# 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 *
###
### This algorithm demonstrates the various ways to handle History pandas DataFrame
###
###
###
###
###
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"