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55.63759, "mae": -1952.24, "mfe": 6589.96, "duration": "118.23:00:00", "endTradeDrawdown": 2561.28, "isWin": true, "orderIds": [ 7816, 8108 ] } ] }, "analysis": [ { "name": "FlatEquityCurveAnalysis", "issue": "The equity curve is flat for several days in a row.", "sample": [ { "start": "2016-01-01T05:00:00Z", "end": "2017-01-06T05:00:00", "trading_days": 90 } ], "solutions": [ "Log how often each entry condition passes individually: when several conditions must 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%: 1759", "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": "ParameterCountAnalysis", "issue": "The algorithm has so many numeric parameters it's at risk of overfitting.", "sample": "13 Parameters Detected", "solutions": [ "Try to remove some parameters to make the strategy more robust. The following table shows the criteria for parameters:| Parameter Types | Example Instances |\n|-|-|\n| Numeric Comparison | Numeric operators used to compare numeric arguments: <= < > >= |\n| Time Span | Setting the interval of `TimeSpan` or `timedelta` |\n| Order Event | Inputting numeric arguments when placing orders |\n| Scheduled Event | Inputting numeric arguments when scheduling an algorithm event to occur |\n| Variable Assignment | Assigning numeric values to variables |\n| Mathematical Operation | Any mathematical operation involving explicit numbers |\n| Lean API | Numeric arguments passed to Indicators, Consolidators, Rolling Windows, etc. |The following table shows common expressions that are not parameters:| Non-Parameter Types | Example Instances |\n|---------------------|-------------------|\n| Common APIs | `SetStartDate`, `SetEndDate`, `SetCash`, etc. |\n| Boolean Comparison | Testing for True or False conditions |\n| String Numbers | Numbers formatted as part of `Log` or `Debug` method statements |\n| Variable Names | Any variable names that use numbers as part of the name (for example, `smaIndicator200`) |\n| Common Functions | Rounding, array indexing, boolean comparison using 1/0 for True/False, etc. |" ] }, { "name": "CrisisEventsAnalysis", "issue": "The strategy underperformed the benchmark during some crisis events in terms of risk-adjusted returns.", "sample": { "crisisEvent": "COVID-19 Pandemic 2020", "backtestSharpe": -1.7474253467151577, "benchmarkSharpe": -1.194611439698716 }, "count": 2, "solutions": [ "Consider adding risk management techniques such as stop-loss orders, position sizing, Option hedging, and diversification to mitigate losses during turbulent periods." ] }, { "name": "StatisticalSignificanceOfDailyReturnsAnalysis", "issue": "The distribution of the strategy's daily returns in excess of the benchmark's daily returns has a p-value above 0.05. Therefore, we fail to reject the null hypothesis that the mean of this distribution is above zero.", "sample": { "pValue": 0.38241820276085525 }, "solutions": [ "Try adjusting the trading rules and/or the universe to get a strategy that outperforms the benchmark." ] } ] }