# region imports from AlgorithmImports import * # endregion # https://quantpedia.com/strategies/short-term-reversal-with-futures/ # # The investment universe consists of 24 types of US futures contracts (4 currencies, 5 financials, 8 agricultural, 7 commodities). # A weekly time frame is used – a Wednesday- Wednesday interval. The contract closest to expiration is used, except within the delivery # month, in which the second-nearest contract is used. Rolling into the second nearest contract is done at the beginning of the delivery month. # The contract is defined as the high- (low-) volume contract if the contract’s volume changes between period from t-1 to t and period from t-2 # to t-1 is above (below) the median volume change of all contracts (weekly trading volume is detrended by dividing the trading volume by its # sample mean to make the volume measure comparable across markets). All contracts are also assigned to either high-open interest (top 50% of # changes in open interest) or low-open interest groups (bottom 50% of changes in open interest) based on lagged changes in open interest between # the period from t-1 to t and period from t-2 to t-1. The investor goes long (short) on futures from the high-volume, low-open interest group # with the lowest (greatest) returns in the previous week. The weight of each contract is proportional to the difference between the return # of the contract over the past one week and the equal-weighted average of returns on the N (number of contracts in a group) contracts during that period. from collections import deque import numpy as np class ShortTermReversal(QCAlgorithm): def Initialize(self): self.SetStartDate(2010, 1, 1) self.SetCash(100000) symbols: dict = { "CME_S1": Futures.Grains.Soybeans, "CME_W1": Futures.Grains.Wheat, "CME_BO1": Futures.Grains.SoybeanOil, "CME_C1": Futures.Grains.Corn, "CME_LC1": Futures.Meats.LiveCattle, "CME_FC1": Futures.Meats.FeederCattle, "CME_KW2": Futures.Grains.Wheat, "ICE_CC1": Futures.Softs.Cocoa, "ICE_SB1": Futures.Softs.Sugar11CME, "CME_GC1": Futures.Metals.Gold, "CME_SI1": Futures.Metals.Silver, "CME_PL1": Futures.Metals.Platinum, "CME_RB1": Futures.Energies.Gasoline, "ICE_WT1": Futures.Energies.CrudeOilWTI, "ICE_O1": Futures.Energies.HeatingOil, "CME_BP1": Futures.Currencies.GBP, "CME_EC1": Futures.Currencies.EUR, "CME_JY1": Futures.Currencies.JPY, "CME_SF1": Futures.Currencies.CHF, "CME_ES1": Futures.Indices.SP500EMini, "CME_TY1": Futures.Financials.Y10TreasuryNote, "CME_FV1": Futures.Financials.Y5TreasuryNote, } self.period: int = 14 self.SetWarmUp(self.period, Resolution.Daily) self.futures_info: dict = {} self.min_expiration_days: int = 2 self.max_expiration_days: int = 360 # daily close, volume and open interest data self.data: dict = {} for qp_symbol, qc_future in symbols.items(): # QP futures data: Security = self.AddData( QuantpediaFutures, qp_symbol, Resolution.Daily ) data.SetFeeModel(CustomFeeModel()) data.SetLeverage(5) self.data[data.Symbol] = deque(maxlen=self.period) # QC futures future: Future = self.AddFuture( qc_future, Resolution.Daily, dataNormalizationMode=DataNormalizationMode.Raw, ) future.SetFilter( timedelta(days=self.min_expiration_days), timedelta(days=self.max_expiration_days), ) self.futures_info[future.Symbol.Value] = FuturesInfo(data.Symbol) self.recent_month: int = -1 def find_and_update_contracts(self, futures_chain, symbol) -> None: near_contract: FuturesContract = None if symbol in futures_chain: contracts: list = [ contract for contract in futures_chain[symbol] if contract.Expiry.date() > self.Time.date() ] if len(contracts) >= 1: contracts: list = sorted( contracts, key=lambda x: x.Expiry, reverse=False ) near_contract = contracts[0] self.futures_info[symbol].update_contracts(near_contract) def OnData(self, data): if data.FutureChains.Count > 0: for symbol, futures_info in self.futures_info.items(): # check if near contract is expired or is not initialized if not futures_info.is_initialized() or ( futures_info.is_initialized() and futures_info.near_contract.Expiry.date() == self.Time.date() ): self.find_and_update_contracts(data.FutureChains, symbol) rebalance_flag: bool = False ret_volume_oi_data: dict[Symbol, tuple] = {} # roll return calculation for symbol, futures_info in self.futures_info.items(): # futures data is present in the algorithm if ( futures_info.quantpedia_future in data and data[futures_info.quantpedia_future] ): if futures_info.is_initialized(): near_c: FuturesContract = futures_info.near_contract if self.Securities.ContainsKey(near_c.Symbol): if futures_info.is_initialized(): # store daily data price: float = data[futures_info.quantpedia_future].Value vol: int = self.Securities[near_c.Symbol].Volume oi: int = self.Securities[near_c.Symbol].OpenInterest if price != 0 and vol != 0 and oi != 0: self.data[futures_info.quantpedia_future].append( (price, vol, oi) ) # new month rebalance if self.Time.month != self.recent_month and not self.IsWarmingUp: self.recent_month = self.Time.month rebalance_flag = True if rebalance_flag: if ( len(self.data[futures_info.quantpedia_future]) == self.data[futures_info.quantpedia_future].maxlen ): # performance prices: list[float] = [ x[0] for x in self.data[futures_info.quantpedia_future] ] half: list[float] = int(len(prices) / 2) prices: list[float] = prices[-half:] ret: float = prices[-1] / prices[0] - 1 # volume change volumes: list[int] = [ x[1] for x in self.data[futures_info.quantpedia_future] ] volumes_t1: list[int] = volumes[-half:] t1_vol_mean: float = np.mean(volumes_t1) t1_vol_total: float = sum(volumes_t1) / t1_vol_mean volumes_t2: list[int] = volumes[:half] t2_vol_mean: float = np.mean(volumes_t2) t2_vol_total: float = sum(volumes_t2) / t2_vol_mean volume_weekly_diff: float = t1_vol_total - t2_vol_total # open interest change interests: list[int] = [ x[2] for x in self.data[futures_info.quantpedia_future] ] t1_oi: list[int] = interests[-half:] t1_oi_total: float = sum(t1_oi) t2_oi: list[int] = interests[:half] t2_oi_total: float = sum(t2_oi) oi_weekly_diff: float = t1_oi_total - t2_oi_total # store weekly diff data ret_volume_oi_data[futures_info.quantpedia_future] = ( ret, volume_weekly_diff, oi_weekly_diff, ) if rebalance_flag: weight: dict[Symbol, float] = {} if len(ret_volume_oi_data) > 4: volume_sorted: list = sorted( ret_volume_oi_data.items(), key=lambda x: x[1][1], reverse=True ) half: int = int(len(volume_sorted) / 2) high_volume: list = [x for x in volume_sorted[:half]] open_interest_sorted: list = sorted( ret_volume_oi_data.items(), key=lambda x: x[1][2], reverse=True ) half = int(len(open_interest_sorted) / 2) low_oi: list = [x for x in open_interest_sorted[-half:]] filtered: list = [x for x in high_volume if x in low_oi] filtered_by_return: list = sorted( filtered, key=lambda x: x[0], reverse=True ) half = int(len(filtered_by_return) / 2) long: list[Symbol] = filtered_by_return[-half:] short: list[Symbol] = filtered_by_return[:half] if len(long + short) >= 2: # return weighting diff: dict[Symbol, float] = {} avg_ret: float = np.average([x[1][0] for x in long + short]) for symbol, ret_volume_oi in long + short: diff[symbol] = ret_volume_oi[0] - avg_ret total_diff: float = sum([abs(x[1]) for x in diff.items()]) long_symbols: list[Symbol] = [x[0] for x in long] if total_diff != 0: for symbol, data in long + short: if symbol in long_symbols: weight[symbol] = diff[symbol] / total_diff else: weight[symbol] = -diff[symbol] / total_diff # trade execution invested: list[Symbol] = [x.Key for x in self.Portfolio if x.Value.Invested] for symbol in invested: if symbol not in weight: self.Liquidate(symbol) for symbol, w in weight.items(): self.SetHoldings(symbol, w) class FuturesInfo: def __init__(self, quantpedia_future: Symbol) -> None: self.quantpedia_future: Symbol = quantpedia_future self.near_contract: FuturesContract = None def update_contracts(self, near_contract: FuturesContract) -> None: self.near_contract = near_contract def is_initialized(self) -> bool: return self.near_contract is not None # Custom fee model. class CustomFeeModel: def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD")) # Quantpedia data. # NOTE: IMPORTANT: Data order must be ascending (datewise) class QuantpediaFutures(PythonData): def GetSource(self, config, date, isLiveMode): return SubscriptionDataSource( "data.quantpedia.com/backtesting_data/futures/{0}.csv".format( config.Symbol.Value ), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv, ) def Reader(self, config, line, date, isLiveMode): data = QuantpediaFutures() data.Symbol = config.Symbol if not line[0].isdigit(): return None split = line.split(";") data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1) data["back_adjusted"] = float(split[1]) data["spliced"] = float(split[2]) data.Value = float(split[1]) return data