13 KiB
Start the bot
This page explains the different parameters of the bot and how to run it.
Bot commands
usage: freqtrade [-h] [-v] [--logfile FILE] [--version] [-c PATH] [-d PATH]
[-s NAME] [--strategy-path PATH] [--db-url PATH]
[--sd-notify]
{backtesting,edge,hyperopt} ...
Free, open source crypto trading bot
positional arguments:
{backtesting,edge,hyperopt}
backtesting Backtesting module.
edge Edge module.
hyperopt Hyperopt module.
optional arguments:
-h, --help show this help message and exit
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: None). Multiple
--config options may be used. Can be set to '-' to
read config from stdin.
-d PATH, --datadir PATH
Path to backtest data.
-s NAME, --strategy NAME
Specify strategy class name (default:
DefaultStrategy).
--strategy-path PATH Specify additional strategy lookup path.
--db-url PATH Override trades database URL, this is useful if
dry_run is enabled or in custom deployments (default:
None).
--sd-notify Notify systemd service manager.
How to use a different configuration file?
The bot allows you to select which configuration file you want to use. Per
default, the bot will load the file ./config.json
freqtrade -c path/far/far/away/config.json
How to use multiple configuration files?
The bot allows you to use multiple configuration files by specifying multiple
-c/--config configuration options in the command line. Configuration parameters
defined in the last configuration file override parameters with the same name
defined in the previous configuration file specified in the command line.
For example, you can make a separate configuration file with your key and secrete for the Exchange you use for trading, specify default configuration file with empty key and secrete values while running in the Dry Mode (which does not actually require them):
freqtrade -c ./config.json
and specify both configuration files when running in the normal Live Trade Mode:
freqtrade -c ./config.json -c path/to/secrets/keys.config.json
This could help you hide your private Exchange key and Exchange secrete on you local machine by setting appropriate file permissions for the file which contains actual secrets and, additionally, prevent unintended disclosure of sensitive private data when you publish examples of your configuration in the project issues or in the Internet.
See more details on this technique with examples in the documentation page on configuration.
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:
freqtrade --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 Strategy Customization.
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 directory!):
freqtrade --strategy AwesomeStrategy --strategy-path /some/directory
How to install a strategy?
This is very simple. Copy paste your strategy file into the directory
user_data/strategies or use --strategy-path. And voila, the bot is ready to use it.
How to use --db-url?
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 --db-url. This can also be used to specify a custom database
in production mode. Example command:
freqtrade -c config.json --db-url sqlite:///tradesv3.dry_run.sqlite
Backtesting commands
Backtesting also uses the config specified via -c/--config.
usage: freqtrade backtesting [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
[--max_open_trades MAX_OPEN_TRADES]
[--stake_amount STAKE_AMOUNT] [-r] [--eps] [--dmmp]
[-l]
[--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]
[--export EXPORT] [--export-filename PATH]
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).
--timerange TIMERANGE
Specify what timerange of data to use.
--max_open_trades MAX_OPEN_TRADES
Specify max_open_trades to use.
--stake_amount STAKE_AMOUNT
Specify stake_amount.
-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 optimization commands with up-to-date data.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
--dmmp, --disable-max-market-positions
Disable applying `max_open_trades` during backtest
(same as setting `max_open_trades` to a very high
number).
-l, --live Use live data.
--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]
Provide a commaseparated list of strategies to
backtest Please note that ticker-interval needs to be
set either in config or via command line. When using
this together with --export trades, the strategy-name
is injected into the filename (so backtest-data.json
becomes backtest-data-DefaultStrategy.json
--export EXPORT Export backtest results, argument are: trades. Example
--export=trades
--export-filename PATH
Save backtest results to this filename requires
--export to be set as well Example --export-
filename=user_data/backtest_data/backtest_today.json
(default: user_data/backtest_data/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 the Exchange.
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.
!!! Note 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
To optimize your strategy, you can use hyperopt parameter hyperoptimization to find optimal parameter values for your stategy.
usage: freqtrade hyperopt [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
[--max_open_trades INT]
[--stake_amount STAKE_AMOUNT] [-r]
[--customhyperopt NAME] [--eps] [-e INT]
[-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]
[--dmmp] [--print-all] [-j JOBS]
[--random-state INT] [--min-trades INT] [--continue]
[--hyperopt-loss NAME]
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`).
--timerange TIMERANGE
Specify what timerange of data to use.
--max_open_trades INT
Specify max_open_trades to use.
--stake_amount STAKE_AMOUNT
Specify stake_amount.
-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 optimization commands with up-to-date data.
--customhyperopt NAME
Specify hyperopt class name (default:
`DefaultHyperOpts`).
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
-e INT, --epochs INT Specify number of epochs (default: 100).
-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...], --spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]
Specify which parameters to hyperopt. Space-separated
list. Default: `all`.
--dmmp, --disable-max-market-positions
Disable applying `max_open_trades` during backtest
(same as setting `max_open_trades` to a very high
number).
--print-all Print all results, not only the best ones.
-j JOBS, --job-workers JOBS
The number of concurrently running jobs for
hyperoptimization (hyperopt worker processes). If -1
(default), all CPUs are used, for -2, all CPUs but one
are used, etc. If 1 is given, no parallel computing
code is used at all.
--random-state INT Set random state to some positive integer for
reproducible hyperopt results.
--min-trades INT Set minimal desired number of trades for evaluations
in the hyperopt optimization path (default: 1).
--continue Continue hyperopt from previous runs. By default,
temporary files will be removed and hyperopt will
start from scratch.
--hyperopt-loss NAME
Specify the class name of the hyperopt loss function
class (IHyperOptLoss). Different functions can
generate completely different results, since the
target for optimization is different. (default:
`DefaultHyperOptLoss`).
Edge commands
To know your trade expectacny and winrate against historical data, you can use Edge.
usage: freqtrade edge [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]
[--max_open_trades MAX_OPEN_TRADES]
[--stake_amount STAKE_AMOUNT] [-r]
[--stoplosses STOPLOSS_RANGE]
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).
--timerange TIMERANGE
Specify what timerange of data to use.
--max_open_trades MAX_OPEN_TRADES
Specify max_open_trades to use.
--stake_amount STAKE_AMOUNT
Specify stake_amount.
-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 optimization commands with up-to-date data.
--stoplosses STOPLOSS_RANGE
Defines a range of stoploss against which edge will
assess the strategy the format is "min,max,step"
(without any space).example:
--stoplosses=-0.01,-0.1,-0.001
To understand edge and how to read the results, please read the edge documentation.
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 Strategy Customization.