The bug happened since it just checked the length of the list itself, not what it represents. in this case .*/USDT could be any amount of pairs
when the user sets a max_open_trades of let's say 3 then the pairs only have 3 trade slots of whatever amount of pairs it really has and thereby creating a bottleneck.
This just sets the max_open_trades to -1 without even checking it, letting freqtrade itself handle the amount of trades allowed at a given time.
AI generated strategies and configs cause a lot of noise.
we should be clear that people shall read the documentation first
(this will also allow us to point people at this within the issue).
I got the following error on Pi 2 (using Debian Bookworm armhf arch)
```
...
running bdist_wheel
running build
running build_py
creating build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/lock.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/backend_ctypes.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/_imp_emulation.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/model.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/ffiplatform.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/setuptools_ext.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/error.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/vengine_gen.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/api.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/__init__.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/recompiler.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/cffi_opcode.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/pkgconfig.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/verifier.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/cparser.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/_shimmed_dist_utils.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/commontypes.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/vengine_cpy.py -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/_cffi_include.h -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/parse_c_type.h -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/_embedding.h -> build/lib.linux-armv7l-cpython-311/cffi
copying src/cffi/_cffi_errors.h -> build/lib.linux-armv7l-cpython-311/cffi
running build_ext
building '_cffi_backend' extension
creating build/temp.linux-armv7l-cpython-311/src/c
arm-linux-gnueabihf-gcc -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -fPIC -DFFI_BUILDING=1 -DUSE__THREAD -DHAVE_SYNC_SYNCHRONIZE -I/usr/include/ffi -I/usr/include/libffi -I/freqtrade/.venv/include -I/usr/include/python3.11 -c src/c/_cffi_backend.c -o build/temp.linux-armv7l-cpython-311/src/c/_cffi_backend.o
src/c/_cffi_backend.c:15:10: fatal error: ffi.h: No such file or directory
15 | #include <ffi.h>
| ^~~~~~~
compilation terminated.
error: command '/usr/bin/arm-linux-gnueabihf-gcc' failed with exit code 1
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for cffi
Failed to build cffi
ERROR: Failed to build installable wheels for some pyproject.toml based projects (cffi)
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× pip subprocess to install build dependencies did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
Failed installing dependencies
```
It can be Easily solved by installing libffi-dev before hand
There are tests failing when using delayed and wrap_non_picklable_objects as decorator.
until I'll find a solution to run generate_optimizer standalone for analyze_per_epoch=True
with warnings.catch_warnings():
from optuna.exceptions import ExperimentalWarning
warnings.filterwarnings("ignore", category=FutureWarning)
this should be when importing sampler
The issue as that `logging.config.dictConfig(log_config)` ends up using the dictionary in place and including non-picklable items (saw an RLock), blowing up backtesting.
freqtrade/optimize/space/optunaspaces.py:39: error: Argument 1 to "__init__" of "IntDistribution" has incompatible type "int | float"; expected "int" [arg-type]
freqtrade/optimize/space/optunaspaces.py:39: error: Argument 2 to "__init__" of "IntDistribution" has incompatible type "int | float"; expected "int" [arg-type]
remove all references for ExtraTreesRegressor and skopt.space
"description":"Telegram chat or group ID. Recommended to be set via environment variable FREQTRADE__TELEGRAM__CHAT_ID",
"type":"string"
},
"topic_id":{
"description":"Telegram topic ID - only applicable for group chats",
"description":"Telegram topic ID - only applicable for group chats. Recommended to be set via environment variable FREQTRADE__TELEGRAM__TOPIC_ID",
"type":"string"
},
"authorized_users":{
"description":"Authorized users for the bot.",
"type":"array",
"items":{
"type":"string"
},
"uniqueItems":true
},
"allow_custom_messages":{
"description":"Allow sending custom messages from the Strategy.",
"type":"boolean",
@@ -760,9 +773,11 @@
"type":"object",
"properties":{
"enabled":{
"description":"Enable webhook notifications.",
"type":"boolean"
},
"url":{
"description":"Webhook URL. Recommended to be set via environment variable FREQTRADE__WEBHOOK__URL",
"type":"string"
},
"format":{
@@ -840,6 +855,7 @@
"type":"boolean"
},
"webhook_url":{
"description":"Discord webhook URL. Recommended to be set via environment variable FREQTRADE__DISCORD__WEBHOOK_URL",
"type":"string"
},
"exit_fill":{
@@ -1019,6 +1035,7 @@
"type":"string",
"enum":[
"running",
"paused",
"stopped"
]
},
@@ -1154,28 +1171,35 @@
"description":"Name of the exchange.",
"type":"string"
},
"enable_ws":{
"description":"Enable WebSocket connections to the exchange.",
"type":"boolean",
"default":true
},
"key":{
"description":"API key for the exchange.",
"description":"API key for the exchange. Recommended to be set via environment variable FREQTRADE__EXCHANGE__KEY",
"type":"string",
"default":""
},
"secret":{
"description":"API secret for the exchange.",
"description":"API secret for the exchange. Recommended to be set via environment variable FREQTRADE__EXCHANGE__SECRET",
"type":"string",
"default":""
},
"password":{
"description":"Password for the exchange, if required.",
"description":"Password for the exchange, if required. Recommended to be set via environment variable FREQTRADE__EXCHANGE__PASSWORD",
"type":"string",
"default":""
},
"uid":{
"description":"User ID for the exchange, if required.",
"description":"User ID for the exchange, if required. Recommended to be set via environment variable FREQTRADE__EXCHANGE__UID",
"type":"string"
},
"account_id":{
"description":"Account ID for the exchange, if required. Recommended to be set via environment variable FREQTRADE__EXCHANGE__ACCOUNT_ID",
"type":"string"
},
"wallet_address":{
"description":"Wallet address for the exchange, if required. Usually used by DEX exchanges. Recommended to be set via environment variable FREQTRADE__EXCHANGE__WALLET_ADDRESS",
"type":"string"
},
"private_key":{
"description":"Private key for the exchange, if required. Usually used by DEX exchanges. Recommended to be set via environment variable FREQTRADE__EXCHANGE__PRIVATE_KEY",
"type":"string"
},
"pair_whitelist":{
@@ -1199,6 +1223,11 @@
"type":"boolean",
"default":false
},
"enable_ws":{
"description":"Enable WebSocket connections to the exchange.",
"type":"boolean",
"default":true
},
"unknown_fee_rate":{
"description":"Fee rate for unknown markets.",
"type":"number"
@@ -1272,6 +1301,30 @@
"allowed_risk"
]
},
"logging":{
"type":"object",
"properties":{
"version":{
"type":"number",
"const":1
},
"formatters":{
"type":"object"
},
"handlers":{
"type":"object"
},
"root":{
"type":"object"
}
},
"required":[
"version",
"formatters",
"handlers",
"root"
]
},
"external_message_consumer":{
"description":"Configuration for external message consumer.",
"type":"object",
@@ -1366,10 +1419,10 @@
"type":"boolean",
"default":false
},
"keras":{
"description":"Use Keras for model training.",
"type":"boolean",
"default":false
"identifier":{
"description":"A unique ID for the current model. Must be changed when modifying features.",
"type":"string",
"default":"example"
},
"write_metrics_to_disk":{
"description":"Write metrics to disk?",
@@ -1399,16 +1452,49 @@
"type":"number",
"default":7
},
"identifier":{
"description":"A unique ID for the current model. Must be changed when modifying features.",
"type":"string",
"default":"example"
"live_retrain_hours":{
"description":"Frequency of retraining during dry/live runs.",
"type":"number",
"default":0
},
"expiration_hours":{
"description":"Avoid making predictions if a model is more than `expiration_hours` old. Defaults to 0 (no expiration).",
"type":"number",
"default":0
},
"save_backtest_models":{
"description":"Save models to disk when running backtesting.",
"type":"boolean",
"default":false
},
"fit_live_predictions_candles":{
"description":"Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset.",
"type":"integer"
},
"data_kitchen_thread_count":{
"description":"Designate the number of threads you want to use for data processing (outlier methods, normalization, etc.).",
"type":"integer"
},
"activate_tensorboard":{
"description":"Indicate whether or not to activate tensorboard",
"type":"boolean",
"default":true
},
"wait_for_training_iteration_on_reload":{
"description":"Wait for the next training iteration to complete after /reload or ctrl+c.",
"type":"boolean",
"default":true
},
"continual_learning":{
"description":"Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning.",
"type":"boolean",
"default":false
},
"keras":{
"description":"Use Keras for model training.",
"type":"boolean",
"default":false
},
"feature_parameters":{
"description":"The parameters used to engineer the feature set",
"type":"object",
@@ -1445,6 +1531,14 @@
"type":"boolean",
"default":false
},
"indicator_periods_candles":{
"description":"Time periods to calculate indicators for. The indicators are added to the base indicator dataset.",
"type":"array",
"items":{
"type":"number",
"minimum":1
}
},
"use_SVM_to_remove_outliers":{
"description":"Use SVM to remove outliers from the features.",
Possible values are either one of "GP", "RF", "ET", "GBRT" (Details can be found in the [scikit-optimize documentation](https://scikit-optimize.github.io/)), or "an instance of a class that inherits from `RegressorMixin` (from sklearn) and where the `predict` method has an optional `return_std` argument, which returns `std(Y | x)` along with `E[Y | x]`".
Possible values are either one of "NSGAIISampler", "TPESampler", "GPSampler", "CmaEsSampler", "NSGAIIISampler", "QMCSampler" (Details can be found in the [optuna-samplers documentation](https://optuna.readthedocs.io/en/stable/reference/samplers/index.html)), or "an instance of a class that inherits from `optuna.samplers.BaseSampler`".
Some research will be necessary to find additional Regressors.
Example for `ExtraTreesRegressor` ("ET") with additional parameters:
# Corresponds to "ET" - but allows additional parameters.
return ExtraTreesRegressor(n_estimators=100)
```
The `dimensions` parameter is the list of `skopt.space.Dimension` objects corresponding to the parameters to be optimized. It can be used to create isotropic kernels for the `skopt.learning.GaussianProcessRegressor` estimator. Here's an example:
Some research will be necessary to find additional Samplers (from optunahub) for example.
!!! Note
While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
If you're unsure about this, best use one of the Defaults (`"ET"` has proven to be the most versatile) without further parameters.
If you're unsure about this, best use one of the Defaults (`"NSGAIIISampler"` has proven to be the most versatile) without further parameters.
If you run the bot as a service, you can use systemd service manager as a software watchdog monitoring freqtrade bot
state and restarting it in the case of failures. If the `internals.sd_notify` parameter is set to true in the
configuration or the `--sd-notify` command line option is used, the bot will send keep-alive ping messages to systemd
using the sd_notify (systemd notifications) protocol and will also tell systemd its current state (Running or Stopped)
using the sd_notify (systemd notifications) protocol and will also tell systemd its current state (Running, Paused or Stopped)
when it changes.
The `freqtrade.service.watchdog` file contains an example of the service unit configuration file which uses systemd
@@ -188,30 +188,113 @@ as the watchdog.
## Advanced Logging
Freqtrade uses the default logging module provided by python.
Python allows for extensive [logging configuration](https://docs.python.org/3/library/logging.config.html#logging.config.dictConfig) in this regard - way more than what can be covered here.
Default logging format (coloured terminal output) is set up by default if no `log_config` is provided in your freqtrade configuration.
Using `--logfile logfile.log` will enable the RotatingFileHandler.
If you're not content with the log format, or with the default settings provided for the RotatingFileHandler, you can customize logging to your liking by adding the `log_config` configuration to your freqtrade configuration file(s).
The default configuration looks roughly like the below, with the file handler being provided but not enabled as the `filename` is commented out.
Uncomment this line and supply a valid path/filename to enable it.
Highlighted lines in the above code-block define the Rich handler and belong together.
The formatter "standard" and "file" will belong to the FileHandler.
Each handler must use one of the defined formatters (by name), its class must be available, and must be a valid logging class.
To actually use a handler, it must be in the "handlers" section inside the "root" segment.
If this section is left out, freqtrade will provide no output (in the non-configured handler, anyway).
!!! Tip "Explicit log configuration"
We recommend to extract the logging configuration from your main freqtrade configuration file, and provide it to your bot via [multiple configuration files](configuration.md#multiple-configuration-files) functionality. This will avoid unnecessary code duplication.
---
On many Linux systems the bot can be configured to send its log messages to `syslog` or `journald` system services. Logging to a remote `syslog` server is also available on Windows. The special values for the `--logfile` command line option can be used for this.
### Logging to syslog
To send Freqtrade log messages to a local or remote `syslog` service use the `--logfile` command line option with the value in the following format:
To send Freqtrade log messages to a local or remote `syslog` service use the `"log_config"` setup option to configure logging.
* `--logfile syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
// Use one of the other options above as address instead?
"address": "/dev/log"
}
},
"root": {
"handlers": [
// other handlers
"syslog",
]
}
The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
}
}
```
So, the following are the examples of possible usages:
[Additional log-handlers](#advanced-logging) may need to be configured to for example also have log output in the console.
* `--logfile syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
* `--logfile syslog` -- same as above, the shortcut for `/dev/log`.
* `--logfile syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
* `--logfile syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
* `--logfile syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
#### Syslog usage
Log messages are send to `syslog` with the `user` facility. So you can see them with the following commands:
* `tail -f /var/log/user`, or
* `tail -f /var/log/user`, or
* install a comprehensive graphical viewer (for instance, 'Log File Viewer' for Ubuntu).
On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both syslog or journald can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
For `rsyslog` the messages from the bot can be redirected into a separate dedicated log file. To achieve this, add
@@ -228,13 +311,66 @@ For `syslog` (`rsyslog`), the reduction mode can be switched on. This will reduc
$RepeatedMsgReduction on
```
#### Syslog addressing
The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
So, the following are the examples of possible addresses:
* `"address": "/dev/log"` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
* `"address": "/var/run/syslog"` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
* `"address": "localhost:514"` -- log to local syslog using UDP socket, if it listens on port 514.
* `"address": "<ip>:514"` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
??? Info "Deprecated - configure syslog via command line"
`--logfile syslog:<syslog_address>` -- send log messages to `syslog` service using the `<syslog_address>` as the syslog address.
The syslog address can be either a Unix domain socket (socket filename) or a UDP socket specification, consisting of IP address and UDP port, separated by the `:` character.
So, the following are the examples of possible usages:
* `--logfile syslog:/dev/log` -- log to syslog (rsyslog) using the `/dev/log` socket, suitable for most systems.
* `--logfile syslog` -- same as above, the shortcut for `/dev/log`.
* `--logfile syslog:/var/run/syslog` -- log to syslog (rsyslog) using the `/var/run/syslog` socket. Use this on MacOS.
* `--logfile syslog:localhost:514` -- log to local syslog using UDP socket, if it listens on port 514.
* `--logfile syslog:<ip>:514` -- log to remote syslog at IP address and port 514. This may be used on Windows for remote logging to an external syslog server.
### Logging to journald
This needs the `cysystemd` python package installed as dependency (`pip install cysystemd`), which is not available on Windows. Hence, the whole journald logging functionality is not available for a bot running on Windows.
To send Freqtrade log messages to `journald` system service use the `--logfile` command line option with the value in the following format:
To send Freqtrade log messages to `journald` system service, add the following configuration snippet to your configuration.
* `--logfile journald` -- send log messages to `journald`.
[Additional log-handlers](#advanced-logging) may need to be configured to for example also have log output in the console.
Log messages are send to `journald` with the `user` facility. So you can see them with the following commands:
@@ -244,3 +380,51 @@ Log messages are send to `journald` with the `user` facility. So you can see the
There are many other options in the `journalctl` utility to filter the messages, see manual pages for this utility.
On many systems `syslog` (`rsyslog`) fetches data from `journald` (and vice versa), so both `--logfile syslog` or `--logfile journald` can be used and the messages be viewed with both `journalctl` and a syslog viewer utility. You can combine this in any way which suites you better.
??? Info "Deprecated - configure journald via command line"
To send Freqtrade log messages to `journald` system service use the `--logfile` command line option with the value in the following format:
`--logfile journald` -- send log messages to `journald`.
### Log format as JSON
You can also configure the default output stream to use JSON format instead.
The "fmt_dict" attribute defines the keys for the json output - as well as the [python logging LogRecord attributes](https://docs.python.org/3/library/logging.html#logrecord-attributes).
The below configuration will change the default output to JSON. The same formatter could however also be used in combination with the `RotatingFileHandler`.
We recommend to keep one format in human readable form.
@@ -432,6 +435,20 @@ To save time, by default backtest will reuse a cached result from within the las
To further analyze your backtest results, freqtrade will export the trades to file by default.
You can then load the trades to perform further analysis as shown in the [data analysis](strategy_analysis_example.md#load-backtest-results-to-pandas-dataframe) backtesting section.
### Backtest output file
The output file freqtrade produces is a zip file containing the following files:
- The backtest report in json format
- the market change data in feather format
- a copy of the strategy file
- a copy of the strategy parameters (if a parameter file was used)
- a sanitized copy of the config file
This will ensure results are reproducible - under the assumption that the same data is available.
Only the strategy file and the config file are included in the zip file, eventual dependencies are not included.
## Assumptions made by backtesting
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
@@ -180,7 +180,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `minimal_roi` | **Required.** Set the threshold as ratio the bot will use to exit a trade. [More information below](#understand-minimal_roi). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
| `stoploss` | **Required.** Value as ratio of the stoploss used by the bot. More details in the [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float (as ratio)
| `trailing_stop` | Enables trailing stoploss (based on `stoploss` in either configuration or strategy file). More details in the [stoploss documentation](stoploss.md#trailing-stop-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Boolean
| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-custom-positive-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
| `trailing_stop_positive` | Changes stoploss once profit has been reached. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-different-positive-loss). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Float
| `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
| `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `fee` | Fee used during backtesting / dry-runs. Should normally not be configured, which has freqtrade fall back to the exchange default fee. Set as ratio (e.g. 0.001 = 0.1%). Fee is applied twice for each trade, once when buying, once when selling. <br> **Datatype:** Float (as ratio)
@@ -205,7 +205,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `exit_pricing.use_order_book` | Enable exiting of open trades using [Order Book Exit](#exit-price-with-orderbook-enabled). <br> *Defaults to `true`.*<br> **Datatype:** Boolean
| `exit_pricing.order_book_top` | Bot will use the top N rate in Order Book "price_side" to exit. I.e. a value of 2 will allow the bot to pick the 2nd ask rate in [Order Book Exit](#exit-price-with-orderbook-enabled)<br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `custom_price_max_distance_ratio` | Configure maximum distance ratio between current and custom entry or exit price. <br>*Defaults to `0.02` 2%).*<br> **Datatype:** Positive float
| | **TODO**
| | **Order/Signal handling**
| `use_exit_signal` | Use exit signals produced by the strategy in addition to the `minimal_roi`. <br>Setting this to false disables the usage of `"exit_long"` and `"exit_short"` columns. Has no influence on other exit methods (Stoploss, ROI, callbacks). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `exit_profit_only` | Wait until the bot reaches `exit_profit_offset` before taking an exit decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `exit_profit_offset` | Exit-signal is only active above this value. Only active in combination with `exit_profit_only=True`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0`.* <br> **Datatype:** Float (as ratio)
@@ -266,7 +266,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `bot_name` | Name of the bot. Passed via API to a client - can be shown to distinguish / name bots.<br> *Defaults to `freqtrade`*<br> **Datatype:** String
| `external_message_consumer` | Enable [Producer/Consumer mode](producer-consumer.md) for more details. <br> **Datatype:** Dict
| | **Other**
| `initial_state` | Defines the initial application state. If set to stopped, then the bot has to be explicitly started via `/start` RPC command. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `stopped` or `running`
| `initial_state` | Defines the initial application state. If set to stopped, then the bot has to be explicitly started via `/start` RPC command. <br>*Defaults to `stopped`.* <br> **Datatype:** Enum, either `running`, `paused` or `stopped`
| `force_entry_enable` | Enables the RPC Commands to force a Trade entry. More information below. <br> **Datatype:** Boolean
| `disable_dataframe_checks` | Disable checking the OHLCV dataframe returned from the strategy methods for correctness. Only use when intentionally changing the dataframe and understand what you are doing. [Strategy Override](#parameters-in-the-strategy).<br> *Defaults to `False`*. <br> **Datatype:** Boolean
| `internals.process_throttle_secs` | Set the process throttle, or minimum loop duration for one bot iteration loop. Value in second. <br>*Defaults to `5` seconds.* <br> **Datatype:** Positive Integer
@@ -281,7 +281,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `add_config_files` | Additional config files. These files will be loaded and merged with the current config file. The files are resolved relative to the initial file.<br> *Defaults to `[]`*. <br> **Datatype:** List of strings
| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `feather`*. <br> **Datatype:** String
| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `feather`*. <br> **Datatype:** String
| `reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage (and decreasing train/inference timing in FreqAI). (Currently only affects FreqAI use-cases) <br> **Datatype:** Boolean. <br> Default: `False`.
| `reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage (and decreasing train/inference timing backtesting/hyperopt and in FreqAI). <br> **Datatype:** Boolean. <br> Default: `False`.
| `log_config` | Dictionary containing the log config for python logging. [more info](advanced-setup.md#advanced-logging) <br> **Datatype:** dict. <br> Default: `FtRichHandler`
### Parameters in the strategy
@@ -671,7 +672,7 @@ Should you experience problems you suspect are caused by websockets, you can dis
}
```
Should you be required to use a proxy, please refer to the [proxy section](#using-proxy-with-freqtrade) for more information.
Should you be required to use a proxy, please refer to the [proxy section](#using-a-proxy-with-freqtrade) for more information.
!!! Info "Rollout"
We're implementing this out slowly, ensuring stability of your bots.
@@ -88,3 +88,8 @@ Setting protections from the configuration via `"protections": [],` has been rem
Using hdf5 as data storage has been deprecated in 2024.12 and was removed in 2025.1. We recommend switching to the feather data format.
Please use the [`convert-data` subcommand](data-download.md#sub-command-convert-data) to convert your existing data to one of the supported formats before updating.
## Configuring advanced logging via config
Configuring syslog and journald via `--logfile systemd` and `--logfile journald` respectively has been deprecated in 2025.3.
Please use configuration based [log setup](advanced-setup.md#advanced-logging) instead.
@@ -363,6 +363,29 @@ Hyperliquid handles deposits and withdrawals on the Arbitrum One chain, a Layer
* Create a different software wallet, only transfer the funds you want to trade with to that wallet, and use that wallet to trade on Hyperliquid.
* If you have funds you don't want to use for trading (after making a profit for example), transfer them back to your hardware wallet.
### Historic Hyperliquid data
The Hyperliquid API does not provide historic data beyond the single call to fetch current data, so downloading data is not possible, as the downloaded data would not constitute proper historic data.
## Bitvavo
If your account is required to use an operatorId, you can set it in the configuration file as follows:
``` json
"exchange": {
"name": "bitvavo",
"key": "",
"secret": "",
"ccxt_config": {
"options": {
"operatorId": "123567"
}
},
}
```
Bitvavo expects the `operatorId` to be an integer.
## All exchanges
Should you experience constant errors with Nonce (like `InvalidNonce`), it is best to regenerate the API keys. Resetting Nonce is difficult and it's usually easier to regenerate the API keys.
@@ -102,6 +102,14 @@ You can use "current" market data by using the [dataprovider](strategy-customiza
You can use the `/stopentry` command in Telegram to prevent future trade entry, followed by `/forceexit all` (sell all open trades).
### I sold the bot's capital and now there's errors in the log
Freqtrade assumes that the trades it opens are managed only though the bot.
If you happen to (accidentally) sell the bot's capital, freqtrade will try to recover by trying to re-find on-exchange orders.
This is a best-effort approach, and will not work in all cases, especially when using order types that are not supported by freqtrade (OCO, iceberg, etc.), or when working with older trades (where the exchange no longer provides full order information).
The exact limits will vary between exchanges - with the details usually being documented in the exchange's API documentation.
### I want to run multiple bots on the same machine
Please look at the [advanced setup documentation Page](advanced-setup.md#running-multiple-instances-of-freqtrade).
@@ -219,10 +227,7 @@ On Windows, the `--logfile` option is also supported by Freqtrade and you can us
First of all, most indicator libraries don't have GPU support - as such, there would be little benefit for indicator calculations.
The GPU improvements would only apply to pandas-native calculations - or ones written by yourself.
For hyperopt, freqtrade is using scikit-optimize, which is built on top of scikit-learn.
Their statement about GPU support is [pretty clear](https://scikit-learn.org/stable/faq.html#will-you-add-gpu-support).
GPU's also are only good at crunching numbers (floating point operations).
GPU's are only good at crunching numbers (floating point operations).
For hyperopt, we need both number-crunching (find next parameters) and running python code (running backtesting).
As such, GPU's are not too well suited for most parts of hyperopt.
@@ -258,6 +258,8 @@ freqtrade trade --config config_examples/config_freqai.example.json --strategy F
We do provide an explicit docker-compose file for this in `docker/docker-compose-freqai.yml` - which can be used via `docker compose -f docker/docker-compose-freqai.yml run ...` - or can be copied to replace the original docker file.
This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.
PyTorch dropped support for macOS x64 (intel based Apple devices) in version 2.3. Subsequently, freqtrade also dropped support for PyTorch on this platform.
@@ -181,7 +181,7 @@ You can ask for each of the defined features to be included also for informative
In total, the number of features the user of the presented example strategy has created is: length of `include_timeframes`* no. features in `feature_engineering_expand_*()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
$= 3 * 3 * 3 * 2 * 2 = 108$.
!!! note "Learn more about creative feature engineering"
!!! note "Learn more about creative feature engineering"
Check out our [medium article](https://emergentmethods.medium.com/freqai-from-price-to-prediction-6fadac18b665) geared toward helping users learn how to creatively engineer features.
### Gain finer control over `feature_engineering_*` functions with `metadata`
@@ -310,7 +310,7 @@ class MyCoolTransform(BaseTransform):
If you have created your own custom `IFreqaiModel` with a custom `train()`/`predict()` function, *and* you still rely on `data_cleaning_train/predict()`, then you will need to migrate to the new pipeline. If your model does *not* rely on `data_cleaning_train/predict()`, then you do not need to worry about this migration.
More details about the migration can be found [here](strategy_migration.md#freqai---new-data-pipeline).
More details about the migration can be found [here](strategy_migration.md#freqai-new-data-pipeline).
If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker compose file with `image: freqtradeorg/freqtrade:stable_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices. If you would like to use PyTorch or Reinforcement learning, you should use the torch or RL tags, `image: freqtradeorg/freqtrade:stable_freqaitorch`, `image: freqtradeorg/freqtrade:stable_freqairl`.
!!! note "docker-compose-freqai.yml"
We do provide an explicit docker-compose file for this in `docker/docker-compose-freqai.yml` - which can be used via `docker compose -f docker/docker-compose-freqai.yml run ...` - or can be copied to replace the original docker file. This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.
We do provide an explicit docker-compose file for this in `docker/docker-compose-freqai.yml` - which can be used via `docker compose -f docker/docker-compose-freqai.yml run ...` - or can be copied to replace the original docker file. This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.
### FreqAI position in open-source machine learning landscape
This page explains how to tune your strategy by finding the optimal
parameters, a process called hyperparameter optimization. The bot uses algorithms included in the `scikit-optimize` package to accomplish this.
parameters, a process called hyperparameter optimization. The bot uses algorithms included in the `optuna` package to accomplish this.
The search will burn all your CPU cores, make your laptop sound like a fighter jet and still take a long time.
In general, the search for best parameters starts with a few random combinations (see [below](#reproducible-results) for more details) and then uses Bayesian search with a ML regressor algorithm (currently ExtraTreesRegressor) to quickly find a combination of parameters in the search hyperspace that minimizes the value of the [loss function](#loss-functions).
In general, the search for best parameters starts with a few random combinations (see [below](#reproducible-results) for more details) and then uses one of optuna's sampler algorithms (currently NSGAIIISampler) to quickly find a combination of parameters in the search hyperspace that minimizes the value of the [loss function](#loss-functions).
Hyperopt requires historic data to be available, just as backtesting does (hyperopt runs backtesting many times with different parameters).
To learn how to get data for the pairs and exchange you're interested in, head over to the [Data Downloading](data-download.md) section of the documentation.
@@ -471,6 +471,7 @@ Currently, the following loss functions are builtin:
* `SortinoHyperOptLossDaily` - optimizes Sortino Ratio calculated on **daily** trade returns relative to **downside** standard deviation.
* `MaxDrawDownHyperOptLoss` - Optimizes Maximum absolute drawdown.
* `MaxDrawDownRelativeHyperOptLoss` - Optimizes both maximum absolute drawdown while also adjusting for maximum relative drawdown.
* `MaxDrawDownPerPairHyperOptLoss` - Calculates the profit/drawdown ratio per pair and returns the worst result as objective, forcing hyperopt to optimize the parameters for all pairs in the pairlist. This way, we prevent one or more pairs with good results from inflating the metrics, while the pairs with poor results are not represented and therefore not optimized.
* `CalmarHyperOptLoss` - Optimizes Calmar Ratio calculated on trade returns relative to max drawdown.
* `ProfitDrawDownHyperOptLoss` - Optimizes by max Profit & min Drawdown objective. `DRAWDOWN_MULT` variable within the hyperoptloss file can be adjusted to be stricter or more flexible on drawdown purposes.
* `MultiMetricHyperOptLoss` - Optimizes by several key metrics to achieve balanced performance. The primary focus is on maximizing Profit and minimizing Drawdown, while also considering additional metrics such as Profit Factor, Expectancy Ratio and Winrate. Moreover, it applies a penalty for epochs with a low number of trades, encouraging strategies with adequate trade frequency.
The `-e` option will set how many evaluations hyperopt will do. Since hyperopt uses Bayesian search, running too many epochs at once may not produce greater results. Experience has shown that best results are usually not improving much after 500-1000 epochs.
The `--early-stop` option will set after how many epochs with no improvements hyperopt will stop. A good value is 20-30% of the total epochs. Any value greater than 0 and lower than 20 it will be replaced by 20. Early stop is by default disabled (`--early-stop=0`)
Doing multiple runs (executions) with a few 1000 epochs and different random state will most likely produce different results.
The `--spaces all` option determines that all possible parameters should be optimized. Possibilities are listed below.
@@ -531,7 +534,7 @@ Legal values are:
* `trailing`: search for the best trailing stop values
* `trades`: search for the best max open trades values
* `protection`: search for the best protection parameters (read the [protections section](#optimizing-protections) on how to properly define these)
* `default`: `all` except `trailing` and `protection`
* `default`: `all` except `trailing`, `trades` and `protection`
* space-separated list of any of the above values for example `--spaces roi stoploss`
The default Hyperopt Search Space, used when no `--space` command line option is specified, does not include the `trailing` hyperspace. We recommend you to run optimization for the `trailing` hyperspace separately, when the best parameters for other hyperspaces were found, validated and pasted into your custom strategy.
@@ -44,9 +44,24 @@ You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged
By default, the `StaticPairList` method is used, which uses a statically defined pair whitelist from the configuration. The pairlist also supports wildcards (in regex-style) - so `.*/BTC` will include all pairs with BTC as a stake.
It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`.
It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`, which in the below example, will trade BTC/USDT and ETH/USDT - and will prevent BNB/USDT trading.
Both `pair_*list` parameters support regex - so values like `.*/USDT` would enable trading all pairs that are not in the blacklist.
Freqtrade allows your strategy to implement different exit logic using signal-based or callback-based functions.
This section aims to compare each different function, helping you to choose the one that best fits your needs.
* **`populate_exit_trend()`** - Vectorized signal-based exit logic using indicators in the main dataframe
✅ **Use** to define exit signals based on indicators or other data that can be calculated in a vectorized manner.
🚫 **Don't use** to customize exit conditions for each individual trade, or if trade data is necessary to make an exit decision.
* **`custom_exit()`** - Custom exit logic that will fully exit a trade immediately, called for every open trade at every bot loop iteration until a trade is closed.
✅ **Use** to specify exit conditions for each individual trade (including any additional adjusted orders using `adjust_trade_position()`), or if trade data is necessary to make an exit decision, e.g. using profit data to exit.
🚫 **Don't use** when you want to exit using vectorised indicator-based data (use a `populate_exit_trend()` signal instead), or as a proxy for `custom_stoploss()`, and be aware that rate-based exits in backtesting can be inaccurate.
* **`custom_stoploss()`** - Custom trailing stoploss, called for every open trade every iteration until a trade is closed. The value returned here is also used for [stoploss on exchange](stoploss.md#stop-loss-on-exchangefreqtrade).
✅ **Use** to customize the stoploss logic to set a dynamic stoploss based on trade data or other conditions.
🚫 **Don't use** to exit a trade immediately based on a specific condition. Use `custom_exit()` for that purpose.
* **`custom_roi()`** - Custom ROI, called for every open trade every iteration until a trade is closed.
✅ **Use** to specify a minimum ROI threshold ("take-profit") to exit a trade at this ROI level at some point within the trade duration, based on profit or other conditions.
🚫 **Don't use** to exit a trade immediately based on a specific condition. Use `custom_exit()`.
🚫 **Don't use** for static ROI. Use `minimal_roi`.
@@ -50,6 +50,7 @@ Enable subscribing to an instance by adding the `external_message_consumer` sect
| `ping_timeout` | Ping timeout <br>*Defaults to `10`.*<br>**Datatype:** Integer - in seconds.
| `sleep_time` | Sleep time before retrying to connect.<br>*Defaults to `10`.*<br>**Datatype:** Integer - in seconds.
| `remove_entry_exit_signals` | Remove signal columns from the dataframe (set them to 0) on dataframe receipt.<br>*Defaults to `false`.*<br>**Datatype:** Boolean.
| `initial_candle_limit` | Initial candles to expect from the Producer.<br>*Defaults to `1500`.*<br>**Datatype:** Integer - Number of candles.
| `message_size_limit` | Size limit per message<br>*Defaults to `8`.*<br>**Datatype:** Integer - Megabytes.
Instead of (or as well as) calculating indicators in `populate_indicators()` the follower instance listens on the connection to a producer instance's messages (or multiple producer instances in advanced configurations) and requests the producer's most recently analyzed dataframes for each pair in the active whitelist.
@@ -165,18 +165,23 @@ If there is any significant difference, verify that your entry and exit signals
## Controlling or monitoring a running bot
Once your bot is running in dry or live mode, Freqtrade has five mechanisms to control or monitor a running bot:
Once your bot is running in dry or live mode, Freqtrade has six mechanisms to control or monitor a running bot:
- **[FreqUI](freq-ui.md)**: The easiest to get started with, FreqUI is a web interface to see and control current activity of your bot.
- **[Telegram](telegram-usage.md)**: On mobile devices, Telegram integration is available to get alerts about your bot activity and to control certain aspects.
- **[FTUI](https://github.com/freqtrade/ftui)**: FTUI is a terminal (command line) interface to Freqtrade, and allows monitoring of a running bot only.
- **[REST API](rest-api.md)**: The REST API allows programmers to develop their own tools to interact with a Freqtrade bot.
- **[freqtrade-client](rest-api.md#consuming-the-api)**: A python implementation of the REST API, making it easy to make requests and consume bot responses from your python apps or the command line.
- **[REST API endpoints](rest-api.md#available-endpoints)**: The REST API allows programmers to develop their own tools to interact with a Freqtrade bot.
- **[Webhooks](webhook-config.md)**: Freqtrade can send information to other services, e.g. discord, by webhooks.
### Logs
Freqtrade generates extensive debugging logs to help you understand what's happening. Please familiarise yourself with the information and error messages you might see in your bot logs.
Logging by default occurs on standard out (the command line). If you want to write out to a file instead, many freqtrade commands, including the `trade` command, accept the `--logfile` option to write to a file.
Check the [FAQ](faq.md#how-do-i-search-the-bot-logs-for-something) for examples.
## Final Thoughts
Algo trading is difficult, and most public strategies are not good performers due to the time and effort to make a strategy work profitably in multiple scenarios.
@@ -121,7 +125,7 @@ Freqtrade will fall back to the `proposed_stake` value should your code raise an
Called for open trade every throttling iteration (roughly every 5 seconds) until a trade is closed.
Allows to define custom exit signals, indicating that specified position should be sold. This is very useful when we need to customize exit conditions for each individual trade, or if you need trade data to make an exit decision.
Allows to define custom exit signals, indicating that specified position should be closed (full exit). This is very useful when we need to customize exit conditions for each individual trade, or if you need trade data to make an exit decision.
For example you could implement a 1:2 risk-reward ROI with `custom_exit()`.
@@ -178,6 +182,8 @@ Returning `None` will be interpreted as "no desire to change", and is the only s
Stoploss on exchange works similar to `trailing_stop`, and the stoploss on exchange is updated as configured in `stoploss_on_exchange_interval` ([More details about stoploss on exchange](stoploss.md#stop-loss-on-exchangefreqtrade)).
If you're on futures markets, please take note of the [stoploss and leverage](stoploss.md#stoploss-and-leverage) section, as the stoploss value returned from `custom_stoploss` is the risk for this trade - not the relative price movement.
!!! Note "Use of dates"
All time-based calculations should be done based on `current_time` - using `datetime.now()` or `datetime.utcnow()` is discouraged, as this will break backtesting support.
@@ -233,7 +239,7 @@ class AwesomeStrategy(IStrategy):
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New stoploss value, relative to the current_rate
"""
return -0.04
return -0.04 * trade.leverage
```
#### Time based trailing stop
@@ -255,9 +261,9 @@ class AwesomeStrategy(IStrategy):
# Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
if current_time - timedelta(minutes=120) > trade.open_date_utc:
@@ -497,6 +503,135 @@ The helper function `stoploss_from_absolute()` can be used to convert from an ab
---
## Custom ROI
Called for open trade every iteration (roughly every 5 seconds) until a trade is closed.
The usage of the custom ROI method must be enabled by setting `use_custom_roi=True` on the strategy object.
This method allows you to define a custom minimum ROI threshold for exiting a trade, expressed as a ratio (e.g., `0.05` for 5% profit). If both `minimal_roi` and `custom_roi` are defined, the lower of the two thresholds will trigger an exit. For example, if `minimal_roi` is set to `{"0":0.10}` (10% at 0 minutes) and `custom_roi` returns `0.05`, the trade will exit when the profit reaches 5%. Also, if `custom_roi` returns `0.10` and `minimal_roi` is set to `{"0":0.05}` (5% at 0 minutes), the trade will be closed when the profit reaches 5%.
The method must return a float representing the new ROI threshold as a ratio, or `None` to fall back to the `minimal_roi` logic. Returning `NaN` or `inf` values is considered invalid and will be treated as `None`, causing the bot to use the `minimal_roi` configuration.
### Custom ROI examples
The following examples illustrate how to use the `custom_roi` function to implement different ROI logics.
#### Custom ROI per side
Use different ROI thresholds depending on the `side`. In this example, 5% for long entries and 2% for short entries.
By default, freqtrade use the orderbook to automatically set an order price([Relevant documentation](configuration.md#prices-used-for-orders)), you also have the option to create custom order prices based on your strategy.
@@ -758,7 +893,7 @@ For performance reasons, it's disabled by default and freqtrade will show a warn
Additional orders also result in additional fees and those orders don't count towards `max_open_trades`.
This callback is also called when there is an open order (either buy or sell) waiting for execution - and will cancel the existing open order to place a new order if the amount, price or direction is different.
This callback is also called when there is an open order (either buy or sell) waiting for execution - and will cancel the existing open order to place a new order if the amount, price or direction is different. Also partially filled orders will be canceled, and will be replaced with the new amount as returned by the callback.
`adjust_trade_position()` is called very frequently for the duration of a trade, so you must keep your implementation as performant as possible.
@@ -770,9 +905,10 @@ Modifications to leverage are not possible, and the stake-amount returned is ass
The combined stake currently allocated to the position is held in `trade.stake_amount`. Therefore `trade.stake_amount` will always be updated on every additional entry and partial exit made through `adjust_trade_position()`.
!!! Danger "Loose Logic"
On dry and live run, this function will be called every `throttle_process_secs` (default to 5s). If you have a loose logic, for example your logic for extra entry is only to check RSI of last candle is below 30, then when such condition fulfilled, your bot will do extra re-entry every 5 secs until either it run out of money, it hit the `max_position_adjustment` limit, or a new candle with RSI more than 30 arrived.
On dry and live run, this function will be called every `throttle_process_secs` (default to 5s). If you have a loose logic, (e.g. increase position if RSI of the last candle is below 30), your bot will do extra re-entry every 5 secs until you either it run out of money, hit the `max_position_adjustment` limit, or a new candle with RSI more than 30 arrived.
Same thing also can happen with partial exit. So be sure to have a strict logic and/or check for the last filled order.
Same thing also can happen with partial exit.
So be sure to have a strict logic and/or check for the last filled order and if an order is already open.
!!! Warning "Performance with many position adjustments"
Position adjustments can be a good approach to increase a strategy's output - but it can also have drawbacks if using this feature extensively.
@@ -876,6 +1012,9 @@ class DigDeeperStrategy(IStrategy):
Return None for no action.
Optionally, return a tuple with a 2nd element with an order reason
"""
if trade.has_open_orders:
# Only act if no orders are open
return
if current_profit > 0.05 and trade.nr_of_successful_exits == 0:
# Take half of the profit at +5%
@@ -973,7 +1112,7 @@ class AwesomeStrategy(IStrategy):
side: str,
is_entry: bool,
**kwargs,
) -> float:
) -> float | None:
"""
Exit and entry order price re-adjustment logic, returning the user desired limit price.
This only executes when a order was already placed, still open (unfilled fully or partially)
@@ -995,7 +1134,7 @@ class AwesomeStrategy(IStrategy):
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:param is_entry: True if the order is an entry order, False if it's an exit order.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New entry price value if provided
:return float or None: New entry price value if provided
"""
# Limit entry orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
@@ -1103,3 +1242,119 @@ class AwesomeStrategy(IStrategy):
return None
```
## Plot annotations callback
The plot annotations callback is called whenever freqUI requests data to display a chart.
This callback has no meaning in the trade cycle context and is only used for charting purposes.
The strategy can then return a list of `AnnotationType` objects to be displayed on the chart.
Depending on the content returned - the chart can display horizontal areas, vertical areas, or boxes.
The full object looks like this:
``` json
{
"type": "area", // Type of the annotation, currently only "area" is supported
"start": "2024-01-01 15:00:00", // Start date of the area
"end": "2024-01-01 16:00:00", // End date of the area
"y_start": 94000.2, // Price / y axis value
"y_end": 98000, // Price / y axis value
"color": "",
"label": "some label"
}
```
The below example will mark the chart with areas for the hours 8 and 15, with a grey color, highlighting the market open and close hours.
@@ -1122,6 +1122,7 @@ The following list contains some common patterns which should be avoided to prev
- don't use `.iloc[-1]` or any other absolute position in the dataframe within `populate_` functions, as this will be different between dry-run and backtesting. Absolute `iloc` indexing is safe to use in callbacks however - see [Strategy Callbacks](strategy-callbacks.md).
- don't use functions that use all dataframe or column values, e.g. `dataframe['mean_volume'] = dataframe['volume'].mean()`. As backtesting uses the full dataframe, at any point in the dataframe, the `'mean_volume'` series would include data from the future. Use rolling() calculations instead, e.g. `dataframe['volume'].rolling(<window>).mean()`.
- don't use `.resample('1h')`. This uses the left border of the period interval, so moves data from an hour boundary to the start of the hour. Use `.resample('1h', label='right')` instead.
- don't use `.merge()` to combine longer timeframes onto shorter ones. Instead, use the [informative pair](#informative-pairs) helpers. (A plain merge can implicitly cause a lookahead bias as date refers to open date, not close date).
!!! Tip "Identifying problems"
You should always use the two helper commands [lookahead-analysis](lookahead-analysis.md) and [recursive-analysis](recursive-analysis.md), which can each help you figure out problems with your strategy in different ways.
@@ -187,8 +200,8 @@ official commands. You can ask at any moment for help with `/help`.
|----------|-------------|
| **System commands**
| `/start` | Starts the trader
| `/pause | /stopentry | /stopbuy` | Pause the trader. Gracefully handle open trades according to their rules. Do not enter new positions.
| `/stop` | Stops the trader
| `/stopbuy | /stopentry` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `/reload_config` | Reloads the configuration file
| `/show_config` | Shows part of the current configuration with relevant settings to operation
| `/logs [limit]` | Show last log messages.
@@ -237,25 +250,27 @@ Below, example of Telegram message you will receive for each command.
> **Status:** `running`
### /pause | /stopentry | /stopbuy
> **Status:** `paused, no more entries will occur from now. Run /start to enable entries.`
Prevents the bot from opening new trades by changing the state to `paused`.
Open trades will continue to be managed according to their regular rules (ROI/exit signals, stop-loss, etc.).
Note that position adjustment remains active, but only on the exit side — meaning that when the bot is `paused`, it can only reduce the position size of open trades.
After this, give the bot time to close off open trades (can be checked via `/status table`).
Once all positions are closed, run `/stop` to completely stop the bot.
Use `/start` to resume the bot to the `running` state, allowing it to open new positions.
!!! Warning
The pause/stopentry signal is ONLY active while the bot is running, and is not persisted anyway, so restarting the bot will cause this to reset.
### /stop
> `Stopping trader ...`
> **Status:** `stopped`
### /stopbuy
> **status:** `Setting max_open_trades to 0. Run /reload_config to reset.`
Prevents the bot from opening new trades by temporarily setting "max_open_trades" to 0. Open trades will be handled via their regular rules (ROI / Sell-signal, stoploss, ...).
After this, give the bot time to close off open trades (can be checked via `/status table`).
Once all positions are sold, run `/stop` to completely stop the bot.
`/reload_config` resets "max_open_trades" to the value set in the configuration and resets this command.
!!! Warning
The stop-buy signal is ONLY active while the bot is running, and is not persisted anyway, so restarting the bot will cause this to reset.
### /status
For each open trade, the bot will send you the following message.
@@ -25,6 +25,7 @@ The following attributes / properties are available for each individual trade -
| `close_date_utc` | datetime | Timestamp when trade was closed - in UTC. |
| `close_profit` | float | Relative profit at the time of trade closure. `0.01` == 1% |
| `close_profit_abs` | float | Absolute profit (in stake currency) at the time of trade closure. |
| `realized_profit` | float | Absolute already realized profit (in stake currency) while the trade is still open. |
| `leverage` | float | Leverage used for this trade - defaults to 1.0 in spot markets. |
| `enter_tag` | string | Tag provided on entry via the `enter_tag` column in the dataframe. |
| `is_short` | boolean | True for short trades, False otherwise. |
@@ -35,6 +36,7 @@ The following attributes / properties are available for each individual trade -
| `trade_direction` | "long" / "short" | Trade direction in text - long or short. |
| `nr_of_successful_entries` | int | Number of successful (filled) entry orders. |
| `nr_of_successful_exits` | int | Number of successful (filled) exit orders. |
| `has_open_orders` | boolean | Has the trade open orders (excluding stoploss orders). |
## Class methods
@@ -132,15 +134,17 @@ Most properties here can be None as they are dependent on the exchange response.
|------------|-------------|-------------|
| `trade` | Trade | Trade object this order is attached to |
| `ft_pair` | string | Pair this order is for |
| `ft_is_open` | boolean | is the order filled? |
| `ft_is_open` | boolean | is the order still open? |
| `order_type` | string | Order type as defined on the exchange - usually market, limit or stoploss |
| `status` | string | Status as defined by ccxt. Usually open, closed, expired or canceled |
| `side` | string | Buy or Sell |
| `status` | string | Status as defined by [ccxt's order structure](https://docs.ccxt.com/#/README?id=order-structure). Usually open, closed, expired, canceled or rejected |
| `side` | string | buy or sell |
| `price` | float | Price the order was placed at |
| `average` | float | Average price the order filled at |
| `amount` | float | Amount in base currency |
| `filled` | float | Filled amount (in base currency) |
| `remaining` | float | Remaining amount |
| `filled` | float | Filled amount (in base currency) (use `safe_filled` instead) |
| `safe_filled` | float | Filled amount (in base currency) - guaranteed to not be None |
| `safe_remaining` | float | Remaining amount - either taken from the exchange or calculated. |
| `cost` | float | Cost of the order - usually average * filled (*Exchange dependent on futures, may contain the cost with or without leverage and may be in contracts.*) |
| `stake_amount` | float | Stake amount used for this order. *Added in 2023.7.* |
| `stake_amount_filled` | float | Filled Stake amount used for this order. *Added in 2024.11.* |
# Set acknowledged to True to avoid ccxt exception
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