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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] [--dynamic-whitelist [INT]]
                 [--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
  --version             show program's version number and exit
  -c PATH, --config PATH
                        Specify configuration file (default: None). Multiple
                        --config options may be used.
  -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.
  --dynamic-whitelist [INT]
                        Dynamically generate and update whitelist based on 24h
                        BaseVolume (default: 20). DEPRECATED.
  --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

python3 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):

python3 freqtrade -c ./config.json

and specify both configuration files when running in the normal Live Trade Mode:

python3 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:

python3 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 folder!):

python3 freqtrade --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?

DEPRECATED

This command line option is deprecated. Please move your configurations using it

to the configurations that utilize the StaticPairList or VolumePairList methods set in the configuration file as outlined here

Description of this deprecated feature was moved to here. Please no longer 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:

python3 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 MAX_OPEN_TRADES]
                        [--stake_amount STAKE_AMOUNT] [-r]
                        [--customhyperopt NAME] [--eps] [--dmmp] [-e INT]
                        [-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]
                        [--print-all] [-j JOBS]

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.
  --customhyperopt NAME
                        Specify hyperopt class name (default:
                        DefaultHyperOpts).
  --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).
  -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 separate
                        list. Default: all.
  --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.

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.