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Merge branch 'develop' into feat/short

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Matthias
2022-01-29 14:19:30 +01:00
parent fdea4fcb1b e7409e74c2
commit 463714832d
41 changed files with 347 additions and 135 deletions
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25
docs/advanced-hyperopt.md
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@@ -105,7 +105,7 @@ You can define your own estimator for Hyperopt by implementing `generate_estimat
```python
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator():
def generate_estimator(dimensions: List['Dimension'], **kwargs):
return "RF"
```
@@ -119,13 +119,34 @@ Example for `ExtraTreesRegressor` ("ET") with additional parameters:
```python
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator():
def generate_estimator(dimensions: List['Dimension'], **kwargs):
from skopt.learning import ExtraTreesRegressor
# 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:
```python
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator(dimensions: List['Dimension'], **kwargs):
from skopt.utils import cook_estimator
from skopt.learning.gaussian_process.kernels import (Matern, ConstantKernel)
kernel_bounds = (0.0001, 10000)
kernel = (
ConstantKernel(1.0, kernel_bounds) *
Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=2.5)
)
kernel += (
ConstantKernel(1.0, kernel_bounds) *
Matern(length_scale=np.ones(len(dimensions)), length_scale_bounds=[kernel_bounds for d in dimensions], nu=1.5)
)
return cook_estimator("GP", space=dimensions, kernel=kernel, n_restarts_optimizer=2)
```
!!! Note
While custom estimators can be provided, it's up to you as User to do research on possible parameters and analyze / understand which ones should be used.
If you're unsure about this, best use one of the Defaults (`"ET"` has proven to be the most versatile) without further parameters.

2
docs/configuration.md
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@@ -175,6 +175,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `dataformat_ohlcv` | Data format to use to store historical candle (OHLCV) data. <br> *Defaults to `json`*. <br> **Datatype:** String
| `dataformat_trades` | Data format to use to store historical trades data. <br> *Defaults to `jsongz`*. <br> **Datatype:** String
| `position_adjustment_enable` | Enables the strategy to use position adjustments (additional buys or sells). [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*<br> **Datatype:** Boolean
| `max_entry_position_adjustment` | Maximum additional order(s) for each open trade on top of the first entry Order. Set it to `-1` for unlimited additional orders. [More information here](strategy-callbacks.md#adjust-trade-position). <br> [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `-1`.*<br> **Datatype:** Positive Integer or -1
### Parameters in the strategy
@@ -200,6 +201,7 @@ Values set in the configuration file always overwrite values set in the strategy
* `ignore_roi_if_buy_signal`
* `ignore_buying_expired_candle_after`
* `position_adjustment_enable`
* `max_entry_position_adjustment`
### Configuring amount per trade

6
docs/docker_quickstart.md
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@@ -126,6 +126,12 @@ All freqtrade arguments will be available by running `docker-compose run --rm fr
!!! Note "`docker-compose run --rm`"
Including `--rm` will remove the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
??? Note "Using docker without docker-compose"
"`docker-compose run --rm`" will require a compose file to be provided.
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
Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.

4
docs/index.md
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@@ -11,7 +11,7 @@
## Introduction
Freqtrade is a crypto-currency algorithmic trading software developed in python (3.7+) and supported on Windows, macOS and Linux.
Freqtrade is a crypto-currency algorithmic trading software developed in python (3.8+) and supported on Windows, macOS and Linux.
!!! 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.
@@ -67,7 +67,7 @@ To run this bot we recommend you a linux cloud instance with a minimum of:
Alternatively
- Python 3.7+
- Python 3.8+
- pip (pip3)
- git
- TA-Lib

2
docs/installation.md
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@@ -42,7 +42,7 @@ These requirements apply to both [Script Installation](#script-installation) and
### Install guide
* [Python >= 3.7.x](http://docs.python-guide.org/en/latest/starting/installation/)
* [Python >= 3.8.x](http://docs.python-guide.org/en/latest/starting/installation/)
* [pip](https://pip.pypa.io/en/stable/installing/)
* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
* [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)

2
docs/requirements-docs.txt
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@@ -1,4 +1,4 @@
mkdocs==1.2.3
mkdocs-material==8.1.7
mkdocs-material==8.1.8
mdx_truly_sane_lists==1.2
pymdown-extensions==9.1

54
docs/strategy-callbacks.md
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@@ -363,8 +363,8 @@ class AwesomeStrategy(IStrategy):
# ... populate_* methods
def custom_entry_price(self, pair: str, current_time: datetime,
proposed_rate, **kwargs) -> float:
def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
entry_tag: Optional[str], **kwargs) -> float:
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
timeframe=self.timeframe)
@@ -414,7 +414,7 @@ It applies a tight timeout for higher priced assets, while allowing more time to
The function must return either `True` (cancel order) or `False` (keep order alive).
``` python
from datetime import datetime, timedelta, timezone
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
@@ -427,22 +427,24 @@ class AwesomeStrategy(IStrategy):
'sell': 60 * 25
}
def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict,
current_time: datetime, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
elif trade.open_rate > 10 and trade.open_date_utc < current_time - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
elif trade.open_rate < 1 and trade.open_date_utc < current_time - timedelta(hours=24):
return True
return False
def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
def check_sell_timeout(self, pair: str, trade: Trade, order: dict,
current_time: datetime, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date_utc < current_time - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
elif trade.open_rate > 10 and trade.open_date_utc < current_time - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(hours=24):
elif trade.open_rate < 1 and trade.open_date_utc < current_time - timedelta(hours=24):
return True
return False
```
@@ -501,7 +503,7 @@ class AwesomeStrategy(IStrategy):
# ... populate_* methods
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, current_time: datetime,
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
side: str, **kwargs) -> bool:
"""
Called right before placing a entry order.
@@ -579,11 +581,13 @@ The `position_adjustment_enable` strategy property enables the usage of `adjust_
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.
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.
!!! Note "About stake size"
@@ -614,14 +618,14 @@ class DigDeeperStrategy(IStrategy):
# ... populate_* methods
# Example specific variables
max_dca_orders = 3
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)
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
**kwargs) -> float:
entry_tag: Optional[str], **kwargs) -> float:
# We need to leave most of the funds for possible further DCA orders
# This also applies to fixed stakes
@@ -656,8 +660,7 @@ class DigDeeperStrategy(IStrategy):
return None
filled_buys = trade.select_filled_orders('buy')
count_of_buys = len(filled_buys)
count_of_buys = trade.nr_of_successful_buys
# Allow up to 3 additional increasingly larger buys (4 in total)
# Initial buy is 1x
# If that falls to -5% profit, we buy 1.25x more, average profit should increase to roughly -2.2%
@@ -666,15 +669,14 @@ class DigDeeperStrategy(IStrategy):
# Total stake for this trade would be 1 + 1.25 + 1.5 + 1.75 = 5.5x of the initial allowed stake.
# That is why max_dca_multiplier is 5.5
# Hope you have a deep wallet!
if 0 < count_of_buys <= self.max_dca_orders:
try:
# This returns first order stake size
stake_amount = filled_buys[0].cost
# This then calculates current safety order size
stake_amount = stake_amount * (1 + (count_of_buys * 0.25))
return stake_amount
except Exception as exception:
return None
try:
# This returns first order stake size
stake_amount = filled_buys[0].cost
# This then calculates current safety order size
stake_amount = stake_amount * (1 + (count_of_buys * 0.25))
return stake_amount
except Exception as exception:
return None
return None

2
docs/windows_installation.md
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@@ -25,7 +25,7 @@ Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7
As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.24-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
Freqtrade provides these dependencies for the latest 3 Python versions (3.7, 3.8, 3.9 and 3.10) and for 64bit Windows.
Freqtrade provides these dependencies for the latest 3 Python versions (3.8, 3.9 and 3.10) and for 64bit Windows.
Other versions must be downloaded from the above link.
``` powershell