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
import numpy as np
from scipy.optimize import minimize

def MonthDiff(d1, d2):
return (d1.year - d2.year) * 12 + d1.month - d2.month

def Return(values):
return (values[-1] - values) / values

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.isdigit(): return None
split = line.split(';')

data.Time = datetime.strptime(split, "%d.%m.%Y") + timedelta(days=1)
data['settle'] = float(split)
data.Value = float(split)

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.
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.

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:

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:

# 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/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:
else:

data.SetFeeModel(fk_tools.CustomFeeModel(self))
data.SetLeverage(20)

self.data[symbol] = deque(maxlen = self.period)

self.Schedule.On(self.DateRules.MonthStart(self.symbols), self.TimeRules.AfterMarketOpen(self.symbols), 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, reverse = True)
quintile = int(len(sorted_by_performance) / 5)
long = [x for x in sorted_by_performance[:quintile]]
short = [x 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

self.SetHoldings(symbol, -volatility_target_leverage / len(short))