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.
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.
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
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).
Checking log content in tests¶
Freqtrade uses 2 main methods to check log content in tests,
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 def test_method_to_test(caplog): method_to_test() assert log_has("This event happened", caplog) # Check regex with trailing number ... assert log_has_re(r"This dynamic event happened and produced \d+", caplog)
You 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
self._freqtrade = freqtrade self._config = config self._whitelist = self._config['exchange']['pair_whitelist'] self._blacklist = self._config['exchange'].get('pair_blacklist', )
Now, let's step through the methods which require actions:
Configuration for PairListProvider is done in the bot configuration file in the element
This Pairlist-object may contain a
"config" dict with additional configurations for the configured pairlist.
"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.
"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._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: # Generate dynamic whitelist pairs = self._gen_pair_whitelist(self._config['stake_currency'], self._sort_key) # Validate whitelist to only have active market pairs self._whitelist = self._validate_whitelist(pairs)[:self._number_pairs]
This 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.
Implement a new Exchange (WIP)¶
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.
Stoploss On Exchange¶
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 from datetime import datetime from freqtrade.data.converter import parse_ticker_dataframe ct = ccxt.binance() timeframe = "1d" pair = "XLM/BTC" # Make sure to use a pair that exists on that exchange! raw = ct.fetch_ohlcv(pair, timeframe=timeframe) # convert to dataframe df1 = parse_ticker_dataframe(raw, timeframe, pair=pair, drop_incomplete=False) print(df1["date"].tail(1)) print(datetime.utcnow())
19 2019-06-08 00:00:00+00:00 2019-06-09 12:30:27.873327
The 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
False to not drop Candles (shown in the example above).
Creating a release¶
This part of the documentation is aimed at maintainers, and shows how to create a release.
Create release branch¶
# make sure you're in develop branch git checkout develop # create new branch git checkout -b new_release
freqtrade/__init__.pyand add the version matching the current date (for example
2019.7for July 2019). Minor versions can be
2019.7-1should we need to do a second release that month.
- Commit this part
- push that branch to the remote and create a PR against the master branch
Create changelog from git commits¶
Make sure that both master and develop are up-todate!.
# Needs to be done before merging / pulling that branch. git log --oneline --no-decorate --no-merges master..develop
Create github release / tag¶
Once the PR against master is merged (best right after merging):
- Use the button "Draft a new release" in the Github UI (subsection releases)
- Use the version-number specified as tag.
- Use "master" as reference (this step comes after the above PR is merged).
- Use the above changelog as release comment (as codeblock)
- Update version in develop by postfixing that with
2019.6 -> 2019.6-dev).
- Create a PR against develop to update that branch.