Overall Statistics |
Total Trades
10182
Average Win
0.12%
Average Loss
-0.16%
Compounding Annual Return
7.644%
Drawdown
20.000%
Expectancy
0.169
Net Profit
352.086%
Sharpe Ratio
0.793
Probabilistic Sharpe Ratio
12.428%
Loss Rate
34%
Win Rate
66%
Profit-Loss Ratio
0.78
Alpha
0.066
Beta
0
Annual Standard Deviation
0.083
Annual Variance
0.007
Information Ratio
0.016
Tracking Error
0.197
Treynor Ratio
-139.807
Total Fees
$7891.40
|
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) def GetFutureMulitpliers(algorithm): symbol_multiplier = {} csv_string_file = algorithm.Download('data.quantpedia.com/backtesting_data/futures/contract_multiplier.csv') mulitpliers_lines = csv_string_file.split('\r\n') for line in mulitpliers_lines: symbol, multiplier = line.split(';') symbol_multiplier[symbol] = float(multiplier) return symbol_multiplier # 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['back_adjusted'] = float(split[1]) data['spliced'] = float(split[2]) 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
# https://quantpedia.com/strategies/time-series-momentum-effect/ # # The investment universe consists of 24 commodity futures, 12 cross-currency pairs (with 9 underlying currencies), 9 developed equity indices, and 13 developed # government bond futures. # Every month, the investor considers whether the excess return of each asset over the past 12 months is positive or negative and goes long on the contract if it is # positive and short if negative. The position size is set to be inversely proportional to the instrument’s volatility. A univariate GARCH model is used to estimated # ex-ante volatility in the source paper. However, other simple models could probably be easily used with good results (for example, the easiest one would be using # historical volatility instead of estimated volatility). The portfolio is rebalanced monthly. from collections import deque import fk_tools from math import sqrt class TimeSeriesMomentum(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 "CME_AD1", # Australian Dollar Futures, Continuous Contract #1 "CME_BP1", # British Pound Futures, Continuous Contract #1 "CME_CD1", # Canadian Dollar Futures, Continuous Contract #1 "CME_EC1", # Euro FX Futures, Continuous Contract #1 "CME_JY1", # Japanese Yen Futures, Continuous Contract #1 "CME_MP1", # Mexican Peso Futures, Continuous Contract #1 "CME_NE1", # New Zealand Dollar Futures, Continuous Contract #1 "CME_SF1", # Swiss Franc Futures, Continuous Contract #1 "ICE_DX1", # US Dollar Index Futures, Continuous Contract #1 "CME_NQ1", # E-mini NASDAQ 100 Futures, Continuous Contract #1 "EUREX_FDAX1", # DAX Futures, Continuous Contract #1 "CME_ES1", # E-mini S&P 500 Futures, Continuous Contract #1 "EUREX_FSMI1", # SMI Futures, Continuous Contract #1 "EUREX_FSTX1", # STOXX Europe 50 Index Futures, Continuous Contract #1 "LIFFE_FCE1", # CAC40 Index Futures, Continuous Contract #1 "LIFFE_Z1", # FTSE 100 Index Futures, Continuous Contract #1 "SGX_NK1", # SGX Nikkei 225 Index Futures, Continuous Contract #1 "CME_MD1", # E-mini S&P MidCap 400 Futures "CME_TY1", # 10 Yr Note Futures, Continuous Contract #1 "CME_FV1", # 5 Yr Note Futures, Continuous Contract #1 "CME_TU1", # 2 Yr Note Futures, Continuous Contract #1 "ASX_XT1", # 10 Year Commonwealth Treasury Bond Futures, Continuous Contract #1 # 'Settlement price' instead of 'settle' on quandl. "ASX_YT1", # 3 Year Commonwealth Treasury Bond Futures, Continuous Contract #1 # 'Settlement price' instead of 'settle' on quandl. "EUREX_FGBL1", # Euro-Bund (10Y) Futures, Continuous Contract #1 "EUREX_FBTP1", # Long-Term Euro-BTP Futures, Continuous Contract #1 "EUREX_FGBM1", # Euro-Bobl Futures, Continuous Contract #1 "EUREX_FGBS1", # Euro-Schatz Futures, Continuous Contract #1 "SGX_JB1", # SGX 10-Year Mini Japanese Government Bond Futures "LIFFE_R1" # Long Gilt Futures, Continuous Contract #1 "MX_CGB1", # Ten-Year Government of Canada Bond Futures, Continuous Contract #1 # 'Settlement price' instead of 'settle' on quandl. ] self.period = 12 * 21 self.SetWarmUp(self.period) self.targeted_volatility = 0.10 # Daily rolled data. self.data = {} for symbol in self.symbols: data = None # Back adjusted and spliced data import. data = self.AddData(fk_tools.QuantpediaFutures, 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): # Store daily 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): # Performance / volatility data. performance_volatility = {} for symbol in self.symbols: if len(self.data[symbol]) == self.data[symbol].maxlen: back_adjusted_prices = [x for x in self.data[symbol]] performance = fk_tools.Return(back_adjusted_prices) back_adjusted_prices = back_adjusted_prices[-21:] volatility_1M = fk_tools.Volatility(back_adjusted_prices) performance_volatility[symbol] = [performance, volatility_1M] if len(performance_volatility) == 0: return # Performance sorting. long = [x for x in performance_volatility.items() if x[1][0] > 0] short = [x for x in performance_volatility.items() if x[1][0] < 0] # Volatility weighting. total_volatility_inversed = sum([(1 / x[1][1]) for x in long + short]) if total_volatility_inversed == 0: return # count = len(long + short) count = len(long + short) * 2 # Volatility targeting. portfolio_volatility = sum([((x[1][1]) / count) for x in long + short]) * sqrt(252) volatility_target_leverage = 2 * self.targeted_volatility / portfolio_volatility long_symbols = [x[0] for x in long] short_symbols = [x[0] for x in short] weight = {} for symbol_data in long + short: symbol = symbol_data[0] volatility = symbol_data[1][1] if volatility != 0: # 2x leverage - 100% long / 100% short. final_leverage = 2.0 * volatility_target_leverage # self.Log(f"Leverage: {final_leverage}") if symbol in long_symbols: weight[symbol] = (final_leverage / volatility) / total_volatility_inversed else: weight[symbol] = - (final_leverage / volatility) / total_volatility_inversed else: weight[symbol] = 0 # Trade execution. invested = [x.Key.Value for x in self.Portfolio if x.Value.Invested] for symbol in invested: if symbol not in long_symbols + short_symbols: self.Liquidate(symbol) for symbol, w in weight.items(): self.SetHoldings(symbol, w)