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 on discord or slack where you can ask questions.
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 test the documentation locally use the following commands.
pip install -r docs/requirements-docs.txt mkdocs serve
This will spin up a local server (usually on port 8000) so you can see if everything looks as you'd like it to.
To configure a development environment, you can either use the provided DevContainer, or use the
setup.sh script and answer "y" when asked "Do you want to install dependencies for dev [y/N]? ".
Alternatively (e.g. 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
The fastest and easiest way to get started is to use VSCode with the Remote container extension. This gives developers the ability to start the bot with all required dependencies without needing to install any freqtrade specific dependencies on your local machine.
For more information about the Remote container extension, best consult the documentation.
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)
Freqtrade Exceptions all inherit from
This general class of error should however not be used directly. Instead, multiple specialized sub-Exceptions exist.
Below is an outline of exception inheritance hierarchy:
+ FreqtradeException | +---+ OperationalException | +---+ DependencyException | | | +---+ PricingError | | | +---+ ExchangeError | | | +---+ TemporaryError | | | +---+ DDosProtection | | | +---+ InvalidOrderException | | | +---+ RetryableOrderError | | | +---+ InsufficientFundsError | +---+ StrategyError
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 Handler.
First of all, have a look at the VolumePairList Handler, and best copy this file with a name of your new Pairlist Handler.
This is a simple Handler, which however serves as a good example on how to start developing.
Next, modify the class-name of the Handler (ideally align this with the module filename).
The base-class provides an instance of the exchange (
self._exchange) the pairlist manager (
self._pairlistmanager), as well as the main configuration (
self._config), the pairlist dedicated configuration (
self._pairlistconfig) and the absolute position within the list of pairlists.
self._exchange = exchange self._pairlistmanager = pairlistmanager self._config = config self._pairlistconfig = pairlistconfig self._pairlist_pos = pairlist_pos
Don't forget to register your pairlist in
constants.py under the variable
AVAILABLE_PAIRLISTS - otherwise it will not be selectable.
Now, let's step through the methods which require actions:
Configuration for the chain of Pairlist Handlers is done in the bot configuration file in the element
"pairlists", an array of configuration parameters for each Pairlist Handlers in the chain.
"number_assets" is used to specify the maximum number of pairs to keep in the pairlist. Please follow this to ensure a consistent user experience.
Additional parameters can be configured as needed. For instance,
"sort_key" to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successful and dynamic.
Returns a description used for Telegram messages.
This should contain the name of the Pairlist Handler, as well as a short description containing the number of assets. Please follow the format
"PairlistName - top/bottom X pairs".
Override this method if the Pairlist Handler can be used as the leading Pairlist Handler in the chain, defining the initial pairlist which is then handled by all Pairlist Handlers in the chain. Examples are
This is called with each iteration of the bot (only if the Pairlist Handler is at the first location) - so consider implementing caching for compute/network heavy calculations.
It must return the resulting pairlist (which may then be passed into the chain of Pairlist Handlers).
Validations are optional, the parent class exposes a
_whitelist_for_active_markets(pairlist) to do default filtering. Use this if you limit your result to a certain number of pairs - so the end-result is not shorter than expected.
This method is called for each Pairlist Handler in the chain by the pairlist manager.
This is called with each iteration of the bot - so consider implementing caching for compute/network heavy calculations.
It gets passed a pairlist (which can be the result of previous pairlists) as well as
tickers, a pre-fetched version of
The default implementation in the base class simply calls the
_validate_pair() method for each pair in the pairlist, but you may override it. So you should either implement the
_validate_pair() in your Pairlist Handler or override
filter_pairlist() to do something else.
If overridden, it must return the resulting pairlist (which may then be passed into the next Pairlist Handler in the chain).
Validations are optional, the parent class exposes a
_whitelist_for_active_markets(pairlist) to do default filters. Use this if you limit your result to a certain number of pairs - so the end result is not shorter than expected.
VolumePairList, this implements different methods of sorting, does early validation so only the expected number of pairs is returned.
def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]: # Generate dynamic whitelist pairs = self._calculate_pairlist(pairlist, tickers) return pairs
Best read the Protection documentation to understand protections. This Guide is directed towards Developers who want to develop a new protection.
No protection should use datetime directly, but use the provided
date_now variable for date calculations. This preserves the ability to backtest protections.
Writing a new Protection
Best copy one of the existing Protections to have a good example.
Don't forget to register your protection in
constants.py under the variable
AVAILABLE_PROTECTIONS - otherwise it will not be selectable.
Implementation of a new protection¶
All Protection implementations must have
IProtection as parent class.
For that reason, they must implement the following methods:
stop_per_pair() must return a ProtectionReturn tuple, which consists of:
- lock pair - boolean
- lock until - datetime - until when should the pair be locked (will be rounded up to the next new candle)
- reason - string, used for logging and storage in the database
until portion should be calculated using the provided
All Protections should use
"stop_duration_candles" to define how long a a pair (or all pairs) should be locked.
The content of this is made available as
self._stop_duration to the each Protection.
If your protection requires a look-back period, please use
"lockback_period_candles" to keep all protections aligned.
Global vs. local stops¶
Protections can have 2 different ways to stop trading for a limited :
- Per pair (local)
- For all Pairs (globally)
Protections - per pair¶
Protections that implement the per pair approach must set
stop_per_pair() will be called whenever a trade closed (sell order completed).
Protections - global protection¶
These Protections should do their evaluation across all pairs, and consequently will also lock all pairs from trading (called a global PairLock).
Global protection must set
has_global_stop=True to be evaluated for global stops.
global_stop() will be called whenever a trade closed (sell order completed).
Protections - calculating lock end time¶
Protections should calculate the lock end time based on the last trade it considers. This avoids re-locking should the lookback-period be longer than the actual lock period.
IProtection parent class provides a helper method for this in
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.
To quickly test the public endpoints of an exchange, add a configuration for your exchange to
test_ccxt_compat.py and run these tests with
pytest --longrun tests/exchange/test_ccxt_compat.py.
Completing these tests successfully a good basis point (it's a requirement, actually), however these won't guarantee correct exchange functioning, as this only tests public endpoints, but no private endpoint (like generate order or similar).
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 ourselves. 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 candle (OHLCV) data, we 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 ohlcv_to_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 = ohlcv_to_dataframe(raw, timeframe, pair=pair, drop_incomplete=False) print(df1.tail(1)) print(datetime.utcnow())
date open high low close volume 499 2019-06-08 00:00:00+00:00 0.000007 0.000007 0.000007 0.000007 26264344.0 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).
Another way is to run this command multiple times in a row and observe if the volume is changing (while the date remains the same).
Updating example notebooks¶
To keep the jupyter notebooks aligned with the documentation, the following should be ran after updating a example notebook.
jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace freqtrade/templates/strategy_analysis_example.ipynb jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown freqtrade/templates/strategy_analysis_example.ipynb --stdout > docs/strategy_analysis_example.md
This documents some decisions taken for the CI Pipeline.
- CI runs on all OS variants, Linux (ubuntu), macOS and Windows.
- Docker images are build for the branches
- Docker images containing Plot dependencies are also available as
- Raspberry PI Docker images are postfixed with
_pi- so tags will be
- Docker images contain a file,
/freqtrade/freqtrade_commitcontaining the commit this image is based of.
- Full docker image rebuilds are run once a week via schedule.
- Deployments run on ubuntu.
- ta-lib binaries are contained in the build_helpers directory to avoid fails related to external unavailability.
- All tests must pass for a PR to be merged to
Creating a release¶
This part of the documentation is aimed at maintainers, and shows how to create a release.
Create release branch¶
First, pick a commit that's about one week old (to not include latest additions to releases).
# create new branch git checkout -b new_release <commitid>
Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
- Merge the release branch (stable) into this branch.
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. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
- Commit this part
- push that branch to the remote and create a PR against the stable branch
Create changelog from git commits¶
Make sure that the
stable branch is up-to-date!
# Needs to be done before merging / pulling that branch. git log --oneline --no-decorate --no-merges stable..new_release
To keep the release-log short, best wrap the full git changelog into a collapsible details section.
<details> <summary>Expand full changelog</summary> ... Full git changelog </details>
Create github release / tag¶
Once the PR against stable 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 "stable" as reference (this step comes after the above PR is merged).
- Use the above changelog as release comment (as codeblock)
This process is now automated as part of Github Actions.
To create a pypi release, please run the following commands:
twine (for uploading), account on pypi with proper permissions.
python setup.py sdist bdist_wheel # For pypi test (to check if some change to the installation did work) twine upload --repository-url https://test.pypi.org/legacy/ dist/* # For production: twine upload dist/*
Please don't push non-releases to the productive / real pypi instance.