Overall Statistics Total Trades224Average Win4.41%Average Loss-1.40%Compounding Annual Return25.257%Drawdown16.500%Expectancy2.037Net Profit1714.767%Sharpe Ratio1.882Probabilistic Sharpe Ratio99.214%Loss Rate27%Win Rate73%Profit-Loss Ratio3.16Alpha0.257Beta0.068Annual Standard Deviation0.141Annual Variance0.02Information Ratio0.623Tracking Error0.238Treynor Ratio3.882Total Fees\$4714.03
```"""
Based on 'In & Out' strategy by Peter Guenther 4 Oct 2020
expanded/inspired by Tentor Testivis, Dan Whitnable (Quantopian), Vladimir, and Thomas Chang.

https://www.quantopian.com/posts/new-strategy-in-and-out
https://www.quantconnect.com/forum/discussion/9597/the-in-amp-out-strategy-continued-from-quantopian/p1
"""

# 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
res = Resolution.Minute

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

# Holdings
### 'Out' holdings and weights
self.BND1 = self.AddEquity('TLT', res).Symbol #TLT; TMF for 3xlev
self.BND2 = self.AddEquity('IEF', res).Symbol #IEF; TYD for 3xlev
self.HLD_OUT = {self.BND1: .5, self.BND2: .5}
### 'In' holdings and weights (static stock selection strategy)
self.STKS = self.AddEquity('QQQ', res).Symbol #SPY or QQQ; TQQQ for 3xlev
self.HLD_IN = {self.STKS: 1}

# Market and list of signals based on ETFs
self.MRKT = self.AddEquity('SPY', res).Symbol  # market
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.INDU = self.PRDC  # vs industrials
self.SHCU = self.AddEquity('FXF', res).Symbol  # safe haven currency (CHF)
self.RICU = self.AddEquity('FXA', res).Symbol  # vs risk currency (AUD)

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

# Initialize variables
## 'In'/'out' indicator
self.be_in = 999 #initially, set to an arbitrary value different from 1 (in) and 0 (out)
## Day count variables
self.dcount = 0  # count of total days since start
self.outday = 0  # dcount when self.be_in=0
## Flexi wait days

# set a warm-up period to initialize the indicator
self.SetWarmUp(timedelta(350))

self.Schedule.On(
self.DateRules.EveryDay(),
self.TimeRules.AfterMarketOpen('SPY', 120),
self.rebalance_when_out_of_the_market
)

self.Schedule.On(
self.DateRules.WeekEnd(),
self.TimeRules.AfterMarketOpen('SPY', 120),
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.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,
np.where((returns_sample[self.GOLD].iloc[-1]>0) & (returns_sample[self.SLVA].iloc[-1]<0) & (returns_sample[self.SLVA].iloc[-2]>0), self.INI_WAIT_DAYS, 1),
np.where((returns_sample[self.UTIL].iloc[-1]>0) & (returns_sample[self.INDU].iloc[-1]<0) & (returns_sample[self.INDU].iloc[-2]>0), self.INI_WAIT_DAYS, 1),
np.where((returns_sample[self.SHCU].iloc[-1]>0) & (returns_sample[self.RICU].iloc[-1]<0) & (returns_sample[self.RICU].iloc[-2]>0), self.INI_WAIT_DAYS, 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

#self.be_in = True # for testing; sets the algo to being always in

# Swap to 'out' assets if applicable
if not self.be_in:
self.wt = {**dict.fromkeys(self.HLD_IN, 0), **self.HLD_OUT}
# Only trade when changing from in to out
for sec, weight in self.wt.items():
cond1 = (self.Portfolio[sec].Quantity > 0) and (weight == 0)
cond2 = (self.Portfolio[sec].Quantity == 0) and (weight > 0)
if cond1 or cond2:
self.SetHoldings(sec, 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())