Overall Statistics Total Trades3209Average Win0.34%Average Loss-0.08%Compounding Annual Return19.188%Drawdown17.100%Expectancy1.931Net Profit852.149%Sharpe Ratio1.367Probabilistic Sharpe Ratio85.480%Loss Rate44%Win Rate56%Profit-Loss Ratio4.25Alpha0.162Beta0.014Annual Standard Deviation0.12Annual Variance0.014Information Ratio0.333Tracking Error0.22Treynor Ratio11.325Total Fees\$5232.40
"""
Based on 'In & Out' strategy by Peter Guenther 10-04-2020
expanded/inspired by Tentor Testivis, Dan Whitnable (Quantopian), Vladimir, and Thomas Chang.

https://www.quantopian.com/posts/new-strategy-in-and-out
"""

# Import packages
import numpy as np
import pandas as pd
import scipy as sc

class InOut(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2008, 1, 1)  # Set Start Date
self.SetCash(100000)  # Set Strategy Cash
self.UniverseSettings.Resolution = Resolution.Daily

# Feed-in constants
self.INI_WAIT_DAYS = 15  # out for 3 trading weeks

res = Resolution.Daily

# Market and list of signals based on ETFs
self.PRDC = self.AddEquity('XLI', res).Symbol  # production (industrials)
self.METL = self.AddEquity('DBB', res).Symbol  # input prices (metals)
self.NRES = self.AddEquity('IGE', res).Symbol  # input prices (natural res)
self.DEBT = self.AddEquity('SHY', res).Symbol  # cost of debt (bond yield)
self.USDX = self.AddEquity('UUP', res).Symbol  # safe haven (USD)
self.GOLD = self.AddEquity('GLD', res).Symbol  # gold
self.SLVA = self.AddEquity('SLV', res).Symbol  # VS silver
self.UTIL = self.AddEquity('XLU', res).Symbol  # utilities
self.SHCU = self.AddEquity('FXF', res).Symbol  # safe haven (CHF)
self.RICU = self.AddEquity('FXA', res).Symbol  # risk currency (AUD)
self.INDU = self.PRDC  # vs industrials

self.FORPAIRS = [self.GOLD, self.SLVA, self.UTIL, self.SHCU, self.RICU]
self.SIGNALS = [self.PRDC, self.METL, self.NRES, self.DEBT, self.USDX]

# 'In' and 'out' holdings incl. weights
self.HLD_IN = {self.MRKT: 1.0}
self.HLD_OUT = {self.TLT: .5, self.IEF: .5}

# Initialize variables
## 'In'/'out' indicator
self.be_in = 1
## Day count variables
self.dcount = 0  # count of total days since start
self.outday = 0  # dcount when self.be_in=0
## Flexi wait days

self.Schedule.On(
self.DateRules.EveryDay(),
self.TimeRules.AfterMarketOpen('SPY', 75),
self.rebalance_when_out_of_the_market
)

self.Schedule.On(
self.DateRules.WeekEnd(),
self.TimeRules.AfterMarketOpen('SPY', 75),
self.rebalance_when_in_the_market
)

def rebalance_when_out_of_the_market(self):
# Returns sample to detect extreme observations
hist = self.History(
self.SIGNALS + [self.MRKT] + self.FORPAIRS, 252, Resolution.Daily)['close'].unstack(level=0).dropna()

# hist_shift = hist.rolling(66).apply(lambda x: x[:11].mean())

hist_shift = hist.apply(lambda x: (x.shift(65) + x.shift(64) + x.shift(63) + x.shift(62) + x.shift(
61) + x.shift(60) + x.shift(59) + x.shift(58) + x.shift(57) + x.shift(56) + x.shift(55)) / 11)

returns_sample = (hist / hist_shift - 1)
# Reverse code USDX: sort largest changes to bottom
returns_sample[self.USDX] = returns_sample[self.USDX] * (-1)
# For pairs, take returns differential, reverse coded
returns_sample['G_S'] = -(returns_sample[self.GOLD] - returns_sample[self.SLVA])
returns_sample['U_I'] = -(returns_sample[self.UTIL] - returns_sample[self.INDU])
returns_sample['C_A'] = -(returns_sample[self.SHCU] - returns_sample[self.RICU])
self.pairlist = ['G_S', 'U_I', 'C_A']

# Extreme observations; statist. significance = 1%
pctl_b = np.nanpercentile(returns_sample, 1, axis=0)
extreme_b = returns_sample.iloc[-1] < pctl_b

# Determine waitdays empirically via safe haven excess returns, 50% decay
self.INI_WAIT_DAYS * max(1,
returns_sample[self.GOLD].iloc[-1] / returns_sample[self.SLVA].iloc[-1],
returns_sample[self.UTIL].iloc[-1] / returns_sample[self.INDU].iloc[-1],
returns_sample[self.SHCU].iloc[-1] / returns_sample[self.RICU].iloc[-1]
))
)

# Determine whether 'in' or 'out' of the market
if (extreme_b[self.SIGNALS + self.pairlist]).any():
self.be_in = False
self.outday = self.dcount
if self.dcount >= self.outday + adjwaitdays:
self.be_in = True
self.dcount += 1

# Swap to 'out' assets if applicable
if not self.be_in:
# Close 'In' holdings
for asset, weight in self.HLD_IN.items():
self.SetHoldings(asset, 0)

for asset, weight in self.HLD_OUT.items():
self.SetHoldings(asset, weight)

self.Plot("In Out", "in_market", int(self.be_in))
self.Plot("In Out", "num_out_signals", extreme_b[self.SIGNALS + self.pairlist].sum())
self.SetHoldings(asset, weight)