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"},{"location":"#introduction","title":"Introduction","text":"Freqtrade is a cryptocurrency trading bot written in Python.
DISCLAIMER
This software is for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
Always start by running a trading bot in Dry-run and do not engage money before you understand how it works and what profit/loss you should expect.
We strongly recommend you to have basic coding skills and Python knowledge. Do not hesitate to read the source code and understand the mechanisms of this bot, algorithms and techniques implemented in it.
"},{"location":"#features","title":"Features","text":"The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
"},{"location":"#hardware-requirements","title":"Hardware requirements","text":"To run this bot we recommend you a cloud instance with a minimum of:
Help / Slack For any questions not covered by the documentation or for further information about the bot, we encourage you to join our Slack channel.
Click here to join Slack channel.
"},{"location":"#ready-to-try","title":"Ready to try?","text":"Begin by reading our installation guide here.
"},{"location":"backtesting/","title":"Backtesting","text":"This page explains how to validate your strategy performance by using Backtesting.
Backtesting requires historic data to be available. To learn how to get data for the pairs and exchange you're interested in, head over to the Data Downloading section of the documentation.
"},{"location":"backtesting/#test-your-strategy-with-backtesting","title":"Test your strategy with Backtesting","text":"Now you have good Buy and Sell strategies and some historic data, you want to test it against real data. This is what we call backtesting.
Backtesting will use the crypto-currencies (pairs) from your config file and load ticker data from user_data/data/<exchange> by default. If no data is available for the exchange / pair / ticker interval combination, backtesting will ask you to download them first using freqtrade download-data. For details on downloading, please refer to the Data Downloading section in the documentation.
The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
"},{"location":"backtesting/#run-a-backtesting-against-the-currencies-listed-in-your-config-file","title":"Run a backtesting against the currencies listed in your config file","text":""},{"location":"backtesting/#with-5-min-tickers-per-default","title":"With 5 min tickers (Per default)","text":"freqtrade backtesting\nfreqtrade backtesting --ticker-interval 1m\nAssume you downloaded the history data from the Bittrex exchange and kept it in the user_data/data/bittrex-20180101 directory. You can then use this data for backtesting as follows:
freqtrade backtesting --datadir user_data/data/bittrex-20180101\nfreqtrade -s SampleStrategy backtesting\nWhere -s SampleStrategy refers to the class name within the strategy file sample_strategy.py found in the freqtrade/user_data/strategies directory.
freqtrade backtesting --strategy-list SampleStrategy1 AwesomeStrategy --ticker-interval 5m\nWhere SampleStrategy1 and AwesomeStrategy refer to class names of strategies.
freqtrade backtesting --export trades\nThe exported trades can be used for further analysis, or can be used by the plotting script plot_dataframe.py in the scripts directory.
freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json\nUse the --timerange argument to change how much of the testset you want to use.
For example, running backtesting with the --timerange=20190501- option will use all available data starting with May 1st, 2019 from your inputdata.
freqtrade backtesting --timerange=20190501-\nYou can also specify particular dates or a range span indexed by start and stop.
The full timerange specification:
--timerange=-20180131--timerange=20180131---timerange=20180131-20180301--timerange=1527595200-1527618600--timerange=-123--timerange=123---timerange=123-456Warning
Be carefull when using non-date functions - these do not allow you to specify precise dates, so if you updated the test-data it will probably use a different dataset.
"},{"location":"backtesting/#understand-the-backtesting-result","title":"Understand the backtesting result","text":"The most important in the backtesting is to understand the result.
A backtesting result will look like that:
========================================================= BACKTESTING REPORT ========================================================\n| pair | buy count | avg profit % | cum profit % | tot profit BTC | tot profit % | avg duration | profit | loss |\n|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|\n| ADA/BTC | 35 | -0.11 | -3.88 | -0.00019428 | -1.94 | 4:35:00 | 14 | 21 |\n| ARK/BTC | 11 | -0.41 | -4.52 | -0.00022647 | -2.26 | 2:03:00 | 3 | 8 |\n| BTS/BTC | 32 | 0.31 | 9.78 | 0.00048938 | 4.89 | 5:05:00 | 18 | 14 |\n| DASH/BTC | 13 | -0.08 | -1.07 | -0.00005343 | -0.53 | 4:39:00 | 6 | 7 |\n| ENG/BTC | 18 | 1.36 | 24.54 | 0.00122807 | 12.27 | 2:50:00 | 8 | 10 |\n| EOS/BTC | 36 | 0.08 | 3.06 | 0.00015304 | 1.53 | 3:34:00 | 16 | 20 |\n| ETC/BTC | 26 | 0.37 | 9.51 | 0.00047576 | 4.75 | 6:14:00 | 11 | 15 |\n| ETH/BTC | 33 | 0.30 | 9.96 | 0.00049856 | 4.98 | 7:31:00 | 16 | 17 |\n| IOTA/BTC | 32 | 0.03 | 1.09 | 0.00005444 | 0.54 | 3:12:00 | 14 | 18 |\n| LSK/BTC | 15 | 1.75 | 26.26 | 0.00131413 | 13.13 | 2:58:00 | 6 | 9 |\n| LTC/BTC | 32 | -0.04 | -1.38 | -0.00006886 | -0.69 | 4:49:00 | 11 | 21 |\n| NANO/BTC | 17 | 1.26 | 21.39 | 0.00107058 | 10.70 | 1:55:00 | 10 | 7 |\n| NEO/BTC | 23 | 0.82 | 18.97 | 0.00094936 | 9.48 | 2:59:00 | 10 | 13 |\n| REQ/BTC | 9 | 1.17 | 10.54 | 0.00052734 | 5.27 | 3:47:00 | 4 | 5 |\n| XLM/BTC | 16 | 1.22 | 19.54 | 0.00097800 | 9.77 | 3:15:00 | 7 | 9 |\n| XMR/BTC | 23 | -0.18 | -4.13 | -0.00020696 | -2.07 | 5:30:00 | 12 | 11 |\n| XRP/BTC | 35 | 0.66 | 22.96 | 0.00114897 | 11.48 | 3:49:00 | 12 | 23 |\n| ZEC/BTC | 22 | -0.46 | -10.18 | -0.00050971 | -5.09 | 2:22:00 | 7 | 15 |\n| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 243 |\n========================================================= SELL REASON STATS =========================================================\n| Sell Reason | Count |\n|:-------------------|--------:|\n| trailing_stop_loss | 205 |\n| stop_loss | 166 |\n| sell_signal | 56 |\n| force_sell | 2 |\n====================================================== LEFT OPEN TRADES REPORT ======================================================\n| pair | buy count | avg profit % | cum profit % | tot profit BTC | tot profit % | avg duration | profit | loss |\n|:---------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|\n| ADA/BTC | 1 | 0.89 | 0.89 | 0.00004434 | 0.44 | 6:00:00 | 1 | 0 |\n| LTC/BTC | 1 | 0.68 | 0.68 | 0.00003421 | 0.34 | 2:00:00 | 1 | 0 |\n| TOTAL | 2 | 0.78 | 1.57 | 0.00007855 | 0.78 | 4:00:00 | 2 | 0 |\nThe 1st table contains all trades the bot made, including \"left open trades\".
The 2nd table contains a recap of sell reasons.
The 3rd table contains all trades the bot had to forcesell at the end of the backtest period to present a full picture. This is necessary to simulate realistic behaviour, since the backtest period has to end at some point, while realistically, you could leave the bot running forever. These trades are also included in the first table, but are extracted separately for clarity.
The last line will give you the overall performance of your strategy, here:
| TOTAL | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 243 |\nThe bot has made 429 trades for an average duration of 4:12:00, with a performance of 76.20% (profit), that means it has earned a total of 0.00762792 BTC starting with a capital of 0.01 BTC.
The column avg profit % shows the average profit for all trades made while the column cum profit % sums up all the profits/losses. The column tot profit % shows instead the total profit % in relation to allocated capital (max_open_trades * stake_amount). In the above results we have max_open_trades=2 and stake_amount=0.005 in config so tot_profit % will be (76.20/100) * (0.005 * 2) =~ 0.00762792 BTC.
Your strategy performance is influenced by your buy strategy, your sell strategy, and also by the minimal_roi and stop_loss you have set.
For example, if your minimal_roi is only \"0\": 0.01 you cannot expect the bot to make more profit than 1% (because it will sell every time a trade reaches 1%).
\"minimal_roi\": {\n \"0\": 0.01\n},\nOn the other hand, if you set a too high minimal_roi like \"0\": 0.55 (55%), there is almost no chance that the bot will ever reach this profit. Hence, keep in mind that your performance is an integral mix of all different elements of the strategy, your configuration, and the crypto-currency pairs you have set up.
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
To further analyze your backtest results, you can export the trades. You can then load the trades to perform further analysis as shown in our data analysis backtesting section.
"},{"location":"backtesting/#backtesting-multiple-strategies","title":"Backtesting multiple strategies","text":"To compare multiple strategies, a list of Strategies can be provided to backtesting.
This is limited to 1 ticker-interval per run, however, data is only loaded once from disk so if you have multiple strategies you'd like to compare, this will give a nice runtime boost.
All listed Strategies need to be in the same directory.
freqtrade backtesting --timerange 20180401-20180410 --ticker-interval 5m --strategy-list Strategy001 Strategy002 --export trades\nThis will save the results to user_data/backtest_results/backtest-result-<strategy>.json, injecting the strategy-name into the target filename. There will be an additional table comparing win/losses of the different strategies (identical to the \"Total\" row in the first table). Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
=========================================================== Strategy Summary ===========================================================\n| Strategy | buy count | avg profit % | cum profit % | tot profit BTC | tot profit % | avg duration | profit | loss |\n|:------------|------------:|---------------:|---------------:|-----------------:|---------------:|:---------------|---------:|-------:|\n| Strategy1 | 429 | 0.36 | 152.41 | 0.00762792 | 76.20 | 4:12:00 | 186 | 243 |\n| Strategy2 | 1487 | -0.13 | -197.58 | -0.00988917 | -98.79 | 4:43:00 | 662 | 825 |\nGreat, your strategy is profitable. What if the bot can give your the optimal parameters to use for your strategy? Your next step is to learn how to find optimal parameters with Hyperopt
"},{"location":"bot-usage/","title":"Start the bot","text":"This page explains the different parameters of the bot and how to run it.
Note
If you've used setup.sh, don't forget to activate your virtual environment (source .env/bin/activate) before running freqtrade commands.
usage: freqtrade [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]\n [--userdir PATH] [-s NAME] [--strategy-path PATH]\n [--db-url PATH] [--sd-notify]\n {backtesting,edge,hyperopt,create-userdir,list-exchanges} ...\n\nFree, open source crypto trading bot\n\npositional arguments:\n {backtesting,edge,hyperopt,create-userdir,list-exchanges}\n backtesting Backtesting module.\n edge Edge module.\n hyperopt Hyperopt module.\n create-userdir Create user-data directory.\n list-exchanges Print available exchanges.\n\noptional arguments:\n -h, --help show this help message and exit\n -v, --verbose Verbose mode (-vv for more, -vvv to get all messages).\n --logfile FILE Log to the file specified.\n -V, --version show program's version number and exit\n -c PATH, --config PATH\n Specify configuration file (default: `config.json`).\n Multiple --config options may be used. Can be set to\n `-` to read config from stdin.\n -d PATH, --datadir PATH\n Path to directory with historical backtesting data.\n --userdir PATH, --user-data-dir PATH\n Path to userdata directory.\n -s NAME, --strategy NAME\n Specify strategy class name (default:\n `DefaultStrategy`).\n --strategy-path PATH Specify additional strategy lookup path.\n --db-url PATH Override trades database URL, this is useful in custom\n deployments (default: `sqlite:///tradesv3.sqlite` for\n Live Run mode, `sqlite://` for Dry Run).\n --sd-notify Notify systemd service manager.\nThe bot allows you to select which configuration file you want to use by means of the -c/--config command line option:
freqtrade -c path/far/far/away/config.json\nPer default, the bot loads the config.json configuration file from the current working directory.
The bot allows you to use multiple configuration files by specifying multiple -c/--config options in the command line. Configuration parameters defined in the latter configuration files override parameters with the same name defined in the previous configuration files specified in the command line earlier.
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\nand specify both configuration files when running in the normal Live Trade Mode:
freqtrade -c ./config.json -c path/to/secrets/keys.config.json\nThis 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.
"},{"location":"bot-usage/#where-to-store-custom-data","title":"Where to store custom data","text":"Freqtrade allows the creation of a user-data directory using freqtrade create-userdir --userdir someDirectory. This directory will look as follows:
user_data/\n\u251c\u2500\u2500 backtest_results\n\u251c\u2500\u2500 data\n\u251c\u2500\u2500 hyperopts\n\u251c\u2500\u2500 hyperopts_results\n\u251c\u2500\u2500 plot\n\u2514\u2500\u2500 strategies\nYou can add the entry \"user_data_dir\" setting to your configuration, to always point your bot to this directory. Alternatively, pass in --userdir to every command. The bot will fail to start if the directory does not exist, but will create necessary subdirectories.
This directory should contain your custom strategies, custom hyperopts and hyperopt loss functions, backtesting historical data (downloaded using either backtesting command or the download script) and plot outputs.
It is recommended to use version control to keep track of changes to your strategies.
"},{"location":"bot-usage/#how-to-use-strategy","title":"How to use --strategy?","text":"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\nIf 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.
"},{"location":"bot-usage/#how-to-use-strategy-path","title":"How to use --strategy-path?","text":"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\nThis 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.
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\nBacktesting also uses the config specified via -c/--config.
usage: freqtrade backtesting [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]\n [--max_open_trades MAX_OPEN_TRADES]\n [--stake_amount STAKE_AMOUNT] [-r] [--eps] [--dmmp]\n [-l]\n [--strategy-list STRATEGY_LIST [STRATEGY_LIST ...]]\n [--export EXPORT] [--export-filename PATH]\n\noptional arguments:\n -h, --help show this help message and exit\n -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL\n Specify ticker interval (1m, 5m, 30m, 1h, 1d).\n --timerange TIMERANGE\n Specify what timerange of data to use.\n --max_open_trades MAX_OPEN_TRADES\n Specify max_open_trades to use.\n --stake_amount STAKE_AMOUNT\n Specify stake_amount.\n --eps, --enable-position-stacking\n Allow buying the same pair multiple times (position\n stacking).\n --dmmp, --disable-max-market-positions\n Disable applying `max_open_trades` during backtest\n (same as setting `max_open_trades` to a very high\n number).\n --strategy-list STRATEGY_LIST [STRATEGY_LIST ...]\n Provide a space-separated list of strategies to\n backtest Please note that ticker-interval needs to be\n set either in config or via command line. When using\n this together with --export trades, the strategy-name\n is injected into the filename (so backtest-data.json\n becomes backtest-data-DefaultStrategy.json\n --export EXPORT Export backtest results, argument are: trades. Example\n --export=trades\n --export-filename PATH\n Save backtest results to this filename requires\n --export to be set as well Example --export-\n filename=user_data/backtest_results/backtest_today.json\n (default: user_data/backtest_results/backtest-\n result.json)\nThe first time your run Backtesting, you will need to download some historic data first. This can be accomplished by using freqtrade download-data. Check the corresponding help page section for more details
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]\n [--max_open_trades INT]\n [--stake_amount STAKE_AMOUNT] [-r]\n [--customhyperopt NAME] [--hyperopt-path PATH]\n [--eps] [-e INT]\n [-s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]]\n [--dmmp] [--print-all] [--no-color] [-j JOBS]\n [--random-state INT] [--min-trades INT] [--continue]\n [--hyperopt-loss NAME]\n\noptional arguments:\n -h, --help show this help message and exit\n -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL\n Specify ticker interval (`1m`, `5m`, `30m`, `1h`,\n `1d`).\n --timerange TIMERANGE\n Specify what timerange of data to use.\n --max_open_trades INT\n Specify max_open_trades to use.\n --stake_amount STAKE_AMOUNT\n Specify stake_amount.\n --customhyperopt NAME\n Specify hyperopt class name (default:\n `DefaultHyperOpts`).\n --hyperopt-path PATH Specify additional lookup path for Hyperopts and\n Hyperopt Loss functions.\n --eps, --enable-position-stacking\n Allow buying the same pair multiple times (position\n stacking).\n -e INT, --epochs INT Specify number of epochs (default: 100).\n -s {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...], --spaces {all,buy,sell,roi,stoploss} [{all,buy,sell,roi,stoploss} ...]\n Specify which parameters to hyperopt. Space-separated\n list. Default: `all`.\n --dmmp, --disable-max-market-positions\n Disable applying `max_open_trades` during backtest\n (same as setting `max_open_trades` to a very high\n number).\n --print-all Print all results, not only the best ones.\n --no-color Disable colorization of hyperopt results. May be\n useful if you are redirecting output to a file.\n -j JOBS, --job-workers JOBS\n The number of concurrently running jobs for\n hyperoptimization (hyperopt worker processes). If -1\n (default), all CPUs are used, for -2, all CPUs but one\n are used, etc. If 1 is given, no parallel computing\n code is used at all.\n --random-state INT Set random state to some positive integer for\n reproducible hyperopt results.\n --min-trades INT Set minimal desired number of trades for evaluations\n in the hyperopt optimization path (default: 1).\n --continue Continue hyperopt from previous runs. By default,\n temporary files will be removed and hyperopt will\n start from scratch.\n --hyperopt-loss NAME Specify the class name of the hyperopt loss function\n class (IHyperOptLoss). Different functions can\n generate completely different results, since the\n target for optimization is different. Built-in\n Hyperopt-loss-functions are: DefaultHyperOptLoss,\n OnlyProfitHyperOptLoss, SharpeHyperOptLoss.\n (default: `DefaultHyperOptLoss`).\nTo know your trade expectancy and winrate against historical data, you can use Edge.
usage: freqtrade edge [-h] [-i TICKER_INTERVAL] [--timerange TIMERANGE]\n [--max_open_trades MAX_OPEN_TRADES]\n [--stake_amount STAKE_AMOUNT] [-r]\n [--stoplosses STOPLOSS_RANGE]\n\noptional arguments:\n -h, --help show this help message and exit\n -i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL\n Specify ticker interval (1m, 5m, 30m, 1h, 1d).\n --timerange TIMERANGE\n Specify what timerange of data to use.\n --max_open_trades MAX_OPEN_TRADES\n Specify max_open_trades to use.\n --stake_amount STAKE_AMOUNT\n Specify stake_amount.\n --stoplosses STOPLOSS_RANGE\n Defines a range of stoploss against which edge will\n assess the strategy the format is \"min,max,step\"\n (without any space).example:\n --stoplosses=-0.01,-0.1,-0.001\nTo understand edge and how to read the results, please read the edge documentation.
"},{"location":"bot-usage/#next-step","title":"Next step","text":"The optimal strategy of the bot will change with time depending of the market trends. The next step is to Strategy Customization.
"},{"location":"configuration/","title":"Configure the bot","text":"Freqtrade has many configurable features and possibilities. By default, these settings are configured via the configuration file (see below).
"},{"location":"configuration/#the-freqtrade-configuration-file","title":"The Freqtrade configuration file","text":"The bot uses a set of configuration parameters during its operation that all together conform the bot configuration. It normally reads its configuration from a file (Freqtrade configuration file).
Per default, the bot loads the configuration from the config.json file, located in the current working directory.
You can specify a different configuration file used by the bot with the -c/--config command line option.
In some advanced use cases, multiple configuration files can be specified and used by the bot or the bot can read its configuration parameters from the process standard input stream.
If you used the Quick start method for installing the bot, the installation script should have already created the default configuration file (config.json) for you.
If default configuration file is not created we recommend you to copy and use the config.json.example as a template for your bot configuration.
The Freqtrade configuration file is to be written in the JSON format.
Additionally to the standard JSON syntax, you may use one-line // ... and multi-line /* ... */ comments in your configuration files and trailing commas in the lists of parameters.
Do not worry if you are not familiar with JSON format -- simply open the configuration file with an editor of your choice, make some changes to the parameters you need, save your changes and, finally, restart the bot or, if it was previously stopped, run it again with the changes you made to the configuration. The bot validates syntax of the configuration file at startup and will warn you if you made any errors editing it, pointing out problematic lines.
"},{"location":"configuration/#configuration-parameters","title":"Configuration parameters","text":"The table below will list all configuration parameters available.
Freqtrade can also load many options via command line (CLI) arguments (check out the commands --help output for details). The prevelance for all Options is as follows:
Mandatory parameters are marked as Required, which means that they are required to be set in one of the possible ways.
Command Default Descriptionmax_open_trades 3 Required. Number of trades open your bot will have. If -1 then it is ignored (i.e. potentially unlimited open trades) stake_currency BTC Required. Crypto-currency used for trading. stake_amount 0.05 Required. Amount of crypto-currency your bot will use for each trade. Per default, the bot will use (0.05 BTC x 3) = 0.15 BTC in total will be always engaged. Set it to \"unlimited\" to allow the bot to use all available balance. amount_reserve_percent 0.05 Reserve some amount in min pair stake amount. Default is 5%. The bot will reserve amount_reserve_percent + stop-loss value when calculating min pair stake amount in order to avoid possible trade refusals. ticker_interval [1m, 5m, 15m, 30m, 1h, 1d, ...] The ticker interval to use (1min, 5 min, 15 min, 30 min, 1 hour or 1 day). Default is 5 minutes. Strategy Override. fiat_display_currency USD Required. Fiat currency used to show your profits. More information below. dry_run true Required. Define if the bot must be in Dry-run or production mode. dry_run_wallet 999.9 Overrides the default amount of 999.9 stake currency units in the wallet used by the bot running in the Dry Run mode if you need it for any reason. process_only_new_candles false If set to true indicators are processed only once a new candle arrives. If false each loop populates the indicators, this will mean the same candle is processed many times creating system load but can be useful of your strategy depends on tick data not only candle. Strategy Override. minimal_roi See below Set the threshold in percent the bot will use to sell a trade. More information below. Strategy Override. stoploss -0.10 Value of the stoploss in percent used by the bot. More information below. More details in the stoploss documentation. Strategy Override. trailing_stop false Enables trailing stop-loss (based on stoploss in either configuration or strategy file). More details in the stoploss documentation. Strategy Override. trailing_stop_positive 0 Changes stop-loss once profit has been reached. More details in the stoploss documentation. Strategy Override. trailing_stop_positive_offset 0 Offset on when to apply trailing_stop_positive. Percentage value which should be positive. More details in the stoploss documentation. Strategy Override. trailing_only_offset_is_reached false Only apply trailing stoploss when the offset is reached. stoploss documentation. Strategy Override. unfilledtimeout.buy 10 Required. How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled. unfilledtimeout.sell 10 Required. How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled. bid_strategy.ask_last_balance 0.0 Required. Set the bidding price. More information below. bid_strategy.use_order_book false Allows buying of pair using the rates in Order Book Bids. bid_strategy.order_book_top 0 Bot will use the top N rate in Order Book Bids. Ie. a value of 2 will allow the bot to pick the 2nd bid rate in Order Book Bids. bid_strategy. check_depth_of_market.enabled false Does not buy if the % difference of buy orders and sell orders is met in Order Book. bid_strategy. check_depth_of_market.bids_to_ask_delta 0 The % difference of buy orders and sell orders found in Order Book. A value lesser than 1 means sell orders is greater, while value greater than 1 means buy orders is higher. ask_strategy.use_order_book false Allows selling of open traded pair using the rates in Order Book Asks. ask_strategy.order_book_min 0 Bot will scan from the top min to max Order Book Asks searching for a profitable rate. ask_strategy.order_book_max 0 Bot will scan from the top min to max Order Book Asks searching for a profitable rate. order_types None Configure order-types depending on the action (\"buy\", \"sell\", \"stoploss\", \"stoploss_on_exchange\"). More information below. Strategy Override. order_time_in_force None Configure time in force for buy and sell orders. More information below. Strategy Override. exchange.name Required. Name of the exchange class to use. List below. exchange.sandbox false Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See here in more details. exchange.key '' API key to use for the exchange. Only required when you are in production mode. Keep it in secrete, do not disclose publicly. exchange.secret '' API secret to use for the exchange. Only required when you are in production mode. Keep it in secrete, do not disclose publicly. exchange.password '' API password to use for the exchange. Only required when you are in production mode and for exchanges that use password for API requests. Keep it in secrete, do not disclose publicly. exchange.pair_whitelist [] List of pairs to use by the bot for trading and to check for potential trades during backtesting. Can be overriden by dynamic pairlists (see below). exchange.pair_blacklist [] List of pairs the bot must absolutely avoid for trading and backtesting. Can be overriden by dynamic pairlists (see below). exchange.ccxt_config None Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the ccxt documentation exchange.ccxt_async_config None Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange and are documented in the ccxt documentation exchange.markets_refresh_interval 60 The interval in minutes in which markets are reloaded. edge false Please refer to edge configuration document for detailed explanation. experimental.use_sell_signal false Use your sell strategy in addition of the minimal_roi. Strategy Override. experimental.sell_profit_only false Waits until you have made a positive profit before taking a sell decision. Strategy Override. experimental.ignore_roi_if_buy_signal false Does not sell if the buy-signal is still active. Takes preference over minimal_roi and use_sell_signal. Strategy Override. experimental.block_bad_exchanges true Block exchanges known to not work with freqtrade. Leave on default unless you want to test if that exchange works now. pairlist.method StaticPairList Use static or dynamic volume-based pairlist. More information below. pairlist.config None Additional configuration for dynamic pairlists. More information below. telegram.enabled true Required. Enable or not the usage of Telegram. telegram.token token Your Telegram bot token. Only required if telegram.enabled is true. Keep it in secrete, do not disclose publicly. telegram.chat_id chat_id Your personal Telegram account id. Only required if telegram.enabled is true. Keep it in secrete, do not disclose publicly. webhook.enabled false Enable usage of Webhook notifications webhook.url false URL for the webhook. Only required if webhook.enabled is true. See the webhook documentation for more details. webhook.webhookbuy false Payload to send on buy. Only required if webhook.enabled is true. See the webhook documentationV for more details. webhook.webhooksell false Payload to send on sell. Only required if webhook.enabled is true. See the webhook documentationV for more details. webhook.webhookstatus false Payload to send on status calls. Only required if webhook.enabled is true. See the webhook documentationV for more details. db_url sqlite:///tradesv3.sqlite Declares database URL to use. NOTE: This defaults to sqlite:// if dry_run is True. initial_state running Defines the initial application state. More information below. forcebuy_enable false Enables the RPC Commands to force a buy. More information below. strategy DefaultStrategy Defines Strategy class to use. strategy_path null Adds an additional strategy lookup path (must be a directory). internals.process_throttle_secs 5 Required. Set the process throttle. Value in second. internals.sd_notify false Enables use of the sd_notify protocol to tell systemd service manager about changes in the bot state and issue keep-alive pings. See here for more details. logfile Specify Logfile. Uses a rolling strategy of 10 files, with 1Mb per file. user_data_dir cwd()/user_data Directory containing user data. Defaults to ./user_data/."},{"location":"configuration/#parameters-in-the-strategy","title":"Parameters in the strategy","text":"The following parameters can be set in either configuration file or strategy. Values set in the configuration file always overwrite values set in the strategy.
ticker_intervalminimal_roistoplosstrailing_stoptrailing_stop_positivetrailing_stop_positive_offsetprocess_only_new_candlesorder_typesorder_time_in_forceuse_sell_signal (experimental)sell_profit_only (experimental)ignore_roi_if_buy_signal (experimental)The stake_amount configuration parameter is an amount of crypto-currency your bot will use for each trade. The minimal value is 0.0005. If there is not enough crypto-currency in the account an exception is generated. To allow the bot to trade all the available stake_currency in your account set
\"stake_amount\" : \"unlimited\",\nIn this case a trade amount is calclulated as:
currency_balanse / (max_open_trades - current_open_trades)\nThe minimal_roi configuration parameter is a JSON object where the key is a duration in minutes and the value is the minimum ROI in percent. See the example below:
\"minimal_roi\": {\n \"40\": 0.0, # Sell after 40 minutes if the profit is not negative\n \"30\": 0.01, # Sell after 30 minutes if there is at least 1% profit\n \"20\": 0.02, # Sell after 20 minutes if there is at least 2% profit\n \"0\": 0.04 # Sell immediately if there is at least 4% profit\n},\nMost of the strategy files already include the optimal minimal_roi value. This parameter can be set in either Strategy or Configuration file. If you use it in the configuration file, it will override the minimal_roi value from the strategy file. If it is not set in either Strategy or Configuration, a default of 1000% {\"0\": 10} is used, and minimal roi is disabled unless your trade generates 1000% profit.
Go to the stoploss documentation for more details.
"},{"location":"configuration/#understand-trailing-stoploss","title":"Understand trailing stoploss","text":"Go to the trailing stoploss Documentation for details on trailing stoploss.
"},{"location":"configuration/#understand-initial_state","title":"Understand initial_state","text":"The initial_state configuration parameter is an optional field that defines the initial application state. Possible values are running or stopped. (default=running) If the value is stopped the bot has to be started with /start first.
The forcebuy_enable configuration parameter enables the usage of forcebuy commands via Telegram. This is disabled for security reasons by default, and will show a warning message on startup if enabled. For example, you can send /forcebuy ETH/BTC Telegram command when this feature if enabled to the bot, who then buys the pair and holds it until a regular sell-signal (ROI, stoploss, /forcesell) appears.
This can be dangerous with some strategies, so use with care.
See the telegram documentation for details on usage.
"},{"location":"configuration/#understand-process_throttle_secs","title":"Understand process_throttle_secs","text":"The process_throttle_secs configuration parameter is an optional field that defines in seconds how long the bot should wait before asking the strategy if we should buy or a sell an asset. After each wait period, the strategy is asked again for every opened trade wether or not we should sell, and for all the remaining pairs (either the dynamic list of pairs or the static list of pairs) if we should buy.
The ask_last_balance configuration parameter sets the bidding price. Value 0.0 will use ask price, 1.0 will use the last price and values between those interpolate between ask and last price. Using ask price will guarantee quick success in bid, but bot will also end up paying more then would probably have been necessary.
The order_types configuration parameter maps actions (buy, sell, stoploss) to order-types (market, limit, ...) as well as configures stoploss to be on the exchange and defines stoploss on exchange update interval in seconds.
This allows to buy using limit orders, sell using limit-orders, and create stoplosses using using market orders. It also allows to set the stoploss \"on exchange\" which means stoploss order would be placed immediately once the buy order is fulfilled. If stoploss_on_exchange and trailing_stop are both set, then the bot will use stoploss_on_exchange_interval to check and update the stoploss on exchange periodically. order_types can be set in the configuration file or in the strategy. order_types set in the configuration file overwrites values set in the strategy as a whole, so you need to configure the whole order_types dictionary in one place.
If this is configured, the following 4 values (buy, sell, stoploss and stoploss_on_exchange) need to be present, otherwise the bot will fail to start.
emergencysell is an optional value, which defaults to market and is used when creating stoploss on exchange orders fails. The below is the default which is used if this is not configured in either strategy or configuration file.
Syntax for Strategy:
order_types = {\n \"buy\": \"limit\",\n \"sell\": \"limit\",\n \"emergencysell\": \"market\",\n \"stoploss\": \"market\",\n \"stoploss_on_exchange\": False,\n \"stoploss_on_exchange_interval\": 60\n}\nConfiguration:
\"order_types\": {\n \"buy\": \"limit\",\n \"sell\": \"limit\",\n \"emergencysell\": \"market\",\n \"stoploss\": \"market\",\n \"stoploss_on_exchange\": false,\n \"stoploss_on_exchange_interval\": 60\n}\nNote
Not all exchanges support \"market\" orders. The following message will be shown if your exchange does not support market orders: \"Exchange <yourexchange> does not support market orders.\"
Note
Stoploss on exchange interval is not mandatory. Do not change its value if you are unsure of what you are doing. For more information about how stoploss works please refer to the stoploss documentation.
Note
If stoploss_on_exchange is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new order.
Warning
If stoploss on exchange creation fails for some reason, then an \"emergency sell\" is initiated. By default, this will sell the asset using a market order. The order-type for the emergency-sell can be changed by setting the emergencysell value in the order_types dictionary - however this is not advised.
The order_time_in_force configuration parameter defines the policy by which the order is executed on the exchange. Three commonly used time in force are:
GTC (Good Till Canceled):
This is most of the time the default time in force. It means the order will remain on exchange till it is canceled by user. It can be fully or partially fulfilled. If partially fulfilled, the remaining will stay on the exchange till cancelled.
FOK (Full Or Kill):
It means if the order is not executed immediately AND fully then it is canceled by the exchange.
IOC (Immediate Or Canceled):
It is the same as FOK (above) except it can be partially fulfilled. The remaining part is automatically cancelled by the exchange.
The order_time_in_force parameter contains a dict with buy and sell time in force policy values. This can be set in the configuration file or in the strategy. Values set in the configuration file overwrites values set in the strategy.
The possible values are: gtc (default), fok or ioc.
\"order_time_in_force\": {\n \"buy\": \"gtc\",\n \"sell\": \"gtc\"\n},\nWarning
This is an ongoing work. For now it is supported only for binance and only for buy orders. Please don't change the default value unless you know what you are doing.
"},{"location":"configuration/#exchange-configuration","title":"Exchange configuration","text":"Freqtrade is based on CCXT library that supports over 100 cryptocurrency exchange markets and trading APIs. The complete up-to-date list can be found in the CCXT repo homepage. However, the bot was tested with only Bittrex and Binance.
The bot was tested with the following exchanges:
Feel free to test other exchanges and submit your PR to improve the bot.
"},{"location":"configuration/#sample-exchange-configuration","title":"Sample exchange configuration","text":"A exchange configuration for \"binance\" would look as follows:
\"exchange\": {\n \"name\": \"binance\",\n \"key\": \"your_exchange_key\",\n \"secret\": \"your_exchange_secret\",\n \"ccxt_config\": {\"enableRateLimit\": true},\n \"ccxt_async_config\": {\n \"enableRateLimit\": true,\n \"rateLimit\": 200\n },\nThis configuration enables binance, as well as rate limiting to avoid bans from the exchange. \"rateLimit\": 200 defines a wait-event of 0.2s between each call. This can also be completely disabled by setting \"enableRateLimit\" to false.
Note
Optimal settings for rate limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings. We try to provide sensible defaults per exchange where possible, if you encounter bans please make sure that \"enableRateLimit\" is enabled and increase the \"rateLimit\" parameter step by step.
Advanced options can be configured using the _ft_has_params setting, which will override Defaults and exchange-specific behaviours.
Available options are listed in the exchange-class as _ft_has_default.
For example, to test the order type FOK with Kraken, and modify candle_limit to 200 (so you only get 200 candles per call):
\"exchange\": {\n \"name\": \"kraken\",\n \"_ft_has_params\": {\n \"order_time_in_force\": [\"gtc\", \"fok\"],\n \"ohlcv_candle_limit\": 200\n }\nWarning
Please make sure to fully understand the impacts of these settings before modifying them.
"},{"location":"configuration/#what-values-can-be-used-for-fiat_display_currency","title":"What values can be used for fiat_display_currency?","text":"The fiat_display_currency configuration parameter sets the base currency to use for the conversion from coin to fiat in the bot Telegram reports.
The valid values are:
\"AUD\", \"BRL\", \"CAD\", \"CHF\", \"CLP\", \"CNY\", \"CZK\", \"DKK\", \"EUR\", \"GBP\", \"HKD\", \"HUF\", \"IDR\", \"ILS\", \"INR\", \"JPY\", \"KRW\", \"MXN\", \"MYR\", \"NOK\", \"NZD\", \"PHP\", \"PKR\", \"PLN\", \"RUB\", \"SEK\", \"SGD\", \"THB\", \"TRY\", \"TWD\", \"ZAR\", \"USD\"\nIn addition to fiat currencies, a range of cryto currencies are supported.
The valid values are:
\"BTC\", \"ETH\", \"XRP\", \"LTC\", \"BCH\", \"USDT\"\nWe recommend starting the bot in the Dry-run mode to see how your bot will behave and what is the performance of your strategy. In the Dry-run mode the bot does not engage your money. It only runs a live simulation without creating trades on the exchange.
config.json configuration file.dry-run to true and specify db_url for a persistence database.\"dry_run\": true,\n\"db_url\": \"sqlite:///tradesv3.dryrun.sqlite\",\n\"exchange\": {\n \"name\": \"bittrex\",\n \"key\": \"key\",\n \"secret\": \"secret\",\n ...\n}\nOnce you will be happy with your bot performance running in the Dry-run mode, you can switch it to production mode.
"},{"location":"configuration/#dynamic-pairlists","title":"Dynamic Pairlists","text":"Dynamic pairlists select pairs for you based on the logic configured. The bot runs against all pairs (with that stake) on the exchange, and a number of assets (number_assets) is selected based on the selected criteria.
By default, the StaticPairList method is used. The Pairlist method is configured as pair_whitelist parameter under the exchange section of the configuration.
Available Pairlist methods:
StaticPairListexchange.pair_whitelist and exchange.pair_blacklist.VolumePairListnumber_assets top pairs based on sort_key, which can be one of askVolume, bidVolume and quoteVolume, defaults to quoteVolume.precision_filter parameter to true for this).Example:
\"pairlist\": {\n \"method\": \"VolumePairList\",\n \"config\": {\n \"number_assets\": 20,\n \"sort_key\": \"quoteVolume\",\n \"precision_filter\": false\n }\n },\nIn production mode, the bot will engage your money. Be careful, since a wrong strategy can lose all your money. Be aware of what you are doing when you run it in production mode.
"},{"location":"configuration/#to-switch-your-bot-in-production-mode","title":"To switch your bot in production mode","text":"Edit your config.json file.
Switch dry-run to false and don't forget to adapt your database URL if set:
\"dry_run\": false,\nInsert your Exchange API key (change them by fake api keys):
\"exchange\": {\n \"name\": \"bittrex\",\n \"key\": \"af8ddd35195e9dc500b9a6f799f6f5c93d89193b\",\n \"secret\": \"08a9dc6db3d7b53e1acebd9275677f4b0a04f1a5\",\n ...\n}\nNote
If you have an exchange API key yet, see our tutorial.
"},{"location":"configuration/#using-proxy-with-freqtrade","title":"Using proxy with FreqTrade","text":"To use a proxy with freqtrade, add the kwarg \"aiohttp_trust_env\"=true to the \"ccxt_async_kwargs\" dict in the exchange section of the configuration.
An example for this can be found in config_full.json.example
\"ccxt_async_config\": {\n \"aiohttp_trust_env\": true\n}\nThen, export your proxy settings using the variables \"HTTP_PROXY\" and \"HTTPS_PROXY\" set to the appropriate values
export HTTP_PROXY=\"http://addr:port\"\nexport HTTPS_PROXY=\"http://addr:port\"\nfreqtrade\nFreqTrade provides you with with an easy way to embed the strategy into your configuration file. This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field, in your chosen config file.
"},{"location":"configuration/#encoding-a-string-as-base64","title":"Encoding a string as BASE64","text":"This is a quick example, how to generate the BASE64 string in python
from base64 import urlsafe_b64encode\n\nwith open(file, 'r') as f:\n content = f.read()\ncontent = urlsafe_b64encode(content.encode('utf-8'))\nThe variable 'content', will contain the strategy file in a BASE64 encoded form. Which can now be set in your configurations file as following
\"strategy\": \"NameOfStrategy:BASE64String\"\nPlease ensure that 'NameOfStrategy' is identical to the strategy name!
"},{"location":"configuration/#next-step","title":"Next step","text":"Now you have configured your config.json, the next step is to start your bot.
"},{"location":"data-analysis/","title":"Analyzing bot data with Jupyter notebooks","text":"You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at user_data/notebooks/.
Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.
"},{"location":"data-analysis/#recommended-workflow","title":"Recommended workflow","text":"Task Tool Bot operations CLI Repetitive tasks Shell scripts Data analysis & visualization NotebookUse the CLI to
Collect these actions in shell scripts
Use a notebook to
Jupyter notebooks execute from the notebook directory. The following snippet searches for the project root, so relative paths remain consistent.
import os\nfrom pathlib import Path\n\n# Change directory\n# Modify this cell to insure that the output shows the correct path.\n# Define all paths relative to the project root shown in the cell output\nproject_root = \"somedir/freqtrade\"\ni=0\ntry:\n os.chdirdir(project_root)\n assert Path('LICENSE').is_file()\nexcept:\n while i<4 and (not Path('LICENSE').is_file()):\n os.chdir(Path(Path.cwd(), '../'))\n i+=1\n project_root = Path.cwd()\nprint(Path.cwd())\nThese examples assume that you have already generated data using the cli. They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload.
"},{"location":"data-analysis/#load-backtest-results-into-a-pandas-dataframe","title":"Load backtest results into a pandas dataframe","text":"from freqtrade.data.btanalysis import load_backtest_data\n\n# Load backtest results\ndf = load_backtest_data(\"user_data/backtest_results/backtest-result.json\")\n\n# Show value-counts per pair\ndf.groupby(\"pair\")[\"sell_reason\"].value_counts()\nfrom freqtrade.data.btanalysis import load_trades_from_db\n\n# Fetch trades from database\ndf = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n\n# Display results\ndf.groupby(\"pair\")[\"sell_reason\"].value_counts()\nThis option can be useful to inspect the results of passing in multiple configs. This will also run through the whole Configuration initialization, so the configuration is completely initialized to be passed to other methods.
import json\nfrom freqtrade.configuration import Configuration\n\n# Load config from multiple files\nconfig = Configuration.from_files([\"config1.json\", \"config2.json\"])\n\n# Show the config in memory\nprint(json.dumps(config['original_config'], indent=2))\nFor Interactive environments, have an additional configuration specifying user_data_dir and pass this in last, so you don't have to change directories while running the bot. Best avoid relative paths, since this starts at the storage location of the jupyter notebook, unless the directory is changed.
{\n \"user_data_dir\": \"~/.freqtrade/\"\n}\nThis loads candle data to a dataframe
from pathlib import Path\nfrom freqtrade.data.history import load_pair_history\n\n# Load data using values passed to function\nticker_interval = \"5m\"\ndata_location = Path('user_data', 'data', 'bitrex')\npair = \"BTC_USDT\"\ncandles = load_pair_history(datadir=data_location,\n ticker_interval=ticker_interval,\n pair=pair)\n\n# Confirm success\nprint(f\"Loaded len(candles) rows of data for {pair} from {data_location}\")\ncandles.head()\nDebugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
"},{"location":"data-analysis/#define-variables-used-in-analyses","title":"Define variables used in analyses","text":"You can override strategy settings as demonstrated below.
# Customize these according to your needs.\n\n# Define some constants\nticker_interval = \"5m\"\n# Name of the strategy class\nstrategy_name = 'SampleStrategy'\n# Path to user data\nuser_data_dir = 'user_data'\n# Location of the strategy\nstrategy_location = Path(user_data_dir, 'strategies')\n# Location of the data\ndata_location = Path(user_data_dir, 'data', 'binance')\n# Pair to analyze - Only use one pair here\npair = \"BTC_USDT\"\nfrom pathlib import Path\nfrom freqtrade.data.history import load_pair_history\n\n# Load data using values set above\ncandles = load_pair_history(datadir=data_location,\n ticker_interval=ticker_interval,\n pair=pair)\n\n# Confirm success\nprint(f\"Loaded {len(candles)} rows of data for {pair} from {data_location}\")\ncandles.head()\nfrom freqtrade.resolvers import StrategyResolver\n\n# Load strategy using values set above\nstrategy = StrategyResolver({'strategy': strategy_name,\n 'user_data_dir': user_data_dir,\n 'strategy_path': strategy_location}).strategy\n\n# Generate buy/sell signals using strategy\ndf = strategy.analyze_ticker(candles, {'pair': pair})\ndata.tail() is preferable to data.head() as most indicators have some \"startup\" data at the top of the dataframe.crossed*() functions with completely different unitsanalyze_ticker() does not necessarily mean that 200 trades will be made during backtesting.df['rsi'] < 30 as buy condition, this will generate multiple \"buy\" signals for each pair in sequence (until rsi returns > 29). The bot will only buy on the first of these signals (and also only if a trade-slot (\"max_open_trades\") is still available), or on one of the middle signals, as soon as a \"slot\" becomes available. # Report results\nprint(f\"Generated {df['buy'].sum()} buy signals\")\ndata = df.set_index('date', drop=True)\ndata.tail()\nFeel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.
"},{"location":"data-download/","title":"Data Downloading","text":""},{"location":"data-download/#getting-data-for-backtesting-and-hyperopt","title":"Getting data for backtesting and hyperopt","text":"To download data (candles / OHLCV) needed for backtesting and hyperoptimization use the freqtrade download-data command.
If no additional parameter is specified, freqtrade will download data for \"1m\" and \"5m\" timeframes for the last 30 days. Exchange and pairs will come from config.json (if specified using -c/--config). Otherwise --exchange becomes mandatory.
Tip
If you already have backtesting data available in your data-directory and would like to refresh this data up to today, use --days xx with a number slightly higher than the missing number of days. Freqtrade will keep the available data and only download the missing data. Be carefull though: If the number is too small (which would result in a few missing days), the whole dataset will be removed and only xx days will be downloaded.
In alternative to the whitelist from config.json, a pairs.json file can be used.
If you are using Binance for example:
user_data/data/binance and copy or create the pairs.json file in that directory.pairs.json file to contain the currency pairs you are interested in.mkdir -p user_data/data/binance\ncp freqtrade/tests/testdata/pairs.json user_data/data/binance\nThe format of the pairs.json file is a simple json list. Mixing different stake-currencies is allowed for this file, since it's only used for downloading.
[\n \"ETH/BTC\",\n \"ETH/USDT\",\n \"BTC/USDT\",\n \"XRP/ETH\"\n]\nThen run:
freqtrade download-data --exchange binance\nThis will download ticker data for all the currency pairs you defined in pairs.json.
--datadir user_data/data/some_directory.pairs.json from some other directory, use --pairs-file some_other_dir/pairs.json.--days 10 (defaults to 30 days).--timeframes to specify which tickers to download. Default is --timeframes 1m 5m which will download 1-minute and 5-minute tickers.-c/--config option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine -c/--config with most other options.Great, you now have backtest data downloaded, so you can now start backtesting your strategy.
"},{"location":"deprecated/","title":"Deprecated features","text":"This page contains description of the command line arguments, configuration parameters and the bot features that were declared as DEPRECATED by the bot development team and are no longer supported. Please avoid their usage in your configuration.
"},{"location":"deprecated/#removed-features","title":"Removed features","text":""},{"location":"deprecated/#the-refresh-pairs-cached-command-line-option","title":"the--refresh-pairs-cached command line option","text":"--refresh-pairs-cached in the context of backtesting, hyperopt and edge allows to refresh candle data for backtesting. Since this leads to much confusion, and slows down backtesting (while not being part of backtesting) this has been singled out as a seperate freqtrade subcommand freqtrade download-data.
This command line option was deprecated in 2019.7-dev (develop branch) and removed in 2019.9 (master branch).
"},{"location":"deprecated/#the-dynamic-whitelist-command-line-option","title":"The --dynamic-whitelist command line option","text":"This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch) and in freqtrade 2019.7 (master branch).
"},{"location":"deprecated/#the-live-command-line-option","title":"the--live command line option","text":"--live in the context of backtesting allowed to download the latest tick data for backtesting. Did only download the latest 500 candles, so was ineffective in getting good backtest data. Removed in 2019-7-dev (develop branch) and in freqtrade 2019-8 (master branch)
This page is intended for developers of FreqTrade, people who want to contribute to the FreqTrade codebase or documentation, or people who want to understand the source code of the application they're running.
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We track issues on GitHub and also have a dev channel in slack where you can ask questions.
"},{"location":"developer/#documentation","title":"Documentation","text":"Documentation is available at https://freqtrade.io and needs to be provided with every new feature PR.
Special fields for the documentation (like Note boxes, ...) can be found here.
"},{"location":"developer/#developer-setup","title":"Developer setup","text":"To configure a development environment, best use the setup.sh script and answer \"y\" when asked \"Do you want to install dependencies for dev [y/N]? \". Alternatively (if your system is not supported by the setup.sh script), follow the manual installation process and run pip3 install -e .[all].
This will install all required tools for development, including pytest, flake8, mypy, and coveralls.
New code should be covered by basic unittests. Depending on the complexity of the feature, Reviewers may request more in-depth unittests. If necessary, the Freqtrade team can assist and give guidance with writing good tests (however please don't expect anyone to write the tests for you).
"},{"location":"developer/#checking-log-content-in-tests","title":"Checking log content in tests","text":"Freqtrade uses 2 main methods to check log content in tests, log_has() and log_has_re() (to check using regex, in case of dynamic log-messages). These are available from conftest.py and can be imported in any test module.
A sample check looks as follows:
from tests.conftest import log_has, log_has_re\n\ndef test_method_to_test(caplog):\n method_to_test()\n\n assert log_has(\"This event happened\", caplog)\n # Check regex with trailing number ...\n assert log_has_re(r\"This dynamic event happened and produced \\d+\", caplog)\nYou have a great idea for a new pair selection algorithm you would like to try out? Great. Hopefully you also want to contribute this back upstream.
Whatever your motivations are - This should get you off the ground in trying to develop a new Pairlist provider.
First of all, have a look at the VolumePairList provider, and best copy this file with a name of your new Pairlist Provider.
This is a simple provider, which however serves as a good example on how to start developing.
Next, modify the classname of the provider (ideally align this with the Filename).
The base-class provides the an instance of the bot (self._freqtrade), as well as the configuration (self._config), and initiates both _blacklist and _whitelist.
self._freqtrade = freqtrade\n self._config = config\n self._whitelist = self._config['exchange']['pair_whitelist']\n self._blacklist = self._config['exchange'].get('pair_blacklist', [])\nNow, let's step through the methods which require actions:
"},{"location":"developer/#configuration","title":"configuration","text":"Configuration for PairListProvider is done in the bot configuration file in the element \"pairlist\". This Pairlist-object may contain a \"config\" dict with additional configurations for the configured pairlist. By convention, \"number_assets\" is used to specify the maximum number of pairs to keep in the whitelist. Please follow this to ensure a consistent user experience.
Additional elements can be configured as needed. VolumePairList uses \"sort_key\" to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successfull and dynamic.
Returns a description used for Telegram messages. This should contain the name of the Provider, as well as a short description containing the number of assets. Please follow the format \"PairlistName - top/bottom X pairs\".
Override this method and run all calculations needed in this method. This is called with each iteration of the bot - so consider implementing caching for compute/network heavy calculations.
Assign the resulting whiteslist to self._whitelist and self._blacklist respectively. These will then be used to run the bot in this iteration. Pairs with open trades will be added to the whitelist to have the sell-methods run correctly.
Please also run self._validate_whitelist(pairs) and to check and remove pairs with inactive markets. This function is available in the Parent class (StaticPairList) and should ideally not be overwritten.
def refresh_pairlist(self) -> None:\n # Generate dynamic whitelist\n pairs = self._gen_pair_whitelist(self._config['stake_currency'], self._sort_key)\n # Validate whitelist to only have active market pairs\n self._whitelist = self._validate_whitelist(pairs)[:self._number_pairs]\nThis is a simple method used by VolumePairList - however serves as a good example. It implements caching (@cached(TTLCache(maxsize=1, ttl=1800))) as well as a configuration option to allow different (but similar) strategies to work with the same PairListProvider.
Note
This section is a Work in Progress and is not a complete guide on how to test a new exchange with FreqTrade.
Most exchanges supported by CCXT should work out of the box.
"},{"location":"developer/#stoploss-on-exchange","title":"Stoploss On Exchange","text":"Check if the new exchange supports Stoploss on Exchange orders through their API.
Since CCXT does not provide unification for Stoploss On Exchange yet, we'll need to implement the exchange-specific parameters ourselfs. Best look at binance.py for an example implementation of this. You'll need to dig through the documentation of the Exchange's API on how exactly this can be done. CCXT Issues may also provide great help, since others may have implemented something similar for their projects.
While fetching OHLCV data, we're may end up getting incomplete candles (Depending on the exchange). To demonstrate this, we'll use daily candles (\"1d\") to keep things simple. We query the api (ct.fetch_ohlcv()) for the timeframe and look at the date of the last entry. If this entry changes or shows the date of a \"incomplete\" candle, then we should drop this since having incomplete candles is problematic because indicators assume that only complete candles are passed to them, and will generate a lot of false buy signals. By default, we're therefore removing the last candle assuming it's incomplete.
To check how the new exchange behaves, you can use the following snippet:
import ccxt\nfrom datetime import datetime\nfrom freqtrade.data.converter import parse_ticker_dataframe\nct = ccxt.binance()\ntimeframe = \"1d\"\npair = \"XLM/BTC\" # Make sure to use a pair that exists on that exchange!\nraw = ct.fetch_ohlcv(pair, timeframe=timeframe)\n\n# convert to dataframe\ndf1 = parse_ticker_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)\n\nprint(df1[\"date\"].tail(1))\nprint(datetime.utcnow())\n19 2019-06-08 00:00:00+00:00\n2019-06-09 12:30:27.873327\nThe output will show the last entry from the Exchange as well as the current UTC date. If the day shows the same day, then the last candle can be assumed as incomplete and should be dropped (leave the setting \"ohlcv_partial_candle\" from the exchange-class untouched / True). Otherwise, set \"ohlcv_partial_candle\" to False to not drop Candles (shown in the example above).
This part of the documentation is aimed at maintainers, and shows how to create a release.
"},{"location":"developer/#create-release-branch","title":"Create release branch","text":"# make sure you're in develop branch\ngit checkout develop\n\n# create new branch\ngit checkout -b new_release\nfreqtrade/__init__.py and add the version matching the current date (for example 2019.7 for July 2019). Minor versions can be 2019.7-1 should we need to do a second release that month.Note
Make sure that both master and develop are up-todate!.
# Needs to be done before merging / pulling that branch.\ngit log --oneline --no-decorate --no-merges master..develop\nOnce the PR against master is merged (best right after merging):
-dev (2019.6 -> 2019.6-dev).Start by downloading and installing Docker CE for your platform:
Once you have Docker installed, simply prepare the config file (e.g. config.json) and run the image for freqtrade as explained below.
Pull the image from docker hub.
Branches / tags available can be checked out on Dockerhub.
docker pull freqtradeorg/freqtrade:develop\n# Optionally tag the repository so the run-commands remain shorter\ndocker tag freqtradeorg/freqtrade:develop freqtrade\nTo update the image, simply run the above commands again and restart your running container.
Should you require additional libraries, please build the image yourself.
Note
The official docker images with tags master, develop and latest are automatically rebuild once a week to keep the base image uptodate. In addition to that, every merge to develop will trigger a rebuild for develop and latest.
Even though you will use docker, you'll still need some files from the github repository.
"},{"location":"docker/#clone-the-git-repository","title":"Clone the git repository","text":"Linux/Mac/Windows with WSL
git clone https://github.com/freqtrade/freqtrade.git\nWindows with docker
git clone --config core.autocrlf=input https://github.com/freqtrade/freqtrade.git\nconfig.json.example to config.json","text":"cd freqtrade\ncp -n config.json.example config.json\nTo understand the configuration options, please refer to the Bot Configuration page.
"},{"location":"docker/#create-your-database-file","title":"Create your database file","text":"Production
touch tradesv3.sqlite\n````\n\nDry-Run\n\n```bash\ntouch tradesv3.dryrun.sqlite\nNote
Make sure to use the path to this file when starting the bot in docker.
"},{"location":"docker/#build-your-own-docker-image","title":"Build your own Docker image","text":"Best start by pulling the official docker image from dockerhub as explained here to speed up building.
To add additional libraries to your docker image, best check out Dockerfile.technical which adds the technical module to the image.
docker build -t freqtrade -f Dockerfile.technical .\nIf you are developing using Docker, use Dockerfile.develop to build a dev Docker image, which will also set up develop dependencies:
docker build -f Dockerfile.develop -t freqtrade-dev .\nNote
For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount an SQLite database file (see the \"5. Run a restartable docker image\" section) to keep it between updates.
"},{"location":"docker/#verify-the-docker-image","title":"Verify the Docker image","text":"After the build process you can verify that the image was created with:
docker images\nThe output should contain the freqtrade image.
"},{"location":"docker/#run-the-docker-image","title":"Run the Docker image","text":"You can run a one-off container that is immediately deleted upon exiting with the following command (config.json must be in the current working directory):
docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade\nWarning
In this example, the database will be created inside the docker instance and will be lost when you will refresh your image.
"},{"location":"docker/#adjust-timezone","title":"Adjust timezone","text":"By default, the container will use UTC timezone. Should you find this irritating please add the following to your docker commands:
"},{"location":"docker/#linux","title":"Linux","text":"-v /etc/timezone:/etc/timezone:ro\n\n# Complete command:\ndocker run --rm -v /etc/timezone:/etc/timezone:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade\nThere is known issue in OSX Docker versions after 17.09.1, whereby /etc/localtime cannot be shared causing Docker to not start. A work-around for this is to start with the following cmd.
docker run --rm -e TZ=`ls -la /etc/localtime | cut -d/ -f8-9` -v `pwd`/config.json:/freqtrade/config.json -it freqtrade\nMore information on this docker issue and work-around can be read here.
"},{"location":"docker/#run-a-restartable-docker-image","title":"Run a restartable docker image","text":"To run a restartable instance in the background (feel free to place your configuration and database files wherever it feels comfortable on your filesystem).
"},{"location":"docker/#move-your-config-file-and-database","title":"Move your config file and database","text":"The following will assume that you place your configuration / database files to ~/.freqtrade, which is a hidden directory in your home directory. Feel free to use a different directory and replace the directory in the upcomming commands.
mkdir ~/.freqtrade\nmv config.json ~/.freqtrade\nmv tradesv3.sqlite ~/.freqtrade\ndocker run -d \\\n --name freqtrade \\\n -v ~/.freqtrade/config.json:/freqtrade/config.json \\\n -v ~/.freqtrade/user_data/:/freqtrade/user_data \\\n -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \\\n freqtrade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy\nNote
db-url defaults to sqlite:///tradesv3.sqlite but it defaults to sqlite:// if dry_run=True is being used. To override this behaviour use a custom db-url value: i.e.: --db-url sqlite:///tradesv3.dryrun.sqlite
Note
All available bot command line parameters can be added to the end of the docker run command.
You can use the following commands to monitor and manage your container:
docker logs freqtrade\ndocker logs -f freqtrade\ndocker restart freqtrade\ndocker stop freqtrade\ndocker start freqtrade\nFor more information on how to operate Docker, please refer to the official Docker documentation.
Note
You do not need to rebuild the image for configuration changes, it will suffice to edit config.json and restart the container.
The following assumes that the download/setup of the docker image have been completed successfully. Also, backtest-data should be available at ~/.freqtrade/user_data/.
docker run -d \\\n --name freqtrade \\\n -v /etc/localtime:/etc/localtime:ro \\\n -v ~/.freqtrade/config.json:/freqtrade/config.json \\\n -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \\\n -v ~/.freqtrade/user_data/:/freqtrade/user_data/ \\\n freqtrade --strategy AwsomelyProfitableStrategy backtesting\nHead over to the Backtesting Documentation for more details.
Note
Additional bot command line parameters can be appended after the image name (freqtrade in the above example).
This page explains how to use Edge Positioning module in your bot in order to enter into a trade only if the trade has a reasonable win rate and risk reward ratio, and consequently adjust your position size and stoploss.
Warning
Edge positioning is not compatible with dynamic (volume-based) whitelist.
Note
Edge does not consider anything else than buy/sell/stoploss signals. So trailing stoploss, ROI, and everything else are ignored in its calculation.
"},{"location":"edge/#introduction","title":"Introduction","text":"Trading is all about probability. No one can claim that he has a strategy working all the time. You have to assume that sometimes you lose.
But it doesn't mean there is no rule, it only means rules should work \"most of the time\". Let's play a game: we toss a coin, heads: I give you 10, tails: you give me 10. Is it an interesting game? No, it's quite boring, isn't it?
But let's say the probability that we have heads is 80% (because our coin has the displaced distribution of mass or other defect), and the probability that we have tails is 20%. Now it is becoming interesting...
That means 10$ X 80% versus 10$ X 20%. 8$ versus 2. That means over time you will win 8 risking only 2$ on each toss of coin.
Let's complicate it more: you win 80% of the time but only 2, I win 20% of the time but 8. The calculation is: 80% X 2$ versus 20% X 8. It is becoming boring again because overtime you win 1.6 (80% X 2 (80% X 2 (80% X 2 (80% X 2) and me 1.6 (20% X 8) too.
The question is: How do you calculate that? How do you know if you wanna play?
The answer comes to two factors: - Win Rate - Risk Reward Ratio
"},{"location":"edge/#win-rate","title":"Win Rate","text":"Win Rate (W) is is the mean over some amount of trades (N) what is the percentage of winning trades to total number of trades (note that we don't consider how much you gained but only if you won or not).
W = (Number of winning trades) / (Total number of trades) = (Number of winning trades) / N\nComplementary Loss Rate (L) is defined as
L = (Number of losing trades) / (Total number of trades) = (Number of losing trades) / N\nor, which is the same, as
L = 1 \u2013 W\nRisk Reward Ratio (R) is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose:
R = Profit / Loss\nOver time, on many trades, you can calculate your risk reward by dividing your average profit on winning trades by your average loss on losing trades:
Average profit = (Sum of profits) / (Number of winning trades)\n\nAverage loss = (Sum of losses) / (Number of losing trades)\n\nR = (Average profit) / (Average loss)\nAt this point we can combine W and R to create an expectancy ratio. This is a simple process of multiplying the risk reward ratio by the percentage of winning trades and subtracting the percentage of losing trades, which is calculated as follows:
Expectancy Ratio = (Risk Reward Ratio X Win Rate) \u2013 Loss Rate = (R X W) \u2013 L\nSo lets say your Win rate is 28% and your Risk Reward Ratio is 5:
Expectancy = (5 X 0.28) \u2013 0.72 = 0.68\nSuperficially, this means that on average you expect this strategy\u2019s trades to return .68 times the size of your loses. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
It is important to remember that any system with an expectancy greater than 0 is profitable using past data. The key is finding one that will be profitable in the future.
You can also use this value to evaluate the effectiveness of modifications to this system.
NOTICE: It's important to keep in mind that Edge is testing your expectancy using historical data, there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology, but be wary of \"curve-fitting\" your approach to the historical data as things are unlikely to play out the exact same way for future trades.
"},{"location":"edge/#how-does-it-work","title":"How does it work?","text":"If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over N trades for each stoploss. Here is an example:
Pair Stoploss Win Rate Risk Reward Ratio Expectancy XZC/ETH -0.01 0.50 1.176384 0.088 XZC/ETH -0.02 0.51 1.115941 0.079 XZC/ETH -0.03 0.52 1.359670 0.228 XZC/ETH -0.04 0.51 1.234539 0.117The goal here is to find the best stoploss for the strategy in order to have the maximum expectancy. In the above example stoploss at 3% leads to the maximum expectancy according to historical data.
Edge module then forces stoploss value it evaluated to your strategy dynamically.
"},{"location":"edge/#position-size","title":"Position size","text":"Edge also dictates the stake amount for each trade to the bot according to the following factors:
Allowed capital at risk is calculated as follows:
Allowed capital at risk = (Capital available_percentage) X (Allowed risk per trade)\nStoploss is calculated as described above against historical data.
Your position size then will be:
Position size = (Allowed capital at risk) / Stoploss\nExample:
Let's say the stake currency is ETH and you have 10 ETH on the exchange, your capital available percentage is 50% and you would allow 1% of risk for each trade. thus your available capital for trading is 10 x 0.5 = 5 ETH and allowed capital at risk would be 5 x 0.01 = 0.05 ETH.
Let's assume Edge has calculated that for XLM/ETH market your stoploss should be at 2%. So your position size will be 0.05 / 0.02 = 2.5 ETH.
Bot takes a position of 2.5 ETH on XLM/ETH (call it trade 1). Up next, you receive another buy signal while trade 1 is still open. This time on BTC/ETH market. Edge calculated stoploss for this market at 4%. So your position size would be 0.05 / 0.04 = 1.25 ETH (call it trade 2).
Note that available capital for trading didn\u2019t change for trade 2 even if you had already trade 1. The available capital doesn\u2019t mean the free amount on your wallet.
Now you have two trades open. The bot receives yet another buy signal for another market: ADA/ETH. This time the stoploss is calculated at 1%. So your position size is 0.05 / 0.01 = 5 ETH. But there are already 3.75 ETH blocked in two previous trades. So the position size for this third trade would be 5 \u2013 3.75 = 1.25 ETH.
Available capital doesn\u2019t change before a position is sold. Let\u2019s assume that trade 1 receives a sell signal and it is sold with a profit of 1 ETH. Your total capital on exchange would be 11 ETH and the available capital for trading becomes 5.5 ETH.
So the Bot receives another buy signal for trade 4 with a stoploss at 2% then your position size would be 0.055 / 0.02 = 2.75 ETH.
"},{"location":"edge/#configurations","title":"Configurations","text":"Edge module has following configuration options:
"},{"location":"edge/#enabled","title":"enabled","text":"If true, then Edge will run periodically.
(defaults to false)
"},{"location":"edge/#process_throttle_secs","title":"process_throttle_secs","text":"How often should Edge run in seconds?
(defaults to 3600 so one hour)
"},{"location":"edge/#calculate_since_number_of_days","title":"calculate_since_number_of_days","text":"Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy Note that it downloads historical data so increasing this number would lead to slowing down the bot.
(defaults to 7)
"},{"location":"edge/#capital_available_percentage","title":"capital_available_percentage","text":"This is the percentage of the total capital on exchange in stake currency.
As an example if you have 10 ETH available in your wallet on the exchange and this value is 0.5 (which is 50%), then the bot will use a maximum amount of 5 ETH for trading and considers it as available capital.
(defaults to 0.5)
"},{"location":"edge/#allowed_risk","title":"allowed_risk","text":"Percentage of allowed risk per trade.
(defaults to 0.01 so 1%)
"},{"location":"edge/#stoploss_range_min","title":"stoploss_range_min","text":"Minimum stoploss.
(defaults to -0.01)
"},{"location":"edge/#stoploss_range_max","title":"stoploss_range_max","text":"Maximum stoploss.
(defaults to -0.10)
"},{"location":"edge/#stoploss_range_step","title":"stoploss_range_step","text":"As an example if this is set to -0.01 then Edge will test the strategy for [-0.01, -0,02, -0,03 ..., -0.09, -0.10] ranges. Note than having a smaller step means having a bigger range which could lead to slow calculation.
If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10.
(defaults to -0.01)
"},{"location":"edge/#minimum_winrate","title":"minimum_winrate","text":"It filters out pairs which don't have at least minimum_winrate.
This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio.
(defaults to 0.60)
"},{"location":"edge/#minimum_expectancy","title":"minimum_expectancy","text":"It filters out pairs which have the expectancy lower than this number.
Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return.
(defaults to 0.20)
"},{"location":"edge/#min_trade_number","title":"min_trade_number","text":"When calculating W, R and E (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable.
Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something.
(defaults to 10, it is highly recommended not to decrease this number)
"},{"location":"edge/#max_trade_duration_minute","title":"max_trade_duration_minute","text":"Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.
NOTICE: While configuring this value, you should take into consideration your ticker interval. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).
(defaults to 1 day, i.e. to 60 * 24 = 1440 minutes)
"},{"location":"edge/#remove_pumps","title":"remove_pumps","text":"Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.
(defaults to false)
"},{"location":"edge/#running-edge-independently","title":"Running Edge independently","text":"You can run Edge independently in order to see in details the result. Here is an example:
freqtrade edge\nAn example of its output:
pair stoploss win rate risk reward ratio required risk reward expectancy total number of trades average duration (min) AGI/BTC -0.02 0.64 5.86 0.56 3.41 14 54 NXS/BTC -0.03 0.64 2.99 0.57 1.54 11 26 LEND/BTC -0.02 0.82 2.05 0.22 1.50 11 36 VIA/BTC -0.01 0.55 3.01 0.83 1.19 11 48 MTH/BTC -0.09 0.56 2.82 0.80 1.12 18 52 ARDR/BTC -0.04 0.42 3.14 1.40 0.73 12 42 BCPT/BTC -0.01 0.71 1.34 0.40 0.67 14 30 WINGS/BTC -0.02 0.56 1.97 0.80 0.65 27 42 VIBE/BTC -0.02 0.83 0.91 0.20 0.59 12 35 MCO/BTC -0.02 0.79 0.97 0.27 0.55 14 31 GNT/BTC -0.02 0.50 2.06 1.00 0.53 18 24 HOT/BTC -0.01 0.17 7.72 4.81 0.50 209 7 SNM/BTC -0.03 0.71 1.06 0.42 0.45 17 38 APPC/BTC -0.02 0.44 2.28 1.27 0.44 25 43 NEBL/BTC -0.03 0.63 1.29 0.58 0.44 19 59"},{"location":"edge/#update-cached-pairs-with-the-latest-data","title":"Update cached pairs with the latest data","text":"Edge requires historic data the same way as backtesting does. Please refer to the download section of the documentation for details.
"},{"location":"edge/#precising-stoploss-range","title":"Precising stoploss range","text":"freqtrade edge --stoplosses=-0.01,-0.1,-0.001 #min,max,step\nfreqtrade edge --timerange=20181110-20181113\nDoing --timerange=-200 will get the last 200 timeframes from your inputdata. You can also specify specific dates, or a range span indexed by start and stop.
The full timerange specification:
--timerange=-123--timerange=123---timerange=123-456--timerange=-20180131--timerange=20180131---timerange=20180131-20180301--timerange=1527595200-1527618600Running the bot with freqtrade --config config.json does show the output freqtrade: command not found.
This could have the following reasons:
source .env/bin/activate to activate the virtual environmentDepending on the buy strategy, the amount of whitelisted coins, the situation of the market etc, it can take up to hours to find good entry position for a trade. Be patient!
"},{"location":"faq/#i-have-made-12-trades-already-why-is-my-total-profit-negative","title":"I have made 12 trades already, why is my total profit negative?!","text":"I understand your disappointment but unfortunately 12 trades is just not enough to say anything. If you run backtesting, you can see that our current algorithm does leave you on the plus side, but that is after thousands of trades and even there, you will be left with losses on specific coins that you have traded tens if not hundreds of times. We of course constantly aim to improve the bot but it will always be a gamble, which should leave you with modest wins on monthly basis but you can't say much from few trades.
"},{"location":"faq/#id-like-to-change-the-stake-amount-can-i-just-stop-the-bot-with-stop-and-then-change-the-configjson-and-run-it-again","title":"I\u2019d like to change the stake amount. Can I just stop the bot with /stop and then change the config.json and run it again?","text":"Not quite. Trades are persisted to a database but the configuration is currently only read when the bot is killed and restarted. /stop more like pauses. You can stop your bot, adjust settings and start it again.
That's great. We have a nice backtesting and hyperoptimizing setup. See the tutorial here|Testing-new-strategies-with-Hyperopt.
"},{"location":"faq/#is-there-a-setting-to-only-sell-the-coins-being-held-and-not-perform-anymore-buys","title":"Is there a setting to only SELL the coins being held and not perform anymore BUYS?","text":"You can use the /forcesell all command from Telegram.
Currently known to happen for US Bittrex users. Bittrex split its exchange into US and International versions. The International version has more pairs available, however the API always returns all pairs, so there is currently no automated way to detect if you're affected by the restriction.
If you have restricted pairs in your whitelist, you'll get a warning message in the log on FreqTrade startup for each restricted pair. If you're an \"International\" Customer on the Bittrex exchange, then this warning will probably not impact you. If you're a US customer, the bot will fail to create orders for these pairs, and you should remove them from your Whitelist.
"},{"location":"faq/#hyperopt-module","title":"Hyperopt module","text":""},{"location":"faq/#how-many-epoch-do-i-need-to-get-a-good-hyperopt-result","title":"How many epoch do I need to get a good Hyperopt result?","text":"Per default Hyperopts without -e or --epochs parameter will only run 100 epochs, means 100 evals of your triggers, guards, ... Too few to find a great result (unless if you are very lucky), so you probably have to run it for 10.000 or more. But it will take an eternity to compute.
We recommend you to run it at least 10.000 epochs:
freqtrade hyperopt -e 10000\nor if you want intermediate result to see
for i in {1..100}; do freqtrade hyperopt -e 100; done\nFinding a great Hyperopt results takes time.
If you wonder why it takes a while to find great hyperopt results
This answer was written during the under the release 0.15.1, when we had:
The following calculation is still very rough and not very precise but it will give the idea. With only these triggers and guards there is already 8*10^9*10 evaluations. A roughly total of 80 billion evals. Did you run 100 000 evals? Congrats, you've done roughly 1 / 100 000 th of the search space.
"},{"location":"faq/#edge-module","title":"Edge module","text":""},{"location":"faq/#edge-implements-interesting-approach-for-controlling-position-size-is-there-any-theory-behind-it","title":"Edge implements interesting approach for controlling position size, is there any theory behind it?","text":"The Edge module is mostly a result of brainstorming of @mishaker and @creslinux freqtrade team members.
You can find further info on expectancy, winrate, risk management and position size in the following sources:
This page explains how to tune your strategy by finding the optimal parameters, a process called hyperparameter optimization. The bot uses several algorithms included in the scikit-optimize package to accomplish this. The search will burn all your CPU cores, make your laptop sound like a fighter jet and still take a long time.
Hyperopt requires historic data to be available, just as backtesting does. To learn how to get data for the pairs and exchange you're interrested in, head over to the Data Downloading section of the documentation.
Bug
Hyperopt will crash when used with only 1 CPU Core as found out in Issue #1133
"},{"location":"hyperopt/#prepare-hyperopting","title":"Prepare Hyperopting","text":"Before we start digging into Hyperopt, we recommend you to take a look at an example hyperopt file located into user_data/hyperopts/
Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar and a lot of code can be copied across from the strategy.
"},{"location":"hyperopt/#checklist-on-all-tasks-possibilities-in-hyperopt","title":"Checklist on all tasks / possibilities in hyperopt","text":"Depending on the space you want to optimize, only some of the below are required:
populate_indicators - probably a copy from your strategybuy_strategy_generator - for buy signal optimizationindicator_space - for buy signal optimzationsell_strategy_generator - for sell signal optimizationsell_indicator_space - for sell signal optimzationOptional, but recommended:
populate_buy_trend from your strategy - otherwise default-strategy will be usedpopulate_sell_trend from your strategy - otherwise default-strategy will be usedRarely you may also need to override:
roi_space - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)generate_roi_table - for custom ROI optimization (if you need more than 4 entries in the ROI table)stoploss_space - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)Put your hyperopt file into the directory user_data/hyperopts.
Let assume you want a hyperopt file awesome_hyperopt.py: Copy the file user_data/hyperopts/sample_hyperopt.py into user_data/hyperopts/awesome_hyperopt.py
There are two places you need to change in your hyperopt file to add a new buy hyperopt for testing:
indicator_space() - the parameters hyperopt shall be optimizing.populate_buy_trend() - applying the parameters.There you have two different types of indicators: 1. guards and 2. triggers.
Hyperoptimization will, for each eval round, pick one trigger and possibly multiple guards. The constructed strategy will be something like \"buy exactly when close price touches lower bollinger band, BUT only if ADX > 10\".
If you have updated the buy strategy, ie. changed the contents of populate_buy_trend() method you have to update the guards and triggers hyperopts must use.
Similar to the buy-signal above, sell-signals can also be optimized. Place the corresponding settings into the following methods
sell_indicator_space() - the parameters hyperopt shall be optimizing.populate_sell_trend() - applying the parameters.The configuration and rules are the same than for buy signals. To avoid naming collisions in the search-space, please prefix all sell-spaces with sell-.
The Strategy exposes the ticker-interval as self.ticker_interval. The same value is available as class-attribute HyperoptName.ticker_interval. In the case of the linked sample-value this would be SampleHyperOpts.ticker_interval.
Let's say you are curious: should you use MACD crossings or lower Bollinger Bands to trigger your buys. And you also wonder should you use RSI or ADX to help with those buy decisions. If you decide to use RSI or ADX, which values should I use for them? So let's use hyperparameter optimization to solve this mystery.
We will start by defining a search space:
def indicator_space() -> List[Dimension]:\n \"\"\"\n Define your Hyperopt space for searching strategy parameters\n \"\"\"\n return [\n Integer(20, 40, name='adx-value'),\n Integer(20, 40, name='rsi-value'),\n Categorical([True, False], name='adx-enabled'),\n Categorical([True, False], name='rsi-enabled'),\n Categorical(['bb_lower', 'macd_cross_signal'], name='trigger')\n ]\nAbove definition says: I have five parameters I want you to randomly combine to find the best combination. Two of them are integer values (adx-value and rsi-value) and I want you test in the range of values 20 to 40. Then we have three category variables. First two are either True or False. We use these to either enable or disable the ADX and RSI guards. The last one we call trigger and use it to decide which buy trigger we want to use.
So let's write the buy strategy using these values:
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:\n conditions = []\n # GUARDS AND TRENDS\n if 'adx-enabled' in params and params['adx-enabled']:\n conditions.append(dataframe['adx'] > params['adx-value'])\n if 'rsi-enabled' in params and params['rsi-enabled']:\n conditions.append(dataframe['rsi'] < params['rsi-value'])\n\n # TRIGGERS\n if 'trigger' in params:\n if params['trigger'] == 'bb_lower':\n conditions.append(dataframe['close'] < dataframe['bb_lowerband'])\n if params['trigger'] == 'macd_cross_signal':\n conditions.append(qtpylib.crossed_above(\n dataframe['macd'], dataframe['macdsignal']\n ))\n\n if conditions:\n dataframe.loc[\n reduce(lambda x, y: x & y, conditions),\n 'buy'] = 1\n\n return dataframe\n\n return populate_buy_trend\nHyperopting will now call this populate_buy_trend as many times you ask it (epochs) with different value combinations. It will then use the given historical data and make buys based on the buy signals generated with the above function and based on the results it will end with telling you which paramter combination produced the best profits.
The search for best parameters starts with a few random combinations and then uses a regressor algorithm (currently ExtraTreesRegressor) to quickly find a parameter combination that minimizes the value of the loss function.
The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators. When you want to test an indicator that isn't used by the bot currently, remember to add it to the populate_indicators() method in hyperopt.py.
Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
By default, FreqTrade uses a loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
A different loss function can be specified by using the --hyperopt-loss <Class-name> argument. This class should be in its own file within the user_data/hyperopts/ directory.
Currently, the following loss functions are builtin:
DefaultHyperOptLoss (default legacy Freqtrade hyperoptimization loss function)OnlyProfitHyperOptLoss (which takes only amount of profit into consideration)SharpeHyperOptLoss (optimizes Sharpe Ratio calculated on the trade returns)To use a custom loss function class, make sure that the function hyperopt_loss_function is defined in your custom hyperopt loss class. For the sample below, you then need to add the command line parameter --hyperopt-loss SuperDuperHyperOptLoss to your hyperopt call so this fuction is being used.
A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found user_data/hyperopts/
from freqtrade.optimize.hyperopt import IHyperOptLoss\n\nTARGET_TRADES = 600\nEXPECTED_MAX_PROFIT = 3.0\nMAX_ACCEPTED_TRADE_DURATION = 300\n\nclass SuperDuperHyperOptLoss(IHyperOptLoss):\n \"\"\"\n Defines the default loss function for hyperopt\n \"\"\"\n\n @staticmethod\n def hyperopt_loss_function(results: DataFrame, trade_count: int,\n min_date: datetime, max_date: datetime,\n *args, **kwargs) -> float:\n \"\"\"\n Objective function, returns smaller number for better results\n This is the legacy algorithm (used until now in freqtrade).\n Weights are distributed as follows:\n * 0.4 to trade duration\n * 0.25: Avoiding trade loss\n * 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above\n \"\"\"\n total_profit = results.profit_percent.sum()\n trade_duration = results.trade_duration.mean()\n\n trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)\n profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)\n duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)\n result = trade_loss + profit_loss + duration_loss\n return result\nCurrently, the arguments are:
results: DataFrame containing the result The following columns are available in results (corresponds to the output-file of backtesting when used with --export trades): pair, profit_percent, profit_abs, open_time, close_time, open_index, close_index, trade_duration, open_at_end, open_rate, close_rate, sell_reasontrade_count: Amount of trades (identical to len(results))min_date: Start date of the hyperopting TimeFramemin_date: End date of the hyperopting TimeFrameThis function needs to return a floating point number (float). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
Note
This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
Note
Please keep the arguments *args and **kwargs in the interface to allow us to extend this interface later.
Once you have updated your hyperopt configuration you can run it. Because hyperopt tries a lot of combinations to find the best parameters it will take time to get a good result. More time usually results in better results.
We strongly recommend to use screen or tmux to prevent any connection loss.
freqtrade -c config.json hyperopt --customhyperopt <hyperoptname> -e 5000 --spaces all\nUse <hyperoptname> as the name of the custom hyperopt used.
The -e flag will set how many evaluations hyperopt will do. We recommend running at least several thousand evaluations.
The --spaces all flag determines that all possible parameters should be optimized. Possibilities are listed below.
Note
By default, hyperopt will erase previous results and start from scratch. Continuation can be archived by using --continue.
Warning
When switching parameters or changing configuration options, make sure to not use the argument --continue so temporary results can be removed.
If you would like to hyperopt parameters using an alternate ticker data that you have on-disk, use the --datadir PATH option. Default hyperopt will use data from directory user_data/data.
Use the --timerange argument to change how much of the testset you want to use. For example, to use one month of data, pass the following parameter to the hyperopt call:
freqtrade hyperopt --timerange 20180401-20180501\nUse the --spaces argument to limit the search space used by hyperopt. Letting Hyperopt optimize everything is a huuuuge search space. Often it might make more sense to start by just searching for initial buy algorithm. Or maybe you just want to optimize your stoploss or roi table for that awesome new buy strategy you have.
Legal values are:
all: optimize everythingbuy: just search for a new buy strategysell: just search for a new sell strategyroi: just optimize the minimal profit table for your strategystoploss: search for the best stoploss value--spaces roi stoplossIn some situations, you may need to run Hyperopt (and Backtesting) with the --eps/--enable-position-staking and --dmmp/--disable-max-market-positions arguments.
By default, hyperopt emulates the behavior of the Freqtrade Live Run/Dry Run, where only one open trade is allowed for every traded pair. The total number of trades open for all pairs is also limited by the max_open_trades setting. During Hyperopt/Backtesting this may lead to some potential trades to be hidden (or masked) by previosly open trades.
The --eps/--enable-position-stacking argument allows emulation of buying the same pair multiple times, while --dmmp/--disable-max-market-positions disables applying max_open_trades during Hyperopt/Backtesting (which is equal to setting max_open_trades to a very high number).
Note
Dry/live runs will NOT use position stacking - therefore it does make sense to also validate the strategy without this as it's closer to reality.
You can also enable position stacking in the configuration file by explicitly setting \"position_stacking\"=true.
Once Hyperopt is completed you can use the result to create a new strategy. Given the following result from hyperopt:
Best result:\n\n 44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722\u03a3%). Avg duration 180.4 mins. Objective: 1.94367\n\nBuy hyperspace params:\n{ 'adx-value': 44,\n 'rsi-value': 29,\n 'adx-enabled': False,\n 'rsi-enabled': True,\n 'trigger': 'bb_lower'}\nYou should understand this result like:
bb_lower.adx-enabled: False)rsi-enabled: True and the best value is 29.0 (rsi-value: 29.0)You have to look inside your strategy file into buy_strategy_generator() method, what those values match to.
So for example you had rsi-value: 29.0 so we would look at rsi-block, that translates to the following code block:
(dataframe['rsi'] < 29.0)\nTranslating your whole hyperopt result as the new buy-signal would then look like:
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:\n dataframe.loc[\n (\n (dataframe['rsi'] < 29.0) & # rsi-value\n dataframe['close'] < dataframe['bb_lowerband'] # trigger\n ),\n 'buy'] = 1\n return dataframe\nBy default, hyperopt prints colorized results -- epochs with positive profit are printed in the green color. This highlighting helps you find epochs that can be interesting for later analysis. Epochs with zero total profit or with negative profits (losses) are printed in the normal color. If you do not need colorization of results (for instance, when you are redirecting hyperopt output to a file) you can switch colorization off by specifying the --no-color option in the command line.
You can use the --print-all command line option if you would like to see all results in the hyperopt output, not only the best ones. When --print-all is used, current best results are also colorized by default -- they are printed in bold (bright) style. This can also be switched off with the --no-color command line option.
If you are optimizing ROI (i.e. if optimization search-space contains 'all' or 'roi'), your result will look as follows and include a ROI table:
Best result:\n\n 44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722\u03a3%). Avg duration 180.4 mins. Objective: 1.94367\n\nBuy hyperspace params:\n{ 'adx-value': 44,\n 'rsi-value': 29,\n 'adx-enabled': False,\n 'rsi-enabled': True,\n 'trigger': 'bb_lower'}\nROI table:\n{ 0: 0.10674,\n 21: 0.09158,\n 78: 0.03634,\n 118: 0}\nIn order to use this best ROI table found by Hyperopt in backtesting and for live trades/dry-run, copy-paste it as the value of the minimal_roi attribute of your custom strategy:
# Minimal ROI designed for the strategy.\n # This attribute will be overridden if the config file contains \"minimal_roi\"\n minimal_roi = {\n 0: 0.10674,\n 21: 0.09158,\n 78: 0.03634,\n 118: 0\n }\nminimal_roi setting in the configuration file."},{"location":"hyperopt/#default-roi-search-space","title":"Default ROI Search Space","text":"If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace for you -- it's the hyperspace of components for the ROI tables. By default, each ROI table generated by the Freqtrade consists of 4 rows (steps). Hyperopt implements adaptive ranges for ROI tables with ranges for values in the ROI steps that depend on the ticker_interval used. By default the values can vary in the following ranges (for some of the most used ticker intervals, values are rounded to 5 digits after the decimal point):
# step 1m 5m 1h 1d 1 0 0.01161...0.11992 0 0.03...0.31 0 0.06883...0.71124 0 0.12178...1.25835 2 2...8 0.00774...0.04255 10...40 0.02...0.11 120...480 0.04589...0.25238 2880...11520 0.08118...0.44651 3 4...20 0.00387...0.01547 20...100 0.01...0.04 240...1200 0.02294...0.09177 5760...28800 0.04059...0.16237 4 6...44 0.0 30...220 0.0 360...2640 0.0 8640...63360 0.0These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the ticker interval used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the ticker interval used.
If you have the generate_roi_table() and roi_space() methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
Override the roi_space() method if you need components of the ROI tables to vary in other ranges. Override the generate_roi_table() and roi_space() methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps). A sample for these methods can be found in user_data/hyperopts/sample_hyperopt_advanced.py.
If you are optimizing stoploss values (i.e. if optimization search-space contains 'all' or 'stoploss'), your result will look as follows and include stoploss:
Best result:\n\n 44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722\u03a3%). Avg duration 180.4 mins. Objective: 1.94367\n\nBuy hyperspace params:\n{ 'adx-value': 44,\n 'rsi-value': 29,\n 'adx-enabled': False,\n 'rsi-enabled': True,\n 'trigger': 'bb_lower'}\nStoploss: -0.27996\nIn order to use this best stoploss value found by Hyperopt in backtesting and for live trades/dry-run, copy-paste it as the value of the stoploss attribute of your custom strategy:
# Optimal stoploss designed for the strategy\n # This attribute will be overridden if the config file contains \"stoploss\"\n stoploss = -0.27996\nstoploss setting in the configuration file."},{"location":"hyperopt/#default-stoploss-search-space","title":"Default Stoploss Search Space","text":"If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimization hyperspace for you. By default, the stoploss values in that hyperspace can vary in the range -0.35...-0.02, which is sufficient in most cases.
If you have the stoploss_space() method in your custom hyperopt file, remove it in order to utilize Stoploss hyperoptimization space generated by Freqtrade by default.
Override the stoploss_space() method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in user_data/hyperopts/sample_hyperopt_advanced.py.
Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
To achieve same results (number of trades, their durations, profit, etc.) than during Hyperopt, please use same set of arguments --dmmp/--disable-max-market-positions and --eps/--enable-position-stacking for Backtesting.
Now you have a perfect bot and want to control it from Telegram. Your next step is to learn the Telegram usage.
"},{"location":"installation/","title":"Installation","text":"This page explains how to prepare your environment for running the bot.
"},{"location":"installation/#prerequisite","title":"Prerequisite","text":""},{"location":"installation/#requirements","title":"Requirements","text":"Click each one for install guide:
Before running your bot in production you will need to setup few external API. In production mode, the bot will require valid Exchange API credentials. We also recommend a Telegram bot (optional but recommended).
"},{"location":"installation/#setup-your-exchange-account","title":"Setup your exchange account","text":"You will need to create API Keys (Usually you get key and secret) from the Exchange website and insert this into the appropriate fields in the configuration or when asked by the installation script.
Freqtrade provides a Linux/MacOS script to install all dependencies and help you to configure the bot.
Note
Python3.6 or higher and the corresponding pip are assumed to be available. The install-script will warn and stop if that's not the case.
git clone git@github.com:freqtrade/freqtrade.git\ncd freqtrade\ngit checkout develop\n./setup.sh --install\nNote
Windows installation is explained here.
"},{"location":"installation/#easy-installation-linux-script","title":"Easy Installation - Linux Script","text":"If you are on Debian, Ubuntu or MacOS freqtrade provides a script to Install, Update, Configure, and Reset your bot.
$ ./setup.sh\nusage:\n -i,--install Install freqtrade from scratch\n -u,--update Command git pull to update.\n -r,--reset Hard reset your develop/master branch.\n -c,--config Easy config generator (Will override your existing file).\n** --install **
This script will install everything you need to run the bot:
ta-libconfig.json fileThis script is a combination of install script --reset, --config
** --update **
Update parameter will pull the last version of your current branch and update your virtualenv.
** --reset **
Reset parameter will hard reset your branch (only if you are on master or develop) and recreate your virtualenv.
** --config **
Config parameter is a config.json configurator. This script will ask you questions to setup your bot and create your config.json.
We've included/collected install instructions for Ubuntu 16.04, MacOS, and Windows. These are guidelines and your success may vary with other distros. OS Specific steps are listed first, the Common section below is necessary for all systems.
Note
Python3.6 or higher and the corresponding pip are assumed to be available.
"},{"location":"installation/#linux-ubuntu-1604","title":"Linux - Ubuntu 16.04","text":""},{"location":"installation/#install-necessary-dependencies","title":"Install necessary dependencies","text":"sudo apt-get update\nsudo apt-get install build-essential git\nBefore installing FreqTrade on a Raspberry Pi running the official Raspbian Image, make sure you have at least Python 3.6 installed. The default image only provides Python 3.5. Probably the easiest way to get a recent version of python is miniconda.
The following assumes that miniconda3 is installed and available in your environment. Since the last miniconda3 installation file uses python 3.4, we will update to python 3.6 on this installation. It's recommended to use (mini)conda for this as installation/compilation of numpy and pandas takes a long time.
Additional package to install on your Raspbian, libffi-dev required by cryptography (from python-telegram-bot).
conda config --add channels rpi\nconda install python=3.6\nconda create -n freqtrade python=3.6\nconda activate freqtrade\nconda install pandas numpy\n\nsudo apt install libffi-dev\npython3 -m pip install -r requirements-common.txt\npython3 -m pip install -e .\nNote
This does not install hyperopt dependencies. To install these, please use python3 -m pip install -e .[hyperopt]. We do not advise to run hyperopt on a Raspberry Pi, since this is a very resource-heavy operation, which should be done on powerful machine.
Official webpage: https://mrjbq7.github.io/ta-lib/install.html
wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz\ntar xvzf ta-lib-0.4.0-src.tar.gz\ncd ta-lib\nsed -i.bak \"s|0.00000001|0.000000000000000001 |g\" src/ta_func/ta_utility.h\n./configure --prefix=/usr/local\nmake\nsudo make install\ncd ..\nrm -rf ./ta-lib*\nNote
An already downloaded version of ta-lib is included in the repository, as the sourceforge.net source seems to have problems frequently.
"},{"location":"installation/#2-setup-your-python-virtual-environment-virtualenv","title":"2. Setup your Python virtual environment (virtualenv)","text":"Note
This step is optional but strongly recommended to keep your system organized
python3 -m venv .env\nsource .env/bin/activate\nClone the git repository:
git clone https://github.com/freqtrade/freqtrade.git\nOptionally checkout the master branch to get the latest stable release:
git checkout master\ncd freqtrade\ncp config.json.example config.json\nTo edit the config please refer to Bot Configuration.
"},{"location":"installation/#5-install-python-dependencies","title":"5. Install python dependencies","text":"python3 -m pip install --upgrade pip\npython3 -m pip install -e .\nIf this is the first time you run the bot, ensure you are running it in Dry-run \"dry_run\": true, otherwise it will start to buy and sell coins.
freqtrade -c config.json\nNote: If you run the bot on a server, you should consider using Docker or a terminal multiplexer like screen or tmux to avoid that the bot is stopped on logout.
freqtrade as a systemd service","text":"From the freqtrade repo... copy freqtrade.service to your systemd user directory (usually ~/.config/systemd/user) and update WorkingDirectory and ExecStart to match your setup.
After that you can start the daemon with:
systemctl --user start freqtrade\nFor this to be persistent (run when user is logged out) you'll need to enable linger for your freqtrade user.
sudo loginctl enable-linger \"$USER\"\nIf you run the bot as a service, you can use systemd service manager as a software watchdog monitoring freqtrade bot state and restarting it in the case of failures. If the internals.sd_notify parameter is set to true in the configuration or the --sd-notify command line option is used, the bot will send keep-alive ping messages to systemd using the sd_notify (systemd notifications) protocol and will also tell systemd its current state (Running or Stopped) when it changes.
The freqtrade.service.watchdog file contains an example of the service unit configuration file which uses systemd as the watchdog.
Note
The sd_notify communication between the bot and the systemd service manager will not work if the bot runs in a Docker container.
"},{"location":"installation/#using-conda","title":"Using Conda","text":"Freqtrade can also be installed using Anaconda (or Miniconda).
conda env create -f environment.yml\nNote
This requires the ta-lib C-library to be installed first.
"},{"location":"installation/#windows","title":"Windows","text":"We recommend that Windows users use Docker as this will work much easier and smoother (also more secure).
If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work. If that is not available on your system, feel free to try the instructions below, which led to success for some.
"},{"location":"installation/#install-freqtrade-manually","title":"Install freqtrade manually","text":""},{"location":"installation/#clone-the-git-repository","title":"Clone the git repository","text":"git clone https://github.com/freqtrade/freqtrade.git\nInstall ta-lib according to the ta-lib documentation.
As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial precompiled windows Wheels here, which needs to be downloaded and installed using pip install TA_Lib\u20110.4.17\u2011cp36\u2011cp36m\u2011win32.whl (make sure to use the version matching your python version)
>cd \\path\\freqtrade-develop\n>python -m venv .env\n>cd .env\\Scripts\n>activate.bat\n>cd \\path\\freqtrade-develop\nREM optionally install ta-lib from wheel\nREM >pip install TA_Lib\u20110.4.17\u2011cp36\u2011cp36m\u2011win32.whl\n>pip install -r requirements.txt\n>pip install -e .\n>python freqtrade\\main.py\nThanks Owdr for the commands. Source: Issue #222
"},{"location":"installation/#error-during-installation-under-windows","title":"Error during installation under Windows","text":"error: Microsoft Visual C++ 14.0 is required. Get it with \"Microsoft Visual C++ Build Tools\": http://landinghub.visualstudio.com/visual-cpp-build-tools\nUnfortunately, many packages requiring compilation don't provide a pre-build wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.
The easiest way is to download install Microsoft Visual Studio Community here and make sure to install \"Common Tools for Visual C++\" to enable building c code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or docker first.
Now you have an environment ready, the next step is Bot Configuration.
"},{"location":"plotting/","title":"Plotting","text":"This page explains how to plot prices, indicators and profits.
"},{"location":"plotting/#installation-setup","title":"Installation / Setup","text":"Plotting modules use the Plotly library. You can install / upgrade this by running the following command:
pip install -U -r requirements-plot.txt\nThe freqtrade plot-dataframe subcommand shows an interactive graph with three subplots:
--indicators2Possible arguments:
usage: freqtrade plot-dataframe [-h] [-p PAIRS [PAIRS ...]]\n [--indicators1 INDICATORS1 [INDICATORS1 ...]]\n [--indicators2 INDICATORS2 [INDICATORS2 ...]]\n [--plot-limit INT] [--db-url PATH]\n [--trade-source {DB,file}] [--export EXPORT]\n [--export-filename PATH]\n [--timerange TIMERANGE]\n\noptional arguments:\n -h, --help show this help message and exit\n -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]\n Show profits for only these pairs. Pairs are space-\n separated.\n --indicators1 INDICATORS1 [INDICATORS1 ...]\n Set indicators from your strategy you want in the\n first row of the graph. Space-separated list. Example:\n `ema3 ema5`. Default: `['sma', 'ema3', 'ema5']`.\n --indicators2 INDICATORS2 [INDICATORS2 ...]\n Set indicators from your strategy you want in the\n third row of the graph. Space-separated list. Example:\n `fastd fastk`. Default: `['macd', 'macdsignal']`.\n --plot-limit INT Specify tick limit for plotting. Notice: too high\n values cause huge files. Default: 750.\n --db-url PATH Override trades database URL, this is useful in custom\n deployments (default: `sqlite:///tradesv3.sqlite` for\n Live Run mode, `sqlite://` for Dry Run).\n --trade-source {DB,file}\n Specify the source for trades (Can be DB or file\n (backtest file)) Default: file\n --export EXPORT Export backtest results, argument are: trades.\n Example: `--export=trades`\n --export-filename PATH\n Save backtest results to the file with this filename\n (default: `user_data/backtest_results/backtest-\n result.json`). Requires `--export` to be set as well.\n Example: `--export-filename=user_data/backtest_results\n /backtest_today.json`\n --timerange TIMERANGE\n Specify what timerange of data to use.\nExample:
freqtrade plot-dataframe -p BTC/ETH\nThe -p/--pairs argument can be used to specify pairs you would like to plot.
Note
The freqtrade plot-dataframe subcommand generates one plot-file per pair.
Specify custom indicators. Use --indicators1 for the main plot and --indicators2 for the subplot below (if values are in a different range than prices).
Tip
You will almost certainly want to specify a custom strategy! This can be done by adding -s Classname / --strategy ClassName to the command.
freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH --indicators1 sma ema --indicators2 macd\nTo plot multiple pairs, separate them with a space:
freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH XRP/ETH\nTo plot a timerange (to zoom in)
freqtrade --strategy AwesomeStrategy plot-dataframe -p BTC/ETH --timerange=20180801-20180805\nTo plot trades stored in a database use --db-url in combination with --trade-source DB:
freqtrade --strategy AwesomeStrategy plot-dataframe --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH --trade-source DB\nTo plot trades from a backtesting result, use --export-filename <filename>
freqtrade --strategy AwesomeStrategy plot-dataframe --export-filename user_data/backtest_results/backtest-result.json -p BTC/ETH\nThe freqtrade plot-profit subcommand shows an interactive graph with three plots:
1) Average closing price for all pairs 2) The summarized profit made by backtesting. Note that this is not the real-world profit, but more of an estimate. 3) Profit for each individual pair
The first graph is good to get a grip of how the overall market progresses.
The second graph will show if your algorithm works or doesn't. Perhaps you want an algorithm that steadily makes small profits, or one that acts less often, but makes big swings.
The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
Possible options for the freqtrade plot-profit subcommand:
usage: freqtrade plot-profit [-h] [-p PAIRS [PAIRS ...]]\n [--timerange TIMERANGE] [--export EXPORT]\n [--export-filename PATH] [--db-url PATH]\n [--trade-source {DB,file}]\n\noptional arguments:\n -h, --help show this help message and exit\n -p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]\n Show profits for only these pairs. Pairs are space-\n separated.\n --timerange TIMERANGE\n Specify what timerange of data to use.\n --export EXPORT Export backtest results, argument are: trades.\n Example: `--export=trades`\n --export-filename PATH\n Save backtest results to the file with this filename\n (default: `user_data/backtest_results/backtest-\n result.json`). Requires `--export` to be set as well.\n Example: `--export-filename=user_data/backtest_results\n /backtest_today.json`\n --db-url PATH Override trades database URL, this is useful in custom\n deployments (default: `sqlite:///tradesv3.sqlite` for\n Live Run mode, `sqlite://` for Dry Run).\n --trade-source {DB,file}\n Specify the source for trades (Can be DB or file\n (backtest file)) Default: file\nThe -p/--pairs argument, can be used to limit the pairs that are considered for this calculation.
Examples:
Use custom backtest-export file
freqtrade plot-profit -p LTC/BTC --export-filename user_data/backtest_results/backtest-result-Strategy005.json\nUse custom database
freqtrade plot-profit -p LTC/BTC --db-url sqlite:///tradesv3.sqlite --trade-source DB\nfreqtrade plot-profit --datadir user_data/data/binance_save/ -p LTC/BTC\nEnable the rest API by adding the api_server section to your configuration and setting api_server.enabled to true.
Sample configuration:
\"api_server\": {\n \"enabled\": true,\n \"listen_ip_address\": \"127.0.0.1\",\n \"listen_port\": 8080,\n \"username\": \"Freqtrader\",\n \"password\": \"SuperSecret1!\"\n },\nDanger
By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
Danger
Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
You can then access the API by going to http://127.0.0.1:8080/api/v1/version to check if the API is running correctly.
To generate a secure password, either use a password manager, or use the below code snipped.
import secrets\nsecrets.token_hex()\nIf you run your bot using docker, you'll need to have the bot listen to incomming connections. The security is then handled by docker.
\"api_server\": {\n \"enabled\": true,\n \"listen_ip_address\": \"0.0.0.0\",\n \"listen_port\": 8080\n },\nAdd the following to your docker command:
-p 127.0.0.1:8080:8080\nA complete sample-command may then look as follows:
docker run -d \\\n --name freqtrade \\\n -v ~/.freqtrade/config.json:/freqtrade/config.json \\\n -v ~/.freqtrade/user_data/:/freqtrade/user_data \\\n -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \\\n -p 127.0.0.1:8080:8080 \\\n freqtrade --db-url sqlite:///tradesv3.sqlite --strategy MyAwesomeStrategy\nSecurity warning
By using -p 8080:8080 the API is available to everyone connecting to the server under the correct port, so others may be able to control your bot.
You can consume the API by using the script scripts/rest_client.py. The client script only requires the requests module, so FreqTrade does not need to be installed on the system.
python3 scripts/rest_client.py <command> [optional parameters]\nBy default, the script assumes 127.0.0.1 (localhost) and port 8080 to be used, however you can specify a configuration file to override this behaviour.
{\n \"api_server\": {\n \"enabled\": true,\n \"listen_ip_address\": \"0.0.0.0\",\n \"listen_port\": 8080\n }\n}\npython3 scripts/rest_client.py --config rest_config.json <command> [optional parameters]\nstart Starts the trader stop Stops the trader stopbuy Stops the trader from opening new trades. Gracefully closes open trades according to their rules. reload_conf Reloads the configuration file status Lists all open trades status table List all open trades in a table format count Displays number of trades used and available profit Display a summary of your profit/loss from close trades and some stats about your performance forcesell <trade_id> Instantly sells the given trade (Ignoring minimum_roi). forcesell all Instantly sells all open trades (Ignoring minimum_roi). forcebuy <pair> [rate] Instantly buys the given pair. Rate is optional. (forcebuy_enable must be set to True) performance Show performance of each finished trade grouped by pair balance Show account balance per currency daily <n> 7 Shows profit or loss per day, over the last n days whitelist Show the current whitelist blacklist [pair] Show the current blacklist, or adds a pair to the blacklist. edge Show validated pairs by Edge if it is enabled. version Show version Possible commands can be listed from the rest-client script using the help command.
python3 scripts/rest_client.py help\nPossible commands:\nbalance\n Get the account balance\n :returns: json object\n\nblacklist\n Show the current blacklist\n :param add: List of coins to add (example: \"BNB/BTC\")\n :returns: json object\n\ncount\n Returns the amount of open trades\n :returns: json object\n\ndaily\n Returns the amount of open trades\n :returns: json object\n\nedge\n Returns information about edge\n :returns: json object\n\nforcebuy\n Buy an asset\n :param pair: Pair to buy (ETH/BTC)\n :param price: Optional - price to buy\n :returns: json object of the trade\n\nforcesell\n Force-sell a trade\n :param tradeid: Id of the trade (can be received via status command)\n :returns: json object\n\nperformance\n Returns the performance of the different coins\n :returns: json object\n\nprofit\n Returns the profit summary\n :returns: json object\n\nreload_conf\n Reload configuration\n :returns: json object\n\nstart\n Start the bot if it's in stopped state.\n :returns: json object\n\nstatus\n Get the status of open trades\n :returns: json object\n\nstop\n Stop the bot. Use start to restart\n :returns: json object\n\nstopbuy\n Stop buying (but handle sells gracefully).\n use reload_conf to reset\n :returns: json object\n\nversion\n Returns the version of the bot\n :returns: json object containing the version\n\nwhitelist\n Show the current whitelist\n :returns: json object\nWhere an exchange provides a sandbox for risk-free integration, or end-to-end, testing CCXT provides access to these.
This document is a *light overview of configuring Freqtrade and GDAX sandbox. This can be useful to developers and trader alike as Freqtrade is quite customisable.
When testing your API connectivity, make sure to use the following URLs. Website* https://public.sandbox.gdax.com REST API* https://api-public.sandbox.gdax.com
"},{"location":"sandbox-testing/#configure-a-sandbox-account-on-gdax","title":"Configure a Sandbox account on Gdax","text":"Aim of this document section
This link will redirect to the sandbox main page to login / create account dialogues: https://public.sandbox.pro.coinbase.com/orders/
After registration and Email confimation you wil be redirected into your sanbox account. It is easy to verify you're in sandbox by checking the URL bar.
https://public.sandbox.pro.coinbase.com/
"},{"location":"sandbox-testing/#enable-2fa-a-prerequisite-to-creating-sandbox-api-keys","title":"Enable 2Fa (a prerequisite to creating sandbox API Keys)","text":"From within sand box site select your profile, top right.
Or as a direct link: https://public.sandbox.pro.coinbase.com/profile
From the menu panel to the left of the screen select
Security: \"View or Update\"
In the new site select \"enable authenticator\" as typical google Authenticator.
From within sandbox select profile>api>create api-keys
or as a direct link: https://public.sandbox.pro.coinbase.com/profile/api
Click on \"create one\" and ensure view and trade are \"checked\" and sumbit your 2FA
To add funds, use the web interface deposit and withdraw buttons.
To begin select 'Wallets' from the top menu.
Or as a direct link: https://public.sandbox.pro.coinbase.com/wallets
This process may be repeated for other currencies, ETH as example
"},{"location":"sandbox-testing/#configure-freqtrade-to-use-gax-sandbox","title":"Configure Freqtrade to use Gax Sandbox","text":"The aim of this document section
Freqtrade makes use of CCXT which in turn provides a list of URLs to Freqtrade. These include ['test'] and ['api'].
[Test] if available will point to an Exchanges sandbox. [Api] normally used, and resolves to live API target on the exchange To make use of sandbox / test add \"sandbox\": true, to your config.json
\"exchange\": {\n \"name\": \"gdax\",\n \"sandbox\": true,\n \"key\": \"5wowfxemogxeowo;heiohgmd\",\n \"secret\": \"/ZMH1P62rCVmwefewrgcewX8nh4gob+lywxfwfxwwfxwfNsH1ySgvWCUR/w==\",\n \"password\": \"1bkjfkhfhfu6sr\",\n \"outdated_offset\": 5\n \"pair_whitelist\": [\n \"BTC/USD\"\nAlso insert your
Ensure Freqtrade logs show the sandbox URL, and trades made are shown in sandbox. ** Typically the BTC/USD has the most activity in sandbox to test against.
"},{"location":"sandbox-testing/#gdax-old-candles-problem","title":"GDAX - Old Candles problem","text":"It is my experience that GDAX sandbox candles may be 20+- minutes out of date. This can cause trades to fail as one of Freqtrades safety checks.
To disable this check, add / change the \"outdated_offset\" parameter in the exchange section of your configuration to adjust for this delay. Example based on the above configuration:
\"exchange\": {\n \"name\": \"gdax\",\n \"sandbox\": true,\n \"key\": \"5wowfxemogxeowo;heiohgmd\",\n \"secret\": \"/ZMH1P62rCVmwefewrgcewX8nh4gob+lywxfwfxwwfxwfNsH1ySgvWCUR/w==\",\n \"password\": \"1bkjfkhfhfu6sr\",\n \"outdated_offset\": 30\n \"pair_whitelist\": [\n \"BTC/USD\"\nThis page contains some help if you want to edit your sqlite db.
"},{"location":"sql_cheatsheet/#install-sqlite3","title":"Install sqlite3","text":"Ubuntu/Debian installation
sudo apt-get install sqlite3\nsqlite3\n.open <filepath>\n.tables\n.schema <table_name>\nCREATE TABLE trades (\n id INTEGER NOT NULL,\n exchange VARCHAR NOT NULL,\n pair VARCHAR NOT NULL,\n is_open BOOLEAN NOT NULL,\n fee_open FLOAT NOT NULL,\n fee_close FLOAT NOT NULL,\n open_rate FLOAT,\n open_rate_requested FLOAT,\n close_rate FLOAT,\n close_rate_requested FLOAT,\n close_profit FLOAT,\n stake_amount FLOAT NOT NULL,\n amount FLOAT,\n open_date DATETIME NOT NULL,\n close_date DATETIME,\n open_order_id VARCHAR,\n stop_loss FLOAT,\n initial_stop_loss FLOAT,\n stoploss_order_id VARCHAR,\n stoploss_last_update DATETIME,\n max_rate FLOAT,\n sell_reason VARCHAR,\n strategy VARCHAR,\n ticker_interval INTEGER,\n PRIMARY KEY (id),\n CHECK (is_open IN (0, 1))\n);\nSELECT * FROM trades;\nWarning
Manually selling a pair on the exchange will not be detected by the bot and it will try to sell anyway. Whenever possible, forcesell should be used to accomplish the same thing. It is strongly advised to backup your database file before making any manual changes.
Note
This should not be necessary after /forcesell, as forcesell orders are closed automatically by the bot on the next iteration.
UPDATE trades\nSET is_open=0, close_date=<close_date>, close_rate=<close_rate>, close_profit=close_rate/open_rate-1, sell_reason=<sell_reason> \nWHERE id=<trade_ID_to_update>;\nUPDATE trades\nSET is_open=0, close_date='2017-12-20 03:08:45.103418', close_rate=0.19638016, close_profit=0.0496, sell_reason='force_sell' \nWHERE id=31;\nINSERT INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date)\nVALUES ('bittrex', 'ETH/BTC', 1, 0.0025, 0.0025, <open_rate>, <stake_amount>, <amount>, '<datetime>')\nINSERT INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date)\nVALUES ('bittrex', 'ETH/BTC', 1, 0.0025, 0.0025, 0.00258580, 0.002, 0.7715262081, '2017-11-28 12:44:24.000000')\nIf your DB was created before PR#200 was merged (before 12/23/17).
UPDATE trades SET fee=0.0025 WHERE fee=0.005;\nThe stoploss configuration parameter is loss in percentage that should trigger a sale. For example, value -0.10 will cause immediate sell if the profit dips below -10% for a given trade. This parameter is optional.
Most of the strategy files already include the optimal stoploss value. This parameter is optional. If you use it in the configuration file, it will take over the stoploss value from the strategy file.
At this stage the bot contains the following stoploss support modes:
Note
All stoploss properties can be configured in either Strategy or configuration. Configuration values override strategy values.
Those stoploss modes can be on exchange or off exchange. If the stoploss is on exchange it means a stoploss limit order is placed on the exchange immediately after buy order happens successfuly. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
In case of stoploss on exchange there is another parameter called stoploss_on_exchange_interval. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary. As an example in case of trailing stoploss if the order is on the exchange and the market is going up then the bot automatically cancels the previous stoploss order and put a new one with a stop value higher than previous one. It is clear that the bot cannot do it every 5 seconds otherwise it gets banned. So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
Note
Stoploss on exchange is only supported for Binance as of now.
"},{"location":"stoploss/#static-stop-loss","title":"Static Stop Loss","text":"This is very simple, basically you define a stop loss of x in your strategy file or alternative in the configuration, which will overwrite the strategy definition. This will basically try to sell your asset, the second the loss exceeds the defined loss.
"},{"location":"stoploss/#trailing-stop-loss","title":"Trailing Stop Loss","text":"The initial value for this stop loss, is defined in your strategy or configuration. Just as you would define your Stop Loss normally. To enable this Feauture all you have to do is to define the configuration element:
\"trailing_stop\" : True\nThis will now activate an algorithm, which automatically moves your stop loss up every time the price of your asset increases.
For example, simplified math,
basically what this means is that your stop loss will be adjusted to be always be 2% of the highest observed price
"},{"location":"stoploss/#custom-positive-loss","title":"Custom positive loss","text":"Due to demand, it is possible to have a default stop loss, when you are in the red with your buy, but once your profit surpasses a certain percentage, the system will utilize a new stop loss, which can be a different value. For example your default stop loss is 5%, but once you have 1.1% profit, it will be changed to be only a 1% stop loss, which trails the green candles until it goes below them.
Both values can be configured in the main configuration file and requires \"trailing_stop\": true to be set to true.
\"trailing_stop_positive\": 0.01,\n \"trailing_stop_positive_offset\": 0.011,\n \"trailing_only_offset_is_reached\": false\nThe 0.01 would translate to a 1% stop loss, once you hit 1.1% profit.
You should also make sure to have this value (trailing_stop_positive_offset) lower than your minimal ROI, otherwise minimal ROI will apply first and sell your trade.
If \"trailing_only_offset_is_reached\": true then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configuredstoploss.
A stoploss on an open trade can be changed by changing the value in the configuration or strategy and use the /reload_conf command (alternatively, completely stopping and restarting the bot also works).
The new stoploss value will be applied to open trades (and corresponding log-messages will be generated).
"},{"location":"stoploss/#limitations","title":"Limitations","text":"Stoploss values cannot be changed if trailing_stop is enabled and the stoploss has already been adjusted, or if Edge is enabled (since Edge would recalculate stoploss based on the current market situation).
This page explains where to customize your strategies, and add new indicators.
"},{"location":"strategy-customization/#install-a-custom-strategy-file","title":"Install a custom strategy file","text":"This is very simple. Copy paste your strategy file into the directory user_data/strategies.
Let assume you have a class called AwesomeStrategy in the file awesome-strategy.py:
user_data/strategies (you should have user_data/strategies/awesome-strategy.py--strategy AwesomeStrategy (the parameter is the class name)freqtrade --strategy AwesomeStrategy\nThe bot includes a default strategy file. However, we recommend you to use your own file to not have to lose your parameters every time the default strategy file will be updated on Github. Put your custom strategy file into the directory user_data/strategies.
Best copy the test-strategy and modify this copy to avoid having bot-updates override your changes. cp user_data/strategies/sample_strategy.py user_data/strategies/awesome-strategy.py
A strategy file contains all the information needed to build a good strategy:
The bot also include a sample strategy called SampleStrategy you can update: user_data/strategies/sample_strategy.py. You can test it with the parameter: --strategy SampleStrategy
Additionally, there is an attribute called INTERFACE_VERSION, which defines the version of the strategy interface the bot should use. The current version is 2 - which is also the default when it's not set explicitly in the strategy.
Future versions will require this to be set.
freqtrade --strategy AwesomeStrategy\nFor the following section we will use the user_data/strategies/sample_strategy.py file as reference.
Note
To avoid problems and unexpected differences between Backtesting and dry/live modes, please be aware that during backtesting the full time-interval is passed to the populate_*() methods at once. It is therefore best to use vectorized operations (across the whole dataframe, not loops) and avoid index referencing (df.iloc[-1]), but instead use df.shift() to get to the previous candle.
Warning
Since backtesting passes the full time interval to the populate_*() methods, the strategy author needs to take care to avoid having the strategy utilize data from the future. Samples for usage of future data are dataframe.shift(-1), dataframe.resample(\"1h\") (this uses the left border of the interval, so moves data from an hour to the start of the hour). They all use data which is not available during regular operations, so these strategies will perform well during backtesting, but will fail / perform badly in dry-runs.
Buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method populate_indicators() from your strategy file.
You should only add the indicators used in either populate_buy_trend(), populate_sell_trend(), or to populate another indicator, otherwise performance may suffer.
It's important to always return the dataframe without removing/modifying the columns \"open\", \"high\", \"low\", \"close\", \"volume\", otherwise these fields would contain something unexpected.
Sample:
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:\n \"\"\"\n Adds several different TA indicators to the given DataFrame\n\n Performance Note: For the best performance be frugal on the number of indicators\n you are using. Let uncomment only the indicator you are using in your strategies\n or your hyperopt configuration, otherwise you will waste your memory and CPU usage.\n :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()\n :param metadata: Additional information, like the currently traded pair\n :return: a Dataframe with all mandatory indicators for the strategies\n \"\"\"\n dataframe['sar'] = ta.SAR(dataframe)\n dataframe['adx'] = ta.ADX(dataframe)\n stoch = ta.STOCHF(dataframe)\n dataframe['fastd'] = stoch['fastd']\n dataframe['fastk'] = stoch['fastk']\n dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']\n dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)\n dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)\n dataframe['mfi'] = ta.MFI(dataframe)\n dataframe['rsi'] = ta.RSI(dataframe)\n dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)\n dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)\n dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)\n dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)\n dataframe['ao'] = awesome_oscillator(dataframe)\n macd = ta.MACD(dataframe)\n dataframe['macd'] = macd['macd']\n dataframe['macdsignal'] = macd['macdsignal']\n dataframe['macdhist'] = macd['macdhist']\n hilbert = ta.HT_SINE(dataframe)\n dataframe['htsine'] = hilbert['sine']\n dataframe['htleadsine'] = hilbert['leadsine']\n dataframe['plus_dm'] = ta.PLUS_DM(dataframe)\n dataframe['plus_di'] = ta.PLUS_DI(dataframe)\n dataframe['minus_dm'] = ta.MINUS_DM(dataframe)\n dataframe['minus_di'] = ta.MINUS_DI(dataframe)\n return dataframe\nWant more indicator examples?
Look into the user_data/strategies/sample_strategy.py. Then uncomment indicators you need.
"},{"location":"strategy-customization/#buy-signal-rules","title":"Buy signal rules","text":"Edit the method populate_buy_trend() in your strategy file to update your buy strategy.
It's important to always return the dataframe without removing/modifying the columns \"open\", \"high\", \"low\", \"close\", \"volume\", otherwise these fields would contain something unexpected.
This will method will also define a new column, \"buy\", which needs to contain 1 for buys, and 0 for \"no action\".
Sample from user_data/strategies/sample_strategy.py:
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:\n \"\"\"\n Based on TA indicators, populates the buy signal for the given dataframe\n :param dataframe: DataFrame populated with indicators\n :param metadata: Additional information, like the currently traded pair\n :return: DataFrame with buy column\n \"\"\"\n dataframe.loc[\n (\n (dataframe['adx'] > 30) &\n (dataframe['tema'] <= dataframe['bb_middleband']) &\n (dataframe['tema'] > dataframe['tema'].shift(1))\n ),\n 'buy'] = 1\n\n return dataframe\nEdit the method populate_sell_trend() into your strategy file to update your sell strategy. Please note that the sell-signal is only used if use_sell_signal is set to true in the configuration.
It's important to always return the dataframe without removing/modifying the columns \"open\", \"high\", \"low\", \"close\", \"volume\", otherwise these fields would contain something unexpected.
This will method will also define a new column, \"sell\", which needs to contain 1 for sells, and 0 for \"no action\".
Sample from user_data/strategies/sample_strategy.py:
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:\n \"\"\"\n Based on TA indicators, populates the sell signal for the given dataframe\n :param dataframe: DataFrame populated with indicators\n :param metadata: Additional information, like the currently traded pair\n :return: DataFrame with buy column\n \"\"\"\n dataframe.loc[\n (\n (dataframe['adx'] > 70) &\n (dataframe['tema'] > dataframe['bb_middleband']) &\n (dataframe['tema'] < dataframe['tema'].shift(1))\n ),\n 'sell'] = 1\n return dataframe\nThis dict defines the minimal Return On Investment (ROI) a trade should reach before selling, independent from the sell signal.
It is of the following format, with the dict key (left side of the colon) being the minutes passed since the trade opened, and the value (right side of the colon) being the percentage.
minimal_roi = {\n \"40\": 0.0,\n \"30\": 0.01,\n \"20\": 0.02,\n \"0\": 0.04\n}\nThe above configuration would therefore mean:
The calculation does include fees.
To disable ROI completely, set it to an insanely high number:
minimal_roi = {\n \"0\": 100\n}\nWhile technically not completely disabled, this would sell once the trade reaches 10000% Profit.
"},{"location":"strategy-customization/#stoploss","title":"Stoploss","text":"Setting a stoploss is highly recommended to protect your capital from strong moves against you.
Sample:
stoploss = -0.10\nThis would signify a stoploss of -10%.
For the full documentation on stoploss features, look at the dedicated stoploss page.
If your exchange supports it, it's recommended to also set \"stoploss_on_exchange\" in the order_types dictionary, so your stoploss is on the exchange and cannot be missed due to network problems, high load or other reasons.
For more information on order_types please look here.
"},{"location":"strategy-customization/#ticker-interval","title":"Ticker interval","text":"This is the set of candles the bot should download and use for the analysis. Common values are \"1m\", \"5m\", \"15m\", \"1h\", however all values supported by your exchange should work.
Please note that the same buy/sell signals may work with one interval, but not the other. This setting is accessible within the strategy by using self.ticker_interval.
The metadata-dict (available for populate_buy_trend, populate_sell_trend, populate_indicators) contains additional information. Currently this is pair, which can be accessed using metadata['pair'] - and will return a pair in the format XRP/BTC.
The Metadata-dict should not be modified and does not persist information across multiple calls. Instead, have a look at the section Storing information
"},{"location":"strategy-customization/#storing-information","title":"Storing information","text":"Storing information can be accomplished by crating a new dictionary within the strategy class.
The name of the variable can be choosen at will, but should be prefixed with cust_ to avoid naming collisions with predefined strategy variables.
class Awesomestrategy(IStrategy):\n # Create custom dictionary\n cust_info = {}\n def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:\n # Check if the entry already exists\n if \"crosstime\" in self.cust_info[metadata[\"pair\"]:\n self.cust_info[metadata[\"pair\"][\"crosstime\"] += 1\n else:\n self.cust_info[metadata[\"pair\"][\"crosstime\"] = 1\nWarning
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
"},{"location":"strategy-customization/#additional-data-dataprovider","title":"Additional data (DataProvider)","text":"The strategy provides access to the DataProvider. This allows you to get additional data to use in your strategy.
All methods return None in case of failure (do not raise an exception).
Please always check the mode of operation to select the correct method to get data (samples see below).
"},{"location":"strategy-customization/#possible-options-for-dataprovider","title":"Possible options for DataProvider","text":"available_pairs - Property with tuples listing cached pairs with their intervals (pair, interval).ohlcv(pair, ticker_interval) - Currently cached ticker data for the pair, returns DataFrame or empty DataFrame.historic_ohlcv(pair, ticker_interval) - Returns historical data stored on disk.get_pair_dataframe(pair, ticker_interval) - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).runmode - Property containing the current runmode.if self.dp:\n inf_pair, inf_timeframe = self.informative_pairs()[0]\n informative = self.dp.get_pair_dataframe(pair=inf_pair,\n ticker_interval=inf_timeframe)\nWarning
Be carefull when using dataprovider in backtesting. historic_ohlcv() (and get_pair_dataframe() for the backtesting runmode) provides the full time-range in one go, so please be aware of it and make sure to not \"look into the future\" to avoid surprises when running in dry/live mode).
Warning
This option cannot currently be used during hyperopt.
"},{"location":"strategy-customization/#orderbook","title":"Orderbook","text":"if self.dp:\n if self.dp.runmode in ('live', 'dry_run'):\n ob = self.dp.orderbook(metadata['pair'], 1)\n dataframe['best_bid'] = ob['bids'][0][0]\n dataframe['best_ask'] = ob['asks'][0][0]\nWarning
The order book is not part of the historic data which means backtesting and hyperopt will not work if this method is used.
"},{"location":"strategy-customization/#available-pairs","title":"Available Pairs","text":"if self.dp:\n for pair, ticker in self.dp.available_pairs:\n print(f\"available {pair}, {ticker}\")\nData for additional, informative pairs (reference pairs) can be beneficial for some strategies. Ohlcv data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via DataProvider just as other pairs (see above). These parts will not be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting.
The pairs need to be specified as tuples in the format (\"pair\", \"interval\"), with pair as the first and time interval as the second argument.
Sample:
def informative_pairs(self):\n return [(\"ETH/USDT\", \"5m\"),\n (\"BTC/TUSD\", \"15m\"),\n ]\nWarning
As these pairs will be refreshed as part of the regular whitelist refresh, it's best to keep this list short. All intervals and all pairs can be specified as long as they are available (and active) on the used exchange. It is however better to use resampling to longer time-intervals when possible to avoid hammering the exchange with too many requests and risk beeing blocked.
"},{"location":"strategy-customization/#additional-data-wallets","title":"Additional data - Wallets","text":"The strategy provides access to the Wallets object. This contains the current balances on the exchange.
Note
Wallets is not available during backtesting / hyperopt.
Please always check if Wallets is available to avoid failures during backtesting.
if self.wallets:\n free_eth = self.wallets.get_free('ETH')\n used_eth = self.wallets.get_used('ETH')\n total_eth = self.wallets.get_total('ETH')\nget_free(asset) - currently available balance to tradeget_used(asset) - currently tied up balance (open orders)get_total(asset) - total available balance - sum of the 2 aboveTo inspect the created dataframe, you can issue a print-statement in either populate_buy_trend() or populate_sell_trend(). You may also want to print the pair so it's clear what data is currently shown.
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:\n dataframe.loc[\n (\n #>> whatever condition<<<\n ),\n 'buy'] = 1\n\n # Print the Analyzed pair\n print(f\"result for {metadata['pair']}\")\n\n # Inspect the last 5 rows\n print(dataframe.tail())\n\n return dataframe\nPrinting more than a few rows is also possible (simply use print(dataframe) instead of print(dataframe.tail())), however not recommended, as that will be very verbose (~500 lines per pair every 5 seconds).
The default buy strategy is located in the file freqtrade/default_strategy.py.
"},{"location":"strategy-customization/#specify-custom-strategy-location","title":"Specify custom strategy location","text":"If you want to use a strategy from a different directory you can pass --strategy-path
freqtrade --strategy AwesomeStrategy --strategy-path /some/directory\nTo get additional Ideas for strategies, head over to our strategy repository. Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk. Feel free to use any of them as inspiration for your own strategies. We're happy to accept Pull Requests containing new Strategies to that repo.
We also got a strategy-sharing channel in our Slack community which is a great place to get and/or share ideas.
"},{"location":"strategy-customization/#next-step","title":"Next step","text":"Now you have a perfect strategy you probably want to backtest it. Your next step is to learn How to use the Backtesting.
"},{"location":"telegram-usage/","title":"Telegram usage","text":""},{"location":"telegram-usage/#setup-your-telegram-bot","title":"Setup your Telegram bot","text":"Below we explain how to create your Telegram Bot, and how to get your Telegram user id.
"},{"location":"telegram-usage/#1-create-your-telegram-bot","title":"1. Create your Telegram bot","text":"Start a chat with the Telegram BotFather
Send the message /newbot.
BotFather response:
Alright, a new bot. How are we going to call it? Please choose a name for your bot.
Choose the public name of your bot (e.x. Freqtrade bot)
BotFather response:
Good. Now let's choose a username for your bot. It must end in bot. Like this, for example: TetrisBot or tetris_bot.
Choose the name id of your bot and send it to the BotFather (e.g. \"My_own_freqtrade_bot\")
BotFather response:
Done! Congratulations on your new bot. You will find it at t.me/yourbots_name_bot. You can now add a description, about section and profile picture for your bot, see /help for a list of commands. By the way, when you've finished creating your cool bot, ping our Bot Support if you want a better username for it. Just make sure the bot is fully operational before you do this.
Use this token to access the HTTP API: 22222222:APITOKEN
For a description of the Bot API, see this page: https://core.telegram.org/bots/api Father bot will return you the token (API key)
Copy the API Token (22222222:APITOKEN in the above example) and keep use it for the config parameter token.
Don't forget to start the conversation with your bot, by clicking /START button
Talk to the userinfobot
Get your \"Id\", you will use it for the config parameter chat_id.
Per default, the Telegram bot shows predefined commands. Some commands are only available by sending them to the bot. The table below list the official commands. You can ask at any moment for help with /help.
/start Starts the trader /stop Stops the trader /stopbuy Stops the trader from opening new trades. Gracefully closes open trades according to their rules. /reload_conf Reloads the configuration file /status Lists all open trades /status table List all open trades in a table format /count Displays number of trades used and available /profit Display a summary of your profit/loss from close trades and some stats about your performance /forcesell <trade_id> Instantly sells the given trade (Ignoring minimum_roi). /forcesell all Instantly sells all open trades (Ignoring minimum_roi). /forcebuy <pair> [rate] Instantly buys the given pair. Rate is optional. (forcebuy_enable must be set to True) /performance Show performance of each finished trade grouped by pair /balance Show account balance per currency /daily <n> 7 Shows profit or loss per day, over the last n days /whitelist Show the current whitelist /blacklist [pair] Show the current blacklist, or adds a pair to the blacklist. /edge Show validated pairs by Edge if it is enabled. /help Show help message /version Show version"},{"location":"telegram-usage/#telegram-commands-in-action","title":"Telegram commands in action","text":"Below, example of Telegram message you will receive for each command.
"},{"location":"telegram-usage/#start","title":"/start","text":"Status: running
Stopping trader ... Status: stopped
status: Setting max_open_trades to 0. Run /reload_conf to reset.
Prevents the bot from opening new trades by temporarily setting \"max_open_trades\" to 0. Open trades will be handled via their regular rules (ROI / Sell-signal, stoploss, ...).
After this, give the bot time to close off open trades (can be checked via /status table). Once all positions are sold, run /stop to completely stop the bot.
/reload_conf resets \"max_open_trades\" to the value set in the configuration and resets this command.
Warning
The stop-buy signal is ONLY active while the bot is running, and is not persisted anyway, so restarting the bot will cause this to reset.
"},{"location":"telegram-usage/#status","title":"/status","text":"For each open trade, the bot will send you the following message.
Trade ID: 123 (since 1 days ago) Current Pair: CVC/BTC Open Since: 1 days ago Amount: 26.64180098 Open Rate: 0.00007489 Current Rate: 0.00007489 Current Profit: 12.95% Stoploss: 0.00007389 (-0.02%)
Return the status of all open trades in a table format.
ID Pair Since Profit\n---- -------- ------- --------\n 67 SC/BTC 1 d 13.33%\n 123 CVC/BTC 1 h 12.95%\nReturn the number of trades used and available.
current max\n--------- -----\n 2 10\nReturn a summary of your profit/loss and performance.
ROI: Close trades \u2219 0.00485701 BTC (258.45%) \u2219 62.968 USD ROI: All trades \u2219 0.00255280 BTC (143.43%) \u2219 33.095 EUR
Total Trade Count: 138 First Trade opened: 3 days ago Latest Trade opened: 2 minutes ago Avg. Duration: 2:33:45 Best Performing: PAY/BTC: 50.23%
BITTREX: Selling BTC/LTC with limit 0.01650000 (profit: ~-4.07%, -0.00008168)
BITTREX: Buying ETH/BTC with limit 0.03400000 (1.000000 ETH, 225.290 USD)
Note that for this to work, forcebuy_enable needs to be set to true.
More details
","text":""},{"location":"telegram-usage/#performance","title":"/performanceReturn the performance of each crypto-currency the bot has sold.
Performance: 1. RCN/BTC 57.77% 2. PAY/BTC 56.91% 3. VIB/BTC 47.07% 4. SALT/BTC 30.24% 5. STORJ/BTC 27.24% ...
Return the balance of all crypto-currency your have on the exchange.
Currency: BTC Available: 3.05890234 Balance: 3.05890234 Pending: 0.0
Currency: CVC Available: 86.64180098 Balance: 86.64180098 Pending: 0.0
","text":""},{"location":"telegram-usage/#daily","title":"/dailyPer default /daily will return the 7 last days. The example below if for /daily 3:
Daily Profit over the last 3 days:
Day Profit BTC Profit USD\n---------- -------------- ------------\n2018-01-03 0.00224175 BTC 29,142 USD\n2018-01-02 0.00033131 BTC 4,307 USD\n2018-01-01 0.00269130 BTC 34.986 USD\nShows the current whitelist
Using whitelist StaticPairList with 22 pairs IOTA/BTC, NEO/BTC, TRX/BTC, VET/BTC, ADA/BTC, ETC/BTC, NCASH/BTC, DASH/BTC, XRP/BTC, XVG/BTC, EOS/BTC, LTC/BTC, OMG/BTC, BTG/BTC, LSK/BTC, ZEC/BTC, HOT/BTC, IOTX/BTC, XMR/BTC, AST/BTC, XLM/BTC, NANO/BTC
Shows the current blacklist. If Pair is set, then this pair will be added to the pairlist. Also supports multiple pairs, seperated by a space. Use /reload_conf to reset the blacklist.
Using blacklist StaticPairList with 2 pairs DODGE/BTC, HOT/BTC.
Shows pairs validated by Edge along with their corresponding winrate, expectancy and stoploss values.
Edge only validated following pairs:
Pair Winrate Expectancy Stoploss\n-------- --------- ------------ ----------\nDOCK/ETH 0.522727 0.881821 -0.03\nPHX/ETH 0.677419 0.560488 -0.03\nHOT/ETH 0.733333 0.490492 -0.03\nHC/ETH 0.588235 0.280988 -0.02\nARDR/ETH 0.366667 0.143059 -0.01\nVersion: 0.14.3
Enable webhooks by adding a webhook-section to your configuration file, and setting webhook.enabled to true.
Sample configuration (tested using IFTTT).
\"webhook\": {\n \"enabled\": true,\n \"url\": \"https://maker.ifttt.com/trigger/<YOUREVENT>/with/key/<YOURKEY>/\",\n \"webhookbuy\": {\n \"value1\": \"Buying {pair}\",\n \"value2\": \"limit {limit:8f}\",\n \"value3\": \"{stake_amount:8f} {stake_currency}\"\n },\n \"webhooksell\": {\n \"value1\": \"Selling {pair}\",\n \"value2\": \"limit {limit:8f}\",\n \"value3\": \"profit: {profit_amount:8f} {stake_currency}\"\n },\n \"webhookstatus\": {\n \"value1\": \"Status: {status}\",\n \"value2\": \"\",\n \"value3\": \"\"\n }\n },\nThe url in webhook.url should point to the correct url for your webhook. If you're using IFTTT (as shown in the sample above) please insert our event and key to the url.
Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.
"},{"location":"webhook-config/#webhookbuy","title":"Webhookbuy","text":"The fields in webhook.webhookbuy are filled when the bot executes a buy. Parameters are filled using string.format. Possible parameters are:
exchangepairlimitstake_amountstake_currencyfiat_currencyorder_typeThe fields in webhook.webhooksell are filled when the bot sells a trade. Parameters are filled using string.format. Possible parameters are:
exchangepairgainlimitamountopen_ratecurrent_rateprofit_amountprofit_percentstake_currencyfiat_currencysell_reasonorder_typeThe fields in webhook.webhookstatus are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
The only possible value here is {status}.