# 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.SetStartDate(2014, 6, 9) # Set Start Date
self.SetEndDate(2014, 6, 9) # Set End Date
self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol
self.eur = self.AddForex("EURUSD", Resolution.Daily).Symbol
aapl = self.AddEquity("AAPL", Resolution.Minute).Symbol
self.option = Symbol.CreateOption(aapl, Market.USA, OptionStyle.American, OptionRight.Call, 750, datetime(2014, 10, 18))
self.AddOptionContract(self.option)
sp1 = self.AddData(QuandlFuture,"CHRIS/CME_SP1", Resolution.Daily)
sp1.Exchange = EquityExchange()
self.sp1 = sp1.Symbol
self.AddUniverse(self.CoarseSelection)
def CoarseSelection(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
universeHistory = self.History(selected, 10, Resolution.Daily)
for symbol in selected:
self.AssertHistoryIndex(universeHistory, "close", 10, "", symbol)
return selected
def OnData(self, data):
if self.Portfolio.Invested:
return
# we can get history in initialize to set up indicators and such
self.spyDailySma = SimpleMovingAverage(14)
# get the last calendar year's worth of SPY data at the configured resolution (daily)
tradeBarHistory = self.History(["SPY"], timedelta(365))
self.AssertHistoryIndex(tradeBarHistory, "close", 251, "SPY", self.spy)
# get the last calendar year's worth of EURUSD data at the configured resolution (daily)
quoteBarHistory = self.History(["EURUSD"], timedelta(298))
self.AssertHistoryIndex(quoteBarHistory, "bidclose", 251, "EURUSD", self.eur)
optionHistory = self.History([self.option], timedelta(3))
optionHistory.index = optionHistory.index.droplevel(level=[0,1,2])
self.AssertHistoryIndex(optionHistory, "bidclose", 390, "", self.option)
# get the last calendar year's worth of quandl data at the configured resolution (daily)
quandlHistory = self.History(QuandlFuture, "CHRIS/CME_SP1", timedelta(365))
self.AssertHistoryIndex(quandlHistory, "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.spyDailySma.Reset()
for index, tradeBar in tradeBarHistory.loc["SPY"].iterrows():
self.spyDailySma.Update(index, tradeBar["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 tradeBarHistory above
self.spyDailySma.Reset()
for index, quandl in quandlHistory.loc["CHRIS/CME_SP1"].iterrows():
self.spyDailySma.Update(index, quandl["settle"])
self.SetHoldings(self.eur, 1)
def AssertHistoryIndex(self, df, column, expected, ticker, symbol):
if df.empty:
raise Exception(f"Empty history data frame for {symbol}")
if column not in df:
raise Exception(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.AssertHistoryCount(f"df.iloc[0]", df.iloc[0], len(df.columns))
self.AssertHistoryCount(f"df.loc[str({symbol.ID})]", df.loc[str(symbol.ID)], expected)
self.AssertHistoryCount(f"df.xs(str({symbol.ID}))", df.xs(str(symbol.ID)), expected)
self.AssertHistoryCount(f"df.at[(str({symbol.ID}),), '{column}']", list(df.at[(str(symbol.ID),), column]), expected)
self.AssertHistoryCount(f"df2.loc[str({symbol.ID})]", df2.loc[str(symbol.ID)], len(df2.columns))
self.AssertHistoryCount(f"df3[str({symbol.ID})]", df3[str(symbol.ID)], expected)
self.AssertHistoryCount(f"df3.get(str({symbol.ID}))", df3.get(str(symbol.ID)), expected)
# str(Symbol)
self.AssertHistoryCount(f"df.loc[str({symbol})]", df.loc[str(symbol)], expected)
self.AssertHistoryCount(f"df.xs(str({symbol}))", df.xs(str(symbol)), expected)
self.AssertHistoryCount(f"df.at[(str({symbol}),), '{column}']", list(df.at[(str(symbol),), column]), expected)
self.AssertHistoryCount(f"df2.loc[str({symbol})]", df2.loc[str(symbol)], len(df2.columns))
self.AssertHistoryCount(f"df3[str({symbol})]", df3[str(symbol)], expected)
self.AssertHistoryCount(f"df3.get(str({symbol}))", df3.get(str(symbol)), expected)
# str : Symbol.Value
if len(ticker) == 0:
return
self.AssertHistoryCount(f"df.loc[{ticker}]", df.loc[ticker], expected)
self.AssertHistoryCount(f"df.xs({ticker})", df.xs(ticker), expected)
self.AssertHistoryCount(f"df.at[(ticker,), '{column}']", list(df.at[(ticker,), column]), expected)
self.AssertHistoryCount(f"df2.loc[{ticker}]", df2.loc[ticker], len(df2.columns))
self.AssertHistoryCount(f"df3[{ticker}]", df3[ticker], expected)
self.AssertHistoryCount(f"df3.get({ticker})", df3.get(ticker), expected)
except Exception as e:
symbols = set(df.index.get_level_values(level='symbol'))
raise Exception(f"{symbols}, {symbol.ID}, {symbol}, {ticker}. {e}")
def AssertHistoryCount(self, methodCall, tradeBarHistory, expected):
if isinstance(tradeBarHistory, list):
count = len(tradeBarHistory)
else:
count = len(tradeBarHistory.index)
if count != expected:
raise Exception(f"{methodCall} 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.ValueColumnName = "Settle"