Overall Statistics Total Trades3224Average Win0.32%Average Loss-0.07%Compounding Annual Return20.154%Drawdown15.400%Expectancy2.124Net Profit955.931%Sharpe Ratio1.62Probabilistic Sharpe Ratio95.432%Loss Rate42%Win Rate58%Profit-Loss Ratio4.37Alpha0.207Beta0.048Annual Standard Deviation0.131Annual Variance0.017Information Ratio0.428Tracking Error0.236Treynor Ratio4.423Total Fees\$5497.85
```"""
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.cap = 100000
self.SetCash(self.cap)  # Set Strategy Cash
self.UniverseSettings.Resolution = Resolution.Minute

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

res = Resolution.Minute

# 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.spy = []

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())

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())

# to plot SPY on the same chart as the performance of our algo
self.spy.append(hist[self.MRKT].iloc[-1])
spy_perf = self.spy[-1] / self.spy[0] * self.cap
self.Plot("Strategy Equity", "SPY", spy_perf)

def rebalance_when_in_the_market(self):
# Swap to 'in' assets if applicable
if self.be_in:
# Close 'Out' holdings
for asset, weight in self.HLD_OUT.items():
self.SetHoldings(asset, 0)

for asset, weight in self.HLD_IN.items():
self.SetHoldings(asset, weight)```
```"""
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

def initialize(context):
# Feed-in constants
context.INI_WAIT_DAYS = 15 # out for 3 trading weeks

# 'In' and 'out' holdings incl. weights
context.HLD_IN = {symbol('SPY'): 1.0}
context.HLD_OUT = {symbol('TLT'): .5, symbol('IEF'): .5}

# Market and list of signals based on ETFs
context.MRKT = symbol('SPY')
context.PRDC = symbol('XLI') # production (industrials)
context.METL = symbol('DBB') # input prices (metals)
context.NRES = symbol('IGE') # input prices (natural res)
context.DEBT = symbol('SHY') # cost of debt (bond yield)
context.USDX = symbol('UUP') # safe haven (USD)
context.SIGNALS = [context.PRDC, context.METL, context.NRES, context.DEBT, context.USDX]

# Pairs for comparative returns signals
context.GOLD = symbol('GLD') # gold
context.SLVA = symbol('SLV') # VS silver
context.UTIL = symbol('XLU') # utilities
context.INDU = context.PRDC # vs industrials
context.SHCU = symbol('FXF') # safe haven (CHF)
context.RICU = symbol('FXA') # risk currency (AUD)
context.FORPAIRS = [context.GOLD, context.SLVA, context.UTIL, context.SHCU, context.RICU]

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

# Commission

# Schedule functions
schedule_function(
# daily rebalance if OUT of the market
rebalance_when_out_of_the_market,
date_rules.every_day(),
time_rules.market_op en(minutes = 75)
)
schedule_function(
# weekly rebalance if IN the market
rebalance_when_in_the_market,
date_rules.week_start(days_offset=4),
time_rules.market_op en(minutes = 75)
)

def rebalance_when_out_of_the_market(context, data):
# Returns sample to detect extreme observations
hist = data.history(context.SIGNALS+[context.MRKT]+context.FORPAIRS, 'close', 253, '1d').iloc[:-1]
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[context.USDX] = returns_sample[context.USDX]*(-1)
# For pairs, take returns differential, reverse coded
returns_sample['G_S'] = -(returns_sample[context.GOLD] - returns_sample[context.SLVA])
returns_sample['U_I'] = -(returns_sample[context.UTIL] - returns_sample[context.INDU])
returns_sample['C_A'] = -(returns_sample[context.SHCU] - returns_sample[context.RICU])
context.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
context.WDadjvar = int(max(0.50*context.WDadjvar, context.INI_WAIT_DAYS * max(1,returns_sample[context.GOLD].iloc[-1] / returns_sample[context.SLVA].iloc[-1],returns_sample[context.UTIL].iloc[-1] / returns_sample[context.INDU].iloc[-1],returns_sample[context.SHCU].iloc[-1] / returns_sample[context.RICU].iloc[-1])))

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

# Swap to 'out' assets if applicable
if not context.be_in:
for asset, weight in context.HLD_OUT.items():
order_target_percent(asset, weight)
for asset in context.portfolio.positions:
# Close 'In' holdings
if asset not in context.HLD_OUT:
order_target_percent(asset, 0)

# Record