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align (trend, volume, indicator thresholds), it is common that they are never all true at the same time. Relax the single most restrictive condition and re-run, changing one condition per backtest instead of redesigning the strategy.","Check if you need to warm-up some data structures, including indicators, RollingWindow objects, and training data. Log how many assets pass IsReady or warm-up gates: they can silently exclude most of the universe.","Check if the algorithm subscribes to any assets. Is the universe selection actually selecting anything? Log the selection count on each rebalance.","Check custom data sources actually deliver data by logging the first points received: a reader or date-format error can silently produce no data and therefore no signals.","Check minimum-history or training-window requirements against the history the data source actually provides: if a model requires more history than exists, the trading logic may never activate.","Check if there is enough cash to satisfy the minimum order sizes."]},{"name":"PortfolioMarginUsageAnalysis","issue":"The algorithm sometimes only utilizes a small proportion of the margin available.","sample":"Number of days when the 3-day SMA of the margin usage drops below 50%: 600","solutions":["Adjust the strategy logic or position sizing to utilize more margin.","If the algorithm logic leads to periods of time when the portfolio sits in cash, consider holding a \"risk-free\" asset during these periods."]},{"name":"PerformanceRelativeToBenchmarkAnalysis","issue":"The strategy has a lower Sharpe ratio than the benchmark.","sample":{"backtestSharpe":-5.5547761223975387,"benchmarkSharpe":-1.1085894562560332},"solutions":["Try adjusting the trading rules and\/or the universe to get a strategy that outperforms the benchmark."]},{"name":"CrisisEventsAnalysis","issue":"The strategy underperformed the benchmark during some crisis events in terms of risk-adjusted returns.","sample":{"crisisEvent":"DotCom Bubble 2000","backtestSharpe":-2.0805976705679572,"benchmarkSharpe":-1.0742405870482774},"count":9,"solutions":["Consider adding risk management techniques such as stop-loss orders, position sizing, Option hedging, and diversification to mitigate losses during turbulent periods."]}]},"backtestId":"f34ab591b5377be1339c55a37f55e4aa","files":[{"id":null,"content":"# https:\/\/quantpedia.com\/strategies\/payday-anomaly\/\n#\n# The investment universe consists of the S&P500 index. Simply, buy and hold the index during the 16th day in the month during each month of the year.\n\nfrom dateutil.relativedelta import relativedelta\nfrom AlgorithmImports import *\n\nclass PayDayAnomaly(QCAlgorithm):\n\n    def Initialize(self):\n        self.SetStartDate(2000, 1, 1)\n        self.SetCash(100000)\n        \n        self.market: Symbol = self.AddEquity('SPY', Resolution.Minute).Symbol\n        self.liquidate_next_day: bool = False\n        \n        self.Schedule.On(self.DateRules.EveryDay(self.market), self.TimeRules.BeforeMarketClose(self.market, 1), self.Purchase)\n    \n    def Purchase(self) -> None:\n        alg_time = self.Time\n        paydate = self.PaydayDate(alg_time)\n\n        if alg_time.date() == paydate:\n            self.SetHoldings(self.market, 1)\n            self.liquidate_next_day = True\n\n        if self.liquidate_next_day:\n            self.liquidate_next_day = False\n            return\n        \n        if self.Portfolio[self.market].IsLong:\n            self.Liquidate(self.market)\n\n    def PaydayDate(self, date_time):\n        payday = date(date_time.year, date_time.month, 1) + relativedelta(day=15)\n        \n        if payday.weekday() == 5: # Is saturday.\n            payday = payday - timedelta(days=1)\n        elif payday.weekday() == 6: # Is sunday.\n            payday = payday - timedelta(days=2)\n        \n        return payday","modified":null,"name":"main.py","open":false,"isLibrary":false,"projectId":37344498}],"success":true}