Overall Statistics Total Trades 1490 Average Win 0.18% Average Loss -0.22% Compounding Annual Return 2.282% Drawdown 29.500% Expectancy 0.261 Net Profit 58.744% Sharpe Ratio 0.257 Probabilistic Sharpe Ratio 0.011% Loss Rate 30% Win Rate 70% Profit-Loss Ratio 0.81 Alpha 0.024 Beta -0.017 Annual Standard Deviation 0.088 Annual Variance 0.008 Information Ratio -0.199 Tracking Error 0.201 Treynor Ratio -1.31 Total Fees \$169.25
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[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 = {}

mulitpliers_lines = csv_string_file.split('\r\n')
for line in mulitpliers_lines:
symbol, multiplier = line.split(';')
symbol_multiplier[symbol] = 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['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.
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.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, 1 / self.long_size)
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, - 1 / self.short_size)
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/fx-carry-trade/
#
# Create an investment universe consisting of several currencies (10-20). Go long three currencies with the highest central bank prime rates and
# go short three currencies with the lowest central bank prime rates. The cash not used as the margin is invested in overnight rates. The strategy
# is rebalanced monthly.

import fk_tools

def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)

# Source: https://www.quandl.com/data/OECD-Organisation-for-Economic-Co-operation-and-Development
self.symbols = {
"CME_AD1" : "OECD/KEI_IR3TIB01_AUS_ST_M",   # Australian Dollar Futures, Continuous Contract #1
"CME_BP1" : "OECD/KEI_IR3TIB01_GBR_ST_M",   # British Pound Futures, Continuous Contract #1
"CME_CD1" : "OECD/KEI_IR3TIB01_CAN_ST_M",   # Canadian Dollar Futures, Continuous Contract #1
"CME_EC1" : "OECD/KEI_IR3TIB01_EA19_ST_M",  # Euro FX Futures, Continuous Contract #1
"CME_JY1" : "OECD/KEI_IR3TIB01_JPN_ST_M",   # Japanese Yen Futures, Continuous Contract #1
"CME_MP1" : "OECD/KEI_IR3TIB01_MEX_ST_M",   # Mexican Peso Futures, Continuous Contract #1
"CME_NE1" : "OECD/KEI_IR3TIB01_NZL_ST_M",   # New Zealand Dollar Futures, Continuous Contract #1
"CME_SF1" : "SNB/ZIMOMA"                    # Swiss Franc Futures, Continuous Contract #1

# OECD 3 month interbank rate for Switzerland is missing.
# "CME_SF1",                                # Swiss Franc Futures, Continuous Contract #1
}

for symbol, rate_symbol in self.symbols.items():

data.SetFeeModel(fk_tools.CustomFeeModel(self))
data.SetLeverage(5)

def Rebalance(self):
# Interbank rate sorting.
sorted_by_rate = sorted([y for y in self.symbols if self.Securities.ContainsKey(self.symbols[y]) and self.Securities[y].Price != 0], key = lambda x: self.Securities[self.symbols[x]].Price, reverse = True)
long = [x for x in sorted_by_rate[:traded_count]]
short = [x for x in sorted_by_rate[-traded_count:]]

self.ValueColumnName = 'Value'