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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%: 154","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":-6.0534876698371471,"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":-4.2335218358985038,"benchmarkSharpe":-1.0742405870482774},"count":8,"solutions":["Consider adding risk management techniques such as stop-loss orders, position sizing, Option hedging, and diversification to mitigate losses during turbulent periods."]}]},"backtestId":"9b5680274da76368695f47252723916e","files":[{"id":null,"content":"# https:\/\/quantpedia.com\/strategies\/federal-open-market-committee-meeting-effect-in-stocks\/\r\n#\r\n# The investor is invested in stocks during FOMC meetings (going long S&P 500 ETF, fund, future, or CFD on a close one day before the meeting and closing position on close after the meeting).\r\n# Otherwise, he is invested in cash during the remaining days. The strategy has very low exposure to the stock market (8 days during the average year); therefore, it can be very easily leveraged\r\n# to gain very significant returns.\r\n#\r\n# QC implementation changes:\r\n#   - FED dates are imported from text file.\r\n\r\nfrom AlgorithmImports import *\r\nfrom pandas.tseries.offsets import BDay\r\nfrom datetime import datetime\r\n\r\nclass FederalOpenMarketCommitteeMeetingEffectinStocks(QCAlgorithm):\r\n    \r\n    def Initialize(self) -> None:\r\n        self.SetStartDate(2000, 1, 1)\r\n        self.SetCash(100000)\r\n        \r\n        self.market:Symbol = self.AddEquity(\"SPY\", Resolution.Minute).Symbol\r\n\r\n        self.fed_days_symbol:Symbol = self.AddData(FedDays, 'fed_days', Resolution.Daily, TimeZones.NewYork).Symbol\r\n        self.SetWarmUp(1, Resolution.Daily)\r\n        FedDays.set_algo(self)\r\n        \r\n        self.recent_day:int = -1\r\n\r\n    def OnData(self, data: Slice) -> None:\r\n        if self.IsWarmingUp: return\r\n\r\n        # custom data is still coming\r\n        if self.securities[self.fed_days_symbol].get_last_data() and self.time.date() > FedDays.get_last_update_date()[self.fed_days_symbol]:\r\n            self.liquidate()\r\n            return\r\n\r\n        if self.fed_days_symbol in data and data[self.fed_days_symbol]:\r\n            self.Log(f\"New FOMC meeting data arrived: {self.Time}; submitting an MOC order...\")\r\n\r\n            # new fed day data arrived\r\n            quantity:float = self.CalculateOrderQuantity(self.market, 1.)\r\n            self.MarketOnCloseOrder(self.market, quantity)\r\n\r\n            self.recent_day = self.Time.day\r\n        else:\r\n            # other new minute resolution data arrived\r\n            if self.Portfolio[self.market].Invested:\r\n                if self.Time.day != self.recent_day:\r\n                    self.recent_day = self.Time.day\r\n\r\n                    self.Log(f\"FOMC meeting day; submitting an MOC order to close opened position...\")\r\n                \r\n                    self.MarketOnCloseOrder(self.market, -self.Portfolio[self.market].Quantity)\r\n        \r\nclass FedDays(PythonData):\r\n    _last_update_date:Dict[str, datetime.date] = {}\r\n\r\n    @staticmethod\r\n    def get_last_update_date() -> Dict[str, datetime.date]:\r\n       return FedDays._last_update_date\r\n\r\n    algo = None\r\n    \r\n    @staticmethod\r\n    def set_algo(algo):\r\n        FedDays.algo = algo\r\n\r\n    def GetSource(self, config:SubscriptionDataConfig, date:datetime, isLiveMode:bool) -> SubscriptionDataSource:\r\n        if isLiveMode:\r\n            # FedDays.algo.Log(f\"Edited GetSource date {FedDays.algo.Time}\")\r\n            return SubscriptionDataSource(\"https:\/\/data.quantpedia.com\/backtesting_data\/economic\/fed_days.json\", SubscriptionTransportMedium.RemoteFile, FileFormat.UnfoldingCollection)\r\n\r\n        return SubscriptionDataSource(\"https:\/\/data.quantpedia.com\/backtesting_data\/economic\/fed_days.csv\", SubscriptionTransportMedium.RemoteFile)\r\n        \r\n    def Reader(self, config:SubscriptionDataConfig, line:str, date:datetime, isLiveMode:bool) -> BaseData:\r\n        if isLiveMode:\r\n            try:\r\n                # FedDays.algo.Log(f\"Reader\")\r\n\r\n                objects = []\r\n                data = json.loads(line)\r\n                end_time = None\r\n\r\n                for index, sample in enumerate(data):\r\n                    custom_data = FedDays()\r\n                    custom_data.Symbol = config.Symbol\r\n\r\n                    custom_data.Time = (datetime.strptime(str(sample[\"fed_date\"]), \"%Y-%m-%d\") - BDay(1)).replace(hour=9, minute=31)\r\n                    # FedDays.algo.Log(f\"{custom_data.Time}\")\r\n\r\n                    end_time = custom_data.Time  \r\n                    objects.append(custom_data)\r\n\r\n                    # store last update dates\r\n                    if config.Symbol not in FedDays._last_update_date:\r\n                        FedDays._last_update_date[config.Symbol] = datetime(1,1,1).date()\r\n                    if custom_data.Time.date() > FedDays._last_update_date[config.Symbol]:\r\n                        FedDays._last_update_date[config.Symbol] = custom_data.Time.date()\r\n\r\n                return BaseDataCollection(end_time, config.Symbol, objects) \r\n\r\n            except ValueError:\r\n                # FedDays.algo.Log(f\"Reader Error\")\r\n                return None\r\n        else:\r\n            if not (line.strip() and line[0].isdigit()):\r\n                return None\r\n            \r\n            custom = FedDays()\r\n            custom.Symbol = config.Symbol\r\n\r\n            custom.Time = (datetime.strptime(line, \"%Y-%m-%d\") - BDay(1)).replace(hour=9, minute=31)\r\n            custom.Value = 0.\r\n            custom[\"fed_date_str\"] = line\r\n\r\n            # store last update dates\r\n            if config.Symbol not in FedDays._last_update_date:\r\n                FedDays._last_update_date[config.Symbol] = datetime(1,1,1).date()\r\n            if custom.Time.date() > FedDays._last_update_date[config.Symbol]:\r\n                FedDays._last_update_date[config.Symbol] = custom.Time.date()\r\n\r\n            return custom","modified":null,"name":"main.py","open":false,"isLibrary":false,"projectId":37298172}],"success":true}