| Overall Statistics |
|
Total Trades 1152 Average Win 1.61% Average Loss -0.88% Compounding Annual Return 33.953% Drawdown 29.000% Expectancy 0.687 Net Profit 4385.070% Sharpe Ratio 1.256 Probabilistic Sharpe Ratio 64.431% Loss Rate 40% Win Rate 60% Profit-Loss Ratio 1.82 Alpha 0.287 Beta 0.252 Annual Standard Deviation 0.248 Annual Variance 0.062 Information Ratio 0.762 Tracking Error 0.281 Treynor Ratio 1.236 Total Fees $12180.71 |
'''
Intersection of ROC comparison using OUT_DAY approach by Vladimir v1.1 (diversified static lists)
inspired by Peter Guenther, Tentor Testivis, Dan Whitnable, Thomas Chang.
'''
import numpy as np
# -------------------------------------------------------------------------------------------
BONDS = ['TLT','TLH']; VOLA = 126; BASE_RET = 85; LEV = 0.99;
# -------------------------------------------------------------------------------------------
class ROC_Comparison_IN_OUT(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2008, 1, 1)
# self.SetEndDate(2021, 1, 1)
self.cap = 100000
self.STOCKS = [] # Selected using the universe selection
self.BONDS = [self.AddEquity(ticker, Resolution.Minute).Symbol for ticker in BONDS]
self.ASSETS = [self.STOCKS, self.BONDS]
self.SLV = self.AddEquity('SLV', Resolution.Minute).Symbol
self.GLD = self.AddEquity('GLD', Resolution.Minute).Symbol
self.XLI = self.AddEquity('XLI', Resolution.Minute).Symbol
self.XLU = self.AddEquity('XLU', Resolution.Minute).Symbol
self.DBB = self.AddEquity('DBB', Resolution.Minute).Symbol
self.UUP = self.AddEquity('UUP', Resolution.Minute).Symbol
self.MKT = self.AddEquity('SPY', Resolution.Minute).Symbol
self.pairs = [self.SLV, self.GLD, self.XLI, self.XLU, self.DBB, self.UUP]
self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.coarseSelector, self.fineSelector))
self.UniverseSettings.Resolution = Resolution.Minute
self.bull = 1
self.count = 0
self.outday = 0
self.wt = {}
self.real_wt = {}
self.mkt = []
self.universeMonth = -1
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 60),
self.daily_check)
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', 120),
self.trade)
symbols = [self.MKT] + self.pairs
for symbol in symbols:
self.consolidator = TradeBarConsolidator(timedelta(days=1))
self.consolidator.DataConsolidated += self.consolidation_handler
self.SubscriptionManager.AddConsolidator(symbol, self.consolidator)
self.history = self.History(symbols, VOLA + 1, Resolution.Daily)
if self.history.empty or 'close' not in self.history.columns:
return
self.history = self.history['close'].unstack(level=0).dropna()
def consolidation_handler(self, sender, consolidated):
self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close
self.history = self.history.iloc[-(VOLA + 1):]
def daily_check(self):
vola = self.history[[self.MKT]].pct_change().std() * np.sqrt(252)
wait_days = int(vola * BASE_RET)
period = int((1.0 - vola) * BASE_RET)
r = self.history.pct_change(period).iloc[-1]
exit = ((r[self.SLV] < r[self.GLD]) and (r[self.XLI] < r[self.XLU]) and (r[self.DBB] < r[self.UUP]))
if exit:
self.bull = 0
self.outday = self.count
if self.count >= self.outday + wait_days:
self.bull = 1
self.count += 1
def trade(self):
# Delete non-tradable stocks
for sym in self.STOCKS:
if self.Securities[sym].IsTradable == False:
del self.Securities[sym]
# Set all non-selected stocks as zero
for pi in self.Portfolio.Values:
eq = self.Symbol(str(pi.Symbol))
if eq not in self.STOCKS:
self.wt[eq] = 0.
for sec in self.STOCKS:
self.wt[sec] = LEV/len(self.STOCKS) if self.bull else 0;
for sec in self.BONDS:
self.wt[sec] = 0 if self.bull else LEV/len(self.BONDS);
for mode in ['sell', 'buy']: # First sell, then buy to make sure there is margin
for sec, weight in self.wt.items():
if weight == 0 and self.Portfolio[sec].IsLong:
self.Liquidate(sec)
cond1 = weight == 0 and self.Portfolio[sec].IsLong
cond2 = weight > 0 and not self.Portfolio[sec].Invested
if cond1 or cond2:
currentWeight = (self.Portfolio[sec].Quantity * self.Securities[sec].Price) / self.Portfolio.TotalPortfolioValue
if ((mode == 'buy' and weight > currentWeight) or
(mode == 'sell' and weight < currentWeight)):
self.SetHoldings(sec, weight)
def NOTINUSE_OnEndOfDay(self):
mkt_price = self.Securities[self.MKT].Close
self.mkt.append(mkt_price)
mkt_perf = self.mkt[-1] / self.mkt[0] * self.cap
self.Plot('Strategy Equity', 'SPY', mkt_perf)
account_leverage = self.Portfolio.TotalHoldingsValue / self.Portfolio.TotalPortfolioValue
self.Plot('Holdings', 'leverage', round(account_leverage, 1))
for sec, weight in self.wt.items():
self.real_wt[sec] = round(self.ActiveSecurities[sec].Holdings.Quantity * self.Securities[sec].Price / self.Portfolio.TotalPortfolioValue,4)
self.Plot('Holdings', self.Securities[sec].Symbol, round(self.real_wt[sec], 3))
def coarseSelector(self, coarse):
if self.Time.month == self.universeMonth:
return self.STOCKS
eqs = [x for x in coarse if (x.HasFundamentalData == True)]
eqs_volume_sorted = sorted(eqs, key=lambda x: x.DollarVolume, reverse=True)
# Top 1000 in stock volume
top = eqs_volume_sorted[:1000]
# self.Debug(f"{self.Time} Coarse: {len(eqs)} => {len(top)}")
top_eqs = [x.Symbol for x in top]
return top_eqs
def fineSelector(self, fine):
if self.Time.month == self.universeMonth:
return self.STOCKS
# No idea what class fine is, len or shape do not work
fineLen = 0
for i in fine:
fineLen += 1
tech = [x for x in fine if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology]
selected_symbols = [str(x.Symbol) for x in tech]
hist = self.History(selected_symbols, 21 * 12, Resolution.Daily)
o = hist['open'].unstack(level=0)
# Pure Profit
scores = o.ix[-1] / o.ix[0] - 1.
# Sharpe
# scores = (o.ix[-1] / o.ix[0] - 1.) / o.std()
companyCount = 10
top_eqs = scores.sort_values(ascending=False)[:companyCount]
self.STOCKS = [self.Symbol(str(x)) for x in top_eqs.index]
# self.Debug(f"{self.Time} Fine: selecting {fineLen} => {scores.shape[0]} => {len(self.STOCKS)}")
self.universeMonth = self.Time.month
return self.STOCKS