wehub-resource-sync
152 lines
7.3 KiB
Python
152 lines
7.3 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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### <summary>
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### This algorithm demonstrates the various ways to handle History pandas DataFrame
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="history and warm up" />
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### <meta name="tag" content="history" />
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### <meta name="tag" content="warm up" />
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class PandasDataFrameHistoryAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2014, 6, 9) # Set Start Date
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self.set_end_date(2014, 6, 9) # Set End Date
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self.spy = self.add_equity("SPY", Resolution.DAILY).symbol
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self.eur = self.add_forex("EURUSD", Resolution.DAILY).symbol
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aapl = self.add_equity("AAPL", Resolution.MINUTE).symbol
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self.option = Symbol.create_option(aapl, Market.USA, OptionStyle.AMERICAN, OptionRight.CALL, 750, datetime(2014, 10, 18))
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self.add_option_contract(self.option)
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sp1 = self.add_data(QuandlFuture,"CHRIS/CME_SP1", Resolution.DAILY)
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sp1.exchange = EquityExchange()
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self.sp1 = sp1.symbol
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self.add_universe(self.coarse_selection)
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def coarse_selection(self, coarse):
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if self.portfolio.invested:
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return Universe.UNCHANGED
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selected = [x.symbol for x in coarse if x.symbol.value in ["AAA", "AIG", "BAC"]]
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if len(selected) == 0:
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return Universe.UNCHANGED
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universe_history = self.history(selected, 10, Resolution.DAILY)
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for symbol in selected:
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self.assert_history_index(universe_history, "close", 10, "", symbol)
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return selected
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def on_data(self, data):
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if self.portfolio.invested:
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return
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# we can get history in initialize to set up indicators and such
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self.spy_daily_sma = SimpleMovingAverage(14)
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# get the last calendar year's worth of SPY data at the configured resolution (daily)
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trade_bar_history = self.history(["SPY"], timedelta(365))
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self.assert_history_index(trade_bar_history, "close", 251, "SPY", self.spy)
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# get the last calendar year's worth of EURUSD data at the configured resolution (daily)
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quote_bar_history = self.history(["EURUSD"], timedelta(298))
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self.assert_history_index(quote_bar_history, "bidclose", 251, "EURUSD", self.eur)
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option_history = self.history([self.option], timedelta(3))
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option_history.index = option_history.index.droplevel(level=[0,1,2])
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self.assert_history_index(option_history, "bidclose", 390, "", self.option)
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# get the last calendar year's worth of quandl data at the configured resolution (daily)
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quandl_history = self.history(QuandlFuture, "CHRIS/CME_SP1", timedelta(365))
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self.assert_history_index(quandl_history, "settle", 251, "CHRIS/CME_SP1", self.sp1)
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# we can loop over the return value from these functions and we get TradeBars
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# we can use these TradeBars to initialize indicators or perform other math
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self.spy_daily_sma.reset()
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for index, trade_bar in trade_bar_history.loc["SPY"].iterrows():
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self.spy_daily_sma.update(index, trade_bar["close"])
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# we can loop over the return values from these functions and we'll get Quandl data
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# this can be used in much the same way as the trade_bar_history above
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self.spy_daily_sma.reset()
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for index, quandl in quandl_history.loc["CHRIS/CME_SP1"].iterrows():
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self.spy_daily_sma.update(index, quandl["settle"])
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self.set_holdings(self.eur, 1)
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def assert_history_index(self, df, column, expected, ticker, symbol):
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if df.empty:
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raise AssertionError(f"Empty history data frame for {symbol}")
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if column not in df:
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raise AssertionError(f"Could not unstack df. Columns: {', '.join(df.columns)} | {column}")
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value = df.iat[0,0]
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df2 = df.xs(df.index.get_level_values('time')[0], level='time')
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df3 = df[column].unstack(level=0)
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try:
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# str(Symbol.ID)
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self.assert_history_count(f"df.iloc[0]", df.iloc[0], len(df.columns))
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self.assert_history_count(f"df.loc[str({symbol.id})]", df.loc[str(symbol.id)], expected)
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self.assert_history_count(f"df.xs(str({symbol.id}))", df.xs(str(symbol.id)), expected)
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self.assert_history_count(f"df.at[(str({symbol.id}),), '{column}']", list(df.at[(str(symbol.id),), column]), expected)
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self.assert_history_count(f"df2.loc[str({symbol.id})]", df2.loc[str(symbol.id)], len(df2.columns))
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self.assert_history_count(f"df3[str({symbol.id})]", df3[str(symbol.id)], expected)
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self.assert_history_count(f"df3.get(str({symbol.id}))", df3.get(str(symbol.id)), expected)
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# str(Symbol)
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self.assert_history_count(f"df.loc[str({symbol})]", df.loc[str(symbol)], expected)
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self.assert_history_count(f"df.xs(str({symbol}))", df.xs(str(symbol)), expected)
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self.assert_history_count(f"df.at[(str({symbol}),), '{column}']", list(df.at[(str(symbol),), column]), expected)
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self.assert_history_count(f"df2.loc[str({symbol})]", df2.loc[str(symbol)], len(df2.columns))
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self.assert_history_count(f"df3[str({symbol})]", df3[str(symbol)], expected)
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self.assert_history_count(f"df3.get(str({symbol}))", df3.get(str(symbol)), expected)
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# str : Symbol.VALUE
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if len(ticker) == 0:
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return
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self.assert_history_count(f"df.loc[{ticker}]", df.loc[ticker], expected)
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self.assert_history_count(f"df.xs({ticker})", df.xs(ticker), expected)
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self.assert_history_count(f"df.at[(ticker,), '{column}']", list(df.at[(ticker,), column]), expected)
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self.assert_history_count(f"df2.loc[{ticker}]", df2.loc[ticker], len(df2.columns))
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self.assert_history_count(f"df3[{ticker}]", df3[ticker], expected)
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self.assert_history_count(f"df3.get({ticker})", df3.get(ticker), expected)
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except Exception as e:
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symbols = set(df.index.get_level_values(level='symbol'))
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raise AssertionError(f"{symbols}, {symbol.id}, {symbol}, {ticker}. {e}")
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def assert_history_count(self, method_call, trade_bar_history, expected):
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if isinstance(trade_bar_history, list):
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count = len(trade_bar_history)
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else:
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count = len(trade_bar_history.index)
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if count != expected:
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raise AssertionError(f"{method_call} expected {expected}, but received {count}")
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class QuandlFuture(PythonQuandl):
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'''Custom quandl data type for setting customized value column name. Value column is used for the primary trading calculations and charting.'''
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def __init__(self):
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self.value_column_name = "Settle"
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