# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# 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 AlgorithmImports import *

### <summary>
### This algorithm demonstrates the various ways to handle History pandas DataFrame
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="history and warm up" />
### <meta name="tag" content="history" />
### <meta name="tag" content="warm up" />
class PandasDataFrameHistoryAlgorithm(QCAlgorithm):

 def initialize(self):
 self.set_start_date(2014, 6, 9) # Set Start Date
 self.set_end_date(2014, 6, 9) # Set End Date

 self.spy = self.add_equity("SPY", Resolution.DAILY).symbol
 self.eur = self.add_forex("EURUSD", Resolution.DAILY).symbol

 aapl = self.add_equity("AAPL", Resolution.MINUTE).symbol
 self.option = Symbol.create_option(aapl, Market.USA, OptionStyle.AMERICAN, OptionRight.CALL, 750, datetime(2014, 10, 18))
 self.add_option_contract(self.option)

 sp1 = self.add_data(QuandlFuture,"CHRIS/CME_SP1", Resolution.DAILY)
 sp1.exchange = EquityExchange()
 self.sp1 = sp1.symbol

 self.add_universe(self.coarse_selection)

 def coarse_selection(self, coarse):
 if self.portfolio.invested:
 return Universe.UNCHANGED

 selected = [x.symbol for x in coarse if x.symbol.value in ["AAA", "AIG", "BAC"]]
 if len(selected) == 0:
 return Universe.UNCHANGED

 universe_history = self.history(selected, 10, Resolution.DAILY)
 for symbol in selected:
 self.assert_history_index(universe_history, "close", 10, "", symbol)

 return selected


 def on_data(self, data):
 if self.portfolio.invested:
 return

 # we can get history in initialize to set up indicators and such
 self.spy_daily_sma = SimpleMovingAverage(14)

 # get the last calendar year's worth of SPY data at the configured resolution (daily)
 trade_bar_history = self.history(["SPY"], timedelta(365))
 self.assert_history_index(trade_bar_history, "close", 251, "SPY", self.spy)

 # get the last calendar year's worth of EURUSD data at the configured resolution (daily)
 quote_bar_history = self.history(["EURUSD"], timedelta(298))
 self.assert_history_index(quote_bar_history, "bidclose", 251, "EURUSD", self.eur)

 option_history = self.history([self.option], timedelta(3))
 option_history.index = option_history.index.droplevel(level=[0,1,2])
 self.assert_history_index(option_history, "bidclose", 390, "", self.option)

 # get the last calendar year's worth of quandl data at the configured resolution (daily)
 quandl_history = self.history(QuandlFuture, "CHRIS/CME_SP1", timedelta(365))
 self.assert_history_index(quandl_history, "settle", 251, "CHRIS/CME_SP1", self.sp1)

 # we can loop over the return value from these functions and we get TradeBars
 # we can use these TradeBars to initialize indicators or perform other math
 self.spy_daily_sma.reset()
 for index, trade_bar in trade_bar_history.loc["SPY"].iterrows():
 self.spy_daily_sma.update(index, trade_bar["close"])

 # we can loop over the return values from these functions and we'll get Quandl data
 # this can be used in much the same way as the trade_bar_history above
 self.spy_daily_sma.reset()
 for index, quandl in quandl_history.loc["CHRIS/CME_SP1"].iterrows():
 self.spy_daily_sma.update(index, quandl["settle"])

 self.set_holdings(self.eur, 1)

 def assert_history_index(self, df, column, expected, ticker, symbol):

 if df.empty:
 raise AssertionError(f"Empty history data frame for {symbol}")
 if column not in df:
 raise AssertionError(f"Could not unstack df. Columns: {', '.join(df.columns)} | {column}")

 value = df.iat[0,0]
 df2 = df.xs(df.index.get_level_values('time')[0], level='time')
 df3 = df[column].unstack(level=0)

 try:

 # str(Symbol.ID)
 self.assert_history_count(f"df.iloc[0]", df.iloc[0], len(df.columns))
 self.assert_history_count(f"df.loc[str({symbol.id})]", df.loc[str(symbol.id)], expected)
 self.assert_history_count(f"df.xs(str({symbol.id}))", df.xs(str(symbol.id)), expected)
 self.assert_history_count(f"df.at[(str({symbol.id}),), '{column}']", list(df.at[(str(symbol.id),), column]), expected)
 self.assert_history_count(f"df2.loc[str({symbol.id})]", df2.loc[str(symbol.id)], len(df2.columns))
 self.assert_history_count(f"df3[str({symbol.id})]", df3[str(symbol.id)], expected)
 self.assert_history_count(f"df3.get(str({symbol.id}))", df3.get(str(symbol.id)), expected)

 # str(Symbol)
 self.assert_history_count(f"df.loc[str({symbol})]", df.loc[str(symbol)], expected)
 self.assert_history_count(f"df.xs(str({symbol}))", df.xs(str(symbol)), expected)
 self.assert_history_count(f"df.at[(str({symbol}),), '{column}']", list(df.at[(str(symbol),), column]), expected)
 self.assert_history_count(f"df2.loc[str({symbol})]", df2.loc[str(symbol)], len(df2.columns))
 self.assert_history_count(f"df3[str({symbol})]", df3[str(symbol)], expected)
 self.assert_history_count(f"df3.get(str({symbol}))", df3.get(str(symbol)), expected)

 # str : Symbol.VALUE
 if len(ticker) == 0:
 return
 self.assert_history_count(f"df.loc[{ticker}]", df.loc[ticker], expected)
 self.assert_history_count(f"df.xs({ticker})", df.xs(ticker), expected)
 self.assert_history_count(f"df.at[(ticker,), '{column}']", list(df.at[(ticker,), column]), expected)
 self.assert_history_count(f"df2.loc[{ticker}]", df2.loc[ticker], len(df2.columns))
 self.assert_history_count(f"df3[{ticker}]", df3[ticker], expected)
 self.assert_history_count(f"df3.get({ticker})", df3.get(ticker), expected)

 except Exception as e:
 symbols = set(df.index.get_level_values(level='symbol'))
 raise AssertionError(f"{symbols}, {symbol.id}, {symbol}, {ticker}. {e}")


 def assert_history_count(self, method_call, trade_bar_history, expected):
 if isinstance(trade_bar_history, list):
 count = len(trade_bar_history)
 else:
 count = len(trade_bar_history.index)
 if count != expected:
 raise AssertionError(f"{method_call} expected {expected}, but received {count}")


class QuandlFuture(PythonQuandl):
 '''Custom quandl data type for setting customized value column name. Value column is used for the primary trading calculations and charting.'''
 def __init__(self):
 self.value_column_name = "Settle"