7.7 KiB
Bot usage
This page explains the difference parameters of the bot and how to run it.
Table of Contents
Bot commands
usage: main.py [-h] [-v] [--version] [-c PATH] [-d PATH] [-s NAME]
[--strategy-path PATH] [--dynamic-whitelist [INT]]
[--dry-run-db]
{backtesting,hyperopt} ...
Simple High Frequency Trading Bot for crypto currencies
positional arguments:
{backtesting,hyperopt}
backtesting backtesting module
hyperopt hyperopt module
optional arguments:
-h, --help show this help message and exit
-v, --verbose be verbose
--version show program's version number and exit
-c PATH, --config PATH
specify configuration file (default: config.json)
-d PATH, --datadir PATH
path to backtest data (default:
freqtrade/tests/testdata
-s NAME, --strategy NAME
specify strategy class name (default: DefaultStrategy)
--strategy-path PATH specify additional strategy lookup path
--dynamic-whitelist [INT]
dynamically generate and update whitelist based on 24h
BaseVolume (Default 20 currencies)
--dry-run-db Force dry run to use a local DB
"tradesv3.dry_run.sqlite" instead of memory DB. Work
only if dry_run is enabled.
How to use a different config file?
The bot allows you to select which config file you want to use. Per
default, the bot will load the file ./config.json
python3 ./freqtrade/main.py -c path/far/far/away/config.json
How to use --strategy?
This parameter will allow you to load your custom strategy class.
Per default without --strategy or -s the bot will load the
DefaultStrategy included with the bot (freqtrade/strategy/default_strategy.py).
The bot will search your strategy file within user_data/strategies and freqtrade/strategy.
To load a strategy, simply pass the class name (e.g.: CustomStrategy) in this parameter.
Example:
In user_data/strategies you have a file my_awesome_strategy.py which has
a strategy class called AwesomeStrategy to load it:
python3 ./freqtrade/main.py --strategy AwesomeStrategy
If the bot does not find your strategy file, it will display in an error message the reason (File not found, or errors in your code).
Learn more about strategy file in optimize your bot.
How to use --strategy-path?
This parameter allows you to add an additional strategy lookup path, which gets checked before the default locations (The passed path must be a folder!):
python3 ./freqtrade/main.py --strategy AwesomeStrategy --strategy-path /some/folder
How to install a strategy?
This is very simple. Copy paste your strategy file into the folder
user_data/strategies or use --strategy-path. And voila, the bot is ready to use it.
How to use --dynamic-whitelist?
Per default --dynamic-whitelist will retrieve the 20 currencies based
on BaseVolume. This value can be changed when you run the script.
By Default
Get the 20 currencies based on BaseVolume.
python3 ./freqtrade/main.py --dynamic-whitelist
Customize the number of currencies to retrieve
Get the 30 currencies based on BaseVolume.
python3 ./freqtrade/main.py --dynamic-whitelist 30
Exception
--dynamic-whitelist must be greater than 0. If you enter 0 or a
negative value (e.g -2), --dynamic-whitelist will use the default
value (20).
How to use --dry-run-db?
When you run the bot in Dry-run mode, per default no transactions are
stored in a database. If you want to store your bot actions in a DB
using --dry-run-db. This command will use a separate database file
tradesv3.dry_run.sqlite
python3 ./freqtrade/main.py -c config.json --dry-run-db
Backtesting commands
Backtesting also uses the config specified via -c/--config.
usage: main.py backtesting [-h] [-i TICKER_INTERVAL] [--realistic-simulation]
[--timerange TIMERANGE] [-l] [-r] [--export EXPORT]
[--export-filename EXPORTFILENAME]
optional arguments:
-h, --help show this help message and exit
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
specify ticker interval (1m, 5m, 30m, 1h, 1d)
--realistic-simulation
uses max_open_trades from config to simulate real
world limitations
--timerange TIMERANGE
specify what timerange of data to use.
-l, --live using live data
-r, --refresh-pairs-cached
refresh the pairs files in tests/testdata with the
latest data from the exchange. Use it if you want to
run your backtesting with up-to-date data.
--export EXPORT export backtest results, argument are: trades Example
--export=trades
--export-filename EXPORTFILENAME
Save backtest results to this filename requires
--export to be set as well Example --export-
filename=backtest_today.json (default: backtest-
result.json
How to use --refresh-pairs-cached parameter?
The first time your run Backtesting, it will take the pairs you have set in your config file and download data from Bittrex.
If for any reason you want to update your data set, you use
--refresh-pairs-cached to force Backtesting to update the data it has.
Use it only if you want to update your data set. You will not be able
to come back to the previous version.
To test your strategy with latest data, we recommend continuing using
the parameter -l or --live.
Hyperopt commands
It is possible to use hyperopt for trading strategy optimization.
Hyperopt uses an internal json config return by hyperopt_optimize_conf()
located in freqtrade/optimize/hyperopt_conf.py.
usage: main.py hyperopt [-h] [-i TICKER_INTERVAL] [--realistic-simulation]
[--timerange TIMERANGE] [-e INT] [--use-mongodb]
[-s {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...]]
optional arguments:
-h, --help show this help message and exit
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
specify ticker interval (1m, 5m, 30m, 1h, 1d)
--realistic-simulation
uses max_open_trades from config to simulate real
world limitations
--timerange TIMERANGE
specify what timerange of data to use.
-e INT, --epochs INT specify number of epochs (default: 100)
--use-mongodb parallelize evaluations with mongodb (requires mongod
in PATH)
-s {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...], --spaces {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...]
Specify which parameters to hyperopt. Space separate
list. Default: all
A parameter missing in the configuration?
All parameters for main.py, backtesting, hyperopt are referenced
in misc.py
Next step
The optimal strategy of the bot will change with time depending of the market trends. The next step is to optimize your bot.