chore: import upstream snapshot with attribution
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wehub-resource-sync
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# 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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### Strategy example algorithm using CAPE - a bubble indicator dataset saved in dropbox. CAPE is based on a macroeconomic indicator(CAPE Ratio),
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### we are looking for entry/exit points for momentum stocks CAPE data: January 1990 - December 2014
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### Goals:
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### Capitalize in overvalued markets by generating returns with momentum and selling before the crash
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### Capitalize in undervalued markets by purchasing stocks at bottom of trough
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### </summary>
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### <meta name="tag" content="strategy example" />
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### <meta name="tag" content="custom data" />
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class BubbleAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_cash(100000)
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self.set_start_date(1998,1,1)
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self.set_end_date(2014,6,1)
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self._symbols = []
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self._macd_dic, self._rsi_dic = {},{}
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self._new_low, self._curr_cape = None, None
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self._counter, self._counter2 = 0, 0
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self._c, self._c_copy = np.empty([4]), np.empty([4])
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self._symbols.append("SPY")
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# add CAPE data
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self.add_data(Cape, "CAPE")
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# # Present Social Media Stocks:
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# self._symbols.append("FB"), self._symbols.append("LNKD"),self._symbols.append("GRPN"), self._symbols.append("TWTR")
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# self.set_start_date(2011, 1, 1)
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# self.set_end_date(2014, 12, 1)
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# # 2008 Financials
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# self._symbols.append("C"), self._symbols.append("AIG"), self._symbols.append("BAC"), self._symbols.append("HBOS")
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# self.set_start_date(2003, 1, 1)
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# self.set_end_date(2011, 1, 1)
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# # 2000 Dot.com
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# self._symbols.append("IPET"), self._symbols.append("WBVN"), self._symbols.append("GCTY")
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# self.set_start_date(1998, 1, 1)
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# self.set_end_date(2000, 1, 1)
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for stock in self._symbols:
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self.add_security(SecurityType.EQUITY, stock, Resolution.MINUTE)
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self._macd = self.macd(stock, 12, 26, 9, MovingAverageType.EXPONENTIAL, Resolution.DAILY)
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self._macd_dic[stock] = self._macd
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self._rsi = self.rsi(stock, 14, MovingAverageType.EXPONENTIAL, Resolution.DAILY)
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self._rsi_dic[stock] = self._rsi
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# Trying to find if current Cape is the lowest Cape in three months to indicate selling period
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def on_data(self, data):
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if self._curr_cape and self._new_low is not None:
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try:
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# Bubble territory
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if self._curr_cape > 20 and self._new_low == False:
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for stock in self._symbols:
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# Order stock based on MACD
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# During market hours, stock is trading, and sufficient cash
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if self.securities[stock].holdings.quantity == 0 and self._rsi_dic[stock].current.value < 70 \
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and self.securities[stock].price != 0 \
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and self.portfolio.cash > self.securities[stock].price * 100 \
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and self.time.hour == 9 and self.time.minute == 31:
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self.buy_stock(stock)
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# Utilize RSI for overbought territories and liquidate that stock
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if self._rsi_dic[stock].current.value > 70 and self.securities[stock].holdings.quantity > 0 \
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and self.time.hour == 9 and self.time.minute == 31:
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self.sell_stock(stock)
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# Undervalued territory
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elif self._new_low:
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for stock in self._symbols:
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# Sell stock based on MACD
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if self.securities[stock].holdings.quantity > 0 and self._rsi_dic[stock].current.value > 30 \
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and self.time.hour == 9 and self.time.minute == 31:
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self.sell_stock(stock)
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# Utilize RSI and MACD to understand oversold territories
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elif self.securities[stock].holdings.quantity == 0 and self._rsi_dic[stock].current.value < 30 \
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and self.securities[stock].price != 0 and self.portfolio.cash > self.securities[stock].price * 100 \
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and self.time.hour == 9 and self.time.minute == 31:
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self.buy_stock(stock)
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# Cape Ratio is missing from original data
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# Most recent cape data is most likely to be missing
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elif self._curr_cape == 0:
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self.debug("Exiting due to no CAPE!")
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self.quit("CAPE ratio not supplied in data, exiting.")
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except:
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# Do nothing
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return None
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if not data.contains_key("CAPE"): return
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self._new_low = False
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# Adds first four Cape Ratios to array c
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self._curr_cape = data["CAPE"].cape
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if self._counter < 4:
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self._c[self._counter] = self._curr_cape
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self._counter +=1
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# Replaces oldest Cape with current Cape
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# Checks to see if current Cape is lowest in the previous quarter
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# Indicating a sell off
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else:
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self._c_copy = self._c
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self._c_copy = np.sort(self._c_copy)
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if self._c_copy[0] > self._curr_cape:
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self._new_low = True
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self._c[self._counter2] = self._curr_cape
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self._counter2 += 1
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if self._counter2 == 4: self._counter2 = 0
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self.debug("Current Cape: " + str(self._curr_cape) + " on " + str(self.time))
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if self._new_low:
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self.debug("New Low has been hit on " + str(self.time))
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# Buy this symbol
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def buy_stock(self,symbol):
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s = self.securities[symbol].holdings
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if self._macd_dic[symbol].current.value>0:
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self.set_holdings(symbol, 1)
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self.debug("Purchasing: " + str(symbol) + " MACD: " + str(self._macd_dic[symbol]) + " RSI: " + str(self._rsi_dic[symbol])
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+ " Price: " + str(round(self.securities[symbol].price, 2)) + " Quantity: " + str(s.quantity))
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# Sell this symbol
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def sell_stock(self,symbol):
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s = self.securities[symbol].holdings
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if s.quantity > 0 and self._macd_dic[symbol].current.value < 0:
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self.liquidate(symbol)
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self.debug("Selling: " + str(symbol) + " at sell MACD: " + str(self._macd_dic[symbol]) + " RSI: " + str(self._rsi_dic[symbol])
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+ " Price: " + str(round(self.securities[symbol].price, 2)) + " Profit from sale: " + str(s.last_trade_profit))
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# CAPE Ratio for SP500 PE Ratio for avg inflation adjusted earnings for previous ten years Custom Data from DropBox
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# Original Data from: http://www.econ.yale.edu/~shiller/data.htm
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class Cape(PythonData):
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# Return the URL string source of the file. This will be converted to a stream
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# <param name="config">Configuration object</param>
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# <param name="date">Date of this source file</param>
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# <param name="is_live_mode">true if we're in live mode, false for backtesting mode</param>
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# <returns>String URL of source file.</returns>
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def get_source(self, config, date, is_live_mode):
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# Remember to add the "?dl=1" for dropbox links
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return SubscriptionDataSource("https://www.dropbox.com/s/ggt6blmib54q36e/CAPE.csv?dl=1", SubscriptionTransportMedium.REMOTE_FILE)
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''' Reader Method : using set of arguments we specify read out type. Enumerate until
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the end of the data stream or file. E.g. Read CSV file line by line and convert into data types. '''
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# <returns>BaseData type set by Subscription Method.</returns>
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# <param name="config">Config.</param>
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# <param name="line">Line.</param>
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# <param name="date">Date.</param>
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# <param name="is_live_mode">true if we're in live mode, false for backtesting mode</param>
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def reader(self, config, line, date, is_live_mode):
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if not (line.strip() and line[0].isdigit()): return None
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# New Nifty object
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index = Cape()
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index.symbol = config.symbol
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try:
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# Example File Format:
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# Date | Price | Div | Earning | CPI | FractionalDate | Interest Rate | RealPrice | RealDiv | RealEarnings | CAPE
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# 2014.06 1947.09 37.38 103.12 238.343 2014.37 2.6 1923.95 36.94 101.89 25.55
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data = line.split(',')
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# Dates must be in the format YYYY-MM-DD. If your data source does not have this format, you must use
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# DateTime.parse_exact() and explicit declare the format your data source has.
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index.time = datetime.strptime(data[0], "%Y-%m")
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index["Cape"] = float(data[10])
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index.value = data[10]
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except ValueError:
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# Do nothing
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return None
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return index
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