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GeezerVison is showing github.com — Lean/Algorithm.Python/BubbleAlgorithm.py at master · QuantConnect/Lean Expand file tree/ Copy pathBubbleAlgorithm.pyMore file actions 193 lines (163 loc) · 9.49 KB / Copy pathBubbleAlgorithm.pyFile metadata and controls193 lines (163 loc) · 9.49 KB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 # 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> ### Strategy example algorithm using CAPE - a bubble indicator dataset saved in dropbox. CAPE is based on a macroeconomic indicator(CAPE Ratio), ### we are looking for entry/exit points for momentum stocks CAPE data: January 1990 - December 2014 ### Goals: ### Capitalize in overvalued markets by generating returns with momentum and selling before the crash ### Capitalize in undervalued markets by purchasing stocks at bottom of trough ### </summary> ### <meta name="tag" content="strategy example" /> ### <meta name="tag" content="custom data" /> class BubbleAlgorithm(QCAlgorithm): def initialize(self): self.set_cash(100000) self.set_start_date(1998,1,1) self.set_end_date(2014,6,1) self._symbols = [] self._macd_dic, self._rsi_dic = {},{} self._new_low, self._curr_cape = None, None self._counter, self._counter2 = 0, 0 self._c, self._c_copy = np.empty([4]), np.empty([4]) self._symbols.append("SPY") # add CAPE data self.add_data(Cape, "CAPE") # # Present Social Media Stocks: # self._symbols.append("FB"), self._symbols.append("LNKD"),self._symbols.append("GRPN"), self._symbols.append("TWTR") # self.set_start_date(2011, 1, 1) # self.set_end_date(2014, 12, 1) # # 2008 Financials # self._symbols.append("C"), self._symbols.append("AIG"), self._symbols.append("BAC"), self._symbols.append("HBOS") # self.set_start_date(2003, 1, 1) # self.set_end_date(2011, 1, 1) # # 2000 Dot.com # self._symbols.append("IPET"), self._symbols.append("WBVN"), self._symbols.append("GCTY") # self.set_start_date(1998, 1, 1) # self.set_end_date(2000, 1, 1) for stock in self._symbols: self.add_security(SecurityType.EQUITY, stock, Resolution.MINUTE) self._macd = self.macd(stock, 12, 26, 9, MovingAverageType.EXPONENTIAL, Resolution.DAILY) self._macd_dic[stock] = self._macd self._rsi = self.rsi(stock, 14, MovingAverageType.EXPONENTIAL, Resolution.DAILY) self._rsi_dic[stock] = self._rsi # Trying to find if current Cape is the lowest Cape in three months to indicate selling period def on_data(self, data): if self._curr_cape and self._new_low is not None: try: # Bubble territory if self._curr_cape > 20 and self._new_low == False: for stock in self._symbols: # Order stock based on MACD # During market hours, stock is trading, and sufficient cash if self.securities[stock].holdings.quantity == 0 and self._rsi_dic[stock].current.value < 70 \ and self.securities[stock].price != 0 \ and self.portfolio.cash > self.securities[stock].price * 100 \ and self.time.hour == 9 and self.time.minute == 31: self.buy_stock(stock) # Utilize RSI for overbought territories and liquidate that stock if self._rsi_dic[stock].current.value > 70 and self.securities[stock].holdings.quantity > 0 \ and self.time.hour == 9 and self.time.minute == 31: self.sell_stock(stock) # Undervalued territory elif self._new_low: for stock in self._symbols: # Sell stock based on MACD if self.securities[stock].holdings.quantity > 0 and self._rsi_dic[stock].current.value > 30 \ and self.time.hour == 9 and self.time.minute == 31: self.sell_stock(stock) # Utilize RSI and MACD to understand oversold territories elif self.securities[stock].holdings.quantity == 0 and self._rsi_dic[stock].current.value < 30 \ and self.securities[stock].price != 0 and self.portfolio.cash > self.securities[stock].price * 100 \ and self.time.hour == 9 and self.time.minute == 31: self.buy_stock(stock) # Cape Ratio is missing from original data # Most recent cape data is most likely to be missing elif self._curr_cape == 0: self.debug("Exiting due to no CAPE!") self.quit("CAPE ratio not supplied in data, exiting.") except: # Do nothing return None if not data.contains_key("CAPE"): return self._new_low = False # Adds first four Cape Ratios to array c self._curr_cape = data["CAPE"].cape if self._counter < 4: self._c[self._counter] = self._curr_cape self._counter +=1 # Replaces oldest Cape with current Cape # Checks to see if current Cape is lowest in the previous quarter # Indicating a sell off else: self._c_copy = self._c self._c_copy = np.sort(self._c_copy) if self._c_copy[0] > self._curr_cape: self._new_low = True self._c[self._counter2] = self._curr_cape self._counter2 += 1 if self._counter2 == 4: self._counter2 = 0 self.debug("Current Cape: " + str(self._curr_cape) + " on " + str(self.time)) if self._new_low: self.debug("New Low has been hit on " + str(self.time)) # Buy this symbol def buy_stock(self,symbol): s = self.securities[symbol].holdings if self._macd_dic[symbol].current.value>0: self.set_holdings(symbol, 1) self.debug("Purchasing: " + str(symbol) + " MACD: " + str(self._macd_dic[symbol]) + " RSI: " + str(self._rsi_dic[symbol]) + " Price: " + str(round(self.securities[symbol].price, 2)) + " Quantity: " + str(s.quantity)) # Sell this symbol def sell_stock(self,symbol): s = self.securities[symbol].holdings if s.quantity > 0 and self._macd_dic[symbol].current.value < 0: self.liquidate(symbol) self.debug("Selling: " + str(symbol) + " at sell MACD: " + str(self._macd_dic[symbol]) + " RSI: " + str(self._rsi_dic[symbol]) + " Price: " + str(round(self.securities[symbol].price, 2)) + " Profit from sale: " + str(s.last_trade_profit)) # CAPE Ratio for SP500 PE Ratio for avg inflation adjusted earnings for previous ten years Custom Data from DropBox # Original Data from: http://www.econ.yale.edu/~shiller/data.htm class Cape(PythonData): # Return the URL string source of the file. This will be converted to a stream # <param name="config">Configuration object</param> # <param name="date">Date of this source file</param> # <param name="is_live_mode">true if we're in live mode, false for backtesting mode</param> # <returns>String URL of source file.</returns> def get_source(self, config, date, is_live_mode): # Remember to add the "?dl=1" for dropbox links return SubscriptionDataSource("https://www.dropbox.com/s/ggt6blmib54q36e/CAPE.csv?dl=1", SubscriptionTransportMedium.REMOTE_FILE) ''' Reader Method : using set of arguments we specify read out type. Enumerate until the end of the data stream or file. E.g. Read CSV file line by line and convert into data types. ''' # <returns>BaseData type set by Subscription Method.</returns> # <param name="config">Config.</param> # <param name="line">Line.</param> # <param name="date">Date.</param> # <param name="is_live_mode">true if we're in live mode, false for backtesting mode</param> def reader(self, config, line, date, is_live_mode): if not (line.strip() and line[0].isdigit()): return None # New Nifty object index = Cape() index.symbol = config.symbol try: # Example File Format: # Date | Price | Div | Earning | CPI | FractionalDate | Interest Rate | RealPrice | RealDiv | RealEarnings | CAPE # 2014.06 1947.09 37.38 103.12 238.343 2014.37 2.6 1923.95 36.94 101.89 25.55 data = line.split(',') # Dates must be in the format YYYY-MM-DD. If your data source does not have this format, you must use # DateTime.parse_exact() and explicit declare the format your data source has. index.time = datetime.strptime(data[0], "%Y-%m") index["Cape"] = float(data[10]) index.value = data[10] except ValueError: # Do nothing return None return index |
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