Alexandre Catarino
2b0fd2e607
* Fixes Double to Decimal Cast in GetAnnualPerformance `GetAnnualPerformance` raises an exception if the `AnnualPerformance` calculation returns a double that cannot be cast to decimal (smaller than `decimal.MinValue` or bigger than `decimal.MaxValue`). See `ProbabilisticSharpeRatio` where the same solution was applied. * Updates SPY Market Data SPY is a key asset since it is the default benchmark, and any change can lead to different `Alpha` and `Beta` * Updates Unit Tests to Reflect Data Update * Updates Regression Tests to Reflect Data Update I Most of the regression tests change because of updated data (market and factors) of SPY (default benchmark) while the total trade remain the same. * Updates Regression Tests to Reflect Data Update II The following regression tests were changed to adapt to adjusted prices and keep the total trades: - `BacktestingBrokerageRegressionAlgorithm` - `LimitIfTouchedRegressionAlgorithm` - `PortfolioRebalanceOnCustomFuncRegressionAlgorithm` - `SetAccountCurrencySecurityMarginModelRegressionAlgorithm` - `StopLossOnOrderEventRegressionAlgorithm` - `TimeInForceAlgorithm` The following regression tests have more trades since adjusted prices allowed more 1-2 shares trades that were rounded down to zero before: - `FreePortfolioValueRegressionAlgorithm` 2 -> 3 - `PortfolioRebalanceOnDateRulesRegressionAlgorithm` 291 -> 298 - `TrailingStopRiskFrameworkAlgorithm` 5 -> 7 Especial cases: - `AutoRegressiveIntegratedMovingAverageRegressionAlgorithm` 65 -> 52 - ARIMA model sensibility - `BlackLittermanPortfolioOptimizationFrameworkAlgorithm` 18 -> 19 - BLM model sensibility - `ExtendedMarketHoursHistoryRegressionAlgorithm` 20 -> 18 - Less minute bars before market opens * Addresses Peer-Review Fix `BacktestingBrokerageRegressionAlgorithm` to use `CalculateOrderQuantity` and round down `quantity` to an even number to pass a value assertion and update the expected value from 50 to 52. The quantity calculated by `CalculateOrderQuantity` has changed from 50 to 53 because of factor file update.
LEAN Data Formats
Introduction
From the beginning LEAN strived to use an open, human readible data format - independent of any specific database or file format. From this core philosophy we built LEAN to read its financial data from flat files on disk. Data compression is done in zip format; and all individual files are CSV or JSON.
When there is no activity for a security, the price is omitted to the file. Only new ticks, and price changes are recorded.
File Data Format
Although we strive to make all data formats identical it is often not possible. Below are links to dedicated documentation on the file format of the data in each asset type:
Equity | Forex | Options | Futures | Crypto
Folder Structure
Data files are separated and nested in a few predictable layers:
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Tick, Second and Minute Financial Data:
/data/securityType/marketName/resolution/ticker/date_tradeType.zip -
Hour, Daily Financial Data:
/data/securityType/marketName/resolution/ticker.zip
The marketName value is used to separate different tradable assets with the same ticker. E.g. EURUSD is traded on multiple brokerages all with slightly different prices.
Core Data Types
LEAN has a few core data types which are represented in all the asset classes we support. Below are links to their implementation in LEAN.
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TradeBar - TradeBar represents trade ticks of assets consolidated for a period. TradeBar file format is slightly different for high resolution (second, minute) and low resolution (daily, hour).
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QuoteBar - QuoteBar represents top of book quote data consolidated over a period of time (bid and ask bar).
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Tick - Tick data represents an individual record of trades ("trade ticks") or quote updates ("quote tick") for an asset. Tick data is instantaneous - it does not have a period.
Data Readers
All data is parsed from disk via Reader() methods. The Reader takes a single line of the file and converts it the appropriate type. i.e. TradeBar.Reader() method is a factory which returns TradeBar objects. When implementing custom data Readers are used
Other Data Formats
Theoretically LEAN can accept data in any format (database, API or flatfile). However practically we currently have reader implementations written for a flat file system.