|
|
|
# Reorder table to match order on Yahoo website
|
|
df = df.reindex([k for k in keys if k in df.index])
|
|
df = df[sorted(df.columns, reverse=True)]
|
|
|
|
# Trailing 12 months return only the first column.
|
|
if (timescale == "trailing"):
|
|
df = df.iloc[:, [0]]
|
|
|
|
return df
|