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Merge branch 'develop' into pr/squat0001/5533

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Matthias 2021-10-02 14:45:16 +02:00
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6
.github/workflows/ci.yml vendored
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@ -87,7 +87,7 @@ jobs:
run: |
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
run: |
@ -180,7 +180,7 @@ jobs:
run: |
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
run: |
@ -247,7 +247,7 @@ jobs:
run: |
cp config_examples/config_bittrex.example.json config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
run: |

2
.travis.yml
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@ -33,7 +33,7 @@ jobs:
- script:
- cp config_examples/config_bittrex.example.json config.json
- freqtrade create-userdir --userdir user_data
- freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily
- freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt-loss SharpeHyperOptLossDaily
name: hyperopt
- script: flake8
name: flake8

2
Dockerfile
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@ -13,7 +13,7 @@ RUN mkdir /freqtrade \
&& apt-get update \
&& apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-serial-dev \
&& apt-get clean \
&& useradd -u 1000 -G sudo -U -m ftuser \
&& useradd -u 1000 -G sudo -U -m -s /bin/bash ftuser \
&& chown ftuser:ftuser /freqtrade \
# Allow sudoers
&& echo "ftuser ALL=(ALL) NOPASSWD: /bin/chown" >> /etc/sudoers

9
README.md
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@ -30,6 +30,7 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
- [X] [Bittrex](https://bittrex.com/)
- [X] [Kraken](https://kraken.com/)
- [X] [FTX](https://ftx.com)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
### Community tested
@ -78,22 +79,22 @@ For any other type of installation please refer to [Installation doc](https://ww
```
usage: freqtrade [-h] [-V]
{trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
{trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
...
Free, open source crypto trading bot
positional arguments:
{trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
{trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
trade Trade module.
create-userdir Create user-data directory.
new-config Create new config
new-hyperopt Create new hyperopt
new-strategy Create new strategy
download-data Download backtesting data.
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.
@ -107,8 +108,10 @@ 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

4
config_examples/config_full.example.json
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@ -149,7 +149,9 @@
},
"sell_fill": "on",
"buy_cancel": "on",
"sell_cancel": "on"
"sell_cancel": "on",
"protection_trigger": "off",
"protection_trigger_global": "on"
},
"reload": true,
"balance_dust_level": 0.01

332
docs/advanced-hyperopt.md
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@ -67,10 +67,10 @@ Currently, the arguments are:
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
Please keep the arguments `*args` and `**kwargs` in the interface to allow us to extend this interface later.
!!! 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
@ -80,10 +80,56 @@ To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_sp
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
# Define a custom stoploss space.
def stoploss_space(self):
def stoploss_space():
return [SKDecimal(-0.05, -0.01, decimals=3, name='stoploss')]
# Define custom ROI space
def roi_space() -> List[Dimension]:
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
SKDecimal(0.01, 0.04, decimals=3, name='roi_p1'),
SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
]
```
!!! Note
All overrides are optional and can be mixed/matched as necessary.
### Overriding Base estimator
You can define your own estimator for Hyperopt by implementing `generate_estimator()` in the Hyperopt subclass.
```python
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator():
return "RF"
```
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:
```python
class MyAwesomeStrategy(IStrategy):
class HyperOpt:
def generate_estimator():
from skopt.learning import ExtraTreesRegressor
# Corresponds to "ET" - but allows additional parameters.
return ExtraTreesRegressor(n_estimators=100)
```
!!! 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.
## Space options
For the additional spaces, scikit-optimize (in combination with Freqtrade) provides the following space types:
@ -105,281 +151,3 @@ from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal,
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]`).
---
## Legacy Hyperopt
This Section explains the configuration of an explicit Hyperopt file (separate to the strategy).
!!! Warning "Deprecated / legacy mode"
Since the 2021.4 release you no longer have to write a separate hyperopt class, but all strategies can be hyperopted.
Please read the [main hyperopt page](hyperopt.md) for more details.
### Prepare hyperopt file
Configuring an explicit hyperopt file is similar to writing your own strategy, and many tasks will be similar.
!!! Tip "About this page"
For this page, we will be using a fictional strategy called `AwesomeStrategy` - which will be optimized using the `AwesomeHyperopt` class.
#### Create a Custom Hyperopt File
The simplest way to get started is to use the following command, which will create a new hyperopt file from a template, which will be located under `user_data/hyperopts/AwesomeHyperopt.py`.
Let assume you want a hyperopt file `AwesomeHyperopt.py`:
``` bash
freqtrade new-hyperopt --hyperopt AwesomeHyperopt
```
#### Legacy Hyperopt checklist
Checklist on all tasks / possibilities in hyperopt
Depending on the space you want to optimize, only some of the below are required:
* fill `buy_strategy_generator` - for buy signal optimization
* fill `indicator_space` - for buy signal optimization
* fill `sell_strategy_generator` - for sell signal optimization
* fill `sell_indicator_space` - for sell signal optimization
!!! Note
`populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
Optional in hyperopt - can also be loaded from a strategy (recommended):
* `populate_indicators` - fallback to create indicators
* `populate_buy_trend` - fallback if not optimizing for buy space. should come from strategy
* `populate_sell_trend` - fallback if not optimizing for sell space. should come from strategy
!!! Note
You always have to provide a strategy to Hyperopt, even if your custom Hyperopt class contains all methods.
Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
Rarely you may also need to override:
* `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
* `generate_roi_table` - for custom ROI optimization (if you need the ranges for the values in the ROI table that differ from default or the number of entries (steps) in the ROI table which differs from the default 4 steps)
* `stoploss_space` - for custom stoploss optimization (if you need the range for the stoploss parameter in the optimization hyperspace that differs from default)
* `trailing_space` - for custom trailing stop optimization (if you need the ranges for the trailing stop parameters in the optimization hyperspace that differ from default)
#### Defining a buy signal optimization
Let's say you are curious: should you use MACD crossings or lower Bollinger
Bands to trigger your buys. And you also wonder should you use RSI or ADX to
help with those buy decisions. If you decide to use RSI or ADX, which values
should I use for them? So let's use hyperparameter optimization to solve this
mystery.
We will start by defining a search space:
```python
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return [
Integer(20, 40, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal'], name='trigger')
]
```
Above definition says: I have five parameters I want you to randomly combine
to find the best combination. Two of them are integer values (`adx-value` and `rsi-value`) and I want you test in the range of values 20 to 40.
Then we have three category variables. First two are either `True` or `False`.
We use these to either enable or disable the ADX and RSI guards.
The last one we call `trigger` and use it to decide which buy trigger we want to use.
So let's write the buy strategy generator using these values:
```python
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
```
Hyperopt will now call `populate_buy_trend()` many times (`epochs`) with different value combinations.
It will use the given historical data and make buys based on the buy signals generated with the above function.
Based on the results, hyperopt will tell you which parameter combination produced the best results (based on the configured [loss function](#loss-functions)).
!!! Note
The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
When you want to test an indicator that isn't used by the bot currently, remember to
add it to the `populate_indicators()` method in your strategy or hyperopt file.
#### Sell optimization
Similar to the buy-signal above, sell-signals can also be optimized.
Place the corresponding settings into the following methods
* Inside `sell_indicator_space()` - the parameters hyperopt shall be optimizing.
* Within `sell_strategy_generator()` - populate the nested method `populate_sell_trend()` to apply the parameters.
The configuration and rules are the same than for buy signals.
To avoid naming collisions in the search-space, please prefix all sell-spaces with `sell-`.
### Execute Hyperopt
Once you have updated your hyperopt configuration you can run it.
Because hyperopt tries a lot of combinations to find the best parameters it will take time to get a good result. More time usually results in better results.
We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
```bash
freqtrade hyperopt --config config.json --hyperopt <hyperoptname> --hyperopt-loss <hyperoptlossname> --strategy <strategyname> -e 500 --spaces all
```
Use `<hyperoptname>` as the name of the custom hyperopt used.
The `-e` option will set how many evaluations hyperopt will do. Since hyperopt uses Bayesian search, running too many epochs at once may not produce greater results. Experience has shown that best results are usually not improving much after 500-1000 epochs.
Doing multiple runs (executions) with a few 1000 epochs and different random state will most likely produce different results.
The `--spaces all` option determines that all possible parameters should be optimized. Possibilities are listed below.
!!! Note
Hyperopt will store hyperopt results with the timestamp of the hyperopt start time.
Reading commands (`hyperopt-list`, `hyperopt-show`) can use `--hyperopt-filename <filename>` to read and display older hyperopt results.
You can find a list of filenames with `ls -l user_data/hyperopt_results/`.
#### Running Hyperopt using methods from a strategy
Hyperopt can reuse `populate_indicators`, `populate_buy_trend`, `populate_sell_trend` from your strategy, assuming these methods are **not** in your custom hyperopt file, and a strategy is provided.
```bash
freqtrade hyperopt --hyperopt AwesomeHyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy AwesomeStrategy
```
### Understand the Hyperopt Result
Once Hyperopt is completed you can use the result to create a new strategy.
Given the following result from hyperopt:
```
Best result:
44/100: 135 trades. Avg profit 0.57%. Total profit 0.03871918 BTC (0.7722%). Avg duration 180.4 mins. Objective: 1.94367
Buy hyperspace params:
{ 'adx-value': 44,
'rsi-value': 29,
'adx-enabled': False,
'rsi-enabled': True,
'trigger': 'bb_lower'}
```
You should understand this result like:
* The buy trigger that worked best was `bb_lower`.
* You should not use ADX because `adx-enabled: False`)
* You should **consider** using the RSI indicator (`rsi-enabled: True` and the best value is `29.0` (`rsi-value: 29.0`)
You have to look inside your strategy file into `buy_strategy_generator()`
method, what those values match to.
So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that translates to the following code block:
```python
(dataframe['rsi'] < 29.0)
```
Translating your whole hyperopt result as the new buy-signal would then look like:
```python
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] < 29.0) & # rsi-value
dataframe['close'] < dataframe['bb_lowerband'] # trigger
),
'buy'] = 1
return dataframe
```
### Validate backtesting results
Once the optimized parameters and conditions have been implemented into your strategy, you should backtest the strategy to make sure everything is working as expected.
To achieve same results (number of trades, their durations, profit, etc.) than during Hyperopt, please use same configuration and parameters (timerange, timeframe, ...) used for hyperopt `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
Should results not match, please double-check to make sure you transferred all conditions correctly.
Pay special care to the stoploss (and trailing stoploss) parameters, as these are often set in configuration files, which override changes to the strategy.
You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss` or `trailing_stop`).
### Sharing methods with your strategy
Hyperopt classes provide access to the Strategy via the `strategy` class attribute.
This can be a great way to reduce code duplication if used correctly, but will also complicate usage for inexperienced users.
``` python
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
import freqtrade.vendor.qtpylib.indicators as qtpylib
class MyAwesomeStrategy(IStrategy):
buy_params = {
'rsi-value': 30,
'adx-value': 35,
}
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return self.buy_strategy_generator(self.buy_params, dataframe, metadata)
@staticmethod
def buy_strategy_generator(params, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
qtpylib.crossed_above(dataframe['rsi'], params['rsi-value']) &
dataframe['adx'] > params['adx-value']) &
dataframe['volume'] > 0
)
, 'buy'] = 1
return dataframe
class MyAwesomeHyperOpt(IHyperOpt):
...
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
# Call strategy's buy strategy generator
return self.StrategyClass.buy_strategy_generator(params, dataframe, metadata)
return populate_buy_trend
```

8
docs/bot-usage.md
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@ -12,22 +12,22 @@ This page explains the different parameters of the bot and how to run it.
```
usage: freqtrade [-h] [-V]
{trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
{trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
...
Free, open source crypto trading bot
positional arguments:
{trade,create-userdir,new-config,new-hyperopt,new-strategy,download-data,convert-data,convert-trade-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,plot-dataframe,plot-profit}
{trade,create-userdir,new-config,new-strategy,download-data,convert-data,convert-trade-data,list-data,backtesting,edge,hyperopt,hyperopt-list,hyperopt-show,list-exchanges,list-hyperopts,list-markets,list-pairs,list-strategies,list-timeframes,show-trades,test-pairlist,install-ui,plot-dataframe,plot-profit,webserver}
trade Trade module.
create-userdir Create user-data directory.
new-config Create new config
new-hyperopt Create new hyperopt
new-strategy Create new strategy
download-data Download backtesting data.
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.
@ -41,8 +41,10 @@ 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

55
docs/data-download.md
View File

@ -204,6 +204,61 @@ It'll also remove original jsongz data files (`--erase` parameter).
freqtrade convert-trade-data --format-from jsongz --format-to json --datadir ~/.freqtrade/data/kraken --erase
```
### Sub-command trades to ohlcv
When you need to use `--dl-trades` (kraken only) to download data, conversion of trades data to ohlcv data is the last step.
This command will allow you to repeat this last step for additional timeframes without re-downloading the data.
```
usage: freqtrade trades-to-ohlcv [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
[--exchange EXCHANGE]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--data-format-trades {json,jsongz,hdf5}]
optional arguments:
-h, --help show this help message and exit
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
--data-format-ohlcv {json,jsongz,hdf5}
Storage format for downloaded candle (OHLCV) data.
(default: `json`).
--data-format-trades {json,jsongz,hdf5}
Storage format for downloaded trades data. (default:
`jsongz`).
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.
```
#### Example trade-to-ohlcv conversion
``` bash
freqtrade trades-to-ohlcv --exchange kraken -t 5m 1h 1d --pairs BTC/EUR ETH/EUR
```
### Sub-command list-data
You can get a list of downloaded data using the `list-data` sub-command.

5
docs/deprecated.md
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@ -38,3 +38,8 @@ Since only quoteVolume can be compared between assets, the other options (bidVol
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.

18
docs/docker_quickstart.md
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@ -149,6 +149,24 @@ You'll then also need to modify the `docker-compose.yml` file and uncomment the
You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
### Troubleshooting
#### Docker on Windows
* Error: `"Timestamp for this request is outside of the recvWindow."`
* The market api requests require a synchronized clock but the time in the docker container shifts a bit over time into the past.
To fix this issue temporarily you need to run `wsl --shutdown` and restart docker again (a popup on windows 10 will ask you to do so).
A permanent solution is either to host the docker container on a linux host or restart the wsl from time to time with the scheduler.
```
taskkill /IM "Docker Desktop.exe" /F
wsl --shutdown
start "" "C:\Program Files\Docker\Docker\Docker Desktop.exe"
```
!!! Warning
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.
## 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.

2
docs/edge.md
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@ -3,7 +3,7 @@
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
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.
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.

6
docs/exchanges.md
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@ -58,6 +58,12 @@ Bittrex does not support market orders. If you have a message at the bot startup
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.

2
docs/faq.md
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@ -167,7 +167,7 @@ Since hyperopt uses Bayesian search, running for too many epochs may not produce
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.
```bash
freqtrade hyperopt --hyperopt SampleHyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy SampleStrategy -e 1000
freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy SampleStrategy -e 1000
```
### Why does it take a long time to run hyperopt?

23
docs/hyperopt.md
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@ -44,9 +44,8 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--max-open-trades INT]
[--stake-amount STAKE_AMOUNT] [--fee FLOAT]
[-p PAIRS [PAIRS ...]] [--hyperopt NAME]
[--hyperopt-path PATH] [--eps] [--dmmp]
[--enable-protections]
[-p PAIRS [PAIRS ...]] [--hyperopt-path PATH]
[--eps] [--dmmp] [--enable-protections]
[--dry-run-wallet DRY_RUN_WALLET] [-e INT]
[--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]]
[--print-all] [--no-color] [--print-json] [-j JOBS]
@ -73,10 +72,8 @@ optional arguments:
-p PAIRS [PAIRS ...], --pairs PAIRS [PAIRS ...]
Limit command to these pairs. Pairs are space-
separated.
--hyperopt NAME Specify hyperopt class name which will be used by the
bot.
--hyperopt-path PATH Specify additional lookup path for Hyperopt and
Hyperopt Loss functions.
--hyperopt-path PATH Specify additional lookup path for Hyperopt Loss
functions.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking).
@ -558,7 +555,7 @@ For example, to use one month of data, pass `--timerange 20210101-20210201` (fro
Full command:
```bash
freqtrade hyperopt --hyperopt <hyperoptname> --strategy <strategyname> --timerange 20210101-20210201
freqtrade hyperopt --strategy <strategyname> --timerange 20210101-20210201
```
### Running Hyperopt with Smaller Search Space
@ -680,11 +677,11 @@ If you are optimizing ROI, Freqtrade creates the 'roi' optimization hyperspace f
These ranges should be sufficient in most cases. The minutes in the steps (ROI dict keys) are scaled linearly depending on the timeframe used. The ROI values in the steps (ROI dict values) are scaled logarithmically depending on the timeframe used.
If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
Override the `roi_space()` method if you need components of the ROI tables to vary in other ranges. Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps).
A sample for these methods can be found in [sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
A sample for these methods can be found in the [overriding pre-defined spaces section](advanced-hyperopt.md#overriding-pre-defined-spaces).
!!! Note "Reduced search space"
To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#pverriding-pre-defined-spaces) to change this to your needs.
@ -726,7 +723,7 @@ If you are optimizing stoploss values, Freqtrade creates the 'stoploss' optimiza
If you have the `stoploss_space()` method in your custom hyperopt file, remove it in order to utilize Stoploss hyperoptimization space generated by Freqtrade by default.
Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
Override the `stoploss_space()` method and define the desired range in it if you need stoploss values to vary in other range during hyperoptimization. A sample for this method can be found in the [overriding pre-defined spaces section](advanced-hyperopt.md#overriding-pre-defined-spaces).
!!! Note "Reduced search space"
To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#pverriding-pre-defined-spaces) to change this to your needs.
@ -764,10 +761,10 @@ As stated in the comment, you can also use it as the values of the corresponding
If you are optimizing trailing stop values, Freqtrade creates the 'trailing' optimization hyperspace for you. By default, the `trailing_stop` parameter is always set to True in that hyperspace, the value of the `trailing_only_offset_is_reached` vary between True and False, the values of the `trailing_stop_positive` and `trailing_stop_positive_offset` parameters vary in the ranges 0.02...0.35 and 0.01...0.1 correspondingly, which is sufficient in most cases.
Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
Override the `trailing_space()` method and define the desired range in it if you need values of the trailing stop parameters to vary in other ranges during hyperoptimization. A sample for this method can be found in the [overriding pre-defined spaces section](advanced-hyperopt.md#overriding-pre-defined-spaces).
!!! Note "Reduced search space"
To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#pverriding-pre-defined-spaces) to change this to your needs.
To limit the search space further, Decimals are limited to 3 decimal places (a precision of 0.001). This is usually sufficient, every value more precise than this will usually result in overfitted results. You can however [overriding pre-defined spaces](advanced-hyperopt.md#overriding-pre-defined-spaces) to change this to your needs.
### Reproducible results

34
docs/includes/pairlists.md
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@ -82,6 +82,8 @@ Filtering instances (not the first position in the list) will not apply any cach
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:
@ -105,6 +107,24 @@ For convenience `lookback_days` can be specified, which will imply that 1d candl
!!! 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
@ -145,6 +165,7 @@ Example to remove the first 10 pairs from the pairlist:
```json
"pairlists": [
// ...
{
"method": "OffsetFilter",
"offset": 10
@ -170,6 +191,19 @@ Sorts pairs by past trade performance, as follows:
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.
```json
"pairlists": [
// ...
{
"method": "PerformanceFilter",
"minutes": 1440 // rolling 24h
}
],
```
!!! Note
`PerformanceFilter` does not support backtesting mode.

1
docs/index.md
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@ -40,6 +40,7 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
- [X] [Bittrex](https://bittrex.com/)
- [X] [FTX](https://ftx.com)
- [X] [Kraken](https://kraken.com/)
- [X] [Gate.io](https://www.gate.io/ref/6266643)
- [ ] [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)_
### Community tested

2
docs/requirements-docs.txt
View File

@ -1,4 +1,4 @@
mkdocs==1.2.2
mkdocs-material==7.2.5
mkdocs-material==7.3.0
mdx_truly_sane_lists==1.2
pymdown-extensions==8.2

36
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View File

@ -288,6 +288,12 @@ Stoploss values returned from `custom_stoploss()` always specify a percentage re
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()`.
### Calculating 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()`.
#### Stepped stoploss
Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.
@ -695,3 +701,33 @@ The variable 'content', will contain the strategy file in a BASE64 encoded form.
```
Please ensure that 'NameOfStrategy' is identical to the strategy name!
## Performance warning
When executing a strategy, one can sometimes be greeted by the following in the logs
> PerformanceWarning: DataFrame is highly fragmented.
This is a warning from [`pandas`](https://github.com/pandas-dev/pandas) and as the warning continues to say:
use `pd.concat(axis=1)`.
This can have slight performance implications, which are usually only visible during hyperopt (when optimizing an indicator).
For example:
```python
for val in self.buy_ema_short.range:
dataframe[f'ema_short_{val}'] = ta.EMA(dataframe, timeperiod=val)
```
should be rewritten to
```python
frames = [dataframe]
for val in self.buy_ema_short.range:
frames.append({
f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
})
# Append columns to existing dataframe
merged_frame = pd.concat(frames, axis=1)
```

173
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@ -122,6 +122,16 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
Look into the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_strategy.py).
Then uncomment indicators you need.
#### Indicator libraries
Out of the box, freqtrade installs the following technical libraries:
* [ta-lib](http://mrjbq7.github.io/ta-lib/)
* [pandas-ta](https://twopirllc.github.io/pandas-ta/)
* [technical](https://github.com/freqtrade/technical/)
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.
@ -639,6 +649,167 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
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 `sell_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 proce we can call `stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)`.
``` python
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy, stoploss_from_open
class AwesomeStrategy(IStrategy):
use_custom_stoploss = True
def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
return dataframe
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
candle = dataframe.iloc[-1].squeeze()
return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)
```
### *@informative()*
``` python
def informative(timeframe: str, asset: str = '',
fmt: Optional[Union[str, Callable[[KwArg(str)], str]]] = None,
ffill: bool = True) -> Callable[[PopulateIndicators], PopulateIndicators]:
"""
A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to
define informative indicators.
Example usage:
@informative('1h')
def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
: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.
"""
```
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)
for more information.
??? 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'.
@informative('30m')
@informative('1h')
def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
# Define BTC/STAKE informative pair. Available in populate_indicators and other methods as
# 'btc_rsi_1h'. Current stake currency should be specified as {stake} format variable
# instead of hardcoding actual stake currency. Available in populate_indicators and other
# methods as 'btc_usdt_rsi_1h' (when stake currency is USDT).
@informative('1h', 'BTC/{stake}')
def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
# Define BTC/ETH informative pair. You must specify quote currency if it is different from
# stake currency. Available in populate_indicators and other methods as 'eth_btc_rsi_1h'.
@informative('1h', 'ETH/BTC')
def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
# Define BTC/STAKE informative pair. A custom formatter may be specified for formatting
# column names. A callable `fmt(**kwargs) -> str` may be specified, to implement custom
# formatting. Available in populate_indicators and other methods as 'rsi_upper'.
@informative('1h', 'BTC/{stake}', '{column}')
def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi_upper'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Strategy timeframe indicators for current pair.
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# Informative pairs are available in this method.
dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
return dataframe
```
!!! Note
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.
``` python
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
stake = self.config['stake_currency']
dataframe.loc[
(
(dataframe[f'btc_{stake}_rsi_1h'] < 35)
&
(dataframe['volume'] > 0)
),
['buy', 'buy_tag']] = (1, 'buy_signal_rsi')
return dataframe
```
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 (Wallets)
@ -781,6 +952,8 @@ 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.

5
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@ -93,7 +93,9 @@ Example configuration showing the different settings:
"buy_cancel": "silent",
"sell_cancel": "on",
"buy_fill": "off",
"sell_fill": "off"
"sell_fill": "off",
"protection_trigger": "off",
"protection_trigger_global": "on"
},
"reload": true,
"balance_dust_level": 0.01
@ -103,6 +105,7 @@ Example configuration showing the different settings:
`buy` notifications are sent when the order is placed, while `buy_fill` notifications are sent when the order is filled on the exchange.
`sell` notifications are sent when the order is placed, while `sell_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.

83
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@ -26,9 +26,7 @@ optional arguments:
├── data
├── hyperopt_results
├── hyperopts
│   ├── sample_hyperopt_advanced.py
│   ├── sample_hyperopt_loss.py
│   └── sample_hyperopt.py
├── notebooks
│   └── strategy_analysis_example.ipynb
├── plot
@ -111,46 +109,11 @@ Using the advanced template (populates all optional functions and methods)
freqtrade new-strategy --strategy AwesomeStrategy --template advanced
```
## Create new hyperopt
## List Strategies
Creates a new hyperopt from a template similar to SampleHyperopt.
The file will be named inline with your class name, and will not overwrite existing files.
Use the `list-strategies` subcommand to see all strategies in one particular directory.
Results will be located in `user_data/hyperopts/<classname>.py`.
``` output
usage: freqtrade new-hyperopt [-h] [--userdir PATH] [--hyperopt NAME]
[--template {full,minimal,advanced}]
optional arguments:
-h, --help show this help message and exit
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
--hyperopt NAME Specify hyperopt class name which will be used by the
bot.
--template {full,minimal,advanced}
Use a template which is either `minimal`, `full`
(containing multiple sample indicators) or `advanced`.
Default: `full`.
```
### Sample usage of new-hyperopt
```bash
freqtrade new-hyperopt --hyperopt AwesomeHyperopt
```
With custom user directory
```bash
freqtrade new-hyperopt --userdir ~/.freqtrade/ --hyperopt AwesomeHyperopt
```
## List Strategies and List Hyperopts
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).
```
usage: freqtrade list-strategies [-h] [-v] [--logfile FILE] [-V] [-c PATH]
@ -164,34 +127,6 @@ optional arguments:
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log 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: `config.json`).
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.
```
```
usage: freqtrade list-hyperopts [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[--hyperopt-path PATH] [-1] [--no-color]
optional arguments:
-h, --help show this help message and exit
--hyperopt-path PATH Specify additional lookup path for Hyperopt and
Hyperopt Loss functions.
-1, --one-column Print output in one column.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
@ -211,18 +146,16 @@ 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.
``` bash
freqtrade list-strategies --userdir ~/.freqtrade/
freqtrade list-hyperopts --userdir ~/.freqtrade/
```
Example: Search dedicated strategy path.
@ -231,12 +164,6 @@ Example: Search dedicated strategy path.
freqtrade list-strategies --strategy-path ~/.freqtrade/strategies/
```
Example: Search dedicated hyperopt path.
``` bash
freqtrade list-hyperopt --hyperopt-path ~/.freqtrade/hyperopts/
```
## List Exchanges
Use the `list-exchanges` subcommand to see the exchanges available for the bot.

12
freqtrade/commands/__init__.py
View File

@ -8,14 +8,14 @@ Note: Be careful with file-scoped imports in these subfiles.
"""
from freqtrade.commands.arguments import Arguments
from freqtrade.commands.build_config_commands import start_new_config
from freqtrade.commands.data_commands import (start_convert_data, start_download_data,
start_list_data)
from freqtrade.commands.data_commands import (start_convert_data, start_convert_trades,
start_download_data, start_list_data)
from freqtrade.commands.deploy_commands import (start_create_userdir, start_install_ui,
start_new_hyperopt, start_new_strategy)
start_new_strategy)
from freqtrade.commands.hyperopt_commands import start_hyperopt_list, start_hyperopt_show
from freqtrade.commands.list_commands import (start_list_exchanges, start_list_hyperopts,
start_list_markets, start_list_strategies,
start_list_timeframes, start_show_trades)
from freqtrade.commands.list_commands import (start_list_exchanges, start_list_markets,
start_list_strategies, start_list_timeframes,
start_show_trades)
from freqtrade.commands.optimize_commands import start_backtesting, start_edge, start_hyperopt
from freqtrade.commands.pairlist_commands import start_test_pairlist
from freqtrade.commands.plot_commands import start_plot_dataframe, start_plot_profit

49
freqtrade/commands/arguments.py
View File

@ -55,11 +55,11 @@ ARGS_BUILD_CONFIG = ["config"]
ARGS_BUILD_STRATEGY = ["user_data_dir", "strategy", "template"]
ARGS_BUILD_HYPEROPT = ["user_data_dir", "hyperopt", "template"]
ARGS_CONVERT_DATA = ["pairs", "format_from", "format_to", "erase"]
ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes"]
ARGS_CONVERT_TRADES = ["pairs", "timeframes", "exchange", "dataformat_ohlcv", "dataformat_trades"]
ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs"]
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "new_pairs_days", "timerange",
@ -92,10 +92,10 @@ ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperop
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies", "list-data",
"list-hyperopts", "hyperopt-list", "hyperopt-show",
"plot-dataframe", "plot-profit", "show-trades"]
"hyperopt-list", "hyperopt-show",
"plot-dataframe", "plot-profit", "show-trades", "trades-to-ohlcv"]
NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-hyperopt", "new-strategy"]
NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
class Arguments:
@ -171,15 +171,14 @@ class Arguments:
self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
self._build_args(optionlist=['version'], parser=self.parser)
from freqtrade.commands import (start_backtesting, start_convert_data, start_create_userdir,
start_download_data, start_edge, start_hyperopt,
start_hyperopt_list, start_hyperopt_show, start_install_ui,
start_list_data, start_list_exchanges, start_list_hyperopts,
from freqtrade.commands import (start_backtesting, start_convert_data, start_convert_trades,
start_create_userdir, start_download_data, start_edge,
start_hyperopt, start_hyperopt_list, start_hyperopt_show,
start_install_ui, start_list_data, start_list_exchanges,
start_list_markets, start_list_strategies,
start_list_timeframes, start_new_config, start_new_hyperopt,
start_new_strategy, start_plot_dataframe, start_plot_profit,
start_show_trades, start_test_pairlist, start_trading,
start_webserver)
start_list_timeframes, start_new_config, start_new_strategy,
start_plot_dataframe, start_plot_profit, start_show_trades,
start_test_pairlist, start_trading, start_webserver)
subparsers = self.parser.add_subparsers(dest='command',
# Use custom message when no subhandler is added
@ -206,12 +205,6 @@ class Arguments:
build_config_cmd.set_defaults(func=start_new_config)
self._build_args(optionlist=ARGS_BUILD_CONFIG, parser=build_config_cmd)
# add new-hyperopt subcommand
build_hyperopt_cmd = subparsers.add_parser('new-hyperopt',
help="Create new hyperopt")
build_hyperopt_cmd.set_defaults(func=start_new_hyperopt)
self._build_args(optionlist=ARGS_BUILD_HYPEROPT, parser=build_hyperopt_cmd)
# add new-strategy subcommand
build_strategy_cmd = subparsers.add_parser('new-strategy',
help="Create new strategy")
@ -245,6 +238,15 @@ class Arguments:
convert_trade_data_cmd.set_defaults(func=partial(start_convert_data, ohlcv=False))
self._build_args(optionlist=ARGS_CONVERT_DATA, parser=convert_trade_data_cmd)
# Add trades-to-ohlcv subcommand
convert_trade_data_cmd = subparsers.add_parser(
'trades-to-ohlcv',
help='Convert trade data to OHLCV data.',
parents=[_common_parser],
)
convert_trade_data_cmd.set_defaults(func=start_convert_trades)
self._build_args(optionlist=ARGS_CONVERT_TRADES, parser=convert_trade_data_cmd)
# Add list-data subcommand
list_data_cmd = subparsers.add_parser(
'list-data',
@ -300,15 +302,6 @@ class Arguments:
list_exchanges_cmd.set_defaults(func=start_list_exchanges)
self._build_args(optionlist=ARGS_LIST_EXCHANGES, parser=list_exchanges_cmd)
# Add list-hyperopts subcommand
list_hyperopts_cmd = subparsers.add_parser(
'list-hyperopts',
help='Print available hyperopt classes.',
parents=[_common_parser],
)
list_hyperopts_cmd.set_defaults(func=start_list_hyperopts)
self._build_args(optionlist=ARGS_LIST_HYPEROPTS, parser=list_hyperopts_cmd)
# Add list-markets subcommand
list_markets_cmd = subparsers.add_parser(
'list-markets',

10
freqtrade/commands/cli_options.py
View File

@ -1,7 +1,7 @@
"""
Definition of cli arguments used in arguments.py
"""
from argparse import ArgumentTypeError
from argparse import SUPPRESS, ArgumentTypeError
from freqtrade import __version__, constants
from freqtrade.constants import HYPEROPT_LOSS_BUILTIN
@ -203,13 +203,13 @@ AVAILABLE_CLI_OPTIONS = {
# Hyperopt
"hyperopt": Arg(
'--hyperopt',
help='Specify hyperopt class name which will be used by the bot.',
help=SUPPRESS,
metavar='NAME',
required=False,
),
"hyperopt_path": Arg(
'--hyperopt-path',
help='Specify additional lookup path for Hyperopt and Hyperopt Loss functions.',
help='Specify additional lookup path for Hyperopt Loss functions.',
metavar='PATH',
),
"epochs": Arg(
@ -381,12 +381,12 @@ AVAILABLE_CLI_OPTIONS = {
),
"dataformat_ohlcv": Arg(
'--data-format-ohlcv',
help='Storage format for downloaded candle (OHLCV) data. (default: `%(default)s`).',
help='Storage format for downloaded candle (OHLCV) data. (default: `json`).',
choices=constants.AVAILABLE_DATAHANDLERS,
),
"dataformat_trades": Arg(
'--data-format-trades',
help='Storage format for downloaded trades data. (default: `%(default)s`).',
help='Storage format for downloaded trades data. (default: `jsongz`).',
choices=constants.AVAILABLE_DATAHANDLERS,
),
"exchange": Arg(

35
freqtrade/commands/data_commands.py
View File

@ -89,6 +89,41 @@ def start_download_data(args: Dict[str, Any]) -> None:
f"on exchange {exchange.name}.")
def start_convert_trades(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
timerange = TimeRange()
# Remove stake-currency to skip checks which are not relevant for datadownload
config['stake_currency'] = ''
if 'pairs' not in config:
raise OperationalException(
"Downloading data requires a list of pairs. "
"Please check the documentation on how to configure this.")
# Init exchange
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
# Manual validations of relevant settings
if not config['exchange'].get('skip_pair_validation', False):
exchange.validate_pairs(config['pairs'])
expanded_pairs = expand_pairlist(config['pairs'], list(exchange.markets))
logger.info(f"About to Convert pairs: {expanded_pairs}, "
f"intervals: {config['timeframes']} to {config['datadir']}")
for timeframe in config['timeframes']:
exchange.validate_timeframes(timeframe)
# Convert downloaded trade data to different timeframes
convert_trades_to_ohlcv(
pairs=expanded_pairs, timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
data_format_ohlcv=config['dataformat_ohlcv'],
data_format_trades=config['dataformat_trades'],
)
def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
"""
Convert data from one format to another

52
freqtrade/commands/deploy_commands.py
View File

@ -7,7 +7,7 @@ import requests
from freqtrade.configuration import setup_utils_configuration
from freqtrade.configuration.directory_operations import copy_sample_files, create_userdata_dir
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
from freqtrade.constants import USERPATH_STRATEGIES
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.misc import render_template, render_template_with_fallback
@ -87,56 +87,6 @@ def start_new_strategy(args: Dict[str, Any]) -> None:
raise OperationalException("`new-strategy` requires --strategy to be set.")
def deploy_new_hyperopt(hyperopt_name: str, hyperopt_path: Path, subtemplate: str) -> None:
"""
Deploys a new hyperopt template to hyperopt_path
"""
fallback = 'full'
buy_guards = render_template_with_fallback(
templatefile=f"subtemplates/hyperopt_buy_guards_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/hyperopt_buy_guards_{fallback}.j2",
)
sell_guards = render_template_with_fallback(
templatefile=f"subtemplates/hyperopt_sell_guards_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/hyperopt_sell_guards_{fallback}.j2",
)
buy_space = render_template_with_fallback(
templatefile=f"subtemplates/hyperopt_buy_space_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/hyperopt_buy_space_{fallback}.j2",
)
sell_space = render_template_with_fallback(
templatefile=f"subtemplates/hyperopt_sell_space_{subtemplate}.j2",
templatefallbackfile=f"subtemplates/hyperopt_sell_space_{fallback}.j2",
)
strategy_text = render_template(templatefile='base_hyperopt.py.j2',
arguments={"hyperopt": hyperopt_name,
"buy_guards": buy_guards,
"sell_guards": sell_guards,
"buy_space": buy_space,
"sell_space": sell_space,
})
logger.info(f"Writing hyperopt to `{hyperopt_path}`.")
hyperopt_path.write_text(strategy_text)
def start_new_hyperopt(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if 'hyperopt' in args and args['hyperopt']:
new_path = config['user_data_dir'] / USERPATH_HYPEROPTS / (args['hyperopt'] + '.py')
if new_path.exists():
raise OperationalException(f"`{new_path}` already exists. "
"Please choose another Hyperopt Name.")
deploy_new_hyperopt(args['hyperopt'], new_path, args['template'])
else:
raise OperationalException("`new-hyperopt` requires --hyperopt to be set.")
def clean_ui_subdir(directory: Path):
if directory.is_dir():
logger.info("Removing UI directory content.")

2
freqtrade/commands/hyperopt_commands.py
View File

@ -53,7 +53,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
if epochs and export_csv:
HyperoptTools.export_csv_file(
config, epochs, total_epochs, not config.get('hyperopt_list_best', False), export_csv
config, epochs, export_csv
)

21
freqtrade/commands/list_commands.py
View File

@ -10,7 +10,7 @@ from colorama import init as colorama_init
from tabulate import tabulate
from freqtrade.configuration import setup_utils_configuration
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
from freqtrade.constants import USERPATH_STRATEGIES
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import market_is_active, validate_exchanges
@ -92,25 +92,6 @@ def start_list_strategies(args: Dict[str, Any]) -> None:
_print_objs_tabular(strategy_objs, config.get('print_colorized', False))
def start_list_hyperopts(args: Dict[str, Any]) -> None:
"""
Print files with HyperOpt custom classes available in the directory
"""
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
directory = Path(config.get('hyperopt_path', config['user_data_dir'] / USERPATH_HYPEROPTS))
hyperopt_objs = HyperOptResolver.search_all_objects(directory, not args['print_one_column'])
# Sort alphabetically
hyperopt_objs = sorted(hyperopt_objs, key=lambda x: x['name'])
if args['print_one_column']:
print('\n'.join([s['name'] for s in hyperopt_objs]))
else:
_print_objs_tabular(hyperopt_objs, config.get('print_colorized', False))
def start_list_timeframes(args: Dict[str, Any]) -> None:
"""
Print timeframes available on Exchange

19
freqtrade/configuration/PeriodicCache.py Normal file
View File

@ -0,0 +1,19 @@
from datetime import datetime, timezone
from cachetools.ttl import TTLCache
class PeriodicCache(TTLCache):
"""
Special cache that expires at "straight" times
A timer with ttl of 3600 (1h) will expire at every full hour (:00).
"""
def __init__(self, maxsize, ttl, getsizeof=None):
def local_timer():
ts = datetime.now(timezone.utc).timestamp()
offset = (ts % ttl)
return ts - offset
# Init with smlight offset
super().__init__(maxsize=maxsize, ttl=ttl-1e-5, timer=local_timer, getsizeof=getsizeof)

3
freqtrade/configuration/__init__.py
View File

@ -1,7 +1,8 @@
# flake8: noqa: F401
from freqtrade.configuration.check_exchange import check_exchange, remove_credentials
from freqtrade.configuration.check_exchange import check_exchange
from freqtrade.configuration.config_setup import setup_utils_configuration
from freqtrade.configuration.config_validation import validate_config_consistency
from freqtrade.configuration.configuration import Configuration
from freqtrade.configuration.PeriodicCache import PeriodicCache
from freqtrade.configuration.timerange import TimeRange

13
freqtrade/configuration/check_exchange.py
View File

@ -10,19 +10,6 @@ from freqtrade.exchange import (available_exchanges, is_exchange_known_ccxt,
logger = logging.getLogger(__name__)
def remove_credentials(config: Dict[str, Any]) -> None:
"""
Removes exchange keys from the configuration and specifies dry-run
Used for backtesting / hyperopt / edge and utils.
Modifies the input dict!
"""
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
config['exchange']['password'] = ''
config['exchange']['uid'] = ''
config['dry_run'] = True
def check_exchange(config: Dict[str, Any], check_for_bad: bool = True) -> bool:
"""
Check if the exchange name in the config file is supported by Freqtrade

5
freqtrade/configuration/config_setup.py
View File

@ -3,7 +3,6 @@ from typing import Any, Dict
from freqtrade.enums import RunMode
from .check_exchange import remove_credentials
from .config_validation import validate_config_consistency
from .configuration import Configuration
@ -21,8 +20,8 @@ def setup_utils_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str
configuration = Configuration(args, method)
config = configuration.get_config()
# Ensure we do not use Exchange credentials
remove_credentials(config)
# Ensure these modes are using Dry-run
config['dry_run'] = True
validate_config_consistency(config)
return config

13
freqtrade/constants.py
View File

@ -69,9 +69,7 @@ DUST_PER_COIN = {
# Source files with destination directories within user-directory
USER_DATA_FILES = {
'sample_strategy.py': USERPATH_STRATEGIES,
'sample_hyperopt_advanced.py': USERPATH_HYPEROPTS,
'sample_hyperopt_loss.py': USERPATH_HYPEROPTS,
'sample_hyperopt.py': USERPATH_HYPEROPTS,
'strategy_analysis_example.ipynb': USERPATH_NOTEBOOKS,
}
@ -112,7 +110,7 @@ CONF_SCHEMA = {
},
'tradable_balance_ratio': {
'type': 'number',
'minimum': 0.1,
'minimum': 0.0,
'maximum': 1,
'default': 0.99
},
@ -286,6 +284,15 @@ CONF_SCHEMA = {
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
},
'protection_trigger': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
'default': 'off'
},
'protection_trigger_global': {
'type': 'string',
'enum': TELEGRAM_SETTING_OPTIONS,
},
}
},
'reload': {'type': 'boolean'},

2
freqtrade/data/dataprovider.py
View File

@ -149,6 +149,8 @@ class DataProvider:
Clear pair dataframe cache.
"""
self.__cached_pairs = {}
self.__cached_pairs_backtesting = {}
self.__slice_index = 0
# Exchange functions

3
freqtrade/data/history/history_utils.py
View File

@ -197,7 +197,8 @@ def _download_pair_history(pair: str, *,
timeframe=timeframe,
since_ms=since_ms if since_ms else
arrow.utcnow().shift(
days=-new_pairs_days).int_timestamp * 1000
days=-new_pairs_days).int_timestamp * 1000,
is_new_pair=data.empty
)
# TODO: Maybe move parsing to exchange class (?)
new_dataframe = ohlcv_to_dataframe(new_data, timeframe, pair,

2
freqtrade/edge/edge_positioning.py
View File

@ -119,7 +119,7 @@ class Edge:
)
# Download informative pairs too
res = defaultdict(list)
for p, t in self.strategy.informative_pairs():
for p, t in self.strategy.gather_informative_pairs():
res[t].append(p)
for timeframe, inf_pairs in res.items():
timerange_startup = deepcopy(self._timerange)

2
freqtrade/enums/rpcmessagetype.py
View File

@ -11,6 +11,8 @@ class RPCMessageType(Enum):
SELL = 'sell'
SELL_FILL = 'sell_fill'
SELL_CANCEL = 'sell_cancel'
PROTECTION_TRIGGER = 'protection_trigger'
PROTECTION_TRIGGER_GLOBAL = 'protection_trigger_global'
def __repr__(self):
return self.value

2
freqtrade/exchange/__init__.py
View File

@ -1,6 +1,6 @@
# flake8: noqa: F401
# isort: off
from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS
from freqtrade.exchange.common import remove_credentials, MAP_EXCHANGE_CHILDCLASS
from freqtrade.exchange.exchange import Exchange
# isort: on
from freqtrade.exchange.bibox import Bibox

20
freqtrade/exchange/binance.py
View File

@ -1,7 +1,8 @@
""" Binance exchange subclass """
import logging
from typing import Dict
from typing import Dict, List
import arrow
import ccxt
from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, InvalidOrderException,
@ -90,3 +91,20 @@ class Binance(Exchange):
f'Could not place sell order due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, is_new_pair: bool
) -> List:
"""
Overwrite to introduce "fast new pair" functionality by detecting the pair's listing date
Does not work for other exchanges, which don't return the earliest data when called with "0"
"""
if is_new_pair:
x = await self._async_get_candle_history(pair, timeframe, 0)
if x and x[2] and x[2][0] and x[2][0][0] > since_ms:
# Set starting date to first available candle.
since_ms = x[2][0][0]
logger.info(f"Candle-data for {pair} available starting with "
f"{arrow.get(since_ms // 1000).isoformat()}.")
return await super()._async_get_historic_ohlcv(
pair=pair, timeframe=timeframe, since_ms=since_ms, is_new_pair=is_new_pair)

13
freqtrade/exchange/common.py
View File

@ -51,6 +51,19 @@ EXCHANGE_HAS_OPTIONAL = [
]
def remove_credentials(config) -> None:
"""
Removes exchange keys from the configuration and specifies dry-run
Used for backtesting / hyperopt / edge and utils.
Modifies the input dict!
"""
if config.get('dry_run', False):
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
config['exchange']['password'] = ''
config['exchange']['uid'] = ''
def calculate_backoff(retrycount, max_retries):
"""
Calculate backoff

43
freqtrade/exchange/exchange.py
View File

@ -26,9 +26,9 @@ from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFun
InvalidOrderException, OperationalException, PricingError,
RetryableOrderError, TemporaryError)
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES,
EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED, retrier,
retrier_async)
from freqtrade.misc import deep_merge_dicts, safe_value_fallback2
EXCHANGE_HAS_OPTIONAL, EXCHANGE_HAS_REQUIRED,
remove_credentials, retrier, retrier_async)
from freqtrade.misc import chunks, deep_merge_dicts, safe_value_fallback2
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
@ -104,6 +104,7 @@ class Exchange:
# Holds all open sell orders for dry_run
self._dry_run_open_orders: Dict[str, Any] = {}
remove_credentials(config)
if config['dry_run']:
logger.info('Instance is running with dry_run enabled')
@ -1194,7 +1195,7 @@ class Exchange:
# Historic data
def get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int) -> List:
since_ms: int, is_new_pair: bool = False) -> List:
"""
Get candle history using asyncio and returns the list of candles.
Handles all async work for this.
@ -1206,7 +1207,7 @@ class Exchange:
"""
return asyncio.get_event_loop().run_until_complete(
self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
since_ms=since_ms))
since_ms=since_ms, is_new_pair=is_new_pair))
def get_historic_ohlcv_as_df(self, pair: str, timeframe: str,
since_ms: int) -> DataFrame:
@ -1221,11 +1222,12 @@ class Exchange:
return ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=self._ohlcv_partial_candle)
async def _async_get_historic_ohlcv(self, pair: str,
timeframe: str,
since_ms: int) -> List:
async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, is_new_pair: bool
) -> List:
"""
Download historic ohlcv
:param is_new_pair: used by binance subclass to allow "fast" new pair downloading
"""
one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(timeframe)
@ -1238,21 +1240,22 @@ class Exchange:
pair, timeframe, since) for since in
range(since_ms, arrow.utcnow().int_timestamp * 1000, one_call)]
results = await asyncio.gather(*input_coroutines, return_exceptions=True)
# Combine gathered results
data: List = []
for res in results:
if isinstance(res, Exception):
logger.warning("Async code raised an exception: %s", res.__class__.__name__)
continue
# Deconstruct tuple if it's not an exception
p, _, new_data = res
if p == pair:
data.extend(new_data)
# Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
for input_coro in chunks(input_coroutines, 100):
results = await asyncio.gather(*input_coro, return_exceptions=True)
for res in results:
if isinstance(res, Exception):
logger.warning("Async code raised an exception: %s", res.__class__.__name__)
continue
# Deconstruct tuple if it's not an exception
p, _, new_data = res
if p == pair:
data.extend(new_data)
# Sort data again after extending the result - above calls return in "async order"
data = sorted(data, key=lambda x: x[0])
logger.info("Downloaded data for %s with length %s.", pair, len(data))
logger.info(f"Downloaded data for {pair} with length {len(data)}.")
return data
def refresh_latest_ohlcv(self, pair_list: ListPairsWithTimeframes, *,

2
freqtrade/exchange/gateio.py
View File

@ -21,3 +21,5 @@ class Gateio(Exchange):
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
}
_headers = {'X-Gate-Channel-Id': 'freqtrade'}

132
freqtrade/freqtradebot.py
View File

@ -83,10 +83,10 @@ class FreqtradeBot(LoggingMixin):
self.dataprovider = DataProvider(self.config, self.exchange, self.pairlists)
# Attach Dataprovider to Strategy baseclass
IStrategy.dp = self.dataprovider
# Attach Wallets to Strategy baseclass
IStrategy.wallets = self.wallets
# Attach Dataprovider to strategy instance
self.strategy.dp = self.dataprovider
# Attach Wallets to strategy instance
self.strategy.wallets = self.wallets
# Initializing Edge only if enabled
self.edge = Edge(self.config, self.exchange, self.strategy) if \
@ -99,7 +99,7 @@ class FreqtradeBot(LoggingMixin):
self.state = State[initial_state.upper()] if initial_state else State.STOPPED
# Protect sell-logic from forcesell and vice versa
self._sell_lock = Lock()
self._exit_lock = Lock()
LoggingMixin.__init__(self, logger, timeframe_to_seconds(self.strategy.timeframe))
def notify_status(self, msg: str) -> None:
@ -139,7 +139,7 @@ class FreqtradeBot(LoggingMixin):
# Only update open orders on startup
# This will update the database after the initial migration
self.update_open_orders()
self.startup_update_open_orders()
def process(self) -> None:
"""
@ -160,20 +160,20 @@ class FreqtradeBot(LoggingMixin):
# Refreshing candles
self.dataprovider.refresh(self.pairlists.create_pair_list(self.active_pair_whitelist),
self.strategy.informative_pairs())
self.strategy.gather_informative_pairs())
strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)()
self.strategy.analyze(self.active_pair_whitelist)
with self._sell_lock:
with self._exit_lock:
# Check and handle any timed out open orders
self.check_handle_timedout()
# Protect from collisions with forcesell.
# Without this, freqtrade my try to recreate stoploss_on_exchange orders
# while selling is in process, since telegram messages arrive in an different thread.
with self._sell_lock:
with self._exit_lock:
trades = Trade.get_open_trades()
# First process current opened trades (positions)
self.exit_positions(trades)
@ -237,7 +237,7 @@ class FreqtradeBot(LoggingMixin):
open_trades = len(Trade.get_open_trades())
return max(0, self.config['max_open_trades'] - open_trades)
def update_open_orders(self):
def startup_update_open_orders(self):
"""
Updates open orders based on order list kept in the database.
Mainly updates the state of orders - but may also close trades
@ -296,9 +296,9 @@ class FreqtradeBot(LoggingMixin):
if sell_order:
self.refind_lost_order(trade)
else:
self.reupdate_buy_order_fees(trade)
self.reupdate_enter_order_fees(trade)
def reupdate_buy_order_fees(self, trade: Trade):
def reupdate_enter_order_fees(self, trade: Trade):
"""
Get buy order from database, and try to reupdate.
Handles trades where the initial fee-update did not work.
@ -476,21 +476,21 @@ class FreqtradeBot(LoggingMixin):
time_in_force = self.strategy.order_time_in_force['buy']
if price:
buy_limit_requested = price
enter_limit_requested = price
else:
# Calculate price
proposed_buy_rate = self.exchange.get_rate(pair, refresh=True, side="buy")
proposed_enter_rate = self.exchange.get_rate(pair, refresh=True, side="buy")
custom_entry_price = strategy_safe_wrapper(self.strategy.custom_entry_price,
default_retval=proposed_buy_rate)(
default_retval=proposed_enter_rate)(
pair=pair, current_time=datetime.now(timezone.utc),
proposed_rate=proposed_buy_rate)
proposed_rate=proposed_enter_rate)
buy_limit_requested = self.get_valid_price(custom_entry_price, proposed_buy_rate)
enter_limit_requested = self.get_valid_price(custom_entry_price, proposed_enter_rate)
if not buy_limit_requested:
if not enter_limit_requested:
raise PricingError('Could not determine buy price.')
min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, buy_limit_requested,
min_stake_amount = self.exchange.get_min_pair_stake_amount(pair, enter_limit_requested,
self.strategy.stoploss)
if not self.edge:
@ -498,7 +498,7 @@ class FreqtradeBot(LoggingMixin):
stake_amount = strategy_safe_wrapper(self.strategy.custom_stake_amount,
default_retval=stake_amount)(
pair=pair, current_time=datetime.now(timezone.utc),
current_rate=buy_limit_requested, proposed_stake=stake_amount,
current_rate=enter_limit_requested, proposed_stake=stake_amount,
min_stake=min_stake_amount, max_stake=max_stake_amount)
stake_amount = self.wallets._validate_stake_amount(pair, stake_amount, min_stake_amount)
@ -508,27 +508,27 @@ class FreqtradeBot(LoggingMixin):
logger.info(f"Buy signal found: about create a new trade for {pair} with stake_amount: "
f"{stake_amount} ...")
amount = stake_amount / buy_limit_requested
amount = stake_amount / enter_limit_requested
order_type = self.strategy.order_types['buy']
if forcebuy:
# Forcebuy can define a different ordertype
order_type = self.strategy.order_types.get('forcebuy', order_type)
if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair, order_type=order_type, amount=amount, rate=buy_limit_requested,
pair=pair, order_type=order_type, amount=amount, rate=enter_limit_requested,
time_in_force=time_in_force, current_time=datetime.now(timezone.utc)):
logger.info(f"User requested abortion of buying {pair}")
return False
amount = self.exchange.amount_to_precision(pair, amount)
order = self.exchange.create_order(pair=pair, ordertype=order_type, side="buy",
amount=amount, rate=buy_limit_requested,
amount=amount, rate=enter_limit_requested,
time_in_force=time_in_force)
order_obj = Order.parse_from_ccxt_object(order, pair, 'buy')
order_id = order['id']
order_status = order.get('status', None)
# we assume the order is executed at the price requested
buy_limit_filled_price = buy_limit_requested
enter_limit_filled_price = enter_limit_requested
amount_requested = amount
if order_status == 'expired' or order_status == 'rejected':
@ -551,13 +551,13 @@ class FreqtradeBot(LoggingMixin):
)
stake_amount = order['cost']
amount = safe_value_fallback(order, 'filled', 'amount')
buy_limit_filled_price = safe_value_fallback(order, 'average', 'price')
enter_limit_filled_price = safe_value_fallback(order, 'average', 'price')
# in case of FOK the order may be filled immediately and fully
elif order_status == 'closed':
stake_amount = order['cost']
amount = safe_value_fallback(order, 'filled', 'amount')
buy_limit_filled_price = safe_value_fallback(order, 'average', 'price')
enter_limit_filled_price = safe_value_fallback(order, 'average', 'price')
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
@ -569,8 +569,8 @@ class FreqtradeBot(LoggingMixin):
amount_requested=amount_requested,
fee_open=fee,
fee_close=fee,
open_rate=buy_limit_filled_price,
open_rate_requested=buy_limit_requested,
open_rate=enter_limit_filled_price,
open_rate_requested=enter_limit_requested,
open_date=datetime.utcnow(),
exchange=self.exchange.id,
open_order_id=order_id,
@ -590,11 +590,11 @@ class FreqtradeBot(LoggingMixin):
# Updating wallets
self.wallets.update()
self._notify_buy(trade, order_type)
self._notify_enter(trade, order_type)
return True
def _notify_buy(self, trade: Trade, order_type: str) -> None:
def _notify_enter(self, trade: Trade, order_type: str) -> None:
"""
Sends rpc notification when a buy occurred.
"""
@ -617,7 +617,7 @@ class FreqtradeBot(LoggingMixin):
# Send the message
self.rpc.send_msg(msg)
def _notify_buy_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
def _notify_enter_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
"""
Sends rpc notification when a buy cancel occurred.
"""
@ -643,7 +643,7 @@ class FreqtradeBot(LoggingMixin):
# Send the message
self.rpc.send_msg(msg)
def _notify_buy_fill(self, trade: Trade) -> None:
def _notify_enter_fill(self, trade: Trade) -> None:
msg = {
'trade_id': trade.id,
'type': RPCMessageType.BUY_FILL,
@ -713,8 +713,8 @@ class FreqtradeBot(LoggingMixin):
)
logger.debug('checking sell')
sell_rate = self.exchange.get_rate(trade.pair, refresh=True, side="sell")
if self._check_and_execute_sell(trade, sell_rate, buy, sell):
exit_rate = self.exchange.get_rate(trade.pair, refresh=True, side="sell")
if self._check_and_execute_exit(trade, exit_rate, buy, sell):
return True
logger.debug('Found no sell signal for %s.', trade)
@ -744,7 +744,7 @@ class FreqtradeBot(LoggingMixin):
except InvalidOrderException as e:
trade.stoploss_order_id = None
logger.error(f'Unable to place a stoploss order on exchange. {e}')
logger.warning('Selling the trade forcefully')
logger.warning('Exiting the trade forcefully')
self.execute_trade_exit(trade, trade.stop_loss, sell_reason=SellCheckTuple(
sell_type=SellType.EMERGENCY_SELL))
@ -782,7 +782,7 @@ class FreqtradeBot(LoggingMixin):
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
reason='Auto lock')
self._notify_sell(trade, "stoploss")
self._notify_exit(trade, "stoploss")
return True
if trade.open_order_id or not trade.is_open:
@ -851,19 +851,19 @@ class FreqtradeBot(LoggingMixin):
logger.warning(f"Could not create trailing stoploss order "
f"for pair {trade.pair}.")
def _check_and_execute_sell(self, trade: Trade, sell_rate: float,
def _check_and_execute_exit(self, trade: Trade, exit_rate: float,
buy: bool, sell: bool) -> bool:
"""
Check and execute sell
Check and execute exit
"""
should_sell = self.strategy.should_sell(
trade, sell_rate, datetime.now(timezone.utc), buy, sell,
trade, exit_rate, datetime.now(timezone.utc), buy, sell,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
)
if should_sell.sell_flag:
logger.info(f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}')
self.execute_trade_exit(trade, sell_rate, should_sell)
self.execute_trade_exit(trade, exit_rate, should_sell)
return True
return False
@ -906,7 +906,7 @@ class FreqtradeBot(LoggingMixin):
default_retval=False)(pair=trade.pair,
trade=trade,
order=order))):
self.handle_cancel_buy(trade, order, constants.CANCEL_REASON['TIMEOUT'])
self.handle_cancel_enter(trade, order, constants.CANCEL_REASON['TIMEOUT'])
elif (order['side'] == 'sell' and (order['status'] == 'open' or fully_cancelled) and (
fully_cancelled
@ -915,7 +915,7 @@ class FreqtradeBot(LoggingMixin):
default_retval=False)(pair=trade.pair,
trade=trade,
order=order))):
self.handle_cancel_sell(trade, order, constants.CANCEL_REASON['TIMEOUT'])
self.handle_cancel_exit(trade, order, constants.CANCEL_REASON['TIMEOUT'])
def cancel_all_open_orders(self) -> None:
"""
@ -931,13 +931,13 @@ class FreqtradeBot(LoggingMixin):
continue
if order['side'] == 'buy':
self.handle_cancel_buy(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
self.handle_cancel_enter(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
elif order['side'] == 'sell':
self.handle_cancel_sell(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
self.handle_cancel_exit(trade, order, constants.CANCEL_REASON['ALL_CANCELLED'])
Trade.commit()
def handle_cancel_buy(self, trade: Trade, order: Dict, reason: str) -> bool:
def handle_cancel_enter(self, trade: Trade, order: Dict, reason: str) -> bool:
"""
Buy cancel - cancel order
:return: True if order was fully cancelled
@ -994,11 +994,11 @@ class FreqtradeBot(LoggingMixin):
reason += f", {constants.CANCEL_REASON['PARTIALLY_FILLED']}"
self.wallets.update()
self._notify_buy_cancel(trade, order_type=self.strategy.order_types['buy'],
reason=reason)
self._notify_enter_cancel(trade, order_type=self.strategy.order_types['buy'],
reason=reason)
return was_trade_fully_canceled
def handle_cancel_sell(self, trade: Trade, order: Dict, reason: str) -> str:
def handle_cancel_exit(self, trade: Trade, order: Dict, reason: str) -> str:
"""
Sell cancel - cancel order and update trade
:return: Reason for cancel
@ -1032,14 +1032,14 @@ class FreqtradeBot(LoggingMixin):
reason = constants.CANCEL_REASON['PARTIALLY_FILLED_KEEP_OPEN']
self.wallets.update()
self._notify_sell_cancel(
self._notify_exit_cancel(
trade,
order_type=self.strategy.order_types['sell'],
reason=reason
)
return reason
def _safe_sell_amount(self, pair: str, amount: float) -> float:
def _safe_exit_amount(self, pair: str, amount: float) -> float:
"""
Get sellable amount.
Should be trade.amount - but will fall back to the available amount if necessary.
@ -1111,7 +1111,7 @@ class FreqtradeBot(LoggingMixin):
# but we allow this value to be changed)
order_type = self.strategy.order_types.get("forcesell", order_type)
amount = self._safe_sell_amount(trade.pair, trade.amount)
amount = self._safe_exit_amount(trade.pair, trade.amount)
time_in_force = self.strategy.order_time_in_force['sell']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
@ -1150,11 +1150,11 @@ class FreqtradeBot(LoggingMixin):
self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
reason='Auto lock')
self._notify_sell(trade, order_type)
self._notify_exit(trade, order_type)
return True
def _notify_sell(self, trade: Trade, order_type: str, fill: bool = False) -> None:
def _notify_exit(self, trade: Trade, order_type: str, fill: bool = False) -> None:
"""
Sends rpc notification when a sell occurred.
"""
@ -1196,7 +1196,7 @@ class FreqtradeBot(LoggingMixin):
# Send the message
self.rpc.send_msg(msg)
def _notify_sell_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
def _notify_exit_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
"""
Sends rpc notification when a sell cancel occurred.
"""
@ -1217,7 +1217,7 @@ class FreqtradeBot(LoggingMixin):
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
'gain': gain,
'limit': profit_rate,
'limit': profit_rate or 0,
'order_type': order_type,
'amount': trade.amount,
'open_rate': trade.open_rate,
@ -1226,7 +1226,7 @@ class FreqtradeBot(LoggingMixin):
'profit_ratio': profit_ratio,
'sell_reason': trade.sell_reason,
'open_date': trade.open_date,
'close_date': trade.close_date,
'close_date': trade.close_date or datetime.now(timezone.utc),
'stake_currency': self.config['stake_currency'],
'fiat_currency': self.config.get('fiat_display_currency', None),
'reason': reason,
@ -1291,16 +1291,28 @@ class FreqtradeBot(LoggingMixin):
# Updating wallets when order is closed
if not trade.is_open:
if not stoploss_order and not trade.open_order_id:
self._notify_sell(trade, '', True)
self.protections.stop_per_pair(trade.pair)
self.protections.global_stop()
self._notify_exit(trade, '', True)
self.handle_protections(trade.pair)
self.wallets.update()
elif not trade.open_order_id:
# Buy fill
self._notify_buy_fill(trade)
self._notify_enter_fill(trade)
return False
def handle_protections(self, pair: str) -> None:
prot_trig = self.protections.stop_per_pair(pair)
if prot_trig:
msg = {'type': RPCMessageType.PROTECTION_TRIGGER, }
msg.update(prot_trig.to_json())
self.rpc.send_msg(msg)
prot_trig_glb = self.protections.global_stop()
if prot_trig_glb:
msg = {'type': RPCMessageType.PROTECTION_TRIGGER_GLOBAL, }
msg.update(prot_trig_glb.to_json())
self.rpc.send_msg(msg)
def apply_fee_conditional(self, trade: Trade, trade_base_currency: str,
amount: float, fee_abs: float) -> float:
"""

63
freqtrade/optimize/backtesting.py
View File

@ -11,7 +11,7 @@ from typing import Any, Dict, List, Optional, Tuple
from pandas import DataFrame
from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
from freqtrade.configuration import TimeRange, validate_config_consistency
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data import history
from freqtrade.data.btanalysis import trade_list_to_dataframe
@ -61,8 +61,7 @@ class Backtesting:
self.config = config
self.results: Optional[Dict[str, Any]] = None
# Reset keys for backtesting
remove_credentials(self.config)
config['dry_run'] = True
self.strategylist: List[IStrategy] = []
self.all_results: Dict[str, Dict] = {}
@ -86,18 +85,7 @@ class Backtesting:
"configuration or as cli argument `--timeframe 5m`")
self.timeframe = str(self.config.get('timeframe'))
self.timeframe_min = timeframe_to_minutes(self.timeframe)
# Load detail timeframe if specified
self.timeframe_detail = str(self.config.get('timeframe_detail', ''))
if self.timeframe_detail:
self.timeframe_detail_min = timeframe_to_minutes(self.timeframe_detail)
if self.timeframe_min <= self.timeframe_detail_min:
raise OperationalException(
"Detail timeframe must be smaller than strategy timeframe.")
else:
self.timeframe_detail_min = 0
self.detail_data: Dict[str, DataFrame] = {}
self.init_backtest_detail()
self.pairlists = PairListManager(self.exchange, self.config)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException("VolumePairList not allowed for backtesting.")
@ -120,14 +108,6 @@ class Backtesting:
else:
self.fee = self.exchange.get_fee(symbol=self.pairlists.whitelist[0])
Trade.use_db = False
Trade.reset_trades()
PairLocks.timeframe = self.config['timeframe']
PairLocks.use_db = False
PairLocks.reset_locks()
self.wallets = Wallets(self.config, self.exchange, log=False)
self.timerange = TimeRange.parse_timerange(
None if self.config.get('timerange') is None else str(self.config.get('timerange')))
@ -136,9 +116,7 @@ class Backtesting:
# Add maximum startup candle count to configuration for informative pairs support
self.config['startup_candle_count'] = self.required_startup
self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
self.progress = BTProgress()
self.abort = False
self.init_backtest()
def __del__(self):
self.cleanup()
@ -148,6 +126,28 @@ class Backtesting:
PairLocks.use_db = True
Trade.use_db = True
def init_backtest_detail(self):
# Load detail timeframe if specified
self.timeframe_detail = str(self.config.get('timeframe_detail', ''))
if self.timeframe_detail:
self.timeframe_detail_min = timeframe_to_minutes(self.timeframe_detail)
if self.timeframe_min <= self.timeframe_detail_min:
raise OperationalException(
"Detail timeframe must be smaller than strategy timeframe.")
else:
self.timeframe_detail_min = 0
self.detail_data: Dict[str, DataFrame] = {}
def init_backtest(self):
self.prepare_backtest(False)
self.wallets = Wallets(self.config, self.exchange, log=False)
self.progress = BTProgress()
self.abort = False
def _set_strategy(self, strategy: IStrategy):
"""
Load strategy into backtesting
@ -155,7 +155,7 @@ class Backtesting:
self.strategy: IStrategy = strategy
strategy.dp = self.dataprovider
# Attach Wallets to Strategy baseclass
IStrategy.wallets = self.wallets
strategy.wallets = self.wallets
# Set stoploss_on_exchange to false for backtesting,
# since a "perfect" stoploss-sell is assumed anyway
# And the regular "stoploss" function would not apply to that case
@ -227,7 +227,8 @@ class Backtesting:
Trade.reset_trades()
self.rejected_trades = 0
self.dataprovider.clear_cache()
self._load_protections(self.strategy)
if enable_protections:
self._load_protections(self.strategy)
def check_abort(self):
"""
@ -384,12 +385,12 @@ class Backtesting:
detail_data = detail_data.loc[
(detail_data['date'] >= sell_candle_time) &
(detail_data['date'] < sell_candle_end)
]
].copy()
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
return self._get_sell_trade_entry_for_candle(trade, sell_row)
detail_data['buy'] = sell_row[BUY_IDX]
detail_data['sell'] = sell_row[SELL_IDX]
detail_data.loc[:, 'buy'] = sell_row[BUY_IDX]
detail_data.loc[:, 'sell'] = sell_row[SELL_IDX]
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
for det_row in detail_data[headers].values.tolist():
res = self._get_sell_trade_entry_for_candle(trade, det_row)

8
freqtrade/optimize/edge_cli.py
View File

@ -7,7 +7,8 @@ import logging
from typing import Any, Dict
from freqtrade import constants
from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
from freqtrade.configuration import TimeRange, validate_config_consistency
from freqtrade.data.dataprovider import DataProvider
from freqtrade.edge import Edge
from freqtrade.optimize.optimize_reports import generate_edge_table
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@ -28,11 +29,12 @@ class EdgeCli:
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
# Reset keys for edge
remove_credentials(self.config)
# Ensure using dry-run
self.config['dry_run'] = True
self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
self.strategy = StrategyResolver.load_strategy(self.config)
self.strategy.dp = DataProvider(config, None)
validate_config_consistency(self.config)

83
freqtrade/optimize/hyperopt.py
View File

@ -22,6 +22,7 @@ from pandas import DataFrame
from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.history import get_timerange
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts, file_dump_json, plural
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
@ -30,7 +31,7 @@ from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
# Suppress scikit-learn FutureWarnings from skopt
@ -44,7 +45,7 @@ progressbar.streams.wrap_stdout()
logger = logging.getLogger(__name__)
INITIAL_POINTS = 30
INITIAL_POINTS = 5
# Keep no more than SKOPT_MODEL_QUEUE_SIZE models
# in the skopt model queue, to optimize memory consumption
@ -78,10 +79,10 @@ class Hyperopt:
if not self.config.get('hyperopt'):
self.custom_hyperopt = HyperOptAuto(self.config)
self.auto_hyperopt = True
else:
self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
self.auto_hyperopt = False
raise OperationalException(
"Using separate Hyperopt files has been removed in 2021.9. Please convert "
"your existing Hyperopt file to the new Hyperoptable strategy interface")
self.backtesting._set_strategy(self.backtesting.strategylist[0])
self.custom_hyperopt.strategy = self.backtesting.strategy
@ -103,31 +104,6 @@ class Hyperopt:
self.num_epochs_saved = 0
self.current_best_epoch: Optional[Dict[str, Any]] = None
if not self.auto_hyperopt:
# Populate "fallback" functions here
# (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_indicators'):
logger.warning(
"DEPRECATED: Using `populate_indicators()` in the hyperopt file is deprecated. "
"Please move these methods to your strategy."
)
self.backtesting.strategy.populate_indicators = ( # type: ignore
self.custom_hyperopt.populate_indicators) # type: ignore
if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
logger.warning(
"DEPRECATED: Using `populate_buy_trend()` in the hyperopt file is deprecated. "
"Please move these methods to your strategy."
)
self.backtesting.strategy.populate_buy_trend = ( # type: ignore
self.custom_hyperopt.populate_buy_trend) # type: ignore
if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
logger.warning(
"DEPRECATED: Using `populate_sell_trend()` in the hyperopt file is deprecated. "
"Please move these methods to your strategy."
)
self.backtesting.strategy.populate_sell_trend = ( # type: ignore
self.custom_hyperopt.populate_sell_trend) # type: ignore
# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
self.max_open_trades = self.config['max_open_trades']
@ -256,7 +232,7 @@ class Hyperopt:
"""
Assign the dimensions in the hyperoptimization space.
"""
if self.auto_hyperopt and HyperoptTools.has_space(self.config, 'protection'):
if HyperoptTools.has_space(self.config, 'protection'):
# Protections can only be optimized when using the Parameter interface
logger.debug("Hyperopt has 'protection' space")
# Enable Protections if protection space is selected.
@ -265,7 +241,7 @@ class Hyperopt:
if HyperoptTools.has_space(self.config, 'buy'):
logger.debug("Hyperopt has 'buy' space")
self.buy_space = self.custom_hyperopt.indicator_space()
self.buy_space = self.custom_hyperopt.buy_indicator_space()
if HyperoptTools.has_space(self.config, 'sell'):
logger.debug("Hyperopt has 'sell' space")
@ -285,6 +261,15 @@ class Hyperopt:
self.dimensions = (self.buy_space + self.sell_space + self.protection_space
+ self.roi_space + self.stoploss_space + self.trailing_space)
def assign_params(self, params_dict: Dict, category: str) -> None:
"""
Assign hyperoptable parameters
"""
for attr_name, attr in self.backtesting.strategy.enumerate_parameters(category):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params_dict[attr_name]
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
"""
Used Optimize function.
@ -296,18 +281,13 @@ class Hyperopt:
# Apply parameters
if HyperoptTools.has_space(self.config, 'buy'):
self.backtesting.strategy.advise_buy = ( # type: ignore
self.custom_hyperopt.buy_strategy_generator(params_dict))
self.assign_params(params_dict, 'buy')
if HyperoptTools.has_space(self.config, 'sell'):
self.backtesting.strategy.advise_sell = ( # type: ignore
self.custom_hyperopt.sell_strategy_generator(params_dict))
self.assign_params(params_dict, 'sell')
if HyperoptTools.has_space(self.config, 'protection'):
for attr_name, attr in self.backtesting.strategy.enumerate_parameters('protection'):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params_dict[attr_name]
self.assign_params(params_dict, 'protection')
if HyperoptTools.has_space(self.config, 'roi'):
self.backtesting.strategy.minimal_roi = ( # type: ignore
@ -385,10 +365,20 @@ class Hyperopt:
}
def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
estimator = self.custom_hyperopt.generate_estimator()
acq_optimizer = "sampling"
if isinstance(estimator, str):
if estimator not in ("GP", "RF", "ET", "GBRT"):
raise OperationalException(f"Estimator {estimator} not supported.")
else:
acq_optimizer = "auto"
logger.info(f"Using estimator {estimator}.")
return Optimizer(
dimensions,
base_estimator="ET",
acq_optimizer="auto",
base_estimator=estimator,
acq_optimizer=acq_optimizer,
n_initial_points=INITIAL_POINTS,
acq_optimizer_kwargs={'n_jobs': cpu_count},
random_state=self.random_state,
@ -517,11 +507,10 @@ class Hyperopt:
f"saved to '{self.results_file}'.")
if self.current_best_epoch:
if self.auto_hyperopt:
HyperoptTools.try_export_params(
self.config,
self.backtesting.strategy.get_strategy_name(),
self.current_best_epoch)
HyperoptTools.try_export_params(
self.config,
self.backtesting.strategy.get_strategy_name(),
self.current_best_epoch)
HyperoptTools.show_epoch_details(self.current_best_epoch, self.total_epochs,
self.print_json)

50
freqtrade/optimize/hyperopt_auto.py
View File

@ -4,15 +4,23 @@ This module implements a convenience auto-hyperopt class, which can be used toge
that implement IHyperStrategy interface.
"""
from contextlib import suppress
from typing import Any, Callable, Dict, List
from typing import Callable, Dict, List
from pandas import DataFrame
from freqtrade.exceptions import OperationalException
with suppress(ImportError):
from skopt.space import Dimension
from freqtrade.optimize.hyperopt_interface import IHyperOpt
from freqtrade.optimize.hyperopt_interface import EstimatorType, IHyperOpt
def _format_exception_message(space: str) -> str:
raise OperationalException(
f"The '{space}' space is included into the hyperoptimization "
f"but no parameter for this space was not found in your Strategy. "
f"Please make sure to have parameters for this space enabled for optimization "
f"or remove the '{space}' space from hyperoptimization.")
class HyperOptAuto(IHyperOpt):
@ -22,26 +30,6 @@ class HyperOptAuto(IHyperOpt):
sell_indicator_space methods, but other hyperopt methods can be overridden as well.
"""
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
def populate_buy_trend(dataframe: DataFrame, metadata: dict):
for attr_name, attr in self.strategy.enumerate_parameters('buy'):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params[attr_name]
return self.strategy.populate_buy_trend(dataframe, metadata)
return populate_buy_trend
def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
def populate_sell_trend(dataframe: DataFrame, metadata: dict):
for attr_name, attr in self.strategy.enumerate_parameters('sell'):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params[attr_name]
return self.strategy.populate_sell_trend(dataframe, metadata)
return populate_sell_trend
def _get_func(self, name) -> Callable:
"""
Return a function defined in Strategy.HyperOpt class, or one defined in super() class.
@ -60,21 +48,22 @@ class HyperOptAuto(IHyperOpt):
if attr.optimize:
yield attr.get_space(attr_name)
def _get_indicator_space(self, category, fallback_method_name):
def _get_indicator_space(self, category):
# TODO: is this necessary, or can we call "generate_space" directly?
indicator_space = list(self._generate_indicator_space(category))
if len(indicator_space) > 0:
return indicator_space
else:
return self._get_func(fallback_method_name)()
_format_exception_message(category)
def indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('buy', 'indicator_space')
def buy_indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('buy')
def sell_indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('sell', 'sell_indicator_space')
return self._get_indicator_space('sell')
def protection_space(self) -> List['Dimension']:
return self._get_indicator_space('protection', 'protection_space')
return self._get_indicator_space('protection')
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
return self._get_func('generate_roi_table')(params)
@ -90,3 +79,6 @@ class HyperOptAuto(IHyperOpt):
def trailing_space(self) -> List['Dimension']:
return self._get_func('trailing_space')()
def generate_estimator(self) -> EstimatorType:
return self._get_func('generate_estimator')()

44
freqtrade/optimize/hyperopt_interface.py
View File

@ -5,11 +5,11 @@ This module defines the interface to apply for hyperopt
import logging
import math
from abc import ABC
from typing import Any, Callable, Dict, List
from typing import Dict, List, Union
from sklearn.base import RegressorMixin
from skopt.space import Categorical, Dimension, Integer
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import round_dict
from freqtrade.optimize.space import SKDecimal
@ -18,12 +18,7 @@ from freqtrade.strategy import IStrategy
logger = logging.getLogger(__name__)
def _format_exception_message(method: str, space: str) -> str:
return (f"The '{space}' space is included into the hyperoptimization "
f"but {method}() method is not found in your "
f"custom Hyperopt class. You should either implement this "
f"method or remove the '{space}' space from hyperoptimization.")
EstimatorType = Union[RegressorMixin, str]
class IHyperOpt(ABC):
@ -45,36 +40,13 @@ class IHyperOpt(ABC):
IHyperOpt.ticker_interval = str(config['timeframe']) # DEPRECATED
IHyperOpt.timeframe = str(config['timeframe'])
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
def generate_estimator(self) -> EstimatorType:
"""
Create a buy strategy generator.
Return base_estimator.
Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class
inheriting from RegressorMixin (from sklearn).
"""
raise OperationalException(_format_exception_message('buy_strategy_generator', 'buy'))
def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
"""
Create a sell strategy generator.
"""
raise OperationalException(_format_exception_message('sell_strategy_generator', 'sell'))
def protection_space(self) -> List[Dimension]:
"""
Create a protection space.
Only supported by the Parameter interface.
"""
raise OperationalException(_format_exception_message('indicator_space', 'protection'))
def indicator_space(self) -> List[Dimension]:
"""
Create an indicator space.
"""
raise OperationalException(_format_exception_message('indicator_space', 'buy'))
def sell_indicator_space(self) -> List[Dimension]:
"""
Create a sell indicator space.
"""
raise OperationalException(_format_exception_message('sell_indicator_space', 'sell'))
return 'ET'
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
"""

8
freqtrade/optimize/hyperopt_tools.py
View File

@ -7,6 +7,7 @@ from pathlib import Path
from typing import Any, Dict, Iterator, List, Optional, Tuple
import numpy as np
import pandas as pd
import rapidjson
import tabulate
from colorama import Fore, Style
@ -298,8 +299,8 @@ class HyperoptTools():
f"Objective: {results['loss']:.5f}")
@staticmethod
def prepare_trials_columns(trials, legacy_mode: bool, has_drawdown: bool) -> str:
def prepare_trials_columns(trials: pd.DataFrame, legacy_mode: bool,
has_drawdown: bool) -> pd.DataFrame:
trials['Best'] = ''
if 'results_metrics.winsdrawslosses' not in trials.columns:
@ -435,8 +436,7 @@ class HyperoptTools():
return table
@staticmethod
def export_csv_file(config: dict, results: list, total_epochs: int, highlight_best: bool,
csv_file: str) -> None:
def export_csv_file(config: dict, results: list, csv_file: str) -> None:
"""
Log result to csv-file
"""

10
freqtrade/persistence/models.py
View File

@ -2,7 +2,7 @@
This module contains the class to persist trades into SQLite
"""
import logging
from datetime import datetime, timezone
from datetime import datetime, timedelta, timezone
from decimal import Decimal
from typing import Any, Dict, List, Optional
@ -832,17 +832,21 @@ class Trade(_DECL_BASE, LocalTrade):
return total_open_stake_amount or 0
@staticmethod
def get_overall_performance() -> List[Dict[str, Any]]:
def get_overall_performance(minutes=None) -> List[Dict[str, Any]]:
"""
Returns List of dicts containing all Trades, including profit and trade count
NOTE: Not supported in Backtesting.
"""
filters = [Trade.is_open.is_(False)]
if minutes:
start_date = datetime.now(timezone.utc) - timedelta(minutes=minutes)
filters.append(Trade.close_date >= start_date)
pair_rates = Trade.query.with_entities(
Trade.pair,
func.sum(Trade.close_profit).label('profit_sum'),
func.sum(Trade.close_profit_abs).label('profit_sum_abs'),
func.count(Trade.pair).label('count')
).filter(Trade.is_open.is_(False))\
).filter(*filters)\
.group_by(Trade.pair) \
.order_by(desc('profit_sum_abs')) \
.all()

4
freqtrade/persistence/pairlock_middleware.py
View File

@ -30,7 +30,8 @@ class PairLocks():
PairLocks.locks = []
@staticmethod
def lock_pair(pair: str, until: datetime, reason: str = None, *, now: datetime = None) -> None:
def lock_pair(pair: str, until: datetime, reason: str = None, *,
now: datetime = None) -> PairLock:
"""
Create PairLock from now to "until".
Uses database by default, unless PairLocks.use_db is set to False,
@ -52,6 +53,7 @@ class PairLocks():
PairLock.query.session.commit()
else:
PairLocks.locks.append(lock)
return lock
@staticmethod
def get_pair_locks(pair: Optional[str], now: Optional[datetime] = None) -> List[PairLock]:

16
freqtrade/plugins/pairlist/AgeFilter.py
View File

@ -8,6 +8,7 @@ from typing import Any, Dict, List, Optional
import arrow
from pandas import DataFrame
from freqtrade.configuration import PeriodicCache
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural
from freqtrade.plugins.pairlist.IPairList import IPairList
@ -18,14 +19,15 @@ logger = logging.getLogger(__name__)
class AgeFilter(IPairList):
# Checked symbols cache (dictionary of ticker symbol => timestamp)
_symbolsChecked: Dict[str, int] = {}
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
# Checked symbols cache (dictionary of ticker symbol => timestamp)
self._symbolsChecked: Dict[str, int] = {}
self._symbolsCheckFailed = PeriodicCache(maxsize=1000, ttl=86_400)
self._min_days_listed = pairlistconfig.get('min_days_listed', 10)
self._max_days_listed = pairlistconfig.get('max_days_listed', None)
@ -69,9 +71,12 @@ class AgeFilter(IPairList):
:param tickers: Tickers (from exchange.get_tickers()). May be cached.
:return: new allowlist
"""
needed_pairs = [(p, '1d') for p in pairlist if p not in self._symbolsChecked]
needed_pairs = [
(p, '1d') for p in pairlist
if p not in self._symbolsChecked and p not in self._symbolsCheckFailed]
if not needed_pairs:
return pairlist
# Remove pairs that have been removed before
return [p for p in pairlist if p not in self._symbolsCheckFailed]
since_days = -(
self._max_days_listed if self._max_days_listed else self._min_days_listed
@ -118,5 +123,6 @@ class AgeFilter(IPairList):
" or more than "
f"{self._max_days_listed} {plural(self._max_days_listed, 'day')}"
) if self._max_days_listed else ''), logger.info)
self._symbolsCheckFailed[pair] = arrow.utcnow().int_timestamp * 1000
return False
return False

11
freqtrade/plugins/pairlist/PerformanceFilter.py
View File

@ -2,7 +2,7 @@
Performance pair list filter
"""
import logging
from typing import Dict, List
from typing import Any, Dict, List
import pandas as pd
@ -15,6 +15,13 @@ logger = logging.getLogger(__name__)
class PerformanceFilter(IPairList):
def __init__(self, exchange, pairlistmanager,
config: Dict[str, Any], pairlistconfig: Dict[str, Any],
pairlist_pos: int) -> None:
super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
self._minutes = pairlistconfig.get('minutes', 0)
@property
def needstickers(self) -> bool:
"""
@ -40,7 +47,7 @@ class PerformanceFilter(IPairList):
"""
# Get the trading performance for pairs from database
try:
performance = pd.DataFrame(Trade.get_overall_performance())
performance = pd.DataFrame(Trade.get_overall_performance(self._minutes))
except AttributeError:
# Performancefilter does not work in backtesting.
self.log_once("PerformanceFilter is not available in this mode.", logger.warning)

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

@ -123,7 +123,7 @@ class VolumePairList(IPairList):
filtered_tickers = [
v for k, v in tickers.items()
if (self._exchange.get_pair_quote_currency(k) == self._stake_currency
and v[self._sort_key] is not None)]
and (self._use_range or v[self._sort_key] is not None))]
pairlist = [s['symbol'] for s in filtered_tickers]
pairlist = self.filter_pairlist(pairlist, tickers)

23
freqtrade/plugins/protectionmanager.py
View File

@ -6,6 +6,7 @@ from datetime import datetime, timezone
from typing import Dict, List, Optional
from freqtrade.persistence import PairLocks
from freqtrade.persistence.models import PairLock
from freqtrade.plugins.protections import IProtection
from freqtrade.resolvers import ProtectionResolver
@ -43,30 +44,28 @@ class ProtectionManager():
"""
return [{p.name: p.short_desc()} for p in self._protection_handlers]
def global_stop(self, now: Optional[datetime] = None) -> bool:
def global_stop(self, now: Optional[datetime] = None) -> Optional[PairLock]:
if not now:
now = datetime.now(timezone.utc)
result = False
result = None
for protection_handler in self._protection_handlers:
if protection_handler.has_global_stop:
result, until, reason = protection_handler.global_stop(now)
lock, until, reason = protection_handler.global_stop(now)
# Early stopping - first positive result blocks further trades
if result and until:
if lock and until:
if not PairLocks.is_global_lock(until):
PairLocks.lock_pair('*', until, reason, now=now)
result = True
result = PairLocks.lock_pair('*', until, reason, now=now)
return result
def stop_per_pair(self, pair, now: Optional[datetime] = None) -> bool:
def stop_per_pair(self, pair, now: Optional[datetime] = None) -> Optional[PairLock]:
if not now:
now = datetime.now(timezone.utc)
result = False
result = None
for protection_handler in self._protection_handlers:
if protection_handler.has_local_stop:
result, until, reason = protection_handler.stop_per_pair(pair, now)
if result and until:
lock, until, reason = protection_handler.stop_per_pair(pair, now)
if lock and until:
if not PairLocks.is_pair_locked(pair, until):
PairLocks.lock_pair(pair, until, reason, now=now)
result = True
result = PairLocks.lock_pair(pair, until, reason, now=now)
return result

38
freqtrade/resolvers/hyperopt_resolver.py
View File

@ -9,7 +9,6 @@ from typing import Dict
from freqtrade.constants import HYPEROPT_LOSS_BUILTIN, USERPATH_HYPEROPTS
from freqtrade.exceptions import OperationalException
from freqtrade.optimize.hyperopt_interface import IHyperOpt
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
from freqtrade.resolvers import IResolver
@ -17,43 +16,6 @@ from freqtrade.resolvers import IResolver
logger = logging.getLogger(__name__)
class HyperOptResolver(IResolver):
"""
This class contains all the logic to load custom hyperopt class
"""
object_type = IHyperOpt
object_type_str = "Hyperopt"
user_subdir = USERPATH_HYPEROPTS
initial_search_path = None
@staticmethod
def load_hyperopt(config: Dict) -> IHyperOpt:
"""
Load the custom hyperopt class from config parameter
:param config: configuration dictionary
"""
if not config.get('hyperopt'):
raise OperationalException("No Hyperopt set. Please use `--hyperopt` to specify "
"the Hyperopt class to use.")
hyperopt_name = config['hyperopt']
hyperopt = HyperOptResolver.load_object(hyperopt_name, config,
kwargs={'config': config},
extra_dir=config.get('hyperopt_path'))
if not hasattr(hyperopt, 'populate_indicators'):
logger.info("Hyperopt class does not provide populate_indicators() method. "
"Using populate_indicators from the strategy.")
if not hasattr(hyperopt, 'populate_buy_trend'):
logger.info("Hyperopt class does not provide populate_buy_trend() method. "
"Using populate_buy_trend from the strategy.")
if not hasattr(hyperopt, 'populate_sell_trend'):
logger.info("Hyperopt class does not provide populate_sell_trend() method. "
"Using populate_sell_trend from the strategy.")
return hyperopt
class HyperOptLossResolver(IResolver):
"""
This class contains all the logic to load custom hyperopt loss class

23
freqtrade/rpc/api_server/api_backtest.py
View File

@ -4,6 +4,7 @@ from copy import deepcopy
from fastapi import APIRouter, BackgroundTasks, Depends
from freqtrade.configuration.config_validation import validate_config_consistency
from freqtrade.enums import BacktestState
from freqtrade.exceptions import DependencyException
from freqtrade.rpc.api_server.api_schemas import BacktestRequest, BacktestResponse
@ -42,38 +43,40 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
# Reload strategy
lastconfig = ApiServer._bt_last_config
strat = StrategyResolver.load_strategy(btconfig)
validate_config_consistency(btconfig)
if (
not ApiServer._bt
or lastconfig.get('timeframe') != strat.timeframe
or lastconfig.get('timeframe_detail') != btconfig.get('timeframe_detail')
or lastconfig.get('dry_run_wallet') != btconfig.get('dry_run_wallet', 0)
or lastconfig.get('timerange') != btconfig['timerange']
):
from freqtrade.optimize.backtesting import Backtesting
ApiServer._bt = Backtesting(btconfig)
if ApiServer._bt.timeframe_detail:
ApiServer._bt.load_bt_data_detail()
else:
ApiServer._bt.config = btconfig
ApiServer._bt.init_backtest()
# Only reload data if timeframe changed.
if (
not ApiServer._bt_data
or not ApiServer._bt_timerange
or lastconfig.get('stake_amount') != btconfig.get('stake_amount')
or lastconfig.get('enable_protections') != btconfig.get('enable_protections')
or lastconfig.get('protections') != btconfig.get('protections', [])
or lastconfig.get('timeframe') != strat.timeframe
or lastconfig.get('timerange') != btconfig['timerange']
):
lastconfig['timerange'] = btconfig['timerange']
lastconfig['protections'] = btconfig.get('protections', [])
lastconfig['enable_protections'] = btconfig.get('enable_protections')
lastconfig['dry_run_wallet'] = btconfig.get('dry_run_wallet')
lastconfig['timeframe'] = strat.timeframe
ApiServer._bt_data, ApiServer._bt_timerange = ApiServer._bt.load_bt_data()
lastconfig['timerange'] = btconfig['timerange']
lastconfig['timeframe'] = strat.timeframe
lastconfig['protections'] = btconfig.get('protections', [])
lastconfig['enable_protections'] = btconfig.get('enable_protections')
lastconfig['dry_run_wallet'] = btconfig.get('dry_run_wallet')
ApiServer._bt.abort = False
min_date, max_date = ApiServer._bt.backtest_one_strategy(
strat, ApiServer._bt_data, ApiServer._bt_timerange)
ApiServer._bt.results = generate_backtest_stats(
ApiServer._bt_data, ApiServer._bt.all_results,
min_date=min_date, max_date=max_date)

6
freqtrade/rpc/api_server/api_schemas.py
View File

@ -46,6 +46,12 @@ class Balances(BaseModel):
value: float
stake: str
note: str
starting_capital: float
starting_capital_ratio: float
starting_capital_pct: float
starting_capital_fiat: float
starting_capital_fiat_ratio: float
starting_capital_fiat_pct: float
class Count(BaseModel):

36
freqtrade/rpc/rpc.py
View File

@ -403,8 +403,11 @@ class RPC:
# Doing the sum is not right - overall profit needs to be based on initial capital
profit_all_ratio_sum = sum(profit_all_ratio) if profit_all_ratio else 0.0
starting_balance = self._freqtrade.wallets.get_starting_balance()
profit_closed_ratio_fromstart = profit_closed_coin_sum / starting_balance
profit_all_ratio_fromstart = profit_all_coin_sum / starting_balance
profit_closed_ratio_fromstart = 0
profit_all_ratio_fromstart = 0
if starting_balance:
profit_closed_ratio_fromstart = profit_closed_coin_sum / starting_balance
profit_all_ratio_fromstart = profit_all_coin_sum / starting_balance
profit_all_fiat = self._fiat_converter.convert_amount(
profit_all_coin_sum,
@ -455,6 +458,9 @@ class RPC:
raise RPCException('Error getting current tickers.')
self._freqtrade.wallets.update(require_update=False)
starting_capital = self._freqtrade.wallets.get_starting_balance()
starting_cap_fiat = self._fiat_converter.convert_amount(
starting_capital, stake_currency, fiat_display_currency) if self._fiat_converter else 0
for coin, balance in self._freqtrade.wallets.get_all_balances().items():
if not balance.total:
@ -490,15 +496,25 @@ class RPC:
else:
raise RPCException('All balances are zero.')
symbol = fiat_display_currency
value = self._fiat_converter.convert_amount(total, stake_currency,
symbol) if self._fiat_converter else 0
value = self._fiat_converter.convert_amount(
total, stake_currency, fiat_display_currency) if self._fiat_converter else 0
starting_capital_ratio = 0.0
starting_capital_ratio = (total / starting_capital) - 1 if starting_capital else 0.0
starting_cap_fiat_ratio = (value / starting_cap_fiat) - 1 if starting_cap_fiat else 0.0
return {
'currencies': output,
'total': total,
'symbol': symbol,
'symbol': fiat_display_currency,
'value': value,
'stake': stake_currency,
'starting_capital': starting_capital,
'starting_capital_ratio': starting_capital_ratio,
'starting_capital_pct': round(starting_capital_ratio * 100, 2),
'starting_capital_fiat': starting_cap_fiat,
'starting_capital_fiat_ratio': starting_cap_fiat_ratio,
'starting_capital_fiat_pct': round(starting_cap_fiat_ratio * 100, 2),
'note': 'Simulated balances' if self._freqtrade.config['dry_run'] else ''
}
@ -545,12 +561,12 @@ class RPC:
order = self._freqtrade.exchange.fetch_order(trade.open_order_id, trade.pair)
if order['side'] == 'buy':
fully_canceled = self._freqtrade.handle_cancel_buy(
fully_canceled = self._freqtrade.handle_cancel_enter(
trade, order, CANCEL_REASON['FORCE_SELL'])
if order['side'] == 'sell':
# Cancel order - so it is placed anew with a fresh price.
self._freqtrade.handle_cancel_sell(trade, order, CANCEL_REASON['FORCE_SELL'])
self._freqtrade.handle_cancel_exit(trade, order, CANCEL_REASON['FORCE_SELL'])
if not fully_canceled:
# Get current rate and execute sell
@ -563,7 +579,7 @@ class RPC:
if self._freqtrade.state != State.RUNNING:
raise RPCException('trader is not running')
with self._freqtrade._sell_lock:
with self._freqtrade._exit_lock:
if trade_id == 'all':
# Execute sell for all open orders
for trade in Trade.get_open_trades():
@ -625,7 +641,7 @@ class RPC:
Handler for delete <id>.
Delete the given trade and close eventually existing open orders.
"""
with self._freqtrade._sell_lock:
with self._freqtrade._exit_lock:
c_count = 0
trade = Trade.get_trades(trade_filter=[Trade.id == trade_id]).first()
if not trade:

98
freqtrade/rpc/telegram.py
View File

@ -260,6 +260,50 @@ class Telegram(RPCHandler):
return message
def compose_message(self, msg: Dict[str, Any], msg_type: RPCMessageType) -> str:
if msg_type == RPCMessageType.BUY:
message = self._format_buy_msg(msg)
elif msg_type in (RPCMessageType.BUY_CANCEL, RPCMessageType.SELL_CANCEL):
msg['message_side'] = 'buy' if msg_type == RPCMessageType.BUY_CANCEL else 'sell'
message = ("\N{WARNING SIGN} *{exchange}:* "
"Cancelling open {message_side} Order for {pair} (#{trade_id}). "
"Reason: {reason}.".format(**msg))
elif msg_type == RPCMessageType.BUY_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Buy order for {pair} (#{trade_id}) filled "
"for {open_rate}.".format(**msg))
elif msg_type == RPCMessageType.SELL_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Sell order for {pair} (#{trade_id}) filled "
"for {close_rate}.".format(**msg))
elif msg_type == RPCMessageType.SELL:
message = self._format_sell_msg(msg)
elif msg_type == RPCMessageType.PROTECTION_TRIGGER:
message = (
"*Protection* triggered due to {reason}. "
"`{pair}` will be locked until `{lock_end_time}`."
).format(**msg)
elif msg_type == RPCMessageType.PROTECTION_TRIGGER_GLOBAL:
message = (
"*Protection* triggered due to {reason}. "
"*All pairs* will be locked until `{lock_end_time}`."
).format(**msg)
elif msg_type == RPCMessageType.STATUS:
message = '*Status:* `{status}`'.format(**msg)
elif msg_type == RPCMessageType.WARNING:
message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg)
elif msg_type == RPCMessageType.STARTUP:
message = '{status}'.format(**msg)
else:
raise NotImplementedError('Unknown message type: {}'.format(msg_type))
return message
def send_msg(self, msg: Dict[str, Any]) -> None:
""" Send a message to telegram channel """
@ -284,37 +328,7 @@ class Telegram(RPCHandler):
# Notification disabled
return
if msg_type == RPCMessageType.BUY:
message = self._format_buy_msg(msg)
elif msg_type in (RPCMessageType.BUY_CANCEL, RPCMessageType.SELL_CANCEL):
msg['message_side'] = 'buy' if msg_type == RPCMessageType.BUY_CANCEL else 'sell'
message = ("\N{WARNING SIGN} *{exchange}:* "
"Cancelling open {message_side} Order for {pair} (#{trade_id}). "
"Reason: {reason}.".format(**msg))
elif msg_type == RPCMessageType.BUY_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Buy order for {pair} (#{trade_id}) filled "
"for {open_rate}.".format(**msg))
elif msg_type == RPCMessageType.SELL_FILL:
message = ("\N{LARGE CIRCLE} *{exchange}:* "
"Sell order for {pair} (#{trade_id}) filled "
"for {close_rate}.".format(**msg))
elif msg_type == RPCMessageType.SELL:
message = self._format_sell_msg(msg)
elif msg_type == RPCMessageType.STATUS:
message = '*Status:* `{status}`'.format(**msg)
elif msg_type == RPCMessageType.WARNING:
message = '\N{WARNING SIGN} *Warning:* `{status}`'.format(**msg)
elif msg_type == RPCMessageType.STARTUP:
message = '{status}'.format(**msg)
else:
raise NotImplementedError('Unknown message type: {}'.format(msg_type))
message = self.compose_message(msg, msg_type)
self._send_msg(message, disable_notification=(noti == 'silent'))
@ -603,12 +617,15 @@ class Telegram(RPCHandler):
output = ''
if self._config['dry_run']:
output += (
f"*Warning:* Simulated balances in Dry Mode.\n"
"This mode is still experimental!\n"
"Starting capital: "
f"`{self._config['dry_run_wallet']}` {self._config['stake_currency']}.\n"
)
output += "*Warning:* Simulated balances in Dry Mode.\n"
output += ("Starting capital: "
f"`{result['starting_capital']}` {self._config['stake_currency']}"
)
output += (f" `{result['starting_capital_fiat']}` "
f"{self._config['fiat_display_currency']}.\n"
) if result['starting_capital_fiat'] > 0 else '.\n'
total_dust_balance = 0
total_dust_currencies = 0
for curr in result['currencies']:
@ -641,9 +658,12 @@ class Telegram(RPCHandler):
f"{round_coin_value(total_dust_balance, result['stake'], False)}`\n")
output += ("\n*Estimated Value*:\n"
f"\t`{result['stake']}: {result['total']: .8f}`\n"
f"\t`{result['stake']}: "
f"{round_coin_value(result['total'], result['stake'], False)}`"
f" `({result['starting_capital_pct']}%)`\n"
f"\t`{result['symbol']}: "
f"{round_coin_value(result['value'], result['symbol'], False)}`\n")
f"{round_coin_value(result['value'], result['symbol'], False)}`"
f" `({result['starting_capital_fiat_pct']}%)`\n")
self._send_msg(output, reload_able=True, callback_path="update_balance",
query=update.callback_query)
except RPCException as e:

4
freqtrade/strategy/__init__.py
View File

@ -3,5 +3,7 @@ from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_msecs, timefr
timeframe_to_prev_date, timeframe_to_seconds)
from freqtrade.strategy.hyper import (BooleanParameter, CategoricalParameter, DecimalParameter,
IntParameter, RealParameter)
from freqtrade.strategy.informative_decorator import informative
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_helper import merge_informative_pair, stoploss_from_open
from freqtrade.strategy.strategy_helper import (merge_informative_pair, stoploss_from_absolute,
stoploss_from_open)

128
freqtrade/strategy/informative_decorator.py Normal file
View File

@ -0,0 +1,128 @@
from typing import Any, Callable, NamedTuple, Optional, Union
from pandas import DataFrame
from freqtrade.exceptions import OperationalException
from freqtrade.strategy.strategy_helper import merge_informative_pair
PopulateIndicators = Callable[[Any, DataFrame, dict], DataFrame]
class InformativeData(NamedTuple):
asset: Optional[str]
timeframe: str
fmt: Union[str, Callable[[Any], str], None]
ffill: bool
def informative(timeframe: str, asset: str = '',
fmt: Optional[Union[str, Callable[[Any], str]]] = None,
ffill: bool = True) -> Callable[[PopulateIndicators], PopulateIndicators]:
"""
A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to
define informative indicators.
Example usage:
@informative('1h')
def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
: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.
"""
_asset = asset
_timeframe = timeframe
_fmt = fmt
_ffill = ffill
def decorator(fn: PopulateIndicators):
informative_pairs = getattr(fn, '_ft_informative', [])
informative_pairs.append(InformativeData(_asset, _timeframe, _fmt, _ffill))
setattr(fn, '_ft_informative', informative_pairs)
return fn
return decorator
def _format_pair_name(config, pair: str) -> str:
return pair.format(stake_currency=config['stake_currency'],
stake=config['stake_currency']).upper()
def _create_and_merge_informative_pair(strategy, dataframe: DataFrame, metadata: dict,
inf_data: InformativeData,
populate_indicators: PopulateIndicators):
asset = inf_data.asset or ''
timeframe = inf_data.timeframe
fmt = inf_data.fmt
config = strategy.config
if asset:
# Insert stake currency if needed.
asset = _format_pair_name(config, asset)
else:
# Not specifying an asset will define informative dataframe for current pair.
asset = metadata['pair']
if '/' in asset:
base, quote = asset.split('/')
else:
# When futures are supported this may need reevaluation.
# base, quote = asset, ''
raise OperationalException('Not implemented.')
# Default format. This optimizes for the common case: informative pairs using same stake
# currency. When quote currency matches stake currency, column name will omit base currency.
# This allows easily reconfiguring strategy to use different base currency. In a rare case
# where it is desired to keep quote currency in column name at all times user should specify
# fmt='{base}_{quote}_{column}_{timeframe}' format or similar.
if not fmt:
fmt = '{column}_{timeframe}' # Informatives of current pair
if inf_data.asset:
fmt = '{base}_{quote}_' + fmt # Informatives of other pairs
inf_metadata = {'pair': asset, 'timeframe': timeframe}
inf_dataframe = strategy.dp.get_pair_dataframe(asset, timeframe)
inf_dataframe = populate_indicators(strategy, inf_dataframe, inf_metadata)
formatter: Any = None
if callable(fmt):
formatter = fmt # A custom user-specified formatter function.
else:
formatter = fmt.format # A default string formatter.
fmt_args = {
'BASE': base.upper(),
'QUOTE': quote.upper(),
'base': base.lower(),
'quote': quote.lower(),
'asset': asset,
'timeframe': timeframe,
}
inf_dataframe.rename(columns=lambda column: formatter(column=column, **fmt_args),
inplace=True)
date_column = formatter(column='date', **fmt_args)
if date_column in dataframe.columns:
raise OperationalException(f'Duplicate column name {date_column} exists in '
f'dataframe! Ensure column names are unique!')
dataframe = merge_informative_pair(dataframe, inf_dataframe, strategy.timeframe, timeframe,
ffill=inf_data.ffill, append_timeframe=False,
date_column=date_column)
return dataframe

49
freqtrade/strategy/interface.py
View File

@ -19,6 +19,9 @@ from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.exchange.exchange import timeframe_to_next_date
from freqtrade.persistence import PairLocks, Trade
from freqtrade.strategy.hyper import HyperStrategyMixin
from freqtrade.strategy.informative_decorator import (InformativeData, PopulateIndicators,
_create_and_merge_informative_pair,
_format_pair_name)
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
@ -118,7 +121,7 @@ class IStrategy(ABC, HyperStrategyMixin):
# Class level variables (intentional) containing
# the dataprovider (dp) (access to other candles, historic data, ...)
# and wallets - access to the current balance.
dp: Optional[DataProvider] = None
dp: Optional[DataProvider]
wallets: Optional[Wallets] = None
# Filled from configuration
stake_currency: str
@ -134,6 +137,24 @@ class IStrategy(ABC, HyperStrategyMixin):
self._last_candle_seen_per_pair: Dict[str, datetime] = {}
super().__init__(config)
# Gather informative pairs from @informative-decorated methods.
self._ft_informative: List[Tuple[InformativeData, PopulateIndicators]] = []
for attr_name in dir(self.__class__):
cls_method = getattr(self.__class__, attr_name)
if not callable(cls_method):
continue
informative_data_list = getattr(cls_method, '_ft_informative', None)
if not isinstance(informative_data_list, list):
# Type check is required because mocker would return a mock object that evaluates to
# True, confusing this code.
continue
strategy_timeframe_minutes = timeframe_to_minutes(self.timeframe)
for informative_data in informative_data_list:
if timeframe_to_minutes(informative_data.timeframe) < strategy_timeframe_minutes:
raise OperationalException('Informative timeframe must be equal or higher than '
'strategy timeframe!')
self._ft_informative.append((informative_data, cls_method))
@abstractmethod
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
@ -377,6 +398,23 @@ class IStrategy(ABC, HyperStrategyMixin):
# END - Intended to be overridden by strategy
###
def gather_informative_pairs(self) -> ListPairsWithTimeframes:
"""
Internal method which gathers all informative pairs (user or automatically defined).
"""
informative_pairs = self.informative_pairs()
for inf_data, _ in self._ft_informative:
if inf_data.asset:
pair_tf = (_format_pair_name(self.config, inf_data.asset), inf_data.timeframe)
informative_pairs.append(pair_tf)
else:
if not self.dp:
raise OperationalException('@informative decorator with unspecified asset '
'requires DataProvider instance.')
for pair in self.dp.current_whitelist():
informative_pairs.append((pair, inf_data.timeframe))
return list(set(informative_pairs))
def get_strategy_name(self) -> str:
"""
Returns strategy class name
@ -777,10 +815,11 @@ class IStrategy(ABC, HyperStrategyMixin):
Does not run advise_buy or advise_sell!
Used by optimize operations only, not during dry / live runs.
Using .copy() to get a fresh copy of the dataframe for every strategy run.
Also copy on output to avoid PerformanceWarnings pandas 1.3.0 started to show.
Has positive effects on memory usage for whatever reason - also when
using only one strategy.
"""
return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair})
return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy()
for pair, pair_data in data.items()}
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@ -792,6 +831,12 @@ class IStrategy(ABC, HyperStrategyMixin):
:return: a Dataframe with all mandatory indicators for the strategies
"""
logger.debug(f"Populating indicators for pair {metadata.get('pair')}.")
# call populate_indicators_Nm() which were tagged with @informative decorator.
for inf_data, populate_fn in self._ft_informative:
dataframe = _create_and_merge_informative_pair(
self, dataframe, metadata, inf_data, populate_fn)
if self._populate_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)

45
freqtrade/strategy/strategy_helper.py
View File

@ -4,7 +4,9 @@ from freqtrade.exchange import timeframe_to_minutes
def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
timeframe: str, timeframe_inf: str, ffill: bool = True) -> pd.DataFrame:
timeframe: str, timeframe_inf: str, ffill: bool = True,
append_timeframe: bool = True,
date_column: str = 'date') -> pd.DataFrame:
"""
Correctly merge informative samples to the original dataframe, avoiding lookahead bias.
@ -24,6 +26,8 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
:param timeframe: Timeframe of the original pair sample.
:param timeframe_inf: Timeframe of the informative pair sample.
:param ffill: Forwardfill missing values - optional but usually required
:param append_timeframe: Rename columns by appending timeframe.
:param date_column: A custom date column name.
:return: Merged dataframe
:raise: ValueError if the secondary timeframe is shorter than the dataframe timeframe
"""
@ -32,25 +36,29 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
minutes = timeframe_to_minutes(timeframe)
if minutes == minutes_inf:
# No need to forwardshift if the timeframes are identical
informative['date_merge'] = informative["date"]
informative['date_merge'] = informative[date_column]
elif minutes < minutes_inf:
# Subtract "small" timeframe so merging is not delayed by 1 small candle
# Detailed explanation in https://github.com/freqtrade/freqtrade/issues/4073
informative['date_merge'] = (
informative["date"] + pd.to_timedelta(minutes_inf, 'm') - pd.to_timedelta(minutes, 'm')
informative[date_column] + pd.to_timedelta(minutes_inf, 'm') -
pd.to_timedelta(minutes, 'm')
)
else:
raise ValueError("Tried to merge a faster timeframe to a slower timeframe."
"This would create new rows, and can throw off your regular indicators.")
# Rename columns to be unique
informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
date_merge = 'date_merge'
if append_timeframe:
date_merge = f'date_merge_{timeframe_inf}'
informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
# Combine the 2 dataframes
# all indicators on the informative sample MUST be calculated before this point
dataframe = pd.merge(dataframe, informative, left_on='date',
right_on=f'date_merge_{timeframe_inf}', how='left')
dataframe = dataframe.drop(f'date_merge_{timeframe_inf}', axis=1)
right_on=date_merge, how='left')
dataframe = dataframe.drop(date_merge, axis=1)
if ffill:
dataframe = dataframe.ffill()
@ -83,3 +91,28 @@ def stoploss_from_open(open_relative_stop: float, current_profit: float) -> floa
# negative stoploss values indicate the requested stop price is higher than the current price
return max(stoploss, 0.0)
def stoploss_from_absolute(stop_rate: float, current_rate: float) -> float:
"""
Given current price and desired stop price, return a stop loss value that is relative to current
price.
The requested stop can be positive for a stop above the open price, or negative for
a stop below the open price. The return value is always >= 0.
Returns 0 if the resulting stop price would be above the current price.
:param stop_rate: Stop loss price.
:param current_rate: Current asset price.
:return: Positive stop loss value relative to current price
"""
# formula is undefined for current_rate 0, return maximum value
if current_rate == 0:
return 1
stoploss = 1 - (stop_rate / current_rate)
# negative stoploss values indicate the requested stop price is higher than the current price
return max(stoploss, 0.0)

137
freqtrade/templates/base_hyperopt.py.j2
View File

@ -1,137 +0,0 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# --- Do not remove these libs ---
from functools import reduce
from typing import Any, Callable, Dict, List
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer, Real # noqa
from freqtrade.optimize.hyperopt_interface import IHyperOpt
# --------------------------------
# Add your lib to import here
import talib.abstract as ta # noqa
import freqtrade.vendor.qtpylib.indicators as qtpylib
class {{ hyperopt }}(IHyperOpt):
"""
This is a Hyperopt template to get you started.
More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
You should:
- Add any lib you need to build your hyperopt.
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The methods roi_space, generate_roi_table and stoploss_space are not required
and are provided by default.
However, you may override them if you need 'roi' and 'stoploss' spaces that
differ from the defaults offered by Freqtrade.
Sample implementation of these methods will be copied to `user_data/hyperopts` when
creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
or is available online under the following URL:
https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
"""
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
"""
return [
{{ buy_space | indent(12) }}
]
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
{{ buy_guards | indent(12) }}
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
# Check that the candle had volume
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
{{ sell_space | indent(12) }}
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by Hyperopt.
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
{{ sell_guards | indent(12) }}
# TRIGGERS
if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-bb_upper':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
))
if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['sar'], dataframe['close']
))
# Check that the candle had volume
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend

174
freqtrade/templates/sample_hyperopt.py
View File

@ -1,174 +0,0 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
from functools import reduce
from typing import Any, Callable, Dict, List
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer, Real # noqa
from freqtrade.optimize.hyperopt_interface import IHyperOpt
# --------------------------------
# Add your lib to import here
import talib.abstract as ta # noqa
import freqtrade.vendor.qtpylib.indicators as qtpylib
class SampleHyperOpt(IHyperOpt):
"""
This is a sample Hyperopt to inspire you.
More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
You should:
- Rename the class name to some unique name.
- Add any methods you want to build your hyperopt.
- Add any lib you need to build your hyperopt.
An easier way to get a new hyperopt file is by using
`freqtrade new-hyperopt --hyperopt MyCoolHyperopt`.
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The methods roi_space, generate_roi_table and stoploss_space are not required
and are provided by default.
However, you may override them if you need 'roi' and 'stoploss' spaces that
differ from the defaults offered by Freqtrade.
Sample implementation of these methods will be copied to `user_data/hyperopts` when
creating the user-data directory using `freqtrade create-userdir --userdir user_data`,
or is available online under the following URL:
https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py.
"""
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(60, 100, name='sell-rsi-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper',
'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger')
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by Hyperopt.
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
conditions.append(dataframe['adx'] < params['sell-adx-value'])
if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
# TRIGGERS
if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-bb_upper':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
))
if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['sar'], dataframe['close']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend

269
freqtrade/templates/sample_hyperopt_advanced.py
View File

@ -1,269 +0,0 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
from functools import reduce
from typing import Any, Callable, Dict, List
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa
from freqtrade.optimize.hyperopt_interface import IHyperOpt
# --------------------------------
# Add your lib to import here
import talib.abstract as ta # noqa
import freqtrade.vendor.qtpylib.indicators as qtpylib
class AdvancedSampleHyperOpt(IHyperOpt):
"""
This is a sample hyperopt to inspire you.
Feel free to customize it.
More information in the documentation: https://www.freqtrade.io/en/latest/hyperopt/
You should:
- Rename the class name to some unique name.
- Add any methods you want to build your hyperopt.
- Add any lib you need to build your hyperopt.
You must keep:
- The prototypes for the methods: populate_indicators, indicator_space, buy_strategy_generator.
The methods roi_space, generate_roi_table and stoploss_space are not required
and are provided by default.
However, you may override them if you need the
'roi' and the 'stoploss' spaces that differ from the defaults offered by Freqtrade.
This sample illustrates how to override these methods.
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class.
"""
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by hyperopt
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(60, 100, name='sell-rsi-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper',
'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger')
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by hyperopt
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use
"""
# print(params)
conditions = []
# GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
conditions.append(dataframe['adx'] < params['sell-adx-value'])
if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
# TRIGGERS
if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-bb_upper':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
))
if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['sar'], dataframe['close']
))
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def generate_roi_table(params: Dict) -> Dict[int, float]:
"""
Generate the ROI table that will be used by Hyperopt
This implementation generates the default legacy Freqtrade ROI tables.
Change it if you need different number of steps in the generated
ROI tables or other structure of the ROI tables.
Please keep it aligned with parameters in the 'roi' optimization
hyperspace defined by the roi_space method.
"""
roi_table = {}
roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
return roi_table
@staticmethod
def roi_space() -> List[Dimension]:
"""
Values to search for each ROI steps
Override it if you need some different ranges for the parameters in the
'roi' optimization hyperspace.
Please keep it aligned with the implementation of the
generate_roi_table method.
"""
return [
Integer(10, 120, name='roi_t1'),
Integer(10, 60, name='roi_t2'),
Integer(10, 40, name='roi_t3'),
SKDecimal(0.01, 0.04, decimals=3, name='roi_p1'),
SKDecimal(0.01, 0.07, decimals=3, name='roi_p2'),
SKDecimal(0.01, 0.20, decimals=3, name='roi_p3'),
]
@staticmethod
def stoploss_space() -> List[Dimension]:
"""
Stoploss Value to search
Override it if you need some different range for the parameter in the
'stoploss' optimization hyperspace.
"""
return [
SKDecimal(-0.35, -0.02, decimals=3, name='stoploss'),
]
@staticmethod
def trailing_space() -> List[Dimension]:
"""
Create a trailing stoploss space.
You may override it in your custom Hyperopt class.
"""
return [
# It was decided to always set trailing_stop is to True if the 'trailing' hyperspace
# is used. Otherwise hyperopt will vary other parameters that won't have effect if
# trailing_stop is set False.
# This parameter is included into the hyperspace dimensions rather than assigning
# it explicitly in the code in order to have it printed in the results along with
# other 'trailing' hyperspace parameters.
Categorical([True], name='trailing_stop'),
SKDecimal(0.01, 0.35, decimals=3, name='trailing_stop_positive'),
# 'trailing_stop_positive_offset' should be greater than 'trailing_stop_positive',
# so this intermediate parameter is used as the value of the difference between
# them. The value of the 'trailing_stop_positive_offset' is constructed in the
# generate_trailing_params() method.
# This is similar to the hyperspace dimensions used for constructing the ROI tables.
SKDecimal(0.001, 0.1, decimals=3, name='trailing_stop_positive_offset_p1'),
Categorical([True, False], name='trailing_only_offset_is_reached'),
]

2
freqtrade/templates/subtemplates/exchange_kucoin.j2
View File

@ -4,7 +4,7 @@
"secret": "{{ exchange_secret }}",
"password": "{{ exchange_key_password }}",
"ccxt_config": {
"enableRateLimit": true
"enableRateLimit": true,
"rateLimit": 200
},
"ccxt_async_config": {

8
freqtrade/templates/subtemplates/hyperopt_buy_guards_full.j2
View File

@ -1,8 +0,0 @@
if params.get('mfi-enabled'):
conditions.append(dataframe['mfi'] < params['mfi-value'])
if params.get('fastd-enabled'):
conditions.append(dataframe['fastd'] < params['fastd-value'])
if params.get('adx-enabled'):
conditions.append(dataframe['adx'] > params['adx-value'])
if params.get('rsi-enabled'):
conditions.append(dataframe['rsi'] < params['rsi-value'])

2
freqtrade/templates/subtemplates/hyperopt_buy_guards_minimal.j2
View File

@ -1,2 +0,0 @@
if params.get('rsi-enabled'):
conditions.append(dataframe['rsi'] < params['rsi-value'])

9
freqtrade/templates/subtemplates/hyperopt_buy_space_full.j2
View File

@ -1,9 +0,0 @@
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')

3
freqtrade/templates/subtemplates/hyperopt_buy_space_minimal.j2
View File

@ -1,3 +0,0 @@
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')

8
freqtrade/templates/subtemplates/hyperopt_sell_guards_full.j2
View File

@ -1,8 +0,0 @@
if params.get('sell-mfi-enabled'):
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if params.get('sell-fastd-enabled'):
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if params.get('sell-adx-enabled'):
conditions.append(dataframe['adx'] < params['sell-adx-value'])
if params.get('sell-rsi-enabled'):
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])

2
freqtrade/templates/subtemplates/hyperopt_sell_guards_minimal.j2
View File

@ -1,2 +0,0 @@
if params.get('sell-rsi-enabled'):
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])

11
freqtrade/templates/subtemplates/hyperopt_sell_space_full.j2
View File

@ -1,11 +0,0 @@
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(60, 100, name='sell-rsi-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper',
'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger')

5
freqtrade/templates/subtemplates/hyperopt_sell_space_minimal.j2
View File

@ -1,5 +0,0 @@
Integer(60, 100, name='sell-rsi-value'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper',
'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger')

8
requirements-dev.txt
View File

@ -8,18 +8,20 @@ flake8==3.9.2
flake8-type-annotations==0.1.0
flake8-tidy-imports==4.4.1
mypy==0.910
pytest==6.2.4
pytest==6.2.5
pytest-asyncio==0.15.1
pytest-cov==2.12.1
pytest-mock==3.6.1
pytest-random-order==1.0.4
isort==5.9.3
# For datetime mocking
time-machine==2.4.0
# Convert jupyter notebooks to markdown documents
nbconvert==6.1.0
nbconvert==6.2.0
# mypy types
types-cachetools==4.2.0
types-filelock==0.1.5
types-requests==2.25.6
types-requests==2.25.9
types-tabulate==0.8.2

2
requirements-hyperopt.txt
View File

@ -8,4 +8,4 @@ scikit-optimize==0.8.1
filelock==3.0.12
joblib==1.0.1
psutil==5.8.0
progressbar2==3.53.1
progressbar2==3.53.3

2
requirements-plot.txt
View File

@ -1,5 +1,5 @@
# Include all requirements to run the bot.
-r requirements.txt
plotly==5.3.0
plotly==5.3.1

9
requirements.txt
View File

@ -1,16 +1,17 @@
numpy==1.21.2
pandas==1.3.2
pandas==1.3.3
pandas-ta==0.3.14b
ccxt==1.55.56
ccxt==1.57.3
# Pin cryptography for now due to rust build errors with piwheels
cryptography==3.4.8
aiohttp==3.7.4.post0
SQLAlchemy==1.4.23
SQLAlchemy==1.4.25
python-telegram-bot==13.7
arrow==1.1.1
cachetools==4.2.2
requests==2.26.0
urllib3==1.26.6
urllib3==1.26.7
wrapt==1.12.1
jsonschema==3.2.0
TA-Lib==0.4.21

2
scripts/rest_client.py
View File

@ -312,7 +312,7 @@ class FtRestClient():
:param limit: Limit result to the last n candles.
:return: json object
"""
return self._get("available_pairs", params={
return self._get("pair_candles", params={
"pair": pair,
"timeframe": timeframe,
"limit": limit,

1
setup.py
View File

@ -54,6 +54,7 @@ setup(
'wrapt',
'jsonschema',
'TA-Lib',
'pandas-ta',
'technical',
'tabulate',
'pycoingecko',

2
setup.sh
View File

@ -62,7 +62,7 @@ function updateenv() {
then
REQUIREMENTS_PLOT="-r requirements-plot.txt"
fi
if [ "${SYS_ARCH}" == "armv7l" ]; then
if [ "${SYS_ARCH}" == "armv7l" ] || [ "${SYS_ARCH}" == "armv6l" ]; then
echo "Detected Raspberry, installing cython, skipping hyperopt installation."
${PYTHON} -m pip install --upgrade cython
else

103
tests/commands/test_commands.py
View File

@ -8,12 +8,12 @@ from zipfile import ZipFile
import arrow
import pytest
from freqtrade.commands import (start_convert_data, start_create_userdir, start_download_data,
start_hyperopt_list, start_hyperopt_show, start_install_ui,
start_list_data, start_list_exchanges, start_list_hyperopts,
from freqtrade.commands import (start_convert_data, start_convert_trades, start_create_userdir,
start_download_data, start_hyperopt_list, start_hyperopt_show,
start_install_ui, start_list_data, start_list_exchanges,
start_list_markets, start_list_strategies, start_list_timeframes,
start_new_hyperopt, start_new_strategy, start_show_trades,
start_test_pairlist, start_trading, start_webserver)
start_new_strategy, start_show_trades, start_test_pairlist,
start_trading, start_webserver)
from freqtrade.commands.deploy_commands import (clean_ui_subdir, download_and_install_ui,
get_ui_download_url, read_ui_version)
from freqtrade.configuration import setup_utils_configuration
@ -32,8 +32,6 @@ def test_setup_utils_configuration():
config = setup_utils_configuration(get_args(args), RunMode.OTHER)
assert "exchange" in config
assert config['dry_run'] is True
assert config['exchange']['key'] == ''
assert config['exchange']['secret'] == ''
def test_start_trading_fail(mocker, caplog):
@ -210,11 +208,10 @@ def test_list_timeframes(mocker, capsys):
assert re.search(r"^1d$", captured.out, re.MULTILINE)
def test_list_markets(mocker, markets, capsys):
def test_list_markets(mocker, markets_static, capsys):
api_mock = MagicMock()
api_mock.markets = markets
patch_exchange(mocker, api_mock=api_mock, id='bittrex')
patch_exchange(mocker, api_mock=api_mock, id='bittrex', mock_markets=markets_static)
# Test with no --config
args = [
@ -239,7 +236,7 @@ def test_list_markets(mocker, markets, capsys):
"TKN/BTC, XLTCUSDT, XRP/BTC.\n"
in captured.out)
patch_exchange(mocker, api_mock=api_mock, id="binance")
patch_exchange(mocker, api_mock=api_mock, id="binance", mock_markets=markets_static)
# Test with --exchange
args = [
"list-markets",
@ -252,7 +249,7 @@ def test_list_markets(mocker, markets, capsys):
assert re.match("\nExchange Binance has 10 active markets:\n",
captured.out)
patch_exchange(mocker, api_mock=api_mock, id="bittrex")
patch_exchange(mocker, api_mock=api_mock, id="bittrex", mock_markets=markets_static)
# Test with --all: all markets
args = [
"list-markets", "--all",
@ -519,37 +516,6 @@ def test_start_new_strategy_no_arg(mocker, caplog):
start_new_strategy(get_args(args))
def test_start_new_hyperopt(mocker, caplog):
wt_mock = mocker.patch.object(Path, "write_text", MagicMock())
mocker.patch.object(Path, "exists", MagicMock(return_value=False))
args = [
"new-hyperopt",
"--hyperopt",
"CoolNewhyperopt"
]
start_new_hyperopt(get_args(args))
assert wt_mock.call_count == 1
assert "CoolNewhyperopt" in wt_mock.call_args_list[0][0][0]
assert log_has_re("Writing hyperopt to .*", caplog)
mocker.patch('freqtrade.commands.deploy_commands.setup_utils_configuration')
mocker.patch.object(Path, "exists", MagicMock(return_value=True))
with pytest.raises(OperationalException,
match=r".* already exists. Please choose another Hyperopt Name\."):
start_new_hyperopt(get_args(args))
def test_start_new_hyperopt_no_arg(mocker):
args = [
"new-hyperopt",
]
with pytest.raises(OperationalException,
match="`new-hyperopt` requires --hyperopt to be set."):
start_new_hyperopt(get_args(args))
def test_start_install_ui(mocker):
clean_mock = mocker.patch('freqtrade.commands.deploy_commands.clean_ui_subdir')
get_url_mock = mocker.patch('freqtrade.commands.deploy_commands.get_ui_download_url',
@ -793,6 +759,22 @@ def test_download_data_trades(mocker, caplog):
assert convert_mock.call_count == 1
def test_start_convert_trades(mocker, caplog):
convert_mock = mocker.patch('freqtrade.commands.data_commands.convert_trades_to_ohlcv',
MagicMock(return_value=[]))
patch_exchange(mocker)
mocker.patch(
'freqtrade.exchange.Exchange.markets', PropertyMock(return_value={})
)
args = [
"trades-to-ohlcv",
"--exchange", "kraken",
"--pairs", "ETH/BTC", "XRP/BTC",
]
start_convert_trades(get_args(args))
assert convert_mock.call_count == 1
def test_start_list_strategies(mocker, caplog, capsys):
args = [
@ -824,37 +806,20 @@ def test_start_list_strategies(mocker, caplog, capsys):
assert "legacy_strategy_v1.py" in captured.out
assert "StrategyTestV2" in captured.out
def test_start_list_hyperopts(mocker, caplog, capsys):
# Test color output
args = [
"list-hyperopts",
"--hyperopt-path",
str(Path(__file__).parent.parent / "optimize" / "hyperopts"),
"-1"
"list-strategies",
"--strategy-path",
str(Path(__file__).parent.parent / "strategy" / "strats"),
]
pargs = get_args(args)
# pargs['config'] = None
start_list_hyperopts(pargs)
start_list_strategies(pargs)
captured = capsys.readouterr()
assert "TestHyperoptLegacy" not in captured.out
assert "legacy_hyperopt.py" not in captured.out
assert "HyperoptTestSepFile" in captured.out
assert "test_hyperopt.py" not in captured.out
# Test regular output
args = [
"list-hyperopts",
"--hyperopt-path",
str(Path(__file__).parent.parent / "optimize" / "hyperopts"),
]
pargs = get_args(args)
# pargs['config'] = None
start_list_hyperopts(pargs)
captured = capsys.readouterr()
assert "TestHyperoptLegacy" not in captured.out
assert "legacy_hyperopt.py" not in captured.out
assert "HyperoptTestSepFile" in captured.out
assert "TestStrategyLegacyV1" in captured.out
assert "legacy_strategy_v1.py" in captured.out
assert "StrategyTestV2" in captured.out
assert "LOAD FAILED" in captured.out
def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):

29
tests/conftest.py
View File

@ -90,8 +90,10 @@ def patch_exchange(mocker, api_mock=None, id='binance', mock_markets=True) -> No
mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value=id.title()))
mocker.patch('freqtrade.exchange.Exchange.precisionMode', PropertyMock(return_value=2))
if mock_markets:
if isinstance(mock_markets, bool):
mock_markets = get_markets()
mocker.patch('freqtrade.exchange.Exchange.markets',
PropertyMock(return_value=get_markets()))
PropertyMock(return_value=mock_markets))
if api_mock:
mocker.patch('freqtrade.exchange.Exchange._init_ccxt', MagicMock(return_value=api_mock))
@ -376,6 +378,8 @@ def markets():
def get_markets():
# See get_markets_static() for immutable markets and do not modify them unless absolutely
# necessary!
return {
'ETH/BTC': {
'id': 'ethbtc',
@ -675,11 +679,22 @@ def get_markets():
@pytest.fixture
def shitcoinmarkets(markets):
def markets_static():
# These markets are used in some tests that would need adaptation should anything change in
# market list. Do not modify this list without a good reason! Do not modify market parameters
# of listed pairs in get_markets() without a good reason either!
static_markets = ['BLK/BTC', 'BTT/BTC', 'ETH/BTC', 'ETH/USDT', 'LTC/BTC', 'LTC/ETH', 'LTC/USD',
'LTC/USDT', 'NEO/BTC', 'TKN/BTC', 'XLTCUSDT', 'XRP/BTC']
all_markets = get_markets()
return {m: all_markets[m] for m in static_markets}
@pytest.fixture
def shitcoinmarkets(markets_static):
"""
Fixture with shitcoin markets - used to test filters in pairlists
"""
shitmarkets = deepcopy(markets)
shitmarkets = deepcopy(markets_static)
shitmarkets.update({
'HOT/BTC': {
'id': 'HOTBTC',
@ -1685,14 +1700,6 @@ def trades_for_order2():
'fee': {'cost': 0.004, 'currency': 'LTC'}}]
@pytest.fixture(scope="function")
def trades_for_order3(trades_for_order2):
# Different fee currencies for each trade
trades_for_order = deepcopy(trades_for_order2)
trades_for_order[0]['fee'] = {'cost': 0.02, 'currency': 'BNB'}
return trades_for_order
@pytest.fixture
def buy_order_fee():
return {

35
tests/exchange/test_binance.py
View File

@ -1,3 +1,4 @@
from datetime import datetime, timezone
from random import randint
from unittest.mock import MagicMock
@ -5,7 +6,7 @@ import ccxt
import pytest
from freqtrade.exceptions import DependencyException, InvalidOrderException, OperationalException
from tests.conftest import get_patched_exchange
from tests.conftest import get_mock_coro, get_patched_exchange, log_has_re
from tests.exchange.test_exchange import ccxt_exceptionhandlers
@ -105,3 +106,35 @@ def test_stoploss_adjust_binance(mocker, default_conf):
# Test with invalid order case
order['type'] = 'stop_loss'
assert not exchange.stoploss_adjust(1501, order)
@pytest.mark.asyncio
async def test__async_get_historic_ohlcv_binance(default_conf, mocker, caplog):
ohlcv = [
[
int((datetime.now(timezone.utc).timestamp() - 1000) * 1000),
1, # open
2, # high
3, # low
4, # close
5, # volume (in quote currency)
]
]
exchange = get_patched_exchange(mocker, default_conf, id='binance')
# Monkey-patch async function
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
pair = 'ETH/BTC'
res = await exchange._async_get_historic_ohlcv(pair, "5m",
1500000000000, is_new_pair=False)
# Call with very old timestamp - causes tons of requests
assert exchange._api_async.fetch_ohlcv.call_count > 400
# assert res == ohlcv
exchange._api_async.fetch_ohlcv.reset_mock()
res = await exchange._async_get_historic_ohlcv(pair, "5m", 1500000000000, is_new_pair=True)
# Called twice - one "init" call - and one to get the actual data.
assert exchange._api_async.fetch_ohlcv.call_count == 2
assert res == ohlcv
assert log_has_re(r"Candle-data for ETH/BTC available starting with .*", caplog)

2
tests/exchange/test_ccxt_compat.py
View File

@ -54,6 +54,8 @@ EXCHANGES = {
def exchange_conf():
config = get_default_conf((Path(__file__).parent / "testdata").resolve())
config['exchange']['pair_whitelist'] = []
config['exchange']['key'] = ''
config['exchange']['secret'] = ''
config['dry_run'] = False
return config

53
tests/exchange/test_exchange.py
View File

@ -1,5 +1,6 @@
import copy
import logging
from copy import deepcopy
from datetime import datetime, timedelta, timezone
from math import isclose
from random import randint
@ -14,7 +15,7 @@ from freqtrade.exceptions import (DDosProtection, DependencyException, InvalidOr
OperationalException, PricingError, TemporaryError)
from freqtrade.exchange import Binance, Bittrex, Exchange, Kraken
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, API_RETRY_COUNT,
calculate_backoff)
calculate_backoff, remove_credentials)
from freqtrade.exchange.exchange import (market_is_active, timeframe_to_minutes, timeframe_to_msecs,
timeframe_to_next_date, timeframe_to_prev_date,
timeframe_to_seconds)
@ -78,6 +79,22 @@ def test_init(default_conf, mocker, caplog):
assert log_has('Instance is running with dry_run enabled', caplog)
def test_remove_credentials(default_conf, caplog) -> None:
conf = deepcopy(default_conf)
conf['dry_run'] = False
remove_credentials(conf)
assert conf['exchange']['key'] != ''
assert conf['exchange']['secret'] != ''
conf['dry_run'] = True
remove_credentials(conf)
assert conf['exchange']['key'] == ''
assert conf['exchange']['secret'] == ''
assert conf['exchange']['password'] == ''
assert conf['exchange']['uid'] == ''
def test_init_ccxt_kwargs(default_conf, mocker, caplog):
mocker.patch('freqtrade.exchange.Exchange._load_markets', MagicMock(return_value={}))
mocker.patch('freqtrade.exchange.Exchange.validate_stakecurrency')
@ -185,7 +202,7 @@ def test_exchange_resolver(default_conf, mocker, caplog):
def test_validate_order_time_in_force(default_conf, mocker, caplog):
caplog.set_level(logging.INFO)
# explicitly test bittrex, exchanges implementing other policies need seperate tests
# explicitly test bittrex, exchanges implementing other policies need separate tests
ex = get_patched_exchange(mocker, default_conf, id="bittrex")
tif = {
"buy": "gtc",
@ -1551,6 +1568,32 @@ def test_get_historic_ohlcv_as_df(default_conf, mocker, exchange_name):
assert 'high' in ret.columns
@pytest.mark.asyncio
@pytest.mark.parametrize("exchange_name", EXCHANGES)
async def test__async_get_historic_ohlcv(default_conf, mocker, caplog, exchange_name):
ohlcv = [
[
int((datetime.now(timezone.utc).timestamp() - 1000) * 1000),
1, # open
2, # high
3, # low
4, # close
5, # volume (in quote currency)
]
]
exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
# Monkey-patch async function
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
pair = 'ETH/USDT'
res = await exchange._async_get_historic_ohlcv(pair, "5m",
1500000000000, is_new_pair=False)
# Call with very old timestamp - causes tons of requests
assert exchange._api_async.fetch_ohlcv.call_count > 200
assert res[0] == ohlcv[0]
assert log_has_re(r'Downloaded data for .* with length .*\.', caplog)
def test_refresh_latest_ohlcv(mocker, default_conf, caplog) -> None:
ohlcv = [
[
@ -2438,7 +2481,7 @@ def test_fetch_order(default_conf, mocker, exchange_name, caplog):
@pytest.mark.parametrize("exchange_name", EXCHANGES)
def test_fetch_stoploss_order(default_conf, mocker, exchange_name):
# Don't test FTX here - that needs a seperate test
# Don't test FTX here - that needs a separate test
if exchange_name == 'ftx':
return
default_conf['dry_run'] = True
@ -2692,7 +2735,7 @@ def test_get_valid_pair_combination(default_conf, mocker, markets):
(['LTC'], ['NONEXISTENT'], False, False,
[]),
])
def test_get_markets(default_conf, mocker, markets,
def test_get_markets(default_conf, mocker, markets_static,
base_currencies, quote_currencies, pairs_only, active_only,
expected_keys):
mocker.patch.multiple('freqtrade.exchange.Exchange',
@ -2700,7 +2743,7 @@ def test_get_markets(default_conf, mocker, markets,
_load_async_markets=MagicMock(),
validate_pairs=MagicMock(),
validate_timeframes=MagicMock(),
markets=PropertyMock(return_value=markets))
markets=PropertyMock(return_value=markets_static))
ex = Exchange(default_conf)
pairs = ex.get_markets(base_currencies, quote_currencies, pairs_only, active_only)
assert sorted(pairs.keys()) == sorted(expected_keys)

15
tests/optimize/conftest.py
View File

@ -16,7 +16,7 @@ def hyperopt_conf(default_conf):
hyperconf.update({
'datadir': Path(default_conf['datadir']),
'runmode': RunMode.HYPEROPT,
'hyperopt': 'HyperoptTestSepFile',
'strategy': 'HyperoptableStrategy',
'hyperopt_loss': 'ShortTradeDurHyperOptLoss',
'hyperopt_path': str(Path(__file__).parent / 'hyperopts'),
'epochs': 1,
@ -39,16 +39,17 @@ def hyperopt(hyperopt_conf, mocker):
def hyperopt_results():
return pd.DataFrame(
{
'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
'profit_ratio': [-0.1, 0.2, 0.3],
'profit_abs': [-0.2, 0.4, 0.6],
'trade_duration': [10, 30, 10],
'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.ROI],
'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
'profit_ratio': [-0.1, 0.2, -0.1, 0.3],
'profit_abs': [-0.2, 0.4, -0.2, 0.6],
'trade_duration': [10, 30, 10, 10],
'sell_reason': [SellType.STOP_LOSS, SellType.ROI, SellType.STOP_LOSS, SellType.ROI],
'close_date':
[
datetime(2019, 1, 1, 9, 26, 3, 478039),
datetime(2019, 2, 1, 9, 26, 3, 478039),
datetime(2019, 3, 1, 9, 26, 3, 478039)
datetime(2019, 3, 1, 9, 26, 3, 478039),
datetime(2019, 4, 1, 9, 26, 3, 478039),
]
}
)

202
tests/optimize/hyperopts/hyperopt_test_sep_file.py
View File

@ -1,202 +0,0 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from functools import reduce
from typing import Any, Callable, Dict, List
import talib.abstract as ta
from pandas import DataFrame
from skopt.space import Categorical, Dimension, Integer
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
class HyperoptTestSepFile(IHyperOpt):
"""
Default hyperopt provided by the Freqtrade bot.
You can override it with your own Hyperopt
"""
@staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Add several indicators needed for buy and sell strategies defined below.
"""
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
# Minus-DI
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
# SAR
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
@staticmethod
def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'mfi-enabled' in params and params['mfi-enabled']:
conditions.append(dataframe['mfi'] < params['mfi-value'])
if 'fastd-enabled' in params and params['fastd-enabled']:
conditions.append(dataframe['fastd'] < params['fastd-value'])
if 'adx-enabled' in params and params['adx-enabled']:
conditions.append(dataframe['adx'] > params['adx-value'])
if 'rsi-enabled' in params and params['rsi-enabled']:
conditions.append(dataframe['rsi'] < params['rsi-value'])
# TRIGGERS
if 'trigger' in params:
if params['trigger'] == 'bb_lower':
conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
if params['trigger'] == 'macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
))
if params['trigger'] == 'sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
@staticmethod
def indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching buy strategy parameters.
"""
return [
Integer(10, 25, name='mfi-value'),
Integer(15, 45, name='fastd-value'),
Integer(20, 50, name='adx-value'),
Integer(20, 40, name='rsi-value'),
Categorical([True, False], name='mfi-enabled'),
Categorical([True, False], name='fastd-enabled'),
Categorical([True, False], name='adx-enabled'),
Categorical([True, False], name='rsi-enabled'),
Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
]
@staticmethod
def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
"""
Define the sell strategy parameters to be used by Hyperopt.
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use.
"""
conditions = []
# GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:
conditions.append(dataframe['mfi'] > params['sell-mfi-value'])
if 'sell-fastd-enabled' in params and params['sell-fastd-enabled']:
conditions.append(dataframe['fastd'] > params['sell-fastd-value'])
if 'sell-adx-enabled' in params and params['sell-adx-enabled']:
conditions.append(dataframe['adx'] < params['sell-adx-value'])
if 'sell-rsi-enabled' in params and params['sell-rsi-enabled']:
conditions.append(dataframe['rsi'] > params['sell-rsi-value'])
# TRIGGERS
if 'sell-trigger' in params:
if params['sell-trigger'] == 'sell-bb_upper':
conditions.append(dataframe['close'] > dataframe['bb_upperband'])
if params['sell-trigger'] == 'sell-macd_cross_signal':
conditions.append(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
))
if params['sell-trigger'] == 'sell-sar_reversal':
conditions.append(qtpylib.crossed_above(
dataframe['sar'], dataframe['close']
))
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe
return populate_sell_trend
@staticmethod
def sell_indicator_space() -> List[Dimension]:
"""
Define your Hyperopt space for searching sell strategy parameters.
"""
return [
Integer(75, 100, name='sell-mfi-value'),
Integer(50, 100, name='sell-fastd-value'),
Integer(50, 100, name='sell-adx-value'),
Integer(60, 100, name='sell-rsi-value'),
Categorical([True, False], name='sell-mfi-enabled'),
Categorical([True, False], name='sell-fastd-enabled'),
Categorical([True, False], name='sell-adx-enabled'),
Categorical([True, False], name='sell-rsi-enabled'),
Categorical(['sell-bb_upper',
'sell-macd_cross_signal',
'sell-sar_reversal'], name='sell-trigger')
]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include buy space.
"""
dataframe.loc[
(
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['mfi'] < 16) &
(dataframe['adx'] > 25) &
(dataframe['rsi'] < 21)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators. Should be a copy of same method from strategy.
Must align to populate_indicators in this file.
Only used when --spaces does not include sell space.
"""
dataframe.loc[
(
(qtpylib.crossed_above(
dataframe['macdsignal'], dataframe['macd']
)) &
(dataframe['fastd'] > 54)
),
'sell'] = 1
return dataframe

221
tests/optimize/test_hyperopt.py
View File

@ -17,13 +17,10 @@ from freqtrade.optimize.hyperopt_auto import HyperOptAuto
from freqtrade.optimize.hyperopt_tools import HyperoptTools
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.optimize.space import SKDecimal
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
from freqtrade.strategy.hyper import IntParameter
from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
patched_configuration_load_config_file)
from .hyperopts.hyperopt_test_sep_file import HyperoptTestSepFile
def test_setup_hyperopt_configuration_without_arguments(mocker, default_conf, caplog) -> None:
patched_configuration_load_config_file(mocker, default_conf)
@ -31,7 +28,7 @@ def test_setup_hyperopt_configuration_without_arguments(mocker, default_conf, ca
args = [
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'HyperoptTestSepFile',
'--strategy', 'HyperoptableStrategy',
]
config = setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
@ -63,7 +60,7 @@ def test_setup_hyperopt_configuration_with_arguments(mocker, default_conf, caplo
args = [
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'HyperoptTestSepFile',
'--strategy', 'HyperoptableStrategy',
'--datadir', '/foo/bar',
'--timeframe', '1m',
'--timerange', ':100',
@ -115,7 +112,7 @@ def test_setup_hyperopt_configuration_stake_amount(mocker, default_conf) -> None
args = [
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'HyperoptTestSepFile',
'--strategy', 'HyperoptableStrategy',
'--stake-amount', '1',
'--starting-balance', '2'
]
@ -133,47 +130,6 @@ def test_setup_hyperopt_configuration_stake_amount(mocker, default_conf) -> None
setup_optimize_configuration(get_args(args), RunMode.HYPEROPT)
def test_hyperoptresolver(mocker, default_conf, caplog) -> None:
patched_configuration_load_config_file(mocker, default_conf)
hyperopt = HyperoptTestSepFile
delattr(hyperopt, 'populate_indicators')
delattr(hyperopt, 'populate_buy_trend')
delattr(hyperopt, 'populate_sell_trend')
mocker.patch(
'freqtrade.resolvers.hyperopt_resolver.HyperOptResolver.load_object',
MagicMock(return_value=hyperopt(default_conf))
)
default_conf.update({'hyperopt': 'HyperoptTestSepFile'})
x = HyperOptResolver.load_hyperopt(default_conf)
assert not hasattr(x, 'populate_indicators')
assert not hasattr(x, 'populate_buy_trend')
assert not hasattr(x, 'populate_sell_trend')
assert log_has("Hyperopt class does not provide populate_indicators() method. "
"Using populate_indicators from the strategy.", caplog)
assert log_has("Hyperopt class does not provide populate_sell_trend() method. "
"Using populate_sell_trend from the strategy.", caplog)
assert log_has("Hyperopt class does not provide populate_buy_trend() method. "
"Using populate_buy_trend from the strategy.", caplog)
assert hasattr(x, "ticker_interval") # DEPRECATED
assert hasattr(x, "timeframe")
def test_hyperoptresolver_wrongname(default_conf) -> None:
default_conf.update({'hyperopt': "NonExistingHyperoptClass"})
with pytest.raises(OperationalException, match=r'Impossible to load Hyperopt.*'):
HyperOptResolver.load_hyperopt(default_conf)
def test_hyperoptresolver_noname(default_conf):
default_conf['hyperopt'] = ''
with pytest.raises(OperationalException,
match="No Hyperopt set. Please use `--hyperopt` to specify "
"the Hyperopt class to use."):
HyperOptResolver.load_hyperopt(default_conf)
def test_start_not_installed(mocker, default_conf, import_fails) -> None:
start_mock = MagicMock()
patched_configuration_load_config_file(mocker, default_conf)
@ -184,9 +140,7 @@ def test_start_not_installed(mocker, default_conf, import_fails) -> None:
args = [
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'HyperoptTestSepFile',
'--hyperopt-path',
str(Path(__file__).parent / "hyperopts"),
'--strategy', 'HyperoptableStrategy',
'--epochs', '5',
'--hyperopt-loss', 'SharpeHyperOptLossDaily',
]
@ -196,7 +150,7 @@ def test_start_not_installed(mocker, default_conf, import_fails) -> None:
start_hyperopt(pargs)
def test_start(mocker, hyperopt_conf, caplog) -> None:
def test_start_no_hyperopt_allowed(mocker, hyperopt_conf, caplog) -> None:
start_mock = MagicMock()
patched_configuration_load_config_file(mocker, hyperopt_conf)
mocker.patch('freqtrade.optimize.hyperopt.Hyperopt.start', start_mock)
@ -210,10 +164,8 @@ def test_start(mocker, hyperopt_conf, caplog) -> None:
'--epochs', '5'
]
pargs = get_args(args)
start_hyperopt(pargs)
assert log_has('Starting freqtrade in Hyperopt mode', caplog)
assert start_mock.call_count == 1
with pytest.raises(OperationalException, match=r"Using separate Hyperopt files has been.*"):
start_hyperopt(pargs)
def test_start_no_data(mocker, hyperopt_conf) -> None:
@ -225,11 +177,11 @@ def test_start_no_data(mocker, hyperopt_conf) -> None:
)
patch_exchange(mocker)
# TODO: migrate to strategy-based hyperopt
args = [
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'HyperoptTestSepFile',
'--strategy', 'HyperoptableStrategy',
'--hyperopt-loss', 'SharpeHyperOptLossDaily',
'--epochs', '5'
]
@ -247,7 +199,7 @@ def test_start_filelock(mocker, hyperopt_conf, caplog) -> None:
args = [
'hyperopt',
'--config', 'config.json',
'--hyperopt', 'HyperoptTestSepFile',
'--strategy', 'HyperoptableStrategy',
'--hyperopt-loss', 'SharpeHyperOptLossDaily',
'--epochs', '5'
]
@ -427,66 +379,14 @@ def test_hyperopt_format_results(hyperopt):
def test_populate_indicators(hyperopt, testdatadir) -> None:
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
dataframes = hyperopt.backtesting.strategy.advise_all_indicators(data)
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
dataframe = dataframes['UNITTEST/BTC']
# Check if some indicators are generated. We will not test all of them
assert 'adx' in dataframe
assert 'mfi' in dataframe
assert 'macd' in dataframe
assert 'rsi' in dataframe
def test_buy_strategy_generator(hyperopt, testdatadir) -> None:
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
dataframes = hyperopt.backtesting.strategy.advise_all_indicators(data)
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
populate_buy_trend = hyperopt.custom_hyperopt.buy_strategy_generator(
{
'adx-value': 20,
'fastd-value': 20,
'mfi-value': 20,
'rsi-value': 20,
'adx-enabled': True,
'fastd-enabled': True,
'mfi-enabled': True,
'rsi-enabled': True,
'trigger': 'bb_lower'
}
)
result = populate_buy_trend(dataframe, {'pair': 'UNITTEST/BTC'})
# Check if some indicators are generated. We will not test all of them
assert 'buy' in result
assert 1 in result['buy']
def test_sell_strategy_generator(hyperopt, testdatadir) -> None:
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
dataframes = hyperopt.backtesting.strategy.advise_all_indicators(data)
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
populate_sell_trend = hyperopt.custom_hyperopt.sell_strategy_generator(
{
'sell-adx-value': 20,
'sell-fastd-value': 75,
'sell-mfi-value': 80,
'sell-rsi-value': 20,
'sell-adx-enabled': True,
'sell-fastd-enabled': True,
'sell-mfi-enabled': True,
'sell-rsi-enabled': True,
'sell-trigger': 'sell-bb_upper'
}
)
result = populate_sell_trend(dataframe, {'pair': 'UNITTEST/BTC'})
# Check if some indicators are generated. We will not test all of them
print(result)
assert 'sell' in result
assert 1 in result['sell']
def test_generate_optimizer(mocker, hyperopt_conf) -> None:
hyperopt_conf.update({'spaces': 'all',
'hyperopt_min_trades': 1,
@ -527,24 +427,12 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
mocker.patch('freqtrade.optimize.hyperopt.load', return_value={'XRP/BTC': None})
optimizer_param = {
'adx-value': 0,
'fastd-value': 35,
'mfi-value': 0,
'rsi-value': 0,
'adx-enabled': False,
'fastd-enabled': True,
'mfi-enabled': False,
'rsi-enabled': False,
'trigger': 'macd_cross_signal',
'sell-adx-value': 0,
'sell-fastd-value': 75,
'sell-mfi-value': 0,
'sell-rsi-value': 0,
'sell-adx-enabled': False,
'sell-fastd-enabled': True,
'sell-mfi-enabled': False,
'sell-rsi-enabled': False,
'sell-trigger': 'macd_cross_signal',
'buy_plusdi': 0.02,
'buy_rsi': 35,
'sell_minusdi': 0.02,
'sell_rsi': 75,
'protection_cooldown_lookback': 20,
'protection_enabled': True,
'roi_t1': 60.0,
'roi_t2': 30.0,
'roi_t3': 20.0,
@ -564,29 +452,19 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
'0.00003100 BTC ( 0.00%). '
'Avg duration 0:50:00 min.'
),
'params_details': {'buy': {'adx-enabled': False,
'adx-value': 0,
'fastd-enabled': True,
'fastd-value': 35,
'mfi-enabled': False,
'mfi-value': 0,
'rsi-enabled': False,
'rsi-value': 0,
'trigger': 'macd_cross_signal'},
'params_details': {'buy': {'buy_plusdi': 0.02,
'buy_rsi': 35,
},
'roi': {"0": 0.12000000000000001,
"20.0": 0.02,
"50.0": 0.01,
"110.0": 0},
'protection': {},
'sell': {'sell-adx-enabled': False,
'sell-adx-value': 0,
'sell-fastd-enabled': True,
'sell-fastd-value': 75,
'sell-mfi-enabled': False,
'sell-mfi-value': 0,
'sell-rsi-enabled': False,
'sell-rsi-value': 0,
'sell-trigger': 'macd_cross_signal'},
'protection': {'protection_cooldown_lookback': 20,
'protection_enabled': True,
},
'sell': {'sell_minusdi': 0.02,
'sell_rsi': 75,
},
'stoploss': {'stoploss': -0.4},
'trailing': {'trailing_only_offset_is_reached': False,
'trailing_stop': True,
@ -808,11 +686,6 @@ def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> Non
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
del hyperopt.custom_hyperopt.__class__.sell_strategy_generator
del hyperopt.custom_hyperopt.__class__.indicator_space
del hyperopt.custom_hyperopt.__class__.sell_indicator_space
hyperopt.start()
parallel.assert_called_once()
@ -843,16 +716,14 @@ def test_simplified_interface_all_failed(mocker, hyperopt_conf) -> None:
hyperopt_conf.update({'spaces': 'all', })
mocker.patch('freqtrade.optimize.hyperopt_auto.HyperOptAuto._generate_indicator_space',
return_value=[])
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
del hyperopt.custom_hyperopt.__class__.sell_strategy_generator
del hyperopt.custom_hyperopt.__class__.indicator_space
del hyperopt.custom_hyperopt.__class__.sell_indicator_space
with pytest.raises(OperationalException, match=r"The 'buy' space is included into *"):
with pytest.raises(OperationalException, match=r"The 'protection' space is included into *"):
hyperopt.start()
@ -889,11 +760,6 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
# TODO: sell_strategy_generator() is actually not called because
# run_optimizer_parallel() is mocked
del hyperopt.custom_hyperopt.__class__.sell_strategy_generator
del hyperopt.custom_hyperopt.__class__.sell_indicator_space
hyperopt.start()
parallel.assert_called_once()
@ -943,11 +809,6 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
# TODO: buy_strategy_generator() is actually not called because
# run_optimizer_parallel() is mocked
del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
del hyperopt.custom_hyperopt.__class__.indicator_space
hyperopt.start()
parallel.assert_called_once()
@ -964,13 +825,12 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
assert hasattr(hyperopt, "position_stacking")
@pytest.mark.parametrize("method,space", [
('buy_strategy_generator', 'buy'),
('indicator_space', 'buy'),
('sell_strategy_generator', 'sell'),
('sell_indicator_space', 'sell'),
@pytest.mark.parametrize("space", [
('buy'),
('sell'),
('protection'),
])
def test_simplified_interface_failed(mocker, hyperopt_conf, method, space) -> None:
def test_simplified_interface_failed(mocker, hyperopt_conf, space) -> None:
mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
@ -979,6 +839,8 @@ def test_simplified_interface_failed(mocker, hyperopt_conf, method, space) -> No
'freqtrade.optimize.hyperopt.get_timerange',
MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
)
mocker.patch('freqtrade.optimize.hyperopt_auto.HyperOptAuto._generate_indicator_space',
return_value=[])
patch_exchange(mocker)
@ -988,8 +850,6 @@ def test_simplified_interface_failed(mocker, hyperopt_conf, method, space) -> No
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
delattr(hyperopt.custom_hyperopt.__class__, method)
with pytest.raises(OperationalException, match=f"The '{space}' space is included into *"):
hyperopt.start()
@ -999,7 +859,6 @@ def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmpdir, fee) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
(Path(tmpdir) / 'hyperopt_results').mkdir(parents=True)
# No hyperopt needed
del hyperopt_conf['hyperopt']
hyperopt_conf.update({
'strategy': 'HyperoptableStrategy',
'user_data_dir': Path(tmpdir),
@ -1025,6 +884,10 @@ def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmpdir, fee) -> None:
assert hyperopt.backtesting.strategy.buy_rsi.value != 35
assert hyperopt.backtesting.strategy.sell_rsi.value != 74
hyperopt.custom_hyperopt.generate_estimator = lambda *args, **kwargs: 'ET1'
with pytest.raises(OperationalException, match="Estimator ET1 not supported."):
hyperopt.get_optimizer([], 2)
def test_SKDecimal():
space = SKDecimal(1, 2, decimals=2)

92
tests/optimize/test_hyperoptloss.py
View File

@ -35,6 +35,7 @@ def test_hyperoptlossresolver_wrongname(default_conf) -> None:
def test_loss_calculation_prefer_correct_trade_count(hyperopt_conf, hyperopt_results) -> None:
hyperopt_conf.update({'hyperopt_loss': "ShortTradeDurHyperOptLoss"})
hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, 600,
datetime(2019, 1, 1), datetime(2019, 5, 1))
@ -50,6 +51,7 @@ def test_loss_calculation_prefer_shorter_trades(hyperopt_conf, hyperopt_results)
resultsb = hyperopt_results.copy()
resultsb.loc[1, 'trade_duration'] = 20
hyperopt_conf.update({'hyperopt_loss': "ShortTradeDurHyperOptLoss"})
hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
longer = hl.hyperopt_loss_function(hyperopt_results, 100,
datetime(2019, 1, 1), datetime(2019, 5, 1))
@ -64,6 +66,7 @@ def test_loss_calculation_has_limited_profit(hyperopt_conf, hyperopt_results) ->
results_under = hyperopt_results.copy()
results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
hyperopt_conf.update({'hyperopt_loss': "ShortTradeDurHyperOptLoss"})
hl = HyperOptLossResolver.load_hyperoptloss(hyperopt_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, 600,
datetime(2019, 1, 1), datetime(2019, 5, 1))
@ -75,91 +78,28 @@ def test_loss_calculation_has_limited_profit(hyperopt_conf, hyperopt_results) ->
assert under > correct
def test_sharpe_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_ratio'] = hyperopt_results['profit_ratio'] * 2
results_under = hyperopt_results.copy()
results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
default_conf.update({'hyperopt_loss': 'SharpeHyperOptLoss'})
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
assert over < correct
assert under > correct
def test_sharpe_loss_daily_prefers_higher_profits(default_conf, hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_ratio'] = hyperopt_results['profit_ratio'] * 2
results_under = hyperopt_results.copy()
results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
default_conf.update({'hyperopt_loss': 'SharpeHyperOptLossDaily'})
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
assert over < correct
assert under > correct
def test_sortino_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_ratio'] = hyperopt_results['profit_ratio'] * 2
results_under = hyperopt_results.copy()
results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
default_conf.update({'hyperopt_loss': 'SortinoHyperOptLoss'})
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
assert over < correct
assert under > correct
def test_sortino_loss_daily_prefers_higher_profits(default_conf, hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_ratio'] = hyperopt_results['profit_ratio'] * 2
results_under = hyperopt_results.copy()
results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
default_conf.update({'hyperopt_loss': 'SortinoHyperOptLossDaily'})
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
assert over < correct
assert under > correct
def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
@pytest.mark.parametrize('lossfunction', [
"OnlyProfitHyperOptLoss",
"SortinoHyperOptLoss",
"SortinoHyperOptLossDaily",
"SharpeHyperOptLoss",
"SharpeHyperOptLossDaily",
])
def test_loss_functions_better_profits(default_conf, hyperopt_results, lossfunction) -> None:
results_over = hyperopt_results.copy()
results_over['profit_abs'] = hyperopt_results['profit_abs'] * 2
results_over['profit_ratio'] = hyperopt_results['profit_ratio'] * 2
results_under = hyperopt_results.copy()
results_under['profit_abs'] = hyperopt_results['profit_abs'] / 2
results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
default_conf.update({'hyperopt_loss': 'OnlyProfitHyperOptLoss'})
default_conf.update({'hyperopt_loss': lossfunction})
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)
correct = hl.hyperopt_loss_function(hyperopt_results, len(hyperopt_results),
datetime(2019, 1, 1), datetime(2019, 5, 1))
over = hl.hyperopt_loss_function(results_over, len(hyperopt_results),
over = hl.hyperopt_loss_function(results_over, len(results_over),
datetime(2019, 1, 1), datetime(2019, 5, 1))
under = hl.hyperopt_loss_function(results_under, len(hyperopt_results),
under = hl.hyperopt_loss_function(results_under, len(results_under),
datetime(2019, 1, 1), datetime(2019, 5, 1))
assert over < correct
assert under > correct

112
tests/plugins/test_pairlist.py
View File

@ -4,6 +4,7 @@ import time
from unittest.mock import MagicMock, PropertyMock
import pytest
import time_machine
from freqtrade.constants import AVAILABLE_PAIRLISTS
from freqtrade.exceptions import OperationalException
@ -11,7 +12,8 @@ from freqtrade.persistence import Trade
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
from freqtrade.plugins.pairlistmanager import PairListManager
from freqtrade.resolvers import PairListResolver
from tests.conftest import get_patched_exchange, get_patched_freqtradebot, log_has, log_has_re
from tests.conftest import (create_mock_trades, get_patched_exchange, get_patched_freqtradebot,
log_has, log_has_re)
@pytest.fixture(scope="function")
@ -129,9 +131,9 @@ def test_load_pairlist_noexist(mocker, markets, default_conf):
default_conf, {}, 1)
def test_load_pairlist_verify_multi(mocker, markets, default_conf):
def test_load_pairlist_verify_multi(mocker, markets_static, default_conf):
freqtrade = get_patched_freqtradebot(mocker, default_conf)
mocker.patch('freqtrade.exchange.Exchange.markets', PropertyMock(return_value=markets))
mocker.patch('freqtrade.exchange.Exchange.markets', PropertyMock(return_value=markets_static))
plm = PairListManager(freqtrade.exchange, default_conf)
# Call different versions one after the other, should always consider what was passed in
# and have no side-effects (therefore the same check multiple times)
@ -662,6 +664,31 @@ def test_PerformanceFilter_error(mocker, whitelist_conf, caplog) -> None:
assert log_has("PerformanceFilter is not available in this mode.", caplog)
@pytest.mark.usefixtures("init_persistence")
def test_PerformanceFilter_lookback(mocker, whitelist_conf, fee) -> None:
whitelist_conf['exchange']['pair_whitelist'].append('XRP/BTC')
whitelist_conf['pairlists'] = [
{"method": "StaticPairList"},
{"method": "PerformanceFilter", "minutes": 60}
]
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
exchange = get_patched_exchange(mocker, whitelist_conf)
pm = PairListManager(exchange, whitelist_conf)
pm.refresh_pairlist()
assert pm.whitelist == ['ETH/BTC', 'TKN/BTC', 'XRP/BTC']
with time_machine.travel("2021-09-01 05:00:00 +00:00") as t:
create_mock_trades(fee)
pm.refresh_pairlist()
assert pm.whitelist == ['XRP/BTC', 'ETH/BTC', 'TKN/BTC']
# Move to "outside" of lookback window, so original sorting is restored.
t.move_to("2021-09-01 07:00:00 +00:00")
pm.refresh_pairlist()
assert pm.whitelist == ['ETH/BTC', 'TKN/BTC', 'XRP/BTC']
def test_gen_pair_whitelist_not_supported(mocker, default_conf, tickers) -> None:
default_conf['pairlists'] = [{'method': 'VolumePairList', 'number_assets': 10}]
@ -815,32 +842,63 @@ def test_agefilter_min_days_listed_too_large(mocker, default_conf, markets, tick
def test_agefilter_caching(mocker, markets, whitelist_conf_agefilter, tickers, ohlcv_history):
ohlcv_data = {
('ETH/BTC', '1d'): ohlcv_history,
('TKN/BTC', '1d'): ohlcv_history,
('LTC/BTC', '1d'): ohlcv_history,
}
mocker.patch.multiple('freqtrade.exchange.Exchange',
markets=PropertyMock(return_value=markets),
exchange_has=MagicMock(return_value=True),
get_tickers=tickers
)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
refresh_latest_ohlcv=MagicMock(return_value=ohlcv_data),
)
with time_machine.travel("2021-09-01 05:00:00 +00:00") as t:
ohlcv_data = {
('ETH/BTC', '1d'): ohlcv_history,
('TKN/BTC', '1d'): ohlcv_history,
('LTC/BTC', '1d'): ohlcv_history,
}
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
markets=PropertyMock(return_value=markets),
exchange_has=MagicMock(return_value=True),
get_tickers=tickers,
refresh_latest_ohlcv=MagicMock(return_value=ohlcv_data),
)
freqtrade = get_patched_freqtradebot(mocker, whitelist_conf_agefilter)
assert freqtrade.exchange.refresh_latest_ohlcv.call_count == 0
freqtrade.pairlists.refresh_pairlist()
assert len(freqtrade.pairlists.whitelist) == 3
assert freqtrade.exchange.refresh_latest_ohlcv.call_count > 0
freqtrade = get_patched_freqtradebot(mocker, whitelist_conf_agefilter)
assert freqtrade.exchange.refresh_latest_ohlcv.call_count == 0
freqtrade.pairlists.refresh_pairlist()
assert len(freqtrade.pairlists.whitelist) == 3
assert freqtrade.exchange.refresh_latest_ohlcv.call_count > 0
previous_call_count = freqtrade.exchange.refresh_latest_ohlcv.call_count
freqtrade.pairlists.refresh_pairlist()
assert len(freqtrade.pairlists.whitelist) == 3
# Called once for XRP/BTC
assert freqtrade.exchange.refresh_latest_ohlcv.call_count == previous_call_count + 1
freqtrade.pairlists.refresh_pairlist()
assert len(freqtrade.pairlists.whitelist) == 3
# Call to XRP/BTC cached
assert freqtrade.exchange.refresh_latest_ohlcv.call_count == 2
ohlcv_data = {
('ETH/BTC', '1d'): ohlcv_history,
('TKN/BTC', '1d'): ohlcv_history,
('LTC/BTC', '1d'): ohlcv_history,
('XRP/BTC', '1d'): ohlcv_history.iloc[[0]],
}
mocker.patch('freqtrade.exchange.Exchange.refresh_latest_ohlcv', return_value=ohlcv_data)
freqtrade.pairlists.refresh_pairlist()
assert len(freqtrade.pairlists.whitelist) == 3
assert freqtrade.exchange.refresh_latest_ohlcv.call_count == 1
# Move to next day
t.move_to("2021-09-02 01:00:00 +00:00")
mocker.patch('freqtrade.exchange.Exchange.refresh_latest_ohlcv', return_value=ohlcv_data)
freqtrade.pairlists.refresh_pairlist()
assert len(freqtrade.pairlists.whitelist) == 3
assert freqtrade.exchange.refresh_latest_ohlcv.call_count == 1
# Move another day with fresh mocks (now the pair is old enough)
t.move_to("2021-09-03 01:00:00 +00:00")
# Called once for XRP/BTC
ohlcv_data = {
('ETH/BTC', '1d'): ohlcv_history,
('TKN/BTC', '1d'): ohlcv_history,
('LTC/BTC', '1d'): ohlcv_history,
('XRP/BTC', '1d'): ohlcv_history,
}
mocker.patch('freqtrade.exchange.Exchange.refresh_latest_ohlcv', return_value=ohlcv_data)
freqtrade.pairlists.refresh_pairlist()
assert len(freqtrade.pairlists.whitelist) == 4
# Called once (only for XRP/BTC)
assert freqtrade.exchange.refresh_latest_ohlcv.call_count == 1
def test_OffsetFilter_error(mocker, whitelist_conf) -> None:

2
tests/plugins/test_pairlocks.py
View File

@ -68,7 +68,7 @@ def test_PairLocks(use_db):
# Global lock
PairLocks.lock_pair('*', lock_time)
assert PairLocks.is_global_lock(lock_time + timedelta(minutes=-50))
# Global lock also locks every pair seperately
# Global lock also locks every pair separately
assert PairLocks.is_pair_locked(pair, lock_time + timedelta(minutes=-50))
assert PairLocks.is_pair_locked('XRP/USDT', lock_time + timedelta(minutes=-50))

4
tests/plugins/test_protections.py
View File

@ -125,7 +125,7 @@ def test_stoploss_guard(mocker, default_conf, fee, caplog):
# Test 5m after lock-period - this should try and relock the pair, but end-time
# should be the previous end-time
end_time = PairLocks.get_pair_longest_lock('*').lock_end_time + timedelta(minutes=5)
assert freqtrade.protections.global_stop(end_time)
freqtrade.protections.global_stop(end_time)
assert not PairLocks.is_global_lock(end_time)
@ -182,7 +182,7 @@ def test_stoploss_guard_perpair(mocker, default_conf, fee, caplog, only_per_pair
min_ago_open=180, min_ago_close=30, profit_rate=0.9,
))
assert freqtrade.protections.stop_per_pair(pair)
freqtrade.protections.stop_per_pair(pair)
assert freqtrade.protections.global_stop() != only_per_pair
assert PairLocks.is_pair_locked(pair)
assert PairLocks.is_global_lock() != only_per_pair

15
tests/rpc/test_rpc_apiserver.py
View File

@ -422,20 +422,22 @@ def test_api_stopbuy(botclient):
assert ftbot.config['max_open_trades'] == 0
def test_api_balance(botclient, mocker, rpc_balance):
def test_api_balance(botclient, mocker, rpc_balance, tickers):
ftbot, client = botclient
ftbot.config['dry_run'] = False
mocker.patch('freqtrade.exchange.Exchange.get_balances', return_value=rpc_balance)
mocker.patch('freqtrade.exchange.Exchange.get_tickers', tickers)
mocker.patch('freqtrade.exchange.Exchange.get_valid_pair_combination',
side_effect=lambda a, b: f"{a}/{b}")
ftbot.wallets.update()
rc = client_get(client, f"{BASE_URI}/balance")
assert_response(rc)
assert "currencies" in rc.json()
assert len(rc.json()["currencies"]) == 5
assert rc.json()['currencies'][0] == {
response = rc.json()
assert "currencies" in response
assert len(response["currencies"]) == 5
assert response['currencies'][0] == {
'currency': 'BTC',
'free': 12.0,
'balance': 12.0,
@ -443,6 +445,10 @@ def test_api_balance(botclient, mocker, rpc_balance):
'est_stake': 12.0,
'stake': 'BTC',
}
assert 'starting_capital' in response
assert 'starting_capital_fiat' in response
assert 'starting_capital_pct' in response
assert 'starting_capital_ratio' in response
def test_api_count(botclient, mocker, ticker, fee, markets):
@ -1218,6 +1224,7 @@ def test_api_strategies(botclient):
assert_response(rc)
assert rc.json() == {'strategies': [
'HyperoptableStrategy',
'InformativeDecoratorTest',
'StrategyTestV2',
'TestStrategyLegacyV1'
]}

30
tests/rpc/test_rpc_telegram.py
View File

@ -576,6 +576,8 @@ def test_balance_handle_too_large_response(default_conf, update, mocker) -> None
'total': 100.0,
'symbol': 100.0,
'value': 1000.0,
'starting_capital': 1000,
'starting_capital_fiat': 1000,
})
telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
@ -1311,6 +1313,34 @@ def test_send_msg_buy_cancel_notification(default_conf, mocker) -> None:
'Reason: cancelled due to timeout.')
def test_send_msg_protection_notification(default_conf, mocker, time_machine) -> None:
default_conf['telegram']['notification_settings']['protection_trigger'] = 'on'
telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
time_machine.move_to("2021-09-01 05:00:00 +00:00")
lock = PairLocks.lock_pair('ETH/BTC', arrow.utcnow().shift(minutes=6).datetime, 'randreason')
msg = {
'type': RPCMessageType.PROTECTION_TRIGGER,
}
msg.update(lock.to_json())
telegram.send_msg(msg)
assert (msg_mock.call_args[0][0] == "*Protection* triggered due to randreason. "
"`ETH/BTC` will be locked until `2021-09-01 05:10:00`.")
msg_mock.reset_mock()
# Test global protection
msg = {
'type': RPCMessageType.PROTECTION_TRIGGER_GLOBAL,
}
lock = PairLocks.lock_pair('*', arrow.utcnow().shift(minutes=100).datetime, 'randreason')
msg.update(lock.to_json())
telegram.send_msg(msg)
assert (msg_mock.call_args[0][0] == "*Protection* triggered due to randreason. "
"*All pairs* will be locked until `2021-09-01 06:45:00`.")
def test_send_msg_buy_fill_notification(default_conf, mocker) -> None:
default_conf['telegram']['notification_settings']['buy_fill'] = 'on'

75
tests/strategy/strats/informative_decorator_strategy.py Normal file
View File

@ -0,0 +1,75 @@
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
from pandas import DataFrame
from freqtrade.strategy import informative, merge_informative_pair
from freqtrade.strategy.interface import IStrategy
class InformativeDecoratorTest(IStrategy):
"""
Strategy used by tests freqtrade bot.
Please do not modify this strategy, it's intended for internal use only.
Please look at the SampleStrategy in the user_data/strategy directory
or strategy repository https://github.com/freqtrade/freqtrade-strategies
for samples and inspiration.
"""
INTERFACE_VERSION = 2
stoploss = -0.10
timeframe = '5m'
startup_candle_count: int = 20
def informative_pairs(self):
return [('BTC/USDT', '5m')]
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['buy'] = 0
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['sell'] = 0
return dataframe
# Decorator stacking test.
@informative('30m')
@informative('1h')
def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = 14
return dataframe
# Simple informative test.
@informative('1h', 'BTC/{stake}')
def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = 14
return dataframe
# Quote currency different from stake currency test.
@informative('1h', 'ETH/BTC')
def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = 14
return dataframe
# Formatting test.
@informative('30m', 'BTC/{stake}', '{column}_{BASE}_{QUOTE}_{base}_{quote}_{asset}_{timeframe}')
def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = 14
return dataframe
# Custom formatter test
@informative('30m', 'ETH/{stake}', fmt=lambda column, **kwargs: column + '_from_callable')
def populate_indicators_eth_30m(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = 14
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Strategy timeframe indicators for current pair.
dataframe['rsi'] = 14
# Informative pairs are available in this method.
dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
# Mixing manual informative pairs with decorators.
informative = self.dp.get_pair_dataframe('BTC/USDT', '5m')
informative['rsi'] = 14
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '5m', ffill=True)
return dataframe

2
tests/strategy/test_interface.py
View File

@ -607,7 +607,7 @@ def test_is_informative_pairs_callback(default_conf):
strategy = StrategyResolver.load_strategy(default_conf)
# Should return empty
# Uses fallback to base implementation
assert [] == strategy.informative_pairs()
assert [] == strategy.gather_informative_pairs()
@pytest.mark.parametrize('error', [

66
tests/strategy/test_strategy_helpers.py
View File

@ -4,7 +4,9 @@ import numpy as np
import pandas as pd
import pytest
from freqtrade.strategy import merge_informative_pair, stoploss_from_open, timeframe_to_minutes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.strategy import (merge_informative_pair, stoploss_from_absolute, stoploss_from_open,
timeframe_to_minutes)
def generate_test_data(timeframe: str, size: int):
@ -132,3 +134,65 @@ def test_stoploss_from_open():
assert stoploss == 0
else:
assert isclose(stop_price, expected_stop_price, rel_tol=0.00001)
def test_stoploss_from_absolute():
assert stoploss_from_absolute(90, 100) == 1 - (90 / 100)
assert stoploss_from_absolute(100, 100) == 0
assert stoploss_from_absolute(110, 100) == 0
assert stoploss_from_absolute(100, 0) == 1
assert stoploss_from_absolute(0, 100) == 1
def test_informative_decorator(mocker, default_conf):
test_data_5m = generate_test_data('5m', 40)
test_data_30m = generate_test_data('30m', 40)
test_data_1h = generate_test_data('1h', 40)
data = {
('XRP/USDT', '5m'): test_data_5m,
('XRP/USDT', '30m'): test_data_30m,
('XRP/USDT', '1h'): test_data_1h,
('LTC/USDT', '5m'): test_data_5m,
('LTC/USDT', '30m'): test_data_30m,
('LTC/USDT', '1h'): test_data_1h,
('BTC/USDT', '30m'): test_data_30m,
('BTC/USDT', '5m'): test_data_5m,
('BTC/USDT', '1h'): test_data_1h,
('ETH/USDT', '1h'): test_data_1h,
('ETH/USDT', '30m'): test_data_30m,
('ETH/BTC', '1h'): test_data_1h,
}
from .strats.informative_decorator_strategy import InformativeDecoratorTest
default_conf['stake_currency'] = 'USDT'
strategy = InformativeDecoratorTest(config=default_conf)
strategy.dp = DataProvider({}, None, None)
mocker.patch.object(strategy.dp, 'current_whitelist', return_value=[
'XRP/USDT', 'LTC/USDT', 'BTC/USDT'
])
assert len(strategy._ft_informative) == 6 # Equal to number of decorators used
informative_pairs = [('XRP/USDT', '1h'), ('LTC/USDT', '1h'), ('XRP/USDT', '30m'),
('LTC/USDT', '30m'), ('BTC/USDT', '1h'), ('BTC/USDT', '30m'),
('BTC/USDT', '5m'), ('ETH/BTC', '1h'), ('ETH/USDT', '30m')]
for inf_pair in informative_pairs:
assert inf_pair in strategy.gather_informative_pairs()
def test_historic_ohlcv(pair, timeframe):
return data[(pair, timeframe or strategy.timeframe)].copy()
mocker.patch('freqtrade.data.dataprovider.DataProvider.historic_ohlcv',
side_effect=test_historic_ohlcv)
analyzed = strategy.advise_all_indicators(
{p: data[(p, strategy.timeframe)] for p in ('XRP/USDT', 'LTC/USDT')})
expected_columns = [
'rsi_1h', 'rsi_30m', # Stacked informative decorators
'btc_usdt_rsi_1h', # BTC 1h informative
'rsi_BTC_USDT_btc_usdt_BTC/USDT_30m', # Column formatting
'rsi_from_callable', # Custom column formatter
'eth_btc_rsi_1h', # Quote currency not matching stake currency
'rsi', 'rsi_less', # Non-informative columns
'rsi_5m', # Manual informative dataframe
]
for _, dataframe in analyzed.items():
for col in expected_columns:
assert col in dataframe.columns

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