freqtrade_origin/docs/developer.md

Development Help

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.

Documentation

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.

Developer setup

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.

Tests

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() 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

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)

Local docker usage

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.

Install

• git
• docker
• docker-compose

Starting the bot

Use the develop dockerfile

rm docker-compose.yml && mv docker-compose.develop.yml docker-compose.yml

Docker Compose

Starting

docker-compose up

Rebuilding

docker-compose build

Execing (effectively SSH into the container)

The exec command requires that the container already be running, if you want to start it that can be effected by docker-compose up or docker-compose run freqtrade_develop

docker-compose exec freqtrade_develop /bin/bash

Modules

Dynamic Pairlist

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 _blacklist and _whitelist.

        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

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.

short_desc

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".

refresh_pairlist

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.

sample

    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]

_gen_pair_whitelist

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)

!!! 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.

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.

Incomplete candles

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 "ohlcv_partial_candle" to False to not drop Candles (shown in the example above).

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 user_data/notebooks/strategy_analysis_example.ipynb
jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown user_data/notebooks/strategy_analysis_example.ipynb --stdout > docs/strategy_analysis_example.md

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.

• 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.
• Commit this part
• push that branch to the remote and create a PR against the master branch

Create changelog from git commits

!!! Note Make sure that the master branch is uptodate!

# Needs to be done before merging / pulling that branch.
git log --oneline --no-decorate --no-merges master..new_release

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).