Ashutosh
ea5e4ac8d0
* Update symbol-properties-database.csv Updating Symbol properties database to reflect kraken API information. #### Description Updated the market_ticker column of Symbol properties database to reflect information as per Kraken API. #### Related Issue Close #7794 #### Motivation and Context We updated symbol mapping for the Kraken market. #### Requires Documentation Change No #### How Has This Been Tested? <!--- Please describe in detail how you tested your changes. --> <!--- Include details of your testing environment, and the tests you ran to --> <!--- see how your change affects other areas of the code, etc. --> #### Types of changes <!--- What types of changes does your code introduce? Put an `x` in all the boxes that apply: --> - [ ] Bug fix (non-breaking change which fixes an issue) - [ ] Refactor (non-breaking change which improves implementation) - [ ] Performance (non-breaking change which improves performance. Please add associated performance test and results) - [X] New feature (non-breaking change which adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Non-functional change (xml comments/documentation/etc) #### Checklist: <!--- The following is a checklist of items that MUST be completed before a PR is accepted --> <!--- If you're unsure about any of these, don't hesitate to ask. We're here to help! --> - [X] My code follows the code style of this project. - [X] I have read the **CONTRIBUTING** [document](https://github.com/QuantConnect/Lean/blob/master/CONTRIBUTING.md). - [X] I have added tests to cover my changes. <!--- If not applicable, please explain why --> - [X] All new and existing tests passed. - [X] My branch follows the naming convention `bug-<issue#>-<description>` or `feature-<issue#>-<description>` <!--- Template inspired by https://www.talater.com/open-source-templates/#/page/99 --> * Update symbol-properties-database.csv - Update as per peer review * Correction
LEAN Data Formats
Introduction
From the beginning, LEAN has strived to use an open, human-readable 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 from 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 impossible. 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:
-
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 represented in all the asset classes we support. Below are links to their implementation in LEAN.
-
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).
-
QuoteBar - QuoteBar represents top of book quote data consolidated over a period of time (bid and ask bar).
-
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, in practice, we currently have reader implementations written for a flat file system.