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2
.github/ISSUE_TEMPLATE/bug_report.md vendored
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@ -20,7 +20,7 @@ Please do not use bug reports to request new features.
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
* Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
Note: All issues other than enhancement requests will be closed without further comment if the above template is deleted or not filled out.

2
.github/ISSUE_TEMPLATE/feature_request.md vendored
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@ -18,7 +18,7 @@ Have you search for this feature before requesting it? It's highly likely that a
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
* Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
## Describe the enhancement

2
.github/ISSUE_TEMPLATE/question.md vendored
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@ -18,7 +18,7 @@ Please do not use the question template to report bugs or to request new feature
* Operating system: ____
* Python Version: _____ (`python -V`)
* CCXT version: _____ (`pip freeze | grep ccxt`)
* Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
* Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
## Your question

6
.github/workflows/ci.yml vendored
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@ -88,7 +88,7 @@ jobs:
run: |
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
freqtrade hyperopt --datadir tests/testdata -e 6 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
run: |
@ -410,7 +410,7 @@ jobs:
python setup.py sdist bdist_wheel
- name: Publish to PyPI (Test)
uses: pypa/gh-action-pypi-publish@v1.6.1
uses: pypa/gh-action-pypi-publish@v1.6.4
if: (github.event_name == 'release')
with:
user: __token__
@ -418,7 +418,7 @@ jobs:
repository_url: https://test.pypi.org/legacy/
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@v1.6.1
uses: pypa/gh-action-pypi-publish@v1.6.4
if: (github.event_name == 'release')
with:
user: __token__

4
config_examples/config_freqai.example.json
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@ -79,9 +79,7 @@
"test_size": 0.33,
"random_state": 1
},
"model_training_parameters": {
"n_estimators": 1000
}
"model_training_parameters": {}
},
"bot_name": "",
"force_entry_enable": true,

2
docs/data-analysis.md
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@ -5,7 +5,7 @@ You can analyze the results of backtests and trading history easily using Jupyte
## 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`
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.

66
docs/docker_quickstart.md
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@ -4,20 +4,22 @@ This page explains how to run the bot with Docker. It is not meant to work out o
## Install Docker
Start by downloading and installing Docker CE for your platform:
Start by downloading and installing Docker / Docker Desktop 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/)
To simplify running freqtrade, [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the below [docker quick start guide](#docker-quick-start).
!!! Info "Docker compose install"
Freqtrade documentation assumes the use of Docker desktop (or the docker compose plugin).
While the docker-compose standalone installation still works, it will require changing all `docker compose` commands from `docker compose` to `docker-compose` to work (e.g. `docker compose up -d` will become `docker-compose up -d`).
## Freqtrade with docker-compose
## Freqtrade with docker
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/stable/docker-compose.yml) ready for usage.
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/stable/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.
- The following section assumes that `docker` is 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
@ -31,13 +33,13 @@ cd ft_userdata/
curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
docker compose pull
# Create user directory structure
docker-compose run --rm freqtrade create-userdir --userdir user_data
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
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.
@ -64,7 +66,7 @@ The `SampleStrategy` is run by default.
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 up -d
```
!!! Warning "Default configuration"
@ -84,27 +86,27 @@ You can now access the UI by typing localhost:8080 in your browser.
#### Monitoring the bot
You can check for running instances with `docker-compose ps`.
You can check for running instances with `docker compose ps`.
This should list the service `freqtrade` as `running`. If that's not the case, best check the logs (see next point).
#### Docker-compose logs
#### Docker compose logs
Logs will be written to: `user_data/logs/freqtrade.log`.
You can also check the latest log with the command `docker-compose logs -f`.
You can also check the latest log with the command `docker compose logs -f`.
#### Database
The database will be located at: `user_data/tradesv3.sqlite`
#### Updating freqtrade with docker-compose
#### Updating freqtrade with docker
Updating freqtrade when using `docker-compose` is as simple as running the following 2 commands:
Updating freqtrade when using `docker` is as simple as running the following 2 commands:
``` bash
# Download the latest image
docker-compose pull
docker compose pull
# Restart the image
docker-compose up -d
docker compose up -d
```
This will first pull the latest image, and will then restart the container with the just pulled version.
@ -116,43 +118,43 @@ This will first pull the latest image, and will then restart the container with
Advanced users may edit the docker-compose file further to include all possible options or arguments.
All freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
All freqtrade arguments will be available by running `docker compose run --rm freqtrade <command> <optional arguments>`.
!!! Warning "`docker-compose` for trade commands"
Trade commands (`freqtrade trade <...>`) should not be ran via `docker-compose run` - but should use `docker-compose up -d` instead.
!!! Warning "`docker compose` for trade commands"
Trade commands (`freqtrade trade <...>`) should not be ran via `docker compose run` - but should use `docker compose up -d` instead.
This makes sure that the container is properly started (including port forwardings) and will make sure that the container will restart after a system reboot.
If you intend to use freqUI, please also ensure to adjust the [configuration accordingly](rest-api.md#configuration-with-docker), otherwise the UI will not be available.
!!! Note "`docker-compose run --rm`"
!!! Note "`docker compose run --rm`"
Including `--rm` will remove the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
??? Note "Using docker without docker-compose"
"`docker-compose run --rm`" will require a compose file to be provided.
??? Note "Using docker without docker"
"`docker compose run --rm`" will require a compose file to be provided.
Some freqtrade commands that don't require authentication such as `list-pairs` can be run with "`docker run --rm`" instead.
For example `docker run --rm freqtradeorg/freqtrade:stable list-pairs --exchange binance --quote BTC --print-json`.
This can be useful for fetching exchange information to add to your `config.json` without affecting your running containers.
#### Example: Download data with docker-compose
#### Example: Download data with docker
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
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
#### Example: Backtest with docker
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
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
### Additional dependencies with docker
If your strategy requires dependencies not included in the default image - 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.custom](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.custom) for an example).
@ -166,15 +168,15 @@ You'll then also need to modify the `docker-compose.yml` file and uncomment the
dockerfile: "./Dockerfile.<yourextension>"
```
You can then run `docker-compose build --pull` to build the docker image, and run it using the commands described above.
You can then run `docker compose build --pull` to build the docker image, and run it using the commands described above.
### Plotting with docker-compose
### Plotting with docker
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
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.
@ -185,7 +187,7 @@ 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 -f docker/docker-compose-jupyter.yml up
docker compose -f docker/docker-compose-jupyter.yml up
```
This will create a docker-container running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
@ -194,7 +196,7 @@ Please use the link that's printed in the console after startup for simplified l
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) up-to-date.
``` bash
docker-compose -f docker/docker-compose-jupyter.yml build --no-cache
docker compose -f docker/docker-compose-jupyter.yml build --no-cache
```
## Troubleshooting

11
docs/freqai-configuration.md
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@ -26,10 +26,7 @@ FreqAI is configured through the typical [Freqtrade config file](configuration.m
},
"data_split_parameters" : {
"test_size": 0.25
},
"model_training_parameters" : {
"n_estimators": 100
},
}
}
```
@ -118,7 +115,7 @@ The FreqAI strategy requires including the following lines of code in the standa
```
Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`.
@ -182,7 +179,7 @@ The `startup_candle_count` in the FreqAI strategy needs to be set up in the same
## Creating a dynamic target threshold
Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out.
Deciding when to enter or exit a trade can be done in a dynamic way to reflect current market conditions. FreqAI allows you to return additional information from the training of a model (more info [here](freqai-feature-engineering.md#returning-additional-info-from-training)). For example, the `&*_std/mean` return values describe the statistical distribution of the target/label *during the most recent training*. Comparing a given prediction to these values allows you to know the rarity of the prediction. In `templates/FreqaiExampleStrategy.py`, the `target_roi` and `sell_roi` are defined to be 1.25 z-scores away from the mean which causes predictions that are closer to the mean to be filtered out.
```python
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
@ -230,7 +227,7 @@ If you want to predict multiple targets, you need to define multiple labels usin
#### Classifiers
If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set
If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set
```python
df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')

26
docs/freqai-reinforcement-learning.md
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@ -247,6 +247,32 @@ where `unique-id` is the `identifier` set in the `freqai` configuration file. Th
![tensorboard](assets/tensorboard.jpg)
### Custom logging
FreqAI also provides a built in episodic summary logger called `self.tensorboard_log` for adding custom information to the Tensorboard log. By default, this function is already called once per step inside the environment to record the agent actions. All values accumulated for all steps in a single episode are reported at the conclusion of each episode, followed by a full reset of all metrics to 0 in preparation for the subsequent episode.
`self.tensorboard_log` can also be used anywhere inside the environment, for example, it can be added to the `calculate_reward` function to collect more detailed information about how often various parts of the reward were called:
```py
class MyRLEnv(Base5ActionRLEnv):
"""
User made custom environment. This class inherits from BaseEnvironment and gym.env.
Users can override any functions from those parent classes. Here is an example
of a user customized `calculate_reward()` function.
"""
def calculate_reward(self, action: int) -> float:
if not self._is_valid(action):
self.tensorboard_log("is_valid")
return -2
```
!!! Note
The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)` would add 0.23 to `float_metric`. In this case you can also disable incrementing using `inc=False` parameter.
### Choosing a base environment
FreqAI provides two base environments, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 4 or 5 actions. In the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Meanwhile, in the `Base5ActionEnvironment`, the agent has the same actions as Base4, but instead of a single exit action, it separates exit long and exit short. The main changes stemming from the environment selection include:

4
docs/sql_cheatsheet.md
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@ -13,12 +13,12 @@ Feel free to use a visual Database editor like SqliteBrowser if you feel more co
sudo apt-get install sqlite3
```
### Using sqlite3 via docker-compose
### Using sqlite3 via docker
The freqtrade docker image does contain sqlite3, so you can edit the database without having to install anything on the host system.
``` bash
docker-compose exec freqtrade /bin/bash
docker compose exec freqtrade /bin/bash
sqlite3 <database-file>.sqlite
```

2
docs/strategy-callbacks.md
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@ -773,7 +773,7 @@ class DigDeeperStrategy(IStrategy):
* Sell 100@10\$ -> Avg price: 8.5\$, realized profit 150\$, 17.65%
* Buy 150@11\$ -> Avg price: 10\$, realized profit 150\$, 17.65%
* Sell 100@12\$ -> Avg price: 10\$, total realized profit 350\$, 20%
* Sell 150@14\$ -> Avg price: 10\$, total realized profit 950\$, 40%
* Sell 150@14\$ -> Avg price: 10\$, total realized profit 950\$, 40% <- *This will be the last "Exit" message*
The total profit for this trade was 950$ on a 3350$ investment (`100@8$ + 100@9$ + 150@11$`). As such - the final relative profit is 28.35% (`950 / 3350`).

6
docs/strategy-customization.md
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@ -363,9 +363,9 @@ class AwesomeStrategy(IStrategy):
timeframe = "1d"
timeframe_mins = timeframe_to_minutes(timeframe)
minimal_roi = {
"0": 0.05, # 5% for the first 3 candles
str(timeframe_mins * 3)): 0.02, # 2% after 3 candles
str(timeframe_mins * 6)): 0.01, # 1% After 6 candles
"0": 0.05, # 5% for the first 3 candles
str(timeframe_mins * 3): 0.02, # 2% after 3 candles
str(timeframe_mins * 6): 0.01, # 1% After 6 candles
}
```

33
docs/strategy_analysis_example.md
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@ -2,12 +2,37 @@
Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.
The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.
Please follow the [documentation](https://www.freqtrade.io/en/stable/data-download/) for more details.
## Setup
### Change Working directory to repository root
```python
import os
from pathlib import Path
# Change directory
# Modify this cell to insure that the output shows the correct path.
# Define all paths relative to the project root shown in the cell output
project_root = "somedir/freqtrade"
i=0
try:
os.chdirdir(project_root)
assert Path('LICENSE').is_file()
except:
while i<4 and (not Path('LICENSE').is_file()):
os.chdir(Path(Path.cwd(), '../'))
i+=1
project_root = Path.cwd()
print(Path.cwd())
```
### Configure Freqtrade environment
```python
from freqtrade.configuration import Configuration
# Customize these according to your needs.
@ -15,14 +40,14 @@ from freqtrade.configuration import Configuration
# Initialize empty configuration object
config = Configuration.from_files([])
# Optionally (recommended), use existing configuration file
# config = Configuration.from_files(["config.json"])
# config = Configuration.from_files(["user_data/config.json"])
# Define some constants
config["timeframe"] = "5m"
# Name of the strategy class
config["strategy"] = "SampleStrategy"
# Location of the data
data_location = config['datadir']
data_location = config["datadir"]
# Pair to analyze - Only use one pair here
pair = "BTC/USDT"
```
@ -36,12 +61,12 @@ from freqtrade.enums import CandleType
candles = load_pair_history(datadir=data_location,
timeframe=config["timeframe"],
pair=pair,
data_format = "hdf5",
data_format = "json", # Make sure to update this to your data
candle_type=CandleType.SPOT,
)
# Confirm success
print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}")
print(f"Loaded {len(candles)} rows of data for {pair} from {data_location}")
candles.head()
```

6
docs/updating.md
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@ -6,14 +6,14 @@ To update your freqtrade installation, please use one of the below methods, corr
Breaking changes / changed behavior will be documented in the changelog that is posted alongside every release.
For the develop branch, please follow PR's to avoid being surprised by changes.
## docker-compose
## docker
!!! Note "Legacy installations using the `master` image"
We're switching from master to stable for the release Images - please adjust your docker-file and replace `freqtradeorg/freqtrade:master` with `freqtradeorg/freqtrade:stable`
``` bash
docker-compose pull
docker-compose up -d
docker compose pull
docker compose up -d
```
## Installation via setup script

4
docs/utils.md
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@ -652,7 +652,7 @@ Common arguments:
You can also use webserver mode via docker.
Starting a one-off container requires the configuration of the port explicitly, as ports are not exposed by default.
You can use `docker-compose run --rm -p 127.0.0.1:8080:8080 freqtrade webserver` to start a one-off container that'll be removed once you stop it. This assumes that port 8080 is still available and no other bot is running on that port.
You can use `docker compose run --rm -p 127.0.0.1:8080:8080 freqtrade webserver` to start a one-off container that'll be removed once you stop it. This assumes that port 8080 is still available and no other bot is running on that port.
Alternatively, you can reconfigure the docker-compose file to have the command updated:
@ -662,7 +662,7 @@ Alternatively, you can reconfigure the docker-compose file to have the command u
--config /freqtrade/user_data/config.json
```
You can now use `docker-compose up` to start the webserver.
You can now use `docker compose up` to start the webserver.
This assumes that the configuration has a webserver enabled and configured for docker (listening port = `0.0.0.0`).
!!! Tip

7
freqtrade/configuration/config_validation.py
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@ -355,6 +355,13 @@ def _validate_freqai_include_timeframes(conf: Dict[str, Any]) -> None:
f"Main timeframe of {main_tf} must be smaller or equal to FreqAI "
f"`include_timeframes`.Offending include-timeframes: {', '.join(offending_lines)}")
# Ensure that the base timeframe is included in the include_timeframes list
if main_tf not in freqai_include_timeframes:
feature_parameters = conf.get('freqai', {}).get('feature_parameters', {})
include_timeframes = [main_tf] + freqai_include_timeframes
conf.get('freqai', {}).get('feature_parameters', {}) \
.update({**feature_parameters, 'include_timeframes': include_timeframes})
def _validate_freqai_backtest(conf: Dict[str, Any]) -> None:
if conf.get('runmode', RunMode.OTHER) == RunMode.BACKTEST:

5
freqtrade/constants.py
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@ -608,9 +608,8 @@ CONF_SCHEMA = {
"backtest_period_days",
"identifier",
"feature_parameters",
"data_split_parameters",
"model_training_parameters"
]
"data_split_parameters"
]
},
},
}

2
freqtrade/freqai/RL/Base4ActionRLEnv.py
View File

@ -46,9 +46,9 @@ class Base4ActionRLEnv(BaseEnvironment):
self._done = True
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
self.tensorboard_log(self.actions._member_names_[action])
trade_type = None
if self.is_tradesignal(action):

1
freqtrade/freqai/RL/Base5ActionRLEnv.py
View File

@ -49,6 +49,7 @@ class Base5ActionRLEnv(BaseEnvironment):
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
self.tensorboard_log(self.actions._member_names_[action])
trade_type = None
if self.is_tradesignal(action):

51
freqtrade/freqai/RL/BaseEnvironment.py
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@ -2,7 +2,7 @@ import logging
import random
from abc import abstractmethod
from enum import Enum
from typing import Optional, Type
from typing import Optional, Type, Union
import gym
import numpy as np
@ -12,6 +12,7 @@ from gym.utils import seeding
from pandas import DataFrame
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import RunMode
logger = logging.getLogger(__name__)
@ -77,7 +78,13 @@ class BaseEnvironment(gym.Env):
# set here to default 5Ac, but all children envs can override this
self.actions: Type[Enum] = BaseActions
self.custom_info: dict = {}
self.tensorboard_metrics: dict = {}
self.live: bool = False
if dp:
self.live = dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
if not self.live and self.add_state_info:
self.add_state_info = False
logger.warning("add_state_info is not available in backtesting. Deactivating.")
def reset_env(self, df: DataFrame, prices: DataFrame, window_size: int,
reward_kwargs: dict, starting_point=True):
@ -132,20 +139,38 @@ class BaseEnvironment(gym.Env):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def tensorboard_log(self, metric: str, value: Union[int, float] = 1, inc: bool = True):
"""
Function builds the tensorboard_metrics dictionary
to be parsed by the TensorboardCallback. This
function is designed for tracking incremented objects,
events, actions inside the training environment.
For example, a user can call this to track the
frequency of occurence of an `is_valid` call in
their `calculate_reward()`:
def calculate_reward(self, action: int) -> float:
if not self._is_valid(action):
self.tensorboard_log("is_valid")
return -2
:param metric: metric to be tracked and incremented
:param value: value to increment `metric` by
:param inc: sets whether the `value` is incremented or not
"""
if not inc or metric not in self.tensorboard_metrics:
self.tensorboard_metrics[metric] = value
else:
self.tensorboard_metrics[metric] += value
def reset_tensorboard_log(self):
self.tensorboard_metrics = {}
def reset(self):
"""
Reset is called at the beginning of every episode
"""
# custom_info is used for episodic reports and tensorboard logging
self.custom_info["Invalid"] = 0
self.custom_info["Hold"] = 0
self.custom_info["Unknown"] = 0
self.custom_info["pnl_factor"] = 0
self.custom_info["duration_factor"] = 0
self.custom_info["reward_exit"] = 0
self.custom_info["reward_hold"] = 0
for action in self.actions:
self.custom_info[f"{action.name}"] = 0
self.reset_tensorboard_log()
self._done = False
@ -188,7 +213,7 @@ class BaseEnvironment(gym.Env):
"""
features_window = self.signal_features[(
self._current_tick - self.window_size):self._current_tick]
if self.add_state_info:
if self.add_state_info and self.live:
features_and_state = DataFrame(np.zeros((len(features_window), 3)),
columns=['current_profit_pct',
'position',

29
freqtrade/freqai/RL/TensorboardCallback.py
View File

@ -42,19 +42,18 @@ class TensorboardCallback(BaseCallback):
)
def _on_step(self) -> bool:
custom_info = self.training_env.get_attr("custom_info")[0]
self.logger.record("_state/position", self.locals["infos"][0]["position"])
self.logger.record("_state/trade_duration", self.locals["infos"][0]["trade_duration"])
self.logger.record("_state/current_profit_pct", self.locals["infos"]
[0]["current_profit_pct"])
self.logger.record("_reward/total_profit", self.locals["infos"][0]["total_profit"])
self.logger.record("_reward/total_reward", self.locals["infos"][0]["total_reward"])
self.logger.record_mean("_reward/mean_trade_duration", self.locals["infos"]
[0]["trade_duration"])
self.logger.record("_actions/action", self.locals["infos"][0]["action"])
self.logger.record("_actions/_Invalid", custom_info["Invalid"])
self.logger.record("_actions/_Unknown", custom_info["Unknown"])
self.logger.record("_actions/Hold", custom_info["Hold"])
for action in self.actions:
self.logger.record(f"_actions/{action.name}", custom_info[action.name])
local_info = self.locals["infos"][0]
tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
for info in local_info:
if info not in ["episode", "terminal_observation"]:
self.logger.record(f"_info/{info}", local_info[info])
for info in tensorboard_metrics:
if info in [action.name for action in self.actions]:
self.logger.record(f"_actions/{info}", tensorboard_metrics[info])
else:
self.logger.record(f"_custom/{info}", tensorboard_metrics[info])
return True

5
freqtrade/freqai/base_models/BaseClassifierModel.py
View File

@ -95,9 +95,14 @@ class BaseClassifierModel(IFreqaiModel):
self.data_cleaning_predict(dk)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
if self.CONV_WIDTH == 1:
predictions = np.reshape(predictions, (-1, len(dk.label_list)))
pred_df = DataFrame(predictions, columns=dk.label_list)
predictions_prob = self.model.predict_proba(dk.data_dictionary["prediction_features"])
if self.CONV_WIDTH == 1:
predictions_prob = np.reshape(predictions_prob, (-1, len(self.model.classes_)))
pred_df_prob = DataFrame(predictions_prob, columns=self.model.classes_)
pred_df = pd.concat([pred_df, pred_df_prob], axis=1)

3
freqtrade/freqai/base_models/BaseRegressionModel.py
View File

@ -95,6 +95,9 @@ class BaseRegressionModel(IFreqaiModel):
self.data_cleaning_predict(dk)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
if self.CONV_WIDTH == 1:
predictions = np.reshape(predictions, (-1, len(dk.label_list)))
pred_df = DataFrame(predictions, columns=dk.label_list)
pred_df = dk.denormalize_labels_from_metadata(pred_df)

11
freqtrade/freqai/prediction_models/ReinforcementLearner.py
View File

@ -61,7 +61,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
tensorboard_log=Path(
dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
**self.freqai_info['model_training_parameters']
**self.freqai_info.get('model_training_parameters', {})
)
else:
logger.info('Continual training activated - starting training from previously '
@ -100,7 +100,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
"""
# first, penalize if the action is not valid
if not self._is_valid(action):
self.custom_info["Invalid"] += 1
self.tensorboard_log("is_valid")
return -2
pnl = self.get_unrealized_profit()
@ -109,15 +109,12 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
# reward agent for entering trades
if (action == Actions.Long_enter.value
and self._position == Positions.Neutral):
self.custom_info[f"{Actions.Long_enter.name}"] += 1
return 25
if (action == Actions.Short_enter.value
and self._position == Positions.Neutral):
self.custom_info[f"{Actions.Short_enter.name}"] += 1
return 25
# discourage agent from not entering trades
if action == Actions.Neutral.value and self._position == Positions.Neutral:
self.custom_info[f"{Actions.Neutral.name}"] += 1
return -1
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
@ -131,22 +128,18 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
# discourage sitting in position
if (self._position in (Positions.Short, Positions.Long) and
action == Actions.Neutral.value):
self.custom_info["Hold"] += 1
return -1 * trade_duration / max_trade_duration
# close long
if action == Actions.Long_exit.value and self._position == Positions.Long:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
self.custom_info[f"{Actions.Long_exit.name}"] += 1
return float(pnl * factor)
# close short
if action == Actions.Short_exit.value and self._position == Positions.Short:
if pnl > self.profit_aim * self.rr:
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
self.custom_info[f"{Actions.Short_exit.name}"] += 1
return float(pnl * factor)
self.custom_info["Unknown"] += 1
return 0.

9
freqtrade/freqtradebot.py
View File

@ -155,6 +155,8 @@ class FreqtradeBot(LoggingMixin):
self.cancel_all_open_orders()
self.check_for_open_trades()
except Exception as e:
logger.warning(f'Exception during cleanup: {e.__class__.__name__} {e}')
finally:
self.strategy.ft_bot_cleanup()
@ -162,8 +164,13 @@ class FreqtradeBot(LoggingMixin):
self.rpc.cleanup()
if self.emc:
self.emc.shutdown()
Trade.commit()
self.exchange.close()
try:
Trade.commit()
except Exception:
# Exeptions here will be happening if the db disappeared.
# At which point we can no longer commit anyway.
pass
def startup(self) -> None:
"""

2
freqtrade/plugins/pairlist/VolumePairList.py
View File

@ -218,7 +218,7 @@ class VolumePairList(IPairList):
else:
filtered_tickers[i]['quoteVolume'] = 0
else:
# Tickers mode - filter based on incomming pairlist.
# Tickers mode - filter based on incoming pairlist.
filtered_tickers = [v for k, v in tickers.items() if k in pairlist]
if self._min_value > 0:

34
freqtrade/rpc/rpc.py
View File

@ -167,6 +167,7 @@ class RPC:
results = []
for trade in trades:
order: Optional[Order] = None
current_profit_fiat: Optional[float] = None
if trade.open_order_id:
order = trade.select_order_by_order_id(trade.open_order_id)
# calculate profit and send message to user
@ -176,23 +177,26 @@ class RPC:
trade.pair, side='exit', is_short=trade.is_short, refresh=False)
except (ExchangeError, PricingError):
current_rate = NAN
if len(trade.select_filled_orders(trade.entry_side)) > 0:
current_profit = trade.calc_profit_ratio(
current_rate) if not isnan(current_rate) else NAN
current_profit_abs = trade.calc_profit(
current_rate) if not isnan(current_rate) else NAN
else:
current_profit = current_profit_abs = current_profit_fiat = 0.0
else:
# Closed trade ...
current_rate = trade.close_rate
if len(trade.select_filled_orders(trade.entry_side)) > 0:
current_profit = trade.calc_profit_ratio(
current_rate) if not isnan(current_rate) else NAN
current_profit_abs = trade.calc_profit(
current_rate) if not isnan(current_rate) else NAN
current_profit_fiat: Optional[float] = None
# Calculate fiat profit
if self._fiat_converter:
current_profit_fiat = self._fiat_converter.convert_amount(
current_profit_abs,
self._freqtrade.config['stake_currency'],
self._freqtrade.config['fiat_display_currency']
)
else:
current_profit = current_profit_abs = current_profit_fiat = 0.0
current_profit = trade.close_profit
current_profit_abs = trade.close_profit_abs
# Calculate fiat profit
if not isnan(current_profit_abs) and self._fiat_converter:
current_profit_fiat = self._fiat_converter.convert_amount(
current_profit_abs,
self._freqtrade.config['stake_currency'],
self._freqtrade.config['fiat_display_currency']
)
# Calculate guaranteed profit (in case of trailing stop)
stoploss_entry_dist = trade.calc_profit(trade.stop_loss)

48
freqtrade/templates/strategy_analysis_example.ipynb
View File

@ -7,14 +7,17 @@
"# Strategy analysis example\n",
"\n",
"Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.\n",
"The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location."
"The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.\n",
"Please follow the [documentation](https://www.freqtrade.io/en/stable/data-download/) for more details."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
"## Setup\n",
"\n",
"### Change Working directory to repository root"
]
},
{
@ -23,7 +26,38 @@
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from pathlib import Path\n",
"\n",
"# Change directory\n",
"# Modify this cell to insure that the output shows the correct path.\n",
"# Define all paths relative to the project root shown in the cell output\n",
"project_root = \"somedir/freqtrade\"\n",
"i=0\n",
"try:\n",
" os.chdirdir(project_root)\n",
" assert Path('LICENSE').is_file()\n",
"except:\n",
" while i<4 and (not Path('LICENSE').is_file()):\n",
" os.chdir(Path(Path.cwd(), '../'))\n",
" i+=1\n",
" project_root = Path.cwd()\n",
"print(Path.cwd())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure Freqtrade environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from freqtrade.configuration import Configuration\n",
"\n",
"# Customize these according to your needs.\n",
@ -31,14 +65,14 @@
"# Initialize empty configuration object\n",
"config = Configuration.from_files([])\n",
"# Optionally (recommended), use existing configuration file\n",
"# config = Configuration.from_files([\"config.json\"])\n",
"# config = Configuration.from_files([\"user_data/config.json\"])\n",
"\n",
"# Define some constants\n",
"config[\"timeframe\"] = \"5m\"\n",
"# Name of the strategy class\n",
"config[\"strategy\"] = \"SampleStrategy\"\n",
"# Location of the data\n",
"data_location = config['datadir']\n",
"data_location = config[\"datadir\"]\n",
"# Pair to analyze - Only use one pair here\n",
"pair = \"BTC/USDT\""
]
@ -56,12 +90,12 @@
"candles = load_pair_history(datadir=data_location,\n",
" timeframe=config[\"timeframe\"],\n",
" pair=pair,\n",
" data_format = \"hdf5\",\n",
" data_format = \"json\", # Make sure to update this to your data\n",
" candle_type=CandleType.SPOT,\n",
" )\n",
"\n",
"# Confirm success\n",
"print(\"Loaded \" + str(len(candles)) + f\" rows of data for {pair} from {data_location}\")\n",
"print(f\"Loaded {len(candles)} rows of data for {pair} from {data_location}\")\n",
"candles.head()"
]
},
@ -365,7 +399,7 @@
"metadata": {
"file_extension": ".py",
"kernelspec": {
"display_name": "Python 3.9.7 64-bit ('trade_397')",
"display_name": "Python 3.9.7 64-bit",
"language": "python",
"name": "python3"
},

4
requirements-dev.txt
View File

@ -12,7 +12,7 @@ flake8-tidy-imports==4.8.0
mypy==0.991
pre-commit==2.20.0
pytest==7.2.0
pytest-asyncio==0.20.2
pytest-asyncio==0.20.3
pytest-cov==4.0.0
pytest-mock==3.10.0
pytest-random-order==1.1.0
@ -23,7 +23,7 @@ time-machine==2.8.2
httpx==0.23.1
# Convert jupyter notebooks to markdown documents
nbconvert==7.2.5
nbconvert==7.2.6
# mypy types
types-cachetools==5.2.1

2
requirements-freqai.txt
View File

@ -7,5 +7,5 @@ scikit-learn==1.1.3
joblib==1.2.0
catboost==1.1.1; platform_machine != 'aarch64'
lightgbm==3.3.3
xgboost==1.7.1
xgboost==1.7.2
tensorboard==2.11.0

2
requirements-hyperopt.txt
View File

@ -5,5 +5,5 @@
scipy==1.9.3
scikit-learn==1.1.3
scikit-optimize==0.9.0
filelock==3.8.0
filelock==3.8.2
progressbar2==4.2.0

11
requirements.txt
View File

@ -2,13 +2,13 @@ numpy==1.23.5
pandas==1.5.2
pandas-ta==0.3.14b
ccxt==2.2.67
ccxt==2.2.92
# Pin cryptography for now due to rust build errors with piwheels
cryptography==38.0.1; platform_machine == 'armv7l'
cryptography==38.0.4; platform_machine != 'armv7l'
aiohttp==3.8.3
SQLAlchemy==1.4.44
python-telegram-bot==13.14
SQLAlchemy==1.4.45
python-telegram-bot==13.15
arrow==1.2.3
cachetools==4.2.2
requests==2.28.1
@ -20,7 +20,8 @@ tabulate==0.9.0
pycoingecko==3.1.0
jinja2==3.1.2
tables==3.7.0
blosc==1.10.6
blosc==1.10.6; platform_machine == 'arm64'
blosc==1.11.0; platform_machine != 'arm64'
joblib==1.2.0
pyarrow==10.0.1; platform_machine != 'armv7l'
@ -47,7 +48,7 @@ psutil==5.9.4
colorama==0.4.6
# Building config files interactively
questionary==1.10.0
prompt-toolkit==3.0.33
prompt-toolkit==3.0.36
# Extensions to datetime library
python-dateutil==2.8.2

5
tests/conftest.py
View File

@ -408,6 +408,11 @@ def create_mock_trades_usdt(fee, is_short: Optional[bool] = False, use_db: bool
Trade.commit()
@pytest.fixture(autouse=True)
def patch_gc(mocker) -> None:
mocker.patch("freqtrade.main.gc_set_threshold")
@pytest.fixture(autouse=True)
def patch_coingekko(mocker) -> None:
"""

9
tests/exchange/test_ccxt_compat.py
View File

@ -224,8 +224,13 @@ class TestCCXTExchange():
for val in [1, 2, 5, 25, 100]:
l2 = exchange.fetch_l2_order_book(pair, val)
if not l2_limit_range or val in l2_limit_range:
assert len(l2['asks']) == val
assert len(l2['bids']) == val
if val > 50:
# Orderbooks are not always this deep.
assert val - 5 < len(l2['asks']) <= val
assert val - 5 < len(l2['bids']) <= val
else:
assert len(l2['asks']) == val
assert len(l2['bids']) == val
else:
next_limit = exchange.get_next_limit_in_list(
val, l2_limit_range, l2_limit_range_required)

291
tests/rpc/test_rpc_telegram.py
View File

@ -12,6 +12,7 @@ from unittest.mock import ANY, MagicMock
import arrow
import pytest
import time_machine
from pandas import DataFrame
from telegram import Chat, Message, ReplyKeyboardMarkup, Update
from telegram.error import BadRequest, NetworkError, TelegramError
@ -1906,119 +1907,120 @@ def test_send_msg_entry_fill_notification(default_conf, mocker, message_type, en
def test_send_msg_sell_notification(default_conf, mocker) -> None:
telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
with time_machine.travel("2022-09-01 05:00:00 +00:00", tick=False):
telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
old_convamount = telegram._rpc._fiat_converter.convert_amount
telegram._rpc._fiat_converter.convert_amount = lambda a, b, c: -24.812
telegram.send_msg({
'type': RPCMessageType.EXIT,
'trade_id': 1,
'exchange': 'Binance',
'pair': 'KEY/ETH',
'leverage': 1.0,
'direction': 'Long',
'gain': 'loss',
'order_rate': 3.201e-05,
'amount': 1333.3333333333335,
'order_type': 'market',
'open_rate': 7.5e-05,
'current_rate': 3.201e-05,
'profit_amount': -0.05746268,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD',
'enter_tag': 'buy_signal1',
'exit_reason': ExitType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(hours=-1),
'close_date': arrow.utcnow(),
})
assert msg_mock.call_args[0][0] == (
'\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
'*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n'
'*Enter Tag:* `buy_signal1`\n'
'*Exit Reason:* `stop_loss`\n'
'*Direction:* `Long`\n'
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Current Rate:* `0.00003201`\n'
'*Exit Rate:* `0.00003201`\n'
'*Duration:* `1:00:00 (60.0 min)`'
)
msg_mock.reset_mock()
telegram.send_msg({
'type': RPCMessageType.EXIT,
'trade_id': 1,
'exchange': 'Binance',
'pair': 'KEY/ETH',
'direction': 'Long',
'gain': 'loss',
'order_rate': 3.201e-05,
'amount': 1333.3333333333335,
'order_type': 'market',
'open_rate': 7.5e-05,
'current_rate': 3.201e-05,
'cumulative_profit': -0.15746268,
'profit_amount': -0.05746268,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD',
'enter_tag': 'buy_signal1',
'exit_reason': ExitType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
'close_date': arrow.utcnow(),
'stake_amount': 0.01,
'sub_trade': True,
})
assert msg_mock.call_args[0][0] == (
'\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
'*Unrealized Sub Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n'
'*Cumulative Profit:* (`-0.15746268 ETH / -24.812 USD`)\n'
'*Enter Tag:* `buy_signal1`\n'
'*Exit Reason:* `stop_loss`\n'
'*Direction:* `Long`\n'
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Current Rate:* `0.00003201`\n'
'*Exit Rate:* `0.00003201`\n'
'*Remaining:* `(0.01 ETH, -24.812 USD)`'
old_convamount = telegram._rpc._fiat_converter.convert_amount
telegram._rpc._fiat_converter.convert_amount = lambda a, b, c: -24.812
telegram.send_msg({
'type': RPCMessageType.EXIT,
'trade_id': 1,
'exchange': 'Binance',
'pair': 'KEY/ETH',
'leverage': 1.0,
'direction': 'Long',
'gain': 'loss',
'order_rate': 3.201e-05,
'amount': 1333.3333333333335,
'order_type': 'market',
'open_rate': 7.5e-05,
'current_rate': 3.201e-05,
'profit_amount': -0.05746268,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD',
'enter_tag': 'buy_signal1',
'exit_reason': ExitType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(hours=-1),
'close_date': arrow.utcnow(),
})
assert msg_mock.call_args[0][0] == (
'\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
'*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n'
'*Enter Tag:* `buy_signal1`\n'
'*Exit Reason:* `stop_loss`\n'
'*Direction:* `Long`\n'
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Current Rate:* `0.00003201`\n'
'*Exit Rate:* `0.00003201`\n'
'*Duration:* `1:00:00 (60.0 min)`'
)
msg_mock.reset_mock()
telegram.send_msg({
'type': RPCMessageType.EXIT,
'trade_id': 1,
'exchange': 'Binance',
'pair': 'KEY/ETH',
'direction': 'Long',
'gain': 'loss',
'order_rate': 3.201e-05,
'amount': 1333.3333333333335,
'order_type': 'market',
'open_rate': 7.5e-05,
'current_rate': 3.201e-05,
'profit_amount': -0.05746268,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'enter_tag': 'buy_signal1',
'exit_reason': ExitType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
'close_date': arrow.utcnow(),
})
assert msg_mock.call_args[0][0] == (
'\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
'*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH)`\n'
'*Enter Tag:* `buy_signal1`\n'
'*Exit Reason:* `stop_loss`\n'
'*Direction:* `Long`\n'
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Current Rate:* `0.00003201`\n'
'*Exit Rate:* `0.00003201`\n'
'*Duration:* `1 day, 2:30:00 (1590.0 min)`'
)
# Reset singleton function to avoid random breaks
telegram._rpc._fiat_converter.convert_amount = old_convamount
msg_mock.reset_mock()
telegram.send_msg({
'type': RPCMessageType.EXIT,
'trade_id': 1,
'exchange': 'Binance',
'pair': 'KEY/ETH',
'direction': 'Long',
'gain': 'loss',
'order_rate': 3.201e-05,
'amount': 1333.3333333333335,
'order_type': 'market',
'open_rate': 7.5e-05,
'current_rate': 3.201e-05,
'cumulative_profit': -0.15746268,
'profit_amount': -0.05746268,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'fiat_currency': 'USD',
'enter_tag': 'buy_signal1',
'exit_reason': ExitType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
'close_date': arrow.utcnow(),
'stake_amount': 0.01,
'sub_trade': True,
})
assert msg_mock.call_args[0][0] == (
'\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
'*Unrealized Sub Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n'
'*Cumulative Profit:* (`-0.15746268 ETH / -24.812 USD`)\n'
'*Enter Tag:* `buy_signal1`\n'
'*Exit Reason:* `stop_loss`\n'
'*Direction:* `Long`\n'
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Current Rate:* `0.00003201`\n'
'*Exit Rate:* `0.00003201`\n'
'*Remaining:* `(0.01 ETH, -24.812 USD)`'
)
msg_mock.reset_mock()
telegram.send_msg({
'type': RPCMessageType.EXIT,
'trade_id': 1,
'exchange': 'Binance',
'pair': 'KEY/ETH',
'direction': 'Long',
'gain': 'loss',
'order_rate': 3.201e-05,
'amount': 1333.3333333333335,
'order_type': 'market',
'open_rate': 7.5e-05,
'current_rate': 3.201e-05,
'profit_amount': -0.05746268,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'enter_tag': 'buy_signal1',
'exit_reason': ExitType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
'close_date': arrow.utcnow(),
})
assert msg_mock.call_args[0][0] == (
'\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
'*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH)`\n'
'*Enter Tag:* `buy_signal1`\n'
'*Exit Reason:* `stop_loss`\n'
'*Direction:* `Long`\n'
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Current Rate:* `0.00003201`\n'
'*Exit Rate:* `0.00003201`\n'
'*Duration:* `1 day, 2:30:00 (1590.0 min)`'
)
# Reset singleton function to avoid random breaks
telegram._rpc._fiat_converter.convert_amount = old_convamount
def test_send_msg_sell_cancel_notification(default_conf, mocker) -> None:
@ -2065,41 +2067,42 @@ def test_send_msg_sell_fill_notification(default_conf, mocker, direction,
default_conf['telegram']['notification_settings']['exit_fill'] = 'on'
telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
telegram.send_msg({
'type': RPCMessageType.EXIT_FILL,
'trade_id': 1,
'exchange': 'Binance',
'pair': 'KEY/ETH',
'leverage': leverage,
'direction': direction,
'gain': 'loss',
'limit': 3.201e-05,
'amount': 1333.3333333333335,
'order_type': 'market',
'open_rate': 7.5e-05,
'close_rate': 3.201e-05,
'profit_amount': -0.05746268,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'enter_tag': enter_signal,
'exit_reason': ExitType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
'close_date': arrow.utcnow(),
})
with time_machine.travel("2022-09-01 05:00:00 +00:00", tick=False):
telegram.send_msg({
'type': RPCMessageType.EXIT_FILL,
'trade_id': 1,
'exchange': 'Binance',
'pair': 'KEY/ETH',
'leverage': leverage,
'direction': direction,
'gain': 'loss',
'limit': 3.201e-05,
'amount': 1333.3333333333335,
'order_type': 'market',
'open_rate': 7.5e-05,
'close_rate': 3.201e-05,
'profit_amount': -0.05746268,
'profit_ratio': -0.57405275,
'stake_currency': 'ETH',
'enter_tag': enter_signal,
'exit_reason': ExitType.STOP_LOSS.value,
'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
'close_date': arrow.utcnow(),
})
leverage_text = f'*Leverage:* `{leverage}`\n' if leverage and leverage != 1.0 else ''
assert msg_mock.call_args[0][0] == (
'\N{WARNING SIGN} *Binance (dry):* Exited KEY/ETH (#1)\n'
'*Profit:* `-57.41% (loss: -0.05746268 ETH)`\n'
f'*Enter Tag:* `{enter_signal}`\n'
'*Exit Reason:* `stop_loss`\n'
f"*Direction:* `{direction}`\n"
f"{leverage_text}"
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Exit Rate:* `0.00003201`\n'
'*Duration:* `1 day, 2:30:00 (1590.0 min)`'
)
leverage_text = f'*Leverage:* `{leverage}`\n' if leverage and leverage != 1.0 else ''
assert msg_mock.call_args[0][0] == (
'\N{WARNING SIGN} *Binance (dry):* Exited KEY/ETH (#1)\n'
'*Profit:* `-57.41% (loss: -0.05746268 ETH)`\n'
f'*Enter Tag:* `{enter_signal}`\n'
'*Exit Reason:* `stop_loss`\n'
f"*Direction:* `{direction}`\n"
f"{leverage_text}"
'*Amount:* `1333.33333333`\n'
'*Open Rate:* `0.00007500`\n'
'*Exit Rate:* `0.00003201`\n'
'*Duration:* `1 day, 2:30:00 (1590.0 min)`'
)
def test_send_msg_status_notification(default_conf, mocker) -> None:

7
tests/test_configuration.py
View File

@ -1046,8 +1046,13 @@ def test__validate_freqai_include_timeframes(default_conf, caplog) -> None:
# Validation pass
conf.update({'timeframe': '1m'})
validate_config_consistency(conf)
conf.update({'analyze_per_epoch': True})
# Ensure base timeframe is in include_timeframes
conf['freqai']['feature_parameters']['include_timeframes'] = ["5m", "15m"]
validate_config_consistency(conf)
assert conf['freqai']['feature_parameters']['include_timeframes'] == ["1m", "5m", "15m"]
conf.update({'analyze_per_epoch': True})
with pytest.raises(OperationalException,
match=r"Using analyze-per-epoch .* not supported with a FreqAI strategy."):
validate_config_consistency(conf)

12
tests/test_freqtradebot.py
View File

@ -88,6 +88,18 @@ def test_bot_cleanup(mocker, default_conf_usdt, caplog) -> None:
assert coo_mock.call_count == 1
def test_bot_cleanup_db_errors(mocker, default_conf_usdt, caplog) -> None:
mocker.patch('freqtrade.freqtradebot.Trade.commit',
side_effect=OperationalException())
mocker.patch('freqtrade.freqtradebot.FreqtradeBot.check_for_open_trades',
side_effect=OperationalException())
freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
freqtrade.emc = MagicMock()
freqtrade.emc.shutdown = MagicMock()
freqtrade.cleanup()
assert freqtrade.emc.shutdown.call_count == 1
@pytest.mark.parametrize('runmode', [
RunMode.DRY_RUN,
RunMode.LIVE