Overall Statistics 
Total Trades
11474
Average Win
0.49%
Average Loss
0.50%
Compounding Annual Return
3.015%
Drawdown
67.100%
Expectancy
0.012
Net Profit
48.238%
Sharpe Ratio
0.098
Probabilistic Sharpe Ratio
0.000%
Loss Rate
50%
Win Rate
50%
ProfitLoss Ratio
0.99
Alpha
0.015
Beta
0.008
Annual Standard Deviation
0.145
Annual Variance
0.021
Information Ratio
0.389
Tracking Error
0.227
Treynor Ratio
1.857
Total Fees
$9014.60
Estimated Strategy Capacity
$0
Lowest Capacity Asset
ICE_O1.QuantpediaFutures 2S

# https://quantpedia.com/strategies/shorttermreversalwithfutures/ # # 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 secondnearest 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 t1 to t and period from t2 # to t1 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 highopen interest (top 50% of # changes in open interest) or lowopen interest groups (bottom 50% of changes in open interest) based on lagged changes in open interest between # the period from t1 to t and period from t2 to t1. The investor goes long (short) on futures from the highvolume, lowopen 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 equalweighted 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(2000, 1, 1) self.SetCash(100000) self.symbols = [ "CME_S1", # Soybean Futures, Continuous Contract "CME_W1", # Wheat Futures, Continuous Contract "CME_BO1", # Soybean Oil Futures, Continuous Contract "CME_C1", # Corn Futures, Continuous Contract "CME_LC1", # Live Cattle Futures, Continuous Contract "CME_FC1", # Feeder Cattle Futures, Continuous Contract "CME_GC1", # Gold Futures, Continuous Contract "CME_SI1", # Silver Futures, Continuous Contract "CME_PL1", # Platinum Futures, Continuous Contract "CME_CL1", # Crude Oil Futures, Continuous Contract "ICE_RS1", # Canola Futures, Continuous Contract "ICE_GO1", # Gas Oil Futures, Continuous Contract "CME_RB2", # Gasoline Futures, Continuous Contract "CME_KW2", # Wheat Kansas, Continuous Contract "ICE_WT1", # WTI Crude Futures, Continuous Contract "ICE_CC1", # Cocoa Futures, Continuous Contract "ICE_CT1", # Cotton No. 2 Futures, Continuous Contract "ICE_KC1", # Coffee C Futures, Continuous Contract "ICE_O1", # Heating Oil Futures, Continuous Contract "ICE_SB1", # Sugar No. 11 Futures, Continuous Contract "CME_BP1", # British Pound Futures, Continuous Contract #1 "CME_EC1", # Euro FX Futures, Continuous Contract #1 "CME_JY1", # Japanese Yen Futures, Continuous Contract #1 "CME_SF1", # Swiss Franc Futures, Continuous Contract #1 "CME_ES1", # Emini S&P 500 Futures, Continuous Contract #1 "CME_TY1", # 10 Yr Note Futures, Continuous Contract #1 "CME_FV1", # 5 Yr Note Futures, Continuous Contract #1 ] self.period = 14 self.SetWarmUp(self.period) # Daily close, volume and open interest data. self.data = {} self.rebalance_flag = False # Price data. for symbol in self.symbols: data = self.AddData(QuantpediaFutures, symbol, Resolution.Daily) data.SetFeeModel(CustomFeeModel(self)) self.data[symbol] = deque(maxlen=self.period) # Open interest and volume data symbol = 'CHRIS/' + symbol if 'CME' in symbol: self.AddData(QuandlFuturesOpenInterestCME, symbol, Resolution.Daily) else: self.AddData(QuandlFuturesOpenInterest, symbol, Resolution.Daily) self.AddData(QuandlFuturesVolume, symbol, Resolution.Daily) # NOTE: Need to do this because of multiple symbol data integration. (settle, volume, open interest) sym = self.symbols[0] + '.QuantpediaFutures' self.Schedule.On(self.DateRules.Every(DayOfWeek.Wednesday), self.TimeRules.AfterMarketOpen(sym), self.Rebalance) def OnData(self, data): for symbol in self.symbols: volume_symbol = 'CHRIS/' + symbol + '.QuandlFuturesVolume' # Open interest update open_interest_type = '' if 'CME' in symbol: open_interest_type = 'QuandlFuturesOpenInterestCME' else: open_interest_type = 'QuandlFuturesOpenInterest' open_interest_symbol = 'CHRIS/' + symbol + '.' + open_interest_type # if self.Securities.ContainsKey(symbol) and self.Securities.ContainsKey(volume_symbol) and self.Securities.ContainsKey(open_interest_symbol): if symbol in data and volume_symbol in data and open_interest_symbol in data: if data[symbol] and data[volume_symbol] and data[open_interest_symbol]: price = data[symbol].Value vol = data[volume_symbol].Value oi = data[open_interest_symbol].Value if price != 0 and vol != 0 and oi != 0: self.data[symbol].append((price, vol, oi)) def Rebalance(self): if self.IsWarmingUp: return ret_volume_oi_data = {} for symbol in self.symbols: # Data is ready. if len(self.data[symbol]) == self.data[symbol].maxlen: # Return calc. prices = [x[0] for x in self.data[symbol]] half = int(len(prices)/2) prices = prices[half:] ret = prices[1] / prices[0]  1 # Volume change calc. volumes = [x[1] for x in self.data[symbol]] volumes_t1 = volumes[half:] t1_vol_mean = np.mean(volumes_t1) t1_vol_total = sum(volumes_t1) / t1_vol_mean volumes_t2 = volumes[:half] t2_vol_mean = np.mean(volumes_t2) t2_vol_total = sum(volumes_t2) / t2_vol_mean volume_weekly_diff = t1_vol_total  t2_vol_total # Open interest change calc. interests = [x[2] for x in self.data[symbol]] t1_oi = interests[half:] t1_oi_total = sum(t1_oi) t2_oi = interests[:half] t2_oi_total = sum(t2_oi) oi_weekly_diff = t1_oi_total  t2_oi_total # Store weekly diff data. ret_volume_oi_data[symbol] = (ret, volume_weekly_diff, oi_weekly_diff) long = [] short = [] if len(ret_volume_oi_data) != 0: volume_sorted = sorted(ret_volume_oi_data.items(), key = lambda x: x[1][1], reverse = True) half = int(len(volume_sorted)/2) high_volume = [x for x in volume_sorted[:half]] open_interest_sorted = sorted(ret_volume_oi_data.items(), key = lambda x: x[1][2], reverse = True) half = int(len(open_interest_sorted)/2) low_oi = [x for x in open_interest_sorted[half:]] filtered = [x for x in high_volume if x in low_oi] filtered_by_return = sorted(filtered, key = lambda x : x[0], reverse = True) half = int(len(filtered_by_return) / 2) long = filtered_by_return[half:] short = filtered_by_return[:half] # Make sure we have at least two values for weighting. if len(long + short) < 2: return # Return weighting. weight = {} diff = {} avg_ret = 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 = sum([abs(x[1]) for x in diff.items()]) long_symbols = [x[0] for x in long] if total_diff == 0: return 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 self.Liquidate() for symbol, w in weight.items(): sym = symbol + '.QuantpediaFutures' self.SetHoldings(sym, w) class QuandlFuturesOpenInterest(PythonQuandl): def __init__(self): self.ValueColumnName = "Prev. Day Open Interest" class QuandlFuturesOpenInterestCME(PythonQuandl): def __init__(self): self.ValueColumnName = "Previous Day Open Interest" class QuandlFuturesVolume(PythonQuandl): def __init__(self): self.ValueColumnName = "Volume" # 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 # Custom fee model. class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD"))