plotting.py was missing a call to strategy.bot_loop_start() resulting in strategies using this callback to not work.
Made changes and confirmed plotting now works for strategies using bot_loop_start() callback.
LMK if anything else needed for PR.
1. Try to get points using `self.opt.ask` first
2. Discard the points that have already been evaluated
3. Retry using `self.opt.ask` up to 3 times
4. If still some points are missing in respect to `n_points`, random sample some points
5. Repeat until at least `n_points` points in the `asked_non_tried` list
6. Return a list with legth truncated at `n_points`
makes import of datetime columns more robust by first checking
if value is null because strftime can't handle NaT values
use `isnull()` because it handles all NaN/None/NaT cases
Ordering of Pairs without history should remain identical, so pairs with
positive performance move to the front, and negative pairs move to the back.
closes#4893
* updated new-config to add trading_mode and margin_mode
* added trading_mode and margin_mode to config examples
* added okex config example
* new file: config_examples/config_binance_futures.example.json
* removed trading_mode and margin_mode from base_config and binance and okex example
* deleted okex and futures config files
* updated full config file
* updated new-config command to add trading_mode and margin_mode to config
* new file: config_examples/config_okex_futures.example.json
* removed config_okex_futures.example.json
* added trading_mode to test_start_new_config
* new-config asks exchange before asking futures
* Simplify trading_mode selection
* margin_mode is empty string for spot new configs
* build_config_commands sorted exchanges
* isort
Co-authored-by: Matthias <xmatthias@outlook.com>
When specifying multiple pairs to download, the json filenames were
inconsistent due to the reassignment of candle_type. Also adds the
candle_type being downloaded to a log message.
Only KuCoin messages for 429000 error code are logged once.
Logs functions are also simplified and optimized.
test_remove_logs_for_pairs_already_in_blacklist is simplified as well.
New functions log_contains, num_log_contains, num_log_has and num_log_has_re
are introduced in the conftest module to help and simplify checking:
- if logs contain a string;
- count how many messages contain a string;
- count how many messages are the given string;
- count how many messages matchs a regex.
A couple of existing tests are changed using the new functions.
More logs are reduced, for KuCoin, on the retrier_async decorator:
_async_get_candle_history() returned exception
retrying _async_get_candle_history() still for
Giving up retrying: _async_get_candle_history()
Applying DDosProtection backoff delay
KuCoin APIs generate A LOT of error messages.
Consequently, logs are flooded with lines like:
2021-12-25 22:30:23 freqtrade.exchange.common: WARNING -
_async_get_candle_history() returned exception:
"kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?
symbol=PDEX-USDT&type=5min&startAt=1640317818&endAt=1640467818
429 Too Many Requests {"code":"429000","msg":"Too Many Requests"}"
2021-12-25 22:30:23 freqtrade.exchange.common: WARNING -
retrying _async_get_candle_history() still for 3 times
2021-12-25 22:30:23 freqtrade.exchange.common: WARNING -
Kucoin 429 error, avoid triggering DDosProtection backoff delay.
2 tries left before giving up
2021-12-25 22:30:24 freqtrade.exchange.common: WARNING -
_async_get_candle_history() returned exception:
"kucoin GET https://openapi-v2.kucoin.com/api/v1/market/candles?
symbol=UBX-USDT&type=5min&startAt=1640317821&endAt=1640467821
429 Too Many Requests {"code":"429000","msg":"Too Many Requests"}"
Messages like:
Kucoin 429 error, avoid triggering DDosProtection backoff delay.
are logged only once for a certain period of time (default is 3600 seconds).
Travisci seems to no longer offer a free plan for open source
repositories, and other repositories report the need to get in touch
with support again and again.
This complication is not necessary with github actions, which covers our
CI needs well.
Every time that there's freqtrade "ticks", pairs in the blacklist are
checked and a warning message is displayed.
So, the logs are continuously flooded with the same warnings.
For example:
2021-07-26 06:24:45 freqtrade.plugins.pairlistmanager: WARNING -
Pair XTZUP/USDT in your blacklist. Removing it from whitelist...
2021-07-26 06:24:45 freqtrade.plugins.pairlistmanager: WARNING -
Pair SUSHIUP/USDT in your blacklist. Removing it from whitelist...
2021-07-26 06:24:45 freqtrade.plugins.pairlistmanager: WARNING -
Pair XTZDOWN/USDT in your blacklist. Removing it from whitelist...
2021-07-26 06:24:50 freqtrade.plugins.pairlistmanager: WARNING -
Pair XTZUP/USDT in your blacklist. Removing it from whitelist...
2021-07-26 06:24:50 freqtrade.plugins.pairlistmanager: WARNING -
Pair SUSHIUP/USDT in your blacklist. Removing it from whitelist...
2021-07-26 06:24:50 freqtrade.plugins.pairlistmanager: WARNING -
Pair XTZDOWN/USDT in your blacklist. Removing it from whitelist...
This patch shows the warning only the first time, by keeping track
of which pairs in the blacklist were already logged.
- Add warning that PrecisionFilter can't be used on backtest that use multiple strategies
- Add note that not all pairlist handlers can be used on backtest
The sentence I've changed was continued on a different paragraph before, even though they were connected ideas. I have changed it so that they are part of the same paragraph now.
/weekly now list weeks starting from monday instead of rolling weeks.
/monthly now list months starting from the 1st.
Signed-off-by: Antoine Merino <antoine.merino.dev@gmail.com>
/weekly now list weeks starting from monday instead of rolling weeks.
/monthly now list months starting from the 1st.
Signed-off-by: Antoine Merino <antoine.merino.dev@gmail.com>
## Summary
Fix very small mistake in docs, that might confuse people. Let me know if this is the correct value now, there is still another 3100 in there, which I think makes sense there and is correct.
## Quick changelog
Changed the `rateLimit` 3100 value to 200, to match the 200ms and thus 0.2s delay.
- introducing filter
- replaced get_all_locks with a query for speed
. removed logging in backtesting mode for speed
. replaced for-loop with map-function for speed
Changes to models.py:
- changed string representation of Pairlock to also contain reason and active-state
Close all file handles that are left dangling to avoid warnings such as
```
ResourceWarning: unclosed file <_io.TextIOWrapper
name='...' mode='r' encoding='UTF-8'> params = json_load(filename.open('r'))
```
added extra key daily_profit in return of optimize_reports.generate_daily_stats
this allows us to analyze and plot a daily profit chart / equity line using snippet below inside jupyter notebook
```
# Plotting equity line (starting with 0 on day 1 and adding daily profit for each backtested day)
from freqtrade.configuration import Configuration
from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats
import plotly.express as px
import pandas as pd
# strategy = 'Strat'
# config = Configuration.from_files(["user_data/config.json"])
# backtest_dir = config["user_data_dir"] / "backtest_results"
stats = load_backtest_stats(backtest_dir)
strategy_stats = stats['strategy'][strategy]
equity = 0
equity_daily = []
for dp in strategy_stats['daily_profit']:
equity_daily.append(equity)
equity += float(dp)
dates = pd.date_range(strategy_stats['backtest_start'], strategy_stats['backtest_end'])
df = pd.DataFrame({'dates':dates,'equity_daily':equity_daily})
fig = px.line(df, x="dates", y="equity_daily")
fig.show()
```
At the moment we can keep a single code path when using IntParameter, but we have to make a special hyperopt case for CategoricalParameter/DecimalParameter. Range property solves this.
Encountering the python header error on a fresh ubuntu install:
``` utils_find_1st/find_1st.cpp:3:10: fatal error: Python.h: No such file or directory
#include "Python.h"
^~~~~~~~~~
compilation terminated.
```
solved by installing python3.7-dev. Also need to ensure python3.7-venv for fresh install.
Without this fix the resolver tries to read from the broken symlink,
resulting in an exception that leads to the the rather confusing
error message
freqtrade.resolvers.iresolver - WARNING - Path "...../user_data/strategies" does not exist.
as a result of a symlink matching .py not being readable.
freqtrade/freqtrade/optimize/hyperopt.py", line 166, in _save_result
rapidjson.dump(epoch, f, default=str, number_mode=rapidjson.NM_NATIVE)
ValueError: Out of range float values are not JSON compliant
Set a future timestamp when we should retry getting coingecko data.
This fixes conversion from stake to fiat when running multiple bots
as we don't simply accept the 429 error from Coingecko but handle it.
Exception is triggered by backtesting 20210301-20210501 range with BAKE/USDT pair (binance). Pair data starts on 2021-04-30 12:00:00 and after adjusting for startup candles pair dataframe is empty.
Solution: Since there are other pairs with enough data - skip pairs with no data and issue a warning.
Exception:
```
Traceback (most recent call last):
File "/home/rk/src/freqtrade/freqtrade/main.py", line 37, in main
return_code = args['func'](args)
File "/home/rk/src/freqtrade/freqtrade/commands/optimize_commands.py", line 53, in start_backtesting
backtesting.start()
File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 502, in start
min_date, max_date = self.backtest_one_strategy(strat, data, timerange)
File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 474, in backtest_one_strategy
results = self.backtest(
File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 365, in backtest
data: Dict = self._get_ohlcv_as_lists(processed)
File "/home/rk/src/freqtrade/freqtrade/optimize/backtesting.py", line 199, in _get_ohlcv_as_lists
pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
File "/home/rk/src/freqtrade/venv/lib/python3.9/site-packages/pandas/core/indexing.py", line 692, in __setitem__
iloc._setitem_with_indexer(indexer, value, self.name)
File "/home/rk/src/freqtrade/venv/lib/python3.9/site-packages/pandas/core/indexing.py", line 1587, in _setitem_with_indexer
raise ValueError(
ValueError: cannot set a frame with no defined index and a scalar
```
* Fix custom_sell() example to use rsi from last-available instead of trade-open candle, add a pointer to "Dataframe access" section for more info.
* Simplify "Custom stoploss using an indicator from dataframe example" greatly, add a pointer to "Dataframe access" section for more info.
Update custom_sell() example to comment that the current trade row is at trade open as written. Change "abstain" to something clearer for non-fluent English speakers.
otherwise doing something like:
```py
dataframe = super().populate_indicators(dataframe, ...)
```
won't work, because `dataframe` becomes `None`.
This is needed if one of those methods uses dataframe.copy() instead of
just working on reference. e.g. using `merge_informative` in
`populate_indicator` in a nested class hierarchy
This parameter allows us to customize a number of days we would like to download for new pairs only. This allows us to achieve efficient data update, downloading all data for new pairs and only missing data for existing pairs. To do that use `freqtrade download-data --new-pairs-days=3650` (not specifying `--days` or `--timerange` causes freqtrade to download only missing data for existing pairs).
* Split Parameter into IntParameter/FloatParameter/CategoricalParameter.
* Rename IHyperStrategy to HyperStrategyMixin and use it as mixin.
* --hyperopt parameter is now optional if strategy uses HyperStrategyMixin.
* Use OperationalException() instead of asserts.
This just extends the HyperOpt result filename by adding the strategy name. This allows analysis of HyperOpt results folder with no additional necessary context. An alternative idea would be to expand the result dict, but the additional static copies are non value added.
Afaik it should return -0.07 for 7% instead of -0.7.
As a side note, really interesting would also be an example for greater than 100% profits. especially when trailing stoploss, like
* Once profit is > 200% - stoploss will be set to 150%.
I assume it could be as simple as
```py
if current_profit > 2:
return (-1.50 + current_profit)
````
to achieve it
But I'm not quite confident, if the bot can handle stuff smaller than `-1`, since `1` and `-1` seem to have some special meaning and are often used to disable stoploss etc.
Only occurs in combination with api-server enabled,
due to some hot-fixing starlette does.
Since we load starlette at a later point, we need to replicate
starlette's behaviour for now, so sameSite cookies don't create a
problem.
closes#4356
Remove randomized exception that was geared toward ShuffleFilter. Remove case involvoing seed, also geared toward ShuffleFilter. Mock get_overall_performance().
Otherwise edge will have strange results, as
edge runs with sell signal, while the bot runs without sell signal,
causing results to be invalid
closes#3900
there should be no difference between current_profit and close_profit
it's always profit, and the information if it's a closed trade is available elsewhere
- New features need to contain unit tests, must conform to PEP8 (max-line-length = 100) and should be documented with the introduction PR.
- PR's can be declared as `[WIP]` - which signify Work in Progress Pull Requests (which are not finished).
If you are unsure, discuss the feature on our [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE)
or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
If you are unsure, discuss the feature on our [discord server](https://discord.gg/p7nuUNVfP7) or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a Pull Request.
## Getting started
@@ -57,6 +56,13 @@ To help with that, we encourage you to install the git pre-commit
hook that will warn you when you try to commit code that fails these checks.
Guide for installing them is [here](http://flake8.pycqa.org/en/latest/user/using-hooks.html).
##### Additional styles applied
* Have docstrings on all public methods
* Use double-quotes for docstrings
* Multiline docstrings should be indented to the level of the first quote
* Doc-strings should follow the reST format (`:param xxx: ...`, `:return: ...`, `:raises KeyError: ... `)
### 3. Test if all type-hints are correct
#### Run mypy
@@ -65,6 +71,14 @@ Guide for installing them is [here](http://flake8.pycqa.org/en/latest/user/using
Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or webUI. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
We strongly recommend you to have coding and Python knowledge. Do not
hesitate to read the source code and understand the mechanism of this bot.
## Exchange marketplaces supported
## Supported Exchange marketplaces
Please read the [exchange specific notes](docs/exchanges.md) to learn about eventual, special configurations needed for each exchange.
- [X] [Binance](https://www.binance.com/)
- [X] [Bittrex](https://bittrex.com/)
- [X] [Binance](https://www.binance.com/) ([*Note for binance users](docs/exchanges.md#blacklists))
- [X] [FTX](https://ftx.com/#a=2258149)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [Huobi](http://huobi.com/)
- [X] [Kraken](https://kraken.com/)
- [] [113 others to tests](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
- [X] [OKX](https://okx.com/) (Former OKEX)
- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
### Supported Futures Exchanges (experimental)
- [X] [Binance](https://www.binance.com/)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [OKX](https://okx.com/).
Please make sure to read the [exchange specific notes](docs/exchanges.md), as well as the [trading with leverage](docs/leverage.md) documentation before diving in.
### Community tested
Exchanges confirmed working by the community:
- [X] [Bitvavo](https://bitvavo.com/)
- [X] [Kucoin](https://www.kucoin.com/)
## Documentation
We invite you to read the bot documentation to ensure you understand how the bot is working.
Please find the complete documentation on our [website](https://www.freqtrade.io).
Please find the complete documentation on the [freqtrade website](https://www.freqtrade.io).
## Features
- [x]**Based on Python 3.6+**: For botting on any operating system - Windows, macOS and Linux.
- [x]**Based on Python 3.8+**: For botting on any operating system - Windows, macOS and Linux.
- [x]**Persistence**: Persistence is achieved through sqlite.
- [x]**Dry-run**: Run the bot without playing money.
- [x]**Dry-run**: Run the bot without paying money.
- [x]**Backtesting**: Run a simulation of your buy/sell strategy.
- [x]**Strategy Optimization by machine learning**: Use machine learning to optimize your buy/sell strategy parameters with real exchange data.
- [x]**Edge position sizing** Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market. [Learn more](https://www.freqtrade.io/en/latest/edge/).
- [x]**Edge position sizing** Calculate your win rate, risk reward ratio, the best stoploss and adjust your position size before taking a position for each specific market. [Learn more](https://www.freqtrade.io/en/stable/edge/).
- [x]**Whitelist crypto-currencies**: Select which crypto-currency you want to trade or use dynamic whitelists.
- [x]**Blacklist crypto-currencies**: Select which crypto-currency you want to avoid.
- [x]**Builtin WebUI**: Builtin web UI to manage your bot.
- [x]**Manageable via Telegram**: Manage the bot with Telegram.
- [x]**Display profit/loss in fiat**: Display your profit/loss in 33 fiat.
- [x]**Daily summary of profit/loss**: Provide a daily summary of your profit/loss.
- [x]**Display profit/loss in fiat**: Display your profit/loss in fiat currency.
- [x]**Performance status report**: Provide a performance status of your current trades.
## Quick start
Freqtrade provides a Linux/macOS script to install all dependencies and help you to configure the bot.
Please refer to the [Docker Quickstart documentation](https://www.freqtrade.io/en/stable/docker_quickstart/) on how to get started quickly.
```bash
git clone git@github.com:freqtrade/freqtrade.git
cd freqtrade
git checkout develop
./setup.sh --install
```
For any other type of installation please refer to [Installation doc](https://www.freqtrade.io/en/latest/installation/).
For further (native) installation methods, please refer to the [Installation documentation page](https://www.freqtrade.io/en/stable/installation/).
## Basic Usage
@@ -69,22 +83,22 @@ For any other type of installation please refer to [Installation doc](https://ww
convert-data Convert candle (OHLCV) data from one format to
another.
convert-trade-data Convert trade data from one format to another.
list-data List downloaded data.
backtesting Backtesting module.
edge Edge module.
hyperopt Hyperopt module.
@@ -98,8 +112,10 @@ positional arguments:
list-timeframes Print available timeframes for the exchange.
show-trades Show trades.
test-pairlist Test your pairlist configuration.
install-ui Install FreqUI
plot-dataframe Plot candles with indicators.
plot-profit Generate plot showing profits.
webserver Webserver module.
optional arguments:
-h, --help show this help message and exit
@@ -109,19 +125,20 @@ optional arguments:
### Telegram RPC commands
Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on our [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on the [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
-`/start`: Starts the trader
-`/stop`: Stops the trader
-`/status [table]`: Lists all open trades
-`/count`: Displays number of open trades
-`/profit`: Lists cumulative profit from all finished trades
-`/forcesell <trade_id>|all`: Instantly sells the given trade (Ignoring `minimum_roi`).
-`/start`: Starts the trader.
-`/stop`: Stops the trader.
-`/stopbuy`: Stopentering new trades.
-`/status <trade_id>|[table]`: Lists all or specific open trades.
-`/profit [<n>]`: Lists cumulative profit from all finished trades, over the last n days.
-`/forceexit <trade_id>|all`: Instantly exits the given trade (Ignoring `minimum_roi`).
-`/fx <trade_id>|all`: Alias to `/forceexit`
-`/performance`: Show performance of each finished trade grouped by pair
-`/balance`: Show account balance per currency
-`/daily <n>`: Shows profit or loss per day, over the last n days
-`/help`: Show help message
-`/version`: Show version
-`/balance`: Show account balance per currency.
-`/daily <n>`: Shows profit or loss per day, over the last n days.
-`/help`: Show help message.
-`/version`: Show version.
## Development branches
@@ -133,20 +150,17 @@ The project is currently setup in two main branches:
## Support
### Help / Slack
### Help / Discord
For any questions not covered by the documentation or for further
information about the bot, we encourage you to join our slack channel.
- [Click here to join Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE).
For any questions not covered by the documentation or for further information about the bot, or to simply engage with like-minded individuals, we encourage you to join the Freqtrade [discord server](https://discord.gg/p7nuUNVfP7).
to understand the requirements before sending your pull-requests.
Coding is not a neccessity to contribute - maybe start with improving our documentation?
Coding is not a necessity to contribute - maybe start with improving the documentation?
Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/good%20first%20issue) can be good first contributions, and will help get you familiar with the codebase.
**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [discord](https://discord.gg/p7nuUNVfP7) (please use the #dev channel for this). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
**Important:** Always create your PR against the `develop` branch, not `stable`.
@@ -177,7 +191,7 @@ Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/
### Up-to-date clock
The clock must be accurate, syncronized to a NTP server very frequently to avoid problems with communication to the exchanges.
The clock must be accurate, synchronized to a NTP server very frequently to avoid problems with communication to the exchanges.
### Min hardware required
@@ -187,9 +201,9 @@ To run this bot we recommend you a cloud instance with a minimum of:
To use a custom loss function class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
@@ -40,6 +12,11 @@ For the sample below, you then need to add the command line parameter `--hyperop
A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found in [userdata/hyperopts](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_loss.py).
``` python
from datetime import datetime
from typing import Any, Dict
from pandas import DataFrame
from freqtrade.optimize.hyperopt import IHyperOptLoss
TARGET_TRADES = 600
@@ -54,6 +31,8 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
* `trade_count`: Amount of trades (identical to `len(results)`)
* `min_date`: Start date of the hyperopting TimeFrame
* `min_date`: End date of the hyperopting TimeFrame
* `min_date`: Start date of the timerange used
* `min_date`: End date of the timerange used
* `config`: Config object used (Note: Not all strategy-related parameters will be updated here if they are part of a hyperopt space).
* `processed`: Dict of Dataframes with the pair as keys containing the data used for backtesting.
* `backtest_stats`: Backtesting statistics using the same format as the backtesting file "strategy" substructure. Available fields can be seen in `generate_strategy_stats()` in `optimize_reports.py`.
This function needs to return a floating point number (`float`). Smaller numbers will be interpreted as better results. The parameters and balancing for this is up to you.
!!! Note
This function is called once per iteration - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
This function is called once per epoch - so please make sure to have this as optimized as possible to not slow hyperopt down unnecessarily.
!!! Note "`*args` and `**kwargs`"
Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface in the future.
## Overriding pre-defined spaces
To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`), define a nested class called Hyperopt and define the required spaces as follows:
Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.
All overrides are optional and can be mixed/matched as necessary.
### Dynamic parameters
Parameters can also be defined dynamically, but must be available to the instance once the * [`bot_start()` callback](strategy-callbacks.md#bot-start) has been called.
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]`".
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:
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.
## Space options
For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types:
* `Categorical` - Pick from a list of categories (e.g. `Categorical(['a', 'b', 'c'], name="cat")`)
* `Integer` - Pick from a range of whole numbers (e.g. `Integer(1, 10, name='rsi')`)
* `SKDecimal` - Pick from a range of decimal numbers with limited precision (e.g. `SKDecimal(0.1, 0.5, decimals=3, name='adx')`). *Available only with freqtrade*.
* `Real` - Pick from a range of decimal numbers with full precision (e.g. `Real(0.1, 0.5, name='adx')`
You can import all of these from `freqtrade.optimize.space`, although `Categorical`, `Integer` and `Real` are only aliases for their corresponding scikit-optimize Spaces. `SKDecimal` is provided by freqtrade for faster optimizations.
``` python
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa
```
!!! Hint "SKDecimal vs. Real"
We recommend to use `SKDecimal` instead of the `Real` space in almost all cases. While the Real space provides full accuracy (up to ~16 decimal places) - this precision is rarely needed, and leads to unnecessary long hyperopt times.
Assuming the definition of a rather small space (`SKDecimal(0.10, 0.15, decimals=2, name='xxx')`) - SKDecimal will have 5 possibilities (`[0.10, 0.11, 0.12, 0.13, 0.14, 0.15]`).
A corresponding real space `Real(0.10, 0.15 name='xxx')` on the other hand has an almost unlimited number of possibilities (`[0.10, 0.010000000001, 0.010000000002, ... 0.014999999999, 0.01500000000]`).
For more information regarding usage of the sqlite databases, for example to manually enter or remove trades, please refer to the [SQL Cheatsheet](sql_cheatsheet.md).
### Multiple instances using docker
To run multiple instances of freqtrade using docker you will need to edit the docker-compose.yml file and add all the instances you want as separate services. Remember, you can separate your configuration into multiple files, so it's a good idea to think about making them modular, then if you need to edit something common to all bots, you can do that in a single config file.
``` yml
---
version: '3'
services:
freqtrade1:
image: freqtradeorg/freqtrade:stable
# image: freqtradeorg/freqtrade:develop
# Use plotting image
# image: freqtradeorg/freqtrade:develop_plot
# Build step - only needed when additional dependencies are needed
# build:
# context: .
# dockerfile: "./docker/Dockerfile.custom"
restart: always
container_name: freqtrade1
volumes:
- "./user_data:/freqtrade/user_data"
# Expose api on port 8080 (localhost only)
# Please read the https://www.freqtrade.io/en/latest/rest-api/ documentation
# before enabling this.
ports:
- "127.0.0.1:8080:8080"
# Default command used when running `docker compose up`
You can use whatever naming convention you want, freqtrade1 and 2 are arbitrary. Note, that you will need to use different database files, port mappings and telegram configurations for each instance, as mentioned above.
## Configure the bot running as a systemd service
Copy the `freqtrade.service` file to your systemd user directory (usually `~/.config/systemd/user`) and update `WorkingDirectory` and `ExecStart` to match your setup.
@@ -111,12 +176,15 @@ Log messages are send to `syslog` with the `user` facility. So you can see them
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.
For `rsyslog` the messages from the bot can be redirected into a separate dedicated log file. To achieve this, add
```
if $programname startswith "freqtrade" then -/var/log/freqtrade.log
```
to one of the rsyslog configuration files, for example at the end of the `/etc/rsyslog.d/50-default.conf`.
For `syslog` (`rsyslog`), the reduction mode can be switched on. This will reduce the number of repeating messages. For instance, multiple bot Heartbeat messages will be reduced to a single message when nothing else happens with the bot. To achieve this, set in `/etc/rsyslog.conf`:
@@ -5,57 +5,181 @@ This page explains how to validate your strategy performance by using Backtestin
Backtesting requires historic data to be available.
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.
real data. This is what we call [backtesting](https://en.wikipedia.org/wiki/Backtesting).
Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHCLV) data from `user_data/data/<exchange>` by default.
Backtesting will use the crypto-currencies (pairs) from your config file and load historical candle (OHLCV) data from `user_data/data/<exchange>` by default.
If no data is available for the exchange / pair / timeframe combination, backtesting will ask you to download them first using `freqtrade download-data`.
For details on downloading, please refer to the [Data Downloading](data-download.md) section in the documentation.
The result of backtesting will confirm if your bot has better odds of making a profit than a loss.
All profit calculations include fees, and freqtrade will use the exchange's default fees for the calculation.
!!! Warning "Using dynamic pairlists for backtesting"
Using dynamic pairlists is possible, however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
Also, when using pairlists other than StaticPairlist, reproducability of backtesting-results cannot be guaranteed.
Please read the [pairlists documentation](configuration.md#pairlists) for more information.
Using dynamic pairlists is possible (not all of the handlers are allowed to be used in backtest mode), however it relies on the current market conditions - which will not reflect the historic status of the pairlist.
Also, when using pairlists other than StaticPairlist, reproducibility of backtesting-results cannot be guaranteed.
Please read the [pairlists documentation](plugins.md#pairlists) for more information.
To achieve reproducible results, best generate a pairlist via the [`test-pairlist`](utils.md#test-pairlist) command and use that as static pairlist.
### Run a backtesting against the currencies listed in your config file
!!! Note
By default, Freqtrade will export backtesting results to `user_data/backtest_results`.
The exported trades can be used for [further analysis](#further-backtest-result-analysis) or can be used by the [plotting sub-command](plotting.md#plot-price-and-indicators) (`freqtrade plot-dataframe`) in the scripts directory.
#### With 5 min candle (OHLCV) data (per default)
### Starting balance
Backtesting will require a starting balance, which can be provided as `--dry-run-wallet <balance>` or `--starting-balance <balance>` command line argument, or via `dry_run_wallet` configuration setting.
This amount must be higher than `stake_amount`, otherwise the bot will not be able to simulate any trade.
### Dynamic stake amount
Backtesting supports [dynamic stake amount](configuration.md#dynamic-stake-amount) by configuring `stake_amount` as `"unlimited"`, which will split the starting balance into `max_open_trades` pieces.
Profits from early trades will result in subsequent higher stake amounts, resulting in compounding of profits over the backtesting period.
### Example backtesting commands
With 5 min candle (OHLCV) data (per default)
```bash
freqtrade backtesting
freqtrade backtesting --strategy AwesomeStrategy
```
#### With 1 min candle (OHLCV) data
Where `--strategy AwesomeStrategy` / `-s AwesomeStrategy` refers to the class name of the strategy, which is within a python file in the `user_data/strategies` directory.
Where `-s SampleStrategy` refers to the class name within the strategy file `sample_strategy.py` found in the `freqtrade/user_data/strategies` directory.
The exported trades can be used for [further analysis](#further-backtest-result-analysis), or can be used by the plotting script `plot_dataframe.py` in the scripts directory.
Only use this if you're sure you'll not want to plot or analyze your results further.
#### Exporting trades to file specifying a custom filename
---
Exporting trades to file specifying a custom filename
Only supply this option (or the corresponding configuration parameter) if you want to experiment with different fee values. By default, Backtesting fetches the default fee from the exchange pair/market info.
#### Running backtest with smaller testset by using timerange
---
Use the `--timerange` argument to change how much of the testset you want to use.
Running backtest with smaller test-set by using timerange
Use the `--timerange` argument to change how much of the test-set you want to use.
For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your inputdata.
For example, running backtesting with the `--timerange=20190501-` option will use all available data starting with May 1st, 2019 from your inputdata.
```bash
freqtrade backtesting --timerange=20190501-
```
You can also specify particular dates or a range span indexed by start and stop.
You can also specify particular date ranges.
The full timerange specification:
- Use tickframes till 2018/01/31: `--timerange=-20180131`
- Use tickframes since 2018/01/31: `--timerange=20180131-`
- Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
- Use tickframes between POSIX timestamps 1527595200 1527618600:
`--timerange=1527595200-1527618600`
- Use data until 2018/01/31: `--timerange=-20180131`
- Use data since 2018/01/31: `--timerange=20180131-`
- Use data since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
- Use data between POSIX / epoch timestamps 1527595200 1527618600:`--timerange=1527595200-1527618600`
## Understand the backtesting result
@@ -122,61 +252,90 @@ The most important in the backtesting is to understand the result.
The bot has made `429` trades for an average duration of `4:12:00`, with a performance of `76.20%` (profit), that means it has
earned a total of `0.00762792 BTC` starting with a capital of 0.01 BTC.
The column `avg profit %` shows the average profit for all trades made while the column `cum profit %` sums up all the profits/losses.
The column `tot profit %` shows instead the total profit % in relation to allocated capital (`max_open_trades * stake_amount`).
In the above results we have `max_open_trades=2` and `stake_amount=0.005` in config so`tot_profit %` will be `(76.20/100) * (0.005 * 2) =~ 0.00762792 BTC`.
The column `Avg Profit %` shows the average profit for all trades made while the column `Cum Profit %` sums up all the profits/losses.
The column `Tot Profit %` shows instead the total profit % in relation to the starting balance.
In the above results, we have a starting balance of 0.01 BTC and an absolute profit of 0.00762792 BTC - so the`Tot Profit %` will be `(0.00762792 / 0.01) * 100 ~= 76.2%`.
Your strategy performance is influenced by your buy strategy, your sell strategy, and also by the `minimal_roi` and `stop_loss` you have set.
Your strategy performance is influenced by your buy strategy, your exit strategy, and also by the `minimal_roi` and `stop_loss` you have set.
For example, if your `minimal_roi` is only `"0": 0.01` you cannot expect the bot to make more profit than 1% (because it will sell every time a trade reaches 1%).
For example, if your `minimal_roi` is only `"0": 0.01` you cannot expect the bot to make more profit than 1% (because it will exit every time a trade reaches 1%).
```json
"minimal_roi":{
@@ -211,14 +370,14 @@ On the other hand, if you set a too high `minimal_roi` like `"0": 0.55`
(55%), there is almost no chance that the bot will ever reach this profit.
Hence, keep in mind that your performance is an integral mix of all different elements of the strategy, your configuration, and the crypto-currency pairs you have set up.
### Sell reasons table
### Exit reasons table
The 2nd table contains a recap of sell reasons.
This table can tell you which area needs some additional work (e.g. all or many of the `sell_signal` trades are losses, so you should work on improving the sell signal, or consider disabling it).
The 2nd table contains a recap of exit reasons.
This table can tell you which area needs some additional work (e.g. all or many of the `exit_signal` trades are losses, so you should work on improving the exit signal, or consider disabling it).
### Left open trades table
The 3rd table contains all trades the bot had to `forcesell` at the end of the backtesting period to present you the full picture.
The 3rd table contains all trades the bot had to `force_exit` at the end of the backtesting period to present you the full picture.
This is necessary to simulate realistic behavior, since the backtest period has to end at some point, while realistically, you could leave the bot running forever.
These trades are also included in the first table, but are also shown separately in this table for clarity.
@@ -228,68 +387,186 @@ The last element of the backtest report is the summary metrics table.
It contains some useful key metrics about performance of your strategy on backtesting data.
-`Total trades`: Identical to the total trades of the backtest output table.
-`First trade`: First trade entered.
-`First trade pair`: Which pair was part of the first trade.
-`Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
-`Total Profit %`: Total profit per stake amount. Aligned to the TOTAL column of the first table.
-`Trades per day`: Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
-`Max open trades`: Setting of `max_open_trades` (or `--max-open-trades`) - or number of pairs in the pairlist (whatever is lower).
-`Total/Daily Avg Trades`: Identical to the total trades of the backtest output table / Total trades divided by the backtesting duration in days (this will give you information about how many trades to expect from the strategy).
-`Starting balance`: Start balance - as given by dry-run-wallet (config or command line).
-`Final balance`: Final balance - starting balance + absolute profit.
-`Absolute profit`: Profit made in stake currency.
-`Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
-`CAGR %`: Compound annual growth rate.
-`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.
-`Best Pair` / `Worst Pair`: Best and worst performing pair, and it's corresponding `Cum Profit %`.
-`Best Trade` / `Worst Trade`: Biggest single winning trade and biggest single losing trade.
-`Best day` / `Worst day`: Best and worst day based on daily profit.
-`Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
-`Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
-`Max Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
-`Drawdown Start` / `Drawdown End`: Start and end datetimes for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
-`Rejected Entry signals`: Trade entry signals that could not be acted upon due to `max_open_trades` being reached.
-`Entry/Exit Timeouts`: Entry/exit orders which did not fill (only applicable if custom pricing is used).
-`Canceled Trade Entries`: Number of trades that have been canceled by user request via `adjust_entry_price`.
-`Canceled Entry Orders`: Number of entry orders that have been canceled by user request via `adjust_entry_price`.
-`Replaced Entry Orders`: Number of entry orders that have been replaced by user request via `adjust_entry_price`.
-`Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
-`Max % of account underwater`: Maximum percentage your account has decreased from the top since the simulation started.
Calculated as the maximum of `(Max Balance - Current Balance) / (Max Balance)`.
-`Absolute Drawdown (Account)`: Maximum Account Drawdown experienced. Calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.
-`Drawdown`: Maximum, absolute drawdown experienced. Difference between Drawdown High and Subsequent Low point.
-`Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
-`Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
-`Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.
-`Long / Short`: Split long/short values (Only shown when short trades were made).
-`Total profit Long %` / `Absolute profit Long`: Profit long trades only (Only shown when short trades were made).
-`Total profit Short %` / `Absolute profit Short`: Profit short trades only (Only shown when short trades were made).
### Assumptions made by backtesting
### Daily / Weekly / Monthly breakdown
You can get an overview over daily / weekly or monthly results by using the `--breakdown <>` switch.
To visualize daily and weekly breakdowns, you can use the following:
``` bash
freqtrade backtesting --strategy MyAwesomeStrategy --breakdown day week
```
``` output
======================== DAY BREAKDOWN =========================
The output will show a table containing the realized absolute Profit (in stake currency) for the given timeperiod, as well as wins, draws and losses that materialized (closed) on this day. Below that there will be a second table for the summarized values of weeks indicated by the date of the closing Sunday. The same would apply to a monthly breakdown indicated by the last day of the month.
### Backtest result caching
To save time, by default backtest will reuse a cached result from within the last day when the backtested strategy and config match that of a previous backtest. To force a new backtest despite existing result for an identical run specify `--cache none` parameter.
!!! Warning
Caching is automatically disabled for open-ended timeranges (`--timerange 20210101-`), as freqtrade cannot ensure reliably that the underlying data didn't change. It can also use cached results where it shouldn't if the original backtest had missing data at the end, which was fixed by downloading more data.
In this instance, please use `--cache none` once to force a fresh backtest.
### Further backtest-result analysis
To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
You can then load the trades to perform further analysis as shown in the [data analysis](data-analysis.md#backtesting) backtesting section.
## Assumptions made by backtesting
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
- Buys happen at open-price
- Sell signal sells happen at open-price of the following candle
- Low happens before high for stoploss, protecting capital first
- All orders are filled at the requested price (no slippage, no unfilled orders)
- Exit-signal exits happen at open-price of the consecutive candle
- Exit-signal is favored over Stoploss, because exit-signals are assumed to trigger on candle's open
- ROI
- sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
- sells are never "below the candle", so a ROI of 2% may result in a sell at 2.4% if low was at 2.4% profit
- Forcesells caused by `<N>=-1` ROI entries use low as sell value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
- Stoploss sells happen exactly at stoploss price, even if low was lower
- exits are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the exit will be at 2%)
- exits are never "below the candle", so a ROI of 2% may result in a exit at 2.4% if low was at 2.4% profit
- Forceexits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
- Stoploss exits happen exactly at stoploss price, even if low was lower, but the loss will be `2 * fees` higher than the stoploss price
- Stoploss is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` exit reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes
- Low happens before high for stoploss, protecting capital first
- Trailing stoploss
- Trailing Stoploss is only adjusted if it's below the candle's low (otherwise it would be triggered)
- On trade entry candles that trigger trailing stoploss, the "minimum offset" (`stop_positive_offset`) is assumed (instead of high) - and the stop is calculated from this point
- High happens first - adjusting stoploss
- Low uses the adjusted stoploss (so sells with large high-low difference are backtested correctly)
- Sell-reason does not explain if a trade was positive or negative, just what triggered the sell (this can look odd if negative ROI values are used)
- Stoploss (and trailing stoploss) is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` and/or `trailing_stop` sell reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes.
- Low uses the adjusted stoploss (so exits with large high-low difference are backtested correctly)
- ROI applies before trailing-stop, ensuring profits are "top-capped" at ROI if both ROI and trailing stop applies
- Exit-reason does not explain if a trade was positive or negative, just what triggered the exit (this can look odd if negative ROI values are used)
- Evaluation sequence (if multiple signals happen on the same candle)
- Exit-signal
- Stoploss
- ROI
- Trailing stoploss
Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.
Also, keep in mind that past results don't guarantee future success.
In addition to the above assumptions, strategy authors should carefully read the [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies) section, to avoid using data in backtesting which is not available in real market conditions.
### Further backtest-result analysis
### Improved backtest accuracy
To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).
You can then load the trades to perform further analysis as shown in our [data analysis](data-analysis.md#backtesting) backtesting section.
One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or viceversa?).
So assuming you run backtesting with a 1h timeframe, there will be 4 prices for that candle (Open, High, Low, Close).
While backtesting does take some assumptions (read above) about this - this can never be perfect, and will always be biased in one way or the other.
To mitigate this, freqtrade can use a lower (faster) timeframe to simulate intra-candle movements.
To utilize this, you can append `--timeframe-detail 5m` to your regular backtesting command.
This will load 1h data as well as 5m data for the timeframe. The strategy will be analyzed with the 1h timeframe - and for every "open trade candle" (candles where a trade is open) the 5m data will be used to simulate intra-candle movements.
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).
`--timeframe-detail` must be smaller than the original timeframe, otherwise backtesting will fail to start.
Obviously this will require more memory (5m data is bigger than 1h data), and will also impact runtime (depending on the amount of trades and trade durations).
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).
## Backtesting multiple strategies
@@ -309,15 +586,14 @@ There will be an additional table comparing win/losses of the different strategi
Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
* Calls `populate_buy_trend()` and `populate_sell_trend()`
* Calls `bot_loop_start()` once.
* Calculate indicators (calls `populate_indicators()` once per pair).
* Calculate entry / exit signals (calls `populate_entry_trend()` and `populate_exit_trend()` once per pair).
* Loops per candle simulating entry and exit points.
* 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.
* 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).
* In Margin and Futures mode, `leverage()` strategy callback is called to determine the desired leverage.
* Determine stake size by calling the `custom_stake_amount()` callback.
* Check position adjustments for open trades if enabled and call `adjust_trade_position()` to determine if an additional order is requested.
* Call `custom_stoploss()` and `custom_exit()` to find custom exit points.
* For exits based on exit-signal and custom-exit: Call `custom_exit_price()` to determine exit price (Prices are moved to be within the closing candle).
You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/` after initializing the user directory with `freqtrade create-userdir --userdir user_data`.
You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/` after initializing the user directory with `freqtrade create-userdir --userdir user_data`.
Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.
## Recommended workflow
## Recommended workflow
| Task | Tool |
--- | ---
Bot operations | CLI
| Task | Tool |
--- | ---
Bot operations | CLI
Repetitive tasks | Shell scripts
Data analysis & visualization | Notebook
Data analysis & visualization | Notebook
1. Use the CLI to
* download historical data
* run a backtest
* run with real-time data
* export results
* export results
1. Collect these actions in shell scripts
* save complicated commands with arguments
* execute multi-step operations
* execute multi-step operations
* automate testing strategies and preparing data for analysis
1. Use a notebook to
* visualize data
* munge and plot to generate insights
* mangle and plot to generate insights
## Example utility snippets
## Example utility snippets
### Change directory to root
### Change directory to root
Jupyter notebooks execute from the notebook directory. The following snippet searches for the project root, so relative paths remain consistent.
@@ -119,5 +122,6 @@ Best avoid relative paths, since this starts at the storage location of the jupy
* [Strategy debugging](strategy_analysis_example.md) - also available as Jupyter notebook (`user_data/notebooks/strategy_analysis_example.ipynb`)
* [Plotting](plotting.md)
* [Tag Analysis](advanced-backtesting.md)
Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.
@@ -8,11 +8,12 @@ If no additional parameter is specified, freqtrade will download data for `"1m"`
Exchange and pairs will come from `config.json` (if specified using `-c/--config`).
Otherwise `--exchange` becomes mandatory.
You can use a relative timerange (`--days 20`) or an absolute starting point (`--timerange 20200101`). For incremental downloads, the relative approach should be used.
You can use a relative timerange (`--days 20`) or an absolute starting point (`--timerange 20200101-`). For incremental downloads, the relative approach should be used.
!!! Tip "Tip: Updating existing data"
If you already have backtesting data available in your data-directory and would like to refresh this data up to today, use `--days xx` with a number slightly higher than the missing number of days. Freqtrade will keep the available data and only download the missing data.
Be careful though: If the number is too small (which would result in a few missing days), the whole dataset will be removed and only xx days will be downloaded.
If you already have backtesting data available in your data-directory and would like to refresh this data up to today, freqtrade will automatically calculate the data missing for the existing pairs and the download will occur from the latest available point until "now", neither --days or --timerange parameters are required. Freqtrade will keep the available data and only download the missing data.
If you are updating existing data after inserting new pairs that you have no data for, use `--new-pairs-days xx` parameter. Specified number of days will be downloaded for new pairs while old pairs will be updated with missing data only.
If you use `--days xx` parameter alone - data for specified number of days will be downloaded for _all_ pairs. Be careful, if specified number of days is smaller than gap between now and last downloaded candle - freqtrade will delete all existing data to avoid gaps in candle data.
### Usage
@@ -20,20 +21,28 @@ You can use a relative timerange (`--days 20`) or an absolute starting point (`-
Show profits for only these pairs. Pairs are space-
Limit command to these pairs. Pairs are space-
separated.
--pairs-file FILE File containing a list of pairs to download.
--days INT Download data for given number of days.
--new-pairs-days INT Download data of new pairs for given number of days.
Default: `None`.
--include-inactive-pairs
Also download data from inactive pairs.
--timerange TIMERANGE
Specify what timerange of data to use.
--dl-trades Download trades instead of OHLCV data. The bot will
@@ -52,6 +61,9 @@ optional arguments:
--data-format-trades {json,jsongz,hdf5}
Storage format for downloaded trades data. (default:
`jsongz`).
--trading-mode {spot,margin,futures}
Select Trading mode
--prepend Allow data prepending.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
@@ -76,6 +88,93 @@ Common arguments:
For that reason, `download-data` does not care about the "startup-period" defined in a strategy. It's up to the user to download additional days if the backtest should start at a specific point in time (while respecting startup period).
### Pairs file
In alternative to the whitelist from `config.json`, a `pairs.json` file can be used.
If you are using Binance for example:
- create a directory `user_data/data/binance` and copy or create the `pairs.json` file in that directory.
- update the `pairs.json` file to contain the currency pairs you are interested in.
```bash
mkdir -p user_data/data/binance
touch user_data/data/binance/pairs.json
```
The format of the `pairs.json` file is a simple json list.
Mixing different stake-currencies is allowed for this file, since it's only used for downloading.
``` json
[
"ETH/BTC",
"ETH/USDT",
"BTC/USDT",
"XRP/ETH"
]
```
!!! Tip "Downloading all data for one quote currency"
Often, you'll want to download data for all pairs of a specific quote-currency. In such cases, you can use the following shorthand:
`freqtrade download-data --exchange binance --pairs .*/USDT <...>`. The provided "pairs" string will be expanded to contain all active pairs on the exchange.
To also download data for inactive (delisted) pairs, add `--include-inactive-pairs` to the command.
??? Note "Permission denied errors"
If your configuration directory `user_data` was made by docker, you may get the following error:
- To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust rate limits etc.)
- To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
- To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
- To download historical candle (OHLCV) data from a fixed starting point, use `--timerange 20200101-` - which will download all data from January 1st, 2020.
- Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
- To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
#### Download additional data before the current timerange
Assuming you downloaded all data from 2022 (`--timerange 20220101-`) - but you'd now like to also backtest with earlier data.
You can do so by using the `--prepend` flag, combined with `--timerange` - specifying an end-date.
The format of the `pairs.json` file is a simple json list.
Mixing different stake-currencies is allowed for this file, since it's only used for downloading.
``` json
[
"ETH/BTC",
"ETH/USDT",
"BTC/USDT",
"XRP/ETH"
]
```
### Start download
Then run:
```bash
freqtrade download-data --exchange binance
```
This will download historical candle (OHLCV) data for all the currency pairs you defined in `pairs.json`.
### Other Notes
- To use a different directory than the exchange specific default, use `--datadir user_data/data/some_directory`.
- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust rate limits etc.)
- To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
- To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
- To download historical candle (OHLCV) data from a fixed starting point, use `--timerange 20200101-` - which will download all data from January 1st, 2020. Eventually set end dates are ignored.
- Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
- To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
### Trades (tick) data
By default, `download-data` sub-command downloads Candles (OHLCV) data. Some exchanges also provide historic trade-data via their API.
@@ -308,10 +429,13 @@ Since this data is large by default, the files use gzip by default. They are sto
To use this mode, simply add `--dl-trades` to your call. This will swap the download method to download trades, and resamples the data locally.
!!! Warning "do not use"
You should not use this unless you're a kraken user. Most other exchanges provide OHLCV data with sufficient history.
@@ -15,8 +15,8 @@ This command line option was deprecated in 2019.7-dev (develop branch) and remov
### The **--dynamic-whitelist** command line option
This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch)
and in freqtrade 2019.7.
This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch) and in freqtrade 2019.7.
Please refer to [pairlists](plugins.md#pairlists-and-pairlist-handlers) instead.
### the `--live` command line option
@@ -24,6 +24,10 @@ and in freqtrade 2019.7.
Did only download the latest 500 candles, so was ineffective in getting good backtest data.
Removed in 2019-7-dev (develop branch) and in freqtrade 2019.8.
### `ticker_interval` (now `timeframe`)
Support for `ticker_interval` terminology was deprecated in 2020.6 in favor of `timeframe` - and compatibility code was removed in 2022.3.
### Allow running multiple pairlists in sequence
The former `"pairlist"` section in the configuration has been removed, and is replaced by `"pairlists"` - being a list to specify a sequence of pairlists.
@@ -33,3 +37,40 @@ The old section of configuration parameters (`"pairlist"`) has been deprecated i
### deprecation of bidVolume and askVolume from volume-pairlist
Since only quoteVolume can be compared between assets, the other options (bidVolume, askVolume) have been deprecated in 2020.4, and have been removed in 2020.9.
### Using order book steps for exit price
Using `order_book_min` and `order_book_max` used to allow stepping the orderbook and trying to find the next ROI slot - trying to place sell-orders early.
As this does however increase risk and provides no benefit, it's been removed for maintainability purposes in 2021.7.
### Legacy Hyperopt mode
Using separate hyperopt files was deprecated in 2021.4 and was removed in 2021.9.
Please switch to the new [Parametrized Strategies](hyperopt.md) to benefit from the new hyperopt interface.
## Strategy changes between V2 and V3
Isolated Futures / short trading was introduced in 2022.4. This required major changes to configuration settings, strategy interfaces, ...
We have put a great effort into keeping compatibility with existing strategies, so if you just want to continue using freqtrade in spot markets, there are no changes necessary.
While we may drop support for the current interface sometime in the future, we will announce this separately and have an appropriate transition period.
Please follow the [Strategy migration](strategy_migration.md) guide to migrate your strategy to the new format to start using the new functionalities.
### webhooks - changes with 2022.4
#### `buy_tag` has been renamed to `enter_tag`
This should apply only to your strategy and potentially to webhooks.
We will keep a compatibility layer for 1-2 versions (so both `buy_tag` and `enter_tag` will still work), but support for this in webhooks will disappear after that.
#### Naming changes
Webhook terminology changed from "sell" to "exit", and from "buy" to "entry".
This page is intended for developers of Freqtrade, people who want to contribute to the Freqtrade codebase or documentation, or people who want to understand the source code of the application they're running.
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel in [slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) where you can ask questions.
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We [track issues](https://github.com/freqtrade/freqtrade/issues) on [GitHub](https://github.com) and also have a dev channel on [discord](https://discord.gg/p7nuUNVfP7) where you can ask questions.
## Documentation
Documentation is available at [https://freqtrade.io](https://www.freqtrade.io/) and needs to be provided with every new feature PR.
Special fields for the documentation (like Note boxes, ...) can be found [here](https://squidfunk.github.io/mkdocs-material/extensions/admonition/).
Special fields for the documentation (like Note boxes, ...) can be found [here](https://squidfunk.github.io/mkdocs-material/reference/admonitions/).
To test the documentation locally use the following commands.
@@ -26,6 +26,11 @@ Alternatively (e.g. if your system is not supported by the setup.sh script), fol
This will install all required tools for development, including `pytest`, `flake8`, `mypy`, and `coveralls`.
Then install the git hook scripts by running `pre-commit install`, so your changes will be verified locally before committing.
This avoids a lot of waiting for CI already, as some basic formatting checks are done locally on your machine.
Before opening a pull request, please familiarize yourself with our [Contributing Guidelines](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md).
### Devcontainer setup
The fastest and easiest way to get started is to use [VSCode](https://code.visualstudio.com/) with the Remote container extension.
@@ -94,9 +99,11 @@ Below is an outline of exception inheritance hierarchy:
+---+ StrategyError
```
## Modules
---
### Dynamic Pairlist
## Plugins
### Pairlists
You have a great idea for a new pair selection algorithm you would like to try out? Great.
Hopefully you also want to contribute this back upstream.
@@ -119,6 +126,9 @@ The base-class provides an instance of the exchange (`self._exchange`) the pairl
self._pairlist_pos = pairlist_pos
```
!!! Tip
Don't forget to register your pairlist in `constants.py` under the variable `AVAILABLE_PAIRLISTS` - otherwise it will not be selectable.
Now, let's step through the methods which require actions:
#### Pairlist configuration
@@ -170,13 +180,100 @@ In `VolumePairList`, this implements different methods of sorting, does early va
return pairs
```
### Protections
Best read the [Protection documentation](plugins.md#protections) to understand protections.
This Guide is directed towards Developers who want to develop a new protection.
No protection should use datetime directly, but use the provided `date_now` variable for date calculations. This preserves the ability to backtest protections.
!!! Tip "Writing a new Protection"
Best copy one of the existing Protections to have a good example.
Don't forget to register your protection in `constants.py` under the variable `AVAILABLE_PROTECTIONS` - otherwise it will not be selectable.
#### Implementation of a new protection
All Protection implementations must have `IProtection` as parent class.
For that reason, they must implement the following methods:
* `short_desc()`
* `global_stop()`
* `stop_per_pair()`.
`global_stop()` and `stop_per_pair()` must return a ProtectionReturn object, which consists of:
* lock pair - boolean
* lock until - datetime - until when should the pair be locked (will be rounded up to the next new candle)
* reason - string, used for logging and storage in the database
* lock_side - long, short or '*'.
The `until` portion should be calculated using the provided `calculate_lock_end()` method.
All Protections should use `"stop_duration"` / `"stop_duration_candles"` to define how long a a pair (or all pairs) should be locked.
The content of this is made available as `self._stop_duration` to the each Protection.
If your protection requires a look-back period, please use `"lookback_period"` / `"lockback_period_candles"` to keep all protections aligned.
#### Global vs. local stops
Protections can have 2 different ways to stop trading for a limited :
* Per pair (local)
* For all Pairs (globally)
##### Protections - per pair
Protections that implement the per pair approach must set `has_local_stop=True`.
The method `stop_per_pair()` will be called whenever a trade closed (exit order completed).
##### Protections - global protection
These Protections should do their evaluation across all pairs, and consequently will also lock all pairs from trading (called a global PairLock).
Global protection must set `has_global_stop=True` to be evaluated for global stops.
The method `global_stop()` will be called whenever a trade closed (exit order completed).
##### Protections - calculating lock end time
Protections should calculate the lock end time based on the last trade it considers.
This avoids re-locking should the lookback-period be longer than the actual lock period.
The `IProtection` parent class provides a helper method for this in `calculate_lock_end()`.
---
## Implement a new Exchange (WIP)
!!! Note
This section is a Work in Progress and is not a complete guide on how to test a new exchange with Freqtrade.
!!! Note
Make sure to use an up-to-date version of CCXT before running any of the below tests.
You can get the latest version of ccxt by running `pip install -U ccxt` with activated virtual environment.
Native docker is not supported for these tests, however the available dev-container will support all required actions and eventually necessary changes.
Most exchanges supported by CCXT should work out of the box.
To quickly test the public endpoints of an exchange, add a configuration for your exchange to `test_ccxt_compat.py` and run these tests with `pytest --longrun tests/exchange/test_ccxt_compat.py`.
Completing these tests successfully a good basis point (it's a requirement, actually), however these won't guarantee correct exchange functioning, as this only tests public endpoints, but no private endpoint (like generate order or similar).
Also try to use `freqtrade download-data` for an extended timerange (multiple months) and verify that the data downloaded correctly (no holes, the specified timerange was actually downloaded).
These are prerequisites to have an exchange listed as either Supported or Community tested (listed on the homepage).
The below are "extras", which will make an exchange better (feature-complete) - but are not absolutely necessary for either of the 2 categories.
Additional tests / steps to complete:
* Verify data provided by `fetch_ohlcv()` - and eventually adjust `ohlcv_candle_limit` for this exchange
* Check L2 orderbook limit range (API documentation) - and eventually set as necessary
* Check if balance shows correctly (*)
* Create market order (*)
* Create limit order (*)
* Complete trade (enter + exit) (*)
* Compare result calculation between exchange and bot
* Ensure fees are applied correctly (check the database against the exchange)
(*) Requires API keys and Balance on the exchange.
### Stoploss On Exchange
Check if the new exchange supports Stoploss on Exchange orders through their API.
@@ -217,6 +314,32 @@ The output will show the last entry from the Exchange as well as the current UTC
If the day shows the same day, then the last candle can be assumed as incomplete and should be dropped (leave the setting `"ohlcv_partial_candle"` from the exchange-class untouched / True). Otherwise, set `"ohlcv_partial_candle"` to `False` to not drop Candles (shown in the example above).
Another way is to run this command multiple times in a row and observe if the volume is changing (while the date remains the same).
### Update binance cached leverage tiers
Updating leveraged tiers should be done regularly - and requires an authenticated account with futures enabled.
``` python
import ccxt
import json
from pathlib import Path
exchange = ccxt.binance({
'apiKey': '<apikey>',
'secret': '<secret>'
'options': {'defaultType': 'future'}
})
_ = exchange.load_markets()
lev_tiers = exchange.fetch_leverage_tiers()
# Assumes this is running in the root of the repository.
The below documentation is provided for completeness and assumes that you are familiar with running docker containers. If you're just starting out with Docker, we recommend to follow the [Quickstart](docker.md) instructions.
### Download the official Freqtrade docker image
Pull the image from docker hub.
Branches / tags available can be checked out on [Dockerhub tags page](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
```bash
docker pull freqtradeorg/freqtrade:stable
# Optionally tag the repository so the run-commands remain shorter
docker tag freqtradeorg/freqtrade:stable freqtrade
```
To update the image, simply run the above commands again and restart your running container.
Should you require additional libraries, please [build the image yourself](#build-your-own-docker-image).
!!! Note "Docker image update frequency"
The official docker images with tags `stable`, `develop` and `latest` are automatically rebuild once a week to keep the base image up-to-date.
In addition to that, every merge to `develop` will trigger a rebuild for `develop` and `latest`.
### Prepare the configuration files
Even though you will use docker, you'll still need some files from the github repository.
> To understand the configuration options, please refer to the [Bot Configuration](configuration.md) page.
#### Create your database file
=== "Dry-Run"
``` bash
touch tradesv3.dryrun.sqlite
```
=== "Production"
``` bash
touch tradesv3.sqlite
```
!!! Warning "Database File Path"
Make sure to use the path to the correct database file when starting the bot in Docker.
### Build your own Docker image
Best start by pulling the official docker image from dockerhub as explained [here](#download-the-official-docker-image) to speed up building.
To add additional libraries to your docker image, best check out [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.technical) which adds the [technical](https://github.com/freqtrade/technical) module to the image.
For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount an SQLite database file (see [5. Run a restartable docker image](#run-a-restartable-docker-image)") to keep it between updates.
#### Verify the Docker image
After the build process you can verify that the image was created with:
```bash
docker images
```
The output should contain the freqtrade image.
### Run the Docker image
You can run a one-off container that is immediately deleted upon exiting with the following command (`config.json` must be in the current working directory):
```bash
docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
```
!!! Warning
In this example, the database will be created inside the docker instance and will be lost when you refresh your image.
#### Adjust timezone
By default, the container will use UTC timezone.
If you would like to change the timezone use the following commands:
=== "Linux"
``` bash
-v /etc/timezone:/etc/timezone:ro
# Complete command:
docker run --rm -v /etc/timezone:/etc/timezone:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
The OSX Docker versions after 17.09.1 have a known issue whereby `/etc/localtime` cannot be shared causing Docker to not start.<br>
A work-around for this is to start with the MacOS command above
More information on this docker issue and work-around can be read [here](https://github.com/docker/for-mac/issues/2396).
### Run a restartable docker image
To run a restartable instance in the background (feel free to place your configuration and database files wherever it feels comfortable on your filesystem).
#### 1. Move your config file and database
The following will assume that you place your configuration / database files to `~/.freqtrade`, which is a hidden directory in your home directory. Feel free to use a different directory and replace the directory in the upcomming commands.
When using docker, it's best to specify `--db-url` explicitly to ensure that the database URL and the mounted database file match.
!!! Note
All available bot command line parameters can be added to the end of the `docker run` command.
!!! Note
You can define a [restart policy](https://docs.docker.com/config/containers/start-containers-automatically/) in docker. It can be useful in some cases to use the `--restart unless-stopped` flag (crash of freqtrade or reboot of your system).
### Monitor your Docker instance
You can use the following commands to monitor and manage your container:
```bash
docker logs freqtrade
docker logs -f freqtrade
docker restart freqtrade
docker stop freqtrade
docker start freqtrade
```
For more information on how to operate Docker, please refer to the [official Docker documentation](https://docs.docker.com/).
!!! Note
You do not need to rebuild the image for configuration changes, it will suffice to edit `config.json` and restart the container.
### Backtest with docker
The following assumes that the download/setup of the docker image have been completed successfully.
Also, backtest-data should be available at `~/.freqtrade/user_data/`.
This page explains how to run the bot with Docker. It is not meant to work out of the box. You'll still need to read through the documentation and understand how to properly configure it.
## Install Docker
Start by downloading and installing Docker CE for your platform:
@@ -8,13 +10,11 @@ Start by downloading and installing Docker CE for your platform:
Optionally, [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the [docker quick start guide](#docker-quick-start).
Once you have Docker installed, simply prepare the config file (e.g. `config.json`) and run the image for `freqtrade` as explained below.
To simplify running freqtrade, [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the below [docker quick start guide](#docker-quick-start).
## Freqtrade with docker-compose
Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) ready for usage.
Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/stable/docker-compose.yml) ready for usage.
!!! Note
- The following section assumes that `docker` and `docker-compose` are installed and available to the logged in user.
@@ -22,48 +22,23 @@ Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.co
### Docker quick start
Create a new directory and place the [docker-compose file](https://github.com/freqtrade/freqtrade/blob/develop/docker-compose.yml) in this directory.
Create a new directory and place the [docker-compose file](https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml) in this directory.
=== "PC/MAC/Linux"
``` bash
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
docker-compose run --rm freqtrade new-config --config user_data/config.json
```
The above snippet creates a new directory called `ft_userdata`, downloads the latest compose file and pulls the freqtrade image.
The last 2 steps in the snippet create the directory with `user_data`, as well as (interactively) the default configuration based on your selections.
@@ -71,19 +46,20 @@ The last 2 steps in the snippet create the directory with `user_data`, as well a
!!! Question "How to edit the bot configuration?"
You can edit the configuration at any time, which is available as `user_data/config.json` (within the directory `ft_userdata`) when using the above configuration.
You can also change the both Strategy and commands by editing the `docker-compose.yml` file.
You can also change the both Strategy and commands by editing the command section of your `docker-compose.yml` file.
#### Adding a custom strategy
1. The configuration is now available as `user_data/config.json`
2. Copy a custom strategy to the directory `user_data/strategies/`
3. add the Strategy' class name to the `docker-compose.yml` file
3. Add the Strategy' class name to the `docker-compose.yml` file
The `SampleStrategy` is run by default.
!!! Warning "`SampleStrategy` is just a demo!"
!!! Danger "`SampleStrategy` is just a demo!"
The `SampleStrategy` is there for your reference and give you ideas for your own strategy.
Please always backtest the strategy and use dry-run for some time before risking real money!
Please always backtest your strategy and use dry-run for some time before risking real money!
You will find more information about Strategy development in the [Strategy documentation](strategy-customization.md).
Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
@@ -91,18 +67,38 @@ Once this is done, you're ready to launch the bot in trading mode (Dry-run or Li
docker-compose up -d
```
!!! Warning "Default configuration"
While the configuration generated will be mostly functional, you will still need to verify that all options correspond to what you want (like Pricing, pairlist, ...) before starting the bot.
#### Accessing the UI
If you've selected to enable FreqUI in the `new-config` step, you will have freqUI available at port `localhost:8080`.
You can now access the UI by typing localhost:8080 in your browser.
??? Note "UI Access on a remote servers"
If you're running on a VPS, you should consider using either a ssh tunnel, or setup a VPN (openVPN, wireguard) to connect to your bot.
This will ensure that freqUI is not directly exposed to the internet, which is not recommended for security reasons (freqUI does not support https out of the box).
Setup of these tools is not part of this tutorial, however many good tutorials can be found on the internet.
Please also read the [API configuration with docker](rest-api.md#configuration-with-docker) section to learn more about this configuration.
#### Monitoring the bot
You can check for running instances with `docker-compose ps`.
This should list the service `freqtrade` as `running`. If that's not the case, best check the logs (see next point).
#### Docker-compose logs
Logs will be located at: `user_data/logs/freqtrade.log`.
You can check the latest log with the command `docker-compose logs -f`.
Logs will be written to: `user_data/logs/freqtrade.log`.
You can also check the latest log with the command `docker-compose logs -f`.
#### Database
The database will be at: `user_data/tradesv3.sqlite`
The database will be located at: `user_data/tradesv3.sqlite`
#### Updating freqtrade with docker-compose
To update freqtrade when using `docker-compose` is as simple as running the following 2 commands:
Updating freqtrade when using `docker-compose` is as simple as running the following 2 commands:
``` bash
# Download the latest image
@@ -120,10 +116,21 @@ This will first pull the latest image, and will then restart the container with
Advanced users may edit the docker-compose file further to include all possible options or arguments.
All possible freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command><optionalarguments>`.
All freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command><optionalarguments>`.
!!! Warning "`docker-compose` for trade commands"
Trade commands (`freqtrade trade <...>`) should not be ran via `docker-compose run` - but should use `docker-compose up -d` instead.
This makes sure that the container is properly started (including port forwardings) and will make sure that the container will restart after a system reboot.
If you intend to use freqUI, please also ensure to adjust the [configuration accordingly](rest-api.md#configuration-with-docker), otherwise the UI will not be available.
!!! Note "`docker-compose run --rm`"
Including `--rm` will clean up the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
Including `--rm` will remove the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
??? Note "Using docker without docker-compose"
"`docker-compose run --rm`" will require a compose file to be provided.
Some freqtrade commands that don't require authentication such as `list-pairs` can be run with "`docker run --rm`" instead.
For example `docker run --rm freqtradeorg/freqtrade:stable list-pairs --exchange binance --quote BTC --print-json`.
This can be useful for fetching exchange information to add to your `config.json` without affecting your running containers.
#### Example: Download data with docker-compose
@@ -147,8 +154,8 @@ Head over to the [Backtesting Documentation](backtesting.md) to learn more.
### Additional dependencies with docker-compose
If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [docker/Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.technical) for an example).
If your strategy requires dependencies not included in the default image - it will be necessary to build the image on your host.
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [docker/Dockerfile.custom](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.custom) for an example).
You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
@@ -159,9 +166,9 @@ You'll then also need to modify the `docker-compose.yml` file and uncomment the
dockerfile: "./Dockerfile.<yourextension>"
```
You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
You can then run `docker-compose build --pull` to build the docker image, and run it using the commands described above.
## Plotting with docker-compose
### Plotting with docker-compose
Commands `freqtrade plot-profit` and `freqtrade plot-dataframe` ([Documentation](plotting.md)) are available by changing the image to `*_plot` in your docker-compose.yml file.
You can then use these commands as follows:
@@ -172,20 +179,39 @@ docker-compose run --rm freqtrade plot-dataframe --strategy AwesomeStrategy -p B
The output will be stored in the `user_data/plot` directory, and can be opened with any modern browser.
## Data analayis using docker compose
### Data analysis using docker compose
Freqtrade provides a docker-compose file which starts up a jupyter lab server.
You can run this server using the following command:
``` bash
docker-compose --rm -f docker/docker-compose-jupyter.yml up
docker-compose -f docker/docker-compose-jupyter.yml up
```
This will create a dockercontainer running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
This will create a docker-container running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
Please use the link that's printed in the console after startup for simplified login.
Since part of this image is built on your machine, it is recommended to rebuild the image from time to time to keep freqtrade (and dependencies) uptodate.
Since part of this image is built on your machine, it is recommended to rebuild the image from time to time to keep freqtrade (and dependencies) up-to-date.
Due to the above, we do not recommend the usage of docker on windows for production setups, but only for experimentation, datadownload and backtesting.
Best use a linux-VPS for running freqtrade reliably.
The `Edge Positioning` module uses probability to calculate your win rate and risk reward ration. It will use these statistics to control your strategy trade entry points, position side and, stoploss.
The `Edge Positioning` module uses probability to calculate your win rate and risk reward ratio. It will use these statistics to control your strategy trade entry points, position size and, stoploss.
!!! Warning
`Edge positioning`is not compatible with dynamic (volume-based) whitelist.
When using `Edge positioning`with a dynamic whitelist (VolumePairList), make sure to also use `AgeFilter` and set it to at least `calculate_since_number_of_days` to avoid problems with missing data.
!!! Note
`Edge Positioning` only considers *its own* buy/sell/stoploss signals. It ignores the stoploss, trailing stoploss, and ROI settings in the strategy configuration file.
`Edge Positioning` improves the performance of some trading strategies and *decreases* the performance of others.
## Introduction
Trading strategies are not perfect. They are frameworks that are susceptible to the market and its indicators. Because the market is not at all predictable, sometimes a strategy will win and sometimes the same strategy will lose.
To obtain an edge in the market, a strategy has to make more money than it loses. Making money in trading is not only about *how often* the strategy makes or loses money.
To obtain an edge in the market, a strategy has to make more money than it loses. Making money in trading is not only about *how often* the strategy makes or loses money.
!!! tip "It doesn't matter how often, but how much!"
A bad strategy might make 1 penny in *ten* transactions but lose 1 dollar in *one* transaction. If one only checks the number of winning trades, it would be misleading to think that the strategy is actually making a profit.
@@ -23,8 +24,8 @@ The Edge Positioning module seeks to improve a strategy's winning probability an
We raise the following question[^1]:
!!! Question "Which trade is a better option?"
a) A trade with 80% of chance of losing $100 and 20% chance of winning $200<br/>
b) A trade with 100% of chance of losing $30
a) A trade with 80% of chance of losing 100\$ and 20% chance of winning 200\$<br/>
b) A trade with 100% of chance of losing 30\$
???+ Info "Answer"
The expected value of *a)* is smaller than the expected value of *b)*.<br/>
@@ -34,8 +35,8 @@ We raise the following question[^1]:
Another way to look at it is to ask a similar question:
!!! Question "Which trade is a better option?"
a) A trade with 80% of chance of winning 100 and 20% chance of losing $200<br/>
b) A trade with 100% of chance of winning $30
a) A trade with 80% of chance of winning 100\$ and 20% chance of losing 200\$<br/>
b) A trade with 100% of chance of winning 30\$
Edge positioning tries to answer the hard questions about risk/reward and position size automatically, seeking to minimizes the chances of losing of a given strategy.
@@ -55,7 +56,7 @@ Similarly, we can discover the set of losing trades $T_{lose}$ as follows:
$$ T_{lose} = \{o \in O | o \leq 0\} $$
!!! Example
In a section where a strategy made three transactions $O = \{3.5, -1, 15, 0\}$:<br>
In a section where a strategy made four transactions $O = \{3.5, -1, 15, 0\}$:<br>
$T_{win} = \{3.5, 15\}$<br>
$T_{lose} = \{-1, 0\}$<br>
@@ -82,20 +83,34 @@ Risk Reward Ratio ($R$) is a formula used to measure the expected gains of a giv
$$ R = \frac{\text{potential_profit}}{\text{potential_loss}} $$
???+ Example "Worked example of $R$ calculation"
Let's say that you think that the price of *stonecoin* today is $10.0. You believe that, because they will start mining stonecoin, it will go up to $15.0 tomorrow. There is the risk that the stone is too hard, and the GPUs can't mine it, so the price might go to $0 tomorrow. You are planning to invest $100.<br>
Your potential profit is calculated as:<br>
Let's say that you think that the price of *stonecoin* today is 10.0\$. You believe that, because they will start mining stonecoin, it will go up to 15.0\$ tomorrow. There is the risk that the stone is too hard, and the GPUs can't mine it, so the price might go to 0\$ tomorrow. You are planning to invest 100\$, which will give you 10 shares (100 / 10).
R &= \frac{\text{potential_profit}}{\text{potential_loss}}\\
&= \frac{33.33}{100}\\
&= 0.333...
&= \frac{50}{15}\\
&= 3.33
\end{aligned}$<br>
What it effectivelly means is that the strategy have the potential to make $0.33 for each $1 invested.
What it effectively means is that the strategy have the potential to make 3.33\$ for each 1\$ invested.
On a long horizon, that is, on many trades, we can calculate the risk reward by dividing the strategy' average profit on winning trades by the strategy' average loss on losing trades. We can calculate the average profit, $\mu_{win}$, as follows:
@@ -127,7 +142,7 @@ $$E = R * W - L$$
$E = R * W - L = 5 * 0.28 - 0.72 = 0.68$
<br>
The expectancy worked out in the example above means that, on average, this strategy' trades will return 1.68 times the size of its losses. Said another way, the strategy makes $1.68 for every $1 it loses, on average.
The expectancy worked out in the example above means that, on average, this strategy' trades will return 1.68 times the size of its losses. Said another way, the strategy makes 1.68\$ for every 1\$ it loses, on average.
This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
@@ -192,7 +207,68 @@ Let's say the stake currency is **ETH** and there is $10$ **ETH** on the wallet.
- The strategy detects a sell signal in the **XLM/ETH** market. The bot exits **Trade 1** for a profit of $1$ **ETH**. The total capital in the wallet becomes $11$ **ETH** and the available capital for trading becomes $5.5$ **ETH**.
-**Trade 4** The strategy detects a new buy signal int the **XLM/ETH** market. `Edge Positioning` calculates the stoploss of $2%$, and the position size of $0.055 / 0.02 = 2.75$ **ETH**.
-**Trade 4** The strategy detects a new buy signal int the **XLM/ETH** market. `Edge Positioning` calculates the stoploss of $2\%$, and the position size of $0.055 / 0.02 = 2.75$ **ETH**.
@@ -208,7 +284,7 @@ Edge module has following configuration options:
| `stoploss_range_max` | Maximum stoploss. <br>*Defaults to `-0.10`.* <br>**Datatype:** Float
| `stoploss_range_step` | As an example if this is set to -0.01 then Edge will test the strategy for `[-0.01, -0,02, -0,03 ..., -0.09, -0.10]` ranges. <br>**Note** than having a smaller step means having a bigger range which could lead to slow calculation. <br> If you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br>*Defaults to `-0.001`.* <br>**Datatype:** Float
| `minimum_winrate` | It filters out pairs which don't have at least minimum_winrate. <br>This comes handy if you want to be conservative and don't comprise win rate in favour of risk reward ratio. <br>*Defaults to `0.60`.* <br>**Datatype:** Float
| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return. <br>*Defaults to `0.20`.* <br>**Datatype:** Float
| `minimum_expectancy` | It filters out pairs which have the expectancy lower than this number. <br>Having an expectancy of 0.20 means if you put 10\$ on a trade you expect a 12\$ return. <br>*Defaults to `0.20`.* <br>**Datatype:** Float
| `min_trade_number` | When calculating *W*, *R* and *E* (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. <br>Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br>*Defaults to `10` (it is highly recommended not to decrease this number).* <br>**Datatype:** Integer
| `max_trade_duration_minute` | Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br>**NOTICE:** While configuring this value, you should take into consideration your timeframe. As an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. Default value is set assuming your strategy interval is relatively small (1m or 5m, etc.).<br>*Defaults to `1440` (one day).* <br>**Datatype:** Integer
| `remove_pumps` | Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br>*Defaults to `false`.* <br>**Datatype:** Boolean
However, the bot was tested by the development team with only a few exchanges.
A current list of these can be found in the "Home" section of this documentation.
Feel free to test other exchanges and submit your feedback or PR to improve the bot or confirm exchanges that work flawlessly..
Some exchanges require special configuration, which can be found below.
### Sample exchange configuration
A exchange configuration for "binance" would look as follows:
```json
"exchange":{
"name":"binance",
"key":"your_exchange_key",
"secret":"your_exchange_secret",
"ccxt_config":{},
"ccxt_async_config":{},
// ...
```
### Setting rate limits
Usually, rate limits set by CCXT are reliable and work well.
In case of problems related to rate-limits (usually DDOS Exceptions in your logs), it's easy to change rateLimit settings to other values.
```json
"exchange":{
"name":"kraken",
"key":"your_exchange_key",
"secret":"your_exchange_secret",
"ccxt_config":{"enableRateLimit":true},
"ccxt_async_config":{
"enableRateLimit":true,
"rateLimit":3100
},
```
This configuration enables kraken, as well as rate-limiting to avoid bans from the exchange.
`"rateLimit": 3100` defines a wait-event of 3.1s between each call. This can also be completely disabled by setting `"enableRateLimit"` to false.
!!! Note
Optimal settings for rate-limiting depend on the exchange and the size of the whitelist, so an ideal parameter will vary on many other settings.
We try to provide sensible defaults per exchange where possible, if you encounter bans please make sure that `"enableRateLimit"` is enabled and increase the `"rateLimit"` parameter step by step.
## Binance
!!! Tip "Stoploss on Exchange"
Binance supports `stoploss_on_exchange` and uses stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
Binance supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange..
### Binance Blacklist
For Binance, please add `"BNB/<STAKE>"` to your blacklist to avoid issues.
Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB order unsellable as the expected amount is not there anymore.
Accounts having BNB accounts use this to pay for fees - if your first trade happens to be on `BNB`, further trades will consume this position and make the initial BNB trade unsellable as the expected amount is not there anymore.
### Binance Futures
Binance has specific (unfortunately complex) [Futures Trading Quantitative Rules](https://www.binance.com/en/support/faq/4f462ebe6ff445d4a170be7d9e897272) which need to be followed, and which prohibit a too low stake-amount (among others) for too many orders.
Violating these rules will result in a trading restriction.
When trading on Binance Futures market, orderbook must be used because there is no price ticker data for futures.
``` jsonc
"entry_pricing": {
"use_order_book": true,
"order_book_top": 1,
"check_depth_of_market": {
"enabled": false,
"bids_to_ask_delta": 1
}
},
"exit_pricing": {
"use_order_book": true,
"order_book_top": 1
},
```
### Binance sites
Binance has been split into 3, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
Binance has been split into 2, and users must use the correct ccxt exchange ID for their exchange, otherwise API keys are not recognized.
* [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
* [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
* [binance.je](https://www.binance.je/) - Binance Jersey, trading fiat currencies. Use exchange id: `binanceje`.
## Kraken
!!! Tip "Stoploss on Exchange"
Kraken supports `stoploss_on_exchange` and uses stop-loss-market orders. It provides great advantages, so we recommend to benefit from it, however since the resulting order is a stoploss-market order, sell-rates are not guaranteed, which makes this feature less secure than on other exchanges. This limitation is based on kraken's policy [source](https://blog.kraken.com/post/1234/announcement-delisting-pairs-and-temporary-suspension-of-advanced-order-types/) and [source2](https://blog.kraken.com/post/1494/kraken-enables-advanced-orders-and-adds-10-currency-pairs/) - which has stoploss-limit orders disabled.
Kraken supports `stoploss_on_exchange` and can use both stop-loss-market and stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
### Historic Kraken data
@@ -39,12 +113,29 @@ Due to the heavy rate-limiting applied by Kraken, the following configuration se
},
```
!!! Warning "Downloading data from kraken"
Downloading kraken data will require significantly more memory (RAM) than any other exchange, as the trades-data needs to be converted into candles on your machine.
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.
So, in order to mitigate Kraken API "Rate limit exceeded" exception, this configuration should be increased, NOT decreased.
## Bittrex
### Order types
Bittrex does not support market orders. If you have a message at the bot startup about this, you should change order type values set in your configuration and/or in the strategy from `"market"` to `"limit"`. See some more details on this [here in the FAQ](faq.md#im-getting-the-exchange-bittrex-does-not-support-market-orders-message-and-cannot-run-my-strategy).
Bittrex also does not support `VolumePairlist` due to limited / split API constellation at the moment.
Please use `StaticPairlist`. Other pairlists (other than `VolumePairlist`) should not be affected.
### Volume pairlist
Bittrex does not support the direct usage of VolumePairList. This can however be worked around by using the advanced mode with `lookback_days: 1` (or more), which will emulate 24h volume.
Read more in the [pairlist documentation](plugins.md#volumepairlist-advanced-mode).
### Restricted markets
Bittrex split its exchange into US and International versions.
@@ -66,8 +157,9 @@ You can get a list of restricted markets by using the following snippet:
``` python
import ccxt
ct = ccxt.bittrex()
_ = ct.load_markets()
res = [ f"{x['MarketCurrency']}/{x['BaseCurrency']}" for x in ct.publicGetMarkets()['result'] if x['IsRestricted']]
lm = ct.load_markets()
res = [p for p, x in lm.items() if 'US' in x['info']['prohibitedIn']]
print(res)
```
@@ -75,8 +167,7 @@ print(res)
!!! Tip "Stoploss on Exchange"
FTX supports `stoploss_on_exchange` and can use both stop-loss-market and stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide.
You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type of stoploss shall be used.
### Using subaccounts
@@ -92,17 +183,74 @@ To use subaccounts with FTX, you need to edit the configuration and add the foll
}
```
!!! Note
Older versions of freqtrade may require this key to be added to `"ccxt_async_config"` as well.
## Kucoin
Kucoin requires a passphrase for each api key, you will therefore need to add this key into the configuration so your exchange section looks as follows:
Kucoin supports `stoploss_on_exchange` and can use both stop-loss-market and stop-loss-limit orders. It provides great advantages, so we recommend to benefit from it.
You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type of stoploss shall be used.
### Kucoin Blacklists
For Kucoin, please add `"KCS/<STAKE>"` to your blacklist to avoid issues.
Accounts having KCS accounts use this to pay for fees - if your first trade happens to be on `KCS`, further trades will consume this position and make the initial KCS trade unsellable as the expected amount is not there anymore.
## Huobi
!!! Tip "Stoploss on Exchange"
Huobi supports `stoploss_on_exchange` and uses `stop-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
## OKX (former OKEX)
OKX requires a passphrase for each api key, you will therefore need to add this key into the configuration so your exchange section looks as follows:
```json
"exchange": {
"name": "okx",
"key": "your_exchange_key",
"secret": "your_exchange_secret",
"password": "your_exchange_api_key_password",
// ...
}
```
!!! Warning
OKX only provides 100 candles per api call. Therefore, the strategy will only have a pretty low amount of data available in backtesting mode.
!!! Warning "Futures"
OKX Futures has the concept of "position mode" - which can be Net or long/short (hedge mode).
Freqtrade supports both modes - but changing the mode mid-trading is not supported and will lead to exceptions and failures to place trades.
OKX also only provides MARK candles for the past ~3 months. Backtesting futures prior to that date will therefore lead to slight deviations, as funding-fees cannot be calculated correctly without this data.
## Gate.io
!!! Tip "Stoploss on Exchange"
Gate.io supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange..
Gate.io allows the use of `POINT` to pay for fees. As this is not a tradable currency (no regular market available), automatic fee calculations will fail (and default to a fee of 0).
The configuration parameter `exchange.unknown_fee_rate` can be used to specify the exchange rate between Point and the stake currency. Obviously, changing the stake-currency will also require changes to this value.
## 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.
## Random notes for other exchanges
* The Ocean (exchange id: `theocean`) exchange uses Web3 functionality and requires `web3` python package to be installed:
```shell
$ pip3 install web3
```
@@ -117,3 +265,25 @@ Whether your exchange returns incomplete candles or not can be checked using [th
Due to the danger of repainting, Freqtrade does not allow you to use this incomplete candle.
However, if it is based on the need for the latest price for your strategy - then this requirement can be acquired using the [data provider](strategy-customization.md#possible-options-for-dataprovider) from within the strategy.
### Advanced Freqtrade Exchange configuration
Advanced options can be configured using the `_ft_has_params` setting, which will override Defaults and exchange-specific behavior.
Available options are listed in the exchange-class as `_ft_has_default`.
For example, to test the order type `FOK` with Kraken, and modify candle limit to 200 (so you only get 200 candles per API call):
```json
"exchange": {
"name": "kraken",
"_ft_has_params": {
"order_time_in_force": ["gtc", "fok"],
"ohlcv_candle_limit": 200
}
//...
}
```
!!! Warning
Please make sure to fully understand the impacts of these settings before modifying them.
Freqtrade can open short positions in futures markets.
This requires the strategy to be made for this - and `"trading_mode": "futures"` in the configuration.
Please make sure to read the [relevant documentation page](leverage.md) first.
In spot markets, you can in some cases use leveraged spot tokens, which reflect an inverted pair (eg. BTCUP/USD, BTCDOWN/USD, ETHBULL/USD, ETHBEAR/USD,...) which can be traded with Freqtrade.
### Can I trade options or futures?
Futures trading is supported for selected exchanges.
## Beginner Tips & Tricks
* When you work with your strategy & hyperopt file you should use a proper code editor like vscode or Pycharm. A good code editor will provide syntax highlighting as well as line numbers, making it easy to find syntax errors (most likely, pointed out by Freqtrade during startup).
* When you work with your strategy & hyperopt file you should use a proper code editor like VSCode or PyCharm. A good code editor will provide syntax highlighting as well as line numbers, making it easy to find syntax errors (most likely pointed out by Freqtrade during startup).
## Freqtrade common issues
### The bot does not start
Running the bot with `freqtrade trade --config config.json` does show the output `freqtrade: command not found`.
Running the bot with `freqtrade trade --config config.json` shows the output `freqtrade: command not found`.
This could have the following reasons:
This could be caused by the following reasons:
* The virtual environment is not active
*run `source .env/bin/activate` to activate the virtual environment
* The virtual environment is not active.
*Run `source .env/bin/activate` to activate the virtual environment.
* The installation did not work correctly.
* Please check the [Installation documentation](installation.md).
### I have waited 5 minutes, why hasn't the bot made any trades yet?!
### 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 good entry
situation of the market etc, it can take up to hours to find a good entry
position for a trade. Be patient!
*Or it may because of a configuration error? Best check the logs, it's usually telling you if the bot is simply not getting buy signals (only heartbeat messages), or if there is something wrong (errors / exceptions in the log).
*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?!
### I have made 12 trades already, why is my total profit negative?
I understand your disappointment but unfortunately 12 trades is just
not enough to say anything. If you run backtesting, you can see that our
not enough to say anything. If you run backtesting, you can see that the
current algorithm does leave you on the plus side, but that is after
thousands of trades and even there, you will be left with losses on
specific coins that you have traded tens if not hundreds of times. We
@@ -36,20 +52,34 @@ of course constantly aim to improve the bot but it will _always_ be a
gamble, which should leave you with modest wins on monthly basis but
you can't say much from few trades.
### I’d like to change the stake amount. Can I just stop the bot with /stop and then change the config.json and run it again?
### I’d like to make changes to the config. Can I do that without having to kill the bot?
Not quite. Trades are persisted to a database but the configuration is
currently only read when the bot is killed and restarted. `/stop` more
like pauses. You can stop your bot, adjust settings and start it again.
Yes. You can edit your config and use the `/reload_config` command to reload the configuration. The bot will stop, reload the configuration and strategy and will restart with the new configuration and strategy.
### I want to improve the bot with a new strategy
### Why does my bot not sell everything it bought?
That's great. We have a nice backtesting and hyperoptimization setup. See
the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-commands).
This is called "coin dust" and can happen on all exchanges.
It happens because many exchanges subtract fees from the "receiving currency" - so you buy 100 COIN - but you only get 99.9 COIN.
As COIN is trading in full lot sizes (1COIN steps), you cannot sell 0.9 COIN (or 99.9 COIN) - but you need to round down to 99 COIN.
This is not a bot-problem, but will also happen while manual trading.
While freqtrade can handle this (it'll sell 99 COIN), fees are often below the minimum tradable lot-size (you can only trade full COIN, not 0.9 COIN).
Leaving the dust (0.9 COIN) on the exchange makes usually sense, as the next time freqtrade buys COIN, it'll eat into the remaining small balance, this time selling everything it bought, and therefore slowly declining the dust balance (although it most likely will never reach exactly 0).
Where possible (e.g. on binance), the use of the exchange's dedicated fee currency will fix this.
On binance, it's sufficient to have BNB in your account, and have "Pay fees in BNB" enabled in your profile. Your BNB balance will slowly decline (as it's used to pay fees) - but you'll no longer encounter dust (Freqtrade will include the fees in the profit calculations).
Other exchanges don't offer such possibilities, where it's simply something you'll have to accept or move to a different exchange.
### I want to use incomplete candles
Freqtrade will not provide incomplete candles to strategies. Using incomplete candles will lead to repainting and consequently to strategies with "ghost" buys, which are impossible to both backtest, and verify after they happened.
You can use "current" market data by using the [dataprovider](strategy-customization.md#orderbookpair-maximum)'s orderbook or ticker methods - which however cannot be used during backtesting.
### Is there a setting to only SELL the coins being held and not perform anymore BUYS?
You can use the `/forcesell all` command from Telegram.
You can use the `/stopbuy` command in Telegram to prevent future buys, followed by `/forceexit all` (sell all open trades).
### I want to run multiple bots on the same machine
@@ -59,33 +89,45 @@ Please look at the [advanced setup documentation Page](advanced-setup.md#running
This message is just a warning that the latest candles had missing candles in them.
Depending on the exchange, this can indicate that the pair didn't have a trade for the timeframe you are using - and the exchange does only return candles with volume.
On low volume pairs, this is a rather common occurance.
On low volume pairs, this is a rather common occurrence.
If this happens for all pairs in the pairlist, this might indicate a recent exchange downtime. Please check your exchange's public channels for details.
Irrespectively of the reason, Freqtrade will fill up these candles with "empty" candles, where open, high, low and close are set to the previous candle close - and volume is empty. In a chart, this will look like a `_` - and is aligned with how exchanges usually represent 0 volume candles.
### I'm getting "Outdated history for pair xxx" in the log
The bot is trying to tell you that it got an outdated last candle (not the last complete candle).
As a consequence, Freqtrade will not enter a trade for this pair - as trading on old information is usually not what is desired.
This warning can point to one of the below problems:
* Exchange downtime -> Check your exchange status page / blog / twitter feed for details.
* Wrong system time -> Ensure your system-time is correct.
* Barely traded pair -> Check the pair on the exchange webpage, look at the timeframe your strategy uses. If the pair does not have any volume in some candles (usually visualized with a "volume 0" bar, and a "_" as candle), this pair did not have any trades in this timeframe. These pairs should ideally be avoided, as they can cause problems with order-filling.
* API problem -> API returns wrong data (this only here for completeness, and should not happen with supported exchanges).
### I'm getting the "RESTRICTED_MARKET" message in the log
Currently known to happen for US Bittrex users.
Read [the Bittrex section about restricted markets](exchanges.md#restricted-markets) for more information.
### I'm getting the "Exchange Bittrex does not support market orders." message and cannot run my strategy
### I'm getting the "Exchange XXX does not support market orders." message and cannot run my strategy
As the message says, Bittrex does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Probably your strategy was written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Bittrex).
As the message says, your exchange does not support market orders and you have one of the [order types](configuration.md/#understand-order_types) set to "market". Your strategy was probably written with other exchanges in mind and sets "market" orders for "stoploss" orders, which is correct and preferable for most of the exchanges supporting market orders (but not for Bittrex and Gate.io).
To fix it for Bittrex, redefine order types in the strategy to use "limit" instead of "market":
To fix this, redefine order types in the strategy to use "limit" instead of "market":
```
``` python
order_types = {
...
'stoploss': 'limit',
"stoploss": "limit",
...
}
```
Same fix should be done in the configuration file, if order types are defined in your custom config rather than in the strategy.
The same fix should be applied in the configuration file, if order types are defined in your custom config rather than in the strategy.
### How do I search the bot logs for something?
@@ -127,51 +169,61 @@ On Windows, the `--logfile` option is also supported by Freqtrade and you can us
## Hyperopt module
### How many epoch do I need to get a good Hyperopt result?
### Why does freqtrade not have GPU support?
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).
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.
The benefit of using GPU would therefore be pretty slim - and will not justify the complexity introduced by trying to add GPU support.
There is however nothing preventing you from using GPU-enabled indicators within your strategy if you think you must have this - you will however probably be disappointed by the slim gain that will give you (compared to the complexity).
### How many epochs do I need to get a good Hyperopt result?
Per default Hyperopt called without the `-e`/`--epochs` command line option will only
run 100 epochs, means 100 evals of your triggers, guards, ... Too few
run 100 epochs, means 100 evaluations of your triggers, guards, ... Too few
to find a great result (unless if you are very lucky), so you probably
have to run it for 10.000 or more. But it will take an eternity to
have to run it for 10000 or more. But it will take an eternity to
compute.
Since hyperopt uses Bayesian search, running for too many epochs may not produce greater results.
It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10.000 epochs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10000 epochs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
```bash
freqtrade hyperopt -e 1000
```
or if you want intermediate result to see
```bash
for i in {1..100};do freqtrade hyperopt -e 1000;done
* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) - or the Freqtrade [discord community](https://discord.gg/X89cVG). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [discord community](https://discord.gg/p7nuUNVfP7). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
* If you wonder why it can take from 20 minutes to days to do 1000 epochs here are some answers:
This answer was written during the release 0.15.1, when we had:
- 8 triggers
- 9 guards: let's say we evaluate even 10 values from each
- 1 stoploss calculation: let's say we want 10 values from that too to be evaluated
* 8 triggers
* 9 guards: let's say we evaluate even 10 values from each
* 1 stoploss calculation: let's say we want 10 values from that too to be evaluated
The following calculation is still very rough and not very precise
but it will give the idea. With only these triggers and guards there is
already 8\*10^9\*10 evaluations. A roughly total of 80 billion evals.
Did you run 100 000 evals? Congrats, you've done roughly 1 / 100 000 th
already 8\*10^9\*10 evaluations. A roughly total of 80 billion evaluations.
Did you run 100 000 evaluations? Congrats, you've done roughly 1 / 100 000 th
of the search space, assuming that the bot never tests the same parameters more than once.
* The time it takes to run 1000 hyperopt epochs depends on things like: The available cpu, hard-disk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 10.0000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades.
* The time it takes to run 1000 hyperopt epochs depends on things like: The available cpu, hard-disk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 100000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades.
Example: 4% profit 650 times vs 0,3% profit a trade 10.000 times in a year. If we assume you set the --timerange to 365 days.
Example: 4% profit 650 times vs 0,3% profit a trade 10000 times in a year. If we assume you set the --timerange to 365 days.
Pairlist Handlers define the list of pairs (pairlist) that the bot should trade. They are configured in the `pairlists` section of the configuration settings.
In your configuration, you can use Static Pairlist (defined by the [`StaticPairList`](#static-pair-list) Pairlist Handler) and Dynamic Pairlist (defined by the [`VolumePairList`](#volume-pair-list) Pairlist Handler).
Additionally, [`AgeFilter`](#agefilter), [`PrecisionFilter`](#precisionfilter), [`PriceFilter`](#pricefilter), [`ShuffleFilter`](#shufflefilter), [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) act as Pairlist Filters, removing certain pairs and/or moving their positions in the pairlist.
If multiple Pairlist Handlers are used, they are chained and a combination of all Pairlist Handlers forms the resulting pairlist the bot uses for trading and backtesting. Pairlist Handlers are executed in the sequence they are configured. You should always configure either `StaticPairList` or `VolumePairList` as the starting Pairlist Handler.
Inactive markets are always removed from the resulting pairlist. Explicitly blacklisted pairs (those in the `pair_blacklist` configuration setting) are also always removed from the resulting pairlist.
### Pair blacklist
The pair blacklist (configured via `exchange.pair_blacklist` in the configuration) disallows certain pairs from trading.
This can be as simple as excluding `DOGE/BTC` - which will remove exactly this pair.
The pair-blacklist does also support wildcards (in regex-style) - so `BNB/.*` will exclude ALL pairs that start with BNB.
You may also use something like `.*DOWN/BTC` or `.*UP/BTC` to exclude leveraged tokens (check Pair naming conventions for your exchange!)
### Available Pairlist Handlers
* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
* [`VolumePairList`](#volume-pair-list)
* [`AgeFilter`](#agefilter)
* [`OffsetFilter`](#offsetfilter)
* [`PerformanceFilter`](#performancefilter)
* [`PrecisionFilter`](#precisionfilter)
* [`PriceFilter`](#pricefilter)
* [`ShuffleFilter`](#shufflefilter)
* [`SpreadFilter`](#spreadfilter)
* [`RangeStabilityFilter`](#rangestabilityfilter)
* [`VolatilityFilter`](#volatilityfilter)
!!! Tip "Testing pairlists"
Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) utility sub-command to test your configuration quickly.
#### Static Pair List
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`.
```json
"pairlists":[
{"method":"StaticPairList"}
],
```
By default, only currently enabled pairs are allowed.
To skip pair validation against active markets, set `"allow_inactive": true` within the `StaticPairList` configuration.
This can be useful for backtesting expired pairs (like quarterly spot-markets).
This option must be configured along with `exchange.skip_pair_validation` in the exchange configuration.
When used in a "follow-up" position (e.g. after VolumePairlist), all pairs in `'pair_whitelist'` will be added to the end of the pairlist.
#### Volume Pair List
`VolumePairList` employs sorting/filtering of pairs by their trading volume. It selects `number_assets` top pairs with sorting based on the `sort_key` (which can only be `quoteVolume`).
When used in the chain of Pairlist Handlers in a non-leading position (after StaticPairList and other Pairlist Filters), `VolumePairList` considers outputs of previous Pairlist Handlers, adding its sorting/selection of the pairs by the trading volume.
When used in the leading position of the chain of Pairlist Handlers, the `pair_whitelist` configuration setting is ignored. Instead, `VolumePairList` selects the top assets from all available markets with matching stake-currency on the exchange.
The `refresh_period` setting allows to define the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
The pairlist cache (`refresh_period`) on `VolumePairList` is only applicable to generating pairlists.
Filtering instances (not the first position in the list) will not apply any cache and will always use up-to-date data.
`VolumePairList` is per default based on the ticker data from exchange, as reported by the ccxt library:
* The `quoteVolume` is the amount of quote (stake) currency traded (bought or sold) in last 24 hours.
```json
"pairlists":[
{
"method":"VolumePairList",
"number_assets":20,
"sort_key":"quoteVolume",
"min_value":0,
"refresh_period":1800
}
],
```
You can define a minimum volume with `min_value` - which will filter out pairs with a volume lower than the specified value in the specified timerange.
### VolumePairList Advanced mode
`VolumePairList` can also operate in an advanced mode to build volume over a given timerange of specified candle size. It utilizes exchange historical candle data, builds a typical price (calculated by (open+high+low)/3) and multiplies the typical price with every candle's volume. The sum is the `quoteVolume` over the given range. This allows different scenarios, for a more smoothened volume, when using longer ranges with larger candle sizes, or the opposite when using a short range with small candles.
For convenience `lookback_days` can be specified, which will imply that 1d candles will be used for the lookback. In the example below the pairlist would be created based on the last 7 days:
```json
"pairlists":[
{
"method":"VolumePairList",
"number_assets":20,
"sort_key":"quoteVolume",
"min_value":0,
"refresh_period":86400,
"lookback_days":7
}
],
```
!!! Warning "Range look back and refresh period"
When used in conjunction with `lookback_days` and `lookback_timeframe` the `refresh_period` can not be smaller than the candle size in seconds. As this will result in unnecessary requests to the exchanges API.
!!! Warning "Performance implications when using lookback range"
If used in first position in combination with lookback, the computation of the range based volume can be time and resource consuming, as it downloads candles for all tradable pairs. Hence it's highly advised to use the standard approach with `VolumeFilter` to narrow the pairlist down for further range volume calculation.
??? Tip "Unsupported exchanges (Bittrex, Gemini)"
On some exchanges (like Bittrex and Gemini), regular VolumePairList does not work as the api does not natively provide 24h volume. This can be worked around by using candle data to build the volume.
To roughly simulate 24h volume, you can use the following configuration.
Please note that These pairlists will only refresh once per day.
```json
"pairlists": [
{
"method": "VolumePairList",
"number_assets": 20,
"sort_key": "quoteVolume",
"min_value": 0,
"refresh_period": 86400,
"lookback_days": 1
}
],
```
More sophisticated approach can be used, by using `lookback_timeframe` for candle size and `lookback_period` which specifies the amount of candles. This example will build the volume pairs based on a rolling period of 3 days of 1h candles:
```json
"pairlists": [
{
"method": "VolumePairList",
"number_assets": 20,
"sort_key": "quoteVolume",
"min_value": 0,
"refresh_period": 3600,
"lookback_timeframe": "1h",
"lookback_period": 72
}
],
```
!!! Note
`VolumePairList` does not support backtesting mode.
#### 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).
When pairs are first listed on an exchange they can suffer huge price drops and volatility
in the first few days while the pair goes through its price-discovery period. Bots can often
be caught out buying before the pair has finished dropping in price.
This filter allows freqtrade to ignore pairs until they have been listed for at least `min_days_listed` days and listed before `max_days_listed`.
#### OffsetFilter
Offsets an incoming pairlist by a given `offset` value.
As an example it can be used in conjunction with `VolumeFilter` to remove the top X volume pairs. Or to split a larger pairlist on two bot instances.
Example to remove the first 10 pairs from the pairlist, and takes the next 20 (taking items 10-30 of the initial list):
```json
"pairlists": [
// ...
{
"method": "OffsetFilter",
"offset": 10,
"number_assets": 20
}
],
```
!!! Warning
When `OffsetFilter` is used to split a larger pairlist among multiple bots in combination with `VolumeFilter`
it can not be guaranteed that pairs won't overlap due to slightly different refresh intervals for the
`VolumeFilter`.
!!! Note
An offset larger than the total length of the incoming pairlist will result in an empty pairlist.
#### PerformanceFilter
Sorts pairs by past trade performance, as follows:
1. Positive performance.
2. No closed trades yet.
3. Negative performance.
Trade count is used as a tie breaker.
You can use the `minutes` parameter to only consider performance of the past X minutes (rolling window).
Not defining this parameter (or setting it to 0) will use all-time performance.
The optional `min_profit` (as ratio -> a setting of `0.01` corresponds to 1%) parameter defines the minimum profit a pair must have to be considered.
Pairs below this level will be filtered out.
Using this parameter without `minutes` is highly discouraged, as it can lead to an empty pairlist without a way to recover.
```json
"pairlists": [
// ...
{
"method": "PerformanceFilter",
"minutes": 1440, // rolling 24h
"min_profit": 0.01 // minimal profit 1%
}
],
```
As this Filter uses past performance of the bot, it'll have some startup-period - and should only be used after the bot has a few 100 trades in the database.
!!! Warning "Backtesting"
`PerformanceFilter` does not support backtesting mode.
#### PrecisionFilter
Filters low-value coins which would not allow setting stoplosses.
!!! Warning "Backtesting"
`PrecisionFilter` does not support backtesting mode using multiple strategies.
#### PriceFilter
The `PriceFilter` allows filtering of pairs by price. Currently the following price filters are supported:
* `min_price`
* `max_price`
* `max_value`
* `low_price_ratio`
The `min_price` setting removes pairs where the price is below the specified price. This is useful if you wish to avoid trading very low-priced pairs.
This option is disabled by default, and will only apply if set to > 0.
The `max_price` setting removes pairs where the price is above the specified price. This is useful if you wish to trade only low-priced pairs.
This option is disabled by default, and will only apply if set to > 0.
The `max_value` setting removes pairs where the minimum value change is above a specified value.
This is useful when an exchange has unbalanced limits. For example, if step-size = 1 (so you can only buy 1, or 2, or 3, but not 1.1 Coins) - and the price is pretty high (like 20\$) as the coin has risen sharply since the last limit adaption.
As a result of the above, you can only buy for 20\$, or 40\$ - but not for 25\$.
On exchanges that deduct fees from the receiving currency (e.g. FTX) - this can result in high value coins / amounts that are unsellable as the amount is slightly below the limit.
The `low_price_ratio` setting removes pairs where a raise of 1 price unit (pip) is above the `low_price_ratio` ratio.
This option is disabled by default, and will only apply if set to > 0.
For `PriceFilter` at least one of its `min_price`, `max_price` or `low_price_ratio` settings must be applied.
Calculation example:
Min price precision for SHITCOIN/BTC is 8 decimals. If its price is 0.00000011 - one price step above would be 0.00000012, which is ~9% higher than the previous price value. You may filter out this pair by using PriceFilter with `low_price_ratio` set to 0.09 (9%) or with `min_price` set to 0.00000011, correspondingly.
!!! Warning "Low priced pairs"
Low priced pairs with high "1 pip movements" are dangerous since they are often illiquid and it may also be impossible to place the desired stoploss, which can often result in high losses since price needs to be rounded to the next tradable price - so instead of having a stoploss of -5%, you could end up with a stoploss of -9% simply due to price rounding.
#### ShuffleFilter
Shuffles (randomizes) pairs in the pairlist. It can be used for preventing the bot from trading some of the pairs more frequently then others when you want all pairs be treated with the same priority.
!!! Tip
You may set the `seed` value for this Pairlist to obtain reproducible results, which can be useful for repeated backtesting sessions. If `seed` is not set, the pairs are shuffled in the non-repeatable random order. ShuffleFilter will automatically detect runmodes and apply the `seed` only for backtesting modes - if a `seed` value is set.
#### SpreadFilter
Removes pairs that have a difference between asks and bids above the specified ratio, `max_spread_ratio` (defaults to `0.005`).
Example:
If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027, the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005` and this pair will be filtered out.
#### RangeStabilityFilter
Removes pairs where the difference between lowest low and highest high over `lookback_days` days is below `min_rate_of_change` or above `max_rate_of_change`. Since this is a filter that requires additional data, the results are cached for `refresh_period`.
In the below example:
If the trading range over the last 10 days is <1% or >99%, remove the pair from the whitelist.
```json
"pairlists": [
{
"method": "RangeStabilityFilter",
"lookback_days": 10,
"min_rate_of_change": 0.01,
"max_rate_of_change": 0.99,
"refresh_period": 1440
}
]
```
!!! Tip
This Filter can be used to automatically remove stable coin pairs, which have a very low trading range, and are therefore extremely difficult to trade with profit.
Additionally, it can also be used to automatically remove pairs with extreme high/low variance over a given amount of time.
#### VolatilityFilter
Volatility is the degree of historical variation of a pairs over time, it is measured by the standard deviation of logarithmic daily returns. Returns are assumed to be normally distributed, although actual distribution might be different. In a normal distribution, 68% of observations fall within one standard deviation and 95% of observations fall within two standard deviations. Assuming a volatility of 0.05 means that the expected returns for 20 out of 30 days is expected to be less than 5% (one standard deviation). Volatility is a positive ratio of the expected deviation of return and can be greater than 1.00. Please refer to the wikipedia definition of [`volatility`](https://en.wikipedia.org/wiki/Volatility_(finance)).
This filter removes pairs if the average volatility over a `lookback_days` days is below `min_volatility` or above `max_volatility`. Since this is a filter that requires additional data, the results are cached for `refresh_period`.
This filter can be used to narrow down your pairs to a certain volatility or avoid very volatile pairs.
In the below example:
If the volatility over the last 10 days is not in the range of 0.05-0.50, remove the pair from the whitelist. The filter is applied every 24h.
```json
"pairlists": [
{
"method": "VolatilityFilter",
"lookback_days": 10,
"min_volatility": 0.05,
"max_volatility": 0.50,
"refresh_period": 86400
}
]
```
### Full example of Pairlist Handlers
The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#pricefilter), filtering all assets where 1 price unit is > 1%. Then the [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) is applied and pairs are finally shuffled with the random seed set to some predefined value.
Prices for regular orders can be controlled via the parameter structures `entry_pricing` for trade entries and `exit_pricing` for trade exits.
Prices are always retrieved right before an order is placed, either by querying the exchange tickers or by using the orderbook data.
!!! Note
Orderbook data used by Freqtrade are the data retrieved from exchange by the ccxt's function `fetch_order_book()`, i.e. are usually data from the L2-aggregated orderbook, while the ticker data are the structures returned by the ccxt's `fetch_ticker()`/`fetch_tickers()` functions. Refer to the ccxt library [documentation](https://github.com/ccxt/ccxt/wiki/Manual#market-data) for more details.
!!! Warning "Using market orders"
Please read the section [Market order pricing](#market-order-pricing) section when using market orders.
### Entry price
#### Enter price side
The configuration setting `entry_pricing.price_side` defines the side of the orderbook the bot looks for when buying.
The following displays an orderbook.
``` explanation
...
103
102
101 # ask
-------------Current spread
99 # bid
98
97
...
```
If `entry_pricing.price_side` is set to `"bid"`, then the bot will use 99 as entry price.
In line with that, if `entry_pricing.price_side` is set to `"ask"`, then the bot will use 101 as entry price.
Depending on the order direction (_long_/_short_), this will lead to different results. Therefore we recommend to use `"same"` or `"other"` for this configuration instead.
This would result in the following pricing matrix:
| direction | Order | setting | price | crosses spread |
|------ |--------|-----|-----|-----|
| long | buy | ask | 101 | yes |
| long | buy | bid | 99 | no |
| long | buy | same | 99 | no |
| long | buy | other | 101 | yes |
| short | sell | ask | 101 | no |
| short | sell | bid | 99 | yes |
| short | sell | same | 101 | no |
| short | sell | other | 99 | yes |
Using the other side of the orderbook often guarantees quicker filled orders, but the bot can also end up paying more than what would have been necessary.
Taker fees instead of maker fees will most likely apply even when using limit buy orders.
Also, prices at the "other" side of the spread are higher than prices at the "bid" side in the orderbook, so the order behaves similar to a market order (however with a maximum price).
#### Entry price with Orderbook enabled
When entering a trade with the orderbook enabled (`entry_pricing.use_order_book=True`), Freqtrade fetches the `entry_pricing.order_book_top` entries from the orderbook and uses the entry specified as `entry_pricing.order_book_top` on the configured side (`entry_pricing.price_side`) of the orderbook. 1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
#### Entry price without Orderbook enabled
The following section uses `side` as the configured `entry_pricing.price_side` (defaults to `"same"`).
When not using orderbook (`entry_pricing.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's below the `last` traded price from the ticker. Otherwise (when the `side` price is above the `last` price), it calculates a rate between `side` and `last` price based on `entry_pricing.price_last_balance`.
The `entry_pricing.price_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the `last` price and values between those interpolate between ask and last price.
#### Check depth of market
When check depth of market is enabled (`entry_pricing.check_depth_of_market.enabled=True`), the entry signals are filtered based on the orderbook depth (sum of all amounts) for each orderbook side.
Orderbook `bid` (buy) side depth is then divided by the orderbook `ask` (sell) side depth and the resulting delta is compared to the value of the `entry_pricing.check_depth_of_market.bids_to_ask_delta` parameter. The entry order is only executed if the orderbook delta is greater than or equal to the configured delta value.
!!! Note
A delta value below 1 means that `ask` (sell) orderbook side depth is greater than the depth of the `bid` (buy) orderbook side, while a value greater than 1 means opposite (depth of the buy side is higher than the depth of the sell side).
### Exit price
#### Exit price side
The configuration setting `exit_pricing.price_side` defines the side of the spread the bot looks for when exiting a trade.
The following displays an orderbook:
``` explanation
...
103
102
101 # ask
-------------Current spread
99 # bid
98
97
...
```
If `exit_pricing.price_side` is set to `"ask"`, then the bot will use 101 as exiting price.
In line with that, if `exit_pricing.price_side` is set to `"bid"`, then the bot will use 99 as exiting price.
Depending on the order direction (_long_/_short_), this will lead to different results. Therefore we recommend to use `"same"` or `"other"` for this configuration instead.
This would result in the following pricing matrix:
| Direction | Order | setting | price | crosses spread |
|------ |--------|-----|-----|-----|
| long | sell | ask | 101 | no |
| long | sell | bid | 99 | yes |
| long | sell | same | 101 | no |
| long | sell | other | 99 | yes |
| short | buy | ask | 101 | yes |
| short | buy | bid | 99 | no |
| short | buy | same | 99 | no |
| short | buy | other | 101 | yes |
#### Exit price with Orderbook enabled
When exiting with the orderbook enabled (`exit_pricing.use_order_book=True`), Freqtrade fetches the `exit_pricing.order_book_top` entries in the orderbook and uses the entry specified as `exit_pricing.order_book_top` from the configured side (`exit_pricing.price_side`) as trade exit price.
1 specifies the topmost entry in the orderbook, while 2 would use the 2nd entry in the orderbook, and so on.
#### Exit price without Orderbook enabled
The following section uses `side` as the configured `exit_pricing.price_side` (defaults to `"ask"`).
When not using orderbook (`exit_pricing.use_order_book=False`), Freqtrade uses the best `side` price from the ticker if it's above the `last` traded price from the ticker. Otherwise (when the `side` price is below the `last` price), it calculates a rate between `side` and `last` price based on `exit_pricing.price_last_balance`.
The `exit_pricing.price_last_balance` configuration parameter controls this. A value of `0.0` will use `side` price, while `1.0` will use the last price and values between those interpolate between `side` and last price.
### Market order pricing
When using market orders, prices should be configured to use the "correct" side of the orderbook to allow realistic pricing detection.
Assuming both entry and exits are using market orders, a configuration similar to the following must be used
``` jsonc
"order_types": {
"entry": "market",
"exit": "market"
// ...
},
"entry_pricing": {
"price_side": "other",
// ...
},
"exit_pricing":{
"price_side": "other",
// ...
},
```
Obviously, if only one side is using limit orders, different pricing combinations can be used.
This feature is still in it's testing phase. Should you notice something you think is wrong please let us know via Discord or via Github Issue.
Protections will protect your strategy from unexpected events and market conditions by temporarily stop trading for either one pair, or for all pairs.
All protection end times are rounded up to the next candle to avoid sudden, unexpected intra-candle buys.
!!! Note
Not all Protections will work for all strategies, and parameters will need to be tuned for your strategy to improve performance.
!!! Tip
Each Protection can be configured multiple times with different parameters, to allow different levels of protection (short-term / long-term).
!!! Note "Backtesting"
Protections are supported by backtesting and hyperopt, but must be explicitly enabled by using the `--enable-protections` flag.
!!! Warning "Setting protections from the configuration"
Setting protections from the configuration via `"protections": [],` key should be considered deprecated and will be removed in a future version.
It is also no longer guaranteed that your protections apply to the strategy in cases where the strategy defines [protections as property](hyperopt.md#optimizing-protections).
### Available Protections
* [`StoplossGuard`](#stoploss-guard) Stop trading if a certain amount of stoploss occurred within a certain time window.
* [`MaxDrawdown`](#maxdrawdown) Stop trading if max-drawdown is reached.
* [`LowProfitPairs`](#low-profit-pairs) Lock pairs with low profits
* [`CooldownPeriod`](#cooldown-period) Don't enter a trade right after selling a trade.
### Common settings to all Protections
| Parameter| Description |
|------------|-------------|
| `method` | Protection name to use. <br>**Datatype:** String, selected from [available Protections](#available-protections)
| `stop_duration_candles` | For how many candles should the lock be set? <br>**Datatype:** Positive integer (in candles)
| `stop_duration` | how many minutes should protections be locked. <br>Cannot be used together with `stop_duration_candles`. <br>**Datatype:** Float (in minutes)
| `lookback_period_candles` | Only trades that completed within the last `lookback_period_candles` candles will be considered. This setting may be ignored by some Protections. <br>**Datatype:** Positive integer (in candles).
| `lookback_period` | Only trades that completed after `current_time - lookback_period` will be considered. <br>Cannot be used together with `lookback_period_candles`. <br>This setting may be ignored by some Protections. <br>**Datatype:** Float (in minutes)
| `trade_limit` | Number of trades required at minimum (not used by all Protections). <br>**Datatype:** Positive integer
!!! Note "Durations"
Durations (`stop_duration*` and `lookback_period*` can be defined in either minutes or candles).
For more flexibility when testing different timeframes, all below examples will use the "candle" definition.
#### Stoploss Guard
`StoplossGuard` selects all trades within `lookback_period` in minutes (or in candles when using `lookback_period_candles`).
If `trade_limit` or more trades resulted in stoploss, trading will stop for `stop_duration` in minutes (or in candles when using `stop_duration_candles`).
This applies across all pairs, unless `only_per_pair` is set to true, which will then only look at one pair at a time.
Similarly, this protection will by default look at all trades (long and short). For futures bots, setting `only_per_side` will make the bot only consider one side, and will then only lock this one side, allowing for example shorts to continue after a series of long stoplosses.
The below example stops trading for all pairs for 4 candles after the last trade if the bot hit stoploss 4 times within the last 24 candles.
``` python
@property
def protections(self):
return [
{
"method": "StoplossGuard",
"lookback_period_candles": 24,
"trade_limit": 4,
"stop_duration_candles": 4,
"only_per_pair": False,
"only_per_side": False
}
]
```
!!! Note
`StoplossGuard` considers all trades with the results `"stop_loss"`, `"stoploss_on_exchange"` and `"trailing_stop_loss"` if the resulting profit was negative.
`trade_limit` and `lookback_period` will need to be tuned for your strategy.
#### MaxDrawdown
`MaxDrawdown` uses all trades within `lookback_period` in minutes (or in candles when using `lookback_period_candles`) to determine the maximum drawdown. If the drawdown is below `max_allowed_drawdown`, trading will stop for `stop_duration` in minutes (or in candles when using `stop_duration_candles`) after the last trade - assuming that the bot needs some time to let markets recover.
The below sample stops trading for 12 candles if max-drawdown is > 20% considering all pairs - with a minimum of `trade_limit` trades - within the last 48 candles. If desired, `lookback_period` and/or `stop_duration` can be used.
``` python
@property
def protections(self):
return [
{
"method": "MaxDrawdown",
"lookback_period_candles": 48,
"trade_limit": 20,
"stop_duration_candles": 12,
"max_allowed_drawdown": 0.2
},
]
```
#### Low Profit Pairs
`LowProfitPairs` uses all trades for a pair within `lookback_period` in minutes (or in candles when using `lookback_period_candles`) to determine the overall profit ratio.
If that ratio is below `required_profit`, that pair will be locked for `stop_duration` in minutes (or in candles when using `stop_duration_candles`).
For futures bots, setting `only_per_side` will make the bot only consider one side, and will then only lock this one side, allowing for example shorts to continue after a series of long losses.
The below example will stop trading a pair for 60 minutes if the pair does not have a required profit of 2% (and a minimum of 2 trades) within the last 6 candles.
``` python
@property
def protections(self):
return [
{
"method": "LowProfitPairs",
"lookback_period_candles": 6,
"trade_limit": 2,
"stop_duration": 60,
"required_profit": 0.02,
"only_per_pair": False,
}
]
```
#### Cooldown Period
`CooldownPeriod` locks a pair for `stop_duration` in minutes (or in candles when using `stop_duration_candles`) after selling, avoiding a re-entry for this pair for `stop_duration` minutes.
The below example will stop trading a pair for 2 candles after closing a trade, allowing this pair to "cool down".
``` python
@property
def protections(self):
return [
{
"method": "CooldownPeriod",
"stop_duration_candles": 2
}
]
```
!!! Note
This Protection applies only at pair-level, and will never lock all pairs globally.
This Protection does not consider `lookback_period` as it only looks at the latest trade.
### Full example of Protections
All protections can be combined at will, also with different parameters, creating a increasing wall for under-performing pairs.
All protections are evaluated in the sequence they are defined.
The below example assumes a timeframe of 1 hour:
* Locks each pair after selling for an additional 5 candles (`CooldownPeriod`), giving other pairs a chance to get filled.
* Stops trading for 4 hours (`4 * 1h candles`) if the last 2 days (`48 * 1h candles`) had 20 trades, which caused a max-drawdown of more than 20%. (`MaxDrawdown`).
* Stops trading if more than 4 stoploss occur for all pairs within a 1 day (`24 * 1h candles`) limit (`StoplossGuard`).
* Locks all pairs that had 4 Trades within the last 6 hours (`6 * 1h candles`) with a combined profit ratio of below 0.02 (<2%) (`LowProfitPairs`).
* Locks all pairs for 2 candles that had a profit of below 0.01 (<1%) within the last 24h (`24 * 1h candles`), a minimum of 4 trades.
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## Introduction
Freqtrade is a crypto-currency algorithmic trading software developed in python (3.6+) and supported on Windows, macOS and Linux.
Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram or webUI. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning.
!!! Danger "DISCLAIMER"
This software is for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.
@@ -23,6 +20,8 @@ Freqtrade is a crypto-currency algorithmic trading software developed in python
We strongly recommend you to have basic coding skills and Python knowledge. Do not hesitate to read the source code and understand the mechanisms of this bot, algorithms and techniques implemented in it.
- Develop your Strategy: Write your strategy in python, using [pandas](https://pandas.pydata.org/). Example strategies to inspire you are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
@@ -32,9 +31,37 @@ Freqtrade is a crypto-currency algorithmic trading software developed in python
- Select markets: Create your static list or use an automatic one based on top traded volumes and/or prices (not available during backtesting). You can also explicitly blacklist markets you don't want to trade.
- Run: Test your strategy with simulated money (Dry-Run mode) or deploy it with real money (Live-Trade mode).
- Run using Edge (optional module): The concept is to find the best historical [trade expectancy](edge.md#expectancy) by markets based on variation of the stop-loss and then allow/reject markets to trade. The sizing of the trade is based on a risk of a percentage of your capital.
- Control/Monitor: Use Telegram or a REST API (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
- Control/Monitor: Use Telegram or a WebUI (start/stop the bot, show profit/loss, daily summary, current open trades results, etc.).
- Analyse: Further analysis can be performed on either Backtesting data or Freqtrade trading history (SQL database), including automated standard plots, and methods to load the data into [interactive environments](data-analysis.md).
## Supported exchange marketplaces
Please read the [exchange specific notes](exchanges.md) to learn about eventual, special configurations needed for each exchange.
- [X] [Binance](https://www.binance.com/)
- [X] [Bittrex](https://bittrex.com/)
- [X] [FTX](https://ftx.com/#a=2258149)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [Huobi](http://huobi.com/)
- [X] [Kraken](https://kraken.com/)
- [X] [OKX](https://okx.com/) (Former OKEX)
- [ ] [potentially many others through <img alt="ccxt" width="30px" src="assets/ccxt-logo.svg" />](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
### Supported Futures Exchanges (experimental)
- [X] [Binance](https://www.binance.com/)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [X] [OKX](https://okx.com/).
Please make sure to read the [exchange specific notes](exchanges.md), as well as the [trading with leverage](leverage.md) documentation before diving in.
### Community tested
Exchanges confirmed working by the community:
- [X] [Bitvavo](https://bitvavo.com/)
- [X] [Kucoin](https://www.kucoin.com/)
## Requirements
### Hardware requirements
@@ -51,7 +78,7 @@ To run this bot we recommend you a linux cloud instance with a minimum of:
Alternatively
- Python 3.6.x
- Python 3.8+
- pip (pip3)
- git
- TA-Lib
@@ -59,11 +86,10 @@ Alternatively
## Support
### Help / Slack
For any questions not covered by the documentation or for further information about the bot, we encourage you to join our passionate Slack community.
### Help / Discord
Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) to join the Freqtrade Slack channel.
For any questions not covered by the documentation or for further information about the bot, or to simply engage with like-minded individuals, we encourage you to join the Freqtrade [discord server](https://discord.gg/p7nuUNVfP7).
## Ready to try?
Begin by reading our installation guide [for docker](docker.md), or for [installation without docker](installation.md).
Begin by reading the installation guide [for docker](docker_quickstart.md) (recommended), or for [installation without docker](installation.md).
We also recommend a [Telegram bot](telegram-usage.md#setup-your-telegram-bot), which is optional but recommended.
For Windows installation, please use the [windows installation guide](windows_installation.md).
The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the `./setup.sh` script, if it's available for your platform.
!!! Note "Version considerations"
When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests).
The `stable` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
!!! Note
Python3.8 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.
Also, python headers (`python<yourversion>-dev` / `python<yourversion>-devel`) must be available for the installation to complete successfully.
!!! Warning "Up-to-date clock"
The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
## Quick start
Freqtrade provides the Linux/MacOS Easy Installation script to install all dependencies and help you configure the bot.
!!! Note
Windows installation is explained [here](#windows).
The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the Easy Installation script, if it's available for your platform.
!!! Note "Version considerations"
When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests). The `stable` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
!!! Note
Python3.6 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.
(1) This command switches the cloned repository to the use of the `stable` branch. It's not needed if you wish to stay on the `develop` branch. You may later switch between branches at any time with the `git checkout stable`/`git checkout develop` commands.
## Easy Installation Script (Linux/MacOS)
If you are on Debian, Ubuntu or MacOS Freqtrade provides the script to install, update, configure and reset the codebase of your bot.
```bash
$ ./setup.sh
usage:
-i,--install Install freqtrade from scratch
-u,--update Command git pull to update.
-r,--reset Hard reset your develop/stable branch.
-c,--config Easy config generator (Will override your existing file).
```
** --install **
With this option, the script will install the bot and most dependencies:
You will need to have git and python3.6+ installed beforehand for this to work.
* Mandatory software as: `ta-lib`
* Setup your virtualenv under `.env/`
This option is a combination of installation tasks, `--reset` and `--config`.
** --update **
This option will pull the last version of your current branch and update your virtualenv. Run the script with this option periodically to update your bot.
** --reset **
This option will hard reset your branch (only if you are on either `stable` or `develop`) and recreate your virtualenv.
** --config **
DEPRECATED - use `freqtrade new-config -c config.json` instead.
### Activate your virtual environment
Each time you open a new terminal, you must run `source .env/bin/activate`.
------
## Custom Installation
## Requirements
We've included/collected install instructions for Ubuntu 16.04, MacOS, and Windows. These are guidelines and your success may vary with other distros.
These requirements apply to both [Script Installation](#script-installation) and [Manual Installation](#manual-installation).
!!! Note "ARM64 systems"
If you are running an ARM64 system (like a MacOS M1 or an Oracle VM), please use [docker](docker_quickstart.md) to run freqtrade.
While native installation is possible with some manual effort, this is not supported at the moment.
(1) This command switches the cloned repository to the use of the `stable` branch. It's not needed, if you wish to stay on the (2) `develop` branch.
You may later switch between branches at any time with the `git checkout stable`/`git checkout develop` commands.
??? Note "Install from pypi"
An alternative way to install Freqtrade is from [pypi](https://pypi.org/project/freqtrade/). The downside is that this method requires ta-lib to be correctly installed beforehand, and is therefore currently not the recommended way to install Freqtrade.
``` bash
pip install freqtrade
```
------
## Script Installation
First of the ways to install Freqtrade, is to use provided the Linux/MacOS `./setup.sh` script, which install all dependencies and help you configure the bot.
Make sure you fulfill the [Requirements](#requirements) and have downloaded the [Freqtrade repository](#freqtrade-repository).
### Use /setup.sh -install (Linux/MacOS)
If you are on Debian, Ubuntu or MacOS, freqtrade provides the script to install freqtrade.
```bash
# --install, Install freqtrade from scratch
./setup.sh -i
```
### Activate your virtual environment
Each time you open a new terminal, you must run `source .env/bin/activate` to activate your virtual environment.
```bash
# then activate your .env
source ./.env/bin/activate
```
### Congratulations
[You are ready](#you-are-ready), and run the bot
### Other options of /setup.sh script
You can as well update, configure and reset the codebase of your bot with `./script.sh`
```bash
# --update, Command git pull to update.
./setup.sh -u
# --reset, Hard reset your develop/stable branch.
./setup.sh -r
```
```
** --install **
With this option, the script will install the bot and most dependencies:
You will need to have git and python3.8+ installed beforehand for this to work.
* Mandatory software as: `ta-lib`
* Setup your virtualenv under `.env/`
This option is a combination of installation tasks and `--reset`
** --update **
This option will pull the last version of your current branch and update your virtualenv. Run the script with this option periodically to update your bot.
** --reset **
This option will hard reset your branch (only if you are on either `stable` or `develop`) and recreate your virtualenv.
```
-----
## Manual Installation
Make sure you fulfill the [Requirements](#requirements) and have downloaded the [Freqtrade repository](#freqtrade-repository).
### Install TA-Lib
#### TA-Lib script installation
```bash
sudo ./build_helpers/install_ta-lib.sh
@@ -150,81 +214,199 @@ sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h
./configure --prefix=/usr/local
make
sudo make install
# On debian based systems (debian, ubuntu, ...) - updating ldconfig might be necessary.
sudo ldconfig
cd ..
rm -rf ./ta-lib*
```
!!! Note
An already downloaded version of ta-lib is included in the repository, as the sourceforge.net source seems to have problems frequently.
> *To edit the config please refer to [Bot Configuration](configuration.md).*
!!! Note
If you run the bot on a server, you should consider using [Docker](docker_quickstart.md) or a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
#### 6. Run the Bot
If this is the first time you run the bot, ensure you are running it in Dry-run `"dry_run": true,` otherwise it will start to buy and sell coins.
```bash
freqtrade trade -c config.json
```
*Note*: If you run the bot on a server, you should consider using [Docker](docker.md) or a terminal multiplexer like `screen` or [`tmux`](https://en.wikipedia.org/wiki/Tmux) to avoid that the bot is stopped on logout.
#### 7. (Optional) Post-installation Tasks
On Linux, as an optional post-installation task, you may wish to setup the bot to run as a `systemd` service or configure it to send the log messages to the `syslog`/`rsyslog` or `journald` daemons. See [Advanced Logging](advanced-setup.md#advanced-logging) for details.
On Linux with software suite `systemd`, as an optional post-installation task, you may wish to setup the bot to run as a `systemd service` or configure it to send the log messages to the `syslog`/`rsyslog` or `journald` daemons. See [Advanced Logging](advanced-setup.md#advanced-logging) for details.
------
### Anaconda
## Installation with Conda
Freqtrade can also be installed using Anaconda (or Miniconda).
Freqtrade can also be installed with Miniconda or Anaconda. We recommend using Miniconda as it's installation footprint is smaller. Conda will automatically prepare and manage the extensive library-dependencies of the Freqtrade program.
!!! Note
This requires the [ta-lib](#1-install-ta-lib) C-library to be installed first. See below.
### What is Conda?
``` bash
conda env create -f environment.yml
Conda is a package, dependency and environment manager for multiple programming languages: [conda docs](https://docs.conda.io/projects/conda/en/latest/index.html)
### Installation with conda
#### Install Conda
[Installing on linux](https://conda.io/projects/conda/en/latest/user-guide/install/linux.html#install-linux-silent)
[Installing on windows](https://conda.io/projects/conda/en/latest/user-guide/install/windows.html)
Answer all questions. After installation, it is mandatory to turn your terminal OFF and ON again.
The conda command `create -n` automatically installs all nested dependencies for the selected libraries, general structure of installation command is:
```bash
# choose your own packages
conda env create -n [name of the environment] [python version] [packages]
# point to file with packages
conda env create -n [name of the environment] -f [file]
```
#### Enter/exit freqtrade-conda environment
To check available environments, type
```bash
conda env list
```
Enter installed environment
```bash
# enter conda environment
conda activate freqtrade-conda
# exit conda environment - don't do it now
conda deactivate
```
Install last python dependencies with pip
```bash
python3 -m pip install --upgrade pip
python3 -m pip install -e .
```
### Congratulations
[You are ready](#you-are-ready), and run the bot
### Important shortcuts
```bash
# list installed conda environments
conda env list
# activate base environment
conda activate
# activate freqtrade-conda environment
conda activate freqtrade-conda
#deactivate any conda environments
conda deactivate
```
### Further info on anaconda
!!! Info "New heavy packages"
It may happen that creating a new Conda environment, populated with selected packages at the moment of creation takes less time than installing a large, heavy library or application, into previously set environment.
!!! Warning "pip install within conda"
The documentation of conda says that pip should NOT be used within conda, because internal problems can occur.
However, they are rare. [Anaconda Blogpost](https://www.anaconda.com/blog/using-pip-in-a-conda-environment)
Nevertheless, that is why, the `conda-forge` channel is preferred:
* more libraries are available (less need for `pip`)
* `conda-forge` works better with `pip`
* the libraries are newer
Happy trading!
-----
## Troubleshooting
## You are ready
You've made it this far, so you have successfully installed freqtrade.
### Initialize the configuration
```bash
# Step 1 - Initialize user folder
freqtrade create-userdir --userdir user_data
# Step 2 - Create a new configuration file
freqtrade new-config --config config.json
```
You are ready to run, read [Bot Configuration](configuration.md), remember to start with `dry_run: True` and verify that everything is working.
To learn how to setup your configuration, please refer to the [Bot Configuration](configuration.md) documentation page.
You should read through the rest of the documentation, backtest the strategy you're going to use, and use dry-run before enabling trading with real money.
-----
## Troubleshooting
### Common problem: "command not found"
If you used (1)`Script` or (2)`Manual` installation, you need to run the bot in virtual environment. If you get error as below, make sure venv is active.
```bash
# if:
bash: freqtrade: command not found
# then activate your .env
source ./.env/bin/activate
```
### MacOS installation error
@@ -233,13 +415,8 @@ Newer versions of MacOS may have installation failed with errors like `error: co
This error will require explicit installation of the SDK Headers, which are not installed by default in this version of MacOS.
For MacOS 10.14, this can be accomplished with the below command.
```bash
```bash
open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg
```
If this file is inexistent, then you're probably on a different version of MacOS, so you may need to consult the internet for specific resolution details.
-----
Now you have an environment ready, the next step is
This feature is still in it's testing phase. Should you notice something you think is wrong please let us know via Discord or via Github Issue.
!!! Note "Multiple bots on one account"
You can't run 2 bots on the same account with leverage. For leveraged / margin trading, freqtrade assumes it's the only user of the account, and all liquidation levels are calculated based on this assumption.
!!! Danger "Trading with leverage is very risky"
Do not trade with a leverage > 1 using a strategy that hasn't shown positive results in a live run using the spot market. Check the stoploss of your strategy. With a leverage of 2, a stoploss of 0.5 (50%) would be too low, and these trades would be liquidated before reaching that stoploss.
We do not assume any responsibility for eventual losses that occur from using this software or this mode.
Please only use advanced trading modes when you know how freqtrade (and your strategy) works.
Also, never risk more than what you can afford to lose.
Please read the [strategy migration guide](strategy_migration.md#strategy-migration-between-v2-and-v3) to migrate your strategy from a freqtrade v2 strategy, to v3 strategy that can short and trade futures.
## Shorting
Shorting is not possible when trading with [`trading_mode`](#understand-tradingmode) set to `spot`. To short trade, `trading_mode` must be set to `margin`(currently unavailable) or [`futures`](#futures), with [`margin_mode`](#margin-mode) set to `cross`(currently unavailable) or [`isolated`](#isolated-margin-mode)
For a strategy to short, the strategy class must set the class variable `can_short = True`
Please read [strategy customization](strategy-customization.md#entry-signal-rules) for instructions on how to set signals to enter and exit short trades.
## Understand `trading_mode`
The possible values are: `spot` (default), `margin`(*Currently unavailable*) or `futures`.
### Spot
Regular trading mode (low risk)
- Long trades only (No short trades).
- No leverage.
- No Liquidation.
- Profits gained/lost are equal to the change in value of the assets (minus trading fees).
### Leverage trading modes
With leverage, a trader borrows capital from the exchange. The capital must be re-payed fully to the exchange (potentially with interest), and the trader keeps any profits, or pays any losses, from any trades made using the borrowed capital.
Because the capital must always be re-payed, exchanges will **liquidate** (forcefully sell the traders assets) a trade made using borrowed capital when the total value of assets in the leverage account drops to a certain point (a point where the total value of losses is less than the value of the collateral that the trader actually owns in the leverage account), in order to ensure that the trader has enough capital to pay the borrowed assets back to the exchange. The exchange will also charge a **liquidation fee**, adding to the traders losses.
For this reason, **DO NOT TRADE WITH LEVERAGE IF YOU DON'T KNOW EXACTLY WHAT YOUR DOING. LEVERAGE TRADING IS HIGH RISK, AND CAN RESULT IN THE VALUE OF YOUR ASSETS DROPPING TO 0 VERY QUICKLY, WITH NO CHANCE OF INCREASING IN VALUE AGAIN.**
#### Margin (currently unavailable)
Trading occurs on the spot market, but the exchange lends currency to you in an amount equal to the chosen leverage. You pay the amount lent to you back to the exchange with interest, and your profits/losses are multiplied by the leverage specified.
#### Futures
Perpetual swaps (also known as Perpetual Futures) are contracts traded at a price that is closely tied to the underlying asset they are based off of (ex.). You are not trading the actual asset but instead are trading a derivative contract. Perpetual swap contracts can last indefinitely, in contrast to futures or option contracts.
In addition to the gains/losses from the change in price of the futures contract, traders also exchange _funding fees_, which are gains/losses worth an amount that is derived from the difference in price between the futures contract and the underlying asset. The difference in price between a futures contract and the underlying asset varies between exchanges.
To trade in futures markets, you'll have to set `trading_mode` to "futures".
You will also have to pick a "margin mode" (explanation below) - with freqtrade currently only supporting isolated margin.
``` json
"trading_mode": "futures",
"margin_mode": "isolated"
```
### Margin mode
On top of `trading_mode` - you will also have to configure your `margin_mode`.
While freqtrade currently only supports one margin mode, this will change, and by configuring it now you're all set for future updates.
The possible values are: `isolated`, or `cross`(*currently unavailable*).
#### Isolated margin mode
Each market(trading pair), keeps collateral in a separate account
``` json
"margin_mode": "isolated"
```
#### Cross margin mode (currently unavailable)
One account is used to share collateral between markets (trading pairs). Margin is taken from total account balance to avoid liquidation when needed.
``` json
"margin_mode": "cross"
```
## Set leverage to use
Different strategies and risk profiles will require different levels of leverage.
While you could configure one static leverage value - freqtrade offers you the flexibility to adjust this via [strategy leverage callback](strategy-callbacks.md#leverage-callback) - which allows you to use different leverages by pair, or based on some other factor benefitting your strategy result.
If not implemented, leverage defaults to 1x (no leverage).
!!! Warning
Higher leverage also equals higher risk - be sure you fully understand the implications of using leverage!
## Understand `liquidation_buffer`
*Defaults to `0.05`*
A ratio specifying how large of a safety net to place between the liquidation price and the stoploss to prevent a position from reaching the liquidation price.
This artificial liquidation price is calculated as:
Possible values are any floats between 0.0 and 0.99
**ex:** If a trade is entered at a price of 10 coin/USDT, and the liquidation price of this trade is 8 coin/USDT, then with `liquidation_buffer` set to `0.05` the minimum stoploss for this trade would be $8 + ((10 - 8) * 0.05) = 8 + 0.1 = 8.1$
!!! Danger "A `liquidation_buffer` of 0.0, or a low `liquidation_buffer` is likely to result in liquidations, and liquidation fees"
Currently Freqtrade is able to calculate liquidation prices, but does not calculate liquidation fees. Setting your `liquidation_buffer` to 0.0, or using a low `liquidation_buffer` could result in your positions being liquidated. Freqtrade does not track liquidation fees, so liquidations will result in inaccurate profit/loss results for your bot. If you use a low `liquidation_buffer`, it is recommended to use `stoploss_on_exchange` if your exchange supports this.
## Unavailable funding rates
For futures data, exchanges commonly provide the futures candles, the marks, and the funding rates. However, it is common that whilst candles and marks might be available, the funding rates are not. This can affect backtesting timeranges, i.e. you may only be able to test recent timeranges and not earlier, experiencing the `No data found. Terminating.` error. To get around this, add the `futures_funding_rate` config option as listed in [configuration.md](configuration.md), and it is recommended that you set this to `0`, unless you know a given specific funding rate for your pair, exchange and timerange. Setting this to anything other than `0` can have drastic effects on your profit calculations within strategy, e.g. within the `custom_exit`, `custom_stoploss`, etc functions.
!!! Warning "This will mean your backtests are inaccurate."
This will not overwrite funding rates that are available from the exchange, but bear in mind that setting a false funding rate will mean backtesting results will be inaccurate for historical timeranges where funding rates are not available.
### Developer
#### Margin mode
For shorts, the currency which pays the interest fee for the `borrowed` currency is purchased at the same time of the closing trade (This means that the amount purchased in short closing trades is greater than the amount sold in short opening trades).
For longs, the currency which pays the interest fee for the `borrowed` will already be owned by the user and does not need to be purchased. The interest is subtracted from the `close_value` of the trade.
All Fees are included in `current_profit` calculations during the trade.
#### Futures mode
Funding fees are either added or subtracted from the total amount of a trade
The `-p/--pairs` argument can be used to specify pairs you would like to plot.
@@ -107,9 +107,6 @@ The `-p/--pairs` argument can be used to specify pairs you would like to plot.
Specify custom indicators.
Use `--indicators1` for the main plot and `--indicators2` for the subplot below (if values are in a different range than prices).
!!! Tip
You will almost certainly want to specify a custom strategy! This can be done by adding `-s Classname` / `--strategy ClassName` to the command.
``` bash
freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --indicators1 sma ema --indicators2 macd
```
@@ -164,42 +161,117 @@ The resulting plot will have the following elements:
An advanced plot configuration can be specified in the strategy in the `plot_config` parameter.
Additional features when using plot_config include:
Additional features when using `plot_config` include:
* Specify colors per indicator
* Specify additional subplots
* Specify indicator pairs to fill area in between
The sample plot configuration below specifies fixed colors for the indicators. Otherwise consecutive plots may produce different colorschemes each time, making comparisons difficult.
The sample plot configuration below specifies fixed colors for the indicators. Otherwise, consecutive plots may produce different colorschemes each time, making comparisons difficult.
It also allows multiple subplots to display both MACD and RSI at the same time.
Plot type can be configured using `type` key. Possible types are:
* `scatter` corresponding to `plotly.graph_objects.Scatter` class (default).
* `bar` corresponding to `plotly.graph_objects.Bar` class.
Extra parameters to `plotly.graph_objects.*` constructor can be specified in `plotly` dict.
Sample configuration with inline comments explaining the process:
``` python
plot_config = {
'main_plot': {
# Configuration for main plot indicators.
# Specifies `ema10` to be red, and `ema50` to be a shade of gray
'ema10': {'color': 'red'},
'ema50': {'color': '#CCCCCC'},
# By omitting color, a random color is selected.
'sar': {},
@property
def plot_config(self):
"""
There are a lot of solutions how to build the return dictionary.
The only important point is the return value.
Example:
plot_config = {'main_plot': {}, 'subplots': {}}
"""
plot_config = {}
plot_config['main_plot'] = {
# Configuration for main plot indicators.
# Assumes 2 parameters, emashort and emalong to be specified.
The above configuration assumes that `ema10`, `ema50`, `macd`, `macdsignal` and `rsi` are columns in the DataFrame created by the strategy.
The above configuration assumes that `ema10`, `ema50`, `senkou_a`, `senkou_b`,
`macd`, `macdsignal`, `macdhist` and `rsi` are columns in the DataFrame created by the strategy.
!!! Warning
`plotly` arguments are only supported with plotly library and will not work with freq-ui.
!!! Note "Trade position adjustments"
If `position_adjustment_enable` / `adjust_trade_position()` is used, the trade initial buy price is averaged over multiple orders and the trade start price will most likely appear outside the candle range.
## Plot profit
@@ -211,6 +283,8 @@ The `plot-profit` subcommand shows an interactive graph with three plots:
* The summarized profit made by backtesting.
Note that this is not the real-world profit, but more of an estimate.
* Profit for each individual pair.
* Parallelism of trades.
* Underwater (Periods of drawdown).
The first graph is good to get a grip of how the overall market progresses.
@@ -220,6 +294,8 @@ This graph will also highlight the start (and end) of the Max drawdown period.
The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
The forth graph can help you analyze trade parallelism, showing how often max_open_trades have been maxed out.
Possible options for the `freqtrade plot-profit` subcommand:
Freqtrade provides a builtin webserver, which can serve [FreqUI](https://github.com/freqtrade/frequi), the freqtrade UI.
By default, the UI is not included in the installation (except for docker images), and must be installed explicitly with `freqtrade install-ui`.
This same command can also be used to update freqUI, should there be a new release.
Once the bot is started in trade / dry-run mode (with `freqtrade trade`) - the UI will be available under the configured port below (usually `http://127.0.0.1:8080`).
!!! info "Alpha release"
FreqUI is still considered an alpha release - if you encounter bugs or inconsistencies please open a [FreqUI issue](https://github.com/freqtrade/frequi/issues/new/choose).
!!! Note "developers"
Developers should not use this method, but instead use the method described in the [freqUI repository](https://github.com/freqtrade/frequi) to get the source-code of freqUI.
## Configuration
@@ -11,7 +26,8 @@ Sample configuration:
"enabled": true,
"listen_ip_address": "127.0.0.1",
"listen_port": 8080,
"verbosity": "info",
"verbosity": "error",
"enable_openapi": false,
"jwt_secret_key": "somethingrandom",
"CORS_origins": [],
"username": "Freqtrader",
@@ -22,8 +38,10 @@ Sample configuration:
!!! Danger "Security warning"
By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
!!! Danger "Password selection"
Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
??? Note "API/UI Access on a remote servers"
If you're running on a VPS, you should consider using either a ssh tunnel, or setup a VPN (openVPN, wireguard) to connect to your bot.
This will ensure that freqUI is not directly exposed to the internet, which is not recommended for security reasons (freqUI does not support https out of the box).
Setup of these tools is not part of this tutorial, however many good tutorials can be found on the internet.
You can then access the API by going to `http://127.0.0.1:8080/api/v1/ping` in a browser to check if the API is running correctly.
This should return the response:
@@ -34,16 +52,22 @@ This should return the response:
All other endpoints return sensitive info and require authentication and are therefore not available through a web browser.
To generate a secure password, either use a password manager, or use the below code snipped.
### Security
To generate a secure password, best use a password manager, or use the below code.
``` python
import secrets
secrets.token_hex()
```
!!! Hint
!!! Hint "JWT token"
Use the same method to also generate a JWT secret key (`jwt_secret_key`).
!!! Danger "Password selection"
Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
Also change `jwt_secret_key` to something random (no need to remember this, but it'll be used to encrypt your session, so it better be something unique!).
### Configuration with docker
If you run your bot using docker, you'll need to have the bot listen to incoming connections. The security is then handled by docker.
@@ -52,32 +76,27 @@ If you run your bot using docker, you'll need to have the bot listen to incoming
"api_server": {
"enabled": true,
"listen_ip_address": "0.0.0.0",
"listen_port": 8080
"listen_port": 8080,
"username": "Freqtrader",
"password": "SuperSecret1!",
//...
},
```
Add the following to your dockercommand:
Make sure that the following 2 lines are available in your docker-compose file:
``` bash
-p 127.0.0.1:8080:8080
```
A complete sample-command may then look as follows:
By using `-p 8080:8080` the API is available to everyone connecting to the server under the correct port, so others may be able to control your bot.
By using `8080:8080` in the docker port mapping, the API will be available to everyone connecting to the server under the correct port, so others may be able to control your bot.
## Consuming the API
## Rest API
### Consuming the API
You can consume the API by using the script `scripts/rest_client.py`.
The client script only requires the `requests` module, so Freqtrade does not need to be installed on the system.
By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be used, however you can specify a configuration file to override this behaviour.
### Minimalistic client config
#### Minimalistic client config
``` json
{
"api_server": {
"enabled": true,
"listen_ip_address": "0.0.0.0",
"listen_port": 8080
"listen_port": 8080,
"username": "Freqtrader",
"password": "SuperSecret1!",
//...
}
}
```
@@ -104,32 +126,46 @@ By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be use
| `ping` | Simple command testing the API Readiness - requires no authentication.
| `start` | Starts the trader
| `stop` | Stops the trader
| `start` | Starts the trader.
| `stop` | Stops the trader.
| `stopbuy` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `reload_config` | Reloads the configuration file
| `trades` | List last trades.
| `reload_config` | Reloads the configuration file.
| `trades` | List last trades. Limited to 500 trades per call.
| `trade/<tradeid>` | Get specific trade.
| `delete_trade <trade_id>` | Remove trade from the database. Tries to close open orders. Requires manual handling of this trade on the exchange.
| `show_config` | Shows part of the current configuration with relevant settings to operation
| `logs` | Shows last log messages
| `status` | Lists all open trades
| `count` | Displays number of trades used and available
| `profit` | Display a summary of your profit/loss from close trades and some stats about your performance
| `forcesell <trade_id>` | Instantly sells the given trade (Ignoring `minimum_roi`).
| `forcesell all` | Instantly sells all open trades (Ignoring `minimum_roi`).
| `forcebuy<pair> [rate]` | Instantly buys the given pair. Rate is optional. (`forcebuy_enable` must be set to True)
| `performance` | Show performance of each finished trade grouped by pair
| `balance` | Show account balance per currency
| `daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
| `whitelist` | Show the current whitelist
| `show_config` | Shows part of the current configuration with relevant settings to operation.
| `logs` | Shows last log messages.
| `status` | Lists all open trades.
| `count` | Displays number of trades used and available.
| `locks` | Displays currently locked pairs.
| `delete_lock <lock_id>` | Deletes (disables) the lock by id.
| `profit` | Display a summary of your profit/loss from close trades and some stats about your performance.
| `forceexit<trade_id>` | Instantly exits the given trade (Ignoring `minimum_roi`).
| `forceexit all` | Instantly exits all open trades (Ignoring `minimum_roi`).
| `forceenter <pair> [rate]` | Instantly enters the given pair. Rate is optional. (`force_entry_enable` must be set to True)
| `forceenter <pair><side> [rate]` | Instantly longs or shorts the given pair. Rate is optional. (`force_entry_enable` must be set to True)
| `performance` | Show performance of each finished trade grouped by pair.
| `balance` | Show account balance per currency.
| `daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7).
| `stats` | Display a summary of profit / loss reasons as well as average holding times.
| `whitelist` | Show the current whitelist.
| `blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
| `edge` | Show validated pairs by Edge if it is enabled.
| `version` | Show version
| `pair_candles` | Returns dataframe for a pair / timeframe combination while the bot is running. **Alpha**
| `pair_history` | Returns an analyzed dataframe for a given timerange, analyzed by a given strategy. **Alpha**
| `plot_config` | Get plot config from the strategy (or nothing if not configured). **Alpha**
| `strategies` | List strategies in strategy directory. **Alpha**
| `strategy <strategy>` | Get specific Strategy content. **Alpha**
| `available_pairs` | List available backtest data. **Alpha**
| `version` | Show version.
!!! Warning "Alpha status"
Endpoints labeled with *Alpha status* above may change at any time without notice.
Possible commands can be listed from the rest-client script using the `help` command.
@@ -140,6 +176,12 @@ python3 scripts/rest_client.py help
``` output
Possible commands:
available_pairs
Return available pair (backtest data) based on timeframe / stake_currency selection
:param timeframe: Only pairs with this timeframe available.
:param stake_currency: Only pairs that include this timeframe
balance
Get the account balance.
@@ -152,7 +194,12 @@ count
Return the amount of open trades.
daily
Return the amount of open trades.
Return the profits for each day, and amount of trades.
delete_lock
Delete (disable) lock from the database.
:param lock_id: ID for the lock to delete
delete_trade
Delete trade from the database.
@@ -169,19 +216,50 @@ forcebuy
:param pair: Pair to buy (ETH/BTC)
:param price: Optional - price to buy
forcesell
Force-sell a trade.
forceenter
Force entering a trade
:param pair: Pair to buy (ETH/BTC)
:param side: 'long' or 'short'
:param price: Optional - price to buy
forceexit
Force-exit a trade.
:param tradeid: Id of the trade (can be received via status command)
locks
Return current locks
logs
Show latest logs.
:param limit: Limits log messages to the last <limit> logs. No limit to get all the trades.
:param limit: Limits log messages to the last <limit> logs. No limit to get the entire log.
pair_candles
Return live dataframe for <pair><timeframe>.
:param pair: Pair to get data for
:param timeframe: Only pairs with this timeframe available.
:param limit: Limit result to the last n candles.
pair_history
Return historic, analyzed dataframe
:param pair: Pair to get data for
:param timeframe: Only pairs with this timeframe available.
:param strategy: Strategy to analyze and get values for
:param timerange: Timerange to get data for (same format than --timerange endpoints)
performance
Return the performance of the different coins.
ping
simple ping
plot_config
Return plot configuration if the strategy defines one.
profit
Return the profit summary.
@@ -195,6 +273,9 @@ show_config
start
Start the bot if it's in the stopped state.
stats
Return the stats report (durations, sell-reasons).
status
Get the status of open trades.
@@ -204,21 +285,41 @@ stop
stopbuy
Stop buying (but handle sells gracefully). Use `reload_config` to reset.
trades
Return trades history.
strategies
Lists available strategies
:param limit: Limits trades to the X last trades. No limit to get all the trades.
strategy
Get strategy details
:param strategy: Strategy class name
sysinfo
Provides system information (CPU, RAM usage)
trade
Return specific trade
:param trade_id: Specify which trade to get.
trades
Return trades history, sorted by id
:param limit: Limits trades to the X last trades. Max 500 trades.
:param offset: Offset by this amount of trades.
version
Return the version of the bot.
whitelist
Show the current whitelist.
```
## Advanced API usage using JWT tokens
### OpenAPI interface
To enable the builtin openAPI interface (Swagger UI), specify `"enable_openapi": true` in the api_server configuration.
This will enable the Swagger UI at the `/docs` endpoint. By default, that's running at http://localhost:8080/docs/ - but it'll depend on your settings.
### Advanced API usage using JWT tokens
!!! Note
The below should be done in an application (a Freqtrade REST API client, which fetches info via API), and is not intended to be used on a regular basis.
@@ -243,14 +344,17 @@ Since the access token has a short timeout (15 min) - the `token/refresh` reques
All web-based frontends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.
This whole section is only necessary in cross-origin cases (where you multiple bot API's running on `localhost:8081`, `localhost:8082`, ...), and want to combine them into one FreqUI instance.
Users can configure this themselves via the `CORS_origins` configuration setting.
It consists of a list of allowed sites that are allowed to consume resources from the bot's API.
??? info "Technical explanation"
All web-based front-ends are subject to [CORS](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) - Cross-Origin Resource Sharing.
Since most of the requests to the Freqtrade API must be authenticated, a proper CORS policy is key to avoid security problems.
Also, the standard disallows `*` CORS policies for requests with credentials, so this setting must be set appropriately.
Users can allow access from different origin URL's to the bot API via the `CORS_origins` configuration setting.
It consists of a list of allowed URL's that are allowed to consume resources from the bot's API.
Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - this would mean that the following configuration becomes necessary:
@@ -263,5 +367,19 @@ Assuming your application is deployed as `https://frequi.freqtrade.io/home/` - t
}
```
In the following (pretty common) case, FreqUI is accessible on `http://localhost:8080/trade` (this is what you see in your navbar when navigating to freqUI).
The correct configuration for this case is `http://localhost:8080` - the main part of the URL including the port.
```jsonc
{
//...
"jwt_secret_key": "somethingrandom",
"CORS_origins": ["http://localhost:8080"],
//...
}
```
!!! Note
We strongly recommend to also set `jwt_secret_key` to something random and known only to yourself to avoid unauthorized access to your bot.
@@ -19,7 +19,7 @@ The freqtrade docker image does contain sqlite3, so you can edit the database wi
``` bash
docker-compose exec freqtrade /bin/bash
sqlite3 <databasefile>.sqlite
sqlite3 <database-file>.sqlite
```
## Open the DB
@@ -43,66 +43,20 @@ sqlite3
.schema <table_name>
```
### Trade table structure
```sql
CREATE TABLE trades(
id INTEGER NOT NULL,
exchange VARCHAR NOT NULL,
pair VARCHAR NOT NULL,
is_open BOOLEAN NOT NULL,
fee_open FLOAT NOT NULL,
fee_open_cost FLOAT,
fee_open_currency VARCHAR,
fee_close FLOAT NOT NULL,
fee_close_cost FLOAT,
fee_close_currency VARCHAR,
open_rate FLOAT,
open_rate_requested FLOAT,
open_trade_price FLOAT,
close_rate FLOAT,
close_rate_requested FLOAT,
close_profit FLOAT,
close_profit_abs FLOAT,
stake_amount FLOAT NOT NULL,
amount FLOAT,
open_date DATETIME NOT NULL,
close_date DATETIME,
open_order_id VARCHAR,
stop_loss FLOAT,
stop_loss_pct FLOAT,
initial_stop_loss FLOAT,
initial_stop_loss_pct FLOAT,
stoploss_order_id VARCHAR,
stoploss_last_update DATETIME,
max_rate FLOAT,
min_rate FLOAT,
sell_reason VARCHAR,
strategy VARCHAR,
timeframe INTEGER,
PRIMARY KEY (id),
CHECK (is_open IN (0, 1))
);
CREATE INDEX ix_trades_stoploss_order_id ON trades (stoploss_order_id);
CREATE INDEX ix_trades_pair ON trades (pair);
CREATE INDEX ix_trades_is_open ON trades (is_open);
```
## Get all trades in the table
```sql
SELECT * FROM trades;
```
## Fix trade still open after a manual sell on the exchange
## Fix trade still open after a manual exit on the exchange
!!! Warning
Manually selling a pair on the exchange will not be detected by the bot and it will try to sell anyway. Whenever possible, forcesell <tradeid> should be used to accomplish the same thing.
It is strongly advised to backup your database file before making any manual changes.
Manually selling a pair on the exchange will not be detected by the bot and it will try to sell anyway. Whenever possible, /forceexit <tradeid> should be used to accomplish the same thing.
It is strongly advised to backup your database file before making any manual changes.
!!! Note
This should not be necessary after /forcesell, as forcesell orders are closed automatically by the bot on the next iteration.
This should not be necessary after /forceexit, as force_exit orders are closed automatically by the bot on the next iteration.
Maybe you'd like to remove a trade from the database, because something went wrong.
!!! Tip "Use RPC Methods to delete trades"
Consider using `/delete <tradeid>` via telegram or rest API. That's the recommended way to deleting trades.
If you'd still like to remove a trade from the database directly, you can use the below query.
!!! Danger
Some systems (Ubuntu) disable foreign keys in their sqlite3 packaging. When using sqlite - please ensure that foreign keys are on by running `PRAGMA foreign_keys = ON` before the above query.
```sql
DELETE FROM trades WHERE id = <tradeid>;
```
```sql
DELETE FROM trades WHERE id = 31;
```
!!! Warning
This will remove this trade from the database. Please make sure you got the correct id and **NEVER** run this query without the `where` clause.
## Use a different database system
Freqtrade is using SQLAlchemy, which supports multiple different database systems. As such, a multitude of database systems should be supported.
Freqtrade does not depend or install any additional database driver. Please refer to the [SQLAlchemy docs](https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls) on installation instructions for the respective database systems.
The following systems have been tested and are known to work with freqtrade:
* sqlite (default)
* PostgreSQL)
* MariaDB
!!! Warning
By using one of the below database systems, you acknowledge that you know how to manage such a system. The freqtrade team will not provide any support with setup or maintenance (or backups) of the below database systems.
Freqtrade will automatically create the tables necessary upon startup.
If you're running different instances of Freqtrade, you must either setup one database per Instance or use different users / schemas for your connections.
### MariaDB / MySQL
Freqtrade supports MariaDB by using SQLAlchemy, which supports multiple different database systems.
The `stoploss` configuration parameter is loss as ratio that should trigger a sale.
For example, value `-0.10` will cause immediate sell if the profit dips below -10% for a given trade. This parameter is optional.
Stoploss calculations do include fees, so a stoploss of -10% is placed exactly 10% below the entry point.
Most of the strategy files already include the optimal `stoploss` value.
@@ -16,51 +17,65 @@ Those stoploss modes can be *on exchange* or *off exchange*.
These modes can be configured with these values:
``` python
'emergencysell': 'market',
'emergency_exit': 'market',
'stoploss_on_exchange': False
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_limit_ratio': 0.99
```
!!! Note
Stoploss on exchange is only supported for Binance (stop-loss-limit), Kraken (stop-loss-market) and FTX (stop limit and stop-market) as of now.
<ins>Do not set too low stoploss value if using stop loss on exchange!</ins>
If set to low/tight then you have greater risk of missing fill on the order and stoploss will not work
Stoploss on exchange is only supported for Binance (stop-loss-limit), Huobi (stop-limit), Kraken (stop-loss-market, stop-loss-limit), FTX (stop limit and stop-market) Gateio (stop-limit), and Kucoin (stop-limit and stop-market) as of now.
<ins>Do not set too low/tight stoploss value if using stop loss on exchange!</ins>
If set to low/tight then you have greater risk of missing fill on the order and stoploss will not work.
### stoploss_on_exchange and stoploss_on_exchange_limit_ratio
Enable or Disable stop loss on exchange.
If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order happens successfully. This will protect you against sudden crashes in market as the order will be in the queue immediately and if market goes down then the order has more chance of being fulfilled.
If the stoploss is *on exchange* it means a stoploss limit order is placed on the exchange immediately after buy order fills. This will protect you against sudden crashes in market, as the order execution happens purely within the exchange, and has no potential network overhead.
If `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.
`stoploss` defines the stop-price where the limit order is placed - and limit should be slightly below this.
If an exchange supports both limit and market stoploss orders, then the value of `stoploss` will be used to determine the stoploss type.
Calculation example: we bought the asset at 100$.
Stop-price is 95$, then limit would be `95 * 0.99 = 94.05$` - so the limit order fill can happen between 95$ and 94.05$.
Calculation example: we bought the asset at 100\$.
Stop-price is 95\$, then limit would be `95 * 0.99 = 94.05$` - so the limit order fill can happen between 95$ and 94.05$.
For example, assuming the stoploss is on exchange, and trailing stoploss is enabled, and the market is going up, then the bot automatically cancels the previous stoploss order and puts a new one with a stop value higher than the previous stoploss order.
!!! Note
If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new stoploss order.
### stoploss_on_exchange_interval
In case of stoploss on exchange there is another parameter called `stoploss_on_exchange_interval`. This configures the interval in seconds at which the bot will check the stoploss and update it if necessary.
The bot cannot do these every 5 seconds (at each iteration), otherwise it would get banned by the exchange.
So this parameter will tell the bot how often it should update the stoploss order. The default value is 60 (1 minute).
This same logic will reapply a stoploss order on the exchange should you cancel it accidentally.
### emergencysell
`emergencysell` is an optional value, which defaults to `market` and is used when creating stop loss on exchange orders fails.
### force_exit
`force_exit` is an optional value, which defaults to the same value as `exit` and is used when sending a `/forceexit` command from Telegram or from the Rest API.
### force_entry
`force_entry` is an optional value, which defaults to the same value as `entry` and is used when sending a `/forceentry` command from Telegram or from the Rest API.
### emergency_exit
`emergency_exit` is an optional value, which defaults to `market` and is used when creating stop loss on exchange orders fails.
The below is the default which is used if not changed in strategy or configuration file.
Example from strategy file:
``` python
order_types = {
'buy': 'limit',
'sell': 'limit',
'emergencysell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': True,
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_limit_ratio': 0.99
"entry": "limit",
"exit": "limit",
"emergency_exit": "market",
"stoploss": "market",
"stoploss_on_exchange": True,
"stoploss_on_exchange_interval": 60,
"stoploss_on_exchange_limit_ratio": 0.99
}
```
@@ -72,6 +87,7 @@ At this stage the bot contains the following stoploss support modes:
2. Trailing stop loss.
3. Trailing stop loss, custom positive loss.
4. Trailing stop loss only once the trade has reached a certain offset.
* the stop loss would get triggered once the asset drops below 90$
@@ -107,7 +124,7 @@ For example, simplified math:
* the stop loss would get triggered once the asset drops below 90$
* assuming the asset now increases to 102$
* the stop loss will now be -10% of 102$ = 91.8$
* now the asset drops in value to 101$, the stop loss will still be 91.8$ and would trigger at 91.8$.
* now the asset drops in value to 101\$, the stop loss will still be 91.8$ and would trigger at 91.8$.
In summary: The stoploss will be adjusted to be always be -10% of the highest observed price.
@@ -133,8 +150,8 @@ For example, simplified math:
* the stop loss is defined at -10%
* the stop loss would get triggered once the asset drops below 90$
* assuming the asset now increases to 102$
* the stop loss will now be -2% of 102$ = 99.96$ (99.96$ stop loss will be locked in and will follow asset price increasements with -2%)
* now the asset drops in value to 101$, the stop loss will still be 99.96$ and would trigger at 99.96$
* the stop loss will now be -2% of 102$ = 99.96$ (99.96$ stop loss will be locked in and will follow asset price increments with -2%)
* now the asset drops in value to 101\$, the stop loss will still be 99.96$ and would trigger at 99.96$
The 0.02 would translate to a -2% stop loss.
Before this, `stoploss` is used for the trailing stoploss.
@@ -151,7 +168,7 @@ This option can be used with or without `trailing_stop_positive`, but uses `trai
trailing_only_offset_is_reached = True
```
Configuration (offset is buyprice + 3%):
Configuration (offset is buy-price + 3%):
``` python
stoploss = -0.10
@@ -166,14 +183,27 @@ For example, simplified math:
* the bot buys an asset at a price of 100$
* the stop loss is defined at -10%
* the stop loss would get triggered once the asset drops below 90$
* stoploss will remain at 90$ unless asset increases to or above our configured offset
* stoploss will remain at 90$ unless asset increases to or above the configured offset
* assuming the asset now increases to 103$ (where we have the offset configured)
* the stop loss will now be -2% of 103$ = 100.94$
* now the asset drops in value to 101$, the stop loss will still be 100.94$ and would trigger at 100.94$
* now the asset drops in value to 101\$, the stop loss will still be 100.94$ and would trigger at 100.94$
!!! Tip
Make sure to have this value (`trailing_stop_positive_offset`) lower than minimal ROI, otherwise minimal ROI will apply first and sell the trade.
## Stoploss and Leverage
Stoploss should be thought of as "risk on this trade" - so a stoploss of 10% on a 100$ trade means you are willing to lose 10$ (10%) on this trade - which would trigger if the price moves 10% to the downside.
When using leverage, the same principle is applied - with stoploss defining the risk on the trade (the amount you are willing to lose).
Therefore, a stoploss of 10% on a 10x trade would trigger on a 1% price move.
If your stake amount (own capital) was 100$ - this trade would be 1000$ at 10x (after leverage).
If price moves 1% - you've lost 10$ of your own capital - therfore stoploss will trigger in this case.
Make sure to be aware of this, and avoid using too tight stoploss (at 10x leverage, 10% risk may be too little to allow the trade to "breath" a little).
## Changing stoploss on open trades
A stoploss on an open trade can be changed by changing the value in the configuration or strategy and use the `/reload_config` command (alternatively, completely stopping and restarting the bot also works).
@@ -8,203 +8,145 @@ If you're just getting started, please be familiar with the methods described in
!!! Note
All callback methods described below should only be implemented in a strategy if they are actually used.
## Custom order timeout rules
!!! Tip
You can get a strategy template containing all below methods by running `freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced`
Simple, timebased order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
## Storing information
However, freqtrade also offers a custom callback for both ordertypes, which allows you to decide based on custom criteria if a order did time out or not.
Storing information can be accomplished by creating a new dictionary within the strategy class.
The name of the variable can be chosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables.
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
!!! Note
Unfilled order timeouts are not relevant during backtesting or hyperopt, and are only relevant during real (live) trading. Therefore these methods are only called in these circumstances.
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
### Custom order timeout example
## Dataframe access
A simple example, which applies different unfilled-timeouts depending on the price of the asset can be seen below.
It applies a tight timeout for higher priced assets, while allowing more time to fill on cheap coins.
The function must return either `True` (cancel order) or `False` (keep order alive).
You may access dataframe in various strategy functions by querying it from dataprovider.
``` python
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.exchange import timeframe_to_prev_date
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be sold.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process
"""
if sell_reason == 'force_sell' and trade.calc_profit_ratio(rate) < 0:
# Reject force-sells with negative profit
# This is just a sample, please adjust to your needs
# (this does not necessarily make sense, assuming you know when you're force-selling)
return False
return True
```
!!! Note
You should make sure to implement proper version control (like a git repository) alongside this, as freqtrade will not keep historic versions of your strategy, so it's up to the user to be able to eventually roll back to a prior version of the strategy.
## Derived strategies
The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
Both attributes and methods may be overriden, altering behavior of the original strategy in a way you need.
Both attributes and methods may be overridden, altering behavior of the original strategy in a way you need.
While keeping the subclass in the same file is technically possible, it can lead to some problems with hyperopt parameter files, we therefore recommend to use separate strategy files, and import the parent strategy as shown above.
## Embedding Strategies
Freqtrade provides you with an easy way to embed the strategy into your configuration file.
This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
in your chosen config file.
### Encoding a string as BASE64
This is a quick example, how to generate the BASE64 string in python
While the main strategy functions (`populate_indicators()`, `populate_entry_trend()`, `populate_exit_trend()`) should be used in a vectorized way, and are only called [once during backtesting](bot-basics.md#backtesting-hyperopt-execution-logic), callbacks are called "whenever needed".
As such, you should avoid doing heavy calculations in callbacks to avoid delays during operations.
Depending on the callback used, they may be called when entering / exiting a trade, or throughout the duration of a trade.
Freqtrade will fall back to the `proposed_stake` value should your code raise an exception. The exception itself will be logged.
!!! Tip
You do not _have_ to ensure that `min_stake <= returned_value <= max_stake`. Trades will succeed as the returned value will be clamped to supported range and this action will be logged.
!!! Tip
Returning `0` or `None` will prevent trades from being placed.
## Custom exit signal
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.
For example you could implement a 1:2 risk-reward ROI with `custom_exit()`.
Using `custom_exit()` signals in place of stoploss though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
!!! Note
Returning a (none-empty) `string` or `True` from this method is equal to setting exit signal on a candle at specified time. This method is not called when exit signal is set already, or if exit signals are disabled (`use_exit_signal=False`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
`custom_exit()` will ignore `exit_profit_only`, and will always be called unless `use_exit_signal=False`, even if there is a new enter signal.
An example of how we can use different indicators depending on the current profit and also exit trades that were open longer than one day:
# Between 2% and 10%, sell if EMA-long above EMA-short
if 0.02 < current_profit < 0.1:
if last_candle['emalong'] > last_candle['emashort']:
return 'ema_long_below_80'
# Sell any positions at a loss if they are held for more than one day.
if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
return 'unclog'
```
See [Dataframe access](strategy-advanced.md#dataframe-access) for more information about dataframe use in strategy callbacks.
## Custom stoploss
Called for open trade every iteration (roughly every 5 seconds) until a trade is closed.
The usage of the custom stoploss method must be enabled by setting `use_custom_stoploss=True` on the strategy object.
The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss (before this method is called for the first time for a trade), and is still mandatory.
The method must return a stoploss value (float / number) as a percentage of the current price.
E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
The absolute value of the return value is used (the sign is ignored), so returning `0.05` or `-0.05` have the same result, a stoploss 5% below the current price.
To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:
Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
e.g. returning -0.05 would create a stoploss 5% below current_rate.
The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns the initial stoploss value
Only called when use_custom_stoploss is set to True.
:param pair: Pair that's currently analyzed
:param trade: trade object.
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in exit_pricing.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
: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
```
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-exchange-freqtrade)).
!!! 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.
!!! Tip "Trailing stoploss"
It's recommended to disable `trailing_stop` when using custom stoploss values. Both can work in tandem, but you might encounter the trailing stop to move the price higher while your custom function would not want this, causing conflicting behavior.
### Custom stoploss examples
The next section will show some examples on what's possible with the custom stoploss function.
Of course, many more things are possible, and all examples can be combined at will.
#### Time based trailing stop
Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
In this example, we'll trail the highest price with 10% trailing stoploss for `ETH/BTC` and `XRP/BTC`, with 5% trailing stoploss for `LTC/BTC` and with 15% for all other pairs.
Use the initial stoploss until the profit is above 4%, then use a trailing stoploss of 50% of the current profit with a minimum of 2.5% and a maximum of 5%.
Please note that the stoploss can only increase, values lower than the current stoploss are ignored.
# Convert absolute price to percentage relative to current_rate
if stoploss_price < current_rate:
return (stoploss_price / current_rate) - 1
# return maximum stoploss value, keeping current stoploss price unchanged
return 1
```
See [Dataframe access](strategy-advanced.md#dataframe-access) for more information about dataframe use in strategy callbacks.
### Common helpers for stoploss calculations
#### Stoploss relative to open price
Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss relative to the *open* price, we need to use `current_profit` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
The helper function [`stoploss_from_open()`](strategy-customization.md#stoploss_from_open) can be used to convert from an open price relative stop, to a current price relative stop which can be returned from `custom_stoploss()`.
#### Stoploss percentage from absolute price
Stoploss values returned from `custom_stoploss()` always specify a percentage relative to `current_rate`. In order to set a stoploss at specified absolute price level, we need to use `stop_rate` to calculate what percentage relative to the `current_rate` will give you the same result as if the percentage was specified from the open price.
The helper function [`stoploss_from_absolute()`](strategy-customization.md#stoploss_from_absolute) can be used to convert from an absolute price, to a current price relative stop which can be returned from `custom_stoploss()`.
---
## Custom order price rules
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.
You can use this feature by creating a `custom_entry_price()` function in your strategy file to customize entry prices and `custom_exit_price()` for exits.
Each of these methods are called right before placing an order on the exchange.
!!! Note
If your custom pricing function return None or an invalid value, price will fall back to `proposed_rate`, which is based on the regular pricing configuration.
### Custom order entry and exit price example
``` python
from datetime import datetime, timedelta, timezone
Modifying entry and exit prices will only work for limit orders. Depending on the price chosen, this can result in a lot of unfilled orders. By default the maximum allowed distance between the current price and the custom price is 2%, this value can be changed in config with the `custom_price_max_distance_ratio` parameter.
**Example**:
If the new_entryprice is 97, the proposed_rate is 100 and the `custom_price_max_distance_ratio` is set to 2%, The retained valid custom entry price will be 98, which is 2% below the current (proposed) rate.
!!! Warning "Backtesting"
Custom prices are supported in backtesting (starting with 2021.12), and orders will fill if the price falls within the candle's low/high range.
Orders that don't fill immediately are subject to regular timeout handling, which happens once per (detail) candle.
`custom_exit_price()` is only called for sells of type exit_signal and Custom exit. All other exit-types will use regular backtesting prices.
## Custom order timeout rules
Simple, time-based order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
However, freqtrade also offers a custom callback for both order types, which allows you to decide based on custom criteria if an order did time out or not.
!!! Note
Backtesting fills orders if their price falls within the candle's low/high range.
The below callbacks will be called once per (detail) candle for orders that don't fill immediately (which use custom pricing).
### Custom order timeout example
Called for every open order until that order is either filled or cancelled.
`check_entry_timeout()` is called for trade entries, while `check_exit_timeout()` is called for trade exit orders.
A simple example, which applies different unfilled-timeouts depending on the price of the asset can be seen below.
It applies a tight timeout for higher priced assets, while allowing more time to fill on cheap coins.
The function must return either `True` (cancel order) or `False` (keep order alive).
``` python
from datetime import datetime, timedelta
from freqtrade.persistence import Trade, Order
class AwesomeStrategy(IStrategy):
# ... populate_* methods
# Set unfilledtimeout to 25 hours, since the maximum timeout from below is 24 hours.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair for trade that's about to be exited.
:param trade: trade object.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in base currency.
:param rate: Rate that's going to be used when using limit orders
or current rate for market orders.
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param exit_reason: Exit reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'exit_signal', 'force_exit', 'emergency_exit']
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True, then the exit-order is placed on the exchange.
False aborts the process
"""
if exit_reason == 'force_exit' and trade.calc_profit_ratio(rate) < 0:
# Reject force-sells with negative profit
# This is just a sample, please adjust to your needs
# (this does not necessarily make sense, assuming you know when you're force-selling)
return False
return True
```
!!! Warning
`confirm_trade_exit()` can prevent stoploss exits, causing significant losses as this would ignore stoploss exits.
## Adjust trade position
The `position_adjustment_enable` strategy property enables the usage of `adjust_trade_position()` callback in the strategy.
For performance reasons, it's disabled by default and freqtrade will show a warning message on startup if enabled.
`adjust_trade_position()` can be used to perform additional orders, for example to manage risk with DCA (Dollar Cost Averaging).
`max_entry_position_adjustment` property is used to limit the number of additional buys per trade (on top of the first buy) that the bot can execute. By default, the value is -1 which means the bot have no limit on number of adjustment buys.
The strategy is expected to return a stake_amount (in stake currency) between `min_stake` and `max_stake` if and when an additional buy order should be made (position is increased).
If there are not enough funds in the wallet (the return value is above `max_stake`) then the signal will be ignored.
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, or when you have reached the maximum amount of extra buys that you have set on `max_entry_position_adjustment`.
`adjust_trade_position()` is called very frequently for the duration of a trade, so you must keep your implementation as performant as possible.
Position adjustments will always be applied in the direction of the trade, so a positive value will always increase your position, no matter if it's a long or short trade. Modifications to leverage are not possible.
!!! Note "About stake size"
Using fixed stake size means it will be the amount used for the first order, just like without position adjustment.
If you wish to buy additional orders with DCA, then make sure to leave enough funds in the wallet for that.
Using 'unlimited' stake amount with DCA orders requires you to also implement the `custom_stake_amount()` callback to avoid allocating all funds to the initial order.
!!! Warning
Stoploss is still calculated from the initial opening price, not averaged price.
!!! Warning "/stopbuy"
While `/stopbuy` command stops the bot from entering new trades, the position adjustment feature will continue buying new orders on existing trades.
!!! Warning "Backtesting"
During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so performance will be affected.
``` python
from freqtrade.persistence import Trade
class DigDeeperStrategy(IStrategy):
position_adjustment_enable = True
# Attempts to handle large drops with DCA. High stoploss is required.
stoploss = -0.30
# ... populate_* methods
# Example specific variables
max_entry_position_adjustment = 3
# This number is explained a bit further down
max_dca_multiplier = 5.5
# This is called when placing the initial order (opening trade)
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.
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.
!!! 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.
@@ -4,40 +4,30 @@ This page explains how to customize your strategies, add new indicators and set
Please familiarize yourself with [Freqtrade basics](bot-basics.md) first, which provides overall info on how the bot operates.
## Install a custom strategy file
This is very simple. Copy paste your strategy file into the directory `user_data/strategies`.
Let assume you have a class called `AwesomeStrategy` in the file `AwesomeStrategy.py`:
1. Move your file into `user_data/strategies` (you should have `user_data/strategies/AwesomeStrategy.py`
2. Start the bot with the param `--strategy AwesomeStrategy` (the parameter is the class name)
```bash
freqtrade trade --strategy AwesomeStrategy
```
## Develop your own strategy
The bot includes a default strategy file.
Also, several other strategies are available in the [strategy repository](https://github.com/freqtrade/freqtrade-strategies).
You will however most likely have your own idea for a strategy.
This document intends to help you develop one for yourself.
This document intends to help you convert your strategy idea into your own strategy.
To get started, use `freqtrade new-strategy --strategy AwesomeStrategy`.
To get started, use `freqtrade new-strategy --strategy AwesomeStrategy` (you can obviously use your own naming for your strategy).
This will create a new strategy file from a template, which will be located under `user_data/strategies/AwesomeStrategy.py`.
!!! Note
This is just a template file, which will most likely not be profitable out of the box.
??? Hint "Different template levels"
`freqtrade new-strategy` has an additional parameter, `--template`, which controls the amount of pre-build information you get in the created strategy. Use `--template minimal` to get an empty strategy without any indicator examples, or `--template advanced` to get a template with most callbacks defined.
### Anatomy of a strategy
A strategy file contains all the information needed to build a good strategy:
- Indicators
-Buy strategy rules
-Sell strategy rules
-Entry strategy rules
-Exit strategy rules
- Minimal ROI recommended
- Stoploss strongly recommended
@@ -45,7 +35,7 @@ The bot also include a sample strategy called `SampleStrategy` you can update: `
You can test it with the parameter: `--strategy SampleStrategy`
Additionally, there is an attribute called `INTERFACE_VERSION`, which defines the version of the strategy interface the bot should use.
The current version is 2 - which is also the default when it's not set explicitly in the strategy.
The current version is 3 - which is also the default when it's not set explicitly in the strategy.
Future versions will require this to be set.
@@ -67,11 +57,51 @@ file as reference.**
needs to take care to avoid having the strategy utilize data from the future.
Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
### Dataframe
Freqtrade uses [pandas](https://pandas.pydata.org/) to store/provide the candlestick (OHLCV) data.
Pandas is a great library developed for processing large amounts of data.
Each row in a dataframe corresponds to one candle on a chart, with the latest candle always being the last in the dataframe (sorted by date).
Pandas provides fast ways to calculate metrics. To benefit from this speed, it's advised to not use loops, but use vectorized methods instead.
Vectorized operations perform calculations across the whole range of data and are therefore, compared to looping through each row, a lot faster when calculating indicators.
As a dataframe is a table, simple python comparisons like the following will not work
``` python
if dataframe['rsi'] > 30:
dataframe['enter_long'] = 1
```
The above section will fail with `The truth value of a Series is ambiguous. [...]`.
This must instead be written in a pandas-compatible way, so the operation is performed across the whole dataframe.
``` python
dataframe.loc[
(dataframe['rsi'] > 30)
, 'enter_long'] = 1
```
With this section, you have a new column in your dataframe, which has `1` assigned whenever RSI is above 30.
### Customize Indicators
Buy and sell strategies need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
Buy and sell signals need indicators. You can add more indicators by extending the list contained in the method `populate_indicators()` from your strategy file.
You should only add the indicators used in either `populate_buy_trend()`, `populate_sell_trend()`, or to populate another indicator, otherwise performance may suffer.
You should only add the indicators used in either `populate_entry_trend()`, `populate_exit_trend()`, or to populate another indicator, otherwise performance may suffer.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
Additional technical libraries can be installed as necessary, or custom indicators may be written / invented by the strategy author.
### Strategy startup period
Most indicators have an instable startup period, in which they are either not available, or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
Most indicators have an instable startup period, in which they are either not available (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
To account for this, the strategy can be assigned the `startup_candle_count` attribute.
This should be set to the maximum number of candles that the strategy requires to calculate stable indicators.
@@ -136,8 +176,14 @@ In this example strategy, this should be set to 100 (`startup_candle_count = 100
By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt.
!!! Warning "Using x calls to get OHLCV"
If you receive a warning like `WARNING - Using 3 calls to get OHLCV. This can result in slower operations for the bot. Please check if you really need 1500 candles for your strategy` - you should consider if you really need this much historic data for your signals.
Having this will cause Freqtrade to make multiple calls for the same pair, which will obviously be slower than one network request.
As a consequence, Freqtrade will take longer to refresh candles - and should therefore be avoided if possible.
This is capped to 5 total calls to avoid overloading the exchange, or make freqtrade too slow.
!!! Warning
`startup_candle_count` should be below `ohlcv_candle_limit` (which is 500 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
`startup_candle_count` should be below `ohlcv_candle_limit* 5` (which is 500 * 5 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
#### Example
@@ -147,24 +193,24 @@ Let's try to backtest 1 month (January 2019) of 5m candles using an example stra
Assuming `startup_candle_count` is set to 100, backtesting knows it needs 100 candles to generate valid buy signals. It will load data from `20190101 - (100 * 5m)` - which is ~2019-12-31 15:30:00.
Assuming `startup_candle_count` is set to 100, backtesting knows it needs 100 candles to generate valid buy signals. It will load data from `20190101 - (100 * 5m)` - which is ~2018-12-31 15:30:00.
If this data is available, indicators will be calculated with this extended timerange. The instable startup period (up to 2019-01-01 00:00:00) will then be removed before starting backtesting.
!!! Note
If data for the startup period is not available, then the timerange will be adjusted to account for this startup period - so Backtesting would start at 2019-01-01 08:30:00.
### Buy signal rules
### Entry signal rules
Edit the method `populate_buy_trend()` in your strategy file to update your buy strategy.
Edit the method `populate_entry_trend()` in your strategy file to update your entry strategy.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
This will method will also define a new column, `"buy"`, which needs to contain 1 for buys, and 0 for "no action".
This method will also define a new column, `"enter_long"` (`"enter_short"` for shorts), which needs to contain 1 for entries, and 0 for "no action". `enter_long` is a mandatory column that must be set even if the strategy is shorting only.
Sample from `user_data/strategies/sample_strategy.py`:
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['enter_short', 'enter_tag']] = (1, 'rsi_cross')
return dataframe
```
!!! Note
Buying requires sellers to buy from - therefore volume needs to be > 0 (`dataframe['volume'] > 0`) to make sure that the bot does not buy/sell in no-activity periods.
### Sell signal rules
### Exit signal rules
Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
Please note that the sell-signal is only used if `use_sell_signal` is set to true in the configuration.
Edit the method `populate_exit_trend()` into your strategy file to update your exit strategy.
Please note that the exit-signal is only used if `use_exit_signal` is set to true in the configuration.
It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
This will method will also define a new column, `"sell"`, which needs to contain 1 for sells, and 0 for "no action".
This method will also define a new column, `"exit_long"` (`"exit_short"` for shorts), which needs to contain 1 for exits, and 0 for "no action".
Sample from `user_data/strategies/sample_strategy.py`:
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['exit_short', 'exit_tag']] = (1, 'rsi_too_low')
return dataframe
```
### Minimal ROI
This dict defines the minimal Return On Investment (ROI) a trade should reach before selling, independent from the sell signal.
This dict defines the minimal Return On Investment (ROI) a trade should reach before exiting, independent from the exit signal.
It is of the following format, with the dict key (left side of the colon) being the minutes passed since the trade opened, and the value (right side of the colon) being the percentage.
@@ -233,10 +334,10 @@ minimal_roi = {
The above configuration would therefore mean:
- Sell whenever 4% profit was reached
- Sell when 2% profit was reached (in effect after 20 minutes)
- Sell when 1% profit was reached (in effect after 30 minutes)
- Sell when trade is non-loosing (in effect after 40 minutes)
- Exit whenever 4% profit was reached
- Exit when 2% profit was reached (in effect after 20 minutes)
- Exit when 1% profit was reached (in effect after 30 minutes)
- Exit when trade is non-loosing (in effect after 40 minutes)
The calculation does include fees.
@@ -248,7 +349,7 @@ minimal_roi = {
}
```
While technically not completely disabled, this would sell once the trade reaches 10000% Profit.
While technically not completely disabled, this would exit once the trade reaches 10000% Profit.
To use times based on candle duration (timeframe), the following snippet can be handy.
This will allow you to change the timeframe for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)
@@ -271,64 +372,51 @@ class AwesomeStrategy(IStrategy):
Setting a stoploss is highly recommended to protect your capital from strong moves against you.
Sample:
Sample of setting a 10% stoploss:
``` python
stoploss = -0.10
```
This would signify a stoploss of -10%.
For the full documentation on stoploss features, look at the dedicated [stoploss page](stoploss.md).
If your exchange supports it, it's recommended to also set `"stoploss_on_exchange"` in the order_types dictionary, so your stoploss is on the exchange and cannot be missed due to network problems, high load or other reasons.
For more information on order_types please look [here](configuration.md#understand-order_types).
### Timeframe (formerly ticker interval)
### Timeframe
This is the set of candles the bot should download and use for the analysis.
Common values are `"1m"`, `"5m"`, `"15m"`, `"1h"`, however all values supported by your exchange should work.
Please note that the same buy/sell signals may work well with one timeframe, but not with the others.
Please note that the same entry/exit signals may work well with one timeframe, but not with the others.
This setting is accessible within the strategy methods as the `self.timeframe` attribute.
### Can short
To use short signals in futures markets, you will have to let us know to do so by setting `can_short=True`.
Strategies which enable this will fail to load on spot markets.
Disabling of this will have short signals ignored (also in futures markets).
### Metadata dict
The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `populate_indicators`) contains additional information.
The metadata-dict (available for `populate_entry_trend`, `populate_exit_trend`, `populate_indicators`) contains additional information.
Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
The Metadata-dict should not be modified and does not persist information across multiple calls.
Instead, have a look at the section [Storing information](#Storing-information)
Instead, have a look at the [Storing information](strategy-advanced.md#Storing-information) section.
### Storing information
## Strategy file loading
Storing information can be accomplished by creating a new dictionary within the strategy class.
By default, freqtrade will attempt to load strategies from all `.py` files within `user_data/strategies`.
The name of the variable can be chosen at will, but should be prefixed with `cust_` to avoid naming collisions with predefined strategy variables.
Assuming your strategy is called `AwesomeStrategy`, stored in the file `user_data/strategies/AwesomeStrategy.py`, then you can start freqtrade with `freqtrade trade --strategy AwesomeStrategy`.
Note that we're using the class-name, not the file name.
if "crosstime" in self.cust_info[metadata["pair"]:
self.cust_info[metadata["pair"]["crosstime"] += 1
else:
self.cust_info[metadata["pair"]["crosstime"] = 1
```
You can use `freqtrade list-strategies` to see a list of all strategies Freqtrade is able to load (all strategies in the correct folder).
It will also include a "status" field, highlighting potential problems.
!!! Warning
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
??? Hint "Customize strategy directory"
You can use a different directory by using `--strategy-path user_data/otherPath`. This parameteris available to all commands that require a strategy.
!!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
***
## Additional data (informative_pairs)
## Informative Pairs
### Get data for non-tradeable pairs
@@ -355,8 +443,150 @@ A full sample can be found [in the DataProvider section](#complete-data-provider
It is however better to use resampling to longer timeframes whenever possible
to avoid hammering the exchange with too many requests and risk being blocked.
??? Note "Alternative candle types"
Informative_pairs can also provide a 3rd tuple element defining the candle type explicitly.
Availability of alternative candle-types will depend on the trading-mode and the exchange. Details about this can be found in the exchange documentation.
``` python
def informative_pairs(self):
return [
("ETH/USDT", "5m", ""), # Uses default candletype, depends on trading_mode
("ETH/USDT", "5m", "spot"), # Forces usage of spot candles
In most common case it is possible to easily define informative pairs by using a decorator. All decorated `populate_indicators_*` methods run in isolation,
not having access to data from other informative pairs, in the end all informative dataframes are merged and passed to main `populate_indicators()` method.
When hyperopting, use of hyperoptable parameter `.value` attribute is not supported. Please use `.range` attribute. See [optimizing an indicator parameter](hyperopt.md#optimizing-an-indicator-parameter)
:param timeframe: Informative timeframe. Must always be equal or higher than strategy timeframe.
:param asset: Informative asset, for example BTC, BTC/USDT, ETH/BTC. Do not specify to use
current pair.
:param fmt: Column format (str) or column formatter (callable(name, asset, timeframe)). When not
specified, defaults to:
* {base}_{quote}_{column}_{timeframe} if asset is specified.
* {column}_{timeframe} if asset is not specified.
Format string supports these format variables:
* {asset} - full name of the asset, for example 'BTC/USDT'.
* {base} - base currency in lower case, for example 'eth'.
* {BASE} - same as {base}, except in upper case.
* {quote} - quote currency in lower case, for example 'usdt'.
* {QUOTE} - same as {quote}, except in upper case.
* {column} - name of dataframe column.
* {timeframe} - timeframe of informative dataframe.
:param ffill: ffill dataframe after merging informative pair.
:param candle_type: '', mark, index, premiumIndex, or funding_rate
"""
```
??? Example "Fast and easy way to define informative pairs"
Most of the time we do not need power and flexibility offered by `merge_informative_pair()`, therefore we can use a decorator to quickly define informative pairs.
``` python
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy, informative
class AwesomeStrategy(IStrategy):
# This method is not required.
# def informative_pairs(self): ...
# Define informative upper timeframe for each pair. Decorators can be stacked on same
# method. Available in populate_indicators as 'rsi_30m' and 'rsi_1h'.
Do not use `@informative` decorator if you need to use data of one informative pair when generating another informative pair. Instead, define informative pairs
manually as described [in the DataProvider section](#complete-data-provider-sample).
!!! Note
Use string formatting when accessing informative dataframes of other pairs. This will allow easily changing stake currency in config without having to adjust strategy code.
Alternatively column renaming may be used to remove stake currency from column names: `@informative('1h', 'BTC/{stake}', fmt='{base}_{column}_{timeframe}')`.
!!! Warning "Duplicate method names"
Methods tagged with `@informative()` decorator must always have unique names! Re-using same name (for example when copy-pasting already defined informative method)
will overwrite previously defined method and not produce any errors due to limitations of Python programming language. In such cases you will find that indicators
created in earlier-defined methods are not available in the dataframe. Carefully review method names and make sure they are unique!
## Additional data (DataProvider)
The strategy provides access to the `DataProvider`. This allows you to get additional data to use in your strategy.
@@ -394,15 +624,15 @@ if self.dp:
### *current_whitelist()*
Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 volume pairs by volume.
Imagine you've developed a strategy that trades the `5m` timeframe using signals generated from a `1d` timeframe on the top 10 volume pairs by volume.
The strategy might look something like this:
*Scan through the top 10 pairs by volume using the `VolumePairList` every 5 minutes and use a 14 day RSI to buy and sell.*
Due to the limited available data, it's very difficult to resample our `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
Due to the limited available data, it's very difficult to resample `5m` candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!
Since we can't resample our data we will have to use an informative pair; and since our whitelist will be dynamic we don't know which pair(s) to use.
Since we can't resample the data we will have to use an informative pair; and since the whitelist will be dynamic we don't know which pair(s) to use.
This is where calling `self.dp.current_whitelist()` comes in handy.
@@ -413,7 +643,7 @@ This is where calling `self.dp.current_whitelist()` comes in handy.
pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, '1d') for pair in pairs]
return informative_pairs
return informative_pairs
```
### *get_pair_dataframe(pair, timeframe)*
@@ -439,8 +669,9 @@ It can also be used in specific callbacks to get the signal that caused the acti
The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used.
The orderbook structure is aligned with the order structure from [ccxt](https://github.com/ccxt/ccxt/wiki/Manual#order-book-structure), so the result will look as follows:
``` js
{
'bids': [
[ price, amount ], // [ float, float ]
[ price, amount ],
...
],
'asks': [
[ price, amount ],
[ price, amount ],
//...
],
//...
}
```
Therefore, using `ob['bids'][0][0]` as demonstrated above will result in using the best bid price. `ob['bids'][0][1]` would look at the amount at this orderbook position.
!!! Warning "Warning about backtesting"
The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used, as the method will return uptodate values.
### *ticker(pair)*
@@ -531,7 +782,7 @@ class SampleStrategy(IStrategy):
(dataframe['volume'] > 0) # Ensure this candle had volume (important for backtesting)
),
'buy'] = 1
['enter_long', 'enter_tag']] = (1, 'rsi_cross')
```
@@ -573,7 +824,7 @@ All columns of the informative dataframe will be available on the returning data
``` python
'date', 'open', 'high', 'low', 'close', 'rsi' # from the original dataframe
'date_1h', 'open_1h', 'high_1h', 'low_1h', 'close_1h', 'rsi_1h' # from the informative dataframe
'date_1h', 'open_1h', 'high_1h', 'low_1h', 'close_1h', 'rsi_1h' # from the informative dataframe
```
??? Example "Custom implementation"
@@ -608,6 +859,79 @@ All columns of the informative dataframe will be available on the returning data
***
### *stoploss_from_open()*
Stoploss values returned from `custom_stoploss` must specify a percentage relative to `current_rate`, but sometimes you may want to specify a stoploss relative to the open price instead. `stoploss_from_open()` is a helper function to calculate a stoploss value that can be returned from `custom_stoploss` which will be equivalent to the desired percentage above the open price.
??? Example "Returning a stoploss relative to the open price from the custom stoploss function"
Say the open price was $100, and `current_price` is $121 (`current_profit` will be `0.21`).
If we want a stop price at 7% above the open price we can call `stoploss_from_open(0.07, current_profit, False)` which will return `0.1157024793`. 11.57% below $121 is $107, which is the same as 7% above $100.
``` python
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy, stoploss_from_open
Full examples can be found in the [Custom stoploss](strategy-advanced.md#custom-stoploss) section of the Documentation.
!!! Note
Providing invalid input to `stoploss_from_open()` may produce "CustomStoploss function did not return valid stoploss" warnings.
This may happen if `current_profit` parameter is below specified `open_relative_stop`. Such situations may arise when closing trade
is blocked by `confirm_trade_exit()` method. Warnings can be solved by never blocking stop loss sells by checking `exit_reason` in
`confirm_trade_exit()`, or by using `return stoploss_from_open(...) or 1` idiom, which will request to not change stop loss when
`current_profit <open_relative_stop`.
### *stoploss_from_absolute()*
In some situations it may be confusing to deal with stops relative to current rate. Instead, you may define a stoploss level using an absolute price.
??? Example "Returning a stoploss using absolute price from the custom stoploss function"
If we want to trail a stop price at 2xATR below current price we can call `stoploss_from_absolute(current_rate-(candle['atr']*2),current_rate,is_short=trade.is_short)`.
``` python
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy, stoploss_from_absolute
curdayprofit = sum(trade.close_profit for trade in trades)
@@ -672,7 +996,7 @@ if self.config['runmode'].value in ('live', 'dry_run'):
Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of 0.015).
``` json
{'pair': "ETH/BTC", 'profit': 0.015, 'count': 5}
{"pair": "ETH/BTC", "profit": 0.015, "count": 5}
```
!!! Warning
@@ -688,18 +1012,19 @@ Locked pairs will show the message `Pair <pair> is currently locked.`.
Sometimes it may be desired to lock a pair after certain events happen (e.g. multiple losing trades in a row).
Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until)`.
`until` must be a datetime object in the future, after which trading will be reenabled for that pair.
Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until, [reason])`.
`until` must be a datetime object in the future, after which trading will be re-enabled for that pair, while `reason` is an optional string detailing why the pair was locked.
Locks can also be lifted manually, by calling `self.unlock_pair(pair)`.
Locks can also be lifted manually, by calling `self.unlock_pair(pair)` or `self.unlock_reason(<reason>)` - providing reason the pair was locked with.
`self.unlock_reason(<reason>)` will unlock all pairs currently locked with the provided reason.
To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
!!! Note
Locked pairs are not persisted, so a restart of the bot, or calling `/reload_config` will reset locked pairs.
Locked pairs will always be rounded up to the next candle. So assuming a `5m` timeframe, a lock with `until` set to 10:18 will lock the pair until the candle from 10:15-10:20 will be finished.
!!! Warning
Locking pairs is not functioning during backtesting.
Manually locking pairs is not available during backtesting, only locks via Protections are allowed.
#### Pair locking example
@@ -714,7 +1039,7 @@ if self.config['runmode'].value in ('live', 'dry_run'):
@@ -749,6 +1074,8 @@ Printing more than a few rows is also possible (simply use `print(dataframe)` i
## Common mistakes when developing strategies
### Peeking into the future while backtesting
Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future.
This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods, since they all use data which is not available during dry/live runs, so these strategies will perform well during backtesting, but will fail / perform badly in real conditions.
@@ -759,14 +1086,21 @@ The following lists some common patterns which should be avoided to prevent frus
- don't use `dataframe['volume'].mean()`. This uses the full DataFrame for backtesting, including data from the future. Use `dataframe['volume'].rolling(<window>).mean()` instead
- don't use `.resample('1h')`. This uses the left border of the interval, so moves data from an hour to the start of the hour. Use `.resample('1h', label='right')` instead.
### Colliding signals
When conflicting signals collide (e.g. both `'enter_long'` and `'exit_long'` are 1), freqtrade will do nothing and ignore the entry signal. This will avoid trades that enter, and exit immediately. Obviously, this can potentially lead to missed entries.
The following rules apply, and entry signals will be ignored if more than one of the 3 signals is set:
- `enter_long` -> `exit_long`, `enter_short`
- `enter_short` -> `exit_short`, `enter_long`
## Further strategy ideas
To get additional Ideas for strategies, head over to our [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
To get additional Ideas for strategies, head over to the [strategy repository](https://github.com/freqtrade/freqtrade-strategies). Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk.
Feel free to use any of them as inspiration for your own strategies.
We're happy to accept Pull Requests containing new Strategies to that repo.
We also got a *strategy-sharing* channel in our [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) which is a great place to get and/or share ideas.
## Next step
Now you have a perfect strategy you probably want to backtest it.
To support new markets and trade-types (namely short trades / trades with leverage), some things had to change in the interface.
If you intend on using markets other than spot markets, please migrate your strategy to the new format.
We have put a great effort into keeping compatibility with existing strategies, so if you just want to continue using freqtrade in __spot markets__, there should be no changes necessary for now.
You can use the quick summary as checklist. Please refer to the detailed sections below for full migration details.
## Quick summary / migration checklist
Note : `forcesell`, `forcebuy`, `emergencysell` are changed to `force_exit`, `force_enter`, `emergency_exit` respectively.
* New `side` argument to callbacks without trade object
* [`custom_stake_amount`](#custom-stake-amount)
* [`confirm_trade_entry`](#confirm_trade_entry)
* [`custom_entry_price`](#custom_entry_price)
* [Changed argument name in `confirm_trade_exit`](#confirm_trade_exit)
* Dataframe columns:
* [`buy` -> `enter_long`](#populate_buy_trend)
* [`sell` -> `exit_long`](#populate_sell_trend)
* [`buy_tag` -> `enter_tag` (used for both long and short trades)](#populate_buy_trend)
* [New column `enter_short` and corresponding new column `exit_short`](#populate_sell_trend)
* trade-object now has the following new properties:
*`is_short`
*`entry_side`
*`exit_side`
*`trade_direction`
* renamed: `sell_reason` -> `exit_reason`
* [Renamed `trade.nr_of_successful_buys` to `trade.nr_of_successful_entries` (mostly relevant for `adjust_trade_position()`)](#adjust-trade-position-changes)
* Introduced new [`leverage` callback](strategy-callbacks.md#leverage-callback).
* Informative pairs can now pass a 3rd element in the Tuple, defining the candle type.
*`@informative` decorator now takes an optional `candle_type` argument.
* [helper methods](#helper-methods) `stoploss_from_open` and `stoploss_from_absolute` now take `is_short` as additional argument.
* Sell reasons changed to reflect the new naming of "exit" instead of sells. Be careful in your strategy if you're using `exit_reason` checks and eventually update your strategy.
*`sell_signal` -> `exit_signal`
*`custom_sell` -> `custom_exit`
*`force_sell` -> `force_exit`
*`emergency_sell` -> `emergency_exit`
* Webhook terminology changed from "sell" to "exit", and from "buy" to entry
In `populate_buy_trend()` - you will want to change the columns you assign from `'buy`' to `'enter_long'`, as well as the method name from `populate_buy_trend` to `populate_entry_trend`.
While adjust-trade-position itself did not change, you should no longer use `trade.nr_of_successful_buys` - and instead use `trade.nr_of_successful_entries`, which will also include short entries.
### Helper methods
Added argument "is_short" to `stoploss_from_open` and `stoploss_from_absolute`.
This should be given the value of `trade.is_short`.
@@ -35,12 +35,32 @@ Copy the API Token (`22222222:APITOKEN` in the above example) and keep use it fo
Don't forget to start the conversation with your bot, by clicking `/START` button
### 2. Get your userid
### 2. Telegram user_id
#### Get your user id
Talk to the [userinfobot](https://telegram.me/userinfobot)
Get your "Id", you will use it for the config parameter `chat_id`.
#### Use Group id
You can use bots in telegram groups by just adding them to the group. You can find the group id by first adding a [RawDataBot](https://telegram.me/rawdatabot) to your group. The Group id is shown as id in the `"chat"` section, which the RawDataBot will send to you:
``` json
"chat":{
"id":-1001332619709
}
```
For the Freqtrade configuration, you can then use the the full value (including `-` if it's there) as string:
```json
"chat_id": "-1001332619709"
```
!!! Warning "Using telegram groups"
When using telegram groups, you're giving every member of the telegram group access to your freqtrade bot and to all commands possible via telegram. Please make sure that you can trust everyone in the telegram group to avoid unpleasent surprises.
## Control telegram noise
Freqtrade provides means to control the verbosity of your telegram bot.
@@ -52,23 +72,82 @@ Each setting has the following possible values:
Example configuration showing the different settings:
``` json
"telegram": {
"enabled": true,
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id",
"notification_settings": {
"status": "silent",
"warning": "on",
"startup": "off",
"entry": "silent",
"exit": {
"roi": "silent",
"emergency_exit": "on",
"force_exit": "on",
"exit_signal": "silent",
"trailing_stop_loss": "on",
"stop_loss": "on",
"stoploss_on_exchange": "on",
"custom_exit": "silent"
},
"entry_cancel": "silent",
"exit_cancel": "on",
"entry_fill": "off",
"exit_fill": "off",
"protection_trigger": "off",
"protection_trigger_global": "on"
},
"reload": true,
"balance_dust_level": 0.01
},
```
`entry` notifications are sent when the order is placed, while `entry_fill` notifications are sent when the order is filled on the exchange.
`exit` notifications are sent when the order is placed, while `exit_fill` notifications are sent when the order is filled on the exchange.
`*_fill` notifications are off by default and must be explicitly enabled.
`protection_trigger` notifications are sent when a protection triggers and `protection_trigger_global` notifications trigger when global protections are triggered.
`balance_dust_level` will define what the `/balance` command takes as "dust" - Currencies with a balance below this will be shown.
`reload` allows you to disable reload-buttons on selected messages.
## Create a custom keyboard (command shortcut buttons)
Telegram allows us to create a custom keyboard with buttons for commands.
Per default, the Telegram bot shows predefined commands. Some commands
@@ -84,17 +163,27 @@ official commands. You can ask at any moment for help with `/help`.
| `/show_config` | Shows part of the current configuration with relevant settings to operation
| `/logs [limit]` | Show last log messages.
| `/status` | Lists all open trades
| `/status <trade_id>` | Lists one or more specific trade. Separate multiple <trade_id> with a blank space.
| `/status table` | List all open trades in a table format. Pending buy orders are marked with an asterisk (*) Pending sell orders are marked with a double asterisk (**)
| `/trades [limit]` | List all recently closed trades in a table format.
| `/delete <trade_id>` | Delete a specific trade from the Database. Tries to close open orders. Requires manual handling of this trade on the exchange.
| `/count` | Displays number of trades used and available
| `/profit` | Display a summary of your profit/loss from close trades and some stats about your performance
| `/forcesell <trade_id>` | Instantly sells the given trade (Ignoring `minimum_roi`).
| `/forcesell all` | Instantly sells all open trades (Ignoring `minimum_roi`).
| `/forcebuy<pair> [rate]` | Instantly buys the given pair. Rate is optional. (`forcebuy_enable` must be set to True)
| `/locks` | Show currently locked pairs.
| `/unlock <pairorlock_id>` | Remove the lock for this pair (or for this lock id).
| `/profit [<n>]` | Display a summary of your profit/loss from close trades and some stats about your performance, over the last n days (all trades by default)
| `/forceexit<trade_id> | /fx <tradeid>` | Instantly exits the given trade (Ignoring `minimum_roi`).
| `/forceexit all | /fx all` | Instantly exits all open trades (Ignoring `minimum_roi`).
| `/fx` | alias for `/forceexit`
| `/forcelong <pair> [rate]` | Instantly buys the given pair. Rate is optional and only applies to limit orders. (`force_entry_enable` must be set to True)
| `/forceshort <pair> [rate]` | Instantly shorts the given pair. Rate is optional and only applies to limit orders. This will only work on non-spot markets. (`force_entry_enable` must be set to True)
| `/performance` | Show performance of each finished trade grouped by pair
| `/balance` | Show account balance per currency
| `/daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
| `/weekly <n>` | Shows profit or loss per week, over the last n weeks (n defaults to 8)
| `/monthly <n>` | Shows profit or loss per month, over the last n months (n defaults to 6)
| `/stats` | Shows Wins / losses by Exit reason as well as Avg. holding durations for buys and sells
| `/exits` | Shows Wins / losses by Exit reason as well as Avg. holding durations for buys and sells
| `/entries` | Shows Wins / losses by Exit reason as well as Avg. holding durations for buys and sells
| `/whitelist` | Show the current whitelist
| `/blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
| `/edge` | Show validated pairs by Edge if it is enabled.
@@ -131,11 +220,14 @@ Once all positions are sold, run `/stop` to completely stop the bot.
### /status
For each open trade, the bot will send you the following message.
Enter Tag is configurable via Strategy.
> **Trade ID:** `123` `(since 1 days ago)`
> **Current Pair:** CVC/BTC
> **Open Since:** `1 days ago`
> **Direction:** Long
> **Leverage:** 1.0
> **Amount:** `26.64180098`
> **Enter Tag:** Awesome Long Signal
> **Open Rate:** `0.00007489`
> **Current Rate:** `0.00007489`
> **Current Profit:** `12.95%`
@@ -146,10 +238,10 @@ For each open trade, the bot will send you the following message.
Return the status of all open trades in a table format.
```
ID Pair Since Profit
---- -------- ------- --------
67 SC/BTC 1 d 13.33%
123 CVC/BTC 1 h 12.95%
ID L/S Pair Since Profit
---- -------- ------- --------
67 L SC/BTC 1 d 13.33%
123 S CVC/BTC 1 h 12.95%
```
### /count
@@ -167,10 +259,10 @@ current max
Return a summary of your profit/loss and performance.
> **ROI:** Close trades
> ∙ `0.00485701 BTC (258.45%)`
> ∙ `0.00485701 BTC (2.2%) (15.2 Σ%)`
> ∙ `62.968 USD`
> **ROI:** All trades
> ∙ `0.00255280 BTC (143.43%)`
> ∙ `0.00255280 BTC (1.5%) (6.43 Σ%)`
> ∙ `33.095 EUR`
>
> **Total Trade Count:** `138`
@@ -178,28 +270,48 @@ Return a summary of your profit/loss and performance.
> **Latest Trade opened:** `2 minutes ago`
> **Avg. Duration:** `2:33:45`
> **Best Performing:** `PAY/BTC: 50.23%`
> **Trading volume:** `0.5 BTC`
> **Profit factor:** `1.04`
> **Max Drawdown:** `9.23% (0.01255 BTC)`
### /forcesell <trade_id>
The relative profit of `1.2%` is the average profit per trade.
The relative profit of `15.2 Σ%` is be based on the starting capital - so in this case, the starting capital was `0.00485701 * 1.152 = 0.00738 BTC`.
Starting capital is either taken from the `available_capital` setting, or calculated by using current wallet size - profits.
Profit Factor is calculated as gross profits / gross losses - and should serve as an overall metric for the strategy.
Max drawdown corresponds to the backtesting metric `Absolute Drawdown (Account)` - calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.
> **BITTREX:** Selling BTC/LTC with limit `0.01650000 (profit: ~-4.07%, -0.00008168)`
### /forceexit <trade_id>
### /forcebuy <pair>
> **BINANCE:** Exiting BTC/LTC with limit `0.01650000 (profit: ~-4.07%, -0.00008168)`
To update your freqtrade installation, please use one of the below methods, corresponding to your installation method.
!!! Note "Tracking changes"
Breaking changes / changed behavior will be documented in the changelog that is posted alongside every release.
For the develop branch, please follow PR's to avoid being surprised by changes.
## docker-compose
!!! Note "Legacy installations using the `master` image"
We're switching from master to stable for the release Images - please adjust your docker-file and replace `freqtradeorg/freqtrade:master` with `freqtradeorg/freqtrade:stable`
``` bash
docker-compose pull
docker-compose up -d
```
## Installation via setup script
``` bash
./setup.sh --update
```
!!! Note
Make sure to run this command with your virtual environment disabled!
## Plain native installation
Please ensure that you're also updating dependencies - otherwise things might break without you noticing.
Use the `list-strategies` subcommand to see all strategies in one particular directory and the `list-hyperopts` subcommand to list custom Hyperopts.
These subcommands are useful for finding problems in your environment with loading strategies or hyperopt classes: modules with strategies or hyperopt classes that contain errors and failed to load are printed in red (LOAD FAILED), while strategies or hyperopt classes with duplicate names are printed in yellow (DUPLICATE NAME).
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).
Multiple --config options may be used. Can be set to
`-` to read config from stdin.
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
@@ -211,18 +151,16 @@ Common arguments:
!!! 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.
Example: Search default strategies and hyperopts directories (within the default userdir).
Example: Search default strategies directories (within the default userdir).
``` bash
freqtrade list-strategies
freqtrade list-hyperopts
```
Example: Search strategies and hyperopts directory within the userdir.
Example: Search strategies directory within the userdir.
Values with "missing opt:" might need special configuration (e.g. using orderbook if `fetchTickers` is missing) - but should in theory work (although we cannot guarantee they will).
* Example: see all exchanges supported by the ccxt library (including 'bad' ones, i.e. those that are known to not work with Freqtrade):
`freqtrade convert-db` can be used to convert your database from one system to another (sqlite -> postgres, postgres -> other postgres), migrating all trades, orders and Pairlocks.
Please refer to the [SQL cheatsheet](sql_cheatsheet.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.
```
## Show previous Backtest results
Allows you to show previous backtest results.
Adding `--show-pair-list` outputs a sorted pair list you can easily copy/paste into your configuration (omitting bad pairs).
??? 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.
Show backtesting breakdown per [day, week, month].
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
!!! Note
`hyperopt-show` will automatically use the latest available hyperopt results file.
You can override this using the `--hyperopt-filename` argument, and specify another, available filename (without path!).
### Examples
Print details for the epoch 168 (the number of the epoch is shown by the `hyperopt-list` subcommand or by Hyperopt itself during hyperoptimization run):
@@ -38,74 +48,177 @@ Sample configuration (tested using IFTTT).
},
```
The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert our event and key to the url.
The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert your event and key to the url.
You can set the POST body format to Form-Encoded (default), JSON-Encoded, or raw data. Use `"format": "form"`, `"format": "json"`, or `"format": "raw"` respectively. Example configuration for Mattermost Cloud integration:
The result would be a POST request with e.g. `{"text":"Status: running"}` body and `Content-Type: application/json` header which results `Status: running` message in the Mattermost channel.
When using the Form-Encoded or JSON-Encoded configuration you can configure any number of payload values, and both the key and value will be ouput in the POST request. However, when using the raw data format you can only configure one value and it **must** be named `"data"`. In this instance the data key will not be output in the POST request, only the value. For example:
```json
"webhook":{
"enabled":true,
"url":"https://<YOURHOOKURL>",
"format":"raw",
"webhookstatus":{
"data":"Status: {status}"
}
},
```
The result would be a POST request with e.g. `Status: running` body and `Content-Type: text/plain` header.
Optional parameters are available to enable automatic retries for webhook messages. The `webhook.retries` parameter can be set for the maximum number of retries the webhook request should attempt if it is unsuccessful (i.e. HTTP response status is not 200). By default this is set to `0` which is disabled. An additional `webhook.retry_delay` parameter can be set to specify the time in seconds between retry attempts. By default this is set to `0.1` (i.e. 100ms). Note that increasing the number of retries or retry delay may slow down the trader if there are connectivity issues with the webhook. Example configuration for retries:
```json
"webhook":{
"enabled":true,
"url":"https://<YOURHOOKURL>",
"retries":3,
"retry_delay":0.2,
"webhookstatus":{
"status":"Status: {status}"
}
},
```
Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.
### Webhookbuy
### Webhookentry
The fields in `webhook.webhookbuy` are filled when the bot executes a buy. Parameters are filled using string.format.
The fields in `webhook.webhookentry` are filled when the bot executes a long/short. Parameters are filled using string.format.
Possible parameters are:
*`trade_id`
*`exchange`
*`pair`
*`direction`
*`leverage`
* ~~`limit` # Deprecated - should no longer be used.~~
*`open_rate`
*`amount`
*`open_date`
*`stake_amount`
*`stake_currency`
*`base_currency`
*`fiat_currency`
*`order_type`
*`current_rate`
*`enter_tag`
### Webhookentrycancel
The fields in `webhook.webhookentrycancel` are filled when the bot cancels a long/short order. Parameters are filled using string.format.
Possible parameters are:
*`trade_id`
*`exchange`
*`pair`
*`direction`
*`leverage`
*`limit`
*`amount`
*`open_date`
*`stake_amount`
*`stake_currency`
*`base_currency`
*`fiat_currency`
*`order_type`
*`current_rate`
*`enter_tag`
### Webhookbuycancel
### Webhookentryfill
The fields in `webhook.webhookbuycancel` are filled when the bot cancels a buy order. Parameters are filled using string.format.
The fields in `webhook.webhookentryfill` are filled when the bot filled a long/short order. Parameters are filled using string.format.
Possible parameters are:
*`trade_id`
*`exchange`
*`pair`
*`limit`
*`direction`
*`leverage`
*`open_rate`
*`amount`
*`open_date`
*`stake_amount`
*`stake_currency`
*`base_currency`
*`fiat_currency`
*`order_type`
*`current_rate`
*`enter_tag`
### Webhooksell
### Webhookexit
The fields in `webhook.webhooksell` are filled when the bot sells a trade. Parameters are filled using string.format.
The fields in `webhook.webhookexit` are filled when the bot exits a trade. Parameters are filled using string.format.
Possible parameters are:
*`trade_id`
*`exchange`
*`pair`
*`direction`
*`leverage`
*`gain`
*`limit`
*`amount`
*`open_rate`
*`current_rate`
*`profit_amount`
*`profit_ratio`
*`stake_currency`
*`base_currency`
*`fiat_currency`
*`sell_reason`
*`exit_reason`
*`order_type`
*`open_date`
*`close_date`
### Webhooksellcancel
### Webhookexitfill
The fields in `webhook.webhooksellcancel` are filled when the bot cancels a sell order. Parameters are filled using string.format.
The fields in `webhook.webhookexitfill` are filled when the bot fills a exit order (closes a Trade). Parameters are filled using string.format.
Possible parameters are:
*`trade_id`
*`exchange`
*`pair`
*`direction`
*`leverage`
*`gain`
*`close_rate`
*`amount`
*`open_rate`
*`current_rate`
*`profit_amount`
*`profit_ratio`
*`stake_currency`
*`base_currency`
*`fiat_currency`
*`exit_reason`
*`order_type`
*`open_date`
*`close_date`
### Webhookexitcancel
The fields in `webhook.webhookexitcancel` are filled when the bot cancels a exit order. Parameters are filled using string.format.
Possible parameters are:
*`trade_id`
*`exchange`
*`pair`
*`direction`
*`leverage`
*`gain`
*`limit`
*`amount`
@@ -114,8 +227,9 @@ Possible parameters are:
*`profit_amount`
*`profit_ratio`
*`stake_currency`
*`base_currency`
*`fiat_currency`
*`sell_reason`
*`exit_reason`
*`order_type`
*`open_date`
*`close_date`
@@ -125,3 +239,52 @@ Possible parameters are:
The fields in `webhook.webhookstatus` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
The only possible value here is `{status}`.
## Discord
A special form of webhooks is available for discord.
The above represents the default (`exit_fill` and `entry_fill` are optional and will default to the above configuration) - modifications are obviously possible.
Available fields correspond to the fields for webhooks and are documented in the corresponding webhook sections.
The notifications will look as follows by default.
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