Open Source

Generating Random Backtest Data

Introduction

Real market data is often expensive and governed by difficult redistribution licenses. QuantConnect wanted to give the LEAN users a way to design and test their algorithms, without needing to download and buy real financial data. The RandomDataGenerator seeks to address that by randomly generating data in the LEAN format, for the various asset types we support.

Using the Random Data Generator

The LEAN Random Data Generator is located in the LEAN ToolBox and can be accessed by executing the QuantConnect.ToolBox.exe CLI program.

We recommend cloning LEAN and building from the source code directly from Github. However, if this is not possible for you we have made a snapshot of the build available for your convenience here:
2019-01-19-QuantConnect.ToolBox.Binaries.zip

To run the ToolBox open a command window and execute it from the command line. To execute it you will need C# or Mono installed:

Windows:

 .\QuantConnect.ToolBox.exe --app=randomdatagenerator

Linux and Mac:

mono QuantConnect.ToolBox.exe --app=randomdatagenerator

Complete Example:

QuantConnect.ToolBox.exe 
    --app=randomdatagenerator 
    --resolution=Minute 
    --from-date=2018-01-01 
    --to-date=2019-01-01 
    --market=usa 
    --data-density=Dense 
    --include-coarse=true 
    --symbol-count=100

The Random Data Generator has many parameters you can configure to generate the various datasets LEAN supports. Please see the next section for documentation on these parameters.

Command Parameters

Random Data Generator Parameters
security-type	Security type to generate. Possible Values: `Equity, Crypto, Forex, Cfd, Option, Future` Default: Equity Example: `--security-type=Equity`
resolution	Resolution of data to store. Possible Values: `Tick, Second, Minute, Hour, Daily` Default: Minute Example: `--resolution=Minute`
from-date	Start date for the data to generate: format yyyy-mm-dd. Example: `--from-date=2018-01-01`
to-date	Final date of the generated data: format yyyy-mm-dd. Example: `--to-date=2019-01-01`
Symbol Count	Number of tickers to generate Example: `--symbol-count=100`
data-density	Tick density of the data generated to model low volume data. Possible Values: `Dense, Sparse, VerySparse` Default: Dense Example: `--density=Dense`
include-coarse	Generate the coarse universe data when generating Equity data. Default: true Example: `--include-coarse=true`

Supported Assets and Resolutions

The Random Data Generator currently supports the following asset types and resolutions:

Supported Data Types and Resolutions
Equity Trade Only	`Resolutions Tick, Second, Minute, Hour and Daily` All Equity data resolutions are supported.
Forex, CFD Quotes Only	`Resolutions Tick, Second, Minute, Hour and Daily` Forex and CFD generator supports quote (spread) data in all resolutions.
Futures, Crypto Trades and Quotes	`Resolutions Tick, Second, Minute, Hour and Daily` All Futures and crypto data types and resolutions are supported
Options Trades and Quotes	`Resolution Minute` Options are partially supported. Currently option random data is generated independent of a random underlying asset making its usefulness limited. This will be fixed by Github Issue #2839.

Final Notes

The Random Data Generator uses Brownian motion to generate the data prices. You can see how these prices and ticker strings are generated in the RandomValueGenerator class.

If you'd like to use a different system for providing the randomness you can implement your own IRandomValueGenerator or extend the RandomValueGenerator class.

You can also see our Documentation and Videos. You can also get in touch with us via Chat.

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