Overall Statistics |
Total Trades
1001
Average Win
1.25%
Average Loss
-1.25%
Compounding Annual Return
-0.019%
Drawdown
45.600%
Expectancy
0.020
Net Profit
-0.239%
Sharpe Ratio
0.057
Probabilistic Sharpe Ratio
0.003%
Loss Rate
49%
Win Rate
51%
Profit-Loss Ratio
1.00
Alpha
0.012
Beta
-0.056
Annual Standard Deviation
0.117
Annual Variance
0.014
Information Ratio
-0.487
Tracking Error
0.192
Treynor Ratio
-0.12
Total Fees
$1617.30
Estimated Strategy Capacity
$0
Lowest Capacity Asset
CME_BO1.QuantpediaFutures 2S
|
#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