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":"The timeframe to use (e.g `1m`, `5m`, `15m`, `30m`, `1h` ...). \nUsually specified in the strategy and missing in the configuration.",
"type":"string"
},
"proxy_coin":{
"description":"Proxy coin - must be used for specific futures modes (e.g. BNFCR)",
"type":"string"
},
"stake_currency":{
"description":"Currency used for staking.",
"type":"string"
@@ -253,7 +257,8 @@
"enum":[
"day",
"week",
"month"
"month",
"year"
]
}
},
@@ -537,6 +542,10 @@
"description":"Edge configuration.",
"$ref":"#/definitions/edge"
},
"log_config":{
"description":"Logging configuration.",
"$ref":"#/definitions/logging"
},
"freqai":{
"description":"FreqAI configuration.",
"$ref":"#/definitions/freqai"
@@ -601,13 +610,21 @@
"type":"string"
},
"chat_id":{
"description":"Telegram chat or group ID",
"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",
@@ -756,9 +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":{
@@ -836,6 +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":{
@@ -975,7 +995,7 @@
"type":"string"
}
},
"x":{
"verbosity":{
"description":"Logging verbosity level.",
"type":"string",
"enum":[
@@ -1015,6 +1035,7 @@
"type":"string",
"enum":[
"running",
"paused",
"stopped"
]
},
@@ -1051,7 +1072,6 @@
"enum":[
"json",
"jsongz",
"hdf5",
"feather",
"parquet"
],
@@ -1063,7 +1083,6 @@
"enum":[
"json",
"jsongz",
"hdf5",
"feather",
"parquet"
],
@@ -1152,28 +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":{
@@ -1197,6 +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"
@@ -1270,6 +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",
@@ -1364,10 +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?",
@@ -1397,16 +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",
@@ -1443,6 +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.
Recursively search for a strategy in the strategies
folder.
--freqaimodel NAME Specify a custom freqaimodels.
--freqaimodel-path PATH
Specify additional lookup path for freqaimodels.
```
--8<-- "commands/backtesting.md"
## Test your strategy with Backtesting
@@ -313,6 +209,7 @@ A backtesting result will look like that:
| Sortino | 1.88 |
| Sharpe | 2.97 |
| Calmar | 6.29 |
| SQN | 2.45 |
| Profit factor | 1.11 |
| Expectancy (Ratio) | -0.15 (-0.05) |
| Avg. stake amount | 0.001 BTC |
@@ -419,6 +316,7 @@ It contains some useful key metrics about performance of your strategy on backte
| Sortino | 1.88 |
| Sharpe | 2.97 |
| Calmar | 6.29 |
| SQN | 2.45 |
| Profit factor | 1.11 |
| Expectancy (Ratio) | -0.15 (-0.05) |
| Avg. stake amount | 0.001 BTC |
@@ -472,6 +370,7 @@ It contains some useful key metrics about performance of your strategy on backte
-`Sortino`: Annualized Sortino ratio.
-`Sharpe`: Annualized Sharpe ratio.
-`Calmar`: Annualized Calmar ratio.
-`SQN`: System Quality Number (SQN) - by Van Tharp.
-`Profit factor`: profit / loss.
-`Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
-`Total trade volume`: Volume generated on the exchange to reach the above profit.
@@ -536,6 +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:
@@ -612,7 +525,12 @@ To utilize this, you can append `--timeframe-detail 5m` to your regular backtest
This will load 1h data as well as 5m data for the timeframe. The strategy will be analyzed with the 1h timeframe, and Entry orders will only be placed at the main timeframe, however Order fills and exit signals will be evaluated at the 5m candle, simulating intra-candle movements.
This will load 1h data (the main timeframe) as well as 5m data (detail timeframe) for the selected timerange.
The strategy will be analyzed with the 1h timeframe.
Candles where activity may take place (there's an active signal, the pair is in a trade) are evaluated at the 5m timeframe.
This will allow for a more accurate simulation of intra-candle movements - and can lead to different results, especially on higher timeframes.
Entries will generally still happen at the main candle's open, however freed trade slots may be freed earlier (if the exit signal is triggered on the 5m candle), which can then be used for a new trade of a different pair.
All callback functions (`custom_exit()`, `custom_stoploss()`, ... ) will be running for each 5m candle once the trade is opened (so 12 times in the above example of 1h timeframe, and 5m detailed timeframe).
@@ -624,6 +542,27 @@ Also, data must be available / downloaded already.
!!! Tip
You can use this function as the last part of strategy development, to ensure your strategy is not exploiting one of the [backtesting assumptions](#assumptions-made-by-backtesting). Strategies that perform similarly well with this mode have a good chance to perform well in dry/live modes too (although only forward-testing (dry-mode) can really confirm a strategy).
??? Sample "Extreme Difference Example"
Using `--timeframe-detail` on an extreme example (all below pairs have the 10:00 candle with an entry signal) may lead to the following backtesting Trade sequence with 1 max_open_trades:
The difference is significant, as without detail data, only the first `max_open_trades` signals per candle are evaluated, and the trade slots are only freed at the end of the candle, allowing for a new trade to be opened at the next candle.
## Backtesting multiple strategies
To compare multiple strategies, a list of Strategies can be provided to backtesting.
@@ -54,11 +54,13 @@ By default, the bot loop runs every few seconds (`internals.process_throttle_sec
* Check timeouts for open orders.
* Calls `check_entry_timeout()` strategy callback for open entry orders.
* Calls `check_exit_timeout()` strategy callback for open exit orders.
* Calls `adjust_entry_price()` strategy callback for open entry orders.
* Calls `adjust_order_price()` strategy callback for open orders.
* Calls `adjust_entry_price()` strategy callback for open entry orders. *only called when `adjust_order_price()` is not implemented*
* Calls `adjust_exit_price()` strategy callback for open exit orders. *only called when `adjust_order_price()` is not implemented*
* Verifies existing positions and eventually places exit orders.
* Considers stoploss, ROI and exit-signal, `custom_exit()` and `custom_stoploss()`.
* Determine exit-price based on `exit_pricing` configuration setting or by using the `custom_exit_price()` callback.
* Before a exit order is placed, `confirm_trade_exit()` strategy callback is called.
* Before an exit order is placed, `confirm_trade_exit()` strategy callback is called.
* Check position adjustments for open trades if enabled by calling `adjust_trade_position()` and place additional order if required.
* Check if trade-slots are still available (if `max_open_trades` is reached).
* Verifies entry signal trying to enter new positions.
@@ -80,7 +82,9 @@ This loop will be repeated again and again until the bot is stopped.
* Loops per candle simulating entry and exit points.
* Calls `bot_loop_start()` strategy callback.
* Check for Order timeouts, either via the `unfilledtimeout` configuration, or via `check_entry_timeout()` / `check_exit_timeout()` strategy callbacks.
* Calls `adjust_entry_price()` strategy callback for open entry orders.
* Calls `adjust_order_price()` strategy callback for open orders.
* Calls `adjust_entry_price()` strategy callback for open entry orders. *only called when `adjust_order_price()` is not implemented!*
* Calls `adjust_exit_price()` strategy callback for open exit orders. *only called when `adjust_order_price()` is not implemented!*
* Check for trade entry signals (`enter_long` / `enter_short` columns).
* Confirm trade entry / exits (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy).
* Call `custom_entry_price()` (if implemented in the strategy) to determine entry price (Prices are moved to be within the opening candle).
@@ -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.
@@ -85,6 +85,11 @@ Setting protections from the configuration via `"protections": [],` has been rem
## hdf5 data storage
Using hdf5 as data storage has been deprecated in 2024.12 and will be removed in 2025.1. We recommend switching to the feather data format.
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.
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.
It will also take a long time, as freqtrade will need to download every single trade that happened on the exchange for the pair / timerange combination, therefore please be patient.
!!! Warning "rateLimit tuning"
Please pay attention that rateLimit configuration entry holds delay in milliseconds between requests, NOT requests\sec rate.
Please pay attention that rateLimit configuration entry holds delay in milliseconds between requests, NOT requests/sec rate.
So, in order to mitigate Kraken API "Rate limit exceeded" exception, this configuration should be increased, NOT decreased.
## Kucoin
@@ -342,6 +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.
@@ -46,10 +46,12 @@ Make sure you set the `initial_state` config option to `"running"` in your confi
### I have waited 5 minutes, why hasn't the bot made any trades yet?
* Depending on the buy strategy, the amount of whitelisted coins, the
situation of the market etc, it can take up to hours to find a good entry
* Depending on the entry strategy, the amount of whitelisted coins, the
situation of the market etc, it can take up to hours or days to find a good entry
position for a trade. Be patient!
* Backtesting will tell you roughly how many trades to expect - but that won't guarantee that they'll be distributed evenly across time - so you could have 20 trades on one day, and 0 for the rest of the week.
* It may be because of a configuration error. It's best to check the logs, they usually tell you if the bot is simply not getting buy signals (only heartbeat messages), or if there is something wrong (errors / exceptions in the log).
### I have made 12 trades already, why is my total profit negative?
@@ -100,6 +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).
@@ -217,10 +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).
* **Extensibility** - The generalized and robust architecture allows for incorporating any [machine learning library/method](freqai-configuration.md#using-different-prediction-models) available in Python. Eight examples are currently available, including classifiers, regressors, and a convolutional neural network
* **Smart outlier removal** - Remove outliers from training and prediction data sets using a variety of [outlier detection techniques](freqai-feature-engineering.md#outlier-detection)
* **Crash resilience** - Store trained models to disk to make reloading from a crash fast and easy, and [purge obsolete files](freqai-running.md#purging-old-model-data) for sustained dry/live runs
* **Automatic data normalization** - [Normalize the data](freqai-feature-engineering.md#feature-normalization) in a smart and statistically safe way
* **Automatic data normalization** - [Normalize the data](freqai-feature-engineering.md#building-the-data-pipeline) in a smart and statistically safe way
* **Automatic data download** - Compute timeranges for data downloads and update historic data (in live deployments)
* **Cleaning of incoming data** - Handle NaNs safely before training and model inferencing
* **Dimensionality reduction** - Reduce the size of the training data via [Principal Component Analysis](freqai-feature-engineering.md#data-dimensionality-reduction-with-principal-component-analysis)
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:develop_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:develop_freqaitorch`, `image: freqtradeorg/freqtrade:develop_freqairl`.
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
Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. `scikit-learn`) has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citzen scientists" to use their basic Python skills for data exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data collection, storage, and handling presents a disparate challenge. [`FreqAI`](#freqai) aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The `FreqAI` framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the `FreqAI` sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data.
Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. `scikit-learn`) has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citizen scientists" to use their basic Python skills for data exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data collection, storage, and handling presents a disparate challenge. [`FreqAI`](#freqai) aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The `FreqAI` framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the `FreqAI` sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data.
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.
Recursively search for a strategy in the strategies
folder.
--freqaimodel NAME Specify a custom freqaimodels.
--freqaimodel-path PATH
Specify additional lookup path for freqaimodels.
```
--8<-- "commands/hyperopt.md"
### Hyperopt checklist
@@ -591,6 +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.
@@ -651,7 +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.
```json
"exchange": {
"name": "...",
// ...
"pair_whitelist": [
"BTC/USDT",
"ETH/USDT",
// ...
],
"pair_blacklist": [
"BNB/USDT",
// ...
]
},
"pairlists": [
{"method": "StaticPairList"}
],
@@ -377,6 +392,9 @@ If an incorrect category string is chosen, the plugin will print the available c
!!! Warning "Many categories"
Each added category corresponds to one API call to CoinGecko. The more categories you add, the longer the pairlist generation will take, potentially causing rate limit issues.
!!! Danger "Duplicate symbols in coingecko"
Coingecko often has duplicate symbols, where the same symbol is used for different coins. Freqtrade will use the symbol as is and try to search for it on the exchange. If the symbol exists - it will be used. Freqtrade will however not check if the _intended_ symbol is the one coingecko meant. This can sometimes lead to unexpected results, especially on low volume coins or with meme coin categories.
#### AgeFilter
Removes pairs that have been listed on the exchange for less than `min_days_listed` days (defaults to `10`) or more than `max_days_listed` days (defaults `None` mean infinity).
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`.
Use this csv-filename to store lookahead-analysis-
results
```
!!! Note ""
The above Output was reduced to options `lookahead-analysis` adds on top of regular backtesting commands.
### Summary
Checks a given strategy for look ahead bias via lookahead-analysis
Look ahead bias means that the backtest uses data from future candles thereby not making it viable beyond backtesting
and producing false hopes for the one backtesting.
!!! Note
The above output was reduced to options that `lookahead-analysis` adds on top of regular backtesting commands.
### Introduction
Many strategies - without the programmer knowing - have fallen prey to lookahead bias.
Many strategies, without the programmer knowing, have fallen prey to lookahead bias.
This typically makes the strategy backtest look profitable, sometimes to extremes, but this is not realistic as the strategy is "cheating" by looking at data it would not have in dry or live modes.
Any backtest will populate the full dataframe including all timestamps at the beginning.
If the programmer is not careful or oblivious how things work internally
(which sometimes can be really hard to find out) then it will just look into the future making the strategy amazing
but not realistic.
The reason why strategies can "cheat" is because the freqtrade backtesting process populates the full dataframe including all candle timestamps at the outset.
If the programmer is not careful or oblivious how things work internally
(which sometimes can be really hard to find out) then the strategy will look into the future.
This command is made to try to verify the validity in the form of the aforementioned lookahead bias.
This command is made to try to verify the validity in the form of the aforementioned lookahead bias.
### How does the command work?
It will start with a backtest of all pairs to generate a baseline for indicators and entries/exits.
After the backtest ran, it will look if the `minimum-trade-amount` is met
and if not cancel the lookahead-analysis for this strategy.
After this initial backtest runs, it will look if the `minimum-trade-amount` is met and if not cancel the lookahead-analysis for this strategy.
If this happens, use a wider timerange to get more trades for the analysis, or use a timerange where more trades occur.
After setting the baseline it will then do additional runs for every entry and exit separately.
When a verification-backtest is done, it will compare the indicators as the signal (either entry or exit) and report the bias.
After all signals have been verified or falsified a result-table will be generated for the user to see.
After setting the baseline it will then do additional backtest runs for every entry and exit separately.
When these verificationbacktests complete, it will compare the indicators at the signal candles (both entry or exit)
and report the bias.
After all signals have been verified or falsified a result table will be generated for the user to see.
### How to find and remove bias? How can I salvage a biased strategy?
If you found a biased strategy online and want to have the same results, just without bias,
then you will be out of luck most of the time.
Usually the bias in the strategy is THE driving factor for "too good to be true" profits.
Removing conditions or indicators that push the profits up from bias will usually make the strategy significantly worse.
You might be able to salvage it partially if the biased indicators or conditions are not the core of the strategy, or there
are other entry and exit signals that are not biased.
### Examples of lookahead-bias
- `shift(-10)` looks 10 candles into the future.
- Using `iloc[]` in populate_* functions to access a specific row in the dataframe.
- For-loops are prone to introduce lookahead bias if you don't tightly control which numbers are looped through.
- Aggregation functions like `.mean()`, `.min()` and `.max()`, without a rolling window,
will calculate the value over the **whole** dataframe, so the signal candle will "see" a value including future candles.
A non-biased example would be to look back candles using `rolling()` instead:
e.g. `dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean()`
- `ta.MACD(dataframe, 12, 26, 1)` will introduce bias with a signalperiod of 1.
### What do the columns in the results table mean?
- `filename`: name of the checked strategy file
- `strategy`: checked strategy class name
- `has_bias`: result of the lookahead-analysis. `No` would be good, `Yes` would be bad.
- `total_signals`: number of checked signals (default is 20)
- `biased_entry_signals`: found bias in that many entries
- `biased_exit_signals`: found bias in that many exits
- `biased_indicators`: shows you the indicators themselves that are defined in populate_indicators
You might get false positives in the `biased_exit_signals` if you have biased entry signals paired with those exits.
However, a biased entry will usually result in a biased exit too,
even if the exit itself does not produce the bias -
especially if your entry and exit conditions use the same biased indicator.
**Address the bias in the entries first, then address the exits.**
### Caveats
- `lookahead-analysis` can only verify / falsify the trades it calculated and verified.
If the strategy has many different signals / signal types, it's up to you to select appropriate parameters to ensure that all signals have triggered at least once. Not triggered signals will not have been verified.
This could lead to a false-negative (the strategy will then be reported as non-biased).
- `lookahead-analysis` has access to everything that backtesting has too.
Please don't provoke any configs like enabling position stacking.
If you decide to do so, then make doubly sure that you won't ever run out of `max_open_trades`amount and neither leftover money in your wallet.
- In the results table, the `biased_indicators` column will falsely flag FreqAI target indicators defined in `set_freqai_targets()` as biased. These are not biased and can safely be ignored.
If the strategy has many different signals / signal types, it's up to you to select appropriate parameters to ensure that all signals have triggered at least once. Signals that are not triggered will not have been verified.
This would lead to a false-negative, i.e. the strategy will be reported as non-biased.
- `lookahead-analysis` has access to the same backtesting options and this can introduce problems.
Please don't use any options like enabling position stacking as this will distort the number of checked signals.
If you decide to do so, then make doubly sure that you won't ever run out of `max_open_trades`slots,
and that you have enough capital in the backtest wallet configuration.
- In the results table, the `biased_indicators` column
will falsely flag FreqAI target indicators defined in `set_freqai_targets()` as biased.
**These are not biased and can safely be ignored.**
@@ -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.
@@ -154,10 +154,10 @@ For example, simplified math:
In summary: The stoploss will be adjusted to be always be -10% of the highest observed price.
### Trailing stop loss, custom positive loss
### Trailing stop loss, different positive loss
You could also have a default stop loss when you are in the red with your buy (buy - fee), but once you hit a positive result (or an offset you define) the system will utilize a new stop loss, which can have a different value.
For example, your default stop loss is -10%, but once you have more than 0% profit (example 0.1%) a different trailing stoploss will be used.
You could also have a default stop loss when you are in the red with your buy (buy - fee), but once you hit a positive result (or an offset you define) the system will utilize a new stop loss, with a different value.
For example, your default stop loss is -10%, but once you have reached profitability (example 0.1%) a different trailing stoploss will be used.
!!! Note
If you want the stoploss to only be changed when you break even of making a profit (what most users want) please refer to next section with [offset enabled](#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset).
@@ -208,7 +208,9 @@ Before this, `stoploss` is used for the trailing stoploss.
You can also keep a static stoploss until the offset is reached, and then trail the trade to take profits once the market turns.
If `trailing_only_offset_is_reached = True` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss`.
If `trailing_only_offset_is_reached = True` then the trailing stoploss is only activated once the offset is reached. Until then, the stoploss remains at the configured `stoploss` and is not trailing.
Leaving this value as `trailing_only_offset_is_reached=False` will allow the trailing stoploss to start trailing as soon as the asset price increases above the initial entry price.
This option can be used with or without `trailing_stop_positive`, but uses `trailing_stop_positive_offset` as offset.
@@ -163,18 +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.
A simple callback which is called once when the strategy is loaded.
@@ -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 **not** called when there is an open order (either buy or sell) waiting for execution.
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.
@@ -767,10 +902,18 @@ Adjustment orders can be assigned with a tag by returning a 2 element Tuple, wit
Modifications to leverage are not possible, and the stake-amount returned is assumed to be before applying leverage.
!!! 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.
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()`.
Same thing also can happen with partial exit. So be sure to have a strict logic and/or check for the last filled order.
!!! 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, (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 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.
Each of the orders will be attached to the trade object for the duration of the trade - hence increasing memory usage.
Trades with long duration and 10s or even 100ds of position adjustments are therefore not recommended, and should be closed at regular intervals to not affect performance.
!!! Warning "Backtesting"
During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.
@@ -808,11 +951,6 @@ Back to the example above, since current rate is 200, the current USDT value of
While `/stopentry` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades.
!!! 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.
Each of the orders will be attached to the trade object for the duration of the trade - hence increasing memory usage.
Trades with long duration and 10s or even 100ds of position adjustments are therefore not recommended, and should be closed at regular intervals to not affect performance.
``` python
# Default imports
@@ -874,6 +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%
@@ -932,24 +1073,25 @@ class DigDeeperStrategy(IStrategy):
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`).
## Adjust Entry Price
## Adjust order Price
The `adjust_entry_price()` callback may be used by strategy developer to refresh/replace limit orders upon arrival of new candles.
Be aware that `custom_entry_price()` is still the one dictating initial entry limit order price target at the time of entry trigger.
The `adjust_order_price()` callback may be used by strategy developer to refresh/replace limit orders upon arrival of new candles.
This callback is called once every iteration unless the order has been (re)placed within the current candle - limiting the maximum (re)placement of each order to once per candle.
This also means that the first call will be at the start of the next candle after the initial order was placed.
Be aware that `custom_entry_price()`/`custom_exit_price()` is still the one dictating initial limit order price target at the time of the signal.
Orders can be cancelled out of this callback by returning `None`.
Returning `current_order_rate` will keep the order on the exchange "as is".
Returning any other price will cancel the existing order, and replace it with a new order.
The trade open-date (`trade.open_date_utc`) will remain at the time of the very first order placed.
Please make sure to be aware of this - and eventually adjust your logic in other callbacks to account for this, and use the date of the first filled order instead.
If the cancellation of the original order fails, then the order will not be replaced - though the order will most likely have been canceled on exchange. Having this happen on initial entries will result in the deletion of the order, while on position adjustment orders, it'll result in the trade size remaining as is.
If the cancellation of the original order fails, then the order will not be replaced - though the order will most likely have been canceled on exchange. Having this happen on initial entries will result in the deletion of the order, while on position adjustment orders, it'll result in the trade size remaining as is.
If the order has been partially filled, the order will not be replaced. You can however use [`adjust_trade_position()`](#adjust-trade-position) to adjust the trade size to the expected position size, should this be necessary / desired.
!!! Warning "Regular timeout"
Entry `unfilledtimeout` mechanism (as well as `check_entry_timeout()`) takes precedence over this.
Entry Orders that are cancelled via the above methods will not have this callback called. Be sure to update timeout values to match your expectations.
Entry `unfilledtimeout` mechanism (as well as `check_entry_timeout()`/`check_exit_timeout()`) takes precedence over this callback.
Orders that are cancelled via the above methods will not have this callback called. Be sure to update timeout values to match your expectations.
```python
# Default imports
@@ -958,14 +1100,26 @@ class AwesomeStrategy(IStrategy):
# ... populate_* methods
def adjust_entry_price(self, trade: Trade, order: Order | None, pair: str,
Entry price re-adjustment logic, returning the user desired limit price.
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)
and not timed out on subsequent candles after entry trigger.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-callbacks/
When not implemented by a strategy, returns current_order_rate as default.
If current_order_rate is returned then the existing order is maintained.
If None is returned then order gets canceled but not replaced by a new one.
@@ -977,14 +1131,16 @@ class AwesomeStrategy(IStrategy):
:param proposed_rate: Rate, calculated based on pricing settings in entry_pricing.
:param current_order_rate: Rate of the existing order in place.
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
:param side: "long" or "short" - indicating the direction of the proposed trade
: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 orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
# Limit entry orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
if (
pair == "BTC/USDT"
is_entry
and pair == "BTC/USDT"
and entry_tag == "long_sma200"
and side == "long"
and (current_time - timedelta(minutes=10)) <= trade.open_date_utc
@@ -1001,6 +1157,26 @@ class AwesomeStrategy(IStrategy):
return current_order_rate
```
!!! danger "Incompatibility with `adjust_*_price()`"
If you have both `adjust_order_price()` and `adjust_entry_price()`/`adjust_exit_price()` implemented, only `adjust_order_price()` will be used.
If you need to adjust entry/exit prices, you can either implement the logic in `adjust_order_price()`, or use the split `adjust_entry_price()` / `adjust_exit_price()` callbacks, but not both.
Mixing these is not supported and will raise an error during bot startup.
### Adjust Entry Price
The `adjust_entry_price()` callback may be used by strategy developer to refresh/replace entry limit orders upon arrival.
It's a sub-set of `adjust_order_price()` and is called only for entry orders.
All remaining behavior is identical to `adjust_order_price()`.
The trade open-date (`trade.open_date_utc`) will remain at the time of the very first order placed.
Please make sure to be aware of this - and eventually adjust your logic in other callbacks to account for this, and use the date of the first filled order instead.
### Adjust Exit Price
The `adjust_exit_price()` callback may be used by strategy developer to refresh/replace exit limit orders upon arrival.
It's a sub-set of `adjust_order_price()` and is called only for exit orders.
All remaining behavior is identical to `adjust_order_price()`.
## Leverage Callback
When trading in markets that allow leverage, this method must return the desired Leverage (Defaults to 1 -> No leverage).
@@ -1066,3 +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.
@@ -513,7 +513,7 @@ By default, freqtrade will attempt to load strategies from all `.py` files withi
Assuming your strategy is called `AwesomeStrategy`, stored in the file `user_data/strategies/AwesomeStrategy.py`, then you can start freqtrade in dry (or live, depending on your configuration) mode with:
```bash
freqtrade trade --strategy AwesomeStrategy`
freqtrade trade --strategy AwesomeStrategy
```
Note that we're using the class name, not the file name.
@@ -545,7 +545,7 @@ def informative_pairs(self):
]
```
A full sample can be found [in the DataProvider section](#complete-data-provider-sample).
A full sample can be found [in the DataProvider section](#complete-dataprovider-sample).
!!! Warning
As these pairs will be refreshed as part of the regular whitelist refresh, it's best to keep this list short.
@@ -576,7 +576,7 @@ To easily define informative pairs, use the `@informative` decorator. All decora
and do not have access to data from other informative pairs. However, all informative dataframes for each pair are merged and passed to main `populate_indicators()` method.
!!! Note
Do not use the `@informative` decorator if you need to use data from one informative pair when generating another informative pair. Instead, define informative pairs manually as described [in the DataProvider section](#complete-data-provider-sample).
Do not use the `@informative` decorator if you need to use data from one informative pair when generating another informative pair. Instead, define informative pairs manually as described [in the DataProvider section](#complete-dataprovider-sample).
When hyperopting, use of the hyperoptable parameter `.value` attribute is not supported. Please use the `.range` attribute. See [optimizing an indicator parameter](hyperopt.md#optimizing-an-indicator-parameter) for more information.
@@ -710,7 +710,7 @@ Options:
- Merge the dataframe without lookahead bias
- Forward-fill (optional)
For a full sample, please refer to the [complete data provider example](#complete-data-provider-sample) below.
For a full sample, please refer to the [complete data provider example](#complete-dataprovider-sample) below.
All columns of the informative dataframe will be available on the returning dataframe in a renamed fashion:
@@ -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.
@@ -360,6 +375,8 @@ Return the performance of each crypto-currency the bot has sold.
> 5. `STORJ/BTC 0.0009 BTC (27.24%) (1)`
> ...
The relative performance is calculated against the total investment in the currency, aggregating all filled entries for the currency.
### /balance
Return the balance of all crypto-currency your have on the exchange.
@@ -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.* |
--reset Reset sample files to their original state.
```
--8<-- "commands/create-userdir.md"
!!! Warning
Using `--reset` may result in loss of data, since this will overwrite all sample files without asking again.
@@ -38,15 +30,7 @@ optional arguments:
Creates a new configuration file, asking some questions which are important selections for a configuration.
```
usage: freqtrade new-config [-h] [-c PATH]
optional arguments:
-h, --help show this help message and exit
-c PATH, --config PATH
Specify configuration file (default: `config.json`). Multiple --config options may be used. Can be set to `-`
to read config from stdin.
```
--8<-- "commands/new-config.md"
!!! Warning
Only vital questions are asked. Freqtrade offers a lot more configuration possibilities, which are listed in the [Configuration documentation](configuration.md#configuration-parameters)
@@ -73,21 +57,7 @@ Especially useful with [split configuration files](configuration.md#multiple-con
Specify strategy class name which will be used by the
bot.
--template {full,minimal,advanced}
Use a template which is either `minimal`, `full`
(containing multiple sample indicators) or `advanced`.
Default: `full`.
```
--8<-- "commands/new-strategy.md"
### Sample usage of new-strategy
@@ -162,38 +116,7 @@ Use the `list-strategies` subcommand to see all strategies in one particular dir
This subcommand is useful for finding problems in your environment with loading strategies: modules with strategies that contain errors and failed to load are printed in red (LOAD FAILED), while strategies with duplicate names are printed in yellow (DUPLICATE NAME).
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--recursive-strategy-search
Recursively search for a strategy in the strategies
folder.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
--8<-- "commands/list-strategies.md"
!!! Warning
Using these commands will try to load all python files from a directory. This can be a security risk if untrusted files reside in this directory, since all module-level code is executed.
@@ -224,36 +147,7 @@ It provides a quick list of all available loss functions in your environment.
This subcommand can be useful for finding problems in your environment with loading loss functions: modules with Hyperopt-Loss functions that contain errors and failed to load are printed in red (LOAD FAILED), while hyperopt-Loss functions with duplicate names are printed in yellow (DUPLICATE NAME).
--hyperopt-path PATH Specify additional lookup path for Hyperopt Loss
functions.
-1, --one-column Print output in one column.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE, --log-file FILE
Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH, --data-dir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
--8<-- "commands/list-hyperoptloss.md"
## List freqAI models
@@ -261,49 +155,13 @@ Use the `list-freqaimodels` subcommand to see all freqAI models available.
This subcommand is useful for finding problems in your environment with loading freqAI models: modules with models that contain errors and failed to load are printed in red (LOAD FAILED), while models with duplicate names are printed in yellow (DUPLICATE NAME).
Please refer to the [corresponding documentation](advanced-setup.md#use-a-different-database-system) to learn about requirements for different database systems.
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
--8<-- "commands/webserver.md"
### Webserver mode - docker
@@ -609,37 +338,7 @@ Adding `--show-pair-list` outputs a sorted pair list you can easily copy/paste i
??? Warning "Strategy overfitting"
Only using winning pairs can lead to an overfitted strategy, which will not work well on future data. Make sure to extensively test your strategy in dry-run before risking real money.
"--days and --timerange are mutually exclusive. You can only specify one or the other."
)
if"pairs"notinconfig:
Some files were not shown because too many files have changed in this diff
Show More
Reference in New Issue
Block a user
Blocking a user prevents them from interacting with repositories, such as opening or commenting on pull requests or issues. Learn more about blocking a user.
Matthias
