| Overall Statistics |
|
Total Trades 968 Average Win 0.33% Average Loss -0.33% Compounding Annual Return -73.614% Drawdown 34.300% Expectancy -0.217 Net Profit -30.063% Sharpe Ratio -2.283 Probabilistic Sharpe Ratio 0.668% Loss Rate 61% Win Rate 39% Profit-Loss Ratio 0.98 Alpha -0.688 Beta -0.096 Annual Standard Deviation 0.286 Annual Variance 0.082 Information Ratio -0.463 Tracking Error 0.631 Treynor Ratio 6.796 Total Fees $1833.45 |
from System.Collections.Generic import List
from QuantConnect.Data.UniverseSelection import *
import operator
from math import ceil,floor
from scipy import stats
import numpy as np
from datetime import timedelta
class Piotroski(QCAlgorithm):
def Initialize(self):
''' Backtesting Parameters '''
self.SetStartDate(2020, 1, 1)
#self.SetEndDate(2020, 1, 1)
self.minutesAfterOpen = 10
self.SetCash(50000)
''' Universe Settings '''
self.benchmark = Symbol.Create("SPY", SecurityType.Equity, Market.USA)
self.UniverseSettings.Resolution = Resolution.Minute
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.topScoreSymbolsCoarse = 10000
self.topScoreSymbolsFine = 50
''' Schedule Settings '''
self.AddEquity("SPY", Resolution.Minute)
self.SetBenchmark("SPY")
self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 1), Action(self.Buy))
self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 60), Action(self.Liquidate))
''' Other Settings '''
self.month = -1
self.symbols = []
self.changes = {}
def CoarseSelectionFunction(self, coarse):
if self.month != self.Time.month:
sortedCoarse = [x for x in coarse
if x.HasFundamentalData
and x.Price > 5]
sortedDollarVolume = sorted(sortedCoarse, key=lambda x: x.DollarVolume, reverse=True)
topCoarse = sortedDollarVolume[:self.topScoreSymbolsCoarse]
return [x.Symbol for x in topCoarse]
else: return self.symbols
def FineSelectionFunction(self, fine):
if self.month != self.Time.month:
self.month = self.Time.month
''' Retrieve all stocks that have the valid variation ratios that we want '''
filteredFine = [x for x in fine if x.FinancialStatements.IncomeStatement.NetIncome.TwelveMonths
and x.FinancialStatements.CashFlowStatement.CashFlowFromContinuingOperatingActivities.TwelveMonths
and x.OperationRatios.ROA.ThreeMonths
and x.OperationRatios.ROA.OneYear
and x.FinancialStatements.BalanceSheet.ShareIssued.ThreeMonths
and x.FinancialStatements.BalanceSheet.ShareIssued.TwelveMonths
and x.OperationRatios.GrossMargin.ThreeMonths
and x.OperationRatios.GrossMargin.OneYear
and x.OperationRatios.LongTermDebtEquityRatio.ThreeMonths
and x.OperationRatios.LongTermDebtEquityRatio.OneYear
and x.OperationRatios.CurrentRatio.ThreeMonths
and x.OperationRatios.CurrentRatio.OneYear
and x.OperationRatios.AssetsTurnover.ThreeMonths
and x.OperationRatios.AssetsTurnover.OneYear
and x.ValuationRatios.NormalizedPERatio
and x.EarningReports.BasicAverageShares.ThreeMonths
and x.EarningReports.BasicEPS.TwelveMonths]
''' Using the FScore class, retrieve the stocks that have a score of X or higher '''
sortedByFScore = [x for x in filteredFine if FScore(x.FinancialStatements.IncomeStatement.NetIncome.TwelveMonths,
x.FinancialStatements.CashFlowStatement.CashFlowFromContinuingOperatingActivities.TwelveMonths,
x.OperationRatios.ROA.ThreeMonths,
x.OperationRatios.ROA.OneYear,
x.FinancialStatements.BalanceSheet.ShareIssued.ThreeMonths,
x.FinancialStatements.BalanceSheet.ShareIssued.TwelveMonths,
x.OperationRatios.GrossMargin.ThreeMonths,
x.OperationRatios.GrossMargin.OneYear,
x.OperationRatios.LongTermDebtEquityRatio.ThreeMonths,
x.OperationRatios.LongTermDebtEquityRatio.OneYear,
x.OperationRatios.CurrentRatio.ThreeMonths,
x.OperationRatios.CurrentRatio.OneYear,
x.OperationRatios.AssetsTurnover.ThreeMonths,
x.OperationRatios.AssetsTurnover.OneYear).ObjectiveScore() > 6
]
self.qualityStocks = sortedByFScore
''' The Piotroski score ranks quality stocks, but we still need to determine value by filtering it more '''
sortedByNormalizedPE = sorted(sortedByFScore, key=lambda x: x.EarningReports.BasicAverageShares.ThreeMonths *
x.EarningReports.BasicEPS.TwelveMonths *
x.ValuationRatios.NormalizedPERatio, reverse = True)
self.Debug(str(len(self.qualityStocks)))
topFine = sortedByNormalizedPE[:self.topScoreSymbolsFine]
self.symbols = [i.Symbol for i in topFine]
return self.symbols
else: return self.symbols
def Buy(self):
self.changes = {}
for symbol in self.symbols:
history = self.History(symbol, 2, Resolution.Minute)
if history.empty: continue
self.Debug(history)
first = history.head(1)['close'].iloc[0]
last = history.tail(1)['open'].iloc[0]
if last < first: self.changes[symbol] = (last - first)/first
if len(self.changes) > 0:
sortedSymbols = sorted(self.changes.items(), key=lambda x: x[1], reverse=False)[:10]
for symbol in sortedSymbols:
self.Debug(str(self.Time) + " - BUY: " + str(symbol[0]) + " - PERCENT GAINED: " + str(symbol[1]))
self.SetHoldings(symbol[0], .25)
class FScore(object):
def __init__(self,
netincome,
operating_cashflow,
roa_current,
roa_past,
issued_current,
issued_past,
grossm_current,
grossm_past,
longterm_current,
longterm_past,
curratio_current,
curratio_past,
assetturn_current,
assetturn_past):
self.netincome = netincome
self.operating_cashflow = operating_cashflow
self.roa_current = roa_current
self.roa_past = roa_past
self.issued_current = issued_current
self.issued_past = issued_past
self.grossm_current = grossm_current
self.grossm_past = grossm_past
self.longterm_current = longterm_current
self.longterm_past = longterm_past
self.curratio_current = curratio_current
self.curratio_past = curratio_past
self.assetturn_current = assetturn_current
self.assetturn_past = assetturn_past
def ObjectiveScore(self):
''' The Piotroski score is broken down into profitability; leverage, liquidity, and source of funds; and operating efficiency categories, as follows: '''
fscore = 0
''' Profitability Criteria '''
fscore += np.where(self.netincome > 0, 1, 0) # Positive Net Income (X Months?)
fscore += np.where(self.operating_cashflow > 0, 1, 0) # Positive Operating Cash Flow
fscore += np.where(self.roa_current > self.roa_past, 1, 0) # Positive Return on Assets
fscore += np.where(self.operating_cashflow > self.roa_current, 1, 0) # Cash flow from operations being greater than net income (quality of earnings)
''' Leverage, Liquidity, and Source of Dunds Criteria '''
fscore += np.where(self.longterm_current <= self.longterm_past, 1, 0) # Lower ratio of long term debt in the current period, compared to the previous year (decreased leverage)
fscore += np.where(self.curratio_current >= self.curratio_past, 1, 0) # Higher current ratio this year compared to the previous year (more liquidity)
fscore += np.where(self.issued_current <= self.issued_past, 1, 0) # No new shares were issued in the last year
''' Operating Efficiency Criteria '''
# A higher gross margin compared to the previous year
fscore += np.where(self.grossm_current >= self.grossm_past, 1, 0) # A higher gross margin compared to the previous year
fscore += np.where(self.assetturn_current >= self.assetturn_past, 1, 0) # A higher asset turnover ratio compared to the previous year (1 point)
return fscore