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Merge pull request #3805 from freqtrade/new_release

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New release 2020.9
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Matthias 2020-09-28 07:48:54 +02:00 committed by GitHub
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18
.devcontainer/Dockerfile Normal file
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@ -0,0 +1,18 @@
FROM freqtradeorg/freqtrade:develop
# Install dependencies
COPY requirements-dev.txt /freqtrade/
RUN apt-get update \
&& apt-get -y install git sudo vim \
&& apt-get clean \
&& pip install autopep8 -r docs/requirements-docs.txt -r requirements-dev.txt --no-cache-dir \
&& useradd -u 1000 -U -m ftuser \
&& mkdir -p /home/ftuser/.vscode-server /home/ftuser/.vscode-server-insiders /home/ftuser/commandhistory \
&& echo "export PROMPT_COMMAND='history -a'" >> /home/ftuser/.bashrc \
&& echo "export HISTFILE=~/commandhistory/.bash_history" >> /home/ftuser/.bashrc \
&& chown ftuser: -R /home/ftuser/
USER ftuser
# Empty the ENTRYPOINT to allow all commands
ENTRYPOINT []

44
.devcontainer/devcontainer.json Normal file
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@ -0,0 +1,44 @@
{
"name": "freqtrade Develop",
"dockerComposeFile": [
"docker-compose.yml"
],
"service": "ft_vscode",
"workspaceFolder": "/freqtrade/",
"settings": {
"terminal.integrated.shell.linux": "/bin/bash",
"editor.insertSpaces": true,
"files.trimTrailingWhitespace": true,
"[markdown]": {
"files.trimTrailingWhitespace": false,
},
"python.pythonPath": "/usr/local/bin/python",
},
// Add the IDs of extensions you want installed when the container is created.
"extensions": [
"ms-python.python",
"ms-python.vscode-pylance",
"davidanson.vscode-markdownlint",
"ms-azuretools.vscode-docker",
],
// Use 'forwardPorts' to make a list of ports inside the container available locally.
// "forwardPorts": [],
// Uncomment the next line if you want start specific services in your Docker Compose config.
// "runServices": [],
// Uncomment the next line if you want to keep your containers running after VS Code shuts down.
// "shutdownAction": "none",
// Uncomment the next line to run commands after the container is created - for example installing curl.
// "postCreateCommand": "sudo apt-get update && apt-get install -y git",
// Uncomment to connect as a non-root user if you've added one. See https://aka.ms/vscode-remote/containers/non-root.
"remoteUser": "ftuser"
}

24
.devcontainer/docker-compose.yml Normal file
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@ -0,0 +1,24 @@
---
version: '3'
services:
ft_vscode:
build:
context: ..
dockerfile: ".devcontainer/Dockerfile"
volumes:
# Allow git usage within container
- "/home/${USER}/.ssh:/home/ftuser/.ssh:ro"
- "/home/${USER}/.gitconfig:/home/ftuser/.gitconfig:ro"
- ..:/freqtrade:cached
# Persist bash-history
- freqtrade-vscode-server:/home/ftuser/.vscode-server
- freqtrade-bashhistory:/home/ftuser/commandhistory
# Expose API port
ports:
- "127.0.0.1:8080:8080"
command: /bin/sh -c "while sleep 1000; do :; done"
volumes:
freqtrade-vscode-server:
freqtrade-bashhistory:

1
.dockerignore
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@ -13,3 +13,4 @@ CONTRIBUTING.md
MANIFEST.in
README.md
freqtrade.service
user_data

14
.github/workflows/ci.yml vendored
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@ -4,11 +4,11 @@ on:
push:
branches:
- master
- stable
- develop
- github_actions_tests
tags:
release:
types: [published]
release:
types: [published]
pull_request:
schedule:
- cron: '0 5 * * 4'
@ -194,7 +194,7 @@ jobs:
steps:
- name: Cleanup previous runs on this branch
uses: rokroskar/workflow-run-cleanup-action@v0.2.2
if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/master' && github.repository == 'freqtrade/freqtrade'"
if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/stable' && github.repository == 'freqtrade/freqtrade'"
env:
GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"
@ -226,7 +226,7 @@ jobs:
- name: Extract branch name
shell: bash
run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF#refs/heads/})"
run: echo "##[set-output name=branch;]$(echo ${GITHUB_REF##*/})"
id: extract_branch
- name: Build distribution
@ -236,7 +236,7 @@ jobs:
- name: Publish to PyPI (Test)
uses: pypa/gh-action-pypi-publish@master
if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
if: (steps.extract_branch.outputs.branch == 'stable' || github.event_name == 'release')
with:
user: __token__
password: ${{ secrets.pypi_test_password }}
@ -244,7 +244,7 @@ jobs:
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@master
if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
if: (steps.extract_branch.outputs.branch == 'stable' || github.event_name == 'release')
with:
user: __token__
password: ${{ secrets.pypi_password }}

2
.github/workflows/docker_update_readme.yml vendored
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@ -2,7 +2,7 @@ name: Update Docker Hub Description
on:
push:
branches:
- master
- stable
jobs:
dockerHubDescription:

9
CONTRIBUTING.md
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@ -8,8 +8,9 @@ Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/
Few pointers for contributions:
- Create your PR against the `develop` branch, not `master`.
- New features need to contain unit tests and must be PEP8 conformant (max-line-length = 100).
- Create your PR against the `develop` branch, not `stable`.
- New features need to contain unit tests, must conform to PEP8 (max-line-length = 100) and should be documented with the introduction PR.
- PR's can be declared as `[WIP]` - which signify Work in Progress Pull Requests (which are not finished).
If you are unsure, discuss the feature on our [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE)
or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
@ -18,7 +19,7 @@ or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
Best start by reading the [documentation](https://www.freqtrade.io/) to get a feel for what is possible with the bot, or head straight to the [Developer-documentation](https://www.freqtrade.io/en/latest/developer/) (WIP) which should help you getting started.
## Before sending the PR:
## Before sending the PR
### 1. Run unit tests
@ -114,6 +115,6 @@ Contributors may be given commit privileges. Preference will be given to those w
1. Access to resources for cross-platform development and testing.
1. Time to devote to the project regularly.
Being a Committer does not grant write permission on `develop` or `master` for security reasons (Users trust Freqtrade with their Exchange API keys).
Being a Committer does not grant write permission on `develop` or `stable` for security reasons (Users trust Freqtrade with their Exchange API keys).
After being Committer for some time, a Committer may be named Core Committer and given full repository access.

7
Dockerfile
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@ -1,4 +1,4 @@
FROM python:3.8.5-slim-buster
FROM python:3.8.6-slim-buster
RUN apt-get update \
&& apt-get -y install curl build-essential libssl-dev sqlite3 \
@ -16,13 +16,14 @@ RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
ENV LD_LIBRARY_PATH /usr/local/lib
# Install dependencies
COPY requirements.txt requirements-common.txt requirements-hyperopt.txt /freqtrade/
COPY requirements.txt requirements-hyperopt.txt /freqtrade/
RUN pip install numpy --no-cache-dir \
&& pip install -r requirements-hyperopt.txt --no-cache-dir
# Install and execute
COPY . /freqtrade/
RUN pip install -e . --no-cache-dir
RUN pip install -e . --no-cache-dir \
&& mkdir /freqtrade/user_data/
ENTRYPOINT ["freqtrade"]
# Default to trade mode
CMD [ "trade" ]

2
Dockerfile.armhf
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@ -17,7 +17,7 @@ RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
ENV LD_LIBRARY_PATH /usr/local/lib
# Install dependencies
COPY requirements.txt requirements-common.txt /freqtrade/
COPY requirements.txt /freqtrade/
RUN pip install numpy --no-cache-dir \
&& pip install -r requirements.txt --no-cache-dir

8
README.md
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@ -127,8 +127,8 @@ Telegram is not mandatory. However, this is a great way to control your bot. Mor
The project is currently setup in two main branches:
- `develop` - This branch has often new features, but might also cause breaking changes.
- `master` - This branch contains the latest stable release. The bot 'should' be stable on this branch, and is generally well tested.
- `develop` - This branch has often new features, but might also contain breaking changes. We try hard to keep this branch as stable as possible.
- `stable` - This branch contains the latest stable release. This branch is generally well tested.
- `feat/*` - These are feature branches, which are being worked on heavily. Please don't use these unless you want to test a specific feature.
## Support
@ -171,11 +171,11 @@ Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/
**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
**Important:** Always create your PR against the `develop` branch, not `master`.
**Important:** Always create your PR against the `develop` branch, not `stable`.
## Requirements
### Uptodate clock
### Up-to-date clock
The clock must be accurate, syncronized to a NTP server very frequently to avoid problems with communication to the exchanges.

3
build_helpers/publish_docker.sh
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@ -2,6 +2,7 @@
# Replace / with _ to create a valid tag
TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
TAG_PLOT=${TAG}_plot
echo "Running for ${TAG}"
# Add commit and commit_message to docker container
@ -16,6 +17,7 @@ else
docker pull ${IMAGE_NAME}:${TAG}
docker build --cache-from ${IMAGE_NAME}:${TAG} -t freqtrade:${TAG} .
fi
docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${TAG} -t freqtrade:${TAG_PLOT} -f docker/Dockerfile.plot .
if [ $? -ne 0 ]; then
echo "failed building image"
@ -32,6 +34,7 @@ fi
# Tag image for upload
docker tag freqtrade:$TAG ${IMAGE_NAME}:$TAG
docker tag freqtrade:$TAG_PLOT ${IMAGE_NAME}:$TAG_PLOT
if [ $? -ne 0 ]; then
echo "failed tagging image"
return 1

1
config.json.example
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@ -7,7 +7,6 @@
"timeframe": "5m",
"dry_run": false,
"cancel_open_orders_on_exit": false,
"trailing_stop": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30

1
config_binance.json.example
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@ -7,7 +7,6 @@
"timeframe": "5m",
"dry_run": true,
"cancel_open_orders_on_exit": false,
"trailing_stop": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30

11
config_full.json.example
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@ -116,7 +116,16 @@
"telegram": {
"enabled": true,
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
"chat_id": "your_telegram_chat_id",
"notification_settings": {
"status": "on",
"warning": "on",
"startup": "on",
"buy": "on",
"sell": "on",
"buy_cancel": "on",
"sell_cancel": "on"
}
},
"api_server": {
"enabled": false,

1
config_kraken.json.example
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@ -7,7 +7,6 @@
"timeframe": "5m",
"dry_run": true,
"cancel_open_orders_on_exit": false,
"trailing_stop": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30

20
docker-compose.develop.yml
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@ -1,20 +0,0 @@
---
version: '3'
services:
freqtrade_develop:
build:
context: .
dockerfile: "./Dockerfile.develop"
volumes:
- ".:/freqtrade"
entrypoint:
- "freqtrade"
freqtrade_bash:
build:
context: .
dockerfile: "./Dockerfile.develop"
volumes:
- ".:/freqtrade"
entrypoint:
- "/bin/bash"

4
docker-compose.yml
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@ -2,8 +2,10 @@
version: '3'
services:
freqtrade:
image: freqtradeorg/freqtrade:master
image: freqtradeorg/freqtrade:stable
# image: freqtradeorg/freqtrade:develop
# Use plotting image
# image: freqtradeorg/freqtrade:develop_plot
# Build step - only needed when additional dependencies are needed
# build:
# context: .

1
Dockerfile.develop → docker/Dockerfile.develop
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@ -2,6 +2,7 @@ FROM freqtradeorg/freqtrade:develop
# Install dependencies
COPY requirements-dev.txt /freqtrade/
RUN pip install numpy --no-cache-dir \
&& pip install -r requirements-dev.txt --no-cache-dir

7
docker/Dockerfile.jupyter Normal file
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@ -0,0 +1,7 @@
FROM freqtradeorg/freqtrade:develop_plot
RUN pip install jupyterlab --no-cache-dir
# Empty the ENTRYPOINT to allow all commands
ENTRYPOINT []

10
docker/Dockerfile.plot Normal file
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@ -0,0 +1,10 @@
ARG sourceimage=develop
FROM freqtradeorg/freqtrade:${sourceimage}
# Install dependencies
COPY requirements-plot.txt /freqtrade/
RUN pip install -r requirements-plot.txt --no-cache-dir
# Empty the ENTRYPOINT to allow all commands
ENTRYPOINT []

0
Dockerfile.technical → docker/Dockerfile.technical
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16
docker/docker-compose-jupyter.yml Normal file
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@ -0,0 +1,16 @@
---
version: '3'
services:
ft_jupyterlab:
build:
context: ..
dockerfile: docker/Dockerfile.jupyter
restart: unless-stopped
container_name: freqtrade
ports:
- "127.0.0.1:8888:8888"
volumes:
- "./user_data:/freqtrade/user_data"
# Default command used when running `docker compose up`
command: >
jupyter lab --port=8888 --ip 0.0.0.0 --allow-root

3
docs/bot-usage.md
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@ -5,6 +5,9 @@ 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.
!!! Warning "Up-to-date clock"
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.
## Bot commands
```

2
docs/configuration.md
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@ -375,7 +375,7 @@ Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports
exchange markets and trading APIs. The complete up-to-date list can be found in the
[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python).
However, the bot was tested by the development team with only Bittrex, Binance and Kraken,
so the these are the only officially supported exhanges:
so the these are the only officially supported exchanges:
- [Bittrex](https://bittrex.com/): "bittrex"
- [Binance](https://www.binance.com/): "binance"

22
docs/data-analysis.md
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@ -1,12 +1,22 @@
# Analyzing bot data with Jupyter notebooks
You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/`.
You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/` after initializing the user directory with `freqtrade create-userdir --userdir user_data`.
## Pro tips
## Quick start with docker
Freqtrade provides a docker-compose file which starts up a jupyter lab server.
You can run this server using the following command: `docker-compose -f docker/docker-compose-jupyter.yml up`
This will create a dockercontainer running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
Please use the link that's printed in the console after startup for simplified login.
For more information, Please visit the [Data analysis with Docker](docker_quickstart.md#data-analayis-using-docker-compose) section.
### Pro tips
* See [jupyter.org](https://jupyter.org/documentation) for usage instructions.
* Don't forget to start a Jupyter notebook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*
* Copy the example notebook before use so your changes don't get clobbered with the next freqtrade update.
* Copy the example notebook before use so your changes don't get overwritten with the next freqtrade update.
### Using virtual environment with system-wide Jupyter installation
@ -28,10 +38,8 @@ ipython kernel install --user --name=freqtrade
!!! Note
This section is provided for completeness, the Freqtrade Team won't provide full support for problems with this setup and will recommend to install Jupyter in the virtual environment directly, as that is the easiest way to get jupyter notebooks up and running. For help with this setup please refer to the [Project Jupyter](https://jupyter.org/) [documentation](https://jupyter.org/documentation) or [help channels](https://jupyter.org/community).
## Fine print
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.
!!! Warning
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.
## Recommended workflow

198
docs/data-download.md
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@ -8,82 +8,121 @@ If no additional parameter is specified, freqtrade will download data for `"1m"`
Exchange and pairs will come from `config.json` (if specified using `-c/--config`).
Otherwise `--exchange` becomes mandatory.
You can use a relative timerange (`--days 20`) or an absolute starting point (`--timerange 20200101`). For incremental downloads, the relative approach should be used.
!!! Tip "Tip: Updating existing data"
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.
Be careful 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.
### Usage
```
usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH] [--userdir PATH] [-p PAIRS [PAIRS ...]]
[--pairs-file FILE] [--days INT] [--dl-trades] [--exchange EXCHANGE]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
[--erase] [--data-format-ohlcv {json,jsongz}] [--data-format-trades {json,jsongz}]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-separated.
--pairs-file FILE File containing a list of pairs to download.
--days INT Download data for given number of days.
--dl-trades Download trades instead of OHLCV data. The bot will resample trades to the desired timeframe as specified as
--timeframes/-t.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no config is provided.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
Specify which tickers to download. Space-separated list. Default: `1m 5m`.
--erase Clean all existing data for the selected exchange/pairs/timeframes.
--data-format-ohlcv {json,jsongz}
Storage format for downloaded candle (OHLCV) data. (default: `json`).
--data-format-trades {json,jsongz}
Storage format for downloaded trades data. (default: `jsongz`).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are: 'syslog', 'journald'. See the documentation for more details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
### Data format
Freqtrade currently supports 2 dataformats, `json` (plain "text" json files) and `jsongz` (a gzipped version of json files).
By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
This can be changed via the `--data-format-ohlcv` and `--data-format-trades` parameters respectivly.
If the default dataformat has been changed during download, then the keys `dataformat_ohlcv` and `dataformat_trades` in the configuration file need to be adjusted to the selected dataformat as well.
!!! Note
You can convert between data-formats using the [convert-data](#subcommand-convert-data) and [convert-trade-data](#subcommand-convert-trade-data) methods.
#### Subcommand convert data
```
usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz} --format-to {json,jsongz}
[--erase]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] [--pairs-file FILE]
[--days INT] [--timerange TIMERANGE]
[--dl-trades] [--exchange EXCHANGE]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
[--erase]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--data-format-trades {json,jsongz,hdf5}]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
separated.
--format-from {json,jsongz}
--pairs-file FILE File containing a list of pairs to download.
--days INT Download data for given number of days.
--timerange TIMERANGE
Specify what timerange of data to use.
--dl-trades Download trades instead of OHLCV data. The bot will
resample trades to the desired timeframe as specified
as --timeframes/-t.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
--data-format-ohlcv {json,jsongz,hdf5}
Storage format for downloaded candle (OHLCV) data.
(default: `json`).
--data-format-trades {json,jsongz,hdf5}
Storage format for downloaded trades data. (default:
`jsongz`).
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
!!! Note "Startup period"
`download-data` is a strategy-independent command. The idea is to download a big chunk of data once, and then iteratively increase the amount of data stored.
For that reason, `download-data` does not care about the "startup-period" defined in a strategy. It's up to the user to download additional days if the backtest should start at a specific point in time (while respecting startup period).
### Data format
Freqtrade currently supports 3 data-formats for both OHLCV and trades data:
* `json` (plain "text" json files)
* `jsongz` (a gzip-zipped version of json files)
* `hdf5` (a high performance datastore)
By default, OHLCV data is stored as `json` data, while trades data is stored as `jsongz` data.
This can be changed via the `--data-format-ohlcv` and `--data-format-trades` command line arguments respectively.
To persist this change, you can should also add the following snippet to your configuration, so you don't have to insert the above arguments each time:
``` jsonc
// ...
"dataformat_ohlcv": "hdf5",
"dataformat_trades": "hdf5",
// ...
```
If the default data-format has been changed during download, then the keys `dataformat_ohlcv` and `dataformat_trades` in the configuration file need to be adjusted to the selected dataformat as well.
!!! Note
You can convert between data-formats using the [convert-data](#sub-command-convert-data) and [convert-trade-data](#sub-command-convert-trade-data) methods.
#### Sub-command convert data
```
usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz,hdf5} --format-to
{json,jsongz,hdf5} [--erase]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
separated.
--format-from {json,jsongz,hdf5}
Source format for data conversion.
--format-to {json,jsongz}
--format-to {json,jsongz,hdf5}
Destination format for data conversion.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
@ -94,9 +133,10 @@ Common arguments:
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
@ -112,23 +152,23 @@ It'll also remove original json data files (`--erase` parameter).
freqtrade convert-data --format-from json --format-to jsongz --datadir ~/.freqtrade/data/binance -t 5m 15m --erase
```
#### Subcommand convert-trade data
#### Sub-command convert trade data
```
usage: freqtrade convert-trade-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz} --format-to {json,jsongz}
[--erase]
{json,jsongz,hdf5} --format-to
{json,jsongz,hdf5} [--erase]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Show profits for only these pairs. Pairs are space-
separated.
--format-from {json,jsongz}
--format-from {json,jsongz,hdf5}
Source format for data conversion.
--format-to {json,jsongz}
--format-to {json,jsongz,hdf5}
Destination format for data conversion.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
@ -140,13 +180,15 @@ Common arguments:
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
##### Example converting trades
@ -158,21 +200,21 @@ It'll also remove original jsongz data files (`--erase` parameter).
freqtrade convert-trade-data --format-from jsongz --format-to json --datadir ~/.freqtrade/data/kraken --erase
```
### Subcommand list-data
### Sub-command list-data
You can get a list of downloaded data using the `list-data` subcommand.
You can get a list of downloaded data using the `list-data` sub-command.
```
usage: freqtrade list-data [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--userdir PATH] [--exchange EXCHANGE]
[--data-format-ohlcv {json,jsongz}]
[--data-format-ohlcv {json,jsongz,hdf5}]
[-p PAIRS [PAIRS ...]]
optional arguments:
-h, --help show this help message and exit
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
--data-format-ohlcv {json,jsongz}
--data-format-ohlcv {json,jsongz,hdf5}
Storage format for downloaded candle (OHLCV) data.
(default: `json`).
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
@ -194,6 +236,7 @@ Common arguments:
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
#### Example list-data
@ -249,15 +292,16 @@ This will download historical candle (OHLCV) data for all the currency pairs you
### Other Notes
- To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust ratelimits etc.)
- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust rate limits etc.)
- To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
- To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
- To download historical candle (OHLCV) data from a fixed starting point, use `--timerange 20200101-` - which will download all data from January 1st, 2020. Eventually set end dates are ignored.
- Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
- To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-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.
### Trades (tick) data
By default, `download-data` subcommand downloads Candles (OHLCV) data. Some exchanges also provide historic trade-data via their API.
By default, `download-data` sub-command downloads Candles (OHLCV) data. Some exchanges also provide historic trade-data via their API.
This data can be useful if you need many different timeframes, since it is only downloaded once, and then resampled locally to the desired timeframes.
Since this data is large by default, the files use gzip by default. They are stored in your data-directory with the naming convention of `<pair>-trades.json.gz` (`ETH_BTC-trades.json.gz`). Incremental mode is also supported, as for historic OHLCV data, so downloading the data once per week with `--days 8` will create an incremental data-repository.

2
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View File

@ -32,4 +32,4 @@ The old section of configuration parameters (`"pairlist"`) has been deprecated i
### deprecation of bidVolume and askVolume from volume-pairlist
Since only quoteVolume can be compared between assets, the other options (bidVolume, askVolume) have been deprecated in 2020.4.
Since only quoteVolume can be compared between assets, the other options (bidVolume, askVolume) have been deprecated in 2020.4, and have been removed in 2020.9.

107
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View File

@ -10,13 +10,35 @@ Documentation is available at [https://freqtrade.io](https://www.freqtrade.io/)
Special fields for the documentation (like Note boxes, ...) can be found [here](https://squidfunk.github.io/mkdocs-material/extensions/admonition/).
To test the documentation locally use the following commands.
``` bash
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.
## 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]`.
To configure a development environment, you can either use the provided [DevContainer](#devcontainer-setup), 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 `pytest`, `flake8`, `mypy`, and `coveralls`.
### Devcontainer setup
The fastest and easiest way to get started is to use [VSCode](https://code.visualstudio.com/) 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.
#### Devcontainer dependencies
* [VSCode](https://code.visualstudio.com/)
* [docker](https://docs.docker.com/install/)
* [Remote container extension documentation](https://code.visualstudio.com/docs/remote)
For more information about the [Remote container extension](https://code.visualstudio.com/docs/remote), best consult the documentation.
### Tests
New code should be covered by basic unittests. Depending on the complexity of the feature, Reviewers may request more in-depth unittests.
@ -41,50 +63,6 @@ def test_method_to_test(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](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
* [docker](https://docs.docker.com/install/)
* [docker-compose](https://docs.docker.com/compose/install/)
#### Starting the bot
##### Use the develop dockerfile
``` bash
rm docker-compose.yml && mv docker-compose.develop.yml docker-compose.yml
```
#### Docker Compose
##### Starting
``` bash
docker-compose up
```
![Docker compose up](https://user-images.githubusercontent.com/419355/65456322-47f63a80-de06-11e9-90c6-3c74d1bad0b8.png)
##### Rebuilding
``` bash
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`
``` bash
docker-compose exec freqtrade_develop /bin/bash
```
![image](https://user-images.githubusercontent.com/419355/65456522-ba671a80-de06-11e9-9598-df9ca0d8dcac.png)
## ErrorHandling
Freqtrade Exceptions all inherit from `FreqtradeException`.
@ -110,6 +88,8 @@ Below is an outline of exception inheritance hierarchy:
| +---+ InvalidOrderException
| |
| +---+ RetryableOrderError
| |
| +---+ InsufficientFundsError
|
+---+ StrategyError
```
@ -127,7 +107,7 @@ First of all, have a look at the [VolumePairList](https://github.com/freqtrade/f
This is a simple Handler, which however serves as a good example on how to start developing.
Next, modify the classname of the Handler (ideally align this with the module filename).
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.
@ -147,7 +127,7 @@ Configuration for the chain of Pairlist Handlers is done in the bot configuratio
By convention, `"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, `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.
Additional parameters can be configured as needed. For instance, `VolumePairList` uses `"sort_key"` to specify the sorting value - however feel free to specify whatever is necessary for your great algorithm to be successful and dynamic.
#### short_desc
@ -163,7 +143,7 @@ This is called with each iteration of the bot (only if the Pairlist Handler is a
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 `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filtering. Use this if you limit your result to a certain number of pairs - so the endresult is not shorter than expected.
Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_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.
#### filter_pairlist
@ -171,13 +151,13 @@ This method is called for each Pairlist Handler in the chain by the pairlist man
This is called with each iteration of the bot - so consider implementing caching for compute/network heavy calculations.
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()`.
It gets passed a pairlist (which can be the result of previous pairlists) as well as `tickers`, a pre-fetched version of `get_tickers()`.
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 `_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.
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 end result is not shorter than expected.
In `VolumePairList`, this implements different methods of sorting, does early validation so only the expected number of pairs is returned.
@ -201,7 +181,7 @@ Most exchanges supported by CCXT should work out of the box.
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](https://github.com/ccxt/ccxt/issues) may also provide great help, since others may have implemented something similar for their projects.
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](https://github.com/ccxt/ccxt/issues) may also provide great help, since others may have implemented something similar for their projects.
### Incomplete candles
@ -251,13 +231,14 @@ jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown freqtrade
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 `master` and `develop`.
* Raspberry PI Docker images are postfixed with `_pi` - so tags will be `:master_pi` and `develop_pi`.
* Docker images are build for the branches `stable` and `develop`.
* Docker images containing Plot dependencies are also available as `stable_plot` and `develop_plot`.
* Raspberry PI Docker images are postfixed with `_pi` - so tags will be `:stable_pi` and `develop_pi`.
* Docker images contain a file, `/freqtrade/freqtrade_commit` containing 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 `master` or `develop`.
* All tests must pass for a PR to be merged to `stable` or `develop`.
## Creating a release
@ -274,21 +255,22 @@ 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.
* 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. 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 master branch
* push that branch to the remote and create a PR against the stable branch
### Create changelog from git commits
!!! Note
Make sure that the master branch is uptodate!
Make sure that the `stable` branch is up-to-date!
``` bash
# Needs to be done before merging / pulling that branch.
git log --oneline --no-decorate --no-merges master..new_release
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 secction.
To keep the release-log short, best wrap the full git changelog into a collapsible details section.
```markdown
<details>
@ -301,17 +283,20 @@ To keep the release-log short, best wrap the full git changelog into a collapsib
### Create github release / tag
Once the PR against master is merged (best right after merging):
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 "master" as reference (this step comes after the above PR is merged).
* Use "stable" as reference (this step comes after the above PR is merged).
* Use the above changelog as release comment (as codeblock)
## Releases
### pypi
!!! Note
This process is now automated as part of Github Actions.
To create a pypi release, please run the following commands:
Additional requirement: `wheel`, `twine` (for uploading), account on pypi with proper permissions.

216
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@ -1,145 +1,7 @@
# Using Freqtrade with Docker
## Install Docker
Start by downloading and installing Docker CE for your platform:
* [Mac](https://docs.docker.com/docker-for-mac/install/)
* [Windows](https://docs.docker.com/docker-for-windows/install/)
* [Linux](https://docs.docker.com/install/)
Optionally, [docker-compose](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
## Freqtrade with docker-compose
Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
!!! Note
The following section assumes that docker and docker-compose is installed and available to the logged in user.
!!! Note
All below comands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
!!! Note "Docker on Raspberry"
If you're running freqtrade on a Raspberry PI, you must change the image from `freqtradeorg/freqtrade:master` to `freqtradeorg/freqtrade:master_pi` or `freqtradeorg/freqtrade:develop_pi`, otherwise the image will not work.
### Docker quick start
Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
``` bash
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
# Create configuration - Requires answering interactive questions
docker-compose run --rm freqtrade new-config --config user_data/config.json
```
The above snippet creates a new directory called "ft_userdata", downloads the latest compose file and pulls the freqtrade image.
The last 2 steps in the snippet create the directory with user-data, as well as (interactively) the default configuration based on your selections.
!!! Note
You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
#### Adding your strategy
The configuration is now available as `user_data/config.json`.
You should now copy your strategy to `user_data/strategies/` - and add the Strategy class name to the `docker-compose.yml` file, replacing `SampleStrategy`. If you wish to run the bot with the SampleStrategy, just leave it as it is.
!!! Warning
The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
Please always backtest the strategy and use dry-run for some time before risking real money!
Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
``` bash
docker-compose up -d
```
#### Docker-compose logs
Logs will be written to `user_data/logs/freqtrade.log`.
Alternatively, you can check the latest logs using `docker-compose logs -f`.
#### Database
The database will be in the user_data directory as well, and will be called `user_data/tradesv3.sqlite`.
#### Updating freqtrade with docker-compose
To update freqtrade when using docker-compose is as simple as running the following 2 commands:
``` bash
# Download the latest image
docker-compose pull
# Restart the image
docker-compose up -d
```
This will first pull the latest image, and will then restart the container with the just pulled version.
!!! Note
You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
#### Going from here
Advanced users may edit the docker-compose file further to include all possible options or arguments.
All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
!!! Note "`docker-compose run --rm`"
Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
##### Example: Download data with docker-compose
Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
``` bash
docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
```
Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
##### Example: Backtest with docker-compose
Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
``` bash
docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
```
Head over to the [Backtesting Documentation](backtesting.md) to learn more.
#### Additional dependencies with docker-compose
If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) for an example).
You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
``` yaml
image: freqtrade_custom
build:
context: .
dockerfile: "./Dockerfile.<yourextension>"
```
You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
## Freqtrade with docker without docker-compose
!!! Warning
The below documentation is provided for completeness and assumes that you are somewhat familiar with running docker containers. If you're just starting out with docker, we recommend to follow the [Freqtrade with docker-compose](#freqtrade-with-docker-compose) instructions.
The below documentation is provided for completeness and assumes that you are familiar with running docker containers. If you're just starting out with Docker, we recommend to follow the [Quickstart](docker.md) instructions.
### Download the official Freqtrade docker image
@ -148,9 +10,9 @@ Pull the image from docker hub.
Branches / tags available can be checked out on [Dockerhub tags page](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
```bash
docker pull freqtradeorg/freqtrade:develop
docker pull freqtradeorg/freqtrade:stable
# Optionally tag the repository so the run-commands remain shorter
docker tag freqtradeorg/freqtrade:develop freqtrade
docker tag freqtradeorg/freqtrade:stable freqtrade
```
To update the image, simply run the above commands again and restart your running container.
@ -158,7 +20,7 @@ To update the image, simply run the above commands again and restart your runnin
Should you require additional libraries, please [build the image yourself](#build-your-own-docker-image).
!!! Note "Docker image update frequency"
The official docker images with tags `master`, `develop` and `latest` are automatically rebuild once a week to keep the base image uptodate.
The official docker images with tags `stable`, `develop` and `latest` are automatically rebuild once a week to keep the base image up-to-date.
In addition to that, every merge to `develop` will trigger a rebuild for `develop` and `latest`.
### Prepare the configuration files
@ -190,39 +52,38 @@ cp -n config.json.example config.json
#### Create your database file
Production
=== "Dry-Run"
``` bash
touch tradesv3.dryrun.sqlite
```
```bash
touch tradesv3.sqlite
````
=== "Production"
``` bash
touch tradesv3.sqlite
```
Dry-Run
```bash
touch tradesv3.dryrun.sqlite
```
!!! Note
Make sure to use the path to this file when starting the bot in docker.
!!! Warning "Database File Path"
Make sure to use the path to the correct database file when starting the bot in Docker.
### Build your own Docker image
Best start by pulling the official docker image from dockerhub as explained [here](#download-the-official-docker-image) to speed up building.
To add additional libraries to your docker image, best check out [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) which adds the [technical](https://github.com/freqtrade/technical) module to the image.
To add additional libraries to your docker image, best check out [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.technical) which adds the [technical](https://github.com/freqtrade/technical) module to the image.
```bash
docker build -t freqtrade -f Dockerfile.technical .
docker build -t freqtrade -f docker/Dockerfile.technical .
```
If you are developing using Docker, use `Dockerfile.develop` to build a dev Docker image, which will also set up develop dependencies:
If you are developing using Docker, use `docker/Dockerfile.develop` to build a dev Docker image, which will also set up develop dependencies:
```bash
docker build -f Dockerfile.develop -t freqtrade-dev .
docker build -f docker/Dockerfile.develop -t freqtrade-dev .
```
!!! Note
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.
!!! Warning "Include your config file manually"
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 [5. Run a restartable docker image](#run-a-restartable-docker-image)") to keep it between updates.
#### Verify the Docker image
@ -243,37 +104,36 @@ docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
!!! Warning
In this example, the database will be created inside the docker instance and will be lost when you will refresh your image.
In this example, the database will be created inside the docker instance and will be lost when you refresh your image.
#### Adjust timezone
By default, the container will use UTC timezone.
Should you find this irritating please add the following to your docker commands:
If you would like to change the timezone use the following commands:
##### Linux
=== "Linux"
``` bash
-v /etc/timezone:/etc/timezone:ro
``` bash
-v /etc/timezone:/etc/timezone:ro
# Complete command:
docker run --rm -v /etc/timezone:/etc/timezone:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
# Complete command:
docker run --rm -v /etc/timezone:/etc/timezone:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
=== "MacOS"
```bash
docker run --rm -e TZ=`ls -la /etc/localtime | cut -d/ -f8-9` -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
##### MacOS
There 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.
```bash
docker run --rm -e TZ=`ls -la /etc/localtime | cut -d/ -f8-9` -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
More information on this docker issue and work-around can be read [here](https://github.com/docker/for-mac/issues/2396).
!!! Note "MacOS Issues"
The OSX Docker versions after 17.09.1 have a known issue whereby `/etc/localtime` cannot be shared causing Docker to not start.<br>
A work-around for this is to start with the MacOS command above
More information on this docker issue and work-around can be read [here](https://github.com/docker/for-mac/issues/2396).
### Run a restartable docker image
To run a restartable instance in the background (feel free to place your configuration and database files wherever it feels comfortable on your filesystem).
#### Move your config file and database
#### 1. Move your config file and database
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.
@ -283,7 +143,7 @@ mv config.json ~/.freqtrade
mv tradesv3.sqlite ~/.freqtrade
```
#### Run the docker image
#### 2. Run the docker image
```bash
docker run -d \

191
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View File

@ -0,0 +1,191 @@
# Using Freqtrade with Docker
## Install Docker
Start by downloading and installing Docker CE for your platform:
* [Mac](https://docs.docker.com/docker-for-mac/install/)
* [Windows](https://docs.docker.com/docker-for-windows/install/)
* [Linux](https://docs.docker.com/install/)
Optionally, [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
## Freqtrade with docker-compose
Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
!!! Note
- The following section assumes that `docker` and `docker-compose` are installed and available to the logged in user.
- All below commands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
### Docker quick start
Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
=== "PC/MAC/Linux"
``` bash
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
# Create configuration - Requires answering interactive questions
docker-compose run --rm freqtrade new-config --config user_data/config.json
```
=== "RaspberryPi"
``` bash
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
# Create configuration - Requires answering interactive questions
docker-compose run --rm freqtrade new-config --config user_data/config.json
```
!!! Note "Change your docker Image"
You have to change the docker image in the docker-compose file for your Raspberry build to work properly.
``` yml
image: freqtradeorg/freqtrade:stable_pi
# image: freqtradeorg/freqtrade:develop_pi
```
The above snippet creates a new directory called `ft_userdata`, downloads the latest compose file and pulls the freqtrade image.
The last 2 steps in the snippet create the directory with `user_data`, as well as (interactively) the default configuration based on your selections.
!!! Question "How to edit the bot configuration?"
You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
You can also change the both Strategy and commands by editing the `docker-compose.yml` file.
#### Adding a custom strategy
1. The configuration is now available as `user_data/config.json`
2. Copy a custom strategy to the directory `user_data/strategies/`
3. add the Strategy' class name to the `docker-compose.yml` file
The `SampleStrategy` is run by default.
!!! Warning "`SampleStrategy` is just a demo!"
The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
Please always backtest the strategy and use dry-run for some time before risking real money!
Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
``` bash
docker-compose up -d
```
#### Docker-compose logs
Logs will be located at: `user_data/logs/freqtrade.log`.
You can check the latest log with the command `docker-compose logs -f`.
#### Database
The database will be at: `user_data/tradesv3.sqlite`
#### Updating freqtrade with docker-compose
To update freqtrade when using `docker-compose` is as simple as running the following 2 commands:
``` bash
# Download the latest image
docker-compose pull
# Restart the image
docker-compose up -d
```
This will first pull the latest image, and will then restart the container with the just pulled version.
!!! Warning "Check the Changelog"
You should always check the changelog for breaking changes / manual interventions required and make sure the bot starts correctly after the update.
### Editing the docker-compose file
Advanced users may edit the docker-compose file further to include all possible options or arguments.
All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
!!! Note "`docker-compose run --rm`"
Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
#### Example: Download data with docker-compose
Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
``` bash
docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
```
Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
#### Example: Backtest with docker-compose
Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
``` bash
docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
```
Head over to the [Backtesting Documentation](backtesting.md) to learn more.
### Additional dependencies with docker-compose
If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [docker/Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.technical) for an example).
You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
``` yaml
image: freqtrade_custom
build:
context: .
dockerfile: "./Dockerfile.<yourextension>"
```
You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
## Plotting with docker-compose
Commands `freqtrade plot-profit` and `freqtrade plot-dataframe` ([Documentation](plotting.md)) are available by changing the image to `*_plot` in your docker-compose.yml file.
You can then use these commands as follows:
``` bash
docker-compose run --rm freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --timerange=20180801-20180805
```
The output will be stored in the `user_data/plot` directory, and can be opened with any modern browser.
## Data analayis using docker compose
Freqtrade provides a docker-compose file which starts up a jupyter lab server.
You can run this server using the following command:
``` bash
docker-compose --rm -f docker/docker-compose-jupyter.yml up
```
This will create a dockercontainer running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
Please use the link that's printed in the console after startup for simplified login.
Since part of this image is built on your machine, it is recommended to rebuild the image from time to time to keep freqtrade (and dependencies) uptodate.
``` bash
docker-compose -f docker/docker-compose-jupyter.yml build --no-cache
```

205
docs/edge.md
View File

@ -1,92 +1,142 @@
# Edge positioning
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.
The `Edge Positioning` module uses probability to calculate your win rate and risk reward ration. It will use these statistics to control your strategy trade entry points, position side and, stoploss.
!!! Warning
Edge positioning is not compatible with dynamic (volume-based) whitelist.
`Edge positioning` is not compatible with dynamic (volume-based) whitelist.
!!! Note
Edge does not consider anything other than *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
Therefore, it is important to understand that Edge can improve the performance of some trading strategies but *decrease* the performance of others.
`Edge Positioning` only considers *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
`Edge Positioning` improves the performance of some trading strategies and *decreases* the performance of others.
## Introduction
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.
Trading strategies are not perfect. They are frameworks that are susceptible to the market and its indicators. Because the market is not at all predictable, sometimes a strategy will win and sometimes the same strategy will 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?
To obtain an edge in the market, a strategy has to make more money than it loses. Making money in trading is not only about *how often* the strategy makes or loses money.
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...
!!! tip "It doesn't matter how often, but how much!"
A bad strategy might make 1 penny in *ten* transactions but lose 1 dollar in *one* transaction. If one only checks the number of winning trades, it would be misleading to think that the strategy is actually making a profit.
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.
The Edge Positioning module seeks to improve a strategy's winning probability and the money that the strategy will make *on the long run*.
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$) and me $1.6 (20% X 8$) too.
We raise the following question[^1]:
The question is: How do you calculate that? How do you know if you wanna play?
!!! Question "Which trade is a better option?"
a) A trade with 80% of chance of losing $100 and 20% chance of winning $200<br/>
b) A trade with 100% of chance of losing $30
The answer comes to two factors:
???+ Info "Answer"
The expected value of *a)* is smaller than the expected value of *b)*.<br/>
Hence, *b*) represents a smaller loss in the long run.<br/>
However, the answer is: *it depends*
- Win Rate
- Risk Reward Ratio
Another way to look at it is to ask a similar question:
### Win Rate
!!! Question "Which trade is a better option?"
a) A trade with 80% of chance of winning 100 and 20% chance of losing $200<br/>
b) A trade with 100% of chance of winning $30
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).
Edge positioning tries to answer the hard questions about risk/reward and position size automatically, seeking to minimizes the chances of losing of a given strategy.
```
W = (Number of winning trades) / (Total number of trades) = (Number of winning trades) / N
```
### Trading, winning and losing
Complementary Loss Rate (*L*) is defined as
Let's call $o$ the return of a single transaction $o$ where $o \in \mathbb{R}$. The collection $O = \{o_1, o_2, ..., o_N\}$ is the set of all returns of transactions made during a trading session. We say that $N$ is the cardinality of $O$, or, in lay terms, it is the number of transactions made in a trading session.
```
L = (Number of losing trades) / (Total number of trades) = (Number of losing trades) / N
```
!!! Example
In a session where a strategy made three transactions we can say that $O = \{3.5, -1, 15\}$. That means that $N = 3$ and $o_1 = 3.5$, $o_2 = -1$, $o_3 = 15$.
or, which is the same, as
A winning trade is a trade where a strategy *made* money. Making money means that the strategy closed the position in a value that returned a profit, after all deducted fees. Formally, a winning trade will have a return $o_i > 0$. Similarly, a losing trade will have a return $o_j \leq 0$. With that, we can discover the set of all winning trades, $T_{win}$, as follows:
```
L = 1 – W
```
$$ T_{win} = \{ o \in O | o > 0 \} $$
Similarly, we can discover the set of losing trades $T_{lose}$ as follows:
$$ T_{lose} = \{o \in O | o \leq 0\} $$
!!! Example
In a section where a strategy made three transactions $O = \{3.5, -1, 15, 0\}$:<br>
$T_{win} = \{3.5, 15\}$<br>
$T_{lose} = \{-1, 0\}$<br>
### Win Rate and Lose Rate
The win rate $W$ is the proportion of winning trades with respect to all the trades made by a strategy. We use the following function to compute the win rate:
$$W = \frac{|T_{win}|}{N}$$
Where $W$ is the win rate, $N$ is the number of trades and, $T_{win}$ is the set of all trades where the strategy made money.
Similarly, we can compute the rate of losing trades:
$$
L = \frac{|T_{lose}|}{N}
$$
Where $L$ is the lose rate, $N$ is the amount of trades made and, $T_{lose}$ is the set of all trades where the strategy lost money. Note that the above formula is the same as calculating $L = 1 – W$ or $W = 1 – L$
### Risk Reward Ratio
Risk 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:
Risk 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. Formally:
```
R = Profit / Loss
```
$$ R = \frac{\text{potential_profit}}{\text{potential_loss}} $$
Over 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:
???+ Example "Worked example of $R$ calculation"
Let's say that you think that the price of *stonecoin* today is $10.0. You believe that, because they will start mining stonecoin, it will go up to $15.0 tomorrow. There is the risk that the stone is too hard, and the GPUs can't mine it, so the price might go to $0 tomorrow. You are planning to invest $100.<br>
Your potential profit is calculated as:<br>
$\begin{aligned}
\text{potential_profit} &= (\text{potential_price} - \text{cost_per_unit}) * \frac{\text{investment}}{\text{cost_per_unit}} \\
&= (15 - 10) * \frac{100}{15}\\
&= 33.33
\end{aligned}$<br>
Since the price might go to $0, the $100 dolars invested could turn into 0. We can compute the Risk Reward Ratio as follows:<br>
$\begin{aligned}
R &= \frac{\text{potential_profit}}{\text{potential_loss}}\\
&= \frac{33.33}{100}\\
&= 0.333...
\end{aligned}$<br>
What it effectivelly means is that the strategy have the potential to make $0.33 for each $1 invested.
```
Average profit = (Sum of profits) / (Number of winning trades)
On a long horizon, that is, on many trades, we can calculate the risk reward by dividing the strategy' average profit on winning trades by the strategy' average loss on losing trades. We can calculate the average profit, $\mu_{win}$, as follows:
Average loss = (Sum of losses) / (Number of losing trades)
$$ \text{average_profit} = \mu_{win} = \frac{\text{sum_of_profits}}{\text{count_winning_trades}} = \frac{\sum^{o \in T_{win}} o}{|T_{win}|} $$
R = (Average profit) / (Average loss)
```
Similarly, we can calculate the average loss, $\mu_{lose}$, as follows:
$$ \text{average_loss} = \mu_{lose} = \frac{\text{sum_of_losses}}{\text{count_losing_trades}} = \frac{\sum^{o \in T_{lose}} o}{|T_{lose}|} $$
Finally, we can calculate the Risk Reward ratio, $R$, as follows:
$$ R = \frac{\text{average_profit}}{\text{average_loss}} = \frac{\mu_{win}}{\mu_{lose}}\\ $$
???+ Example "Worked example of $R$ calculation using mean profit/loss"
Let's say the strategy that we are using makes an average win $\mu_{win} = 2.06$ and an average loss $\mu_{loss} = 4.11$.<br>
We calculate the risk reward ratio as follows:<br>
$R = \frac{\mu_{win}}{\mu_{loss}} = \frac{2.06}{4.11} = 0.5012...$
### Expectancy
At 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:
By combining the Win Rate $W$ and and the Risk Reward ratio $R$ to create an expectancy ratio $E$. A expectance ratio is the expected return of the investment made in a trade. We can compute the value of $E$ as follows:
```
Expectancy Ratio = (Risk Reward Ratio X Win Rate) – Loss Rate = (R X W) – L
```
$$E = R * W - L$$
So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
!!! Example "Calculating $E$"
Let's say that a strategy has a win rate $W = 0.28$ and a risk reward ratio $R = 5$. What this means is that the strategy is expected to make 5 times the investment around on 28% of the trades it makes. Working out the example:<br>
$E = R * W - L = 5 * 0.28 - 0.72 = 0.68$
<br>
```
Expectancy = (5 X 0.28) – 0.72 = 0.68
```
The expectancy worked out in the example above means that, on average, this strategy' trades will return 1.68 times the size of its losses. Said another way, the strategy makes $1.68 for every $1 it loses, on average.
Superficially, this means that on average you expect this strategy’s trades to return 1.68 times the size of your loses. Said another way, you can expect to win $1.68 for every $1 you lose. 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.
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.
!!! Note
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.
## How does it work?
@ -99,13 +149,13 @@ Edge combines dynamic stoploss, dynamic positions, and whitelist generation into
| XZC/ETH | -0.03 | 0.52 |1.359670 | 0.228 |
| XZC/ETH | -0.04 | 0.51 |1.234539 | 0.117 |
The 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.
The 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.
### Position size
Edge also dictates the stake amount for each trade to the bot according to the following factors:
Edge dictates the amount at stake for each trade to the bot according to the following factors:
- Allowed capital at risk
- Stoploss
@ -116,9 +166,9 @@ Allowed capital at risk is calculated as follows:
Allowed capital at risk = (Capital available_percentage) X (Allowed risk per trade)
```
Stoploss is calculated as described above against historical data.
Stoploss is calculated as described above with respect to historical data.
Your position size then will be:
The position size is calculated as follows:
```
Position size = (Allowed capital at risk) / Stoploss
@ -126,19 +176,23 @@ Position size = (Allowed capital at risk) / Stoploss
Example:
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 say the stake currency is **ETH** and there is $10$ **ETH** on the wallet. The capital available percentage is $50%$ and the allowed risk per trade is $1\%$. Thus, the available capital for trading is $10 * 0.5 = 5$ **ETH** and the allowed capital at risk would be $5 * 0.01 = 0.05$ **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**.
- **Trade 1:** The strategy detects a new buy signal in the **XLM/ETH** market. `Edge Positioning` calculates a stoploss of $2\%$ and a position of $0.05 / 0.02 = 2.5$ **ETH**. The bot takes a position of $2.5$ **ETH** in the **XLM/ETH** market.
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).
- **Trade 2:** The strategy detects a buy signal on the **BTC/ETH** market while **Trade 1** is still open. `Edge Positioning` calculates the stoploss of $4\%$ on this market. Thus, **Trade 2** position size is $0.05 / 0.04 = 1.25$ **ETH**.
Note that available capital for trading didn’t change for trade 2 even if you had already trade 1. The available capital doesn’t mean the free amount on your wallet.
!!! Tip "Available Capital $\neq$ Available in wallet"
The available capital for trading didn't change in **Trade 2** even with **Trade 1** still open. The available capital **is not** the free amount in the 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 – 3.75 = 1.25 ETH**.
- **Trade 3:** The strategy detects a buy signal in the **ADA/ETH** market. `Edge Positioning` calculates a stoploss of $1\%$ and a position of $0.05 / 0.01 = 5$ **ETH**. Since **Trade 1** has $2.5$ **ETH** blocked and **Trade 2** has $1.25$ **ETH** blocked, there is only $5 - 1.25 - 2.5 = 1.25$ **ETH** available. Hence, the position size of **Trade 3** is $1.25$ **ETH**.
Available capital doesn’t change before a position is sold. Let’s 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.
!!! Tip "Available Capital Updates"
The available capital does not change before a position is sold. After a trade is closed the Available Capital goes up if the trade was profitable or goes down if the trade was a loss.
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**.
- The strategy detects a sell signal in the **XLM/ETH** market. The bot exits **Trade 1** for a profit of $1$ **ETH**. The total capital in the wallet becomes $11$ **ETH** and the available capital for trading becomes $5.5$ **ETH**.
- **Trade 4** The strategy detects a new buy signal int the **XLM/ETH** market. `Edge Positioning` calculates the stoploss of $2%$, and the position size of $0.055 / 0.02 = 2.75$ **ETH**.
## Configurations
@ -169,23 +223,23 @@ freqtrade edge
An 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 |
| **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 |
Edge produced the above table by comparing `calculate_since_number_of_days` to `minimum_expectancy` to find `min_trade_number` historical information based on the config file. The timerange Edge uses for its comparisons can be further limited by using the `--timerange` switch.
@ -218,3 +272,6 @@ The full timerange specification:
* Use tickframes since 2018/01/31: `--timerange=20180131-`
* Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
* Use tickframes between POSIX timestamps 1527595200 1527618600: `--timerange=1527595200-1527618600`
[^1]: Question extracted from MIT Opencourseware S096 - Mathematics with applications in Finance: https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-with-applications-in-finance-fall-2013/

12
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@ -89,7 +89,7 @@ Same fix should be done in the configuration file, if order types are defined in
### How do I search the bot logs for something?
By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility subcommands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout.
By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility sub-commands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout.
* In unix shells, this normally can be done as simple as:
```shell
@ -114,7 +114,7 @@ and then grep it as:
```shell
$ cat /path/to/mylogfile.log | grep 'something'
```
or even on the fly, as the bot works and the logfile grows:
or even on the fly, as the bot works and the log file grows:
```shell
$ tail -f /path/to/mylogfile.log | grep 'something'
```
@ -137,7 +137,7 @@ compute.
Since hyperopt uses Bayesian search, running for too many epochs may not produce greater results.
It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10.000 epocs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10.000 epochs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
```bash
freqtrade hyperopt -e 1000
@ -153,7 +153,7 @@ for i in {1..100}; do freqtrade hyperopt -e 1000; done
* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) - or the Freqtrade [discord community](https://discord.gg/X89cVG). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
* If you wonder why it can take from 20 minutes to days to do 1000 epocs here are some answers:
* If you wonder why it can take from 20 minutes to days to do 1000 epochs here are some answers:
This answer was written during the release 0.15.1, when we had:
@ -167,7 +167,7 @@ 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, assuming that the bot never tests the same parameters more than once.
* The time it takes to run 1000 hyperopt epocs depends on things like: The available cpu, harddisk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 10.0000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades.
* The time it takes to run 1000 hyperopt epochs depends on things like: The available cpu, hard-disk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 10.0000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades.
Example: 4% profit 650 times vs 0,3% profit a trade 10.000 times in a year. If we assume you set the --timerange to 365 days.
@ -180,7 +180,7 @@ Example:
The Edge module is mostly a result of brainstorming of [@mishaker](https://github.com/mishaker) and [@creslinux](https://github.com/creslinux) freqtrade team members.
You can find further info on expectancy, winrate, risk management and position size in the following sources:
You can find further info on expectancy, win rate, risk management and position size in the following sources:
- https://www.tradeciety.com/ultimate-math-guide-for-traders/
- http://www.vantharp.com/tharp-concepts/expectancy.asp

2
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@ -229,7 +229,7 @@ Because hyperopt tries a lot of combinations to find the best parameters it will
We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
```bash
freqtrade hyperopt --config config.json --hyperopt <hyperoptname> -e 5000 --spaces all
freqtrade hyperopt --config config.json --hyperopt <hyperoptname> -e 500 --spaces all
```
Use `<hyperoptname>` as the name of the custom hyperopt used.

8
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@ -8,7 +8,7 @@
<!-- Place this tag where you want the button to render. -->
<a class="github-button" href="https://github.com/freqtrade/freqtrade/fork" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork freqtrade/freqtrade on GitHub">Fork</a>
<!-- Place this tag where you want the button to render. -->
<a class="github-button" href="https://github.com/freqtrade/freqtrade/archive/master.zip" data-icon="octicon-cloud-download" data-size="large" aria-label="Download freqtrade/freqtrade on GitHub">Download</a>
<a class="github-button" href="https://github.com/freqtrade/freqtrade/archive/stable.zip" data-icon="octicon-cloud-download" data-size="large" aria-label="Download freqtrade/freqtrade on GitHub">Download</a>
<!-- Place this tag where you want the button to render. -->
<a class="github-button" href="https://github.com/freqtrade" data-size="large" aria-label="Follow @freqtrade on GitHub">Follow @freqtrade</a>
@ -37,13 +37,9 @@ Freqtrade is a crypto-currency algorithmic trading software developed in python
## Requirements
### Up to date clock
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.
### Hardware requirements
To run this bot we recommend you a cloud instance with a minimum of:
To run this bot we recommend you a linux cloud instance with a minimum of:
- 2GB RAM
- 1GB disk space

141
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@ -18,6 +18,9 @@ Click each one for install guide:
We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
!!! Warning "Up-to-date clock"
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.
## Quick start
Freqtrade provides the Linux/MacOS Easy Installation script to install all dependencies and help you configure the bot.
@ -28,7 +31,7 @@ Freqtrade provides the Linux/MacOS Easy Installation script to install all depen
The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the Easy Installation script, if it's available for your platform.
!!! Note "Version considerations"
When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests). The `master` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests). The `stable` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
!!! Note
Python3.6 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.
@ -38,11 +41,11 @@ This can be achieved with the following commands:
```bash
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
git checkout master # Optional, see (1)
# git checkout stable # Optional, see (1)
./setup.sh --install
```
(1) This command switches the cloned repository to the use of the `master` branch. It's not needed if you wish to stay on the `develop` branch. You may later switch between branches at any time with the `git checkout master`/`git checkout develop` commands.
(1) This command switches the cloned repository to the use of the `stable` branch. It's not needed if you wish to stay on the `develop` branch. You may later switch between branches at any time with the `git checkout stable`/`git checkout develop` commands.
## Easy Installation Script (Linux/MacOS)
@ -53,7 +56,7 @@ $ ./setup.sh
usage:
-i,--install Install freqtrade from scratch
-u,--update Command git pull to update.
-r,--reset Hard reset your develop/master branch.
-r,--reset Hard reset your develop/stable branch.
-c,--config Easy config generator (Will override your existing file).
```
@ -73,12 +76,16 @@ This option will pull the last version of your current branch and update your vi
** --reset **
This option will hard reset your branch (only if you are on either `master` or `develop`) and recreate your virtualenv.
This option will hard reset your branch (only if you are on either `stable` or `develop`) and recreate your virtualenv.
** --config **
DEPRECATED - use `freqtrade new-config -c config.json` instead.
### Activate your virtual environment
Each time you open a new terminal, you must run `source .env/bin/activate`.
------
## Custom Installation
@ -89,36 +96,34 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
!!! Note
Python3.6 or higher and the corresponding pip are assumed to be available.
### Linux - Ubuntu 16.04
=== "Ubuntu 16.04"
#### Install necessary dependencies
#### Install necessary dependencies
```bash
sudo apt-get update
sudo apt-get install build-essential git
```
```bash
sudo apt-get update
sudo apt-get install build-essential git
```
=== "RaspberryPi/Raspbian"
The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/) from at least September 2019.
This image comes with python3.7 preinstalled, making it easy to get freqtrade up and running.
### Raspberry Pi / Raspbian
Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/) from at least September 2019.
This image comes with python3.7 preinstalled, making it easy to get freqtrade up and running.
``` bash
sudo apt-get install python3-venv libatlas-base-dev
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
bash setup.sh -i
```
``` bash
sudo apt-get install python3-venv libatlas-base-dev
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
!!! Note "Installation duration"
Depending on your internet speed and the Raspberry Pi version, installation can take multiple hours to complete.
bash setup.sh -i
```
!!! Note "Installation duration"
Depending on your internet speed and the Raspberry Pi version, installation can take multiple hours to complete.
!!! Note
The above 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.
!!! Note
The above 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.
### Common
@ -169,12 +174,7 @@ Clone the git repository:
```bash
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
```
Optionally checkout the master branch to get the latest stable release:
```bash
git checkout master
git checkout stable
```
#### 4. Install python dependencies
@ -212,73 +212,19 @@ On Linux, as an optional post-installation task, you may wish to setup the bot t
------
## Using Conda
### Anaconda
Freqtrade can also be installed using Anaconda (or Miniconda).
!!! Note
This requires the [ta-lib](#1-install-ta-lib) C-library to be installed first. See below.
``` bash
conda env create -f environment.yml
```
!!! Note
This requires the [ta-lib](#1-install-ta-lib) C-library to be installed first.
## Windows
We recommend that Windows users use [Docker](docker.md) 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.
### Install freqtrade manually
!!! Note
Make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
!!! Hint
Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Conda section](#using-conda) in this document for more information.
#### Clone the git repository
```bash
git clone https://github.com/freqtrade/freqtrade.git
```
#### Install ta-lib
Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
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](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version)
```cmd
>cd \path\freqtrade-develop
>python -m venv .env
>.env\Scripts\activate.bat
REM optionally install ta-lib from wheel
REM >pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl
>pip install -r requirements.txt
>pip install -e .
>freqtrade
```
> Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/freqtrade/freqtrade/issues/222)
#### Error during installation under Windows
``` bash
error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
```
Unfortunately, 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](https://visualstudio.microsoft.com/downloads/) 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](docker.md) first.
---
Now you have an environment ready, the next step is
[Bot Configuration](configuration.md).
## Troubleshooting
-----
## Troubleshooting
### MacOS installation error
@ -291,4 +237,9 @@ For MacOS 10.14, this can be accomplished with the below command.
open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg
```
If this file is inexistant, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
If this file is inexistent, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
-----
Now you have an environment ready, the next step is
[Bot Configuration](configuration.md).

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@ -0,0 +1,12 @@
window.MathJax = {
tex: {
inlineMath: [["\\(", "\\)"]],
displayMath: [["\\[", "\\]"]],
processEscapes: true,
processEnvironments: true
},
options: {
ignoreHtmlClass: ".*|",
processHtmlClass: "arithmatex"
}
};

2
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@ -1,2 +1,2 @@
mkdocs-material==5.5.8
mkdocs-material==5.5.13
mdx_truly_sane_lists==1.2

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@ -46,7 +46,7 @@ sqlite3
### Trade table structure
```sql
CREATE TABLE trades
CREATE TABLE trades(
id INTEGER NOT NULL,
exchange VARCHAR NOT NULL,
pair VARCHAR NOT NULL,

83
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@ -483,6 +483,9 @@ if self.dp:
### Complete Data-provider sample
```python
from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame
class SampleStrategy(IStrategy):
# strategy init stuff...
@ -513,17 +516,12 @@ class SampleStrategy(IStrategy):
# Get the 14 day rsi
informative['rsi'] = ta.RSI(informative, timeperiod=14)
# Rename columns to be unique
informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
# Assuming inf_tf = '1d' - then the columns will now be:
# date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
# Combine the 2 dataframes
# all indicators on the informative sample MUST be calculated before this point
dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_{inf_tf}', how='left')
# FFill to have the 1d value available in every row throughout the day.
# Without this, comparisons would only work once per day.
dataframe = dataframe.ffill()
# Use the helper function merge_informative_pair to safely merge the pair
# Automatically renames the columns and merges a shorter timeframe dataframe and a longer timeframe informative pair
# use ffill to have the 1d value available in every row throughout the day.
# Without this, comparisons between columns of the original and the informative pair would only work once per day.
# Full documentation of this method, see below
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
# Calculate rsi of the original dataframe (5m timeframe)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
@ -547,6 +545,69 @@ class SampleStrategy(IStrategy):
***
## Helper functions
### *merge_informative_pair()*
This method helps you merge an informative pair to a regular dataframe without lookahead bias.
It's there to help you merge the dataframe in a safe and consistent way.
Options:
- Rename the columns for you to create unique columns
- Merge the dataframe without lookahead bias
- Forward-fill (optional)
All columns of the informative dataframe will be available on the returning dataframe in a renamed fashion:
!!! Example "Column renaming"
Assuming `inf_tf = '1d'` the resulting columns will be:
``` python
'date', 'open', 'high', 'low', 'close', 'rsi' # from the original dataframe
'date_1d', 'open_1d', 'high_1d', 'low_1d', 'close_1d', 'rsi_1d' # from the informative dataframe
```
??? Example "Column renaming - 1h"
Assuming `inf_tf = '1h'` the resulting columns will be:
``` python
'date', 'open', 'high', 'low', 'close', 'rsi' # from the original dataframe
'date_1h', 'open_1h', 'high_1h', 'low_1h', 'close_1h', 'rsi_1h' # from the informative dataframe
```
??? Example "Custom implementation"
A custom implementation for this is possible, and can be done as follows:
``` python
# Shift date by 1 candle
# This is necessary since the data is always the "open date"
# and a 15m candle starting at 12:15 should not know the close of the 1h candle from 12:00 to 13:00
minutes = timeframe_to_minutes(inf_tf)
# Only do this if the timeframes are different:
informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes, 'm')
# Rename columns to be unique
informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
# Assuming inf_tf = '1d' - then the columns will now be:
# date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
# Combine the 2 dataframes
# all indicators on the informative sample MUST be calculated before this point
dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_merge_{inf_tf}', how='left')
# FFill to have the 1d value available in every row throughout the day.
# Without this, comparisons would only work once per day.
dataframe = dataframe.ffill()
```
!!! Warning "Informative timeframe < timeframe"
Using informative timeframes smaller than the dataframe timeframe is not recommended with this method, as it will not use any of the additional information this would provide.
To use the more detailed information properly, more advanced methods should be applied (which are out of scope for freqtrade documentation, as it'll depend on the respective need).
***
## Additional data (Wallets)
The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.

28
docs/telegram-usage.md
View File

@ -41,6 +41,34 @@ Talk to the [userinfobot](https://telegram.me/userinfobot)
Get your "Id", you will use it for the config parameter `chat_id`.
## Control telegram noise
Freqtrade provides means to control the verbosity of your telegram bot.
Each setting has the following possible values:
* `on` - Messages will be sent, and user will be notified.
* `silent` - Message will be sent, Notification will be without sound / vibration.
* `off` - Skip sending a message-type all together.
Example configuration showing the different settings:
``` json
"telegram": {
"enabled": true,
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id",
"notification_settings": {
"status": "silent",
"warning": "on",
"startup": "off",
"buy": "silent",
"sell": "on",
"buy_cancel": "silent",
"sell_cancel": "on"
}
},
```
## Telegram commands
Per default, the Telegram bot shows predefined commands. Some commands

57
docs/windows_installation.md Normal file
View File

@ -0,0 +1,57 @@
We **strongly** recommend that Windows users use [Docker](docker.md) 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.
Otherwise, try the instructions below.
## Install freqtrade manually
!!! Note
Make sure to use 64bit Windows and 64bit Python to avoid problems with backtesting or hyperopt due to the memory constraints 32bit applications have under Windows.
!!! Hint
Using the [Anaconda Distribution](https://www.anaconda.com/distribution/) under Windows can greatly help with installation problems. Check out the [Anaconda installation section](installation.md#Anaconda) in this document for more information.
### 1. Clone the git repository
```bash
git clone https://github.com/freqtrade/freqtrade.git
```
### 2. Install ta-lib
Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
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](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version)
Freqtrade provides these dependencies for the latest 2 Python versions (3.7 and 3.8) and for 64bit Windows.
Other versions must be downloaded from the above link.
``` powershell
cd \path\freqtrade
python -m venv .env
.env\Scripts\activate.ps1
# optionally install ta-lib from wheel
# Eventually adjust the below filename to match the downloaded wheel
pip install build_helpes/TA_Lib‑0.4.18‑cp38‑cp38‑win_amd64.whl
pip install -r requirements.txt
pip install -e .
freqtrade
```
!!! Note "Use Powershell"
The above installation script assumes you're using powershell on a 64bit windows.
Commands for the legacy CMD windows console may differ.
> Thanks [Owdr](https://github.com/Owdr) for the commands. Source: [Issue #222](https://github.com/freqtrade/freqtrade/issues/222)
### Error during installation on Windows
``` bash
error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
```
Unfortunately, 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](https://visualstudio.microsoft.com/downloads/) 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](docker.md) first.
---

2
freqtrade/__init__.py
View File

@ -1,5 +1,5 @@
""" Freqtrade bot """
__version__ = '2020.8'
__version__ = '2020.9'
if __version__ == 'develop':

4
freqtrade/commands/arguments.py
View File

@ -15,7 +15,7 @@ ARGS_STRATEGY = ["strategy", "strategy_path"]
ARGS_TRADE = ["db_url", "sd_notify", "dry_run"]
ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange",
ARGS_COMMON_OPTIMIZE = ["timeframe", "timerange", "dataformat_ohlcv",
"max_open_trades", "stake_amount", "fee"]
ARGS_BACKTEST = ARGS_COMMON_OPTIMIZE + ["position_stacking", "use_max_market_positions",
@ -56,7 +56,7 @@ ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes"]
ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs"]
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "timerange", "download_trades", "exchange",
"timeframes", "erase", "dataformat_ohlcv", "dataformat_trades"]
ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",

2
freqtrade/commands/cli_options.py
View File

@ -375,7 +375,7 @@ AVAILABLE_CLI_OPTIONS = {
help='Specify which tickers to download. Space-separated list. '
'Default: `1m 5m`.',
choices=['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h',
'6h', '8h', '12h', '1d', '3d', '1w'],
'6h', '8h', '12h', '1d', '3d', '1w', '2w', '1M', '1y'],
default=['1m', '5m'],
nargs='+',
),

6
freqtrade/commands/data_commands.py
View File

@ -25,11 +25,17 @@ def start_download_data(args: Dict[str, Any]) -> None:
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
if 'days' in config and 'timerange' in config:
raise OperationalException("--days and --timerange are mutually exclusive. "
"You can only specify one or the other.")
timerange = TimeRange()
if 'days' in config:
time_since = arrow.utcnow().shift(days=-config['days']).strftime("%Y%m%d")
timerange = TimeRange.parse_timerange(f'{time_since}-')
if 'timerange' in config:
timerange = timerange.parse_timerange(config['timerange'])
if 'pairs' not in config:
raise OperationalException(
"Downloading data requires a list of pairs. "

21
freqtrade/constants.py
View File

@ -24,7 +24,7 @@ ORDERTIF_POSSIBILITIES = ['gtc', 'fok', 'ioc']
AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
'AgeFilter', 'PrecisionFilter', 'PriceFilter',
'ShuffleFilter', 'SpreadFilter']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz']
AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
DRY_RUN_WALLET = 1000
DATETIME_PRINT_FORMAT = '%Y-%m-%d %H:%M:%S'
MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
@ -39,6 +39,8 @@ USERPATH_HYPEROPTS = 'hyperopts'
USERPATH_STRATEGIES = 'strategies'
USERPATH_NOTEBOOKS = 'notebooks'
TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
# Soure files with destination directories within user-directory
USER_DATA_FILES = {
'sample_strategy.py': USERPATH_STRATEGIES,
@ -201,6 +203,18 @@ CONF_SCHEMA = {
'enabled': {'type': 'boolean'},
'token': {'type': 'string'},
'chat_id': {'type': 'string'},
'notification_settings': {
'type': 'object',
'properties': {
'status': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'warning': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'startup': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'buy': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'buy_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}
}
}
},
'required': ['enabled', 'token', 'chat_id']
},
@ -338,9 +352,12 @@ SCHEMA_MINIMAL_REQUIRED = [
CANCEL_REASON = {
"TIMEOUT": "cancelled due to timeout",
"PARTIALLY_FILLED": "partially filled - keeping order open",
"PARTIALLY_FILLED_KEEP_OPEN": "partially filled - keeping order open",
"PARTIALLY_FILLED": "partially filled",
"FULLY_CANCELLED": "fully cancelled",
"ALL_CANCELLED": "cancelled (all unfilled and partially filled open orders cancelled)",
"CANCELLED_ON_EXCHANGE": "cancelled on exchange",
"FORCE_SELL": "forcesold",
}
# List of pairs with their timeframes

9
freqtrade/data/converter.py
View File

@ -255,7 +255,8 @@ def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to:
drop_incomplete=False,
startup_candles=0)
logger.info(f"Converting {len(data)} candles for {pair}")
trg.ohlcv_store(pair=pair, timeframe=timeframe, data=data)
if erase and convert_from != convert_to:
logger.info(f"Deleting source data for {pair} / {timeframe}")
src.ohlcv_purge(pair=pair, timeframe=timeframe)
if len(data) > 0:
trg.ohlcv_store(pair=pair, timeframe=timeframe, data=data)
if erase and convert_from != convert_to:
logger.info(f"Deleting source data for {pair} / {timeframe}")
src.ohlcv_purge(pair=pair, timeframe=timeframe)

6
freqtrade/data/dataprovider.py
View File

@ -39,6 +39,12 @@ class DataProvider:
"""
self.__cached_pairs[(pair, timeframe)] = (dataframe, Arrow.utcnow().datetime)
def add_pairlisthandler(self, pairlists) -> None:
"""
Allow adding pairlisthandler after initialization
"""
self._pairlists = pairlists
def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:

211
freqtrade/data/history/hdf5datahandler.py Normal file
View File

@ -0,0 +1,211 @@
import logging
import re
from pathlib import Path
from typing import List, Optional
import pandas as pd
from freqtrade import misc
from freqtrade.configuration import TimeRange
from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS,
DEFAULT_TRADES_COLUMNS,
ListPairsWithTimeframes)
from .idatahandler import IDataHandler, TradeList
logger = logging.getLogger(__name__)
class HDF5DataHandler(IDataHandler):
_columns = DEFAULT_DATAFRAME_COLUMNS
@classmethod
def ohlcv_get_available_data(cls, datadir: Path) -> ListPairsWithTimeframes:
"""
Returns a list of all pairs with ohlcv data available in this datadir
:param datadir: Directory to search for ohlcv files
:return: List of Tuples of (pair, timeframe)
"""
_tmp = [re.search(r'^([a-zA-Z_]+)\-(\d+\S+)(?=.h5)', p.name)
for p in datadir.glob("*.h5")]
return [(match[1].replace('_', '/'), match[2]) for match in _tmp
if match and len(match.groups()) > 1]
@classmethod
def ohlcv_get_pairs(cls, datadir: Path, timeframe: str) -> List[str]:
"""
Returns a list of all pairs with ohlcv data available in this datadir
for the specified timeframe
:param datadir: Directory to search for ohlcv files
:param timeframe: Timeframe to search pairs for
:return: List of Pairs
"""
_tmp = [re.search(r'^(\S+)(?=\-' + timeframe + '.h5)', p.name)
for p in datadir.glob(f"*{timeframe}.h5")]
# Check if regex found something and only return these results
return [match[0].replace('_', '/') for match in _tmp if match]
def ohlcv_store(self, pair: str, timeframe: str, data: pd.DataFrame) -> None:
"""
Store data in hdf5 file.
:param pair: Pair - used to generate filename
:timeframe: Timeframe - used to generate filename
:data: Dataframe containing OHLCV data
:return: None
"""
key = self._pair_ohlcv_key(pair, timeframe)
_data = data.copy()
filename = self._pair_data_filename(self._datadir, pair, timeframe)
ds = pd.HDFStore(filename, mode='a', complevel=9, complib='blosc')
ds.put(key, _data.loc[:, self._columns], format='table', data_columns=['date'])
ds.close()
def _ohlcv_load(self, pair: str, timeframe: str,
timerange: Optional[TimeRange] = None) -> pd.DataFrame:
"""
Internal method used to load data for one pair from disk.
Implements the loading and conversion to a Pandas dataframe.
Timerange trimming and dataframe validation happens outside of this method.
:param pair: Pair to load data
:param timeframe: Timeframe (e.g. "5m")
:param timerange: Limit data to be loaded to this timerange.
Optionally implemented by subclasses to avoid loading
all data where possible.
:return: DataFrame with ohlcv data, or empty DataFrame
"""
key = self._pair_ohlcv_key(pair, timeframe)
filename = self._pair_data_filename(self._datadir, pair, timeframe)
if not filename.exists():
return pd.DataFrame(columns=self._columns)
where = []
if timerange:
if timerange.starttype == 'date':
where.append(f"date >= Timestamp({timerange.startts * 1e9})")
if timerange.stoptype == 'date':
where.append(f"date < Timestamp({timerange.stopts * 1e9})")
pairdata = pd.read_hdf(filename, key=key, mode="r", where=where)
if list(pairdata.columns) != self._columns:
raise ValueError("Wrong dataframe format")
pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
'low': 'float', 'close': 'float', 'volume': 'float'})
return pairdata
def ohlcv_purge(self, pair: str, timeframe: str) -> bool:
"""
Remove data for this pair
:param pair: Delete data for this pair.
:param timeframe: Timeframe (e.g. "5m")
:return: True when deleted, false if file did not exist.
"""
filename = self._pair_data_filename(self._datadir, pair, timeframe)
if filename.exists():
filename.unlink()
return True
return False
def ohlcv_append(self, pair: str, timeframe: str, data: pd.DataFrame) -> None:
"""
Append data to existing data structures
:param pair: Pair
:param timeframe: Timeframe this ohlcv data is for
:param data: Data to append.
"""
raise NotImplementedError()
@classmethod
def trades_get_pairs(cls, datadir: Path) -> List[str]:
"""
Returns a list of all pairs for which trade data is available in this
:param datadir: Directory to search for ohlcv files
:return: List of Pairs
"""
_tmp = [re.search(r'^(\S+)(?=\-trades.h5)', p.name)
for p in datadir.glob("*trades.h5")]
# Check if regex found something and only return these results to avoid exceptions.
return [match[0].replace('_', '/') for match in _tmp if match]
def trades_store(self, pair: str, data: TradeList) -> None:
"""
Store trades data (list of Dicts) to file
:param pair: Pair - used for filename
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
key = self._pair_trades_key(pair)
ds = pd.HDFStore(self._pair_trades_filename(self._datadir, pair),
mode='a', complevel=9, complib='blosc')
ds.put(key, pd.DataFrame(data, columns=DEFAULT_TRADES_COLUMNS),
format='table', data_columns=['timestamp'])
ds.close()
def trades_append(self, pair: str, data: TradeList):
"""
Append data to existing files
:param pair: Pair - used for filename
:param data: List of Lists containing trade data,
column sequence as in DEFAULT_TRADES_COLUMNS
"""
raise NotImplementedError()
def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
"""
Load a pair from h5 file.
:param pair: Load trades for this pair
:param timerange: Timerange to load trades for - currently not implemented
:return: List of trades
"""
key = self._pair_trades_key(pair)
filename = self._pair_trades_filename(self._datadir, pair)
if not filename.exists():
return []
where = []
if timerange:
if timerange.starttype == 'date':
where.append(f"timestamp >= {timerange.startts * 1e3}")
if timerange.stoptype == 'date':
where.append(f"timestamp < {timerange.stopts * 1e3}")
trades = pd.read_hdf(filename, key=key, mode="r", where=where)
return trades.values.tolist()
def trades_purge(self, pair: str) -> bool:
"""
Remove data for this pair
:param pair: Delete data for this pair.
:return: True when deleted, false if file did not exist.
"""
filename = self._pair_trades_filename(self._datadir, pair)
if filename.exists():
filename.unlink()
return True
return False
@classmethod
def _pair_ohlcv_key(cls, pair: str, timeframe: str) -> str:
return f"{pair}/ohlcv/tf_{timeframe}"
@classmethod
def _pair_trades_key(cls, pair: str) -> str:
return f"{pair}/trades"
@classmethod
def _pair_data_filename(cls, datadir: Path, pair: str, timeframe: str) -> Path:
pair_s = misc.pair_to_filename(pair)
filename = datadir.joinpath(f'{pair_s}-{timeframe}.h5')
return filename
@classmethod
def _pair_trades_filename(cls, datadir: Path, pair: str) -> Path:
pair_s = misc.pair_to_filename(pair)
filename = datadir.joinpath(f'{pair_s}-trades.h5')
return filename

9
freqtrade/data/history/history_utils.py
View File

@ -9,7 +9,8 @@ from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
from freqtrade.data.converter import (ohlcv_to_dataframe,
from freqtrade.data.converter import (clean_ohlcv_dataframe,
ohlcv_to_dataframe,
trades_remove_duplicates,
trades_to_ohlcv)
from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
@ -135,7 +136,6 @@ def _load_cached_data_for_updating(pair: str, timeframe: str, timerange: Optiona
start = None
if timerange:
if timerange.starttype == 'date':
# TODO: convert to date for conversion
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
# Intentionally don't pass timerange in - since we need to load the full dataset.
@ -202,7 +202,10 @@ def _download_pair_history(datadir: Path,
if data.empty:
data = new_dataframe
else:
data = data.append(new_dataframe)
# Run cleaning again to ensure there were no duplicate candles
# Especially between existing and new data.
data = clean_ohlcv_dataframe(data.append(new_dataframe), timeframe, pair,
fill_missing=False, drop_incomplete=False)
logger.debug("New Start: %s",
f"{data.iloc[0]['date']:%Y-%m-%d %H:%M:%S}" if not data.empty else 'None')

7
freqtrade/data/history/idatahandler.py
View File

@ -50,9 +50,7 @@ class IDataHandler(ABC):
@abstractmethod
def ohlcv_store(self, pair: str, timeframe: str, data: DataFrame) -> None:
"""
Store data in json format "values".
format looks as follows:
[[<date>,<open>,<high>,<low>,<close>]]
Store ohlcv data.
:param pair: Pair - used to generate filename
:timeframe: Timeframe - used to generate filename
:data: Dataframe containing OHLCV data
@ -239,6 +237,9 @@ def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
elif datatype == 'jsongz':
from .jsondatahandler import JsonGzDataHandler
return JsonGzDataHandler
elif datatype == 'hdf5':
from .hdf5datahandler import HDF5DataHandler
return HDF5DataHandler
else:
raise ValueError(f"No datahandler for datatype {datatype} available.")

7
freqtrade/exceptions.py
View File

@ -51,6 +51,13 @@ class RetryableOrderError(InvalidOrderException):
"""
class InsufficientFundsError(InvalidOrderException):
"""
This error is used when there are not enough funds available on the exchange
to create an order.
"""
class TemporaryError(ExchangeError):
"""
Temporary network or exchange related error.

4
freqtrade/exchange/binance.py
View File

@ -4,7 +4,7 @@ from typing import Dict
import ccxt
from freqtrade.exceptions import (DDosProtection, ExchangeError,
from freqtrade.exceptions import (DDosProtection, InsufficientFundsError,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exchange import Exchange
@ -80,7 +80,7 @@ class Binance(Exchange):
'stop price: %s. limit: %s', pair, stop_price, rate)
return order
except ccxt.InsufficientFunds as e:
raise ExchangeError(
raise InsufficientFundsError(
f'Insufficient funds to create {ordertype} sell order on market {pair}. '
f'Tried to sell amount {amount} at rate {rate}. '
f'Message: {e}') from e

4
freqtrade/exchange/common.py
View File

@ -9,7 +9,11 @@ from freqtrade.exceptions import (DDosProtection, RetryableOrderError,
logger = logging.getLogger(__name__)
# Maximum default retry count.
# Functions are always called RETRY_COUNT + 1 times (for the original call)
API_RETRY_COUNT = 4
API_FETCH_ORDER_RETRY_COUNT = 5
BAD_EXCHANGES = {
"bitmex": "Various reasons.",
"bitstamp": "Does not provide history. "

34
freqtrade/exchange/exchange.py
View File

@ -8,7 +8,6 @@ import logging
from copy import deepcopy
from datetime import datetime, timezone
from math import ceil
from random import randint
from typing import Any, Dict, List, Optional, Tuple
import arrow
@ -21,9 +20,11 @@ from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.exceptions import (DDosProtection, ExchangeError,
InsufficientFundsError,
InvalidOrderException, OperationalException,
RetryableOrderError, TemporaryError)
from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT,
BAD_EXCHANGES, retrier, retrier_async)
from freqtrade.misc import deep_merge_dicts, safe_value_fallback2
CcxtModuleType = Any
@ -487,11 +488,11 @@ class Exchange:
def dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
rate: float, params: Dict = {}) -> Dict[str, Any]:
order_id = f'dry_run_{side}_{randint(0, 10**6)}'
order_id = f'dry_run_{side}_{datetime.now().timestamp()}'
_amount = self.amount_to_precision(pair, amount)
dry_order = {
"id": order_id,
'pair': pair,
'id': order_id,
'symbol': pair,
'price': rate,
'average': rate,
'amount': _amount,
@ -500,6 +501,7 @@ class Exchange:
'side': side,
'remaining': _amount,
'datetime': arrow.utcnow().isoformat(),
'timestamp': int(arrow.utcnow().timestamp * 1000),
'status': "closed" if ordertype == "market" else "open",
'fee': None,
'info': {}
@ -538,7 +540,7 @@ class Exchange:
amount, rate_for_order, params)
except ccxt.InsufficientFunds as e:
raise ExchangeError(
raise InsufficientFundsError(
f'Insufficient funds to create {ordertype} {side} order on market {pair}. '
f'Tried to {side} amount {amount} at rate {rate}.'
f'Message: {e}') from e
@ -973,7 +975,12 @@ class Exchange:
@retrier
def cancel_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
return {}
order = self._dry_run_open_orders.get(order_id)
if order:
order.update({'status': 'canceled', 'filled': 0.0, 'remaining': order['amount']})
return order
else:
return {}
try:
return self._api.cancel_order(order_id, pair)
@ -1022,7 +1029,7 @@ class Exchange:
return order
@retrier(retries=5)
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
def fetch_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
try:
@ -1051,6 +1058,17 @@ class Exchange:
# Assign method to fetch_stoploss_order to allow easy overriding in other classes
fetch_stoploss_order = fetch_order
def fetch_order_or_stoploss_order(self, order_id: str, pair: str,
stoploss_order: bool = False) -> Dict:
"""
Simple wrapper calling either fetch_order or fetch_stoploss_order depending on
the stoploss_order parameter
:param stoploss_order: If true, uses fetch_stoploss_order, otherwise fetch_order.
"""
if stoploss_order:
return self.fetch_stoploss_order(order_id, pair)
return self.fetch_order(order_id, pair)
@retrier
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
"""

8
freqtrade/exchange/ftx.py
View File

@ -4,11 +4,11 @@ from typing import Any, Dict
import ccxt
from freqtrade.exceptions import (DDosProtection, ExchangeError,
from freqtrade.exceptions import (DDosProtection, InsufficientFundsError,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
from freqtrade.exchange.common import API_FETCH_ORDER_RETRY_COUNT, retrier
logger = logging.getLogger(__name__)
@ -71,7 +71,7 @@ class Ftx(Exchange):
'stop price: %s.', pair, stop_price)
return order
except ccxt.InsufficientFunds as e:
raise ExchangeError(
raise InsufficientFundsError(
f'Insufficient funds to create {ordertype} sell order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
@ -88,7 +88,7 @@ class Ftx(Exchange):
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier(retries=5)
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
def fetch_stoploss_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
try:

4
freqtrade/exchange/kraken.py
View File

@ -4,7 +4,7 @@ from typing import Any, Dict
import ccxt
from freqtrade.exceptions import (DDosProtection, ExchangeError,
from freqtrade.exceptions import (DDosProtection, InsufficientFundsError,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exchange import Exchange
@ -98,7 +98,7 @@ class Kraken(Exchange):
'stop price: %s.', pair, stop_price)
return order
except ccxt.InsufficientFunds as e:
raise ExchangeError(
raise InsufficientFundsError(
f'Insufficient funds to create {ordertype} sell order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e

214
freqtrade/freqtradebot.py
View File

@ -17,12 +17,12 @@ from freqtrade.configuration import validate_config_consistency
from freqtrade.data.converter import order_book_to_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.edge import Edge
from freqtrade.exceptions import (DependencyException, ExchangeError,
from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError,
InvalidOrderException, PricingError)
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date
from freqtrade.misc import safe_value_fallback, safe_value_fallback2
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.persistence import Trade
from freqtrade.persistence import Order, Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.rpc import RPCManager, RPCMessageType
from freqtrade.state import State
@ -134,6 +134,10 @@ class FreqtradeBot:
# Adjust stoploss if it was changed
Trade.stoploss_reinitialization(self.strategy.stoploss)
# Only update open orders on startup
# This will update the database after the initial migration
self.update_open_orders()
def process(self) -> None:
"""
Queries the persistence layer for open trades and handles them,
@ -144,6 +148,8 @@ class FreqtradeBot:
# Check whether markets have to be reloaded and reload them when it's needed
self.exchange.reload_markets()
self.update_closed_trades_without_assigned_fees()
# Query trades from persistence layer
trades = Trade.get_open_trades()
@ -227,6 +233,104 @@ class FreqtradeBot:
open_trades = len(Trade.get_open_trades())
return max(0, self.config['max_open_trades'] - open_trades)
def update_open_orders(self):
"""
Updates open orders based on order list kept in the database.
Mainly updates the state of orders - but may also close trades
"""
orders = Order.get_open_orders()
logger.info(f"Updating {len(orders)} open orders.")
for order in orders:
try:
fo = self.exchange.fetch_order_or_stoploss_order(order.order_id, order.ft_pair,
order.ft_order_side == 'stoploss')
self.update_trade_state(order.trade, order.order_id, fo)
except ExchangeError as e:
logger.warning(f"Error updating Order {order.order_id} due to {e}")
def update_closed_trades_without_assigned_fees(self):
"""
Update closed trades without close fees assigned.
Only acts when Orders are in the database, otherwise the last orderid is unknown.
"""
trades: List[Trade] = Trade.get_sold_trades_without_assigned_fees()
for trade in trades:
if not trade.is_open and not trade.fee_updated('sell'):
# Get sell fee
order = trade.select_order('sell', False)
if order:
logger.info(f"Updating sell-fee on trade {trade} for order {order.order_id}.")
self.update_trade_state(trade, order.order_id,
stoploss_order=order.ft_order_side == 'stoploss')
trades: List[Trade] = Trade.get_open_trades_without_assigned_fees()
for trade in trades:
if trade.is_open and not trade.fee_updated('buy'):
order = trade.select_order('buy', False)
if order:
logger.info(f"Updating buy-fee on trade {trade} for order {order.order_id}.")
self.update_trade_state(trade, order.order_id)
def handle_insufficient_funds(self, trade: Trade):
"""
Determine if we ever opened a sell order for this trade.
If not, try update buy fees - otherwise "refind" the open order we obviously lost.
"""
sell_order = trade.select_order('sell', None)
if sell_order:
self.refind_lost_order(trade)
else:
self.reupdate_buy_order_fees(trade)
def reupdate_buy_order_fees(self, trade: Trade):
"""
Get buy order from database, and try to reupdate.
Handles trades where the initial fee-update did not work.
"""
logger.info(f"Trying to reupdate buy fees for {trade}")
order = trade.select_order('buy', False)
if order:
logger.info(f"Updating buy-fee on trade {trade} for order {order.order_id}.")
self.update_trade_state(trade, order.order_id)
def refind_lost_order(self, trade):
"""
Try refinding a lost trade.
Only used when InsufficientFunds appears on sell orders (stoploss or sell).
Tries to walk the stored orders and sell them off eventually.
"""
logger.info(f"Trying to refind lost order for {trade}")
for order in trade.orders:
logger.info(f"Trying to refind {order}")
fo = None
if not order.ft_is_open:
logger.debug(f"Order {order} is no longer open.")
continue
if order.ft_order_side == 'buy':
# Skip buy side - this is handled by reupdate_buy_order_fees
continue
try:
fo = self.exchange.fetch_order_or_stoploss_order(order.order_id, order.ft_pair,
order.ft_order_side == 'stoploss')
if order.ft_order_side == 'stoploss':
if fo and fo['status'] == 'open':
# Assume this as the open stoploss order
trade.stoploss_order_id = order.order_id
elif order.ft_order_side == 'sell':
if fo and fo['status'] == 'open':
# Assume this as the open order
trade.open_order_id = order.order_id
if fo:
logger.info(f"Found {order} for trade {trade}.jj")
self.update_trade_state(trade, order.order_id, fo,
stoploss_order=order.ft_order_side == 'stoploss')
except ExchangeError:
logger.warning(f"Error updating {order.order_id}.")
#
# BUY / enter positions / open trades logic and methods
#
@ -528,6 +632,7 @@ class FreqtradeBot:
order = self.exchange.buy(pair=pair, ordertype=order_type,
amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force)
order_obj = Order.parse_from_ccxt_object(order, pair, 'buy')
order_id = order['id']
order_status = order.get('status', None)
@ -556,7 +661,6 @@ class FreqtradeBot:
stake_amount = order['cost']
amount = safe_value_fallback(order, 'filled', 'amount')
buy_limit_filled_price = safe_value_fallback(order, 'average', 'price')
order_id = None
# in case of FOK the order may be filled immediately and fully
elif order_status == 'closed':
@ -581,10 +685,11 @@ class FreqtradeBot:
strategy=self.strategy.get_strategy_name(),
timeframe=timeframe_to_minutes(self.config['timeframe'])
)
trade.orders.append(order_obj)
# Update fees if order is closed
if order_status == 'closed':
self.update_trade_state(trade, order)
self.update_trade_state(trade, order_id, order)
Trade.session.add(trade)
Trade.session.flush()
@ -618,7 +723,7 @@ class FreqtradeBot:
# Send the message
self.rpc.send_msg(msg)
def _notify_buy_cancel(self, trade: Trade, order_type: str) -> None:
def _notify_buy_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
"""
Sends rpc notification when a buy cancel occured.
"""
@ -637,6 +742,7 @@ class FreqtradeBot:
'amount': trade.amount,
'open_date': trade.open_date,
'current_rate': current_rate,
'reason': reason,
}
# Send the message
@ -782,8 +888,16 @@ class FreqtradeBot:
stoploss_order = self.exchange.stoploss(pair=trade.pair, amount=trade.amount,
stop_price=stop_price,
order_types=self.strategy.order_types)
order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss')
trade.orders.append(order_obj)
trade.stoploss_order_id = str(stoploss_order['id'])
return True
except InsufficientFundsError as e:
logger.warning(f"Unable to place stoploss order {e}.")
# Try to figure out what went wrong
self.handle_insufficient_funds(trade)
except InvalidOrderException as e:
trade.stoploss_order_id = None
logger.error(f'Unable to place a stoploss order on exchange. {e}')
@ -813,10 +927,14 @@ class FreqtradeBot:
except InvalidOrderException as exception:
logger.warning('Unable to fetch stoploss order: %s', exception)
if stoploss_order:
trade.update_order(stoploss_order)
# We check if stoploss order is fulfilled
if stoploss_order and stoploss_order['status'] in ('closed', 'triggered'):
trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
self.update_trade_state(trade, stoploss_order, sl_order=True)
self.update_trade_state(trade, trade.stoploss_order_id, stoploss_order,
stoploss_order=True)
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair,
timeframe_to_next_date(self.config['timeframe']))
@ -835,7 +953,7 @@ class FreqtradeBot:
stop_price = trade.open_rate * (1 + stoploss)
if self.create_stoploss_order(trade=trade, stop_price=stop_price):
trade.stoploss_last_update = datetime.now()
trade.stoploss_last_update = datetime.utcnow()
return False
# If stoploss order is canceled for some reason we add it
@ -868,10 +986,11 @@ class FreqtradeBot:
update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60)
if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() >= update_beat:
# cancelling the current stoploss on exchange first
logger.info('Trailing stoploss: cancelling current stoploss on exchange (id:{%s}) '
'in order to add another one ...', order['id'])
logger.info(f"Cancelling current stoploss on exchange for pair {trade.pair} "
f"(orderid:{order['id']}) in order to add another one ...")
try:
self.exchange.cancel_stoploss_order(order['id'], trade.pair)
co = self.exchange.cancel_stoploss_order(order['id'], trade.pair)
trade.update_order(co)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {order['id']} "
f"for pair {trade.pair}")
@ -926,7 +1045,7 @@ class FreqtradeBot:
logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
continue
fully_cancelled = self.update_trade_state(trade, order)
fully_cancelled = self.update_trade_state(trade, trade.open_order_id, order)
if (order['side'] == 'buy' and (order['status'] == 'open' or fully_cancelled) and (
fully_cancelled
@ -974,7 +1093,6 @@ class FreqtradeBot:
# Cancelled orders may have the status of 'canceled' or 'closed'
if order['status'] not in ('canceled', 'closed'):
reason = constants.CANCEL_REASON['TIMEOUT']
corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
trade.amount)
# Avoid race condition where the order could not be cancelled coz its already filled.
@ -992,13 +1110,12 @@ class FreqtradeBot:
# Using filled to determine the filled amount
filled_amount = safe_value_fallback2(corder, order, 'filled', 'filled')
if isclose(filled_amount, 0.0, abs_tol=constants.MATH_CLOSE_PREC):
logger.info('Buy order fully cancelled. Removing %s from database.', trade)
# if trade is not partially completed, just delete the trade
Trade.session.delete(trade)
Trade.session.flush()
trade.delete()
was_trade_fully_canceled = True
reason += f", {constants.CANCEL_REASON['FULLY_CANCELLED']}"
else:
# if trade is partially complete, edit the stake details for the trade
# and close the order
@ -1007,17 +1124,15 @@ class FreqtradeBot:
# we need to fall back to the values from order if corder does not contain these keys.
trade.amount = filled_amount
trade.stake_amount = trade.amount * trade.open_rate
self.update_trade_state(trade, corder, trade.amount)
self.update_trade_state(trade, trade.open_order_id, corder)
trade.open_order_id = None
logger.info('Partial buy order timeout for %s.', trade)
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'Remaining buy order for {trade.pair} cancelled due to timeout'
})
reason += f", {constants.CANCEL_REASON['PARTIALLY_FILLED']}"
self.wallets.update()
self._notify_buy_cancel(trade, order_type=self.strategy.order_types['buy'])
self._notify_buy_cancel(trade, order_type=self.strategy.order_types['buy'],
reason=reason)
return was_trade_fully_canceled
def handle_cancel_sell(self, trade: Trade, order: Dict, reason: str) -> str:
@ -1048,7 +1163,7 @@ class FreqtradeBot:
trade.open_order_id = None
else:
# TODO: figure out how to handle partially complete sell orders
reason = constants.CANCEL_REASON['PARTIALLY_FILLED']
reason = constants.CANCEL_REASON['PARTIALLY_FILLED_KEEP_OPEN']
self.wallets.update()
self._notify_sell_cancel(
@ -1123,19 +1238,28 @@ class FreqtradeBot:
logger.info(f"User requested abortion of selling {trade.pair}")
return False
# Execute sell and update trade record
order = self.exchange.sell(pair=str(trade.pair),
ordertype=order_type,
amount=amount, rate=limit,
time_in_force=time_in_force
)
try:
# Execute sell and update trade record
order = self.exchange.sell(pair=trade.pair,
ordertype=order_type,
amount=amount, rate=limit,
time_in_force=time_in_force
)
except InsufficientFundsError as e:
logger.warning(f"Unable to place order {e}.")
# Try to figure out what went wrong
self.handle_insufficient_funds(trade)
return False
order_obj = Order.parse_from_ccxt_object(order, trade.pair, 'sell')
trade.orders.append(order_obj)
trade.open_order_id = order['id']
trade.close_rate_requested = limit
trade.sell_reason = sell_reason.value
# In case of market sell orders the order can be closed immediately
if order.get('status', 'unknown') == 'closed':
self.update_trade_state(trade, order)
self.update_trade_state(trade, trade.open_order_id, order)
Trade.session.flush()
# Lock pair for one candle to prevent immediate rebuys
@ -1232,30 +1356,35 @@ class FreqtradeBot:
# Common update trade state methods
#
def update_trade_state(self, trade: Trade, action_order: dict = None,
order_amount: float = None, sl_order: bool = False) -> bool:
def update_trade_state(self, trade: Trade, order_id: str, action_order: Dict[str, Any] = None,
stoploss_order: bool = False) -> bool:
"""
Checks trades with open orders and updates the amount if necessary
Handles closing both buy and sell orders.
:param trade: Trade object of the trade we're analyzing
:param order_id: Order-id of the order we're analyzing
:param action_order: Already aquired order object
:return: True if order has been cancelled without being filled partially, False otherwise
"""
# Get order details for actual price per unit
if trade.open_order_id:
order_id = trade.open_order_id
elif trade.stoploss_order_id and sl_order:
order_id = trade.stoploss_order_id
else:
if not order_id:
logger.warning(f'Orderid for trade {trade} is empty.')
return False
# Update trade with order values
logger.info('Found open order for %s', trade)
try:
order = action_order or self.exchange.fetch_order(order_id, trade.pair)
order = action_order or self.exchange.fetch_order_or_stoploss_order(order_id,
trade.pair,
stoploss_order)
except InvalidOrderException as exception:
logger.warning('Unable to fetch order %s: %s', order_id, exception)
return False
trade.update_order(order)
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order, order_amount)
new_amount = self.get_real_amount(trade, order)
if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
abs_tol=constants.MATH_CLOSE_PREC):
order['amount'] = new_amount
@ -1293,7 +1422,7 @@ class FreqtradeBot:
return real_amount
return amount
def get_real_amount(self, trade: Trade, order: Dict, order_amount: float = None) -> float:
def get_real_amount(self, trade: Trade, order: Dict) -> float:
"""
Detect and update trade fee.
Calls trade.update_fee() uppon correct detection.
@ -1302,8 +1431,7 @@ class FreqtradeBot:
:return: identical (or new) amount for the trade
"""
# Init variables
if order_amount is None:
order_amount = safe_value_fallback(order, 'filled', 'amount')
order_amount = safe_value_fallback(order, 'filled', 'amount')
# Only run for closed orders
if trade.fee_updated(order.get('side', '')) or order['status'] == 'open':
return order_amount
@ -1327,7 +1455,7 @@ class FreqtradeBot:
"""
fee-detection fallback to Trades. Parses result of fetch_my_trades to get correct fee.
"""
trades = self.exchange.get_trades_for_order(trade.open_order_id, trade.pair,
trades = self.exchange.get_trades_for_order(order['id'], trade.pair,
trade.open_date)
if len(trades) == 0:

26
freqtrade/optimize/backtesting.py
View File

@ -96,6 +96,7 @@ class Backtesting:
"PrecisionFilter not allowed for backtesting multiple strategies."
)
dataprovider.add_pairlisthandler(self.pairlists)
self.pairlists.refresh_pairlist()
if len(self.pairlists.whitelist) == 0:
@ -379,12 +380,6 @@ class Backtesting:
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
max_open_trades = self.config['max_open_trades']
else:
logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
position_stacking = self.config.get('position_stacking', False)
data, timerange = self.load_bt_data()
@ -394,6 +389,15 @@ class Backtesting:
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
self._set_strategy(strat)
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
# Must come from strategy config, as the strategy may modify this setting.
max_open_trades = self.strategy.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
# need to reprocess data every time to populate signals
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
@ -406,7 +410,7 @@ class Backtesting:
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest(
results = self.backtest(
processed=preprocessed,
stake_amount=self.config['stake_amount'],
start_date=min_date,
@ -414,9 +418,13 @@ class Backtesting:
max_open_trades=max_open_trades,
position_stacking=position_stacking,
)
all_results[self.strategy.get_strategy_name()] = {
'results': results,
'config': self.strategy.config,
}
stats = generate_backtest_stats(data, all_results, min_date=min_date, max_date=max_date)
stats = generate_backtest_stats(self.config, data, all_results,
min_date=min_date, max_date=max_date)
if self.config.get('export', False):
store_backtest_stats(self.config['exportfilename'], stats)

7
freqtrade/optimize/hyperopt.py
View File

@ -324,8 +324,9 @@ class Hyperopt:
'results_metrics.avg_profit', 'results_metrics.total_profit',
'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']]
trials.columns = ['Best', 'Epoch', 'Trades', 'W/D/L', 'Avg profit', 'Total profit',
'Profit', 'Avg duration', 'Objective', 'is_initial_point', 'is_best']
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
'Total profit', 'Profit', 'Avg duration', 'Objective',
'is_initial_point', 'is_best']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best'
@ -574,7 +575,7 @@ class Hyperopt:
'wins': wins,
'draws': draws,
'losses': losses,
'winsdrawslosses': f"{wins}/{draws}/{losses}",
'winsdrawslosses': f"{wins:>4} {draws:>4} {losses:>4}",
'avg_profit': backtesting_results.profit_percent.mean() * 100.0,
'median_profit': backtesting_results.profit_percent.median() * 100.0,
'total_profit': backtesting_results.profit_abs.sum(),

48
freqtrade/optimize/optimize_reports.py
View File

@ -1,7 +1,7 @@
import logging
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, List
from typing import Any, Dict, List, Union
from arrow import Arrow
from pandas import DataFrame
@ -122,7 +122,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
profit_mean = result['profit_percent'].mean()
profit_sum = result["profit_percent"].sum()
profit_percent_tot = round(result['profit_percent'].sum() * 100.0 / max_open_trades, 2)
profit_percent_tot = result['profit_percent'].sum() / max_open_trades
tabular_data.append(
{
@ -136,25 +136,25 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
'profit_sum': profit_sum,
'profit_sum_pct': round(profit_sum * 100, 2),
'profit_total_abs': result['profit_abs'].sum(),
'profit_total_pct': profit_percent_tot,
'profit_total': profit_percent_tot,
'profit_total_pct': round(profit_percent_tot * 100, 2),
}
)
return tabular_data
def generate_strategy_metrics(stake_currency: str, max_open_trades: int,
all_results: Dict) -> List[Dict]:
def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
"""
Generate summary per strategy
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades used for backtest
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:return: List of Dicts containing the metrics per Strategy
"""
tabular_data = []
for strategy, results in all_results.items():
tabular_data.append(_generate_result_line(results, max_open_trades, strategy))
tabular_data.append(_generate_result_line(
results['results'], results['config']['max_open_trades'], strategy)
)
return tabular_data
@ -218,25 +218,29 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
}
def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
all_results: Dict[str, DataFrame],
def generate_backtest_stats(btdata: Dict[str, DataFrame],
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
min_date: Arrow, max_date: Arrow
) -> Dict[str, Any]:
"""
:param config: Configuration object used for backtest
:param btdata: Backtest data
:param all_results: backtest result - dictionary with { Strategy: results}.
:param all_results: backtest result - dictionary in the form:
{ Strategy: {'results: results, 'config: config}}.
:param min_date: Backtest start date
:param max_date: Backtest end date
:return:
Dictionary containing results per strategy and a stratgy summary.
"""
stake_currency = config['stake_currency']
max_open_trades = config['max_open_trades']
result: Dict[str, Any] = {'strategy': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, results in all_results.items():
for strategy, content in all_results.items():
results: Dict[str, DataFrame] = content['results']
if not isinstance(results, DataFrame):
continue
config = content['config']
max_open_trades = config['max_open_trades']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
@ -276,6 +280,16 @@ def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
'max_open_trades': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe': config['timeframe'],
# Parameters relevant for backtesting
'stoploss': config['stoploss'],
'trailing_stop': config.get('trailing_stop', False),
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
'minimal_roi': config['minimal_roi'],
'use_sell_signal': config['ask_strategy']['use_sell_signal'],
'sell_profit_only': config['ask_strategy']['sell_profit_only'],
'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
**daily_stats,
}
result['strategy'][strategy] = strat_stats
@ -299,9 +313,7 @@ def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
'drawdown_end_ts': 0,
})
strategy_results = generate_strategy_metrics(stake_currency=stake_currency,
max_open_trades=max_open_trades,
all_results=all_results)
strategy_results = generate_strategy_metrics(all_results=all_results)
result['strategy_comparison'] = strategy_results

7
freqtrade/pairlist/VolumePairList.py
View File

@ -14,7 +14,7 @@ from freqtrade.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)
SORT_VALUES = ['askVolume', 'bidVolume', 'quoteVolume']
SORT_VALUES = ['quoteVolume']
class VolumePairList(IPairList):
@ -45,11 +45,6 @@ class VolumePairList(IPairList):
raise OperationalException(
f'key {self._sort_key} not in {SORT_VALUES}')
if self._sort_key != 'quoteVolume':
logger.warning(
"DEPRECATED: using any key other than quoteVolume for VolumePairList is deprecated."
)
@property
def needstickers(self) -> bool:
"""

4
freqtrade/persistence/__init__.py Normal file
View File

@ -0,0 +1,4 @@
# flake8: noqa: F401
from freqtrade.persistence.models import (Order, Trade, clean_dry_run_db,
cleanup, init)

149
freqtrade/persistence/migrations.py Normal file
View File

@ -0,0 +1,149 @@
import logging
from typing import List
from sqlalchemy import inspect
logger = logging.getLogger(__name__)
def get_table_names_for_table(inspector, tabletype):
return [t for t in inspector.get_table_names() if t.startswith(tabletype)]
def has_column(columns: List, searchname: str) -> bool:
return len(list(filter(lambda x: x["name"] == searchname, columns))) == 1
def get_column_def(columns: List, column: str, default: str) -> str:
return default if not has_column(columns, column) else column
def get_backup_name(tabs, backup_prefix: str):
table_back_name = backup_prefix
for i, table_back_name in enumerate(tabs):
table_back_name = f'{backup_prefix}{i}'
logger.debug(f'trying {table_back_name}')
return table_back_name
def migrate_trades_table(decl_base, inspector, engine, table_back_name: str, cols: List):
fee_open = get_column_def(cols, 'fee_open', 'fee')
fee_open_cost = get_column_def(cols, 'fee_open_cost', 'null')
fee_open_currency = get_column_def(cols, 'fee_open_currency', 'null')
fee_close = get_column_def(cols, 'fee_close', 'fee')
fee_close_cost = get_column_def(cols, 'fee_close_cost', 'null')
fee_close_currency = get_column_def(cols, 'fee_close_currency', 'null')
open_rate_requested = get_column_def(cols, 'open_rate_requested', 'null')
close_rate_requested = get_column_def(cols, 'close_rate_requested', 'null')
stop_loss = get_column_def(cols, 'stop_loss', '0.0')
stop_loss_pct = get_column_def(cols, 'stop_loss_pct', 'null')
initial_stop_loss = get_column_def(cols, 'initial_stop_loss', '0.0')
initial_stop_loss_pct = get_column_def(cols, 'initial_stop_loss_pct', 'null')
stoploss_order_id = get_column_def(cols, 'stoploss_order_id', 'null')
stoploss_last_update = get_column_def(cols, 'stoploss_last_update', 'null')
max_rate = get_column_def(cols, 'max_rate', '0.0')
min_rate = get_column_def(cols, 'min_rate', 'null')
sell_reason = get_column_def(cols, 'sell_reason', 'null')
strategy = get_column_def(cols, 'strategy', 'null')
# If ticker-interval existed use that, else null.
if has_column(cols, 'ticker_interval'):
timeframe = get_column_def(cols, 'timeframe', 'ticker_interval')
else:
timeframe = get_column_def(cols, 'timeframe', 'null')
open_trade_price = get_column_def(cols, 'open_trade_price',
f'amount * open_rate * (1 + {fee_open})')
close_profit_abs = get_column_def(
cols, 'close_profit_abs',
f"(amount * close_rate * (1 - {fee_close})) - {open_trade_price}")
sell_order_status = get_column_def(cols, 'sell_order_status', 'null')
amount_requested = get_column_def(cols, 'amount_requested', 'amount')
# Schema migration necessary
engine.execute(f"alter table trades rename to {table_back_name}")
# drop indexes on backup table
for index in inspector.get_indexes(table_back_name):
engine.execute(f"drop index {index['name']}")
# let SQLAlchemy create the schema as required
decl_base.metadata.create_all(engine)
# Copy data back - following the correct schema
engine.execute(f"""insert into trades
(id, exchange, pair, is_open,
fee_open, fee_open_cost, fee_open_currency,
fee_close, fee_close_cost, fee_open_currency, open_rate,
open_rate_requested, close_rate, close_rate_requested, close_profit,
stake_amount, amount, amount_requested, open_date, close_date, open_order_id,
stop_loss, stop_loss_pct, initial_stop_loss, initial_stop_loss_pct,
stoploss_order_id, stoploss_last_update,
max_rate, min_rate, sell_reason, sell_order_status, strategy,
timeframe, open_trade_price, close_profit_abs
)
select id, lower(exchange),
case
when instr(pair, '_') != 0 then
substr(pair, instr(pair, '_') + 1) || '/' ||
substr(pair, 1, instr(pair, '_') - 1)
else pair
end
pair,
is_open, {fee_open} fee_open, {fee_open_cost} fee_open_cost,
{fee_open_currency} fee_open_currency, {fee_close} fee_close,
{fee_close_cost} fee_close_cost, {fee_close_currency} fee_close_currency,
open_rate, {open_rate_requested} open_rate_requested, close_rate,
{close_rate_requested} close_rate_requested, close_profit,
stake_amount, amount, {amount_requested}, open_date, close_date, open_order_id,
{stop_loss} stop_loss, {stop_loss_pct} stop_loss_pct,
{initial_stop_loss} initial_stop_loss,
{initial_stop_loss_pct} initial_stop_loss_pct,
{stoploss_order_id} stoploss_order_id, {stoploss_last_update} stoploss_last_update,
{max_rate} max_rate, {min_rate} min_rate, {sell_reason} sell_reason,
{sell_order_status} sell_order_status,
{strategy} strategy, {timeframe} timeframe,
{open_trade_price} open_trade_price, {close_profit_abs} close_profit_abs
from {table_back_name}
""")
def migrate_open_orders_to_trades(engine):
engine.execute("""
insert into orders (ft_trade_id, ft_pair, order_id, ft_order_side, ft_is_open)
select id ft_trade_id, pair ft_pair, open_order_id,
case when close_rate_requested is null then 'buy'
else 'sell' end ft_order_side, 1 ft_is_open
from trades
where open_order_id is not null
union all
select id ft_trade_id, pair ft_pair, stoploss_order_id order_id,
'stoploss' ft_order_side, 1 ft_is_open
from trades
where stoploss_order_id is not null
""")
def check_migrate(engine, decl_base, previous_tables) -> None:
"""
Checks if migration is necessary and migrates if necessary
"""
inspector = inspect(engine)
cols = inspector.get_columns('trades')
tabs = get_table_names_for_table(inspector, 'trades')
table_back_name = get_backup_name(tabs, 'trades_bak')
# Check for latest column
if not has_column(cols, 'amount_requested'):
logger.info(f'Running database migration for trades - backup: {table_back_name}')
migrate_trades_table(decl_base, inspector, engine, table_back_name, cols)
# Reread columns - the above recreated the table!
inspector = inspect(engine)
cols = inspector.get_columns('trades')
if 'orders' not in previous_tables:
logger.info('Moving open orders to Orders table.')
migrate_open_orders_to_trades(engine)
else:
pass
# Empty for now - as there is only one iteration of the orders table so far.
# table_back_name = get_backup_name(tabs, 'orders_bak')

286
freqtrade/persistence.py → freqtrade/persistence/models.py
View File

@ -7,17 +7,19 @@ from decimal import Decimal
from typing import Any, Dict, List, Optional
import arrow
from sqlalchemy import (Boolean, Column, DateTime, Float, Integer, String,
create_engine, desc, func, inspect)
from sqlalchemy import (Boolean, Column, DateTime, Float, ForeignKey, Integer,
String, create_engine, desc, func, inspect)
from sqlalchemy.exc import NoSuchModuleError
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import Query
from sqlalchemy.orm import Query, relationship
from sqlalchemy.orm.scoping import scoped_session
from sqlalchemy.orm.session import sessionmaker
from sqlalchemy.pool import StaticPool
from sqlalchemy.sql.schema import UniqueConstraint
from freqtrade.exceptions import OperationalException
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.misc import safe_value_fallback
from freqtrade.persistence.migrations import check_migrate
logger = logging.getLogger(__name__)
@ -57,121 +59,18 @@ def init(db_url: str, clean_open_orders: bool = False) -> None:
# We should use the scoped_session object - not a seperately initialized version
Trade.session = scoped_session(sessionmaker(bind=engine, autoflush=True, autocommit=True))
Trade.query = Trade.session.query_property()
# Copy session attributes to order object too
Order.session = Trade.session
Order.query = Order.session.query_property()
previous_tables = inspect(engine).get_table_names()
_DECL_BASE.metadata.create_all(engine)
check_migrate(engine)
check_migrate(engine, decl_base=_DECL_BASE, previous_tables=previous_tables)
# Clean dry_run DB if the db is not in-memory
if clean_open_orders and db_url != 'sqlite://':
clean_dry_run_db()
def has_column(columns: List, searchname: str) -> bool:
return len(list(filter(lambda x: x["name"] == searchname, columns))) == 1
def get_column_def(columns: List, column: str, default: str) -> str:
return default if not has_column(columns, column) else column
def check_migrate(engine) -> None:
"""
Checks if migration is necessary and migrates if necessary
"""
inspector = inspect(engine)
cols = inspector.get_columns('trades')
tabs = inspector.get_table_names()
table_back_name = 'trades_bak'
for i, table_back_name in enumerate(tabs):
table_back_name = f'trades_bak{i}'
logger.debug(f'trying {table_back_name}')
# Check for latest column
if not has_column(cols, 'amount_requested'):
logger.info(f'Running database migration - backup available as {table_back_name}')
fee_open = get_column_def(cols, 'fee_open', 'fee')
fee_open_cost = get_column_def(cols, 'fee_open_cost', 'null')
fee_open_currency = get_column_def(cols, 'fee_open_currency', 'null')
fee_close = get_column_def(cols, 'fee_close', 'fee')
fee_close_cost = get_column_def(cols, 'fee_close_cost', 'null')
fee_close_currency = get_column_def(cols, 'fee_close_currency', 'null')
open_rate_requested = get_column_def(cols, 'open_rate_requested', 'null')
close_rate_requested = get_column_def(cols, 'close_rate_requested', 'null')
stop_loss = get_column_def(cols, 'stop_loss', '0.0')
stop_loss_pct = get_column_def(cols, 'stop_loss_pct', 'null')
initial_stop_loss = get_column_def(cols, 'initial_stop_loss', '0.0')
initial_stop_loss_pct = get_column_def(cols, 'initial_stop_loss_pct', 'null')
stoploss_order_id = get_column_def(cols, 'stoploss_order_id', 'null')
stoploss_last_update = get_column_def(cols, 'stoploss_last_update', 'null')
max_rate = get_column_def(cols, 'max_rate', '0.0')
min_rate = get_column_def(cols, 'min_rate', 'null')
sell_reason = get_column_def(cols, 'sell_reason', 'null')
strategy = get_column_def(cols, 'strategy', 'null')
# If ticker-interval existed use that, else null.
if has_column(cols, 'ticker_interval'):
timeframe = get_column_def(cols, 'timeframe', 'ticker_interval')
else:
timeframe = get_column_def(cols, 'timeframe', 'null')
open_trade_price = get_column_def(cols, 'open_trade_price',
f'amount * open_rate * (1 + {fee_open})')
close_profit_abs = get_column_def(
cols, 'close_profit_abs',
f"(amount * close_rate * (1 - {fee_close})) - {open_trade_price}")
sell_order_status = get_column_def(cols, 'sell_order_status', 'null')
amount_requested = get_column_def(cols, 'amount_requested', 'amount')
# Schema migration necessary
engine.execute(f"alter table trades rename to {table_back_name}")
# drop indexes on backup table
for index in inspector.get_indexes(table_back_name):
engine.execute(f"drop index {index['name']}")
# let SQLAlchemy create the schema as required
_DECL_BASE.metadata.create_all(engine)
# Copy data back - following the correct schema
engine.execute(f"""insert into trades
(id, exchange, pair, is_open,
fee_open, fee_open_cost, fee_open_currency,
fee_close, fee_close_cost, fee_open_currency, open_rate,
open_rate_requested, close_rate, close_rate_requested, close_profit,
stake_amount, amount, amount_requested, open_date, close_date, open_order_id,
stop_loss, stop_loss_pct, initial_stop_loss, initial_stop_loss_pct,
stoploss_order_id, stoploss_last_update,
max_rate, min_rate, sell_reason, sell_order_status, strategy,
timeframe, open_trade_price, close_profit_abs
)
select id, lower(exchange),
case
when instr(pair, '_') != 0 then
substr(pair, instr(pair, '_') + 1) || '/' ||
substr(pair, 1, instr(pair, '_') - 1)
else pair
end
pair,
is_open, {fee_open} fee_open, {fee_open_cost} fee_open_cost,
{fee_open_currency} fee_open_currency, {fee_close} fee_close,
{fee_close_cost} fee_close_cost, {fee_close_currency} fee_close_currency,
open_rate, {open_rate_requested} open_rate_requested, close_rate,
{close_rate_requested} close_rate_requested, close_profit,
stake_amount, amount, {amount_requested}, open_date, close_date, open_order_id,
{stop_loss} stop_loss, {stop_loss_pct} stop_loss_pct,
{initial_stop_loss} initial_stop_loss,
{initial_stop_loss_pct} initial_stop_loss_pct,
{stoploss_order_id} stoploss_order_id, {stoploss_last_update} stoploss_last_update,
{max_rate} max_rate, {min_rate} min_rate, {sell_reason} sell_reason,
{sell_order_status} sell_order_status,
{strategy} strategy, {timeframe} timeframe,
{open_trade_price} open_trade_price, {close_profit_abs} close_profit_abs
from {table_back_name}
""")
# Reread columns - the above recreated the table!
inspector = inspect(engine)
cols = inspector.get_columns('trades')
def cleanup() -> None:
"""
Flushes all pending operations to disk.
@ -191,13 +90,117 @@ def clean_dry_run_db() -> None:
trade.open_order_id = None
class Order(_DECL_BASE):
"""
Order database model
Keeps a record of all orders placed on the exchange
One to many relationship with Trades:
- One trade can have many orders
- One Order can only be associated with one Trade
Mirrors CCXT Order structure
"""
__tablename__ = 'orders'
# Uniqueness should be ensured over pair, order_id
# its likely that order_id is unique per Pair on some exchanges.
__table_args__ = (UniqueConstraint('ft_pair', 'order_id', name="_order_pair_order_id"),)
id = Column(Integer, primary_key=True)
ft_trade_id = Column(Integer, ForeignKey('trades.id'), index=True)
trade = relationship("Trade", back_populates="orders")
ft_order_side = Column(String, nullable=False)
ft_pair = Column(String, nullable=False)
ft_is_open = Column(Boolean, nullable=False, default=True, index=True)
order_id = Column(String, nullable=False, index=True)
status = Column(String, nullable=True)
symbol = Column(String, nullable=True)
order_type = Column(String, nullable=True)
side = Column(String, nullable=True)
price = Column(Float, nullable=True)
amount = Column(Float, nullable=True)
filled = Column(Float, nullable=True)
remaining = Column(Float, nullable=True)
cost = Column(Float, nullable=True)
order_date = Column(DateTime, nullable=True, default=datetime.utcnow)
order_filled_date = Column(DateTime, nullable=True)
order_update_date = Column(DateTime, nullable=True)
def __repr__(self):
return (f'Order(id={self.id}, order_id={self.order_id}, trade_id={self.ft_trade_id}, '
f'side={self.side}, order_type={self.order_type}, status={self.status})')
def update_from_ccxt_object(self, order):
"""
Update Order from ccxt response
Only updates if fields are available from ccxt -
"""
if self.order_id != str(order['id']):
raise DependencyException("Order-id's don't match")
self.status = order.get('status', self.status)
self.symbol = order.get('symbol', self.symbol)
self.order_type = order.get('type', self.order_type)
self.side = order.get('side', self.side)
self.price = order.get('price', self.price)
self.amount = order.get('amount', self.amount)
self.filled = order.get('filled', self.filled)
self.remaining = order.get('remaining', self.remaining)
self.cost = order.get('cost', self.cost)
if 'timestamp' in order and order['timestamp'] is not None:
self.order_date = datetime.fromtimestamp(order['timestamp'] / 1000, tz=timezone.utc)
self.ft_is_open = True
if self.status in ('closed', 'canceled', 'cancelled'):
self.ft_is_open = False
if order.get('filled', 0) > 0:
self.order_filled_date = arrow.utcnow().datetime
self.order_update_date = arrow.utcnow().datetime
@staticmethod
def update_orders(orders: List['Order'], order: Dict[str, Any]):
"""
Get all non-closed orders - useful when trying to batch-update orders
"""
filtered_orders = [o for o in orders if o.order_id == order['id']]
if filtered_orders:
oobj = filtered_orders[0]
oobj.update_from_ccxt_object(order)
else:
logger.warning(f"Did not find order for {order['id']}.")
@staticmethod
def parse_from_ccxt_object(order: Dict[str, Any], pair: str, side: str) -> 'Order':
"""
Parse an order from a ccxt object and return a new order Object.
"""
o = Order(order_id=str(order['id']), ft_order_side=side, ft_pair=pair)
o.update_from_ccxt_object(order)
return o
@staticmethod
def get_open_orders() -> List['Order']:
"""
"""
return Order.query.filter(Order.ft_is_open.is_(True)).all()
class Trade(_DECL_BASE):
"""
Class used to define a trade structure
Trade database model.
Also handles updating and querying trades
"""
__tablename__ = 'trades'
id = Column(Integer, primary_key=True)
orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
exchange = Column(String, nullable=False)
pair = Column(String, nullable=False, index=True)
is_open = Column(Boolean, nullable=False, default=True, index=True)
@ -380,15 +383,18 @@ class Trade(_DECL_BASE):
self.open_rate = Decimal(safe_value_fallback(order, 'average', 'price'))
self.amount = Decimal(safe_value_fallback(order, 'filled', 'amount'))
self.recalc_open_trade_price()
logger.info('%s_BUY has been fulfilled for %s.', order_type.upper(), self)
if self.is_open:
logger.info(f'{order_type.upper()}_BUY has been fulfilled for {self}.')
self.open_order_id = None
elif order_type in ('market', 'limit') and order['side'] == 'sell':
if self.is_open:
logger.info(f'{order_type.upper()}_SELL has been fulfilled for {self}.')
self.close(safe_value_fallback(order, 'average', 'price'))
logger.info('%s_SELL has been fulfilled for %s.', order_type.upper(), self)
elif order_type in ('stop_loss_limit', 'stop-loss', 'stop'):
self.stoploss_order_id = None
self.close_rate_requested = self.stop_loss
logger.info('%s is hit for %s.', order_type.upper(), self)
if self.is_open:
logger.info(f'{order_type.upper()} is hit for {self}.')
self.close(order['average'])
else:
raise ValueError(f'Unknown order type: {order_type}')
@ -402,7 +408,7 @@ class Trade(_DECL_BASE):
self.close_rate = Decimal(rate)
self.close_profit = self.calc_profit_ratio()
self.close_profit_abs = self.calc_profit()
self.close_date = datetime.utcnow()
self.close_date = self.close_date or datetime.utcnow()
self.is_open = False
self.sell_order_status = 'closed'
self.open_order_id = None
@ -440,6 +446,17 @@ class Trade(_DECL_BASE):
else:
return False
def update_order(self, order: Dict) -> None:
Order.update_orders(self.orders, order)
def delete(self) -> None:
for order in self.orders:
Order.session.delete(order)
Trade.session.delete(self)
Trade.session.flush()
def _calc_open_trade_price(self) -> float:
"""
Calculate the open_rate including open_fee.
@ -506,6 +523,21 @@ class Trade(_DECL_BASE):
profit_ratio = (close_trade_price / self.open_trade_price) - 1
return float(f"{profit_ratio:.8f}")
def select_order(self, order_side: str, is_open: Optional[bool]) -> Optional[Order]:
"""
Finds latest order for this orderside and status
:param order_side: Side of the order (either 'buy' or 'sell')
:param is_open: Only search for open orders?
:return: latest Order object if it exists, else None
"""
orders = [o for o in self.orders if o.side == order_side]
if is_open is not None:
orders = [o for o in orders if o.ft_is_open == is_open]
if len(orders) > 0:
return orders[-1]
else:
return None
@staticmethod
def get_trades(trade_filter=None) -> Query:
"""
@ -537,6 +569,26 @@ class Trade(_DECL_BASE):
"""
return Trade.get_trades(Trade.open_order_id.isnot(None)).all()
@staticmethod
def get_open_trades_without_assigned_fees():
"""
Returns all open trades which don't have open fees set correctly
"""
return Trade.get_trades([Trade.fee_open_currency.is_(None),
Trade.orders.any(),
Trade.is_open.is_(True),
]).all()
@staticmethod
def get_sold_trades_without_assigned_fees():
"""
Returns all closed trades which don't have fees set correctly
"""
return Trade.get_trades([Trade.fee_close_currency.is_(None),
Trade.orders.any(),
Trade.is_open.is_(False),
]).all()
@staticmethod
def total_open_trades_stakes() -> float:
"""

2
freqtrade/resolvers/iresolver.py
View File

@ -59,7 +59,7 @@ class IResolver:
module = importlib.util.module_from_spec(spec)
try:
spec.loader.exec_module(module) # type: ignore # importlib does not use typehints
except (ModuleNotFoundError, SyntaxError) as err:
except (ModuleNotFoundError, SyntaxError, ImportError) as err:
# Catch errors in case a specific module is not installed
logger.warning(f"Could not import {module_path} due to '{err}'")
if enum_failed:

13
freqtrade/rpc/api_server.py
View File

@ -17,8 +17,9 @@ from werkzeug.serving import make_server
from freqtrade.__init__ import __version__
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.rpc.rpc import RPC, RPCException
from freqtrade.persistence import Trade
from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
from freqtrade.rpc.rpc import RPC, RPCException
logger = logging.getLogger(__name__)
@ -70,6 +71,11 @@ def rpc_catch_errors(func: Callable[..., Any]):
return func_wrapper
def shutdown_session(exception=None):
# Remove scoped session
Trade.session.remove()
class ApiServer(RPC):
"""
This class runs api server and provides rpc.rpc functionality to it
@ -104,6 +110,8 @@ class ApiServer(RPC):
self.jwt = JWTManager(self.app)
self.app.json_encoder = ArrowJSONEncoder
self.app.teardown_appcontext(shutdown_session)
# Register application handling
self.register_rest_rpc_urls()
@ -214,9 +222,6 @@ class ApiServer(RPC):
self.app.add_url_rule(f'{BASE_URI}/forcesell', 'forcesell', view_func=self._forcesell,
methods=['POST'])
# TODO: Implement the following
# help (?)
@require_login
def page_not_found(self, error):
"""

43
freqtrade/rpc/rpc.py
View File

@ -11,6 +11,7 @@ from typing import Any, Dict, List, Optional, Tuple, Union
import arrow
from numpy import NAN, mean
from freqtrade.constants import CANCEL_REASON
from freqtrade.exceptions import ExchangeError, PricingError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_msecs
from freqtrade.loggers import bufferHandler
@ -26,7 +27,7 @@ logger = logging.getLogger(__name__)
class RPCMessageType(Enum):
STATUS_NOTIFICATION = 'status'
WARNING_NOTIFICATION = 'warning'
CUSTOM_NOTIFICATION = 'custom'
STARTUP_NOTIFICATION = 'startup'
BUY_NOTIFICATION = 'buy'
BUY_CANCEL_NOTIFICATION = 'buy_cancel'
SELL_NOTIFICATION = 'sell'
@ -35,6 +36,9 @@ class RPCMessageType(Enum):
def __repr__(self):
return self.value
def __str__(self):
return self.value
class RPCException(Exception):
"""
@ -223,7 +227,8 @@ class RPC:
Trade.close_date >= profitday,
Trade.close_date < (profitday + timedelta(days=1))
]).order_by(Trade.close_date).all()
curdayprofit = sum(trade.close_profit_abs for trade in trades)
curdayprofit = sum(
trade.close_profit_abs for trade in trades if trade.close_profit_abs is not None)
profit_days[profitday] = {
'amount': curdayprofit,
'trades': len(trades)
@ -434,7 +439,7 @@ class RPC:
def _rpc_reload_config(self) -> Dict[str, str]:
""" Handler for reload_config. """
self._freqtrade.state = State.RELOAD_CONFIG
return {'status': 'reloading config ...'}
return {'status': 'Reloading config ...'}
def _rpc_stopbuy(self) -> Dict[str, str]:
"""
@ -453,29 +458,22 @@ class RPC:
"""
def _exec_forcesell(trade: Trade) -> None:
# Check if there is there is an open order
fully_canceled = False
if trade.open_order_id:
order = self._freqtrade.exchange.fetch_order(trade.open_order_id, trade.pair)
# Cancel open LIMIT_BUY orders and close trade
if order and order['status'] == 'open' \
and order['type'] == 'limit' \
and order['side'] == 'buy':
self._freqtrade.exchange.cancel_order(trade.open_order_id, trade.pair)
trade.close(order.get('price') or trade.open_rate)
# Do the best effort, if we don't know 'filled' amount, don't try selling
if order['filled'] is None:
return
trade.amount = order['filled']
if order['side'] == 'buy':
fully_canceled = self._freqtrade.handle_cancel_buy(
trade, order, CANCEL_REASON['FORCE_SELL'])
# Ignore trades with an attached LIMIT_SELL order
if order and order['status'] == 'open' \
and order['type'] == 'limit' \
and order['side'] == 'sell':
return
if order['side'] == 'sell':
# Cancel order - so it is placed anew with a fresh price.
self._freqtrade.handle_cancel_sell(trade, order, CANCEL_REASON['FORCE_SELL'])
# Get current rate and execute sell
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
self._freqtrade.execute_sell(trade, current_rate, SellType.FORCE_SELL)
if not fully_canceled:
# Get current rate and execute sell
current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
self._freqtrade.execute_sell(trade, current_rate, SellType.FORCE_SELL)
# ---- EOF def _exec_forcesell ----
if self._freqtrade.state != State.RUNNING:
@ -567,8 +565,7 @@ class RPC:
except (ExchangeError):
pass
Trade.session.delete(trade)
Trade.session.flush()
trade.delete()
self._freqtrade.wallets.update()
return {
'result': 'success',

6
freqtrade/rpc/rpc_manager.py
View File

@ -59,7 +59,7 @@ class RPCManager:
try:
mod.send_msg(msg)
except NotImplementedError:
logger.error(f"Message type {msg['type']} not implemented by handler {mod.name}.")
logger.error(f"Message type '{msg['type']}' not implemented by handler {mod.name}.")
def startup_messages(self, config: Dict[str, Any], pairlist) -> None:
if config['dry_run']:
@ -76,7 +76,7 @@ class RPCManager:
exchange_name = config['exchange']['name']
strategy_name = config.get('strategy', '')
self.send_msg({
'type': RPCMessageType.CUSTOM_NOTIFICATION,
'type': RPCMessageType.STARTUP_NOTIFICATION,
'status': f'*Exchange:* `{exchange_name}`\n'
f'*Stake per trade:* `{stake_amount} {stake_currency}`\n'
f'*Minimum ROI:* `{minimal_roi}`\n'
@ -85,7 +85,7 @@ class RPCManager:
f'*Strategy:* `{strategy_name}`'
})
self.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'type': RPCMessageType.STARTUP_NOTIFICATION,
'status': f'Searching for {stake_currency} pairs to buy and sell '
f'based on {pairlist.short_desc()}'
})

22
freqtrade/rpc/telegram.py
View File

@ -132,6 +132,13 @@ class Telegram(RPC):
def send_msg(self, msg: Dict[str, Any]) -> None:
""" Send a message to telegram channel """
noti = self._config['telegram'].get('notification_settings', {}
).get(str(msg['type']), 'on')
if noti == 'off':
logger.info(f"Notification '{msg['type']}' not sent.")
# Notification disabled
return
if msg['type'] == RPCMessageType.BUY_NOTIFICATION:
if self._fiat_converter:
msg['stake_amount_fiat'] = self._fiat_converter.convert_amount(
@ -151,7 +158,7 @@ class Telegram(RPC):
elif msg['type'] == RPCMessageType.BUY_CANCEL_NOTIFICATION:
message = ("\N{WARNING SIGN} *{exchange}:* "
"Cancelling Open Buy Order for {pair}".format(**msg))
"Cancelling open buy Order for {pair}. Reason: {reason}.".format(**msg))
elif msg['type'] == RPCMessageType.SELL_NOTIFICATION:
msg['amount'] = round(msg['amount'], 8)
@ -190,13 +197,13 @@ class Telegram(RPC):
elif msg['type'] == RPCMessageType.WARNING_NOTIFICATION:
message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg)
elif msg['type'] == RPCMessageType.CUSTOM_NOTIFICATION:
elif msg['type'] == RPCMessageType.STARTUP_NOTIFICATION:
message = '{status}'.format(**msg)
else:
raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
self._send_msg(message)
self._send_msg(message, disable_notification=(noti == 'silent'))
def _get_sell_emoji(self, msg):
"""
@ -773,7 +780,8 @@ class Telegram(RPC):
f"*Current state:* `{val['state']}`"
)
def _send_msg(self, msg: str, parse_mode: ParseMode = ParseMode.MARKDOWN) -> None:
def _send_msg(self, msg: str, parse_mode: ParseMode = ParseMode.MARKDOWN,
disable_notification: bool = False) -> None:
"""
Send given markdown message
:param msg: message
@ -794,7 +802,8 @@ class Telegram(RPC):
self._config['telegram']['chat_id'],
text=msg,
parse_mode=parse_mode,
reply_markup=reply_markup
reply_markup=reply_markup,
disable_notification=disable_notification,
)
except NetworkError as network_err:
# Sometimes the telegram server resets the current connection,
@ -807,7 +816,8 @@ class Telegram(RPC):
self._config['telegram']['chat_id'],
text=msg,
parse_mode=parse_mode,
reply_markup=reply_markup
reply_markup=reply_markup,
disable_notification=disable_notification,
)
except TelegramError as telegram_err:
logger.warning(

4
freqtrade/rpc/webhook.py
View File

@ -48,13 +48,13 @@ class Webhook(RPC):
elif msg['type'] == RPCMessageType.SELL_CANCEL_NOTIFICATION:
valuedict = self._config['webhook'].get('webhooksellcancel', None)
elif msg['type'] in (RPCMessageType.STATUS_NOTIFICATION,
RPCMessageType.CUSTOM_NOTIFICATION,
RPCMessageType.STARTUP_NOTIFICATION,
RPCMessageType.WARNING_NOTIFICATION):
valuedict = self._config['webhook'].get('webhookstatus', None)
else:
raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
if not valuedict:
logger.info("Message type %s not configured for webhooks", msg['type'])
logger.info("Message type '%s' not configured for webhooks", msg['type'])
return
payload = {key: value.format(**msg) for (key, value) in valuedict.items()}

6
freqtrade/strategy/__init__.py
View File

@ -1 +1,5 @@
from freqtrade.strategy.interface import IStrategy # noqa: F401
# flake8: noqa: F401
from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_prev_date,
timeframe_to_seconds, timeframe_to_next_date, timeframe_to_msecs)
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_helper import merge_informative_pair

48
freqtrade/strategy/strategy_helper.py Normal file
View File

@ -0,0 +1,48 @@
import pandas as pd
from freqtrade.exchange import timeframe_to_minutes
def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
timeframe: str, timeframe_inf: str, ffill: bool = True) -> pd.DataFrame:
"""
Correctly merge informative samples to the original dataframe, avoiding lookahead bias.
Since dates are candle open dates, merging a 15m candle that starts at 15:00, and a
1h candle that starts at 15:00 will result in all candles to know the close at 16:00
which they should not know.
Moves the date of the informative pair by 1 time interval forward.
This way, the 14:00 1h candle is merged to 15:00 15m candle, since the 14:00 1h candle is the
last candle that's closed at 15:00, 15:15, 15:30 or 15:45.
Assuming inf_tf = '1d' - then the resulting columns will be:
date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
:param dataframe: Original dataframe
:param informative: Informative pair, most likely loaded via dp.get_pair_dataframe
:param timeframe: Timeframe of the original pair sample.
:param timeframe_inf: Timeframe of the informative pair sample.
:param ffill: Forwardfill missing values - optional but usually required
"""
minutes_inf = timeframe_to_minutes(timeframe_inf)
minutes = timeframe_to_minutes(timeframe)
if minutes >= minutes_inf:
# No need to forwardshift if the timeframes are identical
informative['date_merge'] = informative["date"]
else:
informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes_inf, 'm')
# Rename columns to be unique
informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
# Combine the 2 dataframes
# all indicators on the informative sample MUST be calculated before this point
dataframe = pd.merge(dataframe, informative, left_on='date',
right_on=f'date_merge_{timeframe_inf}', how='left')
dataframe = dataframe.drop(f'date_merge_{timeframe_inf}', axis=1)
if ffill:
dataframe = dataframe.ffill()
return dataframe

5
freqtrade/wallets.py
View File

@ -2,6 +2,7 @@
""" Wallet """
import logging
from copy import deepcopy
from typing import Any, Dict, NamedTuple
import arrow
@ -93,6 +94,10 @@ class Wallets:
balances[currency].get('used', None),
balances[currency].get('total', None)
)
# Remove currencies no longer in get_balances output
for currency in deepcopy(self._wallets):
if currency not in balances:
del self._wallets[currency]
def update(self, require_update: bool = True) -> None:
"""

16
mkdocs.yml
View File

@ -1,8 +1,11 @@
site_name: Freqtrade
nav:
- Home: index.md
- Installation Docker: docker.md
- Installation: installation.md
- Quickstart with Docker: docker_quickstart.md
- Installation:
- Docker without docker-compose: docker.md
- Linux/MacOS/Raspberry: installation.md
- Windows: windows_installation.md
- Freqtrade Basics: bot-basics.md
- Configuration: configuration.md
- Strategy Customization: strategy-customization.md
@ -39,13 +42,19 @@ theme:
accent: 'tear'
extra_css:
- 'stylesheets/ft.extra.css'
extra_javascript:
- javascripts/config.js
- https://polyfill.io/v3/polyfill.min.js?features=es6
- https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
markdown_extensions:
- admonition
- footnotes
- codehilite:
guess_lang: false
- toc:
permalink: true
- pymdownx.arithmatex
- pymdownx.arithmatex:
generic: true
- pymdownx.caret
- pymdownx.critic
- pymdownx.details
@ -53,6 +62,7 @@ markdown_extensions:
- pymdownx.magiclink
- pymdownx.mark
- pymdownx.smartsymbols
- pymdownx.tabbed
- pymdownx.superfences
- pymdownx.tasklist:
custom_checkbox: true

35
requirements-common.txt
View File

@ -1,35 +0,0 @@
# requirements without requirements installable via conda
# mainly used for Raspberry pi installs
ccxt==1.33.52
SQLAlchemy==1.3.19
python-telegram-bot==12.8
arrow==0.16.0
cachetools==4.1.1
requests==2.24.0
urllib3==1.25.10
wrapt==1.12.1
jsonschema==3.2.0
TA-Lib==0.4.18
tabulate==0.8.7
pycoingecko==1.3.0
jinja2==2.11.2
# find first, C search in arrays
py_find_1st==1.1.4
# Load ticker files 30% faster
python-rapidjson==0.9.1
# Notify systemd
sdnotify==0.3.2
# Api server
flask==1.1.2
flask-jwt-extended==3.24.1
flask-cors==3.0.8
# Support for colorized terminal output
colorama==0.4.3
# Building config files interactively
questionary==1.5.2
prompt-toolkit==3.0.6

6
requirements-dev.txt
View File

@ -8,11 +8,11 @@ flake8==3.8.3
flake8-type-annotations==0.1.0
flake8-tidy-imports==4.1.0
mypy==0.782
pytest==6.0.1
pytest==6.0.2
pytest-asyncio==0.14.0
pytest-cov==2.10.1
pytest-mock==3.3.0
pytest-mock==3.3.1
pytest-random-order==1.0.4
# Convert jupyter notebooks to markdown documents
nbconvert==5.6.1
nbconvert==6.0.4

6
requirements-hyperopt.txt
View File

@ -3,8 +3,8 @@
# Required for hyperopt
scipy==1.5.2
scikit-learn==0.23.1
scikit-optimize==0.7.4
scikit-learn==0.23.2
scikit-optimize==0.8.1
filelock==3.0.12
joblib==0.16.0
progressbar2==3.51.4
progressbar2==3.53.1

2
requirements-plot.txt
View File

@ -1,5 +1,5 @@
# Include all requirements to run the bot.
-r requirements.txt
plotly==4.9.0
plotly==4.10.0

41
requirements.txt
View File

@ -1,5 +1,38 @@
# Load common requirements
-r requirements-common.txt
numpy==1.19.2
pandas==1.1.2
numpy==1.19.1
pandas==1.1.1
ccxt==1.34.40
SQLAlchemy==1.3.19
python-telegram-bot==12.8
arrow==0.16.0
cachetools==4.1.1
requests==2.24.0
urllib3==1.25.10
wrapt==1.12.1
jsonschema==3.2.0
TA-Lib==0.4.18
tabulate==0.8.7
pycoingecko==1.3.0
jinja2==2.11.2
tables==3.6.1
blosc==1.9.2
# find first, C search in arrays
py_find_1st==1.1.4
# Load ticker files 30% faster
python-rapidjson==0.9.1
# Notify systemd
sdnotify==0.3.2
# Api server
flask==1.1.2
flask-jwt-extended==3.24.1
flask-cors==3.0.9
# Support for colorized terminal output
colorama==0.4.3
# Building config files interactively
questionary==1.5.2
prompt-toolkit==3.0.7

5
setup.py
View File

@ -62,7 +62,7 @@ setup(name='freqtrade',
setup_requires=['pytest-runner', 'numpy'],
tests_require=['pytest', 'pytest-asyncio', 'pytest-cov', 'pytest-mock', ],
install_requires=[
# from requirements-common.txt
# from requirements.txt
'ccxt>=1.24.96',
'SQLAlchemy',
'python-telegram-bot',
@ -82,9 +82,10 @@ setup(name='freqtrade',
'jinja2',
'questionary',
'prompt-toolkit',
# from requirements.txt
'numpy',
'pandas',
'tables',
'blosc',
],
extras_require={
'api': api,

14
setup.sh
View File

@ -120,13 +120,13 @@ function update() {
updateenv
}
# Reset Develop or Master branch
# Reset Develop or Stable branch
function reset() {
echo "----------------------------"
echo "Reseting branch and virtual env"
echo "----------------------------"
if [ "1" == $(git branch -vv |grep -cE "\* develop|\* master") ]
if [ "1" == $(git branch -vv |grep -cE "\* develop|\* stable") ]
then
read -p "Reset git branch? (This will remove all changes you made!) [y/N]? "
@ -138,14 +138,14 @@ function reset() {
then
echo "- Hard resetting of 'develop' branch."
git reset --hard origin/develop
elif [ "1" == $(git branch -vv |grep -c "* master") ]
elif [ "1" == $(git branch -vv |grep -c "* stable") ]
then
echo "- Hard resetting of 'master' branch."
git reset --hard origin/master
echo "- Hard resetting of 'stable' branch."
git reset --hard origin/stable
fi
fi
else
echo "Reset ignored because you are not on 'master' or 'develop'."
echo "Reset ignored because you are not on 'stable' or 'develop'."
fi
if [ -d ".env" ]; then
@ -270,7 +270,7 @@ function help() {
echo "usage:"
echo " -i,--install Install freqtrade from scratch"
echo " -u,--update Command git pull to update."
echo " -r,--reset Hard reset your develop/master branch."
echo " -r,--reset Hard reset your develop/stable branch."
echo " -c,--config Easy config generator (Will override your existing file)."
echo " -p,--plot Install dependencies for Plotting scripts."
}

48
tests/commands/test_commands.py
View File

@ -2,6 +2,7 @@ import re
from pathlib import Path
from unittest.mock import MagicMock, PropertyMock
import arrow
import pytest
from freqtrade.commands import (start_convert_data, start_create_userdir,
@ -18,6 +19,7 @@ from freqtrade.state import RunMode
from tests.conftest import (create_mock_trades, get_args, log_has, log_has_re,
patch_exchange,
patched_configuration_load_config_file)
from tests.conftest_trades import MOCK_TRADE_COUNT
def test_setup_utils_configuration():
@ -552,6 +554,50 @@ def test_download_data_keyboardInterrupt(mocker, caplog, markets):
assert dl_mock.call_count == 1
def test_download_data_timerange(mocker, caplog, markets):
dl_mock = mocker.patch('freqtrade.commands.data_commands.refresh_backtest_ohlcv_data',
MagicMock(return_value=["ETH/BTC", "XRP/BTC"]))
patch_exchange(mocker)
mocker.patch(
'freqtrade.exchange.Exchange.markets', PropertyMock(return_value=markets)
)
args = [
"download-data",
"--exchange", "binance",
"--pairs", "ETH/BTC", "XRP/BTC",
"--days", "20",
"--timerange", "20200101-"
]
with pytest.raises(OperationalException,
match=r"--days and --timerange are mutually.*"):
start_download_data(get_args(args))
assert dl_mock.call_count == 0
args = [
"download-data",
"--exchange", "binance",
"--pairs", "ETH/BTC", "XRP/BTC",
"--days", "20",
]
start_download_data(get_args(args))
assert dl_mock.call_count == 1
# 20days ago
days_ago = arrow.get(arrow.utcnow().shift(days=-20).date()).timestamp
assert dl_mock.call_args_list[0][1]['timerange'].startts == days_ago
dl_mock.reset_mock()
args = [
"download-data",
"--exchange", "binance",
"--pairs", "ETH/BTC", "XRP/BTC",
"--timerange", "20200101-"
]
start_download_data(get_args(args))
assert dl_mock.call_count == 1
assert dl_mock.call_args_list[0][1]['timerange'].startts == arrow.Arrow(2020, 1, 1).timestamp
def test_download_data_no_markets(mocker, caplog):
dl_mock = mocker.patch('freqtrade.commands.data_commands.refresh_backtest_ohlcv_data',
MagicMock(return_value=["ETH/BTC", "XRP/BTC"]))
@ -1116,7 +1162,7 @@ def test_show_trades(mocker, fee, capsys, caplog):
pargs = get_args(args)
pargs['config'] = None
start_show_trades(pargs)
assert log_has("Printing 4 Trades: ", caplog)
assert log_has(f"Printing {MOCK_TRADE_COUNT} Trades: ", caplog)
captured = capsys.readouterr()
assert "Trade(id=1" in captured.out
assert "Trade(id=2" in captured.out

100
tests/conftest.py
View File

@ -22,6 +22,8 @@ from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver
from freqtrade.worker import Worker
from tests.conftest_trades import (mock_trade_1, mock_trade_2, mock_trade_3,
mock_trade_4, mock_trade_5, mock_trade_6)
logging.getLogger('').setLevel(logging.INFO)
@ -172,64 +174,22 @@ def create_mock_trades(fee):
Create some fake trades ...
"""
# Simulate dry_run entries
trade = Trade(
pair='ETH/BTC',
stake_amount=0.001,
amount=123.0,
amount_requested=123.0,
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.123,
exchange='bittrex',
open_order_id='dry_run_buy_12345',
strategy='DefaultStrategy',
)
trade = mock_trade_1(fee)
Trade.session.add(trade)
trade = Trade(
pair='ETC/BTC',
stake_amount=0.001,
amount=123.0,
amount_requested=123.0,
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.123,
close_rate=0.128,
close_profit=0.005,
exchange='bittrex',
is_open=False,
open_order_id='dry_run_sell_12345',
strategy='DefaultStrategy',
)
trade = mock_trade_2(fee)
Trade.session.add(trade)
trade = Trade(
pair='XRP/BTC',
stake_amount=0.001,
amount=123.0,
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.05,
close_rate=0.06,
close_profit=0.01,
exchange='bittrex',
is_open=False,
)
trade = mock_trade_3(fee)
Trade.session.add(trade)
# Simulate prod entry
trade = Trade(
pair='ETC/BTC',
stake_amount=0.001,
amount=123.0,
amount_requested=124.0,
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.123,
exchange='bittrex',
open_order_id='prod_buy_12345',
strategy='DefaultStrategy',
)
trade = mock_trade_4(fee)
Trade.session.add(trade)
trade = mock_trade_5(fee)
Trade.session.add(trade)
trade = mock_trade_6(fee)
Trade.session.add(trade)
@ -823,22 +783,32 @@ def markets_empty():
@pytest.fixture(scope='function')
def limit_buy_order():
def limit_buy_order_open():
return {
'id': 'mocked_limit_buy',
'type': 'limit',
'side': 'buy',
'symbol': 'mocked',
'datetime': arrow.utcnow().isoformat(),
'timestamp': arrow.utcnow().timestamp,
'price': 0.00001099,
'amount': 90.99181073,
'filled': 90.99181073,
'filled': 0.0,
'cost': 0.0009999,
'remaining': 0.0,
'status': 'closed'
'remaining': 90.99181073,
'status': 'open'
}
@pytest.fixture(scope='function')
def limit_buy_order(limit_buy_order_open):
order = deepcopy(limit_buy_order_open)
order['status'] = 'closed'
order['filled'] = order['amount']
order['remaining'] = 0.0
return order
@pytest.fixture(scope='function')
def market_buy_order():
return {
@ -1021,21 +991,31 @@ def limit_buy_order_canceled_empty(request):
@pytest.fixture
def limit_sell_order():
def limit_sell_order_open():
return {
'id': 'mocked_limit_sell',
'type': 'limit',
'side': 'sell',
'pair': 'mocked',
'datetime': arrow.utcnow().isoformat(),
'timestamp': arrow.utcnow().timestamp,
'price': 0.00001173,
'amount': 90.99181073,
'filled': 90.99181073,
'remaining': 0.0,
'status': 'closed'
'filled': 0.0,
'remaining': 90.99181073,
'status': 'open'
}
@pytest.fixture
def limit_sell_order(limit_sell_order_open):
order = deepcopy(limit_sell_order_open)
order['remaining'] = 0.0
order['filled'] = order['amount']
order['status'] = 'closed'
return order
@pytest.fixture
def order_book_l2():
return MagicMock(return_value={

279
tests/conftest_trades.py Normal file
View File

@ -0,0 +1,279 @@
from freqtrade.persistence.models import Order, Trade
MOCK_TRADE_COUNT = 6
def mock_order_1():
return {
'id': '1234',
'symbol': 'ETH/BTC',
'status': 'closed',
'side': 'buy',
'type': 'limit',
'price': 0.123,
'amount': 123.0,
'filled': 123.0,
'remaining': 0.0,
}
def mock_trade_1(fee):
trade = Trade(
pair='ETH/BTC',
stake_amount=0.001,
amount=123.0,
amount_requested=123.0,
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.123,
exchange='bittrex',
open_order_id='dry_run_buy_12345',
strategy='DefaultStrategy',
)
o = Order.parse_from_ccxt_object(mock_order_1(), 'ETH/BTC', 'buy')
trade.orders.append(o)
return trade
def mock_order_2():
return {
'id': '1235',
'symbol': 'ETC/BTC',
'status': 'closed',
'side': 'buy',
'type': 'limit',
'price': 0.123,
'amount': 123.0,
'filled': 123.0,
'remaining': 0.0,
}
def mock_order_2_sell():
return {
'id': '12366',
'symbol': 'ETC/BTC',
'status': 'closed',
'side': 'sell',
'type': 'limit',
'price': 0.128,
'amount': 123.0,
'filled': 123.0,
'remaining': 0.0,
}
def mock_trade_2(fee):
"""
Closed trade...
"""
trade = Trade(
pair='ETC/BTC',
stake_amount=0.001,
amount=123.0,
amount_requested=123.0,
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.123,
close_rate=0.128,
close_profit=0.005,
exchange='bittrex',
is_open=False,
open_order_id='dry_run_sell_12345',
strategy='DefaultStrategy',
)
o = Order.parse_from_ccxt_object(mock_order_2(), 'ETC/BTC', 'buy')
trade.orders.append(o)
o = Order.parse_from_ccxt_object(mock_order_2_sell(), 'ETC/BTC', 'sell')
trade.orders.append(o)
return trade
def mock_order_3():
return {
'id': '41231a12a',
'symbol': 'XRP/BTC',
'status': 'closed',
'side': 'buy',
'type': 'limit',
'price': 0.05,
'amount': 123.0,
'filled': 123.0,
'remaining': 0.0,
}
def mock_order_3_sell():
return {
'id': '41231a666a',
'symbol': 'XRP/BTC',
'status': 'closed',
'side': 'sell',
'type': 'stop_loss_limit',
'price': 0.06,
'average': 0.06,
'amount': 123.0,
'filled': 123.0,
'remaining': 0.0,
}
def mock_trade_3(fee):
"""
Closed trade
"""
trade = Trade(
pair='XRP/BTC',
stake_amount=0.001,
amount=123.0,
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.05,
close_rate=0.06,
close_profit=0.01,
exchange='bittrex',
is_open=False,
)
o = Order.parse_from_ccxt_object(mock_order_3(), 'XRP/BTC', 'buy')
trade.orders.append(o)
o = Order.parse_from_ccxt_object(mock_order_3_sell(), 'XRP/BTC', 'sell')
trade.orders.append(o)
return trade
def mock_order_4():
return {
'id': 'prod_buy_12345',
'symbol': 'ETC/BTC',
'status': 'open',
'side': 'buy',
'type': 'limit',
'price': 0.123,
'amount': 123.0,
'filled': 0.0,
'remaining': 123.0,
}
def mock_trade_4(fee):
"""
Simulate prod entry
"""
trade = Trade(
pair='ETC/BTC',
stake_amount=0.001,
amount=123.0,
amount_requested=124.0,
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.123,
exchange='bittrex',
open_order_id='prod_buy_12345',
strategy='DefaultStrategy',
)
o = Order.parse_from_ccxt_object(mock_order_4(), 'ETC/BTC', 'buy')
trade.orders.append(o)
return trade
def mock_order_5():
return {
'id': 'prod_buy_3455',
'symbol': 'XRP/BTC',
'status': 'closed',
'side': 'buy',
'type': 'limit',
'price': 0.123,
'amount': 123.0,
'filled': 123.0,
'remaining': 0.0,
}
def mock_order_5_stoploss():
return {
'id': 'prod_stoploss_3455',
'symbol': 'XRP/BTC',
'status': 'open',
'side': 'sell',
'type': 'stop_loss_limit',
'price': 0.123,
'amount': 123.0,
'filled': 0.0,
'remaining': 123.0,
}
def mock_trade_5(fee):
"""
Simulate prod entry with stoploss
"""
trade = Trade(
pair='XRP/BTC',
stake_amount=0.001,
amount=123.0,
amount_requested=124.0,
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.123,
exchange='bittrex',
strategy='SampleStrategy',
stoploss_order_id='prod_stoploss_3455'
)
o = Order.parse_from_ccxt_object(mock_order_5(), 'XRP/BTC', 'buy')
trade.orders.append(o)
o = Order.parse_from_ccxt_object(mock_order_5_stoploss(), 'XRP/BTC', 'stoploss')
trade.orders.append(o)
return trade
def mock_order_6():
return {
'id': 'prod_buy_6',
'symbol': 'LTC/BTC',
'status': 'closed',
'side': 'buy',
'type': 'limit',
'price': 0.15,
'amount': 2.0,
'filled': 2.0,
'remaining': 0.0,
}
def mock_order_6_sell():
return {
'id': 'prod_sell_6',
'symbol': 'LTC/BTC',
'status': 'open',
'side': 'sell',
'type': 'limit',
'price': 0.20,
'amount': 2.0,
'filled': 0.0,
'remaining': 2.0,
}
def mock_trade_6(fee):
"""
Simulate prod entry with open sell order
"""
trade = Trade(
pair='LTC/BTC',
stake_amount=0.001,
amount=2.0,
amount_requested=2.0,
fee_open=fee.return_value,
fee_close=fee.return_value,
open_rate=0.15,
exchange='bittrex',
strategy='SampleStrategy',
open_order_id="prod_sell_6",
)
o = Order.parse_from_ccxt_object(mock_order_6(), 'LTC/BTC', 'buy')
trade.orders.append(o)
o = Order.parse_from_ccxt_object(mock_order_6_sell(), 'LTC/BTC', 'sell')
trade.orders.append(o)
return trade

3
tests/data/test_btanalysis.py
View File

@ -20,6 +20,7 @@ from freqtrade.data.btanalysis import (BT_DATA_COLUMNS,
from freqtrade.data.history import load_data, load_pair_history
from freqtrade.optimize.backtesting import BacktestResult
from tests.conftest import create_mock_trades
from tests.conftest_trades import MOCK_TRADE_COUNT
def test_get_latest_backtest_filename(testdatadir, mocker):
@ -110,7 +111,7 @@ def test_load_trades_from_db(default_conf, fee, mocker):
trades = load_trades_from_db(db_url=default_conf['db_url'])
assert init_mock.call_count == 1
assert len(trades) == 4
assert len(trades) == MOCK_TRADE_COUNT
assert isinstance(trades, DataFrame)
assert "pair" in trades.columns
assert "open_date" in trades.columns

151
tests/data/test_history.py
View File

@ -12,7 +12,9 @@ from pandas import DataFrame
from pandas.testing import assert_frame_equal
from freqtrade.configuration import TimeRange
from freqtrade.constants import AVAILABLE_DATAHANDLERS
from freqtrade.data.converter import ohlcv_to_dataframe
from freqtrade.data.history.hdf5datahandler import HDF5DataHandler
from freqtrade.data.history.history_utils import (
_download_pair_history, _download_trades_history,
_load_cached_data_for_updating, convert_trades_to_ohlcv, get_timerange,
@ -620,7 +622,7 @@ def test_convert_trades_to_ohlcv(mocker, default_conf, testdatadir, caplog):
_clean_test_file(file5)
def test_jsondatahandler_ohlcv_get_pairs(testdatadir):
def test_datahandler_ohlcv_get_pairs(testdatadir):
pairs = JsonDataHandler.ohlcv_get_pairs(testdatadir, '5m')
# Convert to set to avoid failures due to sorting
assert set(pairs) == {'UNITTEST/BTC', 'XLM/BTC', 'ETH/BTC', 'TRX/BTC', 'LTC/BTC',
@ -630,8 +632,11 @@ def test_jsondatahandler_ohlcv_get_pairs(testdatadir):
pairs = JsonGzDataHandler.ohlcv_get_pairs(testdatadir, '8m')
assert set(pairs) == {'UNITTEST/BTC'}
pairs = HDF5DataHandler.ohlcv_get_pairs(testdatadir, '5m')
assert set(pairs) == {'UNITTEST/BTC'}
def test_jsondatahandler_ohlcv_get_available_data(testdatadir):
def test_datahandler_ohlcv_get_available_data(testdatadir):
paircombs = JsonDataHandler.ohlcv_get_available_data(testdatadir)
# Convert to set to avoid failures due to sorting
assert set(paircombs) == {('UNITTEST/BTC', '5m'), ('ETH/BTC', '5m'), ('XLM/BTC', '5m'),
@ -643,6 +648,8 @@ def test_jsondatahandler_ohlcv_get_available_data(testdatadir):
paircombs = JsonGzDataHandler.ohlcv_get_available_data(testdatadir)
assert set(paircombs) == {('UNITTEST/BTC', '8m')}
paircombs = HDF5DataHandler.ohlcv_get_available_data(testdatadir)
assert set(paircombs) == {('UNITTEST/BTC', '5m')}
def test_jsondatahandler_trades_get_pairs(testdatadir):
@ -653,15 +660,17 @@ def test_jsondatahandler_trades_get_pairs(testdatadir):
def test_jsondatahandler_ohlcv_purge(mocker, testdatadir):
mocker.patch.object(Path, "exists", MagicMock(return_value=False))
mocker.patch.object(Path, "unlink", MagicMock())
unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
dh = JsonGzDataHandler(testdatadir)
assert not dh.ohlcv_purge('UNITTEST/NONEXIST', '5m')
assert unlinkmock.call_count == 0
mocker.patch.object(Path, "exists", MagicMock(return_value=True))
assert dh.ohlcv_purge('UNITTEST/NONEXIST', '5m')
assert unlinkmock.call_count == 1
def test_jsondatahandler_trades_load(mocker, testdatadir, caplog):
def test_jsondatahandler_trades_load(testdatadir, caplog):
dh = JsonGzDataHandler(testdatadir)
logmsg = "Old trades format detected - converting"
dh.trades_load('XRP/ETH')
@ -674,26 +683,144 @@ def test_jsondatahandler_trades_load(mocker, testdatadir, caplog):
def test_jsondatahandler_trades_purge(mocker, testdatadir):
mocker.patch.object(Path, "exists", MagicMock(return_value=False))
mocker.patch.object(Path, "unlink", MagicMock())
unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
dh = JsonGzDataHandler(testdatadir)
assert not dh.trades_purge('UNITTEST/NONEXIST')
assert unlinkmock.call_count == 0
mocker.patch.object(Path, "exists", MagicMock(return_value=True))
assert dh.trades_purge('UNITTEST/NONEXIST')
assert unlinkmock.call_count == 1
def test_jsondatahandler_ohlcv_append(testdatadir):
dh = JsonGzDataHandler(testdatadir)
@pytest.mark.parametrize('datahandler', AVAILABLE_DATAHANDLERS)
def test_datahandler_ohlcv_append(datahandler, testdatadir, ):
dh = get_datahandler(testdatadir, datahandler)
with pytest.raises(NotImplementedError):
dh.ohlcv_append('UNITTEST/ETH', '5m', DataFrame())
def test_jsondatahandler_trades_append(testdatadir):
dh = JsonGzDataHandler(testdatadir)
@pytest.mark.parametrize('datahandler', AVAILABLE_DATAHANDLERS)
def test_datahandler_trades_append(datahandler, testdatadir):
dh = get_datahandler(testdatadir, datahandler)
with pytest.raises(NotImplementedError):
dh.trades_append('UNITTEST/ETH', [])
def test_hdf5datahandler_trades_get_pairs(testdatadir):
pairs = HDF5DataHandler.trades_get_pairs(testdatadir)
# Convert to set to avoid failures due to sorting
assert set(pairs) == {'XRP/ETH'}
def test_hdf5datahandler_trades_load(testdatadir):
dh = HDF5DataHandler(testdatadir)
trades = dh.trades_load('XRP/ETH')
assert isinstance(trades, list)
trades1 = dh.trades_load('UNITTEST/NONEXIST')
assert trades1 == []
# data goes from 2019-10-11 - 2019-10-13
timerange = TimeRange.parse_timerange('20191011-20191012')
trades2 = dh._trades_load('XRP/ETH', timerange)
assert len(trades) > len(trades2)
# unfiltered load has trades before starttime
assert len([t for t in trades if t[0] < timerange.startts * 1000]) >= 0
# filtered list does not have trades before starttime
assert len([t for t in trades2 if t[0] < timerange.startts * 1000]) == 0
# unfiltered load has trades after endtime
assert len([t for t in trades if t[0] > timerange.stopts * 1000]) > 0
# filtered list does not have trades after endtime
assert len([t for t in trades2 if t[0] > timerange.stopts * 1000]) == 0
def test_hdf5datahandler_trades_store(testdatadir):
dh = HDF5DataHandler(testdatadir)
trades = dh.trades_load('XRP/ETH')
dh.trades_store('XRP/NEW', trades)
file = testdatadir / 'XRP_NEW-trades.h5'
assert file.is_file()
# Load trades back
trades_new = dh.trades_load('XRP/NEW')
assert len(trades_new) == len(trades)
assert trades[0][0] == trades_new[0][0]
assert trades[0][1] == trades_new[0][1]
# assert trades[0][2] == trades_new[0][2] # This is nan - so comparison does not make sense
assert trades[0][3] == trades_new[0][3]
assert trades[0][4] == trades_new[0][4]
assert trades[0][5] == trades_new[0][5]
assert trades[0][6] == trades_new[0][6]
assert trades[-1][0] == trades_new[-1][0]
assert trades[-1][1] == trades_new[-1][1]
# assert trades[-1][2] == trades_new[-1][2] # This is nan - so comparison does not make sense
assert trades[-1][3] == trades_new[-1][3]
assert trades[-1][4] == trades_new[-1][4]
assert trades[-1][5] == trades_new[-1][5]
assert trades[-1][6] == trades_new[-1][6]
_clean_test_file(file)
def test_hdf5datahandler_trades_purge(mocker, testdatadir):
mocker.patch.object(Path, "exists", MagicMock(return_value=False))
unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
dh = HDF5DataHandler(testdatadir)
assert not dh.trades_purge('UNITTEST/NONEXIST')
assert unlinkmock.call_count == 0
mocker.patch.object(Path, "exists", MagicMock(return_value=True))
assert dh.trades_purge('UNITTEST/NONEXIST')
assert unlinkmock.call_count == 1
def test_hdf5datahandler_ohlcv_load_and_resave(testdatadir):
dh = HDF5DataHandler(testdatadir)
ohlcv = dh.ohlcv_load('UNITTEST/BTC', '5m')
assert isinstance(ohlcv, DataFrame)
assert len(ohlcv) > 0
file = testdatadir / 'UNITTEST_NEW-5m.h5'
assert not file.is_file()
dh.ohlcv_store('UNITTEST/NEW', '5m', ohlcv)
assert file.is_file()
assert not ohlcv[ohlcv['date'] < '2018-01-15'].empty
# Data gores from 2018-01-10 - 2018-01-30
timerange = TimeRange.parse_timerange('20180115-20180119')
# Call private function to ensure timerange is filtered in hdf5
ohlcv = dh._ohlcv_load('UNITTEST/BTC', '5m', timerange)
ohlcv1 = dh._ohlcv_load('UNITTEST/NEW', '5m', timerange)
assert len(ohlcv) == len(ohlcv1)
assert ohlcv.equals(ohlcv1)
assert ohlcv[ohlcv['date'] < '2018-01-15'].empty
assert ohlcv[ohlcv['date'] > '2018-01-19'].empty
_clean_test_file(file)
# Try loading inexisting file
ohlcv = dh.ohlcv_load('UNITTEST/NONEXIST', '5m')
assert ohlcv.empty
def test_hdf5datahandler_ohlcv_purge(mocker, testdatadir):
mocker.patch.object(Path, "exists", MagicMock(return_value=False))
unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
dh = HDF5DataHandler(testdatadir)
assert not dh.ohlcv_purge('UNITTEST/NONEXIST', '5m')
assert unlinkmock.call_count == 0
mocker.patch.object(Path, "exists", MagicMock(return_value=True))
assert dh.ohlcv_purge('UNITTEST/NONEXIST', '5m')
assert unlinkmock.call_count == 1
def test_gethandlerclass():
cl = get_datahandlerclass('json')
assert cl == JsonDataHandler
@ -702,6 +829,9 @@ def test_gethandlerclass():
assert cl == JsonGzDataHandler
assert issubclass(cl, IDataHandler)
assert issubclass(cl, JsonDataHandler)
cl = get_datahandlerclass('hdf5')
assert cl == HDF5DataHandler
assert issubclass(cl, IDataHandler)
with pytest.raises(ValueError, match=r"No datahandler for .*"):
get_datahandlerclass('DeadBeef')
@ -713,3 +843,6 @@ def test_get_datahandler(testdatadir):
assert type(dh) == JsonGzDataHandler
dh1 = get_datahandler(testdatadir, 'jsongz', dh)
assert id(dh1) == id(dh)
dh = get_datahandler(testdatadir, 'hdf5')
assert type(dh) == HDF5DataHandler

52
tests/exchange/test_exchange.py
View File

@ -1,5 +1,3 @@
# pragma pylint: disable=missing-docstring, C0103, bad-continuation, global-statement
# pragma pylint: disable=protected-access
import copy
import logging
from datetime import datetime, timezone
@ -15,7 +13,8 @@ from freqtrade.exceptions import (DDosProtection, DependencyException,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exchange import Binance, Exchange, Kraken
from freqtrade.exchange.common import API_RETRY_COUNT, calculate_backoff
from freqtrade.exchange.common import (API_RETRY_COUNT, API_FETCH_ORDER_RETRY_COUNT,
calculate_backoff)
from freqtrade.exchange.exchange import (market_is_active,
timeframe_to_minutes,
timeframe_to_msecs,
@ -808,7 +807,7 @@ def test_dry_run_order(default_conf, mocker, side, exchange_name):
assert f'dry_run_{side}_' in order["id"]
assert order["side"] == side
assert order["type"] == "limit"
assert order["pair"] == "ETH/BTC"
assert order["symbol"] == "ETH/BTC"
@pytest.mark.parametrize("side", [
@ -1761,6 +1760,14 @@ def test_cancel_order_dry_run(default_conf, mocker, exchange_name):
assert exchange.cancel_order(order_id='123', pair='TKN/BTC') == {}
assert exchange.cancel_stoploss_order(order_id='123', pair='TKN/BTC') == {}
order = exchange.buy('ETH/BTC', 'limit', 5, 0.55, 'gtc')
cancel_order = exchange.cancel_order(order_id=order['id'], pair='ETH/BTC')
assert order['id'] == cancel_order['id']
assert order['amount'] == cancel_order['amount']
assert order['symbol'] == cancel_order['symbol']
assert cancel_order['status'] == 'canceled'
@pytest.mark.parametrize("exchange_name", EXCHANGES)
@pytest.mark.parametrize("order,result", [
@ -1895,12 +1902,14 @@ def test_fetch_order(default_conf, mocker, exchange_name):
# Ensure backoff is called
assert tm.call_args_list[0][0][0] == 1
assert tm.call_args_list[1][0][0] == 2
assert tm.call_args_list[2][0][0] == 5
assert tm.call_args_list[3][0][0] == 10
assert api_mock.fetch_order.call_count == 6
if API_FETCH_ORDER_RETRY_COUNT > 2:
assert tm.call_args_list[2][0][0] == 5
if API_FETCH_ORDER_RETRY_COUNT > 3:
assert tm.call_args_list[3][0][0] == 10
assert api_mock.fetch_order.call_count == API_FETCH_ORDER_RETRY_COUNT + 1
ccxt_exceptionhandlers(mocker, default_conf, api_mock, exchange_name,
'fetch_order', 'fetch_order', retries=6,
'fetch_order', 'fetch_order', retries=API_FETCH_ORDER_RETRY_COUNT + 1,
order_id='_', pair='TKN/BTC')
@ -1933,10 +1942,35 @@ def test_fetch_stoploss_order(default_conf, mocker, exchange_name):
ccxt_exceptionhandlers(mocker, default_conf, api_mock, exchange_name,
'fetch_stoploss_order', 'fetch_order',
retries=6,
retries=API_FETCH_ORDER_RETRY_COUNT + 1,
order_id='_', pair='TKN/BTC')
def test_fetch_order_or_stoploss_order(default_conf, mocker):
exchange = get_patched_exchange(mocker, default_conf, id='binance')
fetch_order_mock = MagicMock()
fetch_stoploss_order_mock = MagicMock()
mocker.patch.multiple('freqtrade.exchange.Exchange',
fetch_order=fetch_order_mock,
fetch_stoploss_order=fetch_stoploss_order_mock,
)
exchange.fetch_order_or_stoploss_order('1234', 'ETH/BTC', False)
assert fetch_order_mock.call_count == 1
assert fetch_order_mock.call_args_list[0][0][0] == '1234'
assert fetch_order_mock.call_args_list[0][0][1] == 'ETH/BTC'
assert fetch_stoploss_order_mock.call_count == 0
fetch_order_mock.reset_mock()
fetch_stoploss_order_mock.reset_mock()
exchange.fetch_order_or_stoploss_order('1234', 'ETH/BTC', True)
assert fetch_order_mock.call_count == 0
assert fetch_stoploss_order_mock.call_count == 1
assert fetch_stoploss_order_mock.call_args_list[0][0][0] == '1234'
assert fetch_stoploss_order_mock.call_args_list[0][0][1] == 'ETH/BTC'
@pytest.mark.parametrize("exchange_name", EXCHANGES)
def test_name(default_conf, mocker, exchange_name):
exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)

5
tests/exchange/test_ftx.py
View File

@ -1,5 +1,3 @@
# pragma pylint: disable=missing-docstring, C0103, bad-continuation, global-statement
# pragma pylint: disable=protected-access
from random import randint
from unittest.mock import MagicMock
@ -7,6 +5,7 @@ import ccxt
import pytest
from freqtrade.exceptions import DependencyException, InvalidOrderException
from freqtrade.exchange.common import API_FETCH_ORDER_RETRY_COUNT
from tests.conftest import get_patched_exchange
from .test_exchange import ccxt_exceptionhandlers
@ -154,5 +153,5 @@ def test_fetch_stoploss_order(default_conf, mocker):
ccxt_exceptionhandlers(mocker, default_conf, api_mock, 'ftx',
'fetch_stoploss_order', 'fetch_orders',
retries=6,
retries=API_FETCH_ORDER_RETRY_COUNT + 1,
order_id='_', pair='TKN/BTC')

2
tests/exchange/test_kraken.py
View File

@ -1,5 +1,3 @@
# pragma pylint: disable=missing-docstring, C0103, bad-continuation, global-statement
# pragma pylint: disable=protected-access
from random import randint
from unittest.mock import MagicMock

5
tests/optimize/test_backtesting.py
View File

@ -14,7 +14,7 @@ from freqtrade.commands.optimize_commands import (setup_optimize_configuration,
start_backtesting)
from freqtrade.configuration import TimeRange
from freqtrade.data import history
from freqtrade.data.btanalysis import evaluate_result_multi
from freqtrade.data.btanalysis import BT_DATA_COLUMNS, evaluate_result_multi
from freqtrade.data.converter import clean_ohlcv_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history import get_timerange
@ -359,6 +359,7 @@ def test_backtesting_start(default_conf, mocker, testdatadir, caplog) -> None:
]
for line in exists:
assert log_has(line, caplog)
assert backtesting.strategy.dp._pairlists is not None
def test_backtesting_start_no_data(default_conf, mocker, caplog, testdatadir) -> None:
@ -693,7 +694,7 @@ def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
patch_exchange(mocker)
backtestmock = MagicMock()
backtestmock = MagicMock(return_value=pd.DataFrame(columns=BT_DATA_COLUMNS + ['profit_abs']))
mocker.patch('freqtrade.pairlist.pairlistmanager.PairListManager.whitelist',
PropertyMock(return_value=['UNITTEST/BTC']))
mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)

2
tests/optimize/test_hyperopt.py
View File

@ -813,7 +813,7 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
'draws': 0,
'duration': 100.0,
'losses': 0,
'winsdrawslosses': '1/0/0',
'winsdrawslosses': ' 1 0 0',
'median_profit': 2.3117,
'profit': 2.3117,
'total_profit': 0.000233,

116
tests/optimize/test_optimize_reports.py
View File

@ -5,7 +5,6 @@ from pathlib import Path
import pandas as pd
import pytest
from arrow import Arrow
from freqtrade.configuration import TimeRange
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.data import history
@ -22,11 +21,12 @@ from freqtrade.optimize.optimize_reports import (generate_backtest_stats,
text_table_bt_results,
text_table_sell_reason,
text_table_strategy)
from freqtrade.resolvers.strategy_resolver import StrategyResolver
from freqtrade.strategy.interface import SellType
from tests.data.test_history import _backup_file, _clean_test_file
def test_text_table_bt_results(default_conf, mocker):
def test_text_table_bt_results():
results = pd.DataFrame(
{
@ -57,32 +57,38 @@ def test_text_table_bt_results(default_conf, mocker):
def test_generate_backtest_stats(default_conf, testdatadir):
results = {'DefStrat': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
"UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [0.003312, 0.010801, 0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime],
"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
"trade_duration": [123, 34, 31, 14],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
})}
default_conf.update({'strategy': 'DefaultStrategy'})
StrategyResolver.load_strategy(default_conf)
results = {'DefStrat': {
'results': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
"UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [0.003312, 0.010801, 0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime],
"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
"trade_duration": [123, 34, 31, 14],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
}),
'config': default_conf}
}
timerange = TimeRange.parse_timerange('1510688220-1510700340')
min_date = Arrow.fromtimestamp(1510688220)
max_date = Arrow.fromtimestamp(1510700340)
btdata = history.load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
fill_up_missing=True)
stats = generate_backtest_stats(default_conf, btdata, results, min_date, max_date)
stats = generate_backtest_stats(btdata, results, min_date, max_date)
assert isinstance(stats, dict)
assert 'strategy' in stats
assert 'DefStrat' in stats['strategy']
@ -90,29 +96,32 @@ def test_generate_backtest_stats(default_conf, testdatadir):
strat_stats = stats['strategy']['DefStrat']
assert strat_stats['backtest_start'] == min_date.datetime
assert strat_stats['backtest_end'] == max_date.datetime
assert strat_stats['total_trades'] == len(results['DefStrat'])
assert strat_stats['total_trades'] == len(results['DefStrat']['results'])
# Above sample had no loosing trade
assert strat_stats['max_drawdown'] == 0.0
results = {'DefStrat': pd.DataFrame(
{"pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [0.003312, 0.010801, -0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, -0.000014, 0.000003],
"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime],
"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.0032903, 0.003217],
"trade_duration": [123, 34, 31, 14],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
})}
results = {'DefStrat': {
'results': pd.DataFrame(
{"pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
"profit_percent": [0.003312, 0.010801, -0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, -0.000014, 0.000003],
"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
Arrow(2017, 11, 14, 21, 36, 00).datetime,
Arrow(2017, 11, 14, 22, 12, 00).datetime,
Arrow(2017, 11, 14, 22, 44, 00).datetime],
"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
Arrow(2017, 11, 14, 22, 10, 00).datetime,
Arrow(2017, 11, 14, 22, 43, 00).datetime,
Arrow(2017, 11, 14, 22, 58, 00).datetime],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.0032903, 0.003217],
"trade_duration": [123, 34, 31, 14],
"open_at_end": [False, False, False, True],
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
SellType.ROI, SellType.FORCE_SELL]
}),
'config': default_conf}
}
assert strat_stats['max_drawdown'] == 0.0
assert strat_stats['drawdown_start'] == Arrow.fromtimestamp(0).datetime
@ -165,7 +174,7 @@ def test_store_backtest_stats(testdatadir, mocker):
assert str(dump_mock.call_args_list[0][0][0]).startswith(str(testdatadir / 'testresult'))
def test_generate_pair_metrics(default_conf, mocker):
def test_generate_pair_metrics():
results = pd.DataFrame(
{
@ -213,7 +222,7 @@ def test_generate_daily_stats(testdatadir):
assert res['losing_days'] == 0
def test_text_table_sell_reason(default_conf):
def test_text_table_sell_reason():
results = pd.DataFrame(
{
@ -245,7 +254,7 @@ def test_text_table_sell_reason(default_conf):
stake_currency='BTC') == result_str
def test_generate_sell_reason_stats(default_conf):
def test_generate_sell_reason_stats():
results = pd.DataFrame(
{
@ -280,9 +289,10 @@ def test_generate_sell_reason_stats(default_conf):
assert stop_result['profit_mean_pct'] == round(stop_result['profit_mean'] * 100, 2)
def test_text_table_strategy(default_conf, mocker):
def test_text_table_strategy(default_conf):
default_conf['max_open_trades'] = 2
results = {}
results['TestStrategy1'] = pd.DataFrame(
results['TestStrategy1'] = {'results': pd.DataFrame(
{
'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
'profit_percent': [0.1, 0.2, 0.3],
@ -293,8 +303,8 @@ def test_text_table_strategy(default_conf, mocker):
'losses': [0, 0, 1],
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
}
)
results['TestStrategy2'] = pd.DataFrame(
), 'config': default_conf}
results['TestStrategy2'] = {'results': pd.DataFrame(
{
'pair': ['LTC/BTC', 'LTC/BTC', 'LTC/BTC'],
'profit_percent': [0.4, 0.2, 0.3],
@ -305,7 +315,7 @@ def test_text_table_strategy(default_conf, mocker):
'losses': [0, 0, 1],
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
}
)
), 'config': default_conf}
result_str = (
'| Strategy | Buys | Avg Profit % | Cum Profit % | Tot'
@ -318,14 +328,12 @@ def test_text_table_strategy(default_conf, mocker):
' 45.00 | 0:20:00 | 3 | 0 | 0 |'
)
strategy_results = generate_strategy_metrics(stake_currency='BTC',
max_open_trades=2,
all_results=results)
strategy_results = generate_strategy_metrics(all_results=results)
assert text_table_strategy(strategy_results, 'BTC') == result_str
def test_generate_edge_table(edge_conf, mocker):
def test_generate_edge_table():
results = {}
results['ETH/BTC'] = PairInfo(-0.01, 0.60, 2, 1, 3, 10, 60)

14
tests/pairlist/test_pairlist.py
View File

@ -231,9 +231,6 @@ def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf):
# VolumePairList only
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"}],
"BTC", ['ETH/BTC', 'TKN/BTC', 'LTC/BTC', 'XRP/BTC', 'HOT/BTC']),
# Different sorting depending on quote or bid volume
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "bidVolume"}],
"BTC", ['HOT/BTC', 'FUEL/BTC', 'XRP/BTC', 'LTC/BTC', 'TKN/BTC']),
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"}],
"USDT", ['ETH/USDT', 'NANO/USDT', 'ADAHALF/USDT', 'ADADOUBLE/USDT']),
# No pair for ETH, VolumePairList
@ -263,10 +260,6 @@ def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf):
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
{"method": "PrecisionFilter"}],
"BTC", ['ETH/BTC', 'TKN/BTC', 'LTC/BTC', 'XRP/BTC']),
# Precisionfilter bid
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "bidVolume"},
{"method": "PrecisionFilter"}],
"BTC", ['FUEL/BTC', 'XRP/BTC', 'LTC/BTC', 'TKN/BTC']),
# PriceFilter and VolumePairList
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
{"method": "PriceFilter", "low_price_ratio": 0.03}],
@ -293,9 +286,6 @@ def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf):
([{"method": "StaticPairList"}],
"BTC", ['ETH/BTC', 'TKN/BTC', 'HOT/BTC']),
# Static Pairlist before VolumePairList - sorting changes
([{"method": "StaticPairList"},
{"method": "VolumePairList", "number_assets": 5, "sort_key": "bidVolume"}],
"BTC", ['HOT/BTC', 'TKN/BTC', 'ETH/BTC']),
# SpreadFilter
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
{"method": "SpreadFilter", "max_spread_ratio": 0.005}],
@ -344,9 +334,9 @@ def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf):
([{"method": "SpreadFilter", "max_spread_ratio": 0.005}],
"BTC", 'filter_at_the_beginning'), # OperationalException expected
# Static Pairlist after VolumePairList, on a non-first position
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "bidVolume"},
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume"},
{"method": "StaticPairList"}],
"BTC", 'static_in_the_middle'),
"BTC", 'static_in_the_middle'),
([{"method": "VolumePairList", "number_assets": 20, "sort_key": "quoteVolume"},
{"method": "PriceFilter", "low_price_ratio": 0.02}],
"USDT", ['ETH/USDT', 'NANO/USDT']),

40
tests/rpc/test_rpc.py
View File

@ -313,7 +313,6 @@ def test_rpc_delete_trade(mocker, default_conf, fee, markets, caplog):
with pytest.raises(RPCException, match='invalid argument'):
rpc._rpc_delete('200')
create_mock_trades(fee)
trades = Trade.query.all()
trades[1].stoploss_order_id = '1234'
trades[2].stoploss_order_id = '1234'
@ -669,7 +668,8 @@ def test_rpc_forcesell(default_conf, ticker, fee, mocker) -> None:
return_value={
'status': 'closed',
'type': 'limit',
'side': 'buy'
'side': 'buy',
'filled': 0.0,
}
),
get_fee=fee,
@ -695,6 +695,7 @@ def test_rpc_forcesell(default_conf, ticker, fee, mocker) -> None:
msg = rpc._rpc_forcesell('all')
assert msg == {'result': 'Created sell orders for all open trades.'}
freqtradebot.enter_positions()
msg = rpc._rpc_forcesell('1')
assert msg == {'result': 'Created sell order for trade 1.'}
@ -707,17 +708,26 @@ def test_rpc_forcesell(default_conf, ticker, fee, mocker) -> None:
freqtradebot.state = State.RUNNING
assert cancel_order_mock.call_count == 0
freqtradebot.enter_positions()
# make an limit-buy open trade
trade = Trade.query.filter(Trade.id == '1').first()
filled_amount = trade.amount / 2
# Fetch order - it's open first, and closed after cancel_order is called.
mocker.patch(
'freqtrade.exchange.Exchange.fetch_order',
return_value={
side_effect=[{
'id': '1234',
'status': 'open',
'type': 'limit',
'side': 'buy',
'filled': filled_amount
}
}, {
'id': '1234',
'status': 'closed',
'type': 'limit',
'side': 'buy',
'filled': filled_amount
}]
)
# check that the trade is called, which is done by ensuring exchange.cancel_order is called
# and trade amount is updated
@ -725,6 +735,16 @@ def test_rpc_forcesell(default_conf, ticker, fee, mocker) -> None:
assert cancel_order_mock.call_count == 1
assert trade.amount == filled_amount
mocker.patch(
'freqtrade.exchange.Exchange.fetch_order',
return_value={
'status': 'open',
'type': 'limit',
'side': 'buy',
'filled': filled_amount
})
freqtradebot.config['max_open_trades'] = 3
freqtradebot.enter_positions()
trade = Trade.query.filter(Trade.id == '2').first()
amount = trade.amount
@ -744,20 +764,22 @@ def test_rpc_forcesell(default_conf, ticker, fee, mocker) -> None:
assert cancel_order_mock.call_count == 2
assert trade.amount == amount
freqtradebot.enter_positions()
# make an limit-sell open trade
mocker.patch(
'freqtrade.exchange.Exchange.fetch_order',
return_value={
'status': 'open',
'type': 'limit',
'side': 'sell'
'side': 'sell',
'amount': amount,
'remaining': amount,
'filled': 0.0
}
)
msg = rpc._rpc_forcesell('3')
assert msg == {'result': 'Created sell order for trade 3.'}
# status quo, no exchange calls
assert cancel_order_mock.call_count == 2
assert cancel_order_mock.call_count == 3
def test_performance_handle(default_conf, ticker, limit_buy_order, fee,
@ -816,10 +838,10 @@ def test_rpc_count(mocker, default_conf, ticker, fee) -> None:
assert counts["current"] == 1
def test_rpcforcebuy(mocker, default_conf, ticker, fee, limit_buy_order) -> None:
def test_rpcforcebuy(mocker, default_conf, ticker, fee, limit_buy_order_open) -> None:
default_conf['forcebuy_enable'] = True
mocker.patch('freqtrade.rpc.telegram.Telegram', MagicMock())
buy_mm = MagicMock(return_value={'id': limit_buy_order['id']})
buy_mm = MagicMock(return_value=limit_buy_order_open)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value=ticker),

6
tests/rpc/test_rpc_apiserver.py
View File

@ -266,7 +266,7 @@ def test_api_reloadconf(botclient):
rc = client_post(client, f"{BASE_URI}/reload_config")
assert_response(rc)
assert rc.json == {'status': 'reloading config ...'}
assert rc.json == {'status': 'Reloading config ...'}
assert ftbot.state == State.RELOAD_CONFIG
@ -435,7 +435,7 @@ def test_api_logs(botclient):
assert len(rc.json) == 2
assert 'logs' in rc.json
# Using a fixed comparison here would make this test fail!
assert rc.json['log_count'] > 10
assert rc.json['log_count'] > 1
assert len(rc.json['logs']) == rc.json['log_count']
assert isinstance(rc.json['logs'][0], list)
@ -471,6 +471,7 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
assert rc.json == {"error": "Error querying _edge: Edge is not enabled."}
@pytest.mark.usefixtures("init_persistence")
def test_api_profit(botclient, mocker, ticker, fee, markets, limit_buy_order, limit_sell_order):
ftbot, client = botclient
patch_get_signal(ftbot, (True, False))
@ -498,6 +499,7 @@ def test_api_profit(botclient, mocker, ticker, fee, markets, limit_buy_order, li
assert rc.json['best_pair'] == ''
assert rc.json['best_rate'] == 0
trade = Trade.query.first()
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()

4
tests/rpc/test_rpc_manager.py
View File

@ -124,10 +124,10 @@ def test_send_msg_webhook_CustomMessagetype(mocker, default_conf, caplog) -> Non
rpc_manager = RPCManager(get_patched_freqtradebot(mocker, default_conf))
assert 'webhook' in [mod.name for mod in rpc_manager.registered_modules]
rpc_manager.send_msg({'type': RPCMessageType.CUSTOM_NOTIFICATION,
rpc_manager.send_msg({'type': RPCMessageType.STARTUP_NOTIFICATION,
'status': 'TestMessage'})
assert log_has(
"Message type RPCMessageType.CUSTOM_NOTIFICATION not implemented by handler webhook.",
"Message type 'startup' not implemented by handler webhook.",
caplog)

49
tests/rpc/test_rpc_telegram.py
View File

@ -14,6 +14,7 @@ from telegram import Chat, Message, Update
from telegram.error import NetworkError
from freqtrade import __version__
from freqtrade.constants import CANCEL_REASON
from freqtrade.edge import PairInfo
from freqtrade.freqtradebot import FreqtradeBot
from freqtrade.loggers import setup_logging
@ -252,7 +253,6 @@ def test_status_table_handle(default_conf, update, ticker, fee, mocker) -> None:
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker,
buy=MagicMock(return_value={'id': 'mocked_order_id'}),
get_fee=fee,
)
msg_mock = MagicMock()
@ -691,7 +691,7 @@ def test_reload_config_handle(default_conf, update, mocker) -> None:
telegram._reload_config(update=update, context=MagicMock())
assert freqtradebot.state == State.RELOAD_CONFIG
assert msg_mock.call_count == 1
assert 'reloading config' in msg_mock.call_args_list[0][0][0]
assert 'Reloading config' in msg_mock.call_args_list[0][0][0]
def test_telegram_forcesell_handle(default_conf, update, ticker, fee,
@ -725,7 +725,7 @@ def test_telegram_forcesell_handle(default_conf, update, ticker, fee,
context.args = ["1"]
telegram._forcesell(update=update, context=context)
assert rpc_mock.call_count == 2
assert rpc_mock.call_count == 3
last_msg = rpc_mock.call_args_list[-1][0][0]
assert {
'type': RPCMessageType.SELL_NOTIFICATION,
@ -784,7 +784,7 @@ def test_telegram_forcesell_down_handle(default_conf, update, ticker, fee,
context.args = ["1"]
telegram._forcesell(update=update, context=context)
assert rpc_mock.call_count == 2
assert rpc_mock.call_count == 3
last_msg = rpc_mock.call_args_list[-1][0][0]
assert {
@ -834,8 +834,9 @@ def test_forcesell_all_handle(default_conf, update, ticker, fee, mocker) -> None
context.args = ["all"]
telegram._forcesell(update=update, context=context)
assert rpc_mock.call_count == 4
msg = rpc_mock.call_args_list[0][0][0]
# Called for each trade 3 times
assert rpc_mock.call_count == 8
msg = rpc_mock.call_args_list[1][0][0]
assert {
'type': RPCMessageType.SELL_NOTIFICATION,
'trade_id': 1,
@ -1005,7 +1006,6 @@ def test_count_handle(default_conf, update, ticker, fee, mocker) -> None:
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker,
buy=MagicMock(return_value={'id': 'mocked_order_id'}),
get_fee=fee,
)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
@ -1299,16 +1299,14 @@ def test_show_config_handle(default_conf, update, mocker) -> None:
assert '*Initial Stoploss:* `-0.1`' in msg_mock.call_args_list[0][0][0]
def test_send_msg_buy_notification(default_conf, mocker) -> None:
def test_send_msg_buy_notification(default_conf, mocker, caplog) -> None:
msg_mock = MagicMock()
mocker.patch.multiple(
'freqtrade.rpc.telegram.Telegram',
_init=MagicMock(),
_send_msg=msg_mock
)
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
telegram = Telegram(freqtradebot)
telegram.send_msg({
msg = {
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
@ -1321,7 +1319,10 @@ def test_send_msg_buy_notification(default_conf, mocker) -> None:
'current_rate': 1.099e-05,
'amount': 1333.3333333333335,
'open_date': arrow.utcnow().shift(hours=-1)
})
}
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
telegram = Telegram(freqtradebot)
telegram.send_msg(msg)
assert msg_mock.call_args[0][0] \
== '\N{LARGE BLUE CIRCLE} *Bittrex:* Buying ETH/BTC\n' \
'*Amount:* `1333.33333333`\n' \
@ -1329,6 +1330,21 @@ def test_send_msg_buy_notification(default_conf, mocker) -> None:
'*Current Rate:* `0.00001099`\n' \
'*Total:* `(0.001000 BTC, 12.345 USD)`'
freqtradebot.config['telegram']['notification_settings'] = {'buy': 'off'}
caplog.clear()
msg_mock.reset_mock()
telegram.send_msg(msg)
msg_mock.call_count == 0
log_has("Notification 'buy' not sent.", caplog)
freqtradebot.config['telegram']['notification_settings'] = {'buy': 'silent'}
caplog.clear()
msg_mock.reset_mock()
telegram.send_msg(msg)
msg_mock.call_count == 1
msg_mock.call_args_list[0][1]['disable_notification'] is True
def test_send_msg_buy_cancel_notification(default_conf, mocker) -> None:
msg_mock = MagicMock()
@ -1343,9 +1359,10 @@ def test_send_msg_buy_cancel_notification(default_conf, mocker) -> None:
'type': RPCMessageType.BUY_CANCEL_NOTIFICATION,
'exchange': 'Bittrex',
'pair': 'ETH/BTC',
'reason': CANCEL_REASON['TIMEOUT']
})
assert msg_mock.call_args[0][0] \
== ('\N{WARNING SIGN} *Bittrex:* Cancelling Open Buy Order for ETH/BTC')
assert (msg_mock.call_args[0][0] == '\N{WARNING SIGN} *Bittrex:* '
'Cancelling open buy Order for ETH/BTC. Reason: cancelled due to timeout.')
def test_send_msg_sell_notification(default_conf, mocker) -> None:
@ -1484,7 +1501,7 @@ def test_warning_notification(default_conf, mocker) -> None:
assert msg_mock.call_args[0][0] == '\N{WARNING SIGN} *Warning:* `message`'
def test_custom_notification(default_conf, mocker) -> None:
def test_startup_notification(default_conf, mocker) -> None:
msg_mock = MagicMock()
mocker.patch.multiple(
'freqtrade.rpc.telegram.Telegram',
@ -1494,7 +1511,7 @@ def test_custom_notification(default_conf, mocker) -> None:
freqtradebot = get_patched_freqtradebot(mocker, default_conf)
telegram = Telegram(freqtradebot)
telegram.send_msg({
'type': RPCMessageType.CUSTOM_NOTIFICATION,
'type': RPCMessageType.STARTUP_NOTIFICATION,
'status': '*Custom:* `Hello World`'
})
assert msg_mock.call_args[0][0] == '*Custom:* `Hello World`'

4
tests/rpc/test_rpc_webhook.py
View File

@ -150,7 +150,7 @@ def test_send_msg(default_conf, mocker):
default_conf["webhook"]["webhooksellcancel"]["value3"].format(**msg))
for msgtype in [RPCMessageType.STATUS_NOTIFICATION,
RPCMessageType.WARNING_NOTIFICATION,
RPCMessageType.CUSTOM_NOTIFICATION]:
RPCMessageType.STARTUP_NOTIFICATION]:
# Test notification
msg = {
'type': msgtype,
@ -174,7 +174,7 @@ def test_exception_send_msg(default_conf, mocker, caplog):
webhook = Webhook(get_patched_freqtradebot(mocker, default_conf))
webhook.send_msg({'type': RPCMessageType.BUY_NOTIFICATION})
assert log_has(f"Message type {RPCMessageType.BUY_NOTIFICATION} not configured for webhooks",
assert log_has(f"Message type '{RPCMessageType.BUY_NOTIFICATION}' not configured for webhooks",
caplog)
default_conf["webhook"] = get_webhook_dict()

88
tests/strategy/test_strategy_helpers.py Normal file
View File

@ -0,0 +1,88 @@
import pandas as pd
import numpy as np
from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes
def generate_test_data(timeframe: str, size: int):
np.random.seed(42)
tf_mins = timeframe_to_minutes(timeframe)
base = np.random.normal(20, 2, size=size)
date = pd.period_range('2020-07-05', periods=size, freq=f'{tf_mins}min').to_timestamp()
df = pd.DataFrame({
'date': date,
'open': base,
'high': base + np.random.normal(2, 1, size=size),
'low': base - np.random.normal(2, 1, size=size),
'close': base + np.random.normal(0, 1, size=size),
'volume': np.random.normal(200, size=size)
}
)
df = df.dropna()
return df
def test_merge_informative_pair():
data = generate_test_data('15m', 40)
informative = generate_test_data('1h', 40)
result = merge_informative_pair(data, informative, '15m', '1h', ffill=True)
assert isinstance(result, pd.DataFrame)
assert len(result) == len(data)
assert 'date' in result.columns
assert result['date'].equals(data['date'])
assert 'date_1h' in result.columns
assert 'open' in result.columns
assert 'open_1h' in result.columns
assert result['open'].equals(data['open'])
assert 'close' in result.columns
assert 'close_1h' in result.columns
assert result['close'].equals(data['close'])
assert 'volume' in result.columns
assert 'volume_1h' in result.columns
assert result['volume'].equals(data['volume'])
# First 4 rows are empty
assert result.iloc[0]['date_1h'] is pd.NaT
assert result.iloc[1]['date_1h'] is pd.NaT
assert result.iloc[2]['date_1h'] is pd.NaT
assert result.iloc[3]['date_1h'] is pd.NaT
# Next 4 rows contain the starting date (0:00)
assert result.iloc[4]['date_1h'] == result.iloc[0]['date']
assert result.iloc[5]['date_1h'] == result.iloc[0]['date']
assert result.iloc[6]['date_1h'] == result.iloc[0]['date']
assert result.iloc[7]['date_1h'] == result.iloc[0]['date']
# Next 4 rows contain the next Hourly date original date row 4
assert result.iloc[8]['date_1h'] == result.iloc[4]['date']
def test_merge_informative_pair_same():
data = generate_test_data('15m', 40)
informative = generate_test_data('15m', 40)
result = merge_informative_pair(data, informative, '15m', '15m', ffill=True)
assert isinstance(result, pd.DataFrame)
assert len(result) == len(data)
assert 'date' in result.columns
assert result['date'].equals(data['date'])
assert 'date_15m' in result.columns
assert 'open' in result.columns
assert 'open_15m' in result.columns
assert result['open'].equals(data['open'])
assert 'close' in result.columns
assert 'close_15m' in result.columns
assert result['close'].equals(data['close'])
assert 'volume' in result.columns
assert 'volume_15m' in result.columns
assert result['volume'].equals(data['volume'])
# Dates match 1:1
assert result['date_15m'].equals(result['date'])

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