introduced new fundamentals + refactoring
ran
parent
cea1ae6163
commit
ad45b73101
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@ -19,668 +19,19 @@
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# limitations under the License.
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#
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from __future__ import print_function
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__version__ = "0.1.45"
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__version__ = "0.1.46"
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__author__ = "Ran Aroussi"
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__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override']
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import time as _time
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import datetime as _datetime
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import requests as _requests
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# import json as _json
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from collections import namedtuple as _namedtuple
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import multitasking as _multitasking
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import pandas as _pd
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import numpy as _np
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import sys as _sys
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from .ticker import Ticker
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from .tickers import Tickers
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from .multi import download
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_DFS = {}
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_PROGRESS_BAR = None
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def genTickers(tickers):
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tickers = tickers if isinstance(
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tickers, list) else tickers.replace(',', ' ').split()
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tickers = [ticker.upper() for ticker in tickers]
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ticker_objects = {}
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for ticker in tickers:
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ticker_objects[ticker] = Ticker(ticker)
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return _namedtuple("Tickers", ticker_objects.keys()
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)(*ticker_objects.values())
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class Tickers():
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def __repr__(self):
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return 'yfinance.Tickers object <%s>' % ",".join(self.symbols)
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def __init__(self, tickers):
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tickers = tickers if isinstance(
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tickers, list) else tickers.replace(',', ' ').split()
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self.symbols = [ticker.upper() for ticker in tickers]
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ticker_objects = {}
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for ticker in self.symbols:
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ticker_objects[ticker] = Ticker(ticker)
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self.tickers = _namedtuple(
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"Tickers", ticker_objects.keys(), rename=True
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)(*ticker_objects.values())
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def download(self, period="1mo", interval="1d",
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start=None, end=None, prepost=False,
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actions=True, auto_adjust=True, proxy=None,
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threads=True, group_by='column', progress=True,
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**kwargs):
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data = download(self.symbols,
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start=start, end=end, actions=actions,
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auto_adjust=auto_adjust,
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period=period,
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interval=interval,
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prepost=prepost,
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proxy=proxy,
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group_by='ticker',
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threads=threads,
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progress=progress,
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**kwargs)
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for symbol in self.symbols:
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getattr(self.tickers, symbol)._history = data[symbol]
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if group_by == 'column':
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data.columns = data.columns.swaplevel(0, 1)
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data.sort_index(level=0, axis=1, inplace=True)
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return data
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class Ticker():
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def __repr__(self):
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return 'yfinance.Ticker object <%s>' % self.ticker
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def __init__(self, ticker):
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self.ticker = ticker.upper()
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self._history = None
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self._base_url = 'https://query1.finance.yahoo.com'
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self._financials = None
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self._balance_sheet = None
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self._cashflow = None
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self._sustainability = None
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self._scrape_url = 'https://finance.yahoo.com/quote'
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self._expirations = {}
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@property
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def info(self):
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""" retreive metadata and currenct price data """
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url = "{}/v7/finance/quote?symbols={}".format(
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self._base_url, self.ticker)
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r = _requests.get(url=url).json()["quoteResponse"]["result"]
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if r:
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return r[0]
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return {}
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def _download_options(self, date=None, proxy=None):
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if date is None:
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url = "{}/v7/finance/options/{}".format(
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self._base_url, self.ticker)
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else:
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url = "{}/v7/finance/options/{}?date={}".format(
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self._base_url, self.ticker, date)
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# setup proxy in requests format
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if proxy is not None:
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if isinstance(proxy, dict) and "https" in proxy:
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proxy = proxy["https"]
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proxy = {"https": proxy}
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r = _requests.get(url=url, proxies=proxy).json()
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if r['optionChain']['result']:
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for exp in r['optionChain']['result'][0]['expirationDates']:
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self._expirations[_datetime.datetime.fromtimestamp(
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exp).strftime('%Y-%m-%d')] = exp
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return r['optionChain']['result'][0]['options'][0]
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return {}
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@property
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def options(self):
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if not self._expirations:
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self._download_options()
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return tuple(self._expirations.keys())
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def _options2df(self, opt):
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return _pd.DataFrame(opt).reindex(columns=[
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'contractSymbol',
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'lastTradeDate',
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'strike',
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'lastPrice',
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'bid',
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'ask',
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'change',
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'percentChange',
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'volume',
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'openInterest',
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'impliedVolatility',
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'inTheMoney',
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'contractSize',
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'currency'])
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def option_chain(self, date=None, proxy=None):
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if date is None:
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options = self._download_options(proxy=proxy)
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else:
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if not self._expirations:
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self._download_options()
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date = self._expirations[date]
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options = self._download_options(date, proxy=proxy)
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return _namedtuple('Options', ['calls', 'puts'])(**{
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"calls": self._options2df(options['calls']),
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"puts": self._options2df(options['puts'])
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})
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@staticmethod
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def _auto_adjust(data):
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df = data.copy()
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ratio = df["Close"] / df["Adj Close"]
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df["Adj Open"] = df["Open"] / ratio
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df["Adj High"] = df["High"] / ratio
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df["Adj Low"] = df["Low"] / ratio
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df.drop(
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["Open", "High", "Low", "Close"],
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axis=1, inplace=True)
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df.rename(columns={
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"Adj Open": "Open", "Adj High": "High",
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"Adj Low": "Low", "Adj Close": "Close"
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}, inplace=True)
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df = df[["Open", "High", "Low", "Close", "Volume"]]
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return df
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@staticmethod
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def _parse_quotes(data):
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timestamps = data["timestamp"]
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ohlc = data["indicators"]["quote"][0]
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volumes = ohlc["volume"]
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opens = ohlc["open"]
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closes = ohlc["close"]
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lows = ohlc["low"]
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highs = ohlc["high"]
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adjclose = closes
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if "adjclose" in data["indicators"]:
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adjclose = data["indicators"]["adjclose"][0]["adjclose"]
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quotes = _pd.DataFrame({"Open": opens,
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"High": highs,
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"Low": lows,
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"Close": closes,
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"Adj Close": adjclose,
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"Volume": volumes})
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quotes.index = _pd.to_datetime(timestamps, unit="s")
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quotes.sort_index(inplace=True)
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return quotes
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@staticmethod
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def _parse_actions(data):
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dividends = _pd.DataFrame(columns=["Dividends"])
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splits = _pd.DataFrame(columns=["Stock Splits"])
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if "events" in data:
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if "dividends" in data["events"]:
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dividends = _pd.DataFrame(
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data=list(data["events"]["dividends"].values()))
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dividends.set_index("date", inplace=True)
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dividends.index = _pd.to_datetime(dividends.index, unit="s")
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dividends.sort_index(inplace=True)
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dividends.columns = ["Dividends"]
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if "splits" in data["events"]:
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splits = _pd.DataFrame(
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data=list(data["events"]["splits"].values()))
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splits.set_index("date", inplace=True)
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splits.index = _pd.to_datetime(
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splits.index, unit="s")
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splits.sort_index(inplace=True)
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splits["Stock Splits"] = splits["numerator"] / \
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splits["denominator"]
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splits = splits["Stock Splits"]
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return dividends, splits
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def get_dividends(self, proxy=None):
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if self._history is None:
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self.history(period="max", proxy=proxy)
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dividends = self._history["Dividends"]
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return dividends[dividends != 0]
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def get_splits(self, proxy=None):
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if self._history is None:
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self.history(period="max", proxy=proxy)
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splits = self._history["Stock Splits"]
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return splits[splits != 0]
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def get_actions(self, proxy=None):
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if self._history is None:
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self.history(period="max", proxy=proxy)
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actions = self._history[["Dividends", "Stock Splits"]]
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return actions[actions != 0].dropna(how='all').fillna(0)
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# ------------------------
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def history(self, period="1mo", interval="1d",
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start=None, end=None, prepost=False, actions=True,
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auto_adjust=True, proxy=None, rounding=False):
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"""
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:Parameters:
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period : str
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Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
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Either Use period parameter or use start and end
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interval : str
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Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
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Intraday data cannot extend last 60 days
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start: str
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Download start date string (YYYY-MM-DD) or _datetime.
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Default is 1900-01-01
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end: str
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Download end date string (YYYY-MM-DD) or _datetime.
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Default is now
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prepost : bool
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Include Pre and Post market data in results?
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Default is False
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auto_adjust: bool
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Adjust all OHLC automatically? Default is True
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proxy: str
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Optional. Proxy server URL scheme. Default is None
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rounding: bool
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Optional. Whether to round the retrieved values to the precision suggested by Yahoo.
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"""
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if start or period is None or period.lower() == "max":
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if start is None:
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start = -2208988800
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elif isinstance(start, _datetime.datetime):
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start = int(_time.mktime(start.timetuple()))
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else:
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start = int(_time.mktime(
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_time.strptime(str(start), '%Y-%m-%d')))
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if end is None:
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end = int(_time.time())
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elif isinstance(end, _datetime.datetime):
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end = int(_time.mktime(end.timetuple()))
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else:
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end = int(_time.mktime(_time.strptime(str(end), '%Y-%m-%d')))
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params = {"period1": start, "period2": end}
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else:
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period = period.lower()
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params = {"range": period}
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params["interval"] = interval.lower()
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params["includePrePost"] = prepost
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params["events"] = "div,splits"
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# 1) fix weired bug with Yahoo! - returning 60m for 30m bars
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if params["interval"] == "30m":
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params["interval"] = "15m"
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# setup proxy in requests format
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if proxy is not None:
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if isinstance(proxy, dict) and "https" in proxy:
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proxy = proxy["https"]
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proxy = {"https": proxy}
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# Getting data from json
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url = "{}/v8/finance/chart/{}".format(self._base_url, self.ticker)
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data = _requests.get(url=url, params=params, proxies=proxy)
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if "Will be right back" in data.text:
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raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
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"Our engineers are working quickly to resolve "
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"the issue. Thank you for your patience.")
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data = data.json()
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# Work with errors
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err_msg = "No data found for this date range, symbol may be delisted"
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if "chart" in data and data["chart"]["error"]:
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err_msg = data["chart"]["error"]["description"]
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_DFS[self.ticker] = _emptydf()
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raise ValueError(self.ticker, err_msg)
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elif "chart" not in data or data["chart"]["result"] is None or \
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not data["chart"]["result"]:
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_DFS[self.ticker] = _emptydf()
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raise ValueError(self.ticker, err_msg)
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# parse quotes
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try:
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quotes = self._parse_quotes(data["chart"]["result"][0])
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except Exception:
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_DFS[self.ticker] = _emptydf()
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raise ValueError(self.ticker, err_msg)
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# 2) fix weired bug with Yahoo! - returning 60m for 30m bars
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if interval.lower() == "30m":
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quotes2 = quotes.resample('30T')
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quotes = _pd.DataFrame(index=quotes2.last().index, data={
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'Open': quotes2['Open'].first(),
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'High': quotes2['High'].max(),
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'Low': quotes2['Low'].min(),
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'Close': quotes2['Close'].last(),
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'Adj Close': quotes2['Adj Close'].last(),
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'Volume': quotes2['Volume'].sum()
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})
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try:
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quotes['Dividends'] = quotes2['Dividends'].max()
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except Exception:
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pass
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try:
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quotes['Stock Splits'] = quotes2['Dividends'].max()
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except Exception:
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pass
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if auto_adjust:
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quotes = self._auto_adjust(quotes)
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if rounding:
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quotes = _np.round(quotes, data[
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"chart"]["result"][0]["meta"]["priceHint"])
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quotes['Volume'] = quotes['Volume'].fillna(0).astype(_np.int64)
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quotes.dropna(inplace=True)
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# actions
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dividends, splits = self._parse_actions(data["chart"]["result"][0])
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# combine
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df = _pd.concat([quotes, dividends, splits], axis=1, sort=True)
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df["Dividends"].fillna(0, inplace=True)
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df["Stock Splits"].fillna(0, inplace=True)
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# index eod/intraday
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df.index = df.index.tz_localize("UTC").tz_convert(
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data["chart"]["result"][0]["meta"]["exchangeTimezoneName"])
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if params["interval"][-1] == "m":
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df.index.name = "Datetime"
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else:
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df.index = _pd.to_datetime(df.index.date)
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df.index.name = "Date"
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self._history = df.copy()
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if not actions:
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df.drop(columns=["Dividends", "Stock Splits"], inplace=True)
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return df
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# ------------------------
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def _get_fundamentals(self, kind, proxy=None):
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# setup proxy in requests format
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if proxy is not None:
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if isinstance(proxy, dict) and "https" in proxy:
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proxy = proxy["https"]
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proxy = {"https": proxy}
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"""
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url = '%s/%s' % (self._scrape_url, self.ticker)
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html = _requests.get(url=url, proxies=proxy).text
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json_str = html.split('root.App.main =')[1].split(
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'(this)')[0].split(';\n}')[0].strip()
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data = _json.loads(json_str)[
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'context']['dispatcher']['stores']['QuoteSummaryStore']
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new_data = ''
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data_parts = _json.dumps(data).replace('{}', 'null').split('{"raw": ')
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for x in range(len(data_parts)):
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if "fmt" in data_parts[x]:
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p = data_parts[x].split(', "fmt":', maxsplit=1)
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new_data += p[0] + p[1].split('}', maxsplit=1)[1]
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else:
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new_data += data_parts[x]
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data = _json.loads(new_data)
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return data
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"""
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url = '%s/%s/%s' % (self._scrape_url, self.ticker, kind)
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try:
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data = _pd.read_html(_requests.get(url=url, proxies=proxy).text)[0]
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except ValueError:
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return _pd.DataFrame()
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if kind == 'sustainability':
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data['Significant Involvement'] = data[
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'Significant Involvement'] != 'No'
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return data
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data.columns = [''] + list(data[:1].values[0][1:])
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data.set_index('', inplace=True)
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if len(data.columns) < 2:
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return _pd.DataFrame()
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for col in data.columns:
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data[col] = _np.where(data[col] == '-', _np.nan, data[col])
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idx = data[data[data.columns[0]] == data[data.columns[1]]].index
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data.loc[idx] = '-'
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return data[1:]
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def get_financials(self, proxy=None):
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if self._financials is None:
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self._financials = self._get_fundamentals(
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'financials', proxy)
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return self._financials
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def get_balance_sheet(self, proxy=None):
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if self._balance_sheet is None:
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self._balance_sheet = self._get_fundamentals(
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'balance-sheet', proxy)
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return self._balance_sheet
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def get_cashflow(self, proxy=None):
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if self._cashflow is None:
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self._cashflow = self._get_fundamentals(
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||||
'cash-flow', proxy)
|
||||
return self._cashflow
|
||||
|
||||
def get_sustainability(self, proxy=None):
|
||||
if self._sustainability is None:
|
||||
self._sustainability = self._get_fundamentals(
|
||||
'sustainability', proxy)
|
||||
return self._sustainability
|
||||
|
||||
# ------------------------
|
||||
|
||||
@property
|
||||
def dividends(self):
|
||||
return self.get_dividends()
|
||||
|
||||
@property
|
||||
def splits(self):
|
||||
return self.get_splits()
|
||||
|
||||
@property
|
||||
def actions(self):
|
||||
return self.get_actions()
|
||||
|
||||
@property
|
||||
def financials(self):
|
||||
return self.get_financials()
|
||||
|
||||
@property
|
||||
def balance_sheet(self):
|
||||
return self.get_balance_sheet()
|
||||
|
||||
@property
|
||||
def balancesheet(self):
|
||||
return self.get_balance_sheet()
|
||||
|
||||
@property
|
||||
def cashflow(self):
|
||||
return self.get_cashflow()
|
||||
|
||||
@property
|
||||
def sustainability(self):
|
||||
return self.get_sustainability()
|
||||
|
||||
|
||||
@_multitasking.task
|
||||
def _download_one_threaded(ticker, start=None, end=None, auto_adjust=False,
|
||||
actions=False, progress=True, period="max",
|
||||
interval="1d", prepost=False, proxy=None, rounding=True):
|
||||
|
||||
global _PROGRESS_BAR, _DFS
|
||||
data = _download_one(ticker, start, end, auto_adjust, actions,
|
||||
period, interval, prepost, proxy, rounding)
|
||||
_DFS[ticker.upper()] = data
|
||||
if progress:
|
||||
_PROGRESS_BAR.animate()
|
||||
|
||||
|
||||
def _download_one(ticker, start=None, end=None, auto_adjust=False,
|
||||
actions=False, period="max", interval="1d",
|
||||
prepost=False, proxy=None, rounding=True):
|
||||
|
||||
return Ticker(ticker).history(period=period, interval=interval,
|
||||
start=start, end=end, prepost=prepost,
|
||||
actions=actions, auto_adjust=auto_adjust,
|
||||
proxy=proxy, rounding=rounding)
|
||||
|
||||
|
||||
def download(tickers, start=None, end=None, actions=False, threads=True,
|
||||
group_by='column', auto_adjust=False, progress=True,
|
||||
period="max", interval="1d", prepost=False, proxy=None,
|
||||
rounding=True,
|
||||
**kwargs):
|
||||
"""Download yahoo tickers
|
||||
:Parameters:
|
||||
tickers : str, list
|
||||
List of tickers to download
|
||||
period : str
|
||||
Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
|
||||
Either Use period parameter or use start and end
|
||||
interval : str
|
||||
Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
|
||||
Intraday data cannot extend last 60 days
|
||||
start: str
|
||||
Download start date string (YYYY-MM-DD) or _datetime.
|
||||
Default is 1900-01-01
|
||||
end: str
|
||||
Download end date string (YYYY-MM-DD) or _datetime.
|
||||
Default is now
|
||||
group_by : str
|
||||
Group by 'ticker' or 'column' (default)
|
||||
prepost : bool
|
||||
Include Pre and Post market data in results?
|
||||
Default is False
|
||||
auto_adjust: bool
|
||||
Adjust all OHLC automatically? Default is False
|
||||
actions: bool
|
||||
Download dividend + stock splits data. Default is False
|
||||
threads: bool / int
|
||||
How many threads to use for mass downloading. Default is True
|
||||
proxy: str
|
||||
Optional. Proxy server URL scheme. Default is None
|
||||
rounding: bool
|
||||
Optional. Whether to round the retrieved values to the precision suggested by Yahoo.
|
||||
"""
|
||||
global _PROGRESS_BAR, _DFS
|
||||
|
||||
# create ticker list
|
||||
tickers = tickers if isinstance(
|
||||
tickers, list) else tickers.replace(',', ' ').split()
|
||||
tickers = list(set([ticker.upper() for ticker in tickers]))
|
||||
|
||||
if progress:
|
||||
_PROGRESS_BAR = _ProgressBar(len(tickers), 'downloaded')
|
||||
|
||||
# reset _DFS
|
||||
_DFS = {}
|
||||
|
||||
# download using threads
|
||||
if threads:
|
||||
if threads is True:
|
||||
threads = min([len(tickers), _multitasking.cpu_count() * 2])
|
||||
_multitasking.set_max_threads(threads)
|
||||
for i, ticker in enumerate(tickers):
|
||||
_download_one_threaded(ticker, period=period, interval=interval,
|
||||
start=start, end=end, prepost=prepost,
|
||||
actions=actions, auto_adjust=auto_adjust,
|
||||
progress=(progress and i > 0), proxy=proxy,
|
||||
rounding=rounding)
|
||||
while len(_DFS) < len(tickers):
|
||||
_time.sleep(0.01)
|
||||
|
||||
# download synchronously
|
||||
else:
|
||||
for i, ticker in enumerate(tickers):
|
||||
data = _download_one(ticker, period=period, interval=interval,
|
||||
start=start, end=end, prepost=prepost,
|
||||
actions=actions, auto_adjust=auto_adjust,
|
||||
rounding=rounding)
|
||||
_DFS[ticker.upper()] = data
|
||||
if progress:
|
||||
_PROGRESS_BAR.animate()
|
||||
|
||||
if progress:
|
||||
_PROGRESS_BAR.completed()
|
||||
|
||||
if len(tickers) == 1:
|
||||
return _DFS[tickers[0]]
|
||||
|
||||
try:
|
||||
data = _pd.concat(_DFS.values(), axis=1, keys=_DFS.keys())
|
||||
except Exception:
|
||||
_realign_dfs()
|
||||
data = _pd.concat(_DFS.values(), axis=1, keys=_DFS.keys())
|
||||
|
||||
if group_by == 'column':
|
||||
data.columns = data.columns.swaplevel(0, 1)
|
||||
data.sort_index(level=0, axis=1, inplace=True)
|
||||
|
||||
return data
|
||||
|
||||
|
||||
def _realign_dfs():
|
||||
idx_len = 0
|
||||
idx = None
|
||||
|
||||
for df in _DFS.values():
|
||||
if len(df) > idx_len:
|
||||
idx_len = len(df)
|
||||
idx = df.index
|
||||
|
||||
for key in _DFS.keys():
|
||||
try:
|
||||
_DFS[key] = _pd.DataFrame(
|
||||
index=idx, data=_DFS[key]).drop_duplicates()
|
||||
except Exception:
|
||||
_DFS[key] = _pd.concat(
|
||||
[_emptydf(idx), _DFS[key].dropna()], axis=0, sort=True)
|
||||
|
||||
# remove duplicate index
|
||||
_DFS[key] = _DFS[key].loc[~_DFS[key].index.duplicated(keep='last')]
|
||||
|
||||
|
||||
def _emptydf(index=[]):
|
||||
empty = _pd.DataFrame(index=index, data={
|
||||
'Open': _np.nan, 'High': _np.nan, 'Low': _np.nan,
|
||||
'Close': _np.nan, 'Adj Close': _np.nan, 'Volume': _np.nan})
|
||||
empty.index.name = 'Date'
|
||||
return empty
|
||||
|
||||
|
||||
# make pandas datareader optional
|
||||
# otherwise can be called via fix_yahoo_finance.download(...)
|
||||
def pdr_override():
|
||||
"""
|
||||
make pandas datareader optional
|
||||
otherwise can be called via fix_yahoo_finance.download(...)
|
||||
"""
|
||||
try:
|
||||
import pandas_datareader
|
||||
pandas_datareader.data.get_data_yahoo = download
|
||||
|
|
@ -690,51 +41,4 @@ def pdr_override():
|
|||
pass
|
||||
|
||||
|
||||
class _ProgressBar:
|
||||
def __init__(self, iterations, text='completed'):
|
||||
self.text = text
|
||||
self.iterations = iterations
|
||||
self.prog_bar = '[]'
|
||||
self.fill_char = '*'
|
||||
self.width = 50
|
||||
self.__update_amount(0)
|
||||
self.elapsed = 1
|
||||
|
||||
def completed(self):
|
||||
if self.elapsed > self.iterations:
|
||||
self.elapsed = self.iterations
|
||||
self.update_iteration(1)
|
||||
print('\r' + str(self), end='')
|
||||
_sys.stdout.flush()
|
||||
print()
|
||||
|
||||
def animate(self, iteration=None):
|
||||
if iteration is None:
|
||||
self.elapsed += 1
|
||||
iteration = self.elapsed
|
||||
else:
|
||||
self.elapsed += iteration
|
||||
|
||||
print('\r' + str(self), end='')
|
||||
_sys.stdout.flush()
|
||||
self.update_iteration()
|
||||
|
||||
def update_iteration(self, val=None):
|
||||
val = val if val is not None else self.elapsed / float(self.iterations)
|
||||
self.__update_amount(val * 100.0)
|
||||
self.prog_bar += ' %s of %s %s' % (
|
||||
self.elapsed, self.iterations, self.text)
|
||||
|
||||
def __update_amount(self, new_amount):
|
||||
percent_done = int(round((new_amount / 100.0) * 100.0))
|
||||
all_full = self.width - 2
|
||||
num_hashes = int(round((percent_done / 100.0) * all_full))
|
||||
self.prog_bar = '[' + self.fill_char * \
|
||||
num_hashes + ' ' * (all_full - num_hashes) + ']'
|
||||
pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))
|
||||
pct_string = '%d%%' % percent_done
|
||||
self.prog_bar = self.prog_bar[0:pct_place] + \
|
||||
(pct_string + self.prog_bar[pct_place + len(pct_string):])
|
||||
|
||||
def __str__(self):
|
||||
return str(self.prog_bar)
|
||||
__all__ = ['download', 'Ticker', 'Tickers', 'pdr_override']
|
||||
|
|
|
|||
|
|
@ -0,0 +1,415 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
|
||||
# https://github.com/ranaroussi/yfinance
|
||||
#
|
||||
# Copyright 2017-2019 Ran Aroussi
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import time as _time
|
||||
import datetime as _datetime
|
||||
import requests as _requests
|
||||
import pandas as _pd
|
||||
import numpy as _np
|
||||
|
||||
from . import utils
|
||||
|
||||
# import json as _json
|
||||
# import re as _re
|
||||
# import sys as _sys
|
||||
|
||||
from . import shared
|
||||
|
||||
|
||||
class TickerBase():
|
||||
def __init__(self, ticker):
|
||||
self.ticker = ticker.upper()
|
||||
self._history = None
|
||||
self._base_url = 'https://query1.finance.yahoo.com'
|
||||
self._scrape_url = 'https://finance.yahoo.com/quote'
|
||||
|
||||
self._fundamentals = False
|
||||
self._info = None
|
||||
self._sustainability = None
|
||||
self._recommendations = None
|
||||
self._calendar = None
|
||||
|
||||
self._earnings = {
|
||||
"yearly": utils.empty_df(),
|
||||
"quarterly": utils.empty_df()}
|
||||
self._financials = {
|
||||
"yearly": utils.empty_df(),
|
||||
"quarterly": utils.empty_df()}
|
||||
self._balancesheet = {
|
||||
"yearly": utils.empty_df(),
|
||||
"quarterly": utils.empty_df()}
|
||||
self._cashflow = {
|
||||
"yearly": utils.empty_df(),
|
||||
"quarterly": utils.empty_df()}
|
||||
|
||||
def history(self, period="1mo", interval="1d",
|
||||
start=None, end=None, prepost=False, actions=True,
|
||||
auto_adjust=True, back_adjust=False,
|
||||
proxy=None, rounding=True, tz=None, **kwargs):
|
||||
"""
|
||||
:Parameters:
|
||||
period : str
|
||||
Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
|
||||
Either Use period parameter or use start and end
|
||||
interval : str
|
||||
Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
|
||||
Intraday data cannot extend last 60 days
|
||||
start: str
|
||||
Download start date string (YYYY-MM-DD) or _datetime.
|
||||
Default is 1900-01-01
|
||||
end: str
|
||||
Download end date string (YYYY-MM-DD) or _datetime.
|
||||
Default is now
|
||||
prepost : bool
|
||||
Include Pre and Post market data in results?
|
||||
Default is False
|
||||
auto_adjust: bool
|
||||
Adjust all OHLC automatically? Default is True
|
||||
back_adjust: bool
|
||||
Back-adjusted data to mimic true historical prices
|
||||
proxy: str
|
||||
Optional. Proxy server URL scheme. Default is None
|
||||
rounding: bool
|
||||
Round values to 2 decimal places?
|
||||
Optional. Default is False = precision suggested by Yahoo!
|
||||
tz: str
|
||||
Optional timezone locale for dates.
|
||||
(default data is returned as non-localized dates)
|
||||
"""
|
||||
|
||||
if start or period is None or period.lower() == "max":
|
||||
if start is None:
|
||||
start = -2208988800
|
||||
elif isinstance(start, _datetime.datetime):
|
||||
start = int(_time.mktime(start.timetuple()))
|
||||
else:
|
||||
start = int(_time.mktime(
|
||||
_time.strptime(str(start), '%Y-%m-%d')))
|
||||
if end is None:
|
||||
end = int(_time.time())
|
||||
elif isinstance(end, _datetime.datetime):
|
||||
end = int(_time.mktime(end.timetuple()))
|
||||
else:
|
||||
end = int(_time.mktime(_time.strptime(str(end), '%Y-%m-%d')))
|
||||
|
||||
params = {"period1": start, "period2": end}
|
||||
else:
|
||||
period = period.lower()
|
||||
params = {"range": period}
|
||||
|
||||
params["interval"] = interval.lower()
|
||||
params["includePrePost"] = prepost
|
||||
params["events"] = "div,splits"
|
||||
|
||||
# 1) fix weired bug with Yahoo! - returning 60m for 30m bars
|
||||
if params["interval"] == "30m":
|
||||
params["interval"] = "15m"
|
||||
|
||||
# setup proxy in requests format
|
||||
if proxy is not None:
|
||||
if isinstance(proxy, dict) and "https" in proxy:
|
||||
proxy = proxy["https"]
|
||||
proxy = {"https": proxy}
|
||||
|
||||
# Getting data from json
|
||||
url = "{}/v8/finance/chart/{}".format(self._base_url, self.ticker)
|
||||
data = _requests.get(url=url, params=params, proxies=proxy)
|
||||
if "Will be right back" in data.text:
|
||||
raise RuntimeError("*** YAHOO! FINANCE IS CURRENTLY DOWN! ***\n"
|
||||
"Our engineers are working quickly to resolve "
|
||||
"the issue. Thank you for your patience.")
|
||||
data = data.json()
|
||||
|
||||
# Work with errors
|
||||
err_msg = "No data found for this date range, symbol may be delisted"
|
||||
if "chart" in data and data["chart"]["error"]:
|
||||
err_msg = data["chart"]["error"]["description"]
|
||||
shared._DFS[self.ticker] = utils.empty_df()
|
||||
shared._ERRORS[self.ticker] = err_msg
|
||||
if "many" not in kwargs:
|
||||
print('- %s: %s' % (self.ticker, err_msg))
|
||||
return shared._DFS[self.ticker]
|
||||
|
||||
elif "chart" not in data or data["chart"]["result"] is None or \
|
||||
not data["chart"]["result"]:
|
||||
shared._DFS[self.ticker] = utils.empty_df()
|
||||
shared._ERRORS[self.ticker] = err_msg
|
||||
if "many" not in kwargs:
|
||||
print('- %s: %s' % (self.ticker, err_msg))
|
||||
return shared._DFS[self.ticker]
|
||||
|
||||
# parse quotes
|
||||
try:
|
||||
quotes = utils.parse_quotes(data["chart"]["result"][0], tz)
|
||||
except Exception:
|
||||
shared._DFS[self.ticker] = utils.empty_df()
|
||||
shared._ERRORS[self.ticker] = err_msg
|
||||
if "many" not in kwargs:
|
||||
print('- %s: %s' % (self.ticker, err_msg))
|
||||
return shared._DFS[self.ticker]
|
||||
|
||||
# 2) fix weired bug with Yahoo! - returning 60m for 30m bars
|
||||
if interval.lower() == "30m":
|
||||
quotes2 = quotes.resample('30T')
|
||||
quotes = _pd.DataFrame(index=quotes2.last().index, data={
|
||||
'Open': quotes2['Open'].first(),
|
||||
'High': quotes2['High'].max(),
|
||||
'Low': quotes2['Low'].min(),
|
||||
'Close': quotes2['Close'].last(),
|
||||
'Adj Close': quotes2['Adj Close'].last(),
|
||||
'Volume': quotes2['Volume'].sum()
|
||||
})
|
||||
try:
|
||||
quotes['Dividends'] = quotes2['Dividends'].max()
|
||||
except Exception:
|
||||
pass
|
||||
try:
|
||||
quotes['Stock Splits'] = quotes2['Dividends'].max()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if auto_adjust:
|
||||
quotes = utils.auto_adjust(quotes)
|
||||
elif back_adjust:
|
||||
quotes = utils.back_adjust(quotes)
|
||||
|
||||
if rounding:
|
||||
quotes = _np.round(quotes, data[
|
||||
"chart"]["result"][0]["meta"]["priceHint"])
|
||||
quotes['Volume'] = quotes['Volume'].fillna(0).astype(_np.int64)
|
||||
|
||||
quotes.dropna(inplace=True)
|
||||
|
||||
# actions
|
||||
dividends, splits = utils.parse_actions(data["chart"]["result"][0], tz)
|
||||
|
||||
# combine
|
||||
df = _pd.concat([quotes, dividends, splits], axis=1, sort=True)
|
||||
df["Dividends"].fillna(0, inplace=True)
|
||||
df["Stock Splits"].fillna(0, inplace=True)
|
||||
|
||||
# index eod/intraday
|
||||
df.index = df.index.tz_localize("UTC").tz_convert(
|
||||
data["chart"]["result"][0]["meta"]["exchangeTimezoneName"])
|
||||
|
||||
if params["interval"][-1] == "m":
|
||||
df.index.name = "Datetime"
|
||||
else:
|
||||
df.index = _pd.to_datetime(df.index.date)
|
||||
if tz is not None:
|
||||
df.index = df.index.tz_localize(tz)
|
||||
df.index.name = "Date"
|
||||
|
||||
self._history = df.copy()
|
||||
|
||||
if not actions:
|
||||
df.drop(columns=["Dividends", "Stock Splits"], inplace=True)
|
||||
|
||||
return df
|
||||
|
||||
# ------------------------
|
||||
|
||||
def _get_fundamentals(self, kind=None, proxy=None):
|
||||
def cleanup(data):
|
||||
df = _pd.DataFrame(data).drop(columns=['maxAge'])
|
||||
for col in df.columns:
|
||||
df[col] = _np.where(df[col] == '-', _np.nan, df[col])
|
||||
|
||||
df.set_index('endDate', inplace=True)
|
||||
df.index = _pd.to_datetime(df.index, unit='s')
|
||||
df = df.T
|
||||
df.columns.name = ''
|
||||
df.index.name = 'Breakdown'
|
||||
|
||||
df.index = utils.camel2title(df.index)
|
||||
return df
|
||||
|
||||
# setup proxy in requests format
|
||||
if proxy is not None:
|
||||
if isinstance(proxy, dict) and "https" in proxy:
|
||||
proxy = proxy["https"]
|
||||
proxy = {"https": proxy}
|
||||
|
||||
if self._fundamentals:
|
||||
return
|
||||
|
||||
# get info and sustainability
|
||||
url = '%s/%s' % (self._scrape_url, self.ticker)
|
||||
data = utils.get_json(url, proxy)
|
||||
|
||||
# sustainability
|
||||
d = {}
|
||||
for item in data['esgScores']:
|
||||
if not isinstance(data['esgScores'][item], dict):
|
||||
d[item] = data['esgScores'][item]
|
||||
|
||||
s = _pd.DataFrame(d)[-1:].T
|
||||
s.columns = ['Value']
|
||||
s.index.name = '%.f-%.f' % (
|
||||
s[s.index == 'ratingYear']['Value'].values[0],
|
||||
s[s.index == 'ratingMonth']['Value'].values[0])
|
||||
|
||||
self._sustainability = s[~s.index.isin(
|
||||
['maxAge', 'ratingYear', 'ratingMonth'])]
|
||||
|
||||
# info
|
||||
self._info = {
|
||||
**data['summaryProfile'],
|
||||
**data['summaryProfile'],
|
||||
**data['defaultKeyStatistics']
|
||||
}
|
||||
|
||||
# events
|
||||
try:
|
||||
cal = _pd.DataFrame(
|
||||
data['calendarEvents']['earnings'])
|
||||
cal['earningsDate'] = _pd.to_datetime(
|
||||
cal['earningsDate'], unit='s')
|
||||
self._calendar = cal.T
|
||||
self._calendar.index = utils.camel2title(self._calendar.index)
|
||||
self._calendar.columns = ['Value']
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# analyst recommendations
|
||||
try:
|
||||
rec = _pd.DataFrame(
|
||||
data['upgradeDowngradeHistory']['history'])
|
||||
rec['earningsDate'] = _pd.to_datetime(
|
||||
rec['epochGradeDate'], unit='s')
|
||||
rec.set_index('earningsDate', inplace=True)
|
||||
rec.index.name = 'Date'
|
||||
rec.columns = utils.camel2title(rec.columns)
|
||||
self._recommendations = rec[[
|
||||
'Firm', 'To Grade', 'From Grade', 'Action']].sort_index()
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# get fundamentals
|
||||
data = utils.get_json(url+'/financials', proxy)
|
||||
|
||||
# generic patterns
|
||||
for key in (
|
||||
(self._cashflow, 'cashflowStatement', 'cashflowStatements'),
|
||||
(self._balancesheet, 'balanceSheet', 'balanceSheetStatements'),
|
||||
(self._financials, 'incomeStatement', 'incomeStatementHistory')
|
||||
):
|
||||
|
||||
item = key[1] + 'History'
|
||||
if item in data:
|
||||
key[0]['yearly'] = cleanup(data[item][key[2]])
|
||||
|
||||
item = key[1]+'HistoryQuarterly'
|
||||
if item in data:
|
||||
key[0]['quarterly'] = cleanup(data[item][key[2]])
|
||||
|
||||
# earnings
|
||||
earnings = data['earnings']['financialsChart']
|
||||
df = _pd.DataFrame(earnings['yearly']).set_index('date')
|
||||
df.columns = utils.camel2title(df.columns)
|
||||
df.index.name = 'Year'
|
||||
self._earnings['yearly'] = df
|
||||
|
||||
df = _pd.DataFrame(earnings['quarterly']).set_index('date')
|
||||
df.columns = utils.camel2title(df.columns)
|
||||
df.index.name = 'Quarter'
|
||||
self._earnings['quarterly'] = df
|
||||
|
||||
self._fundamentals = True
|
||||
|
||||
def get_recommendations(self, proxy=None, as_dict=False, *args, **kwargs):
|
||||
self._get_fundamentals(proxy)
|
||||
data = self._recommendations
|
||||
if as_dict:
|
||||
return data.to_dict()
|
||||
return data
|
||||
|
||||
def get_calendar(self, proxy=None, as_dict=False, *args, **kwargs):
|
||||
self._get_fundamentals(proxy)
|
||||
data = self._calendar
|
||||
if as_dict:
|
||||
return data.to_dict()
|
||||
return data
|
||||
|
||||
def get_info(self, proxy=None, as_dict=False, *args, **kwargs):
|
||||
self._get_fundamentals(proxy)
|
||||
data = self._info
|
||||
if as_dict:
|
||||
return data.to_dict()
|
||||
return data
|
||||
|
||||
def get_sustainability(self, proxy=None, as_dict=False, *args, **kwargs):
|
||||
self._get_fundamentals(proxy)
|
||||
data = self._sustainability
|
||||
if as_dict:
|
||||
return data.to_dict()
|
||||
return data
|
||||
|
||||
def get_earnings(self, proxy=None, as_dict=False, freq="yearly"):
|
||||
self._get_fundamentals(proxy)
|
||||
data = self._earnings[freq]
|
||||
if as_dict:
|
||||
return data.to_dict()
|
||||
return data
|
||||
|
||||
def get_financials(self, proxy=None, as_dict=False, freq="yearly"):
|
||||
self._get_fundamentals(proxy)
|
||||
data = self._financials[freq]
|
||||
if as_dict:
|
||||
return data.to_dict()
|
||||
return data
|
||||
|
||||
def get_balancesheet(self, proxy=None, as_dict=False, freq="yearly"):
|
||||
self._get_fundamentals(proxy)
|
||||
data = self._balancesheet[freq]
|
||||
if as_dict:
|
||||
return data.to_dict()
|
||||
return data
|
||||
|
||||
def get_balance_sheet(self, proxy=None, as_dict=False, freq="yearly"):
|
||||
return self.get_balancesheet(proxy, as_dict, freq)
|
||||
|
||||
def get_cashflow(self, proxy=None, as_dict=False, freq="yearly"):
|
||||
self._get_fundamentals(proxy)
|
||||
data = self._cashflow[freq]
|
||||
if as_dict:
|
||||
return data.to_dict()
|
||||
return data
|
||||
|
||||
def get_dividends(self, proxy=None):
|
||||
if self._history is None:
|
||||
self.history(period="max", proxy=proxy)
|
||||
dividends = self._history["Dividends"]
|
||||
return dividends[dividends != 0]
|
||||
|
||||
def get_splits(self, proxy=None):
|
||||
if self._history is None:
|
||||
self.history(period="max", proxy=proxy)
|
||||
splits = self._history["Stock Splits"]
|
||||
return splits[splits != 0]
|
||||
|
||||
def get_actions(self, proxy=None):
|
||||
if self._history is None:
|
||||
self.history(period="max", proxy=proxy)
|
||||
actions = self._history[["Dividends", "Stock Splits"]]
|
||||
return actions[actions != 0].dropna(how='all').fillna(0)
|
||||
|
|
@ -0,0 +1,182 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
|
||||
# https://github.com/ranaroussi/yfinance
|
||||
#
|
||||
# Copyright 2017-2019 Ran Aroussi
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import time as _time
|
||||
import multitasking as _multitasking
|
||||
import pandas as _pd
|
||||
|
||||
from . import Ticker, utils
|
||||
from . import shared
|
||||
|
||||
|
||||
def download(tickers, start=None, end=None, actions=False, threads=True,
|
||||
group_by='column', auto_adjust=False, back_adjust=False,
|
||||
progress=True, period="max", interval="1d", prepost=False,
|
||||
proxy=None, rounding=False, **kwargs):
|
||||
"""Download yahoo tickers
|
||||
:Parameters:
|
||||
tickers : str, list
|
||||
List of tickers to download
|
||||
period : str
|
||||
Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
|
||||
Either Use period parameter or use start and end
|
||||
interval : str
|
||||
Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
|
||||
Intraday data cannot extend last 60 days
|
||||
start: str
|
||||
Download start date string (YYYY-MM-DD) or _datetime.
|
||||
Default is 1900-01-01
|
||||
end: str
|
||||
Download end date string (YYYY-MM-DD) or _datetime.
|
||||
Default is now
|
||||
group_by : str
|
||||
Group by 'ticker' or 'column' (default)
|
||||
prepost : bool
|
||||
Include Pre and Post market data in results?
|
||||
Default is False
|
||||
auto_adjust: bool
|
||||
Adjust all OHLC automatically? Default is False
|
||||
actions: bool
|
||||
Download dividend + stock splits data. Default is False
|
||||
threads: bool / int
|
||||
How many threads to use for mass downloading. Default is True
|
||||
proxy: str
|
||||
Optional. Proxy server URL scheme. Default is None
|
||||
rounding: bool
|
||||
Optional. Round values to 2 decimal places?
|
||||
"""
|
||||
|
||||
# create ticker list
|
||||
tickers = tickers if isinstance(
|
||||
tickers, (list, set, tuple)) else tickers.replace(',', ' ').split()
|
||||
|
||||
tickers = list(set([ticker.upper() for ticker in tickers]))
|
||||
|
||||
if progress:
|
||||
shared._PROGRESS_BAR = utils.ProgressBar(len(tickers), 'completed')
|
||||
|
||||
# reset shared._DFS
|
||||
shared._DFS = {}
|
||||
shared._ERRORS = {}
|
||||
|
||||
# download using threads
|
||||
if threads:
|
||||
if threads is True:
|
||||
threads = min([len(tickers), _multitasking.cpu_count() * 2])
|
||||
_multitasking.set_max_threads(threads)
|
||||
for i, ticker in enumerate(tickers):
|
||||
_download_one_threaded(ticker, period=period, interval=interval,
|
||||
start=start, end=end, prepost=prepost,
|
||||
actions=actions, auto_adjust=auto_adjust,
|
||||
back_adjust=back_adjust,
|
||||
progress=(progress and i > 0), proxy=proxy,
|
||||
rounding=rounding)
|
||||
while len(shared._DFS) < len(tickers):
|
||||
_time.sleep(0.01)
|
||||
|
||||
# download synchronously
|
||||
else:
|
||||
for i, ticker in enumerate(tickers):
|
||||
data = _download_one(ticker, period=period, interval=interval,
|
||||
start=start, end=end, prepost=prepost,
|
||||
actions=actions, auto_adjust=auto_adjust,
|
||||
back_adjust=back_adjust, rounding=rounding)
|
||||
shared._DFS[ticker.upper()] = data
|
||||
if progress:
|
||||
shared._PROGRESS_BAR.animate()
|
||||
|
||||
if progress:
|
||||
shared._PROGRESS_BAR.completed()
|
||||
|
||||
if shared._ERRORS:
|
||||
print('\n%.f Failed download%s:' % (
|
||||
len(shared._ERRORS), 's' if len(shared._ERRORS) > 1 else ''))
|
||||
# print(shared._ERRORS)
|
||||
print("\n".join(['- %s: %s' %
|
||||
v for v in list(shared._ERRORS.items())]))
|
||||
|
||||
if len(tickers) == 1:
|
||||
return shared._DFS[tickers[0]]
|
||||
|
||||
try:
|
||||
data = _pd.concat(shared._DFS.values(), axis=1,
|
||||
keys=shared._DFS.keys())
|
||||
except Exception:
|
||||
_realign_dfs()
|
||||
data = _pd.concat(shared._DFS.values(), axis=1,
|
||||
keys=shared._DFS.keys())
|
||||
|
||||
if group_by == 'column':
|
||||
data.columns = data.columns.swaplevel(0, 1)
|
||||
data.sort_index(level=0, axis=1, inplace=True)
|
||||
|
||||
return data
|
||||
|
||||
|
||||
def _realign_dfs():
|
||||
idx_len = 0
|
||||
idx = None
|
||||
|
||||
for df in shared._DFS.values():
|
||||
if len(df) > idx_len:
|
||||
idx_len = len(df)
|
||||
idx = df.index
|
||||
|
||||
for key in shared._DFS.keys():
|
||||
try:
|
||||
shared._DFS[key] = _pd.DataFrame(
|
||||
index=idx, data=shared._DFS[key]).drop_duplicates()
|
||||
except Exception:
|
||||
shared._DFS[key] = _pd.concat([
|
||||
utils.empty_df(idx), shared._DFS[key].dropna()
|
||||
], axis=0, sort=True)
|
||||
|
||||
# remove duplicate index
|
||||
shared._DFS[key] = shared._DFS[key].loc[
|
||||
~shared._DFS[key].index.duplicated(keep='last')]
|
||||
|
||||
|
||||
@_multitasking.task
|
||||
def _download_one_threaded(ticker, start=None, end=None,
|
||||
auto_adjust=False, back_adjust=False,
|
||||
actions=False, progress=True, period="max",
|
||||
interval="1d", prepost=False, proxy=None,
|
||||
rounding=False):
|
||||
|
||||
data = _download_one(ticker, start, end, auto_adjust, back_adjust,
|
||||
actions, period, interval, prepost, proxy, rounding)
|
||||
shared._DFS[ticker.upper()] = data
|
||||
if progress:
|
||||
shared._PROGRESS_BAR.animate()
|
||||
|
||||
|
||||
def _download_one(ticker, start=None, end=None,
|
||||
auto_adjust=False, back_adjust=False,
|
||||
actions=False, period="max", interval="1d",
|
||||
prepost=False, proxy=None, rounding=False):
|
||||
|
||||
return Ticker(ticker).history(period=period, interval=interval,
|
||||
start=start, end=end, prepost=prepost,
|
||||
actions=actions, auto_adjust=auto_adjust,
|
||||
back_adjust=back_adjust, proxy=proxy,
|
||||
rounding=rounding, many=True)
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
|
||||
# https://github.com/ranaroussi/yfinance
|
||||
#
|
||||
# Copyright 2017-2019 Ran Aroussi
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
_DFS = {}
|
||||
_PROGRESS_BAR = None
|
||||
_ERRORS = {}
|
||||
|
|
@ -0,0 +1,180 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
|
||||
# https://github.com/ranaroussi/yfinance
|
||||
#
|
||||
# Copyright 2017-2019 Ran Aroussi
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
# import time as _time
|
||||
import datetime as _datetime
|
||||
import requests as _requests
|
||||
import pandas as _pd
|
||||
# import numpy as _np
|
||||
|
||||
# import json as _json
|
||||
# import re as _re
|
||||
from collections import namedtuple as _namedtuple
|
||||
|
||||
from .base import TickerBase
|
||||
|
||||
|
||||
class Ticker(TickerBase):
|
||||
|
||||
def __repr__(self):
|
||||
return 'yfinance.Ticker object <%s>' % self.ticker
|
||||
|
||||
def _download_options(self, date=None, proxy=None):
|
||||
if date is None:
|
||||
url = "{}/v7/finance/options/{}".format(
|
||||
self._base_url, self.ticker)
|
||||
else:
|
||||
url = "{}/v7/finance/options/{}?date={}".format(
|
||||
self._base_url, self.ticker, date)
|
||||
|
||||
# setup proxy in requests format
|
||||
if proxy is not None:
|
||||
if isinstance(proxy, dict) and "https" in proxy:
|
||||
proxy = proxy["https"]
|
||||
proxy = {"https": proxy}
|
||||
|
||||
r = _requests.get(url=url, proxies=proxy).json()
|
||||
if r['optionChain']['result']:
|
||||
for exp in r['optionChain']['result'][0]['expirationDates']:
|
||||
self._expirations[_datetime.datetime.fromtimestamp(
|
||||
exp).strftime('%Y-%m-%d')] = exp
|
||||
return r['optionChain']['result'][0]['options'][0]
|
||||
return {}
|
||||
|
||||
def _options2df(self, opt, tz=None):
|
||||
data = _pd.DataFrame(opt).reindex(columns=[
|
||||
'contractSymbol',
|
||||
'lastTradeDate',
|
||||
'strike',
|
||||
'lastPrice',
|
||||
'bid',
|
||||
'ask',
|
||||
'change',
|
||||
'percentChange',
|
||||
'volume',
|
||||
'openInterest',
|
||||
'impliedVolatility',
|
||||
'inTheMoney',
|
||||
'contractSize',
|
||||
'currency'])
|
||||
|
||||
data['lastTradeDate'] = _pd.to_datetime(
|
||||
data['lastTradeDate'], unit='s')
|
||||
if tz is not None:
|
||||
data['lastTradeDate'] = data['lastTradeDate'].tz_localize(tz)
|
||||
return data
|
||||
|
||||
def option_chain(self, date=None, proxy=None, tz=None):
|
||||
if date is None:
|
||||
options = self._download_options(proxy=proxy)
|
||||
else:
|
||||
if not self._expirations:
|
||||
self._download_options()
|
||||
if date not in self._expirations:
|
||||
raise ValueError(
|
||||
"Expiration `%s` cannot be found. "
|
||||
"Available expiration are: [%s]" % (
|
||||
date, ', '.join(self._expirations)))
|
||||
date = self._expirations[date]
|
||||
options = self._download_options(date, proxy=proxy)
|
||||
|
||||
return _namedtuple('Options', ['calls', 'puts'])(**{
|
||||
"calls": self._options2df(options['calls'], tz=tz),
|
||||
"puts": self._options2df(options['puts'], tz=tz)
|
||||
})
|
||||
|
||||
# ------------------------
|
||||
|
||||
@property
|
||||
def dividends(self):
|
||||
return self.get_dividends()
|
||||
|
||||
@property
|
||||
def splits(self):
|
||||
return self.get_splits()
|
||||
|
||||
@property
|
||||
def actions(self):
|
||||
return self.get_actions()
|
||||
|
||||
@property
|
||||
def info(self):
|
||||
return self.get_info()
|
||||
|
||||
@property
|
||||
def calendar(self):
|
||||
return self.get_calendar()
|
||||
|
||||
@property
|
||||
def recommendations(self):
|
||||
return self.get_recommendations()
|
||||
|
||||
@property
|
||||
def earnings(self):
|
||||
return self.get_earnings()
|
||||
|
||||
@property
|
||||
def quarterly_earnings(self):
|
||||
return self.get_earnings(freq='quarterly')
|
||||
|
||||
@property
|
||||
def financials(self):
|
||||
return self.get_financials()
|
||||
|
||||
@property
|
||||
def quarterly_financials(self):
|
||||
return self.get_financials(freq='quarterly')
|
||||
|
||||
@property
|
||||
def balance_sheet(self):
|
||||
return self.get_balancesheet()
|
||||
|
||||
@property
|
||||
def quarterly_balance_sheet(self):
|
||||
return self.get_balancesheet(freq='quarterly')
|
||||
|
||||
@property
|
||||
def balancesheet(self):
|
||||
return self.get_balancesheet()
|
||||
|
||||
@property
|
||||
def quarterly_balancesheet(self):
|
||||
return self.get_balancesheet(freq='quarterly')
|
||||
|
||||
@property
|
||||
def cashflow(self):
|
||||
return self.get_cashflow()
|
||||
|
||||
@property
|
||||
def quarterly_cashflow(self):
|
||||
return self.get_cashflow(freq='quarterly')
|
||||
|
||||
@property
|
||||
def sustainability(self):
|
||||
return self.get_sustainability()
|
||||
|
||||
@property
|
||||
def options(self):
|
||||
if not self._expirations:
|
||||
self._download_options()
|
||||
return tuple(self._expirations.keys())
|
||||
|
|
@ -0,0 +1,97 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
|
||||
# https://github.com/ranaroussi/yfinance
|
||||
#
|
||||
# Copyright 2017-2019 Ran Aroussi
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
from . import Ticker, multi
|
||||
from collections import namedtuple as _namedtuple
|
||||
|
||||
|
||||
def genTickers(tickers):
|
||||
tickers = tickers if isinstance(
|
||||
tickers, list) else tickers.replace(',', ' ').split()
|
||||
tickers = [ticker.upper() for ticker in tickers]
|
||||
ticker_objects = {}
|
||||
|
||||
for ticker in tickers:
|
||||
ticker_objects[ticker] = Ticker(ticker)
|
||||
return _namedtuple("Tickers", ticker_objects.keys()
|
||||
)(*ticker_objects.values())
|
||||
|
||||
|
||||
class Tickers():
|
||||
|
||||
def __repr__(self):
|
||||
return 'yfinance.Tickers object <%s>' % ",".join(self.symbols)
|
||||
|
||||
def __init__(self, tickers):
|
||||
tickers = tickers if isinstance(
|
||||
tickers, list) else tickers.replace(',', ' ').split()
|
||||
self.symbols = [ticker.upper() for ticker in tickers]
|
||||
ticker_objects = {}
|
||||
|
||||
for ticker in self.symbols:
|
||||
ticker_objects[ticker] = Ticker(ticker)
|
||||
|
||||
self.tickers = _namedtuple(
|
||||
"Tickers", ticker_objects.keys(), rename=True
|
||||
)(*ticker_objects.values())
|
||||
|
||||
def history(self, period="1mo", interval="1d",
|
||||
start=None, end=None, prepost=False,
|
||||
actions=True, auto_adjust=True, proxy=None,
|
||||
threads=True, group_by='column', progress=True,
|
||||
**kwargs):
|
||||
|
||||
return self.download(
|
||||
period, interval,
|
||||
start, end, prepost,
|
||||
actions, auto_adjust, proxy,
|
||||
threads, group_by, progress,
|
||||
**kwargs)
|
||||
|
||||
def download(self, period="1mo", interval="1d",
|
||||
start=None, end=None, prepost=False,
|
||||
actions=True, auto_adjust=True, proxy=None,
|
||||
threads=True, group_by='column', progress=True,
|
||||
**kwargs):
|
||||
|
||||
data = multi.download(self.symbols,
|
||||
start=start, end=end,
|
||||
actions=actions,
|
||||
auto_adjust=auto_adjust,
|
||||
period=period,
|
||||
interval=interval,
|
||||
prepost=prepost,
|
||||
proxy=proxy,
|
||||
group_by='ticker',
|
||||
threads=threads,
|
||||
progress=progress,
|
||||
**kwargs)
|
||||
|
||||
for symbol in self.symbols:
|
||||
getattr(self.tickers, symbol)._history = data[symbol]
|
||||
|
||||
if group_by == 'column':
|
||||
data.columns = data.columns.swaplevel(0, 1)
|
||||
data.sort_index(level=0, axis=1, inplace=True)
|
||||
|
||||
return data
|
||||
|
|
@ -0,0 +1,210 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Yahoo! Finance market data downloader (+fix for Pandas Datareader)
|
||||
# https://github.com/ranaroussi/yfinance
|
||||
#
|
||||
# Copyright 2017-2019 Ran Aroussi
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import requests as _requests
|
||||
import json as _json
|
||||
import re as _re
|
||||
import pandas as _pd
|
||||
import numpy as _np
|
||||
import sys as _sys
|
||||
|
||||
|
||||
def empty_df(index=[]):
|
||||
empty = _pd.DataFrame(index=index, data={
|
||||
'Open': _np.nan, 'High': _np.nan, 'Low': _np.nan,
|
||||
'Close': _np.nan, 'Adj Close': _np.nan, 'Volume': _np.nan})
|
||||
empty.index.name = 'Date'
|
||||
return empty
|
||||
|
||||
|
||||
def get_json(url, proxy=None):
|
||||
html = _requests.get(url=url, proxies=proxy).text
|
||||
json_str = html.split('root.App.main =')[1].split(
|
||||
'(this)')[0].split(';\n}')[0].strip()
|
||||
data = _json.loads(json_str)[
|
||||
'context']['dispatcher']['stores']['QuoteSummaryStore']
|
||||
|
||||
new_data = ''
|
||||
data_parts = _json.dumps(data).replace('{}', 'null').split('{"raw": ')
|
||||
for x in range(len(data_parts)):
|
||||
if "fmt" in data_parts[x]:
|
||||
p = data_parts[x].split(', "fmt":', maxsplit=1)
|
||||
new_data += p[0] + p[1].split('}', maxsplit=1)[1]
|
||||
else:
|
||||
new_data += data_parts[x]
|
||||
return _json.loads(new_data)
|
||||
|
||||
|
||||
def camel2title(o):
|
||||
return [_re.sub("([a-z])([A-Z])", "\g<1> \g<2>", i).title() for i in o]
|
||||
|
||||
|
||||
def auto_adjust(data):
|
||||
df = data.copy()
|
||||
ratio = df["Close"] / df["Adj Close"]
|
||||
df["Adj Open"] = df["Open"] / ratio
|
||||
df["Adj High"] = df["High"] / ratio
|
||||
df["Adj Low"] = df["Low"] / ratio
|
||||
|
||||
df.drop(
|
||||
["Open", "High", "Low", "Close"],
|
||||
axis=1, inplace=True)
|
||||
|
||||
df.rename(columns={
|
||||
"Adj Open": "Open", "Adj High": "High",
|
||||
"Adj Low": "Low", "Adj Close": "Close"
|
||||
}, inplace=True)
|
||||
|
||||
df = df[["Open", "High", "Low", "Close", "Volume"]]
|
||||
return df[["Open", "High", "Low", "Close", "Volume"]]
|
||||
|
||||
|
||||
def back_adjust(data):
|
||||
""" back-adjusted data to mimic true historical prices """
|
||||
|
||||
df = data.copy()
|
||||
ratio = df["Adj Close"] / df["Close"]
|
||||
df["Adj Open"] = df["Open"] * ratio
|
||||
df["Adj High"] = df["High"] * ratio
|
||||
df["Adj Low"] = df["Low"] * ratio
|
||||
|
||||
df.drop(
|
||||
["Open", "High", "Low", "Adj Close"],
|
||||
axis=1, inplace=True)
|
||||
|
||||
df.rename(columns={
|
||||
"Adj Open": "Open", "Adj High": "High",
|
||||
"Adj Low": "Low"
|
||||
}, inplace=True)
|
||||
|
||||
return df[["Open", "High", "Low", "Close", "Volume"]]
|
||||
|
||||
|
||||
def parse_quotes(data, tz=None):
|
||||
timestamps = data["timestamp"]
|
||||
ohlc = data["indicators"]["quote"][0]
|
||||
volumes = ohlc["volume"]
|
||||
opens = ohlc["open"]
|
||||
closes = ohlc["close"]
|
||||
lows = ohlc["low"]
|
||||
highs = ohlc["high"]
|
||||
|
||||
adjclose = closes
|
||||
if "adjclose" in data["indicators"]:
|
||||
adjclose = data["indicators"]["adjclose"][0]["adjclose"]
|
||||
|
||||
quotes = _pd.DataFrame({"Open": opens,
|
||||
"High": highs,
|
||||
"Low": lows,
|
||||
"Close": closes,
|
||||
"Adj Close": adjclose,
|
||||
"Volume": volumes})
|
||||
|
||||
quotes.index = _pd.to_datetime(timestamps, unit="s")
|
||||
quotes.sort_index(inplace=True)
|
||||
|
||||
if tz is not None:
|
||||
quotes.index = quotes.index.tz_localize(tz)
|
||||
|
||||
return quotes
|
||||
|
||||
|
||||
def parse_actions(data, tz=None):
|
||||
dividends = _pd.DataFrame(columns=["Dividends"])
|
||||
splits = _pd.DataFrame(columns=["Stock Splits"])
|
||||
|
||||
if "events" in data:
|
||||
if "dividends" in data["events"]:
|
||||
dividends = _pd.DataFrame(
|
||||
data=list(data["events"]["dividends"].values()))
|
||||
dividends.set_index("date", inplace=True)
|
||||
dividends.index = _pd.to_datetime(dividends.index, unit="s")
|
||||
dividends.sort_index(inplace=True)
|
||||
if tz is not None:
|
||||
dividends.index = dividends.index.tz_localize(tz)
|
||||
|
||||
dividends.columns = ["Dividends"]
|
||||
|
||||
if "splits" in data["events"]:
|
||||
splits = _pd.DataFrame(
|
||||
data=list(data["events"]["splits"].values()))
|
||||
splits.set_index("date", inplace=True)
|
||||
splits.index = _pd.to_datetime(splits.index, unit="s")
|
||||
splits.sort_index(inplace=True)
|
||||
if tz is not None:
|
||||
splits.index = splits.index.tz_localize(tz)
|
||||
splits["Stock Splits"] = splits["numerator"] / \
|
||||
splits["denominator"]
|
||||
splits = splits["Stock Splits"]
|
||||
|
||||
return dividends, splits
|
||||
|
||||
|
||||
class ProgressBar:
|
||||
def __init__(self, iterations, text='completed'):
|
||||
self.text = text
|
||||
self.iterations = iterations
|
||||
self.prog_bar = '[]'
|
||||
self.fill_char = '*'
|
||||
self.width = 50
|
||||
self.__update_amount(0)
|
||||
self.elapsed = 1
|
||||
|
||||
def completed(self):
|
||||
if self.elapsed > self.iterations:
|
||||
self.elapsed = self.iterations
|
||||
self.update_iteration(1)
|
||||
print('\r' + str(self), end='')
|
||||
_sys.stdout.flush()
|
||||
print()
|
||||
|
||||
def animate(self, iteration=None):
|
||||
if iteration is None:
|
||||
self.elapsed += 1
|
||||
iteration = self.elapsed
|
||||
else:
|
||||
self.elapsed += iteration
|
||||
|
||||
print('\r' + str(self), end='')
|
||||
_sys.stdout.flush()
|
||||
self.update_iteration()
|
||||
|
||||
def update_iteration(self, val=None):
|
||||
val = val if val is not None else self.elapsed / float(self.iterations)
|
||||
self.__update_amount(val * 100.0)
|
||||
self.prog_bar += ' %s of %s %s' % (
|
||||
self.elapsed, self.iterations, self.text)
|
||||
|
||||
def __update_amount(self, new_amount):
|
||||
percent_done = int(round((new_amount / 100.0) * 100.0))
|
||||
all_full = self.width - 2
|
||||
num_hashes = int(round((percent_done / 100.0) * all_full))
|
||||
self.prog_bar = '[' + self.fill_char * \
|
||||
num_hashes + ' ' * (all_full - num_hashes) + ']'
|
||||
pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))
|
||||
pct_string = '%d%%' % percent_done
|
||||
self.prog_bar = self.prog_bar[0:pct_place] + \
|
||||
(pct_string + self.prog_bar[pct_place + len(pct_string):])
|
||||
|
||||
def __str__(self):
|
||||
return str(self.prog_bar)
|
||||
Loading…
Reference in New Issue