Overall Statistics Total Trades15Average Win20.53%Average Loss-3.39%Compounding Annual Return14.508%Drawdown16.300%Expectancy2.024Net Profit41.591%Sharpe Ratio0.784Probabilistic Sharpe Ratio31.426%Loss Rate57%Win Rate43%Profit-Loss Ratio6.06Alpha0.083Beta0.28Annual Standard Deviation0.139Annual Variance0.019Information Ratio0.086Tracking Error0.196Treynor Ratio0.388Total Fees\$71.44Estimated Strategy Capacity\$34000.00Lowest Capacity AssetBIL TT1EBZ21QWKL
```#region imports
from AlgorithmImports import *
#endregion
"""
Based on 'In & Out' strategy by Peter Guenther 4 Oct 2020
expanded/inspired by Tentor Testivis, Dan Whitnable (Quantopian), Vladimir, Thomas Chang,
Derek Melchin (QuantConnect), Nathan Swenson, and Goldie Yalamanchi.

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
from scipy import stats
from collections import deque

class InOut(QCAlgorithm):

def Initialize(self):

self.SetStartDate(2020, 1, 1)  # Set Start Date
self.cap = 100000
self.SetCash(self.cap)  # Set Strategy Cash
res = Resolution.Minute

# Holdings
### 'Out' holdings and weights
self.HLD_OUT = {self.AddEquity('BIL', res).Symbol: 1} #TLT; TMF for 3xlev
### 'In' holdings and weights (static stock selection strategy)
# self.HLD_IN = {self.AddEquity('TQQQ', res).Symbol: 1, self.AddEquity('QLD', res).Symbol: 0} #SPY or QQQ; TQQQ for 3xlev
self.sma = self.SMA(self.spy, 50, Resolution.Daily)

# Market and list of signals based on ETFs
self.MRKT = self.AddEquity('QQQ', res).Symbol  # market; QQQ
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.SIGNALS = [self.PRDC, self.METL, self.NRES, self.USDX, self.DEBT, self.MRKT]
self.FORPAIRS = [self.GOLD, self.SLVA, self.UTIL, self.INDU]
self.pairlist = ['G_S', 'U_I']

# Initialize parameters and tracking variables
self.lookback, self.shift_vars, self.stat_alpha, self.ema_f = [252*5, [11, 60, 45], 5, 2/(1+50)]
self.be_in, self.portf_val, self.signal_dens = [[1], [self.cap], deque([0, 0, 0, 0, 0], maxlen = 100)]

self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('QQQ', 120),
self.inout_check)

# Symbols for charts
self.QQQ = self.MRKT

# Setup daily consolidation
symbols = list(set(self.SIGNALS + [self.MRKT] + self.FORPAIRS + list(self.HLD_OUT.keys()) + list(self.HLD_IN.keys()) + [self.SPY] + [self.QQQ]))
for symbol in symbols:
self.consolidator.DataConsolidated += self.consolidation_handler

# Warm up history
self.history = self.History(symbols, self.lookback, Resolution.Daily)
if self.history.empty or 'close' not in self.history.columns:
return
self.history = self.history['close'].unstack(level=0).dropna()
self.update_history_shift()

# Benchmarks for charts
self.benchmarks = [self.history[self.SPY].iloc[-2], self.history[self.QQQ].iloc[-2]]

def consolidation_handler(self, sender, consolidated):
self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close
self.history = self.history.iloc[-self.lookback:]
self.update_history_shift()

def update_history_shift(self):
self.history_shift = self.history.rolling(self.shift_vars[0], center=True).mean().shift(self.shift_vars[1])

def replace_tqqq(self):
if self.Time.date() <= datetime.strptime('2010-02-09', '%Y-%m-%d').date():
self.HLD_IN[list(self.HLD_IN.keys())[0]] = 0; self.HLD_IN[list(self.HLD_IN.keys())[1]] = 1
else: self.HLD_IN[list(self.HLD_IN.keys())[0]] = 1; self.HLD_IN[list(self.HLD_IN.keys())[1]] = 0

def inout_check(self):
if self.history.empty: return

if Symbol.Create('TQQQ', SecurityType.Equity, Market.USA) in self.HLD_IN.keys(): self.replace_tqqq()

# Load saved signal density (for live interruptions):
if self.LiveMode and sum(list(self.signal_dens))==0 and self.ObjectStore.ContainsKey('OS_signal_dens'):
self.signal_dens = deque(OS, maxlen = 100)

# Returns sample to detect extreme observations
returns_sample = (self.history / self.history_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])

# Extreme observations; statistical significance = X% (stat_alpha)
extreme_b = returns_sample.iloc[-1] < np.nanpercentile(returns_sample, self.stat_alpha, axis=0)

# Re-assess/disambiguate double-edged signals
abovemedian = returns_sample.iloc[-1] > np.nanmedian(returns_sample, axis=0)
### Interest rate expectations (cost of debt) may increase because the economic outlook improves (showing in rising input prices) = actually not a negative signal
extreme_b.loc[self.DEBT] = np.where((extreme_b.loc[self.DEBT].any()) & (abovemedian[[self.METL, self.NRES]].any()), False, extreme_b.loc[self.DEBT])

cur_signal_dens = extreme_b[self.SIGNALS + self.pairlist].sum() / len(self.SIGNALS + self.pairlist)
add_dens = np.array((1-self.ema_f) * self.signal_dens[-1] + self.ema_f * cur_signal_dens)

# Determine whether 'in' or 'out' of the market
if self.signal_dens[-1] > self.signal_dens[-2]:
self.be_in.append(0)
if self.signal_dens[-1] < min(list(self.signal_dens)[-(self.shift_vars[2]):-2]):
self.be_in.append(1)

# Swap to 'out' assets if applicable
if not self.be_in[-1]:
if self.be_in[-1] and self.Securities[self.spy].Close > self.sma.Current.Value:

self.charts(extreme_b)

# Save data: signal density from live trading for interruptions (note: backtest saves data at the end so that it's available for live trading).
if self.LiveMode: self.SaveData()

# sort: execute largest sells first, largest buys last
hold_wt = {k: (self.Portfolio[k].Quantity*self.Portfolio[k].Price)/self.Portfolio.TotalPortfolioValue for k in self.Portfolio.Keys}
order_wt = {k: weight_by_sec[k] - hold_wt.get(k, 0) for k in weight_by_sec}
weight_by_sec = {k: weight_by_sec[k] for k in dict(sorted(order_wt.items(), key=lambda item: item[1]))}
for sec, weight in weight_by_sec.items():
# Check that we have data in the algorithm to process a trade
if not self.CurrentSlice.ContainsKey(sec) or self.CurrentSlice[sec] is None:
continue
# Only trade if holdings fundamentally change
cond1 = (weight==0) and self.Portfolio[sec].IsLong
cond2 = (weight>0) and not self.Portfolio[sec].Invested
if cond1 or cond2:
self.SetHoldings(sec, weight)

def charts(self, extreme_b):
# Market comparisons
spy_perf = self.history[self.SPY].iloc[-1] / self.benchmarks[0] * self.cap
qqq_perf = self.history[self.QQQ].iloc[-1] / self.benchmarks[1] * self.cap
self.Plot('Strategy Equity', 'SPY', spy_perf)
self.Plot('Strategy Equity', 'QQQ', qqq_perf)

# Signals
self.Plot("In Out", "in_market", self.be_in[-1])
self.Plot("In Out", "signal_dens", self.signal_dens[-1])

# self.Plot("Signals", "PRDC", int(extreme_b[self.SIGNALS + self.pairlist][0]))
# self.Plot("Signals", "METL", int(extreme_b[self.SIGNALS + self.pairlist][1]))
# self.Plot("Signals", "NRES", int(extreme_b[self.SIGNALS + self.pairlist][2]))
# self.Plot("Signals", "USDX", int(extreme_b[self.SIGNALS + self.pairlist][3]))
# self.Plot("Signals", "DEBT", int(extreme_b[self.SIGNALS + self.pairlist][4]))
# self.Plot("Signals", "MRKT", int(extreme_b[self.SIGNALS + self.pairlist][5]))
# self.Plot("Signals", "G_S", int(extreme_b[self.SIGNALS + self.pairlist][6]))
# self.Plot("Signals", "U_I", int(extreme_b[self.SIGNALS + self.pairlist][7]))

# Comparison of out returns
self.portf_val.append(self.Portfolio.TotalPortfolioValue)
if not self.be_in[-1] and len(self.be_in)>=2:
period = np.where(np.array(self.be_in)[:-1] != np.array(self.be_in)[1:])[0][-1] - len(self.be_in)
mrkt_ret = self.history[self.MRKT].iloc[-1] / self.history[self.MRKT].iloc[period] - 1
strat_ret = self.portf_val[-1] / self.portf_val[period] - 1
strat_vs_mrkt = round(float(strat_ret - mrkt_ret), 4)
else: strat_vs_mrkt = 0
self.Plot('Out return', 'PF vs MRKT', strat_vs_mrkt)

def SaveData(self):
self.ObjectStore.SaveBytes('OS_signal_dens', pickle.dumps(self.signal_dens))```