Overall Statistics |
Total Trades
2602
Average Win
0.39%
Average Loss
-0.50%
Compounding Annual Return
3.836%
Drawdown
25.600%
Expectancy
0.109
Net Profit
116.335%
Sharpe Ratio
0.478
Probabilistic Sharpe Ratio
0.411%
Loss Rate
38%
Win Rate
62%
Profit-Loss Ratio
0.78
Alpha
0.034
Beta
0.008
Annual Standard Deviation
0.071
Annual Variance
0.005
Information Ratio
-0.149
Tracking Error
0.191
Treynor Ratio
4.238
Total Fees
$976.33
|
# https://quantpedia.com/strategies/1-month-momentum-in-commodities/ # # Create a universe of tradable commodity futures. Rank futures performance for each commodity for the last 12 months and divide them into quintiles. # Go long on the quintile with the highest momentum and go short on the quintile with the lowest momentum. Rebalance each month. from collections import deque import fk_tools from math import sqrt class MomentumEffectCommodities(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_SM1", # Soybean Meal Futures, Continuous Contract "CME_BO1", # Soybean Oil Futures, Continuous Contract "CME_C1", # Corn Futures, Continuous Contract "CME_O1", # Oats Futures, Continuous Contract "CME_LC1", # Live Cattle Futures, Continuous Contract "CME_FC1", # Feeder Cattle Futures, Continuous Contract "CME_LN1", # Lean Hog 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 "CME_HG1", # Copper Futures, Continuous Contract "CME_LB1", # Random Length Lumber Futures, Continuous Contract "CME_NG1", # Natural Gas (Henry Hub) Physical Futures, Continuous Contract "CME_PA1", # Palladium Futures, Continuous Contract "CME_RR1", # Rough Rice Futures, Continuous Contract "CME_CU1", # Chicago Ethanol (Platts) Futures "CME_DA1", # Class III Milk Futures "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_OJ1", # Orange Juice Futures, Continuous Contract "ICE_SB1" # Sugar No. 11 Futures, Continuous Contract ] self.period = 12 * 21 self.SetWarmup(self.period) self.targeted_volatility = 0.10 # Daily price data. self.data = {} # True -> Quantpedia data # False -> Quandl free data self.use_quantpedia_data = True if not self.use_quantpedia_data: self.symbols = ['CHRIS/' + x for x in self.symbols] for symbol in self.symbols: data = None if self.use_quantpedia_data: data = self.AddData(fk_tools.QuantpediaFutures, symbol, Resolution.Daily) else: data = self.AddData(fk_tools.QuandlFutures, symbol, Resolution.Daily) data.SetFeeModel(fk_tools.CustomFeeModel(self)) data.SetLeverage(20) self.data[symbol] = deque(maxlen = self.period) self.Schedule.On(self.DateRules.MonthStart(self.symbols[0]), self.TimeRules.AfterMarketOpen(self.symbols[0]), self.Rebalance) def OnData(self, data): for symbol in self.symbols: if self.Securities.ContainsKey(symbol): price = self.Securities[symbol].Price if price != 0: self.data[symbol].append(price) else: # Append latest price as a next one in case there's 0 as price. if len(self.data[symbol]) > 0: last_price = self.data[symbol][-1] self.data[symbol].append(last_price) def Rebalance(self): if self.IsWarmingUp: return performance = {} volatility = {} for symbol in self.symbols: if len(self.data[symbol]) == self.data[symbol].maxlen: price_data = [x for x in self.data[symbol]] performance[symbol] = fk_tools.Return(price_data) volatility[symbol] = fk_tools.Volatility(price_data[-21:]) * sqrt(252) if len(performance) == 0 or len(volatility) == 0: self.Liquidate() return # Return sorting. sorted_by_performance = sorted(performance.items(), key = lambda x: x[1], reverse = True) quintile = int(len(sorted_by_performance) / 5) long = [x[0] for x in sorted_by_performance[:quintile]] short = [x[0] for x in sorted_by_performance[-quintile:]] # Volatility targeting. count = len(long + short) portfolio_volatility = sum([((volatility[x]) / count) for x in long + short]) volatility_target_leverage = self.targeted_volatility / portfolio_volatility # Trade execution. invested = [x.Key.Value for x in self.Portfolio if x.Value.Invested] for symbol in invested: if symbol not in long + short: self.Liquidate(symbol) for symbol in long: self.SetHoldings(symbol, volatility_target_leverage / len(long)) for symbol in short: self.SetHoldings(symbol, -volatility_target_leverage / len(short))
import numpy as np from scipy.optimize import minimize sp100_stocks = ['AAPL','MSFT','AMZN','FB','BRKB','GOOGL','GOOG','JPM','JNJ','V','PG','XOM','UNH','BAC','MA','T','DIS','INTC','HD','VZ','MRK','PFE','CVX','KO','CMCSA','CSCO','PEP','WFC','C','BA','ADBE','WMT','CRM','MCD','MDT','BMY','ABT','NVDA','NFLX','AMGN','PM','PYPL','TMO','COST','ABBV','ACN','HON','NKE','UNP','UTX','NEE','IBM','TXN','AVGO','LLY','ORCL','LIN','SBUX','AMT','LMT','GE','MMM','DHR','QCOM','CVS','MO','LOW','FIS','AXP','BKNG','UPS','GILD','CHTR','CAT','MDLZ','GS','USB','CI','ANTM','BDX','TJX','ADP','TFC','CME','SPGI','COP','INTU','ISRG','CB','SO','D','FISV','PNC','DUK','SYK','ZTS','MS','RTN','AGN','BLK'] def MonthDiff(d1, d2): return (d1.year - d2.year) * 12 + d1.month - d2.month def Return(values): return (values[-1] - values[0]) / values[0] def Volatility(values): values = np.array(values) returns = (values[1:] - values[:-1]) / values[:-1] return np.std(returns) # Custom fee model class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD")) # Quandl free data class QuandlFutures(PythonQuandl): def __init__(self): self.ValueColumnName = "settle" # Quandl short interest data. class QuandlFINRA_ShortVolume(PythonQuandl): def __init__(self): self.ValueColumnName = 'SHORTVOLUME' # also 'TOTALVOLUME' is accesible # 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['settle'] = float(split[1]) data.Value = float(split[1]) return data # NOTE: Manager for new trades. It's represented by certain count of equally weighted brackets for long and short positions. # If there's a place for new trade, it will be managed for time of holding period. class TradeManager(): def __init__(self, algorithm, long_size, short_size, holding_period): self.algorithm = algorithm # algorithm to execute orders in. self.long_size = long_size self.short_size = short_size self.weight = 1 / (self.long_size + self.short_size) self.long_len = 0 self.short_len = 0 # Arrays of ManagedSymbols self.symbols = [] self.holding_period = holding_period # Days of holding. # Add stock symbol object def Add(self, symbol, long_flag): # Open new long trade. managed_symbol = ManagedSymbol(symbol, self.holding_period, long_flag) if long_flag: # If there's a place for it. if self.long_len < self.long_size: self.symbols.append(managed_symbol) self.algorithm.SetHoldings(symbol, self.weight) self.long_len += 1 else: self.algorithm.Log("There's not place for additional trade.") # Open new short trade. else: # If there's a place for it. if self.short_len < self.short_size: self.symbols.append(managed_symbol) self.algorithm.SetHoldings(symbol, - self.weight) self.short_len += 1 else: self.algorithm.Log("There's not place for additional trade.") # Decrement holding period and liquidate symbols. def TryLiquidate(self): symbols_to_delete = [] for managed_symbol in self.symbols: managed_symbol.days_to_liquidate -= 1 # Liquidate. if managed_symbol.days_to_liquidate == 0: symbols_to_delete.append(managed_symbol) self.algorithm.Liquidate(managed_symbol.symbol) if managed_symbol.long_flag: self.long_len -= 1 else: self.short_len -= 1 # Remove symbols from management. for managed_symbol in symbols_to_delete: self.symbols.remove(managed_symbol) def LiquidateTicker(self, ticker): symbol_to_delete = None for managed_symbol in self.symbols: if managed_symbol.symbol.Value == ticker: self.algorithm.Liquidate(managed_symbol.symbol) symbol_to_delete = managed_symbol if managed_symbol.long_flag: self.long_len -= 1 else: self.short_len -= 1 break if symbol_to_delete: self.symbols.remove(symbol_to_delete) else: self.algorithm.Debug("Ticker is not held in portfolio!") class ManagedSymbol(): def __init__(self, symbol, days_to_liquidate, long_flag): self.symbol = symbol self.days_to_liquidate = days_to_liquidate self.long_flag = long_flag class PortfolioOptimization(object): def __init__(self, df_return, risk_free_rate, num_assets): self.daily_return = df_return self.risk_free_rate = risk_free_rate self.n = num_assets # numbers of risk assets in portfolio self.target_vol = 0.05 def annual_port_return(self, weights): # calculate the annual return of portfolio return np.sum(self.daily_return.mean() * weights) * 252 def annual_port_vol(self, weights): # calculate the annual volatility of portfolio return np.sqrt(np.dot(weights.T, np.dot(self.daily_return.cov() * 252, weights))) def min_func(self, weights): # method 1: maximize sharp ratio return - self.annual_port_return(weights) / self.annual_port_vol(weights) # method 2: maximize the return with target volatility #return - self.annual_port_return(weights) / self.target_vol def opt_portfolio(self): # maximize the sharpe ratio to find the optimal weights cons = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1}) bnds = tuple((0, 1) for x in range(2)) + tuple((0, 0.25) for x in range(self.n - 2)) opt = minimize(self.min_func, # object function np.array(self.n * [1. / self.n]), # initial value method='SLSQP', # optimization method bounds=bnds, # bounds for variables constraints=cons) # constraint conditions opt_weights = opt['x'] return opt_weights