mirror of
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257 lines
12 KiB
Markdown
257 lines
12 KiB
Markdown
# Development Help
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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.
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All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel in [slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) where you can ask questions.
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## Documentation
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Documentation is available at [https://freqtrade.io](https://www.freqtrade.io/) and needs to be provided with every new feature PR.
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Special fields for the documentation (like Note boxes, ...) can be found [here](https://squidfunk.github.io/mkdocs-material/extensions/admonition/).
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## Developer setup
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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]? ".
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Alternatively (if your system is not supported by the setup.sh script), follow the manual installation process and run `pip3 install -e .[all]`.
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This will install all required tools for development, including `pytest`, `flake8`, `mypy`, and `coveralls`.
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### Tests
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New code should be covered by basic unittests. Depending on the complexity of the feature, Reviewers may request more in-depth unittests.
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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).
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#### Checking log content in tests
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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).
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These are available from `conftest.py` and can be imported in any test module.
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A sample check looks as follows:
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``` python
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from tests.conftest import log_has, log_has_re
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def test_method_to_test(caplog):
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method_to_test()
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assert log_has("This event happened", caplog)
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# Check regex with trailing number ...
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assert log_has_re(r"This dynamic event happened and produced \d+", caplog)
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```
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### Local docker usage
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The fastest and easiest way to start up is to use docker-compose.develop which gives developers the ability to start the bot up with all the required dependencies, *without* needing to install any freqtrade specific dependencies on your local machine.
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#### Install
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* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
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* [docker](https://docs.docker.com/install/)
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* [docker-compose](https://docs.docker.com/compose/install/)
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#### Starting the bot
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##### Use the develop dockerfile
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``` bash
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rm docker-compose.yml && mv docker-compose.develop.yml docker-compose.yml
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```
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#### Docker Compose
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##### Starting
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``` bash
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docker-compose up
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```
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##### Rebuilding
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``` bash
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docker-compose build
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```
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##### Execing (effectively SSH into the container)
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The `exec` command requires that the container already be running, if you want to start it
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that can be effected by `docker-compose up` or `docker-compose run freqtrade_develop`
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``` bash
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docker-compose exec freqtrade_develop /bin/bash
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```
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## Modules
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### Dynamic Pairlist
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You have a great idea for a new pair selection algorithm you would like to try out? Great.
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Hopefully you also want to contribute this back upstream.
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Whatever your motivations are - This should get you off the ground in trying to develop a new Pairlist provider.
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First of all, have a look at the [VolumePairList](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/pairlist/VolumePairList.py) provider, and best copy this file with a name of your new Pairlist Provider.
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This is a simple provider, which however serves as a good example on how to start developing.
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Next, modify the classname of the provider (ideally align this with the Filename).
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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.
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```python
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self._exchange = exchange
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self._pairlistmanager = pairlistmanager
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self._config = config
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self._pairlistconfig = pairlistconfig
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self._pairlist_pos = pairlist_pos
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```
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Now, let's step through the methods which require actions:
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#### Pairlist configuration
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Configuration for PairListProvider is done in the bot configuration file in the element `"pairlist"`.
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This Pairlist-object may contain configurations with additional configurations for the configured pairlist.
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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.
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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.
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#### short_desc
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Returns a description used for Telegram messages.
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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"`.
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#### filter_pairlist
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Override this method and run all calculations needed in this method.
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This is called with each iteration of the bot - so consider implementing caching for compute/network heavy calculations.
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It get's passed a pairlist (which can be the result of previous pairlists) as well as `tickers`, a pre-fetched version of `get_tickers()`.
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It must return the resulting pairlist (which may then be passed into the next pairlist filter).
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Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filters. Use this if you limit your result to a certain number of pairs - so the endresult is not shorter than expected.
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##### sample
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``` python
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def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
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# Generate dynamic whitelist
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pairs = self._calculate_pairlist(pairlist, tickers)
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return pairs
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```
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#### _gen_pair_whitelist
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This is a simple method used by `VolumePairList` - however serves as a good example.
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In VolumePairList, this implements different methods of sorting, does early validation so only the expected number of pairs is returned.
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## Implement a new Exchange (WIP)
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!!! Note
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This section is a Work in Progress and is not a complete guide on how to test a new exchange with FreqTrade.
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Most exchanges supported by CCXT should work out of the box.
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### Stoploss On Exchange
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Check if the new exchange supports Stoploss on Exchange orders through their API.
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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](https://github.com/ccxt/ccxt/issues) may also provide great help, since others may have implemented something similar for their projects.
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### Incomplete candles
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While fetching OHLCV data, we're may end up getting incomplete candles (Depending on the exchange).
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To demonstrate this, we'll use daily candles (`"1d"`) to keep things simple.
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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.
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To check how the new exchange behaves, you can use the following snippet:
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``` python
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import ccxt
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from datetime import datetime
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from freqtrade.data.converter import parse_ticker_dataframe
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ct = ccxt.binance()
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timeframe = "1d"
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pair = "XLM/BTC" # Make sure to use a pair that exists on that exchange!
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raw = ct.fetch_ohlcv(pair, timeframe=timeframe)
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# convert to dataframe
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df1 = parse_ticker_dataframe(raw, timeframe, pair=pair, drop_incomplete=False)
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print(df1["date"].tail(1))
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print(datetime.utcnow())
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```
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``` output
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19 2019-06-08 00:00:00+00:00
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2019-06-09 12:30:27.873327
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```
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The output will show the last entry from the Exchange as well as the current UTC date.
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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).
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## Updating example notebooks
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To keep the jupyter notebooks aligned with the documentation, the following should be ran after updating a example notebook.
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``` bash
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jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace freqtrade/templates/strategy_analysis_example.ipynb
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jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown freqtrade/templates/strategy_analysis_example.ipynb --stdout > docs/strategy_analysis_example.md
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```
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## Continuous integration
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This documents some decisions taken for the CI Pipeline.
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* CI runs on all OS variants, Linux (ubuntu), macOS and Windows.
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* Docker images are build for the branches `master` and `develop`.
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* Raspberry PI Docker images are postfixed with `_pi` - so tags will be `:master_pi` and `develop_pi`.
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* Docker images contain a file, `/freqtrade/freqtrade_commit` containing the commit this image is based of.
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* Full docker image rebuilds are run once a week via schedule.
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* Deployments run on ubuntu.
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* ta-lib binaries are contained in the build_helpers directory to avoid fails related to external unavailability.
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* All tests must pass for a PR to be merged to `master` or `develop`.
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## Creating a release
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This part of the documentation is aimed at maintainers, and shows how to create a release.
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### Create release branch
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First, pick a commit that's about one week old (to not include latest additions to releases).
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``` bash
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# create new branch
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git checkout -b new_release <commitid>
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```
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Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
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* Edit `freqtrade/__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.
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* Commit this part
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* push that branch to the remote and create a PR against the master branch
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### Create changelog from git commits
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!!! Note
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Make sure that the master branch is uptodate!
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``` bash
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# Needs to be done before merging / pulling that branch.
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git log --oneline --no-decorate --no-merges master..new_release
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```
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### Create github release / tag
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Once the PR against master is merged (best right after merging):
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* Use the button "Draft a new release" in the Github UI (subsection releases).
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* Use the version-number specified as tag.
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* Use "master" as reference (this step comes after the above PR is merged).
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* Use the above changelog as release comment (as codeblock).
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