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Merge pull request #8076 from freqtrade/new_release

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New release 2023.1
This commit is contained in:
Matthias 2023-01-30 18:11:08 +01:00 committed by GitHub
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2
.github/workflows/ci.yml vendored
View File

@ -360,6 +360,8 @@ jobs:
pip install -e .
- name: Tests incl. ccxt compatibility tests
env:
CI_WEB_PROXY: http://152.67.78.211:13128
run: |
pytest --random-order --cov=freqtrade --cov-config=.coveragerc --longrun

6
.pre-commit-config.yaml
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@ -8,16 +8,16 @@ repos:
# stages: [push]
- repo: https://github.com/pre-commit/mirrors-mypy
rev: "v0.942"
rev: "v0.991"
hooks:
- id: mypy
exclude: build_helpers
additional_dependencies:
- types-cachetools==5.2.1
- types-filelock==3.2.7
- types-requests==2.28.11.7
- types-requests==2.28.11.8
- types-tabulate==0.9.0.0
- types-python-dateutil==2.8.19.5
- types-python-dateutil==2.8.19.6
# stages: [push]
- repo: https://github.com/pycqa/isort

9
build_helpers/publish_docker_arm64.sh
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@ -70,20 +70,21 @@ docker push ${CACHE_IMAGE}:$TAG_ARM
# Otherwise installation might fail.
echo "create manifests"
docker manifest create --amend ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
docker manifest create ${IMAGE_NAME}:${TAG} ${CACHE_IMAGE}:${TAG} ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI}
docker manifest push -p ${IMAGE_NAME}:${TAG}
docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM} ${CACHE_IMAGE}:${TAG_PLOT}
docker manifest create ${IMAGE_NAME}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT} ${CACHE_IMAGE}:${TAG_PLOT_ARM}
docker manifest push -p ${IMAGE_NAME}:${TAG_PLOT}
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM} ${CACHE_IMAGE}:${TAG_FREQAI}
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI} ${CACHE_IMAGE}:${TAG_FREQAI_ARM}
docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI}
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM} ${CACHE_IMAGE}:${TAG_FREQAI_RL}
docker manifest create ${IMAGE_NAME}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL} ${CACHE_IMAGE}:${TAG_FREQAI_RL_ARM}
docker manifest push -p ${IMAGE_NAME}:${TAG_FREQAI_RL}
# Tag as latest for develop builds
if [ "${TAG}" = "develop" ]; then
echo 'Tagging image as latest'
docker manifest create ${IMAGE_NAME}:latest ${CACHE_IMAGE}:${TAG_ARM} ${IMAGE_NAME}:${TAG_PI} ${CACHE_IMAGE}:${TAG}
docker manifest push -p ${IMAGE_NAME}:latest
fi

15
build_helpers/publish_docker_multi.sh
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@ -26,7 +26,10 @@ if [ "${GITHUB_EVENT_NAME}" = "schedule" ]; then
--cache-to=type=registry,ref=${CACHE_TAG} \
-f docker/Dockerfile.armhf \
--platform ${PI_PLATFORM} \
-t ${IMAGE_NAME}:${TAG_PI} --push .
-t ${IMAGE_NAME}:${TAG_PI} \
--push \
--provenance=false \
.
else
echo "event ${GITHUB_EVENT_NAME}: building with cache"
# Build regular image
@ -35,12 +38,16 @@ else
# Pull last build to avoid rebuilding the whole image
# docker pull --platform ${PI_PLATFORM} ${IMAGE_NAME}:${TAG}
# disable provenance due to https://github.com/docker/buildx/issues/1509
docker buildx build \
--cache-from=type=registry,ref=${CACHE_TAG} \
--cache-to=type=registry,ref=${CACHE_TAG} \
-f docker/Dockerfile.armhf \
--platform ${PI_PLATFORM} \
-t ${IMAGE_NAME}:${TAG_PI} --push .
-t ${IMAGE_NAME}:${TAG_PI} \
--push \
--provenance=false \
.
fi
if [ $? -ne 0 ]; then
@ -68,12 +75,10 @@ fi
docker images
docker push ${CACHE_IMAGE}
docker push ${CACHE_IMAGE}:$TAG
docker push ${CACHE_IMAGE}:$TAG_PLOT
docker push ${CACHE_IMAGE}:$TAG_FREQAI
docker push ${CACHE_IMAGE}:$TAG_FREQAI_RL
docker push ${CACHE_IMAGE}:$TAG
docker images

14
config_examples/config_binance.example.json
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@ -59,20 +59,6 @@
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",

14
config_examples/config_bittrex.example.json
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@ -56,20 +56,6 @@
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",

8
config_examples/config_freqai.example.json
View File

@ -21,8 +21,8 @@
"ccxt_config": {},
"ccxt_async_config": {},
"pair_whitelist": [
"1INCH/USDT",
"ALGO/USDT"
"1INCH/USDT:USDT",
"ALGO/USDT:USDT"
],
"pair_blacklist": []
},
@ -60,8 +60,8 @@
"1h"
],
"include_corr_pairlist": [
"BTC/USDT",
"ETH/USDT"
"BTC/USDT:USDT",
"ETH/USDT:USDT"
],
"label_period_candles": 20,
"include_shifted_candles": 2,

14
config_examples/config_kraken.example.json
View File

@ -64,20 +64,6 @@
"pairlists": [
{"method": "StaticPairList"}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": false,
"token": "your_telegram_token",

3
docs/advanced-backtesting.md
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@ -32,7 +32,7 @@ To analyze the entry/exit tags, we now need to use the `freqtrade backtesting-an
with `--analysis-groups` option provided with space-separated arguments (default `0 1 2`):
``` bash
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4
freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4 5
```
This command will read from the last backtesting results. The `--analysis-groups` option is
@ -43,6 +43,7 @@ ranging from the simplest (0) to the most detailed per pair, per buy and per sel
* 2: profit summaries grouped by enter_tag and exit_tag
* 3: profit summaries grouped by pair and enter_tag
* 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
* 5: profit summaries grouped by exit_tag
More options are available by running with the `-h` option.

8
docs/advanced-hyperopt.md
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@ -75,7 +75,7 @@ This function needs to return a floating point number (`float`). Smaller numbers
## Overriding pre-defined spaces
To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`), define a nested class called Hyperopt and define the required spaces as follows:
To override a pre-defined space (`roi_space`, `generate_roi_table`, `stoploss_space`, `trailing_space`, `max_open_trades_space`), define a nested class called Hyperopt and define the required spaces as follows:
```python
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal
@ -123,6 +123,12 @@ class MyAwesomeStrategy(IStrategy):
Categorical([True, False], name='trailing_only_offset_is_reached'),
]
# Define a custom max_open_trades space
def max_open_trades_space(self) -> List[Dimension]:
return [
Integer(-1, 10, name='max_open_trades'),
]
```
!!! Note

11
docs/backtesting.md
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@ -300,7 +300,11 @@ A backtesting result will look like that:
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| CAGR % | 460.87% |
| Sortino | 1.88 |
| Sharpe | 2.97 |
| Calmar | 6.29 |
| Profit factor | 1.11 |
| Expectancy | -0.15 |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
@ -400,7 +404,11 @@ It contains some useful key metrics about performance of your strategy on backte
| Absolute profit | 0.00762792 BTC |
| Total profit % | 76.2% |
| CAGR % | 460.87% |
| Sortino | 1.88 |
| Sharpe | 2.97 |
| Calmar | 6.29 |
| Profit factor | 1.11 |
| Expectancy | -0.15 |
| Avg. stake amount | 0.001 BTC |
| Total trade volume | 0.429 BTC |
| | |
@ -447,6 +455,9 @@ It contains some useful key metrics about performance of your strategy on backte
- `Absolute profit`: Profit made in stake currency.
- `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
- `CAGR %`: Compound annual growth rate.
- `Sortino`: Annualized Sortino ratio.
- `Sharpe`: Annualized Sharpe ratio.
- `Calmar`: Annualized Calmar ratio.
- `Profit factor`: profit / loss.
- `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
- `Total trade volume`: Volume generated on the exchange to reach the above profit.

4
docs/bot-basics.md
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@ -75,3 +75,7 @@ This loop will be repeated again and again until the bot is stopped.
!!! Note
Both Backtesting and Hyperopt include exchange default Fees in the calculation. Custom fees can be passed to backtesting / hyperopt by specifying the `--fee` argument.
!!! Warning "Callback call frequency"
Backtesting will call each callback at max. once per candle (`--timeframe-detail` modifies this behavior to once per detailed candle).
Most callbacks will be called once per iteration in live (usually every ~5s) - which can cause backtesting mismatches.

5
docs/configuration.md
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@ -11,7 +11,7 @@ Per default, the bot loads the configuration from the `config.json` file, locate
You can specify a different configuration file used by the bot with the `-c/--config` command-line option.
If you used the [Quick start](installation.md/#quick-start) method for installing
If you used the [Quick start](docker_quickstart.md#docker-quick-start) method for installing
the bot, the installation script should have already created the default configuration file (`config.json`) for you.
If the default configuration file is not created we recommend to use `freqtrade new-config --config config.json` to generate a basic configuration file.
@ -134,7 +134,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| Parameter | Description |
|------------|-------------|
| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation that can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade).<br> **Datatype:** Positive integer or -1.
| `max_open_trades` | **Required.** Number of open trades your bot is allowed to have. Only one open trade per pair is possible, so the length of your pairlist is another limitation that can apply. If -1 then it is ignored (i.e. potentially unlimited open trades, limited by the pairlist). [More information below](#configuring-amount-per-trade). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Positive integer or -1.
| `stake_currency` | **Required.** Crypto-currency used for trading. <br> **Datatype:** String
| `stake_amount` | **Required.** Amount of crypto-currency your bot will use for each trade. Set it to `"unlimited"` to allow the bot to use all available balance. [More information below](#configuring-amount-per-trade). <br> **Datatype:** Positive float or `"unlimited"`.
| `tradable_balance_ratio` | Ratio of the total account balance the bot is allowed to trade. [More information below](#configuring-amount-per-trade). <br>*Defaults to `0.99` 99%).*<br> **Datatype:** Positive float between `0.1` and `1.0`.
@ -263,6 +263,7 @@ Values set in the configuration file always overwrite values set in the strategy
* `minimal_roi`
* `timeframe`
* `stoploss`
* `max_open_trades`
* `trailing_stop`
* `trailing_stop_positive`
* `trailing_stop_positive_offset`

19
docs/exchanges.md
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@ -75,6 +75,25 @@ Binance has been split into 2, and users must use the correct ccxt exchange ID f
* [binance.com](https://www.binance.com/) - International users. Use exchange id: `binance`.
* [binance.us](https://www.binance.us/) - US based users. Use exchange id: `binanceus`.
### Binance RSA keys
Freqtrade supports binance RSA API keys.
We recommend to use them as environment variable.
``` bash
export FREQTRADE__EXCHANGE__SECRET="$(cat ./rsa_binance.private)"
```
They can however also be configured via configuration file. Since json doesn't support multi-line strings, you'll have to replace all newlines with `\n` to have a valid json file.
``` json
// ...
"key": "<someapikey>",
"secret": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBABACAFQA<...>s8KX8=\n-----END PRIVATE KEY-----"
// ...
```
### Binance Futures
Binance has specific (unfortunately complex) [Futures Trading Quantitative Rules](https://www.binance.com/en/support/faq/4f462ebe6ff445d4a170be7d9e897272) which need to be followed, and which prohibit a too low stake-amount (among others) for too many orders.

175
docs/freqai-configuration.md
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@ -43,116 +43,113 @@ The FreqAI strategy requires including the following lines of code in the standa
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# the model will return all labels created by user in `populate_any_indicators`
# the model will return all labels created by user in `set_freqai_labels()`
# (& appended targets), an indication of whether or not the prediction should be accepted,
# the target mean/std values for each of the labels created by user in
# `populate_any_indicators()` for each training period.
# `feature_engineering_*` for each training period.
dataframe = self.freqai.start(dataframe, metadata, self)
return dataframe
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + pair `
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
All features must be prepended with `%` to be recognized by FreqAI internals.
:param df: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
return dataframe
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
def feature_engineering_expand_basic(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
All features must be prepended with `%` to be recognized by FreqAI internals.
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
:param df: strategy dataframe which will receive the features
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
def feature_engineering_standard(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
All features must be prepended with `%` to be recognized by FreqAI internals.
:param df: strategy dataframe which will receive the features
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
:param df: strategy dataframe which will receive the targets
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe["&-s_close"] = (
dataframe["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ dataframe["close"]
- 1
)
return df
```
Notice how the `populate_any_indicators()` is where [features](freqai-feature-engineering.md#feature-engineering) and labels/targets are added. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
Notice also the location of the labels under `if set_generalized_indicators:` at the bottom of the example. This is where single features and labels/targets should be added to the feature set to avoid duplication of them from various configuration parameters that multiply the feature set, such as `include_timeframes`.
Notice how the `feature_engineering_*()` is where [features](freqai-feature-engineering.md#feature-engineering) are added. Meanwhile `set_freqai_targets()` adds the labels/targets. A full example strategy is available in `templates/FreqaiExampleStrategy.py`.
!!! Note
The `self.freqai.start()` function cannot be called outside the `populate_indicators()`.
!!! Note
Features **must** be defined in `populate_any_indicators()`. Defining FreqAI features in `populate_indicators()`
will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, the following structure inside `populate_any_indicators()` should be used
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`):
```python
def populate_any_indicators(self, pair, df, tf, informative=None, set_generalized_indicators=False):
...
# Add generalized indicators here (because in live, it will call only this function to populate
# indicators for retraining). Notice how we ensure not to add them multiple times by associating
# these generalized indicators to the basepair/timeframe
if set_generalized_indicators:
df['%-day_of_week'] = (df["date"].dt.dayofweek + 1) / 7
df['%-hour_of_day'] = (df['date'].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
```
Please see the example script located in `freqtrade/templates/FreqaiExampleStrategy.py` for a full example of `populate_any_indicators()`.
Features **must** be defined in `feature_engineering_*()`. Defining FreqAI features in `populate_indicators()`
will cause the algorithm to fail in live/dry mode. In order to add generalized features that are not associated with a specific pair or timeframe, you should use `feature_engineering_standard()`
(as exemplified in `freqtrade/templates/FreqaiExampleStrategy.py`).
## Important dataframe key patterns
@ -160,11 +157,11 @@ Below are the values you can expect to include/use inside a typical strategy dat
| DataFrame Key | Description |
|------------|-------------|
| `df['&*']` | Any dataframe column prepended with `&` in `populate_any_indicators()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
| `df['&*']` | Any dataframe column prepended with `&` in `set_freqai_targets()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
| `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
| `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
| `df['%*']` | Any dataframe column prepended with `%` in `populate_any_indicators()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
| `df['%*']` | Any dataframe column prepended with `%` in `feature_engineering_*()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
## Setting the `startup_candle_count`

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@ -2,96 +2,130 @@
## Defining the features
Low level feature engineering is performed in the user strategy within a function called `populate_any_indicators()`. That function sets the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. One important syntax rule is that all `base features` string names are prepended with `%-{pair}`, while labels/targets are prepended with `&`.
Low level feature engineering is performed in the user strategy within a set of functions called `feature_engineering_*`. These function set the `base features` such as, `RSI`, `MFI`, `EMA`, `SMA`, time of day, volume, etc. The `base features` can be custom indicators or they can be imported from any technical-analysis library that you can find. FreqAI is equipped with a set of functions to simplify rapid large-scale feature engineering:
!!! Note
Adding the full pair string, e.g. XYZ/USD, in the feature name enables improved performance for dataframe caching on the backend. If you decide *not* to add the full pair string in the feature string, FreqAI will operate in a reduced performance mode.
| Function | Description |
|---------------|-------------|
| `feature_engineering__expand_all()` | This optional function will automatically expand the defined features on the config defined `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
| `feature_engineering__expand_basic()` | This optional function will automatically expand the defined features on the config defined `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. Note: this function does *not* expand across `include_periods_candles`.
| `feature_engineering_standard()` | This optional function will be called once with the dataframe of the base timeframe. This is the final function to be called, which means that the dataframe entering this function will contain all the features and columns from the base asset created by the other `feature_engineering_expand` functions. This function is a good place to do custom exotic feature extractions (e.g. tsfresh). This function is also a good place for any feature that should not be auto-expanded upon (e.g. day of the week).
| `set_freqai_targets()` | Required function to set the targets for the model. All targets must be prepended with `&` to be recognized by the FreqAI internals.
Meanwhile, high level feature engineering is handled within `"feature_parameters":{}` in the FreqAI config. Within this file, it is possible to decide large scale feature expansions on top of the `base_features` such as "including correlated pairs" or "including informative timeframes" or even "including recent candles."
It is advisable to start from the template `populate_any_indicators()` in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
It is advisable to start from the template `feature_engineering_*` functions in the source provided example strategy (found in `templates/FreqaiExampleStrategy.py`) to ensure that the feature definitions are following the correct conventions. Here is an example of how to set the indicators and labels in the strategy:
```python
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
"""
Function designed to automatically generate, name, and merge features
from user-indicated timeframes in the configuration file. The user controls the indicators
passed to the training/prediction by prepending indicators with `'%-' + pair `
(see convention below). I.e., the user should not prepend any supporting metrics
(e.g., bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
All features must be prepended with `%` to be recognized by FreqAI internals.
:param df: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=period, stds=2.2
)
dataframe["bb_lowerband-period"] = bollinger["lower"]
dataframe["bb_middleband-period"] = bollinger["mid"]
dataframe["bb_upperband-period"] = bollinger["upper"]
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
dataframe["%-bb_width-period"] = (
dataframe["bb_upperband-period"]
- dataframe["bb_lowerband-period"]
) / dataframe["bb_middleband-period"]
dataframe["%-close-bb_lower-period"] = (
dataframe["close"] / dataframe["bb_lowerband-period"]
)
informative[f"%-{pair}bb_width-period_{t}"] = (
informative[f"{pair}bb_upperband-period_{t}"]
- informative[f"{pair}bb_lowerband-period_{t}"]
) / informative[f"{pair}bb_middleband-period_{t}"]
informative[f"%-{pair}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
dataframe["%-relative_volume-period"] = (
dataframe["volume"] / dataframe["volume"].rolling(period).mean()
)
return dataframe
def feature_engineering_expand_basic(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
All features must be prepended with `%` to be recognized by FreqAI internals.
:param df: strategy dataframe which will receive the features
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
def feature_engineering_standard(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
All features must be prepended with `%` to be recognized by FreqAI internals.
:param df: strategy dataframe which will receive the features
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
:param df: strategy dataframe which will receive the targets
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe["&-s_close"] = (
dataframe["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ dataframe["close"]
- 1
)
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
return df
return dataframe
```
In the presented example, the user does not wish to pass the `bb_lowerband` as a feature to the model,
@ -118,13 +152,13 @@ After having defined the `base features`, the next step is to expand upon them u
}
```
The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `populate_any_indicators()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
The `include_timeframes` in the config above are the timeframes (`tf`) of each call to `feature_engineering_expand_*()` in the strategy. In the presented case, the user is asking for the `5m`, `15m`, and `4h` timeframes of the `rsi`, `mfi`, `roc`, and `bb_width` to be included in the feature set.
You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `populate_any_indicators` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
You can ask for each of the defined features to be included also for informative pairs using the `include_corr_pairlist`. This means that the feature set will include all the features from `feature_engineering_expand_*()` on all the `include_timeframes` for each of the correlated pairs defined in the config (`ETH/USD`, `LINK/USD`, and `BNB/USD` in the presented example).
`include_shifted_candles` indicates the number of previous candles to include in the feature set. For example, `include_shifted_candles: 2` tells FreqAI to include the past 2 candles for each of the features in the feature set.
In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `populate_any_indicators()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
In total, the number of features the user of the presented example strat has created is: length of `include_timeframes` * no. features in `feature_engineering_expand_*()` * length of `include_corr_pairlist` * no. `include_shifted_candles` * length of `indicator_periods_candles`
$= 3 * 3 * 3 * 2 * 2 = 108$.
### Returning additional info from training

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@ -29,12 +29,12 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
|------------|-------------|
| | **Feature parameters within the `freqai.feature_parameters` sub dictionary**
| `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
| `include_timeframes` | A list of timeframes that all indicators in `populate_any_indicators` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `populate_any_indicators` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `populate_any_indicators` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
| `include_timeframes` | A list of timeframes that all indicators in `feature_engineering_expand_*()` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
| `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `feature_engineering_expand_*()` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `feature_engineering_expand_all()` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
| `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, FreqAI will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
| `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)). <br> **Datatype:** Positive float (typically < 1).
| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `populate_any_indicators()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
| `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `feature_engineering_*()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
| `indicator_periods_candles` | Time periods to calculate indicators for. The indicators are added to the base indicator dataset. <br> **Datatype:** List of positive integers.
| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. <br> Default: `False`.
| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features. Plot is stored in `user_data/models/<identifier>/sub-train-<COIN>_<timestamp>.html`. <br> **Datatype:** Integer. <br> Default: `0`.

68
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@ -34,65 +34,36 @@ Setting up and running a Reinforcement Learning model is the same as running a R
freqtrade trade --freqaimodel ReinforcementLearner --strategy MyRLStrategy --config config.json
```
where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner` (or a custom user defined one located in `user_data/freqaimodels`). The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `populate_any_indicators` as a typical Regressor:
where `ReinforcementLearner` will use the templated `ReinforcementLearner` from `freqai/prediction_models/ReinforcementLearner` (or a custom user defined one located in `user_data/freqaimodels`). The strategy, on the other hand, follows the same base [feature engineering](freqai-feature-engineering.md) with `feature_engineering_*` as a typical Regressor. The difference lies in the creation of the targets, Reinforcement Learning doesn't require them. However, FreqAI requires a default (neutral) value to be set in the action column:
```python
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
def set_freqai_targets(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
More details about feature engineering available:
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
# The following raw price values are necessary for RL models
informative[f"%-{pair}raw_close"] = informative["close"]
informative[f"%-{pair}raw_open"] = informative["open"]
informative[f"%-{pair}raw_high"] = informative["high"]
informative[f"%-{pair}raw_low"] = informative["low"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
# For RL, there are no direct targets to set. This is filler (neutral)
# until the agent sends an action.
df["&-action"] = 0
return df
:param df: strategy dataframe which will receive the targets
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
# For RL, there are no direct targets to set. This is filler (neutral)
# until the agent sends an action.
dataframe["&-action"] = 0
```
Most of the function remains the same as for typical Regressors, however, the function above shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environment:
```python
def feature_engineering_standard(self, dataframe, **kwargs):
# The following features are necessary for RL models
informative[f"%-{pair}raw_close"] = informative["close"]
informative[f"%-{pair}raw_open"] = informative["open"]
informative[f"%-{pair}raw_high"] = informative["high"]
informative[f"%-{pair}raw_low"] = informative["low"]
dataframe[f"%-raw_close"] = dataframe["close"]
dataframe[f"%-raw_open"] = dataframe["open"]
dataframe[f"%-raw_high"] = dataframe["high"]
dataframe[f"%-raw_low"] = dataframe["low"]
```
Finally, there is no explicit "label" to make - instead it is necessary to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action.
@ -272,7 +243,6 @@ FreqAI also provides a built in episodic summary logger called `self.tensorboard
!!! Note
The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)` would add 0.23 to `float_metric`. In this case you can also disable incrementing using `inc=False` parameter.
### Choosing a base environment
FreqAI provides three base environments, `Base3ActionRLEnvironment`, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 3, 4 or 5 actions. The `Base3ActionEnvironment` is the simplest, the agent can select from hold, long, or short. This environment can also be used for long-only bots (it automatically follows the `can_short` flag from the strategy), where long is the enter condition and short is the exit condition. Meanwhile, in the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Finally, in the `Base5ActionEnvironment`, the agent has the same actions as Base4, but instead of a single exit action, it separates exit long and exit short. The main changes stemming from the environment selection include:

6
docs/freqai-running.md
View File

@ -67,6 +67,10 @@ Backtesting mode requires [downloading the necessary data](#downloading-data-to-
*want* to retrain a new model with the same config file, you should simply change the `identifier`.
This way, you can return to using any model you wish by simply specifying the `identifier`.
!!! Note
Backtesting calls `set_freqai_targets()` one time for each backtest window (where the number of windows is the full backtest timerange divided by the `backtest_period_days` parameter). Doing this means that the targets simulate dry/live behavior without look ahead bias. However, the definition of the features in `feature_engineering_*()` is performed once on the entire backtest timerange. This means that you should be sure that features do look-ahead into the future.
More details about look-ahead bias can be found in [Common Mistakes](strategy-customization.md#common-mistakes-when-developing-strategies).
---
### Saving prediction data
@ -135,7 +139,7 @@ freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --strategy FreqaiExampleSt
`hyperopt` requires you to have the data pre-downloaded in the same fashion as if you were doing [backtesting](#backtesting). In addition, you must consider some restrictions when trying to hyperopt FreqAI strategies:
- The `--analyze-per-epoch` hyperopt parameter is not compatible with FreqAI.
- It's not possible to hyperopt indicators in the `populate_any_indicators()` function. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
- It's not possible to hyperopt indicators in the `feature_engineering_*()` and `set_freqai_targets()` functions. This means that you cannot optimize model parameters using hyperopt. Apart from this exception, it is possible to optimize all other [spaces](hyperopt.md#running-hyperopt-with-smaller-search-space).
- The backtesting instructions also apply to hyperopt.
The best method for combining hyperopt and FreqAI is to focus on hyperopting entry/exit thresholds/criteria. You need to focus on hyperopting parameters that are not used in your features. For example, you should not try to hyperopt rolling window lengths in the feature creation, or any part of the FreqAI config which changes predictions. In order to efficiently hyperopt the FreqAI strategy, FreqAI stores predictions as dataframes and reuses them. Hence the requirement to hyperopt entry/exit thresholds/criteria only.

14
docs/hyperopt.md
View File

@ -50,7 +50,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--eps] [--dmmp] [--enable-protections]
[--dry-run-wallet DRY_RUN_WALLET]
[--timeframe-detail TIMEFRAME_DETAIL] [-e INT]
[--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]]
[--spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]]
[--print-all] [--no-color] [--print-json] [-j JOBS]
[--random-state INT] [--min-trades INT]
[--hyperopt-loss NAME] [--disable-param-export]
@ -96,7 +96,7 @@ optional arguments:
Specify detail timeframe for backtesting (`1m`, `5m`,
`30m`, `1h`, `1d`).
-e INT, --epochs INT Specify number of epochs (default: 100).
--spaces {all,buy,sell,roi,stoploss,trailing,protection,default} [{all,buy,sell,roi,stoploss,trailing,protection,default} ...]
--spaces {all,buy,sell,roi,stoploss,trailing,protection,trades,default} [{all,buy,sell,roi,stoploss,trailing,protection,trades,default} ...]
Specify which parameters to hyperopt. Space-separated
list.
--print-all Print all results, not only the best ones.
@ -180,6 +180,7 @@ Rarely you may also need to create a [nested class](advanced-hyperopt.md#overrid
* `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)
* `max_open_trades_space` - for custom max_open_trades optimization (if you need the ranges for the max_open_trades parameter in the optimization hyperspace that differ from default)
!!! Tip "Quickly optimize ROI, stoploss and trailing stoploss"
You can quickly optimize the spaces `roi`, `stoploss` and `trailing` without changing anything in your strategy.
@ -365,7 +366,7 @@ class MyAwesomeStrategy(IStrategy):
timeframe = '15m'
minimal_roi = {
"0": 0.10
},
}
# Define the parameter spaces
buy_ema_short = IntParameter(3, 50, default=5)
buy_ema_long = IntParameter(15, 200, default=50)
@ -400,7 +401,7 @@ class MyAwesomeStrategy(IStrategy):
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions = []
conditions.append(qtpylib.crossed_above(
dataframe[f'ema_long_{self.buy_ema_long.value}'], dataframe[f'ema_short_{self.buy_ema_short.value}']
))
@ -643,6 +644,7 @@ Legal values are:
* `roi`: just optimize the minimal profit table for your strategy
* `stoploss`: search for the best stoploss value
* `trailing`: search for the best trailing stop values
* `trades`: search for the best max open trades values
* `protection`: search for the best protection parameters (read the [protections section](#optimizing-protections) on how to properly define these)
* `default`: `all` except `trailing` and `protection`
* space-separated list of any of the above values for example `--spaces roi stoploss`
@ -916,5 +918,5 @@ Once the optimized strategy has been implemented into your strategy, you should
To achieve same the results (number of trades, their durations, profit, etc.) as during Hyperopt, please use the 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`).
Pay special care to the stoploss, max_open_trades 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`, `max_open_trades` or `trailing_stop`).

4
docs/leverage.md
View File

@ -67,8 +67,6 @@ You will also have to pick a "margin mode" (explanation below) - with freqtrade
Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/en/latest/manual.html?#perpetual-swap-perpetual-future).
A futures pair will therefore have the naming of `base/quote:settle` (e.g. `ETH/USDT:USDT`).
Binance is currently still an exception to this naming scheme, where pairs are named `ETH/USDT` also for futures markets, but will be aligned as soon as CCXT is ready.
### Margin mode
On top of `trading_mode` - you will also have to configure your `margin_mode`.
@ -92,6 +90,8 @@ One account is used to share collateral between markets (trading pairs). Margin
"margin_mode": "cross"
```
Please read the [exchange specific notes](exchanges.md) for exchanges that support this mode and how they differ.
## Set leverage to use
Different strategies and risk profiles will require different levels of leverage.

4
docs/requirements-docs.txt
View File

@ -1,6 +1,6 @@
markdown==3.3.7
mkdocs==1.4.2
mkdocs-material==8.5.11
mkdocs-material==9.0.5
mdx_truly_sane_lists==1.3
pymdown-extensions==9.9
pymdown-extensions==9.9.1
jinja2==3.1.2

2
docs/strategy-advanced.md
View File

@ -80,7 +80,7 @@ class AwesomeStrategy(IStrategy):
## Enter Tag
When your strategy has multiple buy signals, you can name the signal that triggered.
Then you can access you buy signal on `custom_exit`
Then you can access your buy signal on `custom_exit`
```python
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

3
docs/strategy-callbacks.md
View File

@ -659,6 +659,7 @@ Position adjustments will always be applied in the direction of the trade, so a
!!! Warning "Backtesting"
During backtesting this callback is called for each candle in `timeframe` or `timeframe_detail`, so run-time performance will be affected.
This can also cause deviating results between live and backtesting, since backtesting can adjust the trade only once per candle, whereas live could adjust the trade multiple times per candle.
``` python
from freqtrade.persistence import Trade
@ -827,7 +828,7 @@ class AwesomeStrategy(IStrategy):
"""
# Limit orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10) > trade.open_date_utc:
if pair == 'BTC/USDT' and entry_tag == 'long_sma200' and side == 'long' and (current_time - timedelta(minutes=10)) > trade.open_date_utc:
# just cancel the order if it has been filled more than half of the amount
if order.filled > order.remaining:
return None

251
docs/strategy_migration.md
View File

@ -477,3 +477,254 @@ after:
"ignore_buying_expired_candle_after": 120
}
```
## FreqAI strategy
The `populate_any_indicators()` method has been split into `feature_engineering_expand_all()`, `feature_engineering_expand_basic()`, `feature_engineering_standard()` and`set_freqai_targets()`.
For each new function, the pair (and timeframe where necessary) will be automatically added to the column.
As such, the definition of features becomes much simpler with the new logic.
For a full explanation of each method, please go to the corresponding [freqAI documentation page](freqai-feature-engineering.md#defining-the-features)
``` python linenums="1" hl_lines="12-37 39-42 63-65 67-75"
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"%-{pair}bb_width-period_{t}"] = (
informative[f"{pair}bb_upperband-period_{t}"]
- informative[f"{pair}bb_lowerband-period_{t}"]
) / informative[f"{pair}bb_middleband-period_{t}"]
informative[f"%-{pair}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
)
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
) # (1)
informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
informative[f"%-{pair}raw_volume"] = informative["volume"]
informative[f"%-{pair}raw_price"] = informative["close"]
# (2)
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# (3)
# user adds targets here by prepending them with &- (see convention below)
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
) # (4)
return df
```
1. Features - Move to `feature_engineering_expand_all`
2. Basic features, not expanded across `include_periods_candles` - move to`feature_engineering_expand_basic()`.
3. Standard features which should not be expanded - move to `feature_engineering_standard()`.
4. Targets - Move this part to `set_freqai_targets()`.
### freqai - feature engineering expand all
Features will now expand automatically. As such, the expansion loops, as well as the `{pair}` / `{timeframe}` parts will need to be removed.
``` python linenums="1"
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=period, stds=2.2
)
dataframe["bb_lowerband-period"] = bollinger["lower"]
dataframe["bb_middleband-period"] = bollinger["mid"]
dataframe["bb_upperband-period"] = bollinger["upper"]
dataframe["%-bb_width-period"] = (
dataframe["bb_upperband-period"]
- dataframe["bb_lowerband-period"]
) / dataframe["bb_middleband-period"]
dataframe["%-close-bb_lower-period"] = (
dataframe["close"] / dataframe["bb_lowerband-period"]
)
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
dataframe["%-relative_volume-period"] = (
dataframe["volume"] / dataframe["volume"].rolling(period).mean()
)
return dataframe
```
### Freqai - feature engineering basic
Basic features. Make sure to remove the `{pair}` part from your features.
``` python linenums="1"
def feature_engineering_expand_basic(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
```
### FreqAI - feature engineering standard
``` python linenums="1"
def feature_engineering_standard(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
All features must be prepended with `%` to be recognized by FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the features
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
return dataframe
```
### FreqAI - set Targets
Targets now get their own, dedicated method.
``` python linenums="1"
def set_freqai_targets(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the targets
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe["&-s_close"] = (
dataframe["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ dataframe["close"]
- 1
)
return dataframe
```

7
freqtrade/__init__.py
View File

@ -1,19 +1,20 @@
""" Freqtrade bot """
__version__ = '2022.12'
__version__ = '2023.1'
if 'dev' in __version__:
from pathlib import Path
try:
import subprocess
freqtrade_basedir = Path(__file__).parent
__version__ = __version__ + '-' + subprocess.check_output(
['git', 'log', '--format="%h"', '-n 1'],
stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
stderr=subprocess.DEVNULL, cwd=freqtrade_basedir).decode("utf-8").rstrip().strip('"')
except Exception: # pragma: no cover
# git not available, ignore
try:
# Try Fallback to freqtrade_commit file (created by CI while building docker image)
from pathlib import Path
versionfile = Path('./freqtrade_commit')
if versionfile.is_file():
__version__ = f"docker-{__version__}-{versionfile.read_text()[:8]}"

8
freqtrade/commands/cli_options.py
View File

@ -251,7 +251,8 @@ AVAILABLE_CLI_OPTIONS = {
"spaces": Arg(
'--spaces',
help='Specify which parameters to hyperopt. Space-separated list.',
choices=['all', 'buy', 'sell', 'roi', 'stoploss', 'trailing', 'protection', 'default'],
choices=['all', 'buy', 'sell', 'roi', 'stoploss',
'trailing', 'protection', 'trades', 'default'],
nargs='+',
default='default',
),
@ -632,10 +633,11 @@ AVAILABLE_CLI_OPTIONS = {
"1: by enter_tag, "
"2: by enter_tag and exit_tag, "
"3: by pair and enter_tag, "
"4: by pair, enter_ and exit_tag (this can get quite large)"),
"4: by pair, enter_ and exit_tag (this can get quite large), "
"5: by exit_tag"),
nargs='+',
default=['0', '1', '2'],
choices=['0', '1', '2', '3', '4'],
choices=['0', '1', '2', '3', '4', '5'],
),
"enter_reason_list": Arg(
"--enter-reason-list",

3
freqtrade/commands/data_commands.py
View File

@ -14,6 +14,7 @@ from freqtrade.exceptions import OperationalException
from freqtrade.exchange import market_is_active, timeframe_to_minutes
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
from freqtrade.resolvers import ExchangeResolver
from freqtrade.util.binance_mig import migrate_binance_futures_data
logger = logging.getLogger(__name__)
@ -86,6 +87,7 @@ def start_download_data(args: Dict[str, Any]) -> None:
"Please use `--dl-trades` instead for this exchange "
"(will unfortunately take a long time)."
)
migrate_binance_futures_data(config)
pairs_not_available = refresh_backtest_ohlcv_data(
exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange,
@ -145,6 +147,7 @@ def start_convert_data(args: Dict[str, Any], ohlcv: bool = True) -> None:
"""
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if ohlcv:
migrate_binance_futures_data(config)
candle_types = [CandleType.from_string(ct) for ct in config.get('candle_types', ['spot'])]
for candle_type in candle_types:
convert_ohlcv_format(config,

2
freqtrade/configuration/configuration.py
View File

@ -28,7 +28,7 @@ class Configuration:
Reuse this class for the bot, backtesting, hyperopt and every script that required configuration
"""
def __init__(self, args: Dict[str, Any], runmode: RunMode = None) -> None:
def __init__(self, args: Dict[str, Any], runmode: Optional[RunMode] = None) -> None:
self.args = args
self.config: Optional[Config] = None
self.runmode = runmode

5
freqtrade/configuration/load_config.py
View File

@ -6,7 +6,7 @@ import re
import sys
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List
from typing import Any, Dict, List, Optional
import rapidjson
@ -75,7 +75,8 @@ def load_config_file(path: str) -> Dict[str, Any]:
return config
def load_from_files(files: List[str], base_path: Path = None, level: int = 0) -> Dict[str, Any]:
def load_from_files(
files: List[str], base_path: Optional[Path] = None, level: int = 0) -> Dict[str, Any]:
"""
Recursively load configuration files if specified.
Sub-files are assumed to be relative to the initial config.

3
freqtrade/constants.py
View File

@ -636,7 +636,6 @@ SCHEMA_TRADE_REQUIRED = [
SCHEMA_BACKTEST_REQUIRED = [
'exchange',
'max_open_trades',
'stake_currency',
'stake_amount',
'dry_run_wallet',
@ -646,6 +645,7 @@ SCHEMA_BACKTEST_REQUIRED = [
SCHEMA_BACKTEST_REQUIRED_FINAL = SCHEMA_BACKTEST_REQUIRED + [
'stoploss',
'minimal_roi',
'max_open_trades'
]
SCHEMA_MINIMAL_REQUIRED = [
@ -681,3 +681,4 @@ MakerTaker = Literal['maker', 'taker']
BidAsk = Literal['bid', 'ask']
Config = Dict[str, Any]
IntOrInf = float

9
freqtrade/data/btanalysis.py
View File

@ -10,7 +10,7 @@ from typing import Any, Dict, List, Optional, Union
import numpy as np
import pandas as pd
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.constants import LAST_BT_RESULT_FN, IntOrInf
from freqtrade.exceptions import OperationalException
from freqtrade.misc import json_load
from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
@ -90,7 +90,8 @@ def get_latest_hyperopt_filename(directory: Union[Path, str]) -> str:
return 'hyperopt_results.pickle'
def get_latest_hyperopt_file(directory: Union[Path, str], predef_filename: str = None) -> Path:
def get_latest_hyperopt_file(
directory: Union[Path, str], predef_filename: Optional[str] = None) -> Path:
"""
Get latest hyperopt export based on '.last_result.json'.
:param directory: Directory to search for last result
@ -193,7 +194,7 @@ def get_backtest_resultlist(dirname: Path):
def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, str],
min_backtest_date: datetime = None) -> Dict[str, Any]:
min_backtest_date: Optional[datetime] = None) -> Dict[str, Any]:
"""
Find existing backtest stats that match specified run IDs and load them.
:param dirname: pathlib.Path object, or string pointing to the file.
@ -332,7 +333,7 @@ def analyze_trade_parallelism(results: pd.DataFrame, timeframe: str) -> pd.DataF
def evaluate_result_multi(results: pd.DataFrame, timeframe: str,
max_open_trades: int) -> pd.DataFrame:
max_open_trades: IntOrInf) -> pd.DataFrame:
"""
Find overlapping trades by expanding each trade once per period it was open
and then counting overlaps

8
freqtrade/data/dataprovider.py
View File

@ -281,7 +281,7 @@ class DataProvider:
def historic_ohlcv(
self,
pair: str,
timeframe: str = None,
timeframe: Optional[str] = None,
candle_type: str = ''
) -> DataFrame:
"""
@ -333,7 +333,7 @@ class DataProvider:
def get_pair_dataframe(
self,
pair: str,
timeframe: str = None,
timeframe: Optional[str] = None,
candle_type: str = ''
) -> DataFrame:
"""
@ -415,7 +415,7 @@ class DataProvider:
def refresh(self,
pairlist: ListPairsWithTimeframes,
helping_pairs: ListPairsWithTimeframes = None) -> None:
helping_pairs: Optional[ListPairsWithTimeframes] = None) -> None:
"""
Refresh data, called with each cycle
"""
@ -439,7 +439,7 @@ class DataProvider:
def ohlcv(
self,
pair: str,
timeframe: str = None,
timeframe: Optional[str] = None,
copy: bool = True,
candle_type: str = ''
) -> DataFrame:

10
freqtrade/data/entryexitanalysis.py
View File

@ -52,7 +52,7 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
return analysed_trades_dict
def _analyze_candles_and_indicators(pair, trades, signal_candles):
def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles: pd.DataFrame):
buyf = signal_candles
if len(buyf) > 0:
@ -120,7 +120,7 @@ def _do_group_table_output(bigdf, glist):
else:
agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
'profit_ratio': ['sum', 'median', 'mean']}
'profit_ratio': ['median', 'mean', 'sum']}
agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
'total_profit_pct']
@ -141,6 +141,12 @@ def _do_group_table_output(bigdf, glist):
# 4: profit summaries grouped by pair, enter_ and exit_tag (this can get quite large)
if g == "4":
group_mask = ['pair', 'enter_reason', 'exit_reason']
# 5: profit summaries grouped by exit_tag
if g == "5":
group_mask = ['exit_reason']
sortcols = ['exit_reason']
if group_mask:
new = bigdf.groupby(group_mask).agg(agg_mask).reset_index()
new.columns = group_mask + agg_cols

12
freqtrade/data/history/history_utils.py
View File

@ -28,8 +28,8 @@ def load_pair_history(pair: str,
fill_up_missing: bool = True,
drop_incomplete: bool = False,
startup_candles: int = 0,
data_format: str = None,
data_handler: IDataHandler = None,
data_format: Optional[str] = None,
data_handler: Optional[IDataHandler] = None,
candle_type: CandleType = CandleType.SPOT
) -> DataFrame:
"""
@ -69,7 +69,7 @@ def load_data(datadir: Path,
fail_without_data: bool = False,
data_format: str = 'json',
candle_type: CandleType = CandleType.SPOT,
user_futures_funding_rate: int = None,
user_futures_funding_rate: Optional[int] = None,
) -> Dict[str, DataFrame]:
"""
Load ohlcv history data for a list of pairs.
@ -116,7 +116,7 @@ def refresh_data(*, datadir: Path,
timeframe: str,
pairs: List[str],
exchange: Exchange,
data_format: str = None,
data_format: Optional[str] = None,
timerange: Optional[TimeRange] = None,
candle_type: CandleType,
) -> None:
@ -189,7 +189,7 @@ def _download_pair_history(pair: str, *,
timeframe: str = '5m',
process: str = '',
new_pairs_days: int = 30,
data_handler: IDataHandler = None,
data_handler: Optional[IDataHandler] = None,
timerange: Optional[TimeRange] = None,
candle_type: CandleType,
erase: bool = False,
@ -272,7 +272,7 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
datadir: Path, trading_mode: str,
timerange: Optional[TimeRange] = None,
new_pairs_days: int = 30, erase: bool = False,
data_format: str = None,
data_format: Optional[str] = None,
prepend: bool = False,
) -> List[str]:
"""

19
freqtrade/data/history/idatahandler.py
View File

@ -374,6 +374,21 @@ class IDataHandler(ABC):
logger.warning(f"{pair}, {candle_type}, {timeframe}, "
f"data ends at {pairdata.iloc[-1]['date']:%Y-%m-%d %H:%M:%S}")
def rename_futures_data(
self, pair: str, new_pair: str, timeframe: str, candle_type: CandleType):
"""
Temporary method to migrate data from old naming to new naming (BTC/USDT -> BTC/USDT:USDT)
Only used for binance to support the binance futures naming unification.
"""
file_old = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
file_new = self._pair_data_filename(self._datadir, new_pair, timeframe, candle_type)
# print(file_old, file_new)
if file_new.exists():
logger.warning(f"{file_new} exists already, can't migrate {pair}.")
return
file_old.rename(file_new)
def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
"""
@ -403,8 +418,8 @@ def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
raise ValueError(f"No datahandler for datatype {datatype} available.")
def get_datahandler(datadir: Path, data_format: str = None,
data_handler: IDataHandler = None) -> IDataHandler:
def get_datahandler(datadir: Path, data_format: Optional[str] = None,
data_handler: Optional[IDataHandler] = None) -> IDataHandler:
"""
:param datadir: Folder to save data
:param data_format: dataformat to use

118
freqtrade/data/metrics.py
View File

@ -1,4 +1,6 @@
import logging
import math
from datetime import datetime
from typing import Dict, Tuple
import numpy as np
@ -190,3 +192,119 @@ def calculate_cagr(days_passed: int, starting_balance: float, final_balance: flo
:return: CAGR
"""
return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
def calculate_expectancy(trades: pd.DataFrame) -> float:
"""
Calculate expectancy
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
:return: expectancy
"""
if len(trades) == 0:
return 0
expectancy = 1
profit_sum = trades.loc[trades['profit_abs'] > 0, 'profit_abs'].sum()
loss_sum = abs(trades.loc[trades['profit_abs'] < 0, 'profit_abs'].sum())
nb_win_trades = len(trades.loc[trades['profit_abs'] > 0])
nb_loss_trades = len(trades.loc[trades['profit_abs'] < 0])
if (nb_win_trades > 0) and (nb_loss_trades > 0):
average_win = profit_sum / nb_win_trades
average_loss = loss_sum / nb_loss_trades
risk_reward_ratio = average_win / average_loss
winrate = nb_win_trades / len(trades)
expectancy = ((1 + risk_reward_ratio) * winrate) - 1
elif nb_win_trades == 0:
expectancy = 0
return expectancy
def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
starting_balance: float) -> float:
"""
Calculate sortino
:param trades: DataFrame containing trades (requires columns profit_abs)
:return: sortino
"""
if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
return 0
total_profit = trades['profit_abs'] / starting_balance
days_period = max(1, (max_date - min_date).days)
expected_returns_mean = total_profit.sum() / days_period
down_stdev = np.std(trades.loc[trades['profit_abs'] < 0, 'profit_abs'] / starting_balance)
if down_stdev != 0 and not np.isnan(down_stdev):
sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
else:
# Define high (negative) sortino ratio to be clear that this is NOT optimal.
sortino_ratio = -100
# print(expected_returns_mean, down_stdev, sortino_ratio)
return sortino_ratio
def calculate_sharpe(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
starting_balance: float) -> float:
"""
Calculate sharpe
:param trades: DataFrame containing trades (requires column profit_abs)
:return: sharpe
"""
if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
return 0
total_profit = trades['profit_abs'] / starting_balance
days_period = max(1, (max_date - min_date).days)
expected_returns_mean = total_profit.sum() / days_period
up_stdev = np.std(total_profit)
if up_stdev != 0:
sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
sharp_ratio = -100
# print(expected_returns_mean, up_stdev, sharp_ratio)
return sharp_ratio
def calculate_calmar(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
starting_balance: float) -> float:
"""
Calculate calmar
:param trades: DataFrame containing trades (requires columns close_date and profit_abs)
:return: calmar
"""
if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
return 0
total_profit = trades['profit_abs'].sum() / starting_balance
days_period = max(1, (max_date - min_date).days)
# adding slippage of 0.1% per trade
# total_profit = total_profit - 0.0005
expected_returns_mean = total_profit / days_period * 100
# calculate max drawdown
try:
_, _, _, _, _, max_drawdown = calculate_max_drawdown(
trades, value_col="profit_abs", starting_balance=starting_balance
)
except ValueError:
max_drawdown = 0
if max_drawdown != 0:
calmar_ratio = expected_returns_mean / max_drawdown * math.sqrt(365)
else:
# Define high (negative) calmar ratio to be clear that this is NOT optimal.
calmar_ratio = -100
# print(expected_returns_mean, max_drawdown, calmar_ratio)
return calmar_ratio

6
freqtrade/exchange/binance.py
View File

@ -11,7 +11,7 @@ from freqtrade.enums import CandleType, MarginMode, TradingMode
from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
from freqtrade.exchange.types import Tickers
from freqtrade.exchange.types import OHLCVResponse, Tickers
from freqtrade.misc import deep_merge_dicts, json_load
@ -28,7 +28,7 @@ class Binance(Exchange):
"trades_pagination": "id",
"trades_pagination_arg": "fromId",
"l2_limit_range": [5, 10, 20, 50, 100, 500, 1000],
"ccxt_futures_name": "future"
"ccxt_futures_name": "swap"
}
_ft_has_futures: Dict = {
"stoploss_order_types": {"limit": "stop", "market": "stop_market"},
@ -112,7 +112,7 @@ class Binance(Exchange):
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False, raise_: bool = False,
until_ms: Optional[int] = None
) -> Tuple[str, str, str, List]:
) -> OHLCVResponse:
"""
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"

12130
freqtrade/exchange/binance_leverage_tiers.json
View File

File diff suppressed because it is too large Load Diff

110
freqtrade/exchange/exchange.py
View File

@ -3,7 +3,6 @@
Cryptocurrency Exchanges support
"""
import asyncio
import http
import inspect
import logging
from copy import deepcopy
@ -36,7 +35,7 @@ from freqtrade.exchange.exchange_utils import (CcxtModuleType, amount_to_contrac
price_to_precision, timeframe_to_minutes,
timeframe_to_msecs, timeframe_to_next_date,
timeframe_to_prev_date, timeframe_to_seconds)
from freqtrade.exchange.types import Ticker, Tickers
from freqtrade.exchange.types import OHLCVResponse, Ticker, Tickers
from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
safe_value_fallback2)
from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
@ -45,12 +44,6 @@ from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
logger = logging.getLogger(__name__)
# Workaround for adding samesite support to pre 3.8 python
# Only applies to python3.7, and only on certain exchanges (kraken)
# Replicates the fix from starlette (which is actually causing this problem)
http.cookies.Morsel._reserved["samesite"] = "SameSite" # type: ignore
class Exchange:
# Parameters to add directly to buy/sell calls (like agreeing to trading agreement)
@ -474,7 +467,7 @@ class Exchange:
try:
if self._api_async:
self.loop.run_until_complete(
self._api_async.load_markets(reload=reload))
self._api_async.load_markets(reload=reload, params={}))
except (asyncio.TimeoutError, ccxt.BaseError) as e:
logger.warning('Could not load async markets. Reason: %s', e)
@ -483,7 +476,7 @@ class Exchange:
def _load_markets(self) -> None:
""" Initialize markets both sync and async """
try:
self._markets = self._api.load_markets()
self._markets = self._api.load_markets(params={})
self._load_async_markets()
self._last_markets_refresh = arrow.utcnow().int_timestamp
if self._ft_has['needs_trading_fees']:
@ -501,7 +494,7 @@ class Exchange:
return None
logger.debug("Performing scheduled market reload..")
try:
self._markets = self._api.load_markets(reload=True)
self._markets = self._api.load_markets(reload=True, params={})
# Also reload async markets to avoid issues with newly listed pairs
self._load_async_markets(reload=True)
self._last_markets_refresh = arrow.utcnow().int_timestamp
@ -682,7 +675,7 @@ class Exchange:
f"Freqtrade does not support {mm_value} {trading_mode.value} on {self.name}"
)
def get_option(self, param: str, default: Any = None) -> Any:
def get_option(self, param: str, default: Optional[Any] = None) -> Any:
"""
Get parameter value from _ft_has
"""
@ -1357,7 +1350,7 @@ class Exchange:
raise OperationalException(e) from e
@retrier
def fetch_positions(self, pair: str = None) -> List[Dict]:
def fetch_positions(self, pair: Optional[str] = None) -> List[Dict]:
"""
Fetch positions from the exchange.
If no pair is given, all positions are returned.
@ -1705,7 +1698,7 @@ class Exchange:
return self._config['fee']
# validate that markets are loaded before trying to get fee
if self._api.markets is None or len(self._api.markets) == 0:
self._api.load_markets()
self._api.load_markets(params={})
return self._api.calculate_fee(symbol=symbol, type=type, side=side, amount=amount,
price=price, takerOrMaker=taker_or_maker)['rate']
@ -1801,7 +1794,7 @@ class Exchange:
def get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType,
is_new_pair: bool = False,
until_ms: int = None) -> List:
until_ms: Optional[int] = None) -> List:
"""
Get candle history using asyncio and returns the list of candles.
Handles all async work for this.
@ -1813,32 +1806,18 @@ class Exchange:
:param candle_type: '', mark, index, premiumIndex, or funding_rate
:return: List with candle (OHLCV) data
"""
pair, _, _, data = self.loop.run_until_complete(
pair, _, _, data, _ = self.loop.run_until_complete(
self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
since_ms=since_ms, until_ms=until_ms,
is_new_pair=is_new_pair, candle_type=candle_type))
logger.info(f"Downloaded data for {pair} with length {len(data)}.")
return data
def get_historic_ohlcv_as_df(self, pair: str, timeframe: str,
since_ms: int, candle_type: CandleType) -> DataFrame:
"""
Minimal wrapper around get_historic_ohlcv - converting the result into a dataframe
:param pair: Pair to download
:param timeframe: Timeframe to get data for
:param since_ms: Timestamp in milliseconds to get history from
:param candle_type: Any of the enum CandleType (must match trading mode!)
:return: OHLCV DataFrame
"""
ticks = self.get_historic_ohlcv(pair, timeframe, since_ms=since_ms, candle_type=candle_type)
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, candle_type: CandleType,
is_new_pair: bool = False, raise_: bool = False,
until_ms: Optional[int] = None
) -> Tuple[str, str, str, List]:
) -> OHLCVResponse:
"""
Download historic ohlcv
:param is_new_pair: used by binance subclass to allow "fast" new pair downloading
@ -1869,15 +1848,16 @@ class Exchange:
continue
else:
# Deconstruct tuple if it's not an exception
p, _, c, new_data = res
p, _, c, new_data, _ = res
if p == pair and c == candle_type:
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])
return pair, timeframe, candle_type, data
return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
def _build_coroutine(self, pair: str, timeframe: str, candle_type: CandleType,
since_ms: Optional[int], cache: bool) -> Coroutine:
def _build_coroutine(
self, pair: str, timeframe: str, candle_type: CandleType,
since_ms: Optional[int], cache: bool) -> Coroutine[Any, Any, OHLCVResponse]:
not_all_data = cache and self.required_candle_call_count > 1
if cache and (pair, timeframe, candle_type) in self._klines:
candle_limit = self.ohlcv_candle_limit(timeframe, candle_type)
@ -1914,7 +1894,7 @@ class Exchange:
"""
Build Coroutines to execute as part of refresh_latest_ohlcv
"""
input_coroutines = []
input_coroutines: List[Coroutine[Any, Any, OHLCVResponse]] = []
cached_pairs = []
for pair, timeframe, candle_type in set(pair_list):
if (timeframe not in self.timeframes
@ -1978,7 +1958,6 @@ class Exchange:
:return: Dict of [{(pair, timeframe): Dataframe}]
"""
logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
drop_incomplete = self._ohlcv_partial_candle if drop_incomplete is None else drop_incomplete
# Gather coroutines to run
input_coroutines, cached_pairs = self._build_ohlcv_dl_jobs(pair_list, since_ms, cache)
@ -1996,8 +1975,9 @@ class Exchange:
if isinstance(res, Exception):
logger.warning(f"Async code raised an exception: {repr(res)}")
continue
# Deconstruct tuple (has 4 elements)
pair, timeframe, c_type, ticks = res
# Deconstruct tuple (has 5 elements)
pair, timeframe, c_type, ticks, drop_hint = res
drop_incomplete = drop_hint if drop_incomplete is None else drop_incomplete
ohlcv_df = self._process_ohlcv_df(
pair, timeframe, c_type, ticks, cache, drop_incomplete)
@ -2025,7 +2005,7 @@ class Exchange:
timeframe: str,
candle_type: CandleType,
since_ms: Optional[int] = None,
) -> Tuple[str, str, str, List]:
) -> OHLCVResponse:
"""
Asynchronously get candle history data using fetch_ohlcv
:param candle_type: '', mark, index, premiumIndex, or funding_rate
@ -2035,8 +2015,8 @@ class Exchange:
# Fetch OHLCV asynchronously
s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
logger.debug(
"Fetching pair %s, interval %s, since %s %s...",
pair, timeframe, since_ms, s
"Fetching pair %s, %s, interval %s, since %s %s...",
pair, candle_type, timeframe, since_ms, s
)
params = deepcopy(self._ft_has.get('ohlcv_params', {}))
candle_limit = self.ohlcv_candle_limit(
@ -2050,11 +2030,12 @@ class Exchange:
limit=candle_limit, params=params)
else:
# Funding rate
data = await self._api_async.fetch_funding_rate_history(
pair, since=since_ms,
limit=candle_limit)
# Convert funding rate to candle pattern
data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
data = await self._fetch_funding_rate_history(
pair=pair,
timeframe=timeframe,
limit=candle_limit,
since_ms=since_ms,
)
# Some exchanges sort OHLCV in ASC order and others in DESC.
# Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
# while GDAX returns the list of OHLCV in DESC order (newest first, oldest last)
@ -2064,9 +2045,9 @@ class Exchange:
data = sorted(data, key=lambda x: x[0])
except IndexError:
logger.exception("Error loading %s. Result was %s.", pair, data)
return pair, timeframe, candle_type, []
return pair, timeframe, candle_type, [], self._ohlcv_partial_candle
logger.debug("Done fetching pair %s, interval %s ...", pair, timeframe)
return pair, timeframe, candle_type, data
return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
except ccxt.NotSupported as e:
raise OperationalException(
@ -2082,6 +2063,24 @@ class Exchange:
raise OperationalException(f'Could not fetch historical candle (OHLCV) data '
f'for pair {pair}. Message: {e}') from e
async def _fetch_funding_rate_history(
self,
pair: str,
timeframe: str,
limit: int,
since_ms: Optional[int] = None,
) -> List[List]:
"""
Fetch funding rate history - used to selectively override this by subclasses.
"""
# Funding rate
data = await self._api_async.fetch_funding_rate_history(
pair, since=since_ms,
limit=limit)
# Convert funding rate to candle pattern
data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
return data
# Fetch historic trades
@retrier_async
@ -2668,7 +2667,7 @@ class Exchange:
:param amount: Trade amount
:param open_date: Open date of the trade
:return: funding fee since open_date
:raies: ExchangeError if something goes wrong.
:raises: ExchangeError if something goes wrong.
"""
if self.trading_mode == TradingMode.FUTURES:
if self._config['dry_run']:
@ -2745,11 +2744,16 @@ class Exchange:
"""
Important: Must be fetching data from cached values as this is used by backtesting!
PERPETUAL:
gateio: https://www.gate.io/help/futures/perpetual/22160/calculation-of-liquidation-price
gateio: https://www.gate.io/help/futures/futures/27724/liquidation-price-bankruptcy-price
> Liquidation Price = (Entry Price ± Margin / Contract Multiplier / Size) /
[ 1 ± (Maintenance Margin Ratio + Taker Rate)]
Wherein, "+" or "-" depends on whether the contract goes long or short:
"-" for long, and "+" for short.
okex: https://www.okex.com/support/hc/en-us/articles/
360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin
:param exchange_name:
:param pair: Pair to calculate liquidation price for
:param open_rate: Entry price of position
:param is_short: True if the trade is a short, false otherwise
:param amount: Absolute value of position size incl. leverage (in base currency)
@ -2789,7 +2793,7 @@ class Exchange:
def get_maintenance_ratio_and_amt(
self,
pair: str,
nominal_value: float = 0.0,
nominal_value: float,
) -> Tuple[float, Optional[float]]:
"""
Important: Must be fetching data from cached values as this is used by backtesting!

11
freqtrade/exchange/exchange_utils.py
View File

@ -15,18 +15,19 @@ from freqtrade.util import FtPrecise
CcxtModuleType = Any
def is_exchange_known_ccxt(exchange_name: str, ccxt_module: CcxtModuleType = None) -> bool:
def is_exchange_known_ccxt(
exchange_name: str, ccxt_module: Optional[CcxtModuleType] = None) -> bool:
return exchange_name in ccxt_exchanges(ccxt_module)
def ccxt_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
def ccxt_exchanges(ccxt_module: Optional[CcxtModuleType] = None) -> List[str]:
"""
Return the list of all exchanges known to ccxt
"""
return ccxt_module.exchanges if ccxt_module is not None else ccxt.exchanges
def available_exchanges(ccxt_module: CcxtModuleType = None) -> List[str]:
def available_exchanges(ccxt_module: Optional[CcxtModuleType] = None) -> List[str]:
"""
Return exchanges available to the bot, i.e. non-bad exchanges in the ccxt list
"""
@ -86,7 +87,7 @@ def timeframe_to_msecs(timeframe: str) -> int:
return ccxt.Exchange.parse_timeframe(timeframe) * 1000
def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
def timeframe_to_prev_date(timeframe: str, date: Optional[datetime] = None) -> datetime:
"""
Use Timeframe and determine the candle start date for this date.
Does not round when given a candle start date.
@ -102,7 +103,7 @@ def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:
return datetime.fromtimestamp(new_timestamp, tz=timezone.utc)
def timeframe_to_next_date(timeframe: str, date: datetime = None) -> datetime:
def timeframe_to_next_date(timeframe: str, date: Optional[datetime] = None) -> datetime:
"""
Use Timeframe and determine next candle.
:param timeframe: timeframe in string format (e.g. "5m")

7
freqtrade/exchange/types.py
View File

@ -1,4 +1,6 @@
from typing import Dict, Optional, TypedDict
from typing import Dict, List, Optional, Tuple, TypedDict
from freqtrade.enums import CandleType
class Ticker(TypedDict):
@ -14,3 +16,6 @@ class Ticker(TypedDict):
Tickers = Dict[str, Ticker]
# pair, timeframe, candleType, OHLCV, drop last?,
OHLCVResponse = Tuple[str, str, CandleType, List, bool]

38
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@ -280,26 +280,36 @@ class BaseReinforcementLearningModel(IFreqaiModel):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# %-raw_volume_gen_shift-2_ETH/USDT_1h
# price data for model training and evaluation
tf = self.config['timeframe']
ohlc_list = [f'%-{pair}raw_open_{tf}', f'%-{pair}raw_low_{tf}',
f'%-{pair}raw_high_{tf}', f'%-{pair}raw_close_{tf}']
rename_dict = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
rename_dict = {'%-raw_open': 'open', '%-raw_low': 'low',
'%-raw_high': ' high', '%-raw_close': 'close'}
rename_dict_old = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
prices_train = train_df.filter(rename_dict.keys(), axis=1)
prices_train_old = train_df.filter(rename_dict_old.keys(), axis=1)
if prices_train.empty or not prices_train_old.empty:
if not prices_train_old.empty:
prices_train = prices_train_old
rename_dict = rename_dict_old
logger.warning('Reinforcement learning module didnt find the correct raw prices '
'assigned in feature_engineering_standard(). '
'Please assign them with:\n'
'dataframe["%-raw_close"] = dataframe["close"]\n'
'dataframe["%-raw_open"] = dataframe["open"]\n'
'dataframe["%-raw_high"] = dataframe["high"]\n'
'dataframe["%-raw_low"] = dataframe["low"]\n'
'inside `feature_engineering_standard()')
elif prices_train.empty:
raise OperationalException("No prices found, please follow log warning "
"instructions to correct the strategy.")
prices_train = train_df.filter(ohlc_list, axis=1)
if prices_train.empty:
raise OperationalException('Reinforcement learning module didnt find the raw prices '
'assigned in populate_any_indicators. Please assign them '
'with:\n'
'informative[f"%-{pair}raw_close"] = informative["close"]\n'
'informative[f"%-{pair}raw_open"] = informative["open"]\n'
'informative[f"%-{pair}raw_high"] = informative["high"]\n'
'informative[f"%-{pair}raw_low"] = informative["low"]\n')
prices_train.rename(columns=rename_dict, inplace=True)
prices_train.reset_index(drop=True)
prices_test = test_df.filter(ohlc_list, axis=1)
prices_test = test_df.filter(rename_dict.keys(), axis=1)
prices_test.rename(columns=rename_dict, inplace=True)
prices_test.reset_index(drop=True)

191
freqtrade/freqai/data_kitchen.py
View File

@ -1,10 +1,11 @@
import copy
import inspect
import logging
import shutil
from datetime import datetime, timezone
from math import cos, sin
from pathlib import Path
from typing import Any, Dict, List, Tuple
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import numpy.typing as npt
@ -23,6 +24,7 @@ from freqtrade.constants import Config
from freqtrade.data.converter import reduce_dataframe_footprint
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.strategy import merge_informative_pair
from freqtrade.strategy.interface import IStrategy
@ -110,7 +112,7 @@ class FreqaiDataKitchen:
def set_paths(
self,
pair: str,
trained_timestamp: int = None,
trained_timestamp: Optional[int] = None,
) -> None:
"""
Set the paths to the data for the present coin/botloop
@ -1145,9 +1147,9 @@ class FreqaiDataKitchen:
for pair in pairs:
pair = pair.replace(':', '') # lightgbm doesnt like colons
valid_strs = [f"%-{pair}", f"%{pair}", f"%_{pair}"]
pair_cols = [col for col in dataframe.columns if
any(substr in col for substr in valid_strs)]
pair_cols = [col for col in dataframe.columns if col.startswith("%")
and f"{pair}_" in col]
if pair_cols:
pair_cols.insert(0, 'date')
corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
@ -1176,6 +1178,103 @@ class FreqaiDataKitchen:
return dataframe
def get_pair_data_for_features(self,
pair: str,
tf: str,
strategy: IStrategy,
corr_dataframes: dict = {},
base_dataframes: dict = {},
is_corr_pairs: bool = False) -> DataFrame:
"""
Get the data for the pair. If it's not in the dictionary, get it from the data provider
:param pair: str = pair to get data for
:param tf: str = timeframe to get data for
:param strategy: IStrategy = user defined strategy object
:param corr_dataframes: dict = dict containing the df pair dataframes
(for user defined timeframes)
:param base_dataframes: dict = dict containing the current pair dataframes
(for user defined timeframes)
:param is_corr_pairs: bool = whether the pair is a corr pair or not
:return: dataframe = dataframe containing the pair data
"""
if is_corr_pairs:
dataframe = corr_dataframes[pair][tf]
if not dataframe.empty:
return dataframe
else:
dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf)
return dataframe
else:
dataframe = base_dataframes[tf]
if not dataframe.empty:
return dataframe
else:
dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf)
return dataframe
def merge_features(self, df_main: DataFrame, df_to_merge: DataFrame,
tf: str, timeframe_inf: str, suffix: str) -> DataFrame:
"""
Merge the features of the dataframe and remove HLCV and date added columns
:param df_main: DataFrame = main dataframe
:param df_to_merge: DataFrame = dataframe to merge
:param tf: str = timeframe of the main dataframe
:param timeframe_inf: str = timeframe of the dataframe to merge
:param suffix: str = suffix to add to the columns of the dataframe to merge
:return: dataframe = merged dataframe
"""
dataframe = merge_informative_pair(df_main, df_to_merge, tf, timeframe_inf=timeframe_inf,
append_timeframe=False, suffix=suffix, ffill=True)
skip_columns = [
(f"{s}_{suffix}") for s in ["date", "open", "high", "low", "close", "volume"]
]
dataframe = dataframe.drop(columns=skip_columns)
return dataframe
def populate_features(self, dataframe: DataFrame, pair: str, strategy: IStrategy,
corr_dataframes: dict, base_dataframes: dict,
is_corr_pairs: bool = False) -> DataFrame:
"""
Use the user defined strategy functions for populating features
:param dataframe: DataFrame = dataframe to populate
:param pair: str = pair to populate
:param strategy: IStrategy = user defined strategy object
:param corr_dataframes: dict = dict containing the df pair dataframes
:param base_dataframes: dict = dict containing the current pair dataframes
:param is_corr_pairs: bool = whether the pair is a corr pair or not
:return: dataframe = populated dataframe
"""
tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
for tf in tfs:
informative_df = self.get_pair_data_for_features(
pair, tf, strategy, corr_dataframes, base_dataframes, is_corr_pairs)
informative_copy = informative_df.copy()
for t in self.freqai_config["feature_parameters"]["indicator_periods_candles"]:
df_features = strategy.feature_engineering_expand_all(
informative_copy.copy(), t)
suffix = f"{t}"
informative_df = self.merge_features(informative_df, df_features, tf, tf, suffix)
generic_df = strategy.feature_engineering_expand_basic(informative_copy.copy())
suffix = "gen"
informative_df = self.merge_features(informative_df, generic_df, tf, tf, suffix)
indicators = [col for col in informative_df if col.startswith("%")]
for n in range(self.freqai_config["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
df_shift = informative_df[indicators].shift(n)
df_shift = df_shift.add_suffix("_shift-" + str(n))
informative_df = pd.concat((informative_df, df_shift), axis=1)
dataframe = self.merge_features(dataframe.copy(), informative_df,
self.config["timeframe"], tf, f'{pair}_{tf}')
return dataframe
def use_strategy_to_populate_indicators(
self,
strategy: IStrategy,
@ -1188,7 +1287,87 @@ class FreqaiDataKitchen:
"""
Use the user defined strategy for populating indicators during retrain
:param strategy: IStrategy = user defined strategy object
:param corr_dataframes: dict = dict containing the informative pair dataframes
:param corr_dataframes: dict = dict containing the df pair dataframes
(for user defined timeframes)
:param base_dataframes: dict = dict containing the current pair dataframes
(for user defined timeframes)
:param pair: str = pair to populate
:param prediction_dataframe: DataFrame = dataframe containing the pair data
used for prediction
:param do_corr_pairs: bool = whether to populate corr pairs or not
:return:
dataframe: DataFrame = dataframe containing populated indicators
"""
# this is a hack to check if the user is using the populate_any_indicators function
new_version = inspect.getsource(strategy.populate_any_indicators) == (
inspect.getsource(IStrategy.populate_any_indicators))
if new_version:
tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs: List[str] = self.freqai_config["feature_parameters"].get(
"include_corr_pairlist", [])
for tf in tfs:
if tf not in base_dataframes:
base_dataframes[tf] = pd.DataFrame()
for p in pairs:
if p not in corr_dataframes:
corr_dataframes[p] = {}
if tf not in corr_dataframes[p]:
corr_dataframes[p][tf] = pd.DataFrame()
if not prediction_dataframe.empty:
dataframe = prediction_dataframe.copy()
else:
dataframe = base_dataframes[self.config["timeframe"]].copy()
corr_pairs: List[str] = self.freqai_config["feature_parameters"].get(
"include_corr_pairlist", [])
dataframe = self.populate_features(dataframe.copy(), pair, strategy,
corr_dataframes, base_dataframes)
dataframe = strategy.feature_engineering_standard(dataframe.copy())
# ensure corr pairs are always last
for corr_pair in corr_pairs:
if pair == corr_pair:
continue # dont repeat anything from whitelist
if corr_pairs and do_corr_pairs:
dataframe = self.populate_features(dataframe.copy(), corr_pair, strategy,
corr_dataframes, base_dataframes, True)
dataframe = strategy.set_freqai_targets(dataframe.copy())
self.get_unique_classes_from_labels(dataframe)
dataframe = self.remove_special_chars_from_feature_names(dataframe)
if self.config.get('reduce_df_footprint', False):
dataframe = reduce_dataframe_footprint(dataframe)
return dataframe
else:
# the user is using the populate_any_indicators functions which is deprecated
df = self.use_strategy_to_populate_indicators_old_version(
strategy, corr_dataframes, base_dataframes, pair,
prediction_dataframe, do_corr_pairs)
return df
def use_strategy_to_populate_indicators_old_version(
self,
strategy: IStrategy,
corr_dataframes: dict = {},
base_dataframes: dict = {},
pair: str = "",
prediction_dataframe: DataFrame = pd.DataFrame(),
do_corr_pairs: bool = True,
) -> DataFrame:
"""
Use the user defined strategy for populating indicators during retrain
:param strategy: IStrategy = user defined strategy object
:param corr_dataframes: dict = dict containing the df pair dataframes
(for user defined timeframes)
:param base_dataframes: dict = dict containing the current pair dataframes
(for user defined timeframes)

76
freqtrade/freqai/freqai_interface.py
View File

@ -1,3 +1,4 @@
import inspect
import logging
import threading
import time
@ -106,6 +107,8 @@ class IFreqaiModel(ABC):
self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
self.can_short = True # overridden in start() with strategy.can_short
self.warned_deprecated_populate_any_indicators = False
record_params(config, self.full_path)
def __getstate__(self):
@ -136,6 +139,9 @@ class IFreqaiModel(ABC):
self.data_provider = strategy.dp
self.can_short = strategy.can_short
# check if the strategy has deprecated populate_any_indicators function
self.check_deprecated_populate_any_indicators(strategy)
if self.live:
self.inference_timer('start')
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
@ -149,12 +155,9 @@ class IFreqaiModel(ABC):
# the concatenated results for the full backtesting period back to the strategy.
elif not self.follow_mode:
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
)
if not self.config.get("freqai_backtest_live_models", False):
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
dk = self.start_backtesting(dataframe, metadata, self.dk)
dk = self.start_backtesting(dataframe, metadata, self.dk, strategy)
dataframe = dk.remove_features_from_df(dk.return_dataframe)
else:
logger.info(
@ -255,7 +258,7 @@ class IFreqaiModel(ABC):
self.dd.save_metric_tracker_to_disk()
def start_backtesting(
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen, strategy: IStrategy
) -> FreqaiDataKitchen:
"""
The main broad execution for backtesting. For backtesting, each pair enters and then gets
@ -267,19 +270,22 @@ class IFreqaiModel(ABC):
:param dataframe: DataFrame = strategy passed dataframe
:param metadata: Dict = pair metadata
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:param strategy: Strategy to train on
:return:
FreqaiDataKitchen = Data management/analysis tool associated to present pair only
"""
self.pair_it += 1
train_it = 0
pair = metadata["pair"]
populate_indicators = True
check_features = True
# Loop enforcing the sliding window training/backtesting paradigm
# tr_train is the training time range e.g. 1 historical month
# tr_backtest is the backtesting time range e.g. the week directly
# following tr_train. Both of these windows slide through the
# entire backtest
for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
pair = metadata["pair"]
(_, _, _) = self.dd.get_pair_dict_info(pair)
train_it += 1
total_trains = len(dk.backtesting_timeranges)
@ -301,18 +307,42 @@ class IFreqaiModel(ABC):
dk.set_new_model_names(pair, timestamp_model_id)
if dk.check_if_backtest_prediction_is_valid(len_backtest_df):
self.dd.load_metadata(dk)
dk.find_features(dataframe)
self.check_if_feature_list_matches_strategy(dk)
if check_features:
self.dd.load_metadata(dk)
dataframe_dummy_features = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe.tail(1), pair=metadata["pair"]
)
dk.find_features(dataframe_dummy_features)
self.check_if_feature_list_matches_strategy(dk)
check_features = False
append_df = dk.get_backtesting_prediction()
dk.append_predictions(append_df)
else:
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
if populate_indicators:
dataframe = self.dk.use_strategy_to_populate_indicators(
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
)
populate_indicators = False
dataframe_base_train = dataframe.loc[dataframe["date"] < tr_train.stopdt, :]
dataframe_base_train = strategy.set_freqai_targets(dataframe_base_train)
dataframe_base_backtest = dataframe.loc[dataframe["date"] < tr_backtest.stopdt, :]
dataframe_base_backtest = strategy.set_freqai_targets(dataframe_base_backtest)
dataframe_train = dk.slice_dataframe(tr_train, dataframe_base_train)
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe_base_backtest)
if not self.model_exists(dk):
dk.find_features(dataframe_train)
dk.find_labels(dataframe_train)
self.model = self.train(dataframe_train, pair, dk)
try:
self.model = self.train(dataframe_train, pair, dk)
except Exception as msg:
logger.warning(
f"Training {pair} raised exception {msg.__class__.__name__}. "
f"Message: {msg}, skipping.")
self.dd.pair_dict[pair]["trained_timestamp"] = int(
tr_train.stopts)
if self.plot_features:
@ -349,7 +379,6 @@ class IFreqaiModel(ABC):
:returns:
dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
"""
# update follower
if self.follow_mode:
self.dd.update_follower_metadata()
@ -913,9 +942,28 @@ class IFreqaiModel(ABC):
dk.return_dataframe = dk.return_dataframe.drop(columns=list(columns_to_drop))
dk.return_dataframe = pd.merge(
dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
# dk.return_dataframe = dk.return_dataframe[saved_dataframe.columns].fillna(0)
return dk
def check_deprecated_populate_any_indicators(self, strategy: IStrategy):
"""
Check and warn if the deprecated populate_any_indicators function is used.
:param strategy: strategy object
"""
if not self.warned_deprecated_populate_any_indicators:
self.warned_deprecated_populate_any_indicators = True
old_version = inspect.getsource(strategy.populate_any_indicators) != (
inspect.getsource(IStrategy.populate_any_indicators))
if old_version:
logger.warning("DEPRECATION WARNING: "
"You are using the deprecated populate_any_indicators function. "
"This function will raise an error on March 1 2023. "
"Please update your strategy by using "
"the new feature_engineering functions. See \n"
"https://www.freqtrade.io/en/latest/freqai-feature-engineering/"
"for details.")
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.

23
freqtrade/freqtradebot.py
View File

@ -33,6 +33,7 @@ from freqtrade.rpc.external_message_consumer import ExternalMessageConsumer
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.util import FtPrecise
from freqtrade.util.binance_mig import migrate_binance_futures_names
from freqtrade.wallets import Wallets
@ -177,6 +178,8 @@ class FreqtradeBot(LoggingMixin):
Called on startup and after reloading the bot - triggers notifications and
performs startup tasks
"""
migrate_binance_futures_names(self.config)
self.rpc.startup_messages(self.config, self.pairlists, self.protections)
# Update older trades with precision and precision mode
self.startup_backpopulate_precision()
@ -374,7 +377,7 @@ class FreqtradeBot(LoggingMixin):
for trade in trades:
if not trade.is_open and not trade.fee_updated(trade.exit_side):
# Get sell fee
order = trade.select_order(trade.exit_side, False)
order = trade.select_order(trade.exit_side, False, only_filled=True)
if not order:
order = trade.select_order('stoploss', False)
if order:
@ -390,7 +393,7 @@ class FreqtradeBot(LoggingMixin):
for trade in trades:
with self._exit_lock:
if trade.is_open and not trade.fee_updated(trade.entry_side):
order = trade.select_order(trade.entry_side, False)
order = trade.select_order(trade.entry_side, False, only_filled=True)
open_order = trade.select_order(trade.entry_side, True)
if order and open_order is None:
logger.info(
@ -720,7 +723,7 @@ class FreqtradeBot(LoggingMixin):
time_in_force=time_in_force,
leverage=leverage
)
order_obj = Order.parse_from_ccxt_object(order, pair, side)
order_obj = Order.parse_from_ccxt_object(order, pair, side, amount, enter_limit_requested)
order_id = order['id']
order_status = order.get('status')
logger.info(f"Order #{order_id} was created for {pair} and status is {order_status}.")
@ -1094,7 +1097,8 @@ class FreqtradeBot(LoggingMixin):
leverage=trade.leverage
)
order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss')
order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss',
trade.amount, stop_price)
trade.orders.append(order_obj)
trade.stoploss_order_id = str(stoploss_order['id'])
trade.stoploss_last_update = datetime.now(timezone.utc)
@ -1518,7 +1522,7 @@ class FreqtradeBot(LoggingMixin):
*,
exit_tag: Optional[str] = None,
ordertype: Optional[str] = None,
sub_trade_amt: float = None,
sub_trade_amt: Optional[float] = None,
) -> bool:
"""
Executes a trade exit for the given trade and limit
@ -1595,7 +1599,7 @@ class FreqtradeBot(LoggingMixin):
self.handle_insufficient_funds(trade)
return False
order_obj = Order.parse_from_ccxt_object(order, trade.pair, trade.exit_side)
order_obj = Order.parse_from_ccxt_object(order, trade.pair, trade.exit_side, amount, limit)
trade.orders.append(order_obj)
trade.open_order_id = order['id']
@ -1612,7 +1616,7 @@ class FreqtradeBot(LoggingMixin):
return True
def _notify_exit(self, trade: Trade, order_type: str, fill: bool = False,
sub_trade: bool = False, order: Order = None) -> None:
sub_trade: bool = False, order: Optional[Order] = None) -> None:
"""
Sends rpc notification when a sell occurred.
"""
@ -1725,8 +1729,9 @@ class FreqtradeBot(LoggingMixin):
# Common update trade state methods
#
def update_trade_state(self, trade: Trade, order_id: str, action_order: Dict[str, Any] = None,
stoploss_order: bool = False, send_msg: bool = True) -> bool:
def update_trade_state(
self, trade: Trade, order_id: str, action_order: Optional[Dict[str, Any]] = None,
stoploss_order: bool = False, send_msg: bool = True) -> bool:
"""
Checks trades with open orders and updates the amount if necessary
Handles closing both buy and sell orders.

4
freqtrade/main.py
View File

@ -5,7 +5,7 @@ Read the documentation to know what cli arguments you need.
"""
import logging
import sys
from typing import Any, List
from typing import Any, List, Optional
from freqtrade.util.gc_setup import gc_set_threshold
@ -23,7 +23,7 @@ from freqtrade.loggers import setup_logging_pre
logger = logging.getLogger('freqtrade')
def main(sysargv: List[str] = None) -> None:
def main(sysargv: Optional[List[str]] = None) -> None:
"""
This function will initiate the bot and start the trading loop.
:return: None

6
freqtrade/misc.py
View File

@ -6,7 +6,7 @@ import logging
import re
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, Iterator, List, Mapping, Union
from typing import Any, Dict, Iterator, List, Mapping, Optional, Union
from typing.io import IO
from urllib.parse import urlparse
@ -205,7 +205,7 @@ def safe_value_fallback2(dict1: dictMap, dict2: dictMap, key1: str, key2: str, d
return default_value
def plural(num: float, singular: str, plural: str = None) -> str:
def plural(num: float, singular: str, plural: Optional[str] = None) -> str:
return singular if (num == 1 or num == -1) else plural or singular + 's'
@ -269,6 +269,8 @@ def dataframe_to_json(dataframe: pd.DataFrame) -> str:
def default(z):
if isinstance(z, pd.Timestamp):
return z.timestamp() * 1e3
if z is pd.NaT:
return 'NaT'
raise TypeError
return str(orjson.dumps(dataframe.to_dict(orient='split'), default=default), 'utf-8')

101
freqtrade/optimize/backtesting.py
View File

@ -15,7 +15,7 @@ from pandas import DataFrame
from freqtrade import constants
from freqtrade.configuration import TimeRange, validate_config_consistency
from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, LongShort
from freqtrade.constants import DATETIME_PRINT_FORMAT, Config, IntOrInf, LongShort
from freqtrade.data import history
from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
from freqtrade.data.converter import trim_dataframe, trim_dataframes
@ -37,6 +37,7 @@ from freqtrade.plugins.protectionmanager import ProtectionManager
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.util.binance_mig import migrate_binance_futures_data
from freqtrade.wallets import Wallets
@ -157,6 +158,7 @@ class Backtesting:
self._can_short = self.trading_mode != TradingMode.SPOT
self._position_stacking: bool = self.config.get('position_stacking', False)
self.enable_protections: bool = self.config.get('enable_protections', False)
migrate_binance_futures_data(config)
self.init_backtest()
@ -573,26 +575,6 @@ class Backtesting:
""" Rate is within candle, therefore filled"""
return row[LOW_IDX] <= rate <= row[HIGH_IDX]
def _get_exit_trade_entry_for_candle(self, trade: LocalTrade,
row: Tuple) -> Optional[LocalTrade]:
# Check if we need to adjust our current positions
if self.strategy.position_adjustment_enable:
trade = self._get_adjust_trade_entry_for_candle(trade, row)
enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
exits = self.strategy.should_exit(
trade, row[OPEN_IDX], row[DATE_IDX].to_pydatetime(), # type: ignore
enter=enter, exit_=exit_sig,
low=row[LOW_IDX], high=row[HIGH_IDX]
)
for exit_ in exits:
t = self._get_exit_for_signal(trade, row, exit_)
if t:
return t
return None
def _get_exit_for_signal(
self, trade: LocalTrade, row: Tuple, exit_: ExitCheckTuple,
amount: Optional[float] = None) -> Optional[LocalTrade]:
@ -662,7 +644,7 @@ class Backtesting:
return None
def _exit_trade(self, trade: LocalTrade, sell_row: Tuple,
close_rate: float, amount: float = None) -> Optional[LocalTrade]:
close_rate: float, amount: Optional[float] = None) -> Optional[LocalTrade]:
self.order_id_counter += 1
exit_candle_time = sell_row[DATE_IDX].to_pydatetime()
order_type = self.strategy.order_types['exit']
@ -692,11 +674,10 @@ class Backtesting:
trade.orders.append(order)
return trade
def _get_exit_trade_entry(
self, trade: LocalTrade, row: Tuple, is_first: bool) -> Optional[LocalTrade]:
def _check_trade_exit(self, trade: LocalTrade, row: Tuple) -> Optional[LocalTrade]:
exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
if is_first and self.trading_mode == TradingMode.FUTURES:
if self.trading_mode == TradingMode.FUTURES:
trade.funding_fees = self.exchange.calculate_funding_fees(
self.futures_data[trade.pair],
amount=trade.amount,
@ -705,7 +686,22 @@ class Backtesting:
close_date=exit_candle_time,
)
return self._get_exit_trade_entry_for_candle(trade, row)
# Check if we need to adjust our current positions
if self.strategy.position_adjustment_enable:
trade = self._get_adjust_trade_entry_for_candle(trade, row)
enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
exits = self.strategy.should_exit(
trade, row[OPEN_IDX], row[DATE_IDX].to_pydatetime(), # type: ignore
enter=enter, exit_=exit_sig,
low=row[LOW_IDX], high=row[HIGH_IDX]
)
for exit_ in exits:
t = self._get_exit_for_signal(trade, row, exit_)
if t:
return t
return None
def get_valid_price_and_stake(
self, pair: str, row: Tuple, propose_rate: float, stake_amount: float,
@ -779,6 +775,11 @@ class Backtesting:
trade: Optional[LocalTrade] = None,
requested_rate: Optional[float] = None,
requested_stake: Optional[float] = None) -> Optional[LocalTrade]:
"""
:param trade: Trade to adjust - initial entry if None
:param requested_rate: Adjusted entry rate
:param requested_stake: Stake amount for adjusted orders (`adjust_entry_price`).
"""
current_time = row[DATE_IDX].to_pydatetime()
entry_tag = row[ENTER_TAG_IDX] if len(row) >= ENTER_TAG_IDX + 1 else None
@ -804,7 +805,7 @@ class Backtesting:
return trade
time_in_force = self.strategy.order_time_in_force['entry']
if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
if stake_amount and (not min_stake_amount or stake_amount >= min_stake_amount):
self.order_id_counter += 1
base_currency = self.exchange.get_pair_base_currency(pair)
amount_p = (stake_amount / propose_rate) * leverage
@ -920,8 +921,9 @@ class Backtesting:
trade.close(exit_row[OPEN_IDX], show_msg=False)
LocalTrade.close_bt_trade(trade)
def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
def trade_slot_available(self, open_trade_count: int) -> bool:
# Always allow trades when max_open_trades is enabled.
max_open_trades: IntOrInf = self.config['max_open_trades']
if max_open_trades <= 0 or open_trade_count < max_open_trades:
return True
# Rejected trade
@ -1051,7 +1053,8 @@ class Backtesting:
def backtest_loop(
self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
max_open_trades: int, open_trade_count_start: int, is_first: bool = True) -> int:
open_trade_count_start: int, trade_dir: Optional[LongShort],
is_first: bool = True) -> int:
"""
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
@ -1070,11 +1073,10 @@ class Backtesting:
# max_open_trades must be respected
# don't open on the last row
# We only open trades on the main candle, not on detail candles
trade_dir = self.check_for_trade_entry(row)
if (
(self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
and is_first
and self.trade_slot_available(max_open_trades, open_trade_count_start)
and self.trade_slot_available(open_trade_count_start)
and current_time != end_date
and trade_dir is not None
and not PairLocks.is_pair_locked(pair, row[DATE_IDX], trade_dir)
@ -1099,7 +1101,7 @@ class Backtesting:
# 4. Create exit orders (if any)
if not trade.open_order_id:
self._get_exit_trade_entry(trade, row, is_first) # Place exit order if necessary
self._check_trade_exit(trade, row) # Place exit order if necessary
# 5. Process exit orders.
order = trade.select_order(trade.exit_side, is_open=True)
@ -1121,8 +1123,7 @@ class Backtesting:
return open_trade_count_start
def backtest(self, processed: Dict,
start_date: datetime, end_date: datetime,
max_open_trades: int = 0) -> Dict[str, Any]:
start_date: datetime, end_date: datetime) -> Dict[str, Any]:
"""
Implement backtesting functionality
@ -1134,7 +1135,6 @@ class Backtesting:
optimize memory usage!
:param start_date: backtesting timerange start datetime
:param end_date: backtesting timerange end datetime
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
:return: DataFrame with trades (results of backtesting)
"""
self.prepare_backtest(self.enable_protections)
@ -1164,7 +1164,15 @@ class Backtesting:
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
current_detail_time: datetime = row[DATE_IDX].to_pydatetime()
if self.timeframe_detail and pair in self.detail_data:
trade_dir: Optional[LongShort] = self.check_for_trade_entry(row)
if (
(trade_dir is not None or len(LocalTrade.bt_trades_open_pp[pair]) > 0)
and self.timeframe_detail and pair in self.detail_data
):
# Spread out into detail timeframe.
# Should only happen when we are either in a trade for this pair
# or when we got the signal for a new trade.
exit_candle_end = current_detail_time + timedelta(minutes=self.timeframe_min)
detail_data = self.detail_data[pair]
@ -1175,8 +1183,9 @@ class Backtesting:
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date, max_open_trades,
open_trade_count_start)
row, pair, current_time, end_date,
open_trade_count_start, trade_dir)
continue
detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
@ -1187,13 +1196,14 @@ class Backtesting:
current_time_det = current_time
for det_row in detail_data[HEADERS].values.tolist():
open_trade_count_start = self.backtest_loop(
det_row, pair, current_time_det, end_date, max_open_trades,
open_trade_count_start, is_first)
det_row, pair, current_time_det, end_date,
open_trade_count_start, trade_dir, is_first)
current_time_det += timedelta(minutes=self.timeframe_detail_min)
is_first = False
else:
open_trade_count_start = self.backtest_loop(
row, pair, current_time, end_date, max_open_trades, open_trade_count_start)
row, pair, current_time, end_date,
open_trade_count_start, trade_dir)
# Move time one configured time_interval ahead.
self.progress.increment()
@ -1225,13 +1235,11 @@ class Backtesting:
self._set_strategy(strat)
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
# Must come from strategy config, as the strategy may modify this setting.
max_open_trades = self.strategy.config['max_open_trades']
else:
if not self.config.get('use_max_market_positions', True):
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
self.strategy.max_open_trades = float('inf')
self.config.update({'max_open_trades': self.strategy.max_open_trades})
# need to reprocess data every time to populate signals
preprocessed = self.strategy.advise_all_indicators(data)
@ -1254,7 +1262,6 @@ class Backtesting:
processed=preprocessed,
start_date=min_date,
end_date=max_date,
max_open_trades=max_open_trades,
)
backtest_end_time = datetime.now(timezone.utc)
results.update({

38
freqtrade/optimize/hyperopt.py
View File

@ -74,6 +74,7 @@ class Hyperopt:
self.roi_space: List[Dimension] = []
self.stoploss_space: List[Dimension] = []
self.trailing_space: List[Dimension] = []
self.max_open_trades_space: List[Dimension] = []
self.dimensions: List[Dimension] = []
self.config = config
@ -117,11 +118,10 @@ class Hyperopt:
self.current_best_epoch: Optional[Dict[str, Any]] = None
# 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']
else:
if not self.config.get('use_max_market_positions', True):
logger.debug('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
self.max_open_trades = 0
self.backtesting.strategy.max_open_trades = float('inf')
config.update({'max_open_trades': self.backtesting.strategy.max_open_trades})
if HyperoptTools.has_space(self.config, 'sell'):
# Make sure use_exit_signal is enabled
@ -209,6 +209,10 @@ class Hyperopt:
result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
if HyperoptTools.has_space(self.config, 'trailing'):
result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
if HyperoptTools.has_space(self.config, 'trades'):
result['max_open_trades'] = {
'max_open_trades': self.backtesting.strategy.max_open_trades
if self.backtesting.strategy.max_open_trades != float('inf') else -1}
return result
@ -229,6 +233,8 @@ class Hyperopt:
'trailing_stop_positive_offset': strategy.trailing_stop_positive_offset,
'trailing_only_offset_is_reached': strategy.trailing_only_offset_is_reached,
}
if not HyperoptTools.has_space(self.config, 'trades'):
result['max_open_trades'] = {'max_open_trades': strategy.max_open_trades}
return result
def print_results(self, results) -> None:
@ -280,8 +286,13 @@ class Hyperopt:
logger.debug("Hyperopt has 'trailing' space")
self.trailing_space = self.custom_hyperopt.trailing_space()
if HyperoptTools.has_space(self.config, 'trades'):
logger.debug("Hyperopt has 'trades' space")
self.max_open_trades_space = self.custom_hyperopt.max_open_trades_space()
self.dimensions = (self.buy_space + self.sell_space + self.protection_space
+ self.roi_space + self.stoploss_space + self.trailing_space)
+ self.roi_space + self.stoploss_space + self.trailing_space
+ self.max_open_trades_space)
def assign_params(self, params_dict: Dict, category: str) -> None:
"""
@ -328,6 +339,20 @@ class Hyperopt:
self.backtesting.strategy.trailing_only_offset_is_reached = \
d['trailing_only_offset_is_reached']
if HyperoptTools.has_space(self.config, 'trades'):
if self.config["stake_amount"] == "unlimited" and \
(params_dict['max_open_trades'] == -1 or params_dict['max_open_trades'] == 0):
# Ignore unlimited max open trades if stake amount is unlimited
params_dict.update({'max_open_trades': self.config['max_open_trades']})
updated_max_open_trades = int(params_dict['max_open_trades']) \
if (params_dict['max_open_trades'] != -1
and params_dict['max_open_trades'] != 0) else float('inf')
self.config.update({'max_open_trades': updated_max_open_trades})
self.backtesting.strategy.max_open_trades = updated_max_open_trades
with self.data_pickle_file.open('rb') as f:
processed = load(f, mmap_mode='r')
if self.analyze_per_epoch:
@ -337,8 +362,7 @@ class Hyperopt:
bt_results = self.backtesting.backtest(
processed=processed,
start_date=self.min_date,
end_date=self.max_date,
max_open_trades=self.max_open_trades,
end_date=self.max_date
)
backtest_end_time = datetime.now(timezone.utc)
bt_results.update({

3
freqtrade/optimize/hyperopt_auto.py
View File

@ -91,5 +91,8 @@ class HyperOptAuto(IHyperOpt):
def trailing_space(self) -> List['Dimension']:
return self._get_func('trailing_space')()
def max_open_trades_space(self) -> List['Dimension']:
return self._get_func('max_open_trades_space')()
def generate_estimator(self, dimensions: List['Dimension'], **kwargs) -> EstimatorType:
return self._get_func('generate_estimator')(dimensions=dimensions, **kwargs)

10
freqtrade/optimize/hyperopt_interface.py
View File

@ -191,6 +191,16 @@ class IHyperOpt(ABC):
Categorical([True, False], name='trailing_only_offset_is_reached'),
]
def max_open_trades_space(self) -> List[Dimension]:
"""
Create a max open trades space.
You may override it in your custom Hyperopt class.
"""
return [
Integer(-1, 10, name='max_open_trades'),
]
# This is needed for proper unpickling the class attribute timeframe
# which is set to the actual value by the resolver.
# Why do I still need such shamanic mantras in modern python?

41
freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py
View File

@ -5,13 +5,11 @@ This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
from datetime import datetime
from math import sqrt as msqrt
from typing import Any, Dict
from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_max_drawdown
from freqtrade.data.metrics import calculate_calmar
from freqtrade.optimize.hyperopt import IHyperOptLoss
@ -23,42 +21,15 @@ class CalmarHyperOptLoss(IHyperOptLoss):
"""
@staticmethod
def hyperopt_loss_function(
results: DataFrame,
trade_count: int,
min_date: datetime,
max_date: datetime,
config: Config,
processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any],
*args,
**kwargs
) -> float:
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
config: Config, *args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Calmar Ratio calculation.
"""
total_profit = backtest_stats["profit_total"]
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_returns_mean = total_profit.sum() / days_period * 100
# calculate max drawdown
try:
_, _, _, _, _, max_drawdown = calculate_max_drawdown(
results, value_col="profit_abs"
)
except ValueError:
max_drawdown = 0
if max_drawdown != 0:
calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
else:
# Define high (negative) calmar ratio to be clear that this is NOT optimal.
calmar_ratio = -20.0
starting_balance = config['dry_run_wallet']
calmar_ratio = calculate_calmar(results, min_date, max_date, starting_balance)
# print(expected_returns_mean, max_drawdown, calmar_ratio)
return -calmar_ratio

21
freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe.py
View File

@ -6,9 +6,10 @@ Hyperoptimization.
"""
from datetime import datetime
import numpy as np
from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_sharpe
from freqtrade.optimize.hyperopt import IHyperOptLoss
@ -22,25 +23,13 @@ class SharpeHyperOptLoss(IHyperOptLoss):
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
config: Config, *args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Sharpe Ratio calculation.
"""
total_profit = results["profit_ratio"]
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_returns_mean = total_profit.sum() / days_period
up_stdev = np.std(total_profit)
if up_stdev != 0:
sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
sharp_ratio = -20.
starting_balance = config['dry_run_wallet']
sharp_ratio = calculate_sharpe(results, min_date, max_date, starting_balance)
# print(expected_returns_mean, up_stdev, sharp_ratio)
return -sharp_ratio

24
freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py
View File

@ -6,9 +6,10 @@ Hyperoptimization.
"""
from datetime import datetime
import numpy as np
from pandas import DataFrame
from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_sortino
from freqtrade.optimize.hyperopt import IHyperOptLoss
@ -22,28 +23,13 @@ class SortinoHyperOptLoss(IHyperOptLoss):
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
config: Config, *args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Sortino Ratio calculation.
"""
total_profit = results["profit_ratio"]
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_returns_mean = total_profit.sum() / days_period
results['downside_returns'] = 0
results.loc[total_profit < 0, 'downside_returns'] = results['profit_ratio']
down_stdev = np.std(results['downside_returns'])
if down_stdev != 0:
sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
else:
# Define high (negative) sortino ratio to be clear that this is NOT optimal.
sortino_ratio = -20.
starting_balance = config['dry_run_wallet']
sortino_ratio = calculate_sortino(results, min_date, max_date, starting_balance)
# print(expected_returns_mean, down_stdev, sortino_ratio)
return -sortino_ratio

15
freqtrade/optimize/hyperopt_tools.py
View File

@ -96,7 +96,7 @@ class HyperoptTools():
Tell if the space value is contained in the configuration
"""
# 'trailing' and 'protection spaces are not included in the 'default' set of spaces
if space in ('trailing', 'protection'):
if space in ('trailing', 'protection', 'trades'):
return any(s in config['spaces'] for s in [space, 'all'])
else:
return any(s in config['spaces'] for s in [space, 'all', 'default'])
@ -170,7 +170,7 @@ class HyperoptTools():
@staticmethod
def show_epoch_details(results, total_epochs: int, print_json: bool,
no_header: bool = False, header_str: str = None) -> None:
no_header: bool = False, header_str: Optional[str] = None) -> None:
"""
Display details of the hyperopt result
"""
@ -187,7 +187,8 @@ class HyperoptTools():
if print_json:
result_dict: Dict = {}
for s in ['buy', 'sell', 'protection', 'roi', 'stoploss', 'trailing']:
for s in ['buy', 'sell', 'protection',
'roi', 'stoploss', 'trailing', 'max_open_trades']:
HyperoptTools._params_update_for_json(result_dict, params, non_optimized, s)
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
@ -201,6 +202,8 @@ class HyperoptTools():
HyperoptTools._params_pretty_print(params, 'roi', "ROI table:", non_optimized)
HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:", non_optimized)
HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:", non_optimized)
HyperoptTools._params_pretty_print(
params, 'max_open_trades', "Max Open Trades:", non_optimized)
@staticmethod
def _params_update_for_json(result_dict, params, non_optimized, space: str) -> None:
@ -239,7 +242,9 @@ class HyperoptTools():
if space == "stoploss":
stoploss = safe_value_fallback2(space_params, no_params, space, space)
result += (f"stoploss = {stoploss}{appendix}")
elif space == "max_open_trades":
max_open_trades = safe_value_fallback2(space_params, no_params, space, space)
result += (f"max_open_trades = {max_open_trades}{appendix}")
elif space == "roi":
result = result[:-1] + f'{appendix}\n'
minimal_roi_result = rapidjson.dumps({
@ -259,7 +264,7 @@ class HyperoptTools():
print(result)
@staticmethod
def _space_params(params, space: str, r: int = None) -> Dict:
def _space_params(params, space: str, r: Optional[int] = None) -> Dict:
d = params.get(space)
if d:
# Round floats to `r` digits after the decimal point if requested

18
freqtrade/optimize/optimize_reports.py
View File

@ -8,9 +8,10 @@ from pandas import DataFrame, to_datetime
from tabulate import tabulate
from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT,
Config)
from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
calculate_max_drawdown)
Config, IntOrInf)
from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
calculate_expectancy, calculate_market_change,
calculate_max_drawdown, calculate_sharpe, calculate_sortino)
from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
@ -190,7 +191,7 @@ def generate_tag_metrics(tag_type: str,
return []
def generate_exit_reason_stats(max_open_trades: int, results: DataFrame) -> List[Dict]:
def generate_exit_reason_stats(max_open_trades: IntOrInf, results: DataFrame) -> List[Dict]:
"""
Generate small table outlining Backtest results
:param max_open_trades: Max_open_trades parameter
@ -448,6 +449,10 @@ def generate_strategy_stats(pairlist: List[str],
'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
'expectancy': calculate_expectancy(results),
'sortino': calculate_sortino(results, min_date, max_date, start_balance),
'sharpe': calculate_sharpe(results, min_date, max_date, start_balance),
'calmar': calculate_calmar(results, min_date, max_date, start_balance),
'profit_factor': profit_factor,
'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
'backtest_start_ts': int(min_date.timestamp() * 1000),
@ -785,8 +790,13 @@ def text_table_add_metrics(strat_results: Dict) -> str:
strat_results['stake_currency'])),
('Total profit %', f"{strat_results['profit_total']:.2%}"),
('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'),
('Sortino', f"{strat_results['sortino']:.2f}" if 'sortino' in strat_results else 'N/A'),
('Sharpe', f"{strat_results['sharpe']:.2f}" if 'sharpe' in strat_results else 'N/A'),
('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'),
('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
in strat_results else 'N/A'),
('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy'
in strat_results else 'N/A'),
('Trades per day', strat_results['trades_per_day']),
('Avg. daily profit %',
f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),

13
freqtrade/persistence/migrations.py
View File

@ -214,17 +214,22 @@ def migrate_orders_table(engine, table_back_name: str, cols_order: List):
average = get_column_def(cols_order, 'average', 'null')
stop_price = get_column_def(cols_order, 'stop_price', 'null')
funding_fee = get_column_def(cols_order, 'funding_fee', '0.0')
ft_amount = get_column_def(cols_order, 'ft_amount', 'coalesce(amount, 0.0)')
ft_price = get_column_def(cols_order, 'ft_price', 'coalesce(price, 0.0)')
# sqlite does not support literals for booleans
with engine.begin() as connection:
connection.execute(text(f"""
insert into orders (id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
status, symbol, order_type, side, price, amount, filled, average, remaining, cost,
stop_price, order_date, order_filled_date, order_update_date, ft_fee_base, funding_fee)
stop_price, order_date, order_filled_date, order_update_date, ft_fee_base, funding_fee,
ft_amount, ft_price
)
select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
status, symbol, order_type, side, price, amount, filled, {average} average, remaining,
cost, {stop_price} stop_price, order_date, order_filled_date,
order_update_date, {ft_fee_base} ft_fee_base, {funding_fee} funding_fee
order_update_date, {ft_fee_base} ft_fee_base, {funding_fee} funding_fee,
{ft_amount} ft_amount, {ft_price} ft_price
from {table_back_name}
"""))
@ -311,8 +316,8 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
# if ('orders' not in previous_tables
# or not has_column(cols_orders, 'funding_fee')):
migrating = False
# if not has_column(cols_orders, 'funding_fee'):
if not has_column(cols_trades, 'max_stake_amount'):
# if not has_column(cols_trades, 'max_stake_amount'):
if not has_column(cols_orders, 'ft_price'):
migrating = True
logger.info(f"Running database migration for trades - "
f"backup: {table_back_name}, {order_table_bak_name}")

4
freqtrade/persistence/pairlock_middleware.py
View File

@ -30,8 +30,8 @@ class PairLocks():
PairLocks.locks = []
@staticmethod
def lock_pair(pair: str, until: datetime, reason: str = None, *,
now: datetime = None, side: str = '*') -> PairLock:
def lock_pair(pair: str, until: datetime, reason: Optional[str] = None, *,
now: Optional[datetime] = None, side: str = '*') -> PairLock:
"""
Create PairLock from now to "until".
Uses database by default, unless PairLocks.use_db is set to False,

48
freqtrade/persistence/trade_model.py
View File

@ -49,6 +49,8 @@ class Order(_DECL_BASE):
ft_order_side: str = Column(String(25), nullable=False)
ft_pair: str = Column(String(25), nullable=False)
ft_is_open = Column(Boolean, nullable=False, default=True, index=True)
ft_amount = Column(Float, nullable=False)
ft_price = Column(Float, nullable=False)
order_id: str = Column(String(255), nullable=False, index=True)
status = Column(String(255), nullable=True)
@ -82,9 +84,13 @@ class Order(_DECL_BASE):
self.order_filled_date.replace(tzinfo=timezone.utc) if self.order_filled_date else None
)
@property
def safe_amount(self) -> float:
return self.amount or self.ft_amount
@property
def safe_price(self) -> float:
return self.average or self.price or self.stop_price
return self.average or self.price or self.stop_price or self.ft_price
@property
def safe_filled(self) -> float:
@ -94,7 +100,7 @@ class Order(_DECL_BASE):
def safe_remaining(self) -> float:
return (
self.remaining if self.remaining is not None else
self.amount - (self.filled or 0.0)
self.safe_amount - (self.filled or 0.0)
)
@property
@ -140,7 +146,7 @@ class Order(_DECL_BASE):
# Assign funding fee up to this point
# (represents the funding fee since the last order)
self.funding_fee = self.trade.funding_fees
if (order.get('filled', 0.0) or 0.0) > 0:
if (order.get('filled', 0.0) or 0.0) > 0 and not self.order_filled_date:
self.order_filled_date = datetime.now(timezone.utc)
self.order_update_date = datetime.now(timezone.utc)
@ -227,11 +233,20 @@ class Order(_DECL_BASE):
logger.warning(f"Did not find order for {order}.")
@staticmethod
def parse_from_ccxt_object(order: Dict[str, Any], pair: str, side: str) -> 'Order':
def parse_from_ccxt_object(
order: Dict[str, Any], pair: str, side: str,
amount: Optional[float] = None, price: Optional[float] = None) -> 'Order':
"""
Parse an order from a ccxt object and return a new order Object.
Optional support for overriding amount and price is only used for test simplification.
"""
o = Order(order_id=str(order['id']), ft_order_side=side, ft_pair=pair)
o = Order(
order_id=str(order['id']),
ft_order_side=side,
ft_pair=pair,
ft_amount=amount if amount else order['amount'],
ft_price=price if price else order['price'],
)
o.update_from_ccxt_object(order)
return o
@ -784,7 +799,7 @@ class LocalTrade():
else:
return close_trade - fees
def calc_close_trade_value(self, rate: float, amount: float = None) -> float:
def calc_close_trade_value(self, rate: float, amount: Optional[float] = None) -> float:
"""
Calculate the Trade's close value including fees
:param rate: rate to compare with.
@ -822,7 +837,8 @@ class LocalTrade():
raise OperationalException(
f"{self.trading_mode.value} trading is not yet available using freqtrade")
def calc_profit(self, rate: float, amount: float = None, open_rate: float = None) -> float:
def calc_profit(self, rate: float, amount: Optional[float] = None,
open_rate: Optional[float] = None) -> float:
"""
Calculate the absolute profit in stake currency between Close and Open trade
:param rate: close rate to compare with.
@ -843,7 +859,8 @@ class LocalTrade():
return float(f"{profit:.8f}")
def calc_profit_ratio(
self, rate: float, amount: float = None, open_rate: float = None) -> float:
self, rate: float, amount: Optional[float] = None,
open_rate: Optional[float] = None) -> float:
"""
Calculates the profit as ratio (including fee).
:param rate: rate to compare with.
@ -956,11 +973,12 @@ class LocalTrade():
return None
def select_order(self, order_side: Optional[str] = None,
is_open: Optional[bool] = None) -> Optional[Order]:
is_open: Optional[bool] = None, only_filled: bool = False) -> Optional[Order]:
"""
Finds latest order for this orderside and status
:param order_side: ft_order_side of the order (either 'buy', 'sell' or 'stoploss')
:param is_open: Only search for open orders?
:param only_filled: Only search for Filled orders (only valid with is_open=False).
:return: latest Order object if it exists, else None
"""
orders = self.orders
@ -968,6 +986,8 @@ class LocalTrade():
orders = [o for o in orders if o.ft_order_side == order_side]
if is_open is not None:
orders = [o for o in orders if o.ft_is_open == is_open]
if is_open is False and only_filled:
orders = [o for o in orders if o.filled and o.status in NON_OPEN_EXCHANGE_STATES]
if len(orders) > 0:
return orders[-1]
else:
@ -1041,8 +1061,9 @@ class LocalTrade():
return self.exit_reason
@staticmethod
def get_trades_proxy(*, pair: str = None, is_open: bool = None,
open_date: datetime = None, close_date: datetime = None,
def get_trades_proxy(*, pair: Optional[str] = None, is_open: Optional[bool] = None,
open_date: Optional[datetime] = None,
close_date: Optional[datetime] = None,
) -> List['LocalTrade']:
"""
Helper function to query Trades.
@ -1239,8 +1260,9 @@ class Trade(_DECL_BASE, LocalTrade):
Trade.query.session.rollback()
@staticmethod
def get_trades_proxy(*, pair: str = None, is_open: bool = None,
open_date: datetime = None, close_date: datetime = None,
def get_trades_proxy(*, pair: Optional[str] = None, is_open: Optional[bool] = None,
open_date: Optional[datetime] = None,
close_date: Optional[datetime] = None,
) -> List['LocalTrade']:
"""
Helper function to query Trades.j

10
freqtrade/plot/plotting.py
View File

@ -436,11 +436,11 @@ def create_scatter(
return None
def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFrame = None, *,
indicators1: List[str] = [],
indicators2: List[str] = [],
plot_config: Dict[str, Dict] = {},
) -> go.Figure:
def generate_candlestick_graph(
pair: str, data: pd.DataFrame, trades: Optional[pd.DataFrame] = None, *,
indicators1: List[str] = [], indicators2: List[str] = [],
plot_config: Dict[str, Dict] = {},
) -> go.Figure:
"""
Generate the graph from the data generated by Backtesting or from DB
Volume will always be ploted in row2, so Row 1 and 3 are to our disposal for custom indicators

6
freqtrade/plugins/pairlistmanager.py
View File

@ -23,7 +23,8 @@ logger = logging.getLogger(__name__)
class PairListManager(LoggingMixin):
def __init__(self, exchange, config: Config, dataprovider: DataProvider = None) -> None:
def __init__(
self, exchange, config: Config, dataprovider: Optional[DataProvider] = None) -> None:
self._exchange = exchange
self._config = config
self._whitelist = self._config['exchange'].get('pair_whitelist')
@ -153,7 +154,8 @@ class PairListManager(LoggingMixin):
return []
return whitelist
def create_pair_list(self, pairs: List[str], timeframe: str = None) -> ListPairsWithTimeframes:
def create_pair_list(
self, pairs: List[str], timeframe: Optional[str] = None) -> ListPairsWithTimeframes:
"""
Create list of pair tuples with (pair, timeframe)
"""

3
freqtrade/resolvers/iresolver.py
View File

@ -89,7 +89,8 @@ class IResolver:
module = importlib.util.module_from_spec(spec)
try:
spec.loader.exec_module(module) # type: ignore # importlib does not use typehints
except (ModuleNotFoundError, SyntaxError, ImportError, NameError) as err:
except (AttributeError, ModuleNotFoundError, SyntaxError,
ImportError, NameError) as err:
# Catch errors in case a specific module is not installed
logger.warning(f"Could not import {module_path} due to '{err}'")
if enum_failed:

11
freqtrade/resolvers/strategy_resolver.py
View File

@ -33,7 +33,7 @@ class StrategyResolver(IResolver):
extra_path = "strategy_path"
@staticmethod
def load_strategy(config: Config = None) -> IStrategy:
def load_strategy(config: Optional[Config] = None) -> IStrategy:
"""
Load the custom class from config parameter
:param config: configuration dictionary or None
@ -76,6 +76,7 @@ class StrategyResolver(IResolver):
("ignore_buying_expired_candle_after", 0),
("position_adjustment_enable", False),
("max_entry_position_adjustment", -1),
("max_open_trades", -1)
]
for attribute, default in attributes:
StrategyResolver._override_attribute_helper(strategy, config,
@ -110,7 +111,11 @@ class StrategyResolver(IResolver):
val = getattr(strategy, attribute)
# None's cannot exist in the config, so do not copy them
if val is not None:
config[attribute] = val
# max_open_trades set to -1 in the strategy will be copied as infinity in the config
if attribute == 'max_open_trades' and val == -1:
config[attribute] = float('inf')
else:
config[attribute] = val
# Explicitly check for None here as other "falsy" values are possible
elif default is not None:
setattr(strategy, attribute, default)
@ -128,6 +133,8 @@ class StrategyResolver(IResolver):
key=lambda t: t[0]))
if hasattr(strategy, 'stoploss'):
strategy.stoploss = float(strategy.stoploss)
if hasattr(strategy, 'max_open_trades') and strategy.max_open_trades < 0:
strategy.max_open_trades = float('inf')
return strategy
@staticmethod

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

@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional, Union
from pydantic import BaseModel
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.constants import DATETIME_PRINT_FORMAT, IntOrInf
from freqtrade.enums import OrderTypeValues, SignalDirection, TradingMode
@ -165,7 +165,7 @@ class ShowConfig(BaseModel):
stake_amount: str
available_capital: Optional[float]
stake_currency_decimals: int
max_open_trades: int
max_open_trades: IntOrInf
minimal_roi: Dict[str, Any]
stoploss: Optional[float]
trailing_stop: Optional[bool]
@ -422,7 +422,7 @@ class BacktestRequest(BaseModel):
timeframe: Optional[str]
timeframe_detail: Optional[str]
timerange: Optional[str]
max_open_trades: Optional[int]
max_open_trades: Optional[IntOrInf]
stake_amount: Optional[str]
enable_protections: bool
dry_run_wallet: Optional[float]

17
freqtrade/rpc/api_server/api_v1.py
View File

@ -40,7 +40,8 @@ logger = logging.getLogger(__name__)
# 2.20: Add websocket endpoints
# 2.21: Add new_candle messagetype
# 2.22: Add FreqAI to backtesting
API_VERSION = 2.22
# 2.23: Allow plot config request in webserver mode
API_VERSION = 2.23
# Public API, requires no auth.
router_public = APIRouter()
@ -248,8 +249,18 @@ def pair_history(pair: str, timeframe: str, timerange: str, strategy: str,
@router.get('/plot_config', response_model=PlotConfig, tags=['candle data'])
def plot_config(rpc: RPC = Depends(get_rpc)):
return PlotConfig.parse_obj(rpc._rpc_plot_config())
def plot_config(strategy: Optional[str] = None, config=Depends(get_config),
rpc: Optional[RPC] = Depends(get_rpc_optional)):
if not strategy:
if not rpc:
raise RPCException("Strategy is mandatory in webserver mode.")
return PlotConfig.parse_obj(rpc._rpc_plot_config())
else:
config1 = deepcopy(config)
config1.update({
'strategy': strategy
})
return PlotConfig.parse_obj(RPC._rpc_plot_config_with_strategy(config1))
@router.get('/strategies', response_model=StrategyListResponse, tags=['strategy'])

17
freqtrade/rpc/rpc.py
View File

@ -673,6 +673,7 @@ class RPC:
if self._freqtrade.state == State.RUNNING:
# Set 'max_open_trades' to 0
self._freqtrade.config['max_open_trades'] = 0
self._freqtrade.strategy.max_open_trades = 0
return {'status': 'No more entries will occur from now. Run /reload_config to reset.'}
@ -944,7 +945,7 @@ class RPC:
resp['errors'] = errors
return resp
def _rpc_blacklist(self, add: List[str] = None) -> Dict:
def _rpc_blacklist(self, add: Optional[List[str]] = None) -> Dict:
""" Returns the currently active blacklist"""
errors = {}
if add:
@ -1126,12 +1127,12 @@ class RPC:
return self._freqtrade.active_pair_whitelist
@staticmethod
def _rpc_analysed_history_full(config, pair: str, timeframe: str,
def _rpc_analysed_history_full(config: Config, pair: str, timeframe: str,
timerange: str, exchange) -> Dict[str, Any]:
timerange_parsed = TimeRange.parse_timerange(timerange)
_data = load_data(
datadir=config.get("datadir"),
datadir=config["datadir"],
pairs=[pair],
timeframe=timeframe,
timerange=timerange_parsed,
@ -1156,6 +1157,16 @@ class RPC:
self._freqtrade.strategy.plot_config['subplots'] = {}
return self._freqtrade.strategy.plot_config
@staticmethod
def _rpc_plot_config_with_strategy(config: Config) -> Dict[str, Any]:
from freqtrade.resolvers.strategy_resolver import StrategyResolver
strategy = StrategyResolver.load_strategy(config)
if (strategy.plot_config and 'subplots' not in strategy.plot_config):
strategy.plot_config['subplots'] = {}
return strategy.plot_config
@staticmethod
def _rpc_sysinfo() -> Dict[str, Any]:
return {

2
freqtrade/rpc/telegram.py
View File

@ -1605,7 +1605,7 @@ class Telegram(RPCHandler):
def _send_msg(self, msg: str, parse_mode: str = ParseMode.MARKDOWN,
disable_notification: bool = False,
keyboard: List[List[InlineKeyboardButton]] = None,
keyboard: Optional[List[List[InlineKeyboardButton]]] = None,
callback_path: str = "",
reload_able: bool = False,
query: Optional[CallbackQuery] = None) -> None:

7
freqtrade/strategy/hyper.py
View File

@ -4,7 +4,7 @@ This module defines a base class for auto-hyperoptable strategies.
"""
import logging
from pathlib import Path
from typing import Any, Dict, Iterator, List, Tuple, Type, Union
from typing import Any, Dict, Iterator, List, Optional, Tuple, Type, Union
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
@ -36,7 +36,8 @@ class HyperStrategyMixin:
self._ft_params_from_file = params
# Init/loading of parameters is done as part of ft_bot_start().
def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
def enumerate_parameters(
self, category: Optional[str] = None) -> Iterator[Tuple[str, BaseParameter]]:
"""
Find all optimizable parameters and return (name, attr) iterator.
:param category:
@ -80,6 +81,8 @@ class HyperStrategyMixin:
self.stoploss = params.get('stoploss', {}).get(
'stoploss', getattr(self, 'stoploss', -0.1))
self.max_open_trades = params.get('max_open_trades', {}).get(
'max_open_trades', getattr(self, 'max_open_trades', -1))
trailing = params.get('trailing', {})
self.trailing_stop = trailing.get(
'trailing_stop', getattr(self, 'trailing_stop', False))

114
freqtrade/strategy/interface.py
View File

@ -10,7 +10,7 @@ from typing import Dict, List, Optional, Tuple, Union
import arrow
from pandas import DataFrame
from freqtrade.constants import Config, ListPairsWithTimeframes
from freqtrade.constants import Config, IntOrInf, ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, RunMode, SignalDirection,
SignalTagType, SignalType, TradingMode)
@ -54,6 +54,9 @@ class IStrategy(ABC, HyperStrategyMixin):
# associated stoploss
stoploss: float
# max open trades for the strategy
max_open_trades: IntOrInf
# trailing stoploss
trailing_stop: bool = False
trailing_stop_positive: Optional[float] = None
@ -595,9 +598,10 @@ class IStrategy(ABC, HyperStrategyMixin):
return None
def populate_any_indicators(self, pair: str, df: DataFrame, tf: str,
informative: DataFrame = None,
informative: Optional[DataFrame] = None,
set_generalized_indicators: bool = False) -> DataFrame:
"""
DEPRECATED - USE FEATURE ENGINEERING FUNCTIONS INSTEAD
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User can add
additional features here, but must follow the naming convention.
@ -610,6 +614,98 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
return df
def feature_engineering_expand_all(self, dataframe: DataFrame,
period: int, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
return dataframe
def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
return dataframe
def feature_engineering_standard(self, dataframe: DataFrame, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
All features must be prepended with `%` to be recognized by FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the features
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the targets
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
return dataframe
###
# END - Intended to be overridden by strategy
###
@ -663,7 +759,8 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
return self.__class__.__name__
def lock_pair(self, pair: str, until: datetime, reason: str = None, side: str = '*') -> None:
def lock_pair(self, pair: str, until: datetime,
reason: Optional[str] = None, side: str = '*') -> None:
"""
Locks pair until a given timestamp happens.
Locked pairs are not analyzed, and are prevented from opening new trades.
@ -695,7 +792,8 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
PairLocks.unlock_reason(reason, datetime.now(timezone.utc))
def is_pair_locked(self, pair: str, *, candle_date: datetime = None, side: str = '*') -> bool:
def is_pair_locked(self, pair: str, *, candle_date: Optional[datetime] = None,
side: str = '*') -> bool:
"""
Checks if a pair is currently locked
The 2nd, optional parameter ensures that locks are applied until the new candle arrives,
@ -866,7 +964,7 @@ class IStrategy(ABC, HyperStrategyMixin):
pair: str,
timeframe: str,
dataframe: DataFrame,
is_short: bool = None
is_short: Optional[bool] = None
) -> Tuple[bool, bool, Optional[str]]:
"""
Calculates current exit signal based based on the dataframe
@ -965,7 +1063,7 @@ class IStrategy(ABC, HyperStrategyMixin):
def should_exit(self, trade: Trade, rate: float, current_time: datetime, *,
enter: bool, exit_: bool,
low: float = None, high: float = None,
low: Optional[float] = None, high: Optional[float] = None,
force_stoploss: float = 0) -> List[ExitCheckTuple]:
"""
This function evaluates if one of the conditions required to trigger an exit order
@ -1053,8 +1151,8 @@ class IStrategy(ABC, HyperStrategyMixin):
def stop_loss_reached(self, current_rate: float, trade: Trade,
current_time: datetime, current_profit: float,
force_stoploss: float, low: float = None,
high: float = None) -> ExitCheckTuple:
force_stoploss: float, low: Optional[float] = None,
high: Optional[float] = None) -> ExitCheckTuple:
"""
Based on current profit of the trade and configured (trailing) stoploss,
decides to exit or not

165
freqtrade/templates/FreqaiExampleHybridStrategy.py
View File

@ -95,65 +95,132 @@ class FreqaiExampleHybridStrategy(IStrategy):
short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
# FreqAI required function, user can add or remove indicators, but general structure
# must stay the same.
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
"""
User feeds these indicators to FreqAI to train a classifier to decide
if the market will go up or down.
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=period, stds=2.2
)
dataframe["bb_lowerband-period"] = bollinger["lower"]
dataframe["bb_middleband-period"] = bollinger["mid"]
dataframe["bb_upperband-period"] = bollinger["upper"]
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)
dataframe["%-bb_width-period"] = (
dataframe["bb_upperband-period"]
- dataframe["bb_lowerband-period"]
) / dataframe["bb_middleband-period"]
dataframe["%-close-bb_lower-period"] = (
dataframe["close"] / dataframe["bb_lowerband-period"]
)
# FreqAI needs the following lines in order to detect features and automatically
# expand upon them.
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
dataframe["%-relative_volume-period"] = (
dataframe["volume"] / dataframe["volume"].rolling(period).mean()
)
# User can set the "target" here (in present case it is the
# "up" or "down")
if set_generalized_indicators:
# User "looks into the future" here to figure out if the future
# will be "up" or "down". This same column name is available to
# the user
df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
df["close"], 'up', 'down')
return dataframe
return df
def feature_engineering_expand_basic(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
def feature_engineering_standard(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
All features must be prepended with `%` to be recognized by FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the features
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the targets
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) >
dataframe["close"], 'up', 'down')
return dataframe
# flake8: noqa: C901
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

257
freqtrade/templates/FreqaiExampleStrategy.py
View File

@ -1,12 +1,11 @@
import logging
from functools import reduce
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from technical import qtpylib
from freqtrade.strategy import CategoricalParameter, IStrategy, merge_informative_pair
from freqtrade.strategy import CategoricalParameter, IStrategy
logger = logging.getLogger(__name__)
@ -18,8 +17,8 @@ class FreqaiExampleStrategy(IStrategy):
IFreqaiModel to the strategy. Namely, the user uses:
self.freqai.start(dataframe, metadata)
to make predictions on their data. populate_any_indicators() automatically
generates the variety of features indicated by the user in the
to make predictions on their data. feature_engineering_*() automatically
generate the variety of features indicated by the user in the
canonical freqtrade configuration file under config['freqai'].
"""
@ -28,7 +27,7 @@ class FreqaiExampleStrategy(IStrategy):
plot_config = {
"main_plot": {},
"subplots": {
"prediction": {"prediction": {"color": "blue"}},
"&-s_close": {"prediction": {"color": "blue"}},
"do_predict": {
"do_predict": {"color": "brown"},
},
@ -40,133 +39,179 @@ class FreqaiExampleStrategy(IStrategy):
use_exit_signal = True
# this is the maximum period fed to talib (timeframe independent)
startup_candle_count: int = 40
can_short = False
can_short = True
std_dev_multiplier_buy = CategoricalParameter(
[0.75, 1, 1.25, 1.5, 1.75], default=1.25, space="buy", optimize=True)
std_dev_multiplier_sell = CategoricalParameter(
[0.75, 1, 1.25, 1.5, 1.75], space="sell", default=1.25, optimize=True)
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
def feature_engineering_expand_all(self, dataframe, period, **kwargs):
"""
Function designed to automatically generate, name and merge features
from user indicated timeframes in the configuration file. User controls the indicators
passed to the training/prediction by prepending indicators with `f'%-{pair}`
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
`include_corr_pairs`. In other words, a single feature defined in this function
will automatically expand to a total of
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
`include_corr_pairs` numbers of features added to the model.
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
:param period: period of the indicator - usage example:
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
"""
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=period, stds=2.2
)
dataframe["bb_lowerband-period"] = bollinger["lower"]
dataframe["bb_middleband-period"] = bollinger["mid"]
dataframe["bb_upperband-period"] = bollinger["upper"]
t = int(t)
informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
dataframe["%-bb_width-period"] = (
dataframe["bb_upperband-period"]
- dataframe["bb_lowerband-period"]
) / dataframe["bb_middleband-period"]
dataframe["%-close-bb_lower-period"] = (
dataframe["close"] / dataframe["bb_lowerband-period"]
)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
informative[f"%-{pair}bb_width-period_{t}"] = (
informative[f"{pair}bb_upperband-period_{t}"]
- informative[f"{pair}bb_lowerband-period_{t}"]
) / informative[f"{pair}bb_middleband-period_{t}"]
informative[f"%-{pair}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
dataframe["%-relative_volume-period"] = (
dataframe["volume"] / dataframe["volume"].rolling(period).mean()
)
return dataframe
def feature_engineering_expand_basic(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
In other words, a single feature defined in this function
will automatically expand to a total of
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
numbers of features added to the model.
Features defined here will *not* be automatically duplicated on user defined
`indicator_periods_candles`
All features must be prepended with `%` to be recognized by FreqAI internals.
More details on how these config defined parameters accelerate feature engineering
in the documentation at:
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
:param df: strategy dataframe which will receive the features
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
"""
dataframe["%-pct-change"] = dataframe["close"].pct_change()
dataframe["%-raw_volume"] = dataframe["volume"]
dataframe["%-raw_price"] = dataframe["close"]
return dataframe
def feature_engineering_standard(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe.
This is the final function to be called, which means that the dataframe entering this
function will contain all the features and columns created by all other
freqai_feature_engineering_* functions.
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
This function is a good place for any feature that should not be auto-expanded upon
(e.g. day of the week).
All features must be prepended with `%` to be recognized by FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the features
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
"""
dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
return dataframe
def set_freqai_targets(self, dataframe, **kwargs):
"""
*Only functional with FreqAI enabled strategies*
Required function to set the targets for the model.
All targets must be prepended with `&` to be recognized by the FreqAI internals.
More details about feature engineering available:
https://www.freqtrade.io/en/latest/freqai-feature-engineering
:param df: strategy dataframe which will receive the targets
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
"""
dataframe["&-s_close"] = (
dataframe["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ dataframe["close"]
- 1
)
informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
# Classifiers are typically set up with strings as targets:
# df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
# df["close"], 'up', 'down')
informative[f"%-{pair}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)
# If user wishes to use multiple targets, they can add more by
# appending more columns with '&'. User should keep in mind that multi targets
# requires a multioutput prediction model such as
# freqai/prediction_models/CatboostRegressorMultiTarget.py,
# freqtrade trade --freqaimodel CatboostRegressorMultiTarget
informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
informative[f"%-{pair}raw_volume"] = informative["volume"]
informative[f"%-{pair}raw_price"] = informative["close"]
# df["&-s_range"] = (
# df["close"]
# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
# .max()
# -
# df["close"]
# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
# .min()
# )
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
df["&-s_close"] = (
df["close"]
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
.mean()
/ df["close"]
- 1
)
# Classifiers are typically set up with strings as targets:
# df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
# df["close"], 'up', 'down')
# If user wishes to use multiple targets, they can add more by
# appending more columns with '&'. User should keep in mind that multi targets
# requires a multioutput prediction model such as
# templates/CatboostPredictionMultiModel.py,
# df["&-s_range"] = (
# df["close"]
# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
# .max()
# -
# df["close"]
# .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
# .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
# .min()
# )
return df
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# All indicators must be populated by populate_any_indicators() for live functionality
# to work correctly.
# All indicators must be populated by feature_engineering_*() functions
# the model will return all labels created by user in `populate_any_indicators`
# the model will return all labels created by user in `feature_engineering_*`
# (& appended targets), an indication of whether or not the prediction should be accepted,
# the target mean/std values for each of the labels created by user in
# `populate_any_indicators()` for each training period.
# `set_freqai_targets()` for each training period.
dataframe = self.freqai.start(dataframe, metadata, self)
for val in self.std_dev_multiplier_buy.range:
dataframe[f'target_roi_{val}'] = (
dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * val

14
freqtrade/templates/base_config.json.j2
View File

@ -41,20 +41,6 @@
"pairlists": [
{{ '{"method": "StaticPairList"}' if exchange_name == 'bittrex' else volume_pairlist }}
],
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": {
"enabled": {{ telegram | lower }},
"token": "{{ telegram_token }}",

78
freqtrade/util/binance_mig.py Normal file
View File

@ -0,0 +1,78 @@
import logging
from packaging import version
from freqtrade.constants import Config
from freqtrade.enums.tradingmode import TradingMode
from freqtrade.exceptions import OperationalException
from freqtrade.persistence.pairlock import PairLock
from freqtrade.persistence.trade_model import Trade
logger = logging.getLogger(__name__)
def migrate_binance_futures_names(config: Config):
if (
not (config.get('trading_mode', TradingMode.SPOT) == TradingMode.FUTURES
and config['exchange']['name'] == 'binance')
):
# only act on new futures
return
import ccxt
if version.parse("2.6.26") > version.parse(ccxt.__version__):
raise OperationalException(
"Please follow the update instructions in the docs "
"(https://www.freqtrade.io/en/latest/updating/) to install a compatible ccxt version.")
_migrate_binance_futures_db(config)
migrate_binance_futures_data(config)
def _migrate_binance_futures_db(config: Config):
logger.warning('Migrating binance futures pairs in database.')
trades = Trade.get_trades([Trade.exchange == 'binance', Trade.trading_mode == 'FUTURES']).all()
for trade in trades:
if ':' in trade.pair:
# already migrated
continue
new_pair = f"{trade.pair}:{trade.stake_currency}"
trade.pair = new_pair
for order in trade.orders:
order.ft_pair = new_pair
# Should symbol be migrated too?
# order.symbol = new_pair
Trade.commit()
pls = PairLock.query.filter(PairLock.pair.notlike('%:%'))
for pl in pls:
pl.pair = f"{pl.pair}:{config['stake_currency']}"
# print(pls)
# pls.update({'pair': concat(PairLock.pair,':USDT')})
Trade.commit()
logger.warning('Done migrating binance futures pairs in database.')
def migrate_binance_futures_data(config: Config):
if (
not (config.get('trading_mode', TradingMode.SPOT) == TradingMode.FUTURES
and config['exchange']['name'] == 'binance')
):
# only act on new futures
return
from freqtrade.data.history.idatahandler import get_datahandler
dhc = get_datahandler(config['datadir'], config.get('dataformat_ohlcv', 'json'))
paircombs = dhc.ohlcv_get_available_data(
config['datadir'],
config.get('trading_mode', TradingMode.SPOT)
)
for pair, timeframe, candle_type in paircombs:
if ':' in pair:
# already migrated
continue
new_pair = f"{pair}:{config['stake_currency']}"
dhc.rename_futures_data(pair, new_pair, timeframe, candle_type)

14
freqtrade/wallets.py
View File

@ -297,16 +297,16 @@ class Wallets:
logger.debug(f"Stake amount is {stake_amount}, ignoring possible trade for {pair}.")
return 0
max_stake_amount = min(max_stake_amount, self.get_available_stake_amount())
max_allowed_stake = min(max_stake_amount, self.get_available_stake_amount())
if trade_amount:
# if in a trade, then the resulting trade size cannot go beyond the max stake
# Otherwise we could no longer exit.
max_stake_amount = min(max_stake_amount, max_stake_amount - trade_amount)
max_allowed_stake = min(max_allowed_stake, max_stake_amount - trade_amount)
if min_stake_amount is not None and min_stake_amount > max_stake_amount:
if min_stake_amount is not None and min_stake_amount > max_allowed_stake:
if self._log:
logger.warning("Minimum stake amount > available balance. "
f"{min_stake_amount} > {max_stake_amount}")
f"{min_stake_amount} > {max_allowed_stake}")
return 0
if min_stake_amount is not None and stake_amount < min_stake_amount:
if self._log:
@ -325,11 +325,11 @@ class Wallets:
return 0
stake_amount = min_stake_amount
if stake_amount > max_stake_amount:
if stake_amount > max_allowed_stake:
if self._log:
logger.info(
f"Stake amount for pair {pair} is too big "
f"({stake_amount} > {max_stake_amount}), adjusting to {max_stake_amount}."
f"({stake_amount} > {max_allowed_stake}), adjusting to {max_allowed_stake}."
)
stake_amount = max_stake_amount
stake_amount = max_allowed_stake
return stake_amount

2
freqtrade/worker.py
View File

@ -26,7 +26,7 @@ class Worker:
Freqtradebot worker class
"""
def __init__(self, args: Dict[str, Any], config: Config = None) -> None:
def __init__(self, args: Dict[str, Any], config: Optional[Config] = None) -> None:
"""
Init all variables and objects the bot needs to work
"""

4
mkdocs.yml
View File

@ -59,7 +59,11 @@ theme:
favicon: "images/logo.png"
custom_dir: "docs/overrides"
features:
- content.code.annotate
- search.share
- content.code.copy
- navigation.top
- navigation.footer
palette:
- scheme: default
primary: "blue grey"

9
pyproject.toml
View File

@ -31,7 +31,6 @@ asyncio_mode = "auto"
[tool.mypy]
ignore_missing_imports = true
namespace_packages = false
implicit_optional = true
warn_unused_ignores = true
exclude = [
'^build_helpers\.py$'
@ -41,6 +40,11 @@ exclude = [
module = "tests.*"
ignore_errors = true
[[tool.mypy.overrides]]
# Telegram does not use implicit_optional = false in the current version.
module = "telegram.*"
implicit_optional = true
[build-system]
requires = ["setuptools >= 46.4.0", "wheel"]
build-backend = "setuptools.build_meta"
@ -52,6 +56,3 @@ exclude = [
"build_helpers/*.py",
]
ignore = ["freqtrade/vendor/**"]
# Align pyright to mypy config
strictParameterNoneValue = false

12
requirements-dev.txt
View File

@ -11,23 +11,23 @@ flake8==6.0.0
flake8-tidy-imports==4.8.0
mypy==0.991
pre-commit==2.21.0
pytest==7.2.0
pytest==7.2.1
pytest-asyncio==0.20.3
pytest-cov==4.0.0
pytest-mock==3.10.0
pytest-random-order==1.1.0
isort==5.11.4
# For datetime mocking
time-machine==2.8.2
time-machine==2.9.0
# fastapi testing
httpx==0.23.1
httpx==0.23.3
# Convert jupyter notebooks to markdown documents
nbconvert==7.2.7
nbconvert==7.2.8
# mypy types
types-cachetools==5.2.1
types-filelock==3.2.7
types-requests==2.28.11.7
types-requests==2.28.11.8
types-tabulate==0.9.0.0
types-python-dateutil==2.8.19.5
types-python-dateutil==2.8.19.6

6
requirements-freqai.txt
View File

@ -6,6 +6,6 @@
scikit-learn==1.1.3
joblib==1.2.0
catboost==1.1.1; platform_machine != 'aarch64'
lightgbm==3.3.3
xgboost==1.7.2
tensorboard==2.11.0
lightgbm==3.3.4
xgboost==1.7.3
tensorboard==2.11.2

4
requirements-hyperopt.txt
View File

@ -2,8 +2,8 @@
-r requirements.txt
# Required for hyperopt
scipy==1.9.3
scipy==1.10.0
scikit-learn==1.1.3
scikit-optimize==0.9.0
filelock==3.8.2
filelock==3.9.0
progressbar2==4.2.0

20
requirements.txt
View File

@ -1,25 +1,25 @@
numpy==1.24.1
pandas==1.5.2
pandas==1.5.3
pandas-ta==0.3.14b
ccxt==2.4.60
ccxt==2.7.12
# Pin cryptography for now due to rust build errors with piwheels
cryptography==38.0.1; platform_machine == 'armv7l'
cryptography==38.0.4; platform_machine != 'armv7l'
cryptography==39.0.0; platform_machine != 'armv7l'
aiohttp==3.8.3
SQLAlchemy==1.4.45
SQLAlchemy==1.4.46
python-telegram-bot==13.15
arrow==1.2.3
cachetools==4.2.2
requests==2.28.1
urllib3==1.26.13
requests==2.28.2
urllib3==1.26.14
jsonschema==4.17.3
TA-Lib==0.4.25
technical==1.3.0
tabulate==0.9.0
pycoingecko==3.1.0
jinja2==3.1.2
tables==3.7.0
tables==3.8.0
blosc==1.11.1
joblib==1.2.0
pyarrow==10.0.1; platform_machine != 'armv7l'
@ -30,14 +30,14 @@ py_find_1st==1.1.5
# Load ticker files 30% faster
python-rapidjson==1.9
# Properly format api responses
orjson==3.8.3
orjson==3.8.5
# Notify systemd
sdnotify==0.3.2
# API Server
fastapi==0.88.0
pydantic==1.10.2
fastapi==0.89.1
pydantic==1.10.4
uvicorn==0.20.0
pyjwt==2.6.0
aiofiles==22.1.0

9
scripts/rest_client.py
View File

@ -14,6 +14,7 @@ import logging
import re
import sys
from pathlib import Path
from typing import Optional
from urllib.parse import urlencode, urlparse, urlunparse
import rapidjson
@ -36,7 +37,7 @@ class FtRestClient():
self._session = requests.Session()
self._session.auth = (username, password)
def _call(self, method, apipath, params: dict = None, data=None, files=None):
def _call(self, method, apipath, params: Optional[dict] = None, data=None, files=None):
if str(method).upper() not in ('GET', 'POST', 'PUT', 'DELETE'):
raise ValueError(f'invalid method <{method}>')
@ -60,13 +61,13 @@ class FtRestClient():
except ConnectionError:
logger.warning("Connection error")
def _get(self, apipath, params: dict = None):
def _get(self, apipath, params: Optional[dict] = None):
return self._call("GET", apipath, params=params)
def _delete(self, apipath, params: dict = None):
def _delete(self, apipath, params: Optional[dict] = None):
return self._call("DELETE", apipath, params=params)
def _post(self, apipath, params: dict = None, data: dict = None):
def _post(self, apipath, params: Optional[dict] = None, data: Optional[dict] = None):
return self._call("POST", apipath, params=params, data=data)
def start(self):

12
setup.py
View File

@ -25,6 +25,11 @@ freqai_rl = [
'sb3-contrib'
]
hdf5 = [
'tables',
'blosc',
]
develop = [
'coveralls',
'flake8',
@ -44,7 +49,7 @@ jupyter = [
'nbconvert',
]
all_extra = plot + develop + jupyter + hyperopt + freqai + freqai_rl
all_extra = plot + develop + jupyter + hyperopt + hdf5 + freqai + freqai_rl
setup(
tests_require=[
@ -55,7 +60,7 @@ setup(
],
install_requires=[
# from requirements.txt
'ccxt>=1.92.9',
'ccxt>=2.6.26',
'SQLAlchemy',
'python-telegram-bot>=13.4',
'arrow>=0.17.0',
@ -78,8 +83,6 @@ setup(
'prompt-toolkit',
'numpy',
'pandas',
'tables',
'blosc',
'joblib>=1.2.0',
'pyarrow; platform_machine != "armv7l"',
'fastapi',
@ -97,6 +100,7 @@ setup(
'plot': plot,
'jupyter': jupyter,
'hyperopt': hyperopt,
'hdf5': hdf5,
'freqai': freqai,
'freqai_rl': freqai_rl,
'all': all_extra,

14
tests/commands/test_commands.py
View File

@ -746,9 +746,7 @@ def test_download_data_no_exchange(mocker, caplog):
start_download_data(pargs)
def test_download_data_no_pairs(mocker, caplog):
mocker.patch.object(Path, "exists", MagicMock(return_value=False))
def test_download_data_no_pairs(mocker):
mocker.patch('freqtrade.commands.data_commands.refresh_backtest_ohlcv_data',
MagicMock(return_value=["ETH/BTC", "XRP/BTC"]))
@ -770,8 +768,6 @@ def test_download_data_no_pairs(mocker, caplog):
def test_download_data_all_pairs(mocker, markets):
mocker.patch.object(Path, "exists", MagicMock(return_value=False))
dl_mock = mocker.patch('freqtrade.commands.data_commands.refresh_backtest_ohlcv_data',
MagicMock(return_value=["ETH/BTC", "XRP/BTC"]))
patch_exchange(mocker)
@ -1454,10 +1450,10 @@ def test_start_list_data(testdatadir, capsys):
start_list_data(pargs)
captured = capsys.readouterr()
assert "Found 5 pair / timeframe combinations." in captured.out
assert "\n| Pair | Timeframe | Type |\n" in captured.out
assert "\n| XRP/USDT | 1h | futures |\n" in captured.out
assert "\n| XRP/USDT | 1h, 8h | mark |\n" in captured.out
assert "Found 6 pair / timeframe combinations." in captured.out
assert "\n| Pair | Timeframe | Type |\n" in captured.out
assert "\n| XRP/USDT:USDT | 5m, 1h | futures |\n" in captured.out
assert "\n| XRP/USDT:USDT | 1h, 8h | mark |\n" in captured.out
args = [
"list-data",

25
tests/conftest.py
View File

@ -241,7 +241,6 @@ def get_patched_freqtradebot(mocker, config) -> FreqtradeBot:
:return: FreqtradeBot
"""
patch_freqtradebot(mocker, config)
config['datadir'] = Path(config['datadir'])
return FreqtradeBot(config)
@ -510,7 +509,7 @@ def get_default_conf(testdatadir):
"chat_id": "0",
"notification_settings": {},
},
"datadir": str(testdatadir),
"datadir": Path(testdatadir),
"initial_state": "running",
"db_url": "sqlite://",
"user_data_dir": Path("user_data"),
@ -2606,6 +2605,8 @@ def open_trade():
ft_order_side='buy',
ft_pair=trade.pair,
ft_is_open=False,
ft_amount=trade.amount,
ft_price=trade.open_rate,
order_id='123456789',
status="closed",
symbol=trade.pair,
@ -2642,6 +2643,8 @@ def open_trade_usdt():
ft_order_side='buy',
ft_pair=trade.pair,
ft_is_open=False,
ft_amount=trade.amount,
ft_price=trade.open_rate,
order_id='123456789',
status="closed",
symbol=trade.pair,
@ -2659,6 +2662,8 @@ def open_trade_usdt():
ft_order_side='exit',
ft_pair=trade.pair,
ft_is_open=True,
ft_amount=trade.amount,
ft_price=trade.open_rate,
order_id='123456789_exit',
status="open",
symbol=trade.pair,
@ -3103,7 +3108,7 @@ def funding_rate_history_octohourly():
@pytest.fixture(scope='function')
def leverage_tiers():
return {
"1000SHIB/USDT": [
"1000SHIB/USDT:USDT": [
{
'minNotional': 0,
'maxNotional': 50000,
@ -3154,7 +3159,7 @@ def leverage_tiers():
'maintAmt': 654500.0
},
],
"1INCH/USDT": [
"1INCH/USDT:USDT": [
{
'minNotional': 0,
'maxNotional': 5000,
@ -3198,7 +3203,7 @@ def leverage_tiers():
'maintAmt': 386940.0
},
],
"AAVE/USDT": [
"AAVE/USDT:USDT": [
{
'minNotional': 0,
'maxNotional': 5000,
@ -3242,7 +3247,7 @@ def leverage_tiers():
'maintAmt': 386950.0
},
],
"ADA/BUSD": [
"ADA/BUSD:BUSD": [
{
"minNotional": 0,
"maxNotional": 100000,
@ -3286,7 +3291,7 @@ def leverage_tiers():
"maintAmt": 1527500.0
},
],
'BNB/BUSD': [
'BNB/BUSD:BUSD': [
{
"minNotional": 0, # stake(before leverage) = 0
"maxNotional": 100000, # max stake(before leverage) = 5000
@ -3330,7 +3335,7 @@ def leverage_tiers():
"maintAmt": 1527500.0
}
],
'BNB/USDT': [
'BNB/USDT:USDT': [
{
"minNotional": 0, # stake = 0.0
"maxNotional": 10000, # max_stake = 133.33333333333334
@ -3395,7 +3400,7 @@ def leverage_tiers():
"maintAmt": 6233035.0
},
],
'BTC/USDT': [
'BTC/USDT:USDT': [
{
"minNotional": 0, # stake = 0.0
"maxNotional": 50000, # max_stake = 400.0
@ -3467,7 +3472,7 @@ def leverage_tiers():
"maintAmt": 1.997038E8
},
],
"ZEC/USDT": [
"ZEC/USDT:USDT": [
{
'minNotional': 0,
'maxNotional': 50000,

71
tests/data/test_btanalysis.py
View File

@ -12,9 +12,11 @@ from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelis
get_latest_hyperopt_file, load_backtest_data,
load_backtest_metadata, load_trades, load_trades_from_db)
from freqtrade.data.history import load_data, load_pair_history
from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
calculate_max_drawdown, calculate_underwater,
combine_dataframes_with_mean, create_cum_profit)
from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
calculate_expectancy, calculate_market_change,
calculate_max_drawdown, calculate_sharpe, calculate_sortino,
calculate_underwater, combine_dataframes_with_mean,
create_cum_profit)
from freqtrade.exceptions import OperationalException
from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades
from tests.conftest_trades import MOCK_TRADE_COUNT
@ -336,6 +338,69 @@ def test_calculate_csum(testdatadir):
csum_min, csum_max = calculate_csum(DataFrame())
def test_calculate_expectancy(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
expectancy = calculate_expectancy(DataFrame())
assert expectancy == 0.0
expectancy = calculate_expectancy(bt_data)
assert isinstance(expectancy, float)
assert pytest.approx(expectancy) == 0.07151374226574791
def test_calculate_sortino(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
sortino = calculate_sortino(DataFrame(), None, None, 0)
assert sortino == 0.0
sortino = calculate_sortino(
bt_data,
bt_data['open_date'].min(),
bt_data['close_date'].max(),
0.01,
)
assert isinstance(sortino, float)
assert pytest.approx(sortino) == 35.17722
def test_calculate_sharpe(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
sharpe = calculate_sharpe(DataFrame(), None, None, 0)
assert sharpe == 0.0
sharpe = calculate_sharpe(
bt_data,
bt_data['open_date'].min(),
bt_data['close_date'].max(),
0.01,
)
assert isinstance(sharpe, float)
assert pytest.approx(sharpe) == 44.5078669
def test_calculate_calmar(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
calmar = calculate_calmar(DataFrame(), None, None, 0)
assert calmar == 0.0
calmar = calculate_calmar(
bt_data,
bt_data['open_date'].min(),
bt_data['close_date'].max(),
0.01,
)
assert isinstance(calmar, float)
assert pytest.approx(calmar) == 559.040508
@pytest.mark.parametrize('start,end,days, expected', [
(64900, 176000, 3 * 365, 0.3945),
(64900, 176000, 365, 1.7119),

9
tests/data/test_converter.py
View File

@ -294,8 +294,8 @@ def test_convert_trades_format(default_conf, testdatadir, tmpdir):
@pytest.mark.parametrize('file_base,candletype', [
(['XRP_ETH-5m', 'XRP_ETH-1m'], CandleType.SPOT),
(['UNITTEST_USDT-1h-mark', 'XRP_USDT-1h-mark'], CandleType.MARK),
(['XRP_USDT-1h-futures'], CandleType.FUTURES),
(['UNITTEST_USDT_USDT-1h-mark', 'XRP_USDT_USDT-1h-mark'], CandleType.MARK),
(['XRP_USDT_USDT-1h-futures'], CandleType.FUTURES),
])
def test_convert_ohlcv_format(default_conf, testdatadir, tmpdir, file_base, candletype):
tmpdir1 = Path(tmpdir)
@ -315,7 +315,10 @@ def test_convert_ohlcv_format(default_conf, testdatadir, tmpdir, file_base, cand
files_new.append(file_new)
default_conf['datadir'] = tmpdir1
default_conf['pairs'] = ['XRP_ETH', 'XRP_USDT', 'UNITTEST_USDT']
if candletype == CandleType.SPOT:
default_conf['pairs'] = ['XRP/ETH', 'XRP/USDT', 'UNITTEST/USDT']
else:
default_conf['pairs'] = ['XRP/ETH:ETH', 'XRP/USDT:USDT', 'UNITTEST/USDT:USDT']
default_conf['timeframes'] = ['1m', '5m', '1h']
assert not file_new.exists()

19
tests/data/test_datahandler.py
View File

@ -33,10 +33,10 @@ def test_datahandler_ohlcv_get_pairs(testdatadir):
assert set(pairs) == {'UNITTEST/BTC'}
pairs = JsonDataHandler.ohlcv_get_pairs(testdatadir, '1h', candle_type=CandleType.MARK)
assert set(pairs) == {'UNITTEST/USDT', 'XRP/USDT'}
assert set(pairs) == {'UNITTEST/USDT:USDT', 'XRP/USDT:USDT'}
pairs = JsonGzDataHandler.ohlcv_get_pairs(testdatadir, '1h', candle_type=CandleType.FUTURES)
assert set(pairs) == {'XRP/USDT'}
assert set(pairs) == {'XRP/USDT:USDT'}
pairs = HDF5DataHandler.ohlcv_get_pairs(testdatadir, '1h', candle_type=CandleType.MARK)
assert set(pairs) == {'UNITTEST/USDT:USDT'}
@ -104,11 +104,12 @@ def test_datahandler_ohlcv_get_available_data(testdatadir):
paircombs = JsonDataHandler.ohlcv_get_available_data(testdatadir, TradingMode.FUTURES)
# Convert to set to avoid failures due to sorting
assert set(paircombs) == {
('UNITTEST/USDT', '1h', 'mark'),
('XRP/USDT', '1h', 'futures'),
('XRP/USDT', '1h', 'mark'),
('XRP/USDT', '8h', 'mark'),
('XRP/USDT', '8h', 'funding_rate'),
('UNITTEST/USDT:USDT', '1h', 'mark'),
('XRP/USDT:USDT', '5m', 'futures'),
('XRP/USDT:USDT', '1h', 'futures'),
('XRP/USDT:USDT', '1h', 'mark'),
('XRP/USDT:USDT', '8h', 'mark'),
('XRP/USDT:USDT', '8h', 'funding_rate'),
}
paircombs = JsonGzDataHandler.ohlcv_get_available_data(testdatadir, TradingMode.SPOT)
@ -142,7 +143,7 @@ def test_jsondatahandler_ohlcv_load(testdatadir, caplog):
df = dh.ohlcv_load('XRP/ETH', '5m', 'spot')
assert len(df) == 712
df_mark = dh.ohlcv_load('UNITTEST/USDT', '1h', candle_type="mark")
df_mark = dh.ohlcv_load('UNITTEST/USDT:USDT', '1h', candle_type="mark")
assert len(df_mark) == 100
df_no_mark = dh.ohlcv_load('UNITTEST/USDT', '1h', 'spot')
@ -424,7 +425,7 @@ def test_hdf5datahandler_ohlcv_load_and_resave(
# Data goes from 2018-01-10 - 2018-01-30
('UNITTEST/BTC', '5m', 'spot', '', '2018-01-15', '2018-01-19'),
# Mark data goes from to 2021-11-15 2021-11-19
('UNITTEST/USDT', '1h', 'mark', '-mark', '2021-11-16', '2021-11-18'),
('UNITTEST/USDT:USDT', '1h', 'mark', '-mark', '2021-11-16', '2021-11-18'),
])
@pytest.mark.parametrize('datahandler', ['hdf5', 'feather', 'parquet'])
def test_generic_datahandler_ohlcv_load_and_resave(

9
tests/data/test_entryexitanalysis.py
View File

@ -190,6 +190,15 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, tmp
assert '1' in captured.out
assert '2.5' in captured.out
# test group 5
args = get_args(base_args + ['--analysis-groups', "5"])
start_analysis_entries_exits(args)
captured = capsys.readouterr()
assert 'exit_signal' in captured.out
assert 'roi' in captured.out
assert 'stop_loss' in captured.out
assert 'trailing_stop_loss' in captured.out
# test date filtering
args = get_args(base_args + ['--timerange', "20180129-20180130"])
start_analysis_entries_exits(args)

4
tests/data/test_history.py
View File

@ -78,11 +78,11 @@ def test_load_data_1min_timeframe(ohlcv_history, mocker, caplog, testdatadir) ->
def test_load_data_mark(ohlcv_history, mocker, caplog, testdatadir) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_historic_ohlcv', return_value=ohlcv_history)
file = testdatadir / 'futures/UNITTEST_USDT-1h-mark.json'
file = testdatadir / 'futures/UNITTEST_USDT_USDT-1h-mark.json'
load_data(datadir=testdatadir, timeframe='1h', pairs=['UNITTEST/BTC'], candle_type='mark')
assert file.is_file()
assert not log_has(
'Download history data for pair: "UNITTEST/USDT", interval: 1m '
'Download history data for pair: "UNITTEST/USDT:USDT", interval: 1m '
'and store in None.', caplog
)

28
tests/exchange/test_binance.py
View File

@ -557,7 +557,7 @@ async def test__async_get_historic_ohlcv_binance(default_conf, mocker, caplog, c
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
pair = 'ETH/BTC'
respair, restf, restype, res = await exchange._async_get_historic_ohlcv(
respair, restf, restype, res, _ = await exchange._async_get_historic_ohlcv(
pair, "5m", 1500000000000, is_new_pair=False, candle_type=candle_type)
assert respair == pair
assert restf == '5m'
@ -566,7 +566,7 @@ async def test__async_get_historic_ohlcv_binance(default_conf, mocker, caplog, c
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(
_, _, _, res, _ = await exchange._async_get_historic_ohlcv(
pair, "5m", 1500000000000, is_new_pair=True, candle_type=candle_type)
# Called twice - one "init" call - and one to get the actual data.
@ -575,25 +575,13 @@ async def test__async_get_historic_ohlcv_binance(default_conf, mocker, caplog, c
assert log_has_re(r"Candle-data for ETH/BTC available starting with .*", caplog)
@pytest.mark.parametrize("trading_mode,margin_mode,config", [
("spot", "", {}),
("margin", "cross", {"options": {"defaultType": "margin"}}),
("futures", "isolated", {"options": {"defaultType": "future"}}),
])
def test__ccxt_config(default_conf, mocker, trading_mode, margin_mode, config):
default_conf['trading_mode'] = trading_mode
default_conf['margin_mode'] = margin_mode
exchange = get_patched_exchange(mocker, default_conf, id="binance")
assert exchange._ccxt_config == config
@pytest.mark.parametrize('pair,nominal_value,mm_ratio,amt', [
("BNB/BUSD", 0.0, 0.025, 0),
("BNB/USDT", 100.0, 0.0065, 0),
("BTC/USDT", 170.30, 0.004, 0),
("BNB/BUSD", 999999.9, 0.1, 27500.0),
("BNB/USDT", 5000000.0, 0.15, 233035.0),
("BTC/USDT", 600000000, 0.5, 1.997038E8),
("BNB/BUSD:BUSD", 0.0, 0.025, 0),
("BNB/USDT:USDT", 100.0, 0.0065, 0),
("BTC/USDT:USDT", 170.30, 0.004, 0),
("BNB/BUSD:BUSD", 999999.9, 0.1, 27500.0),
("BNB/USDT:USDT", 5000000.0, 0.15, 233035.0),
("BTC/USDT:USDT", 600000000, 0.5, 1.997038E8),
])
def test_get_maintenance_ratio_and_amt_binance(
default_conf,

267
tests/exchange/test_ccxt_compat.py
View File

@ -8,16 +8,20 @@ suitable to run with freqtrade.
from copy import deepcopy
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Tuple
import pytest
from freqtrade.constants import Config
from freqtrade.enums import CandleType
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_prev_date
from freqtrade.exchange.exchange import timeframe_to_msecs
from freqtrade.exchange.exchange import Exchange, timeframe_to_msecs
from freqtrade.resolvers.exchange_resolver import ExchangeResolver
from tests.conftest import get_default_conf_usdt
EXCHANGE_FIXTURE_TYPE = Tuple[Exchange, str]
# Exchanges that should be tested
EXCHANGES = {
'bittrex': {
@ -28,15 +32,61 @@ EXCHANGES = {
'leverage_tiers_public': False,
'leverage_in_spot_market': False,
},
# 'binance': {
# 'pair': 'BTC/USDT',
# 'stake_currency': 'USDT',
# 'hasQuoteVolume': True,
# 'timeframe': '5m',
# 'futures': True,
# 'leverage_tiers_public': False,
# 'leverage_in_spot_market': False,
# },
'binance': {
'pair': 'BTC/USDT',
'stake_currency': 'USDT',
'use_ci_proxy': True,
'hasQuoteVolume': True,
'timeframe': '5m',
'futures': True,
'futures_pair': 'BTC/USDT:USDT',
'hasQuoteVolumeFutures': True,
'leverage_tiers_public': False,
'leverage_in_spot_market': False,
'sample_order': [{
"symbol": "SOLUSDT",
"orderId": 3551312894,
"orderListId": -1,
"clientOrderId": "x-R4DD3S8297c73a11ccb9dc8f2811ba",
"transactTime": 1674493798550,
"price": "15.00000000",
"origQty": "1.00000000",
"executedQty": "0.00000000",
"cummulativeQuoteQty": "0.00000000",
"status": "NEW",
"timeInForce": "GTC",
"type": "LIMIT",
"side": "BUY",
"workingTime": 1674493798550,
"fills": [],
"selfTradePreventionMode": "NONE",
}]
},
'binanceus': {
'pair': 'BTC/USDT',
'stake_currency': 'USDT',
'hasQuoteVolume': True,
'timeframe': '5m',
'futures': False,
'sample_order': [{
"symbol": "SOLUSDT",
"orderId": 3551312894,
"orderListId": -1,
"clientOrderId": "x-R4DD3S8297c73a11ccb9dc8f2811ba",
"transactTime": 1674493798550,
"price": "15.00000000",
"origQty": "1.00000000",
"executedQty": "0.00000000",
"cummulativeQuoteQty": "0.00000000",
"status": "NEW",
"timeInForce": "GTC",
"type": "LIMIT",
"side": "BUY",
"workingTime": 1674493798550,
"fills": [],
"selfTradePreventionMode": "NONE",
}]
},
'kraken': {
'pair': 'BTC/USDT',
'stake_currency': 'USDT',
@ -52,6 +102,40 @@ EXCHANGES = {
'timeframe': '5m',
'leverage_tiers_public': False,
'leverage_in_spot_market': True,
'sample_order': [
{'id': '63d6742d0adc5570001d2bbf7'}, # create order
{
'id': '63d6742d0adc5570001d2bbf7',
'symbol': 'NAKA-USDT',
'opType': 'DEAL',
'type': 'limit',
'side': 'buy',
'price': '30',
'size': '0.1',
'funds': '0',
'dealFunds': '0.032626',
'dealSize': '0.1',
'fee': '0.000065252',
'feeCurrency': 'USDT',
'stp': '',
'stop': '',
'stopTriggered': False,
'stopPrice': '0',
'timeInForce': 'GTC',
'postOnly': False,
'hidden': False,
'iceberg': False,
'visibleSize': '0',
'cancelAfter': 0,
'channel': 'API',
'clientOid': '0a053870-11bf-41e5-be61-b272a4cb62e1',
'remark': None,
'tags': 'partner:ccxt',
'isActive': False,
'cancelExist': False,
'createdAt': 1674493798550,
'tradeType': 'TRADE'
}],
},
'gateio': {
'pair': 'BTC/USDT',
@ -60,6 +144,7 @@ EXCHANGES = {
'timeframe': '5m',
'futures': True,
'futures_pair': 'BTC/USDT:USDT',
'hasQuoteVolumeFutures': True,
'leverage_tiers_public': True,
'leverage_in_spot_market': True,
},
@ -68,14 +153,15 @@ EXCHANGES = {
'stake_currency': 'USDT',
'hasQuoteVolume': True,
'timeframe': '5m',
'futures_pair': 'BTC/USDT:USDT',
'futures': True,
'futures_pair': 'BTC/USDT:USDT',
'hasQuoteVolumeFutures': False,
'leverage_tiers_public': True,
'leverage_in_spot_market': True,
},
'huobi': {
'pair': 'BTC/USDT',
'stake_currency': 'USDT',
'pair': 'ETH/BTC',
'stake_currency': 'BTC',
'hasQuoteVolume': True,
'timeframe': '5m',
'futures': False,
@ -103,8 +189,27 @@ def exchange_conf():
return config
def set_test_proxy(config: Config, use_proxy: bool) -> Config:
# Set proxy to test in CI.
import os
if use_proxy and (proxy := os.environ.get('CI_WEB_PROXY')):
config1 = deepcopy(config)
config1['exchange']['ccxt_config'] = {
"aiohttp_proxy": proxy,
'proxies': {
'https': proxy,
'http': proxy,
}
}
return config1
return config
@pytest.fixture(params=EXCHANGES, scope="class")
def exchange(request, exchange_conf):
exchange_conf = set_test_proxy(
exchange_conf, EXCHANGES[request.param].get('use_ci_proxy', False))
exchange_conf['exchange']['name'] = request.param
exchange_conf['stake_currency'] = EXCHANGES[request.param]['stake_currency']
exchange = ExchangeResolver.load_exchange(request.param, exchange_conf, validate=True)
@ -117,6 +222,8 @@ def exchange_futures(request, exchange_conf, class_mocker):
if not EXCHANGES[request.param].get('futures') is True:
yield None, request.param
else:
exchange_conf = set_test_proxy(
exchange_conf, EXCHANGES[request.param].get('use_ci_proxy', False))
exchange_conf = deepcopy(exchange_conf)
exchange_conf['exchange']['name'] = request.param
exchange_conf['trading_mode'] = 'futures'
@ -141,19 +248,19 @@ def exchange_futures(request, exchange_conf, class_mocker):
@pytest.mark.longrun
class TestCCXTExchange():
def test_load_markets(self, exchange):
exchange, exchangename = exchange
def test_load_markets(self, exchange: EXCHANGE_FIXTURE_TYPE):
exch, exchangename = exchange
pair = EXCHANGES[exchangename]['pair']
markets = exchange.markets
markets = exch.markets
assert pair in markets
assert isinstance(markets[pair], dict)
assert exchange.market_is_spot(markets[pair])
assert exch.market_is_spot(markets[pair])
def test_has_validations(self, exchange):
def test_has_validations(self, exchange: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange
exch, exchangename = exchange
exchange.validate_ordertypes({
exch.validate_ordertypes({
'entry': 'limit',
'exit': 'limit',
'stoploss': 'limit',
@ -162,13 +269,13 @@ class TestCCXTExchange():
if exchangename == 'gateio':
# gateio doesn't have market orders on spot
return
exchange.validate_ordertypes({
exch.validate_ordertypes({
'entry': 'market',
'exit': 'market',
'stoploss': 'market',
})
def test_load_markets_futures(self, exchange_futures):
def test_load_markets_futures(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange_futures
if not exchange:
# exchange_futures only returns values for supported exchanges
@ -181,11 +288,28 @@ class TestCCXTExchange():
assert exchange.market_is_future(markets[pair])
def test_ccxt_fetch_tickers(self, exchange):
exchange, exchangename = exchange
def test_ccxt_order_parse(self, exchange: EXCHANGE_FIXTURE_TYPE):
exch, exchange_name = exchange
if orders := EXCHANGES[exchange_name].get('sample_order'):
for order in orders:
po = exch._api.parse_order(order)
assert isinstance(po['id'], str)
assert po['id'] is not None
if len(order.keys()) > 1:
assert po['timestamp'] == 1674493798550
assert isinstance(po['datetime'], str)
assert isinstance(po['timestamp'], int)
assert isinstance(po['price'], float)
assert isinstance(po['amount'], float)
assert isinstance(po['status'], str)
else:
pytest.skip(f"No sample order available for exchange {exchange_name}")
def test_ccxt_fetch_tickers(self, exchange: EXCHANGE_FIXTURE_TYPE):
exch, exchangename = exchange
pair = EXCHANGES[exchangename]['pair']
tickers = exchange.get_tickers()
tickers = exch.get_tickers()
assert pair in tickers
assert 'ask' in tickers[pair]
assert tickers[pair]['ask'] is not None
@ -195,11 +319,30 @@ class TestCCXTExchange():
if EXCHANGES[exchangename].get('hasQuoteVolume'):
assert tickers[pair]['quoteVolume'] is not None
def test_ccxt_fetch_ticker(self, exchange):
exchange, exchangename = exchange
def test_ccxt_fetch_tickers_futures(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
exch, exchangename = exchange_futures
if not exch or exchangename in ('gateio'):
# exchange_futures only returns values for supported exchanges
return
pair = EXCHANGES[exchangename]['pair']
pair = EXCHANGES[exchangename].get('futures_pair', pair)
tickers = exch.get_tickers()
assert pair in tickers
assert 'ask' in tickers[pair]
assert tickers[pair]['ask'] is not None
assert 'bid' in tickers[pair]
assert tickers[pair]['bid'] is not None
assert 'quoteVolume' in tickers[pair]
if EXCHANGES[exchangename].get('hasQuoteVolumeFutures'):
assert tickers[pair]['quoteVolume'] is not None
def test_ccxt_fetch_ticker(self, exchange: EXCHANGE_FIXTURE_TYPE):
exch, exchangename = exchange
pair = EXCHANGES[exchangename]['pair']
ticker = exchange.fetch_ticker(pair)
ticker = exch.fetch_ticker(pair)
assert 'ask' in ticker
assert ticker['ask'] is not None
assert 'bid' in ticker
@ -208,21 +351,21 @@ class TestCCXTExchange():
if EXCHANGES[exchangename].get('hasQuoteVolume'):
assert ticker['quoteVolume'] is not None
def test_ccxt_fetch_l2_orderbook(self, exchange):
exchange, exchangename = exchange
def test_ccxt_fetch_l2_orderbook(self, exchange: EXCHANGE_FIXTURE_TYPE):
exch, exchangename = exchange
pair = EXCHANGES[exchangename]['pair']
l2 = exchange.fetch_l2_order_book(pair)
l2 = exch.fetch_l2_order_book(pair)
assert 'asks' in l2
assert 'bids' in l2
assert len(l2['asks']) >= 1
assert len(l2['bids']) >= 1
l2_limit_range = exchange._ft_has['l2_limit_range']
l2_limit_range_required = exchange._ft_has['l2_limit_range_required']
l2_limit_range = exch._ft_has['l2_limit_range']
l2_limit_range_required = exch._ft_has['l2_limit_range_required']
if exchangename == 'gateio':
# TODO: Gateio is unstable here at the moment, ignoring the limit partially.
return
for val in [1, 2, 5, 25, 100]:
l2 = exchange.fetch_l2_order_book(pair, val)
l2 = exch.fetch_l2_order_book(pair, val)
if not l2_limit_range or val in l2_limit_range:
if val > 50:
# Orderbooks are not always this deep.
@ -232,7 +375,7 @@ class TestCCXTExchange():
assert len(l2['asks']) == val
assert len(l2['bids']) == val
else:
next_limit = exchange.get_next_limit_in_list(
next_limit = exch.get_next_limit_in_list(
val, l2_limit_range, l2_limit_range_required)
if next_limit is None:
assert len(l2['asks']) > 100
@ -245,23 +388,23 @@ class TestCCXTExchange():
assert len(l2['asks']) == next_limit
assert len(l2['asks']) == next_limit
def test_ccxt_fetch_ohlcv(self, exchange):
exchange, exchangename = exchange
def test_ccxt_fetch_ohlcv(self, exchange: EXCHANGE_FIXTURE_TYPE):
exch, exchangename = exchange
pair = EXCHANGES[exchangename]['pair']
timeframe = EXCHANGES[exchangename]['timeframe']
pair_tf = (pair, timeframe, CandleType.SPOT)
ohlcv = exchange.refresh_latest_ohlcv([pair_tf])
ohlcv = exch.refresh_latest_ohlcv([pair_tf])
assert isinstance(ohlcv, dict)
assert len(ohlcv[pair_tf]) == len(exchange.klines(pair_tf))
# assert len(exchange.klines(pair_tf)) > 200
assert len(ohlcv[pair_tf]) == len(exch.klines(pair_tf))
# assert len(exch.klines(pair_tf)) > 200
# Assume 90% uptime ...
assert len(exchange.klines(pair_tf)) > exchange.ohlcv_candle_limit(
assert len(exch.klines(pair_tf)) > exch.ohlcv_candle_limit(
timeframe, CandleType.SPOT) * 0.90
# Check if last-timeframe is within the last 2 intervals
now = datetime.now(timezone.utc) - timedelta(minutes=(timeframe_to_minutes(timeframe) * 2))
assert exchange.klines(pair_tf).iloc[-1]['date'] >= timeframe_to_prev_date(timeframe, now)
assert exch.klines(pair_tf).iloc[-1]['date'] >= timeframe_to_prev_date(timeframe, now)
def ccxt__async_get_candle_history(self, exchange, exchangename, pair, timeframe, candle_type):
@ -289,17 +432,17 @@ class TestCCXTExchange():
assert len(candles) >= min(candle_count, candle_count1)
assert candles[0][0] == since_ms or (since_ms + timeframe_ms)
def test_ccxt__async_get_candle_history(self, exchange):
exchange, exchangename = exchange
def test_ccxt__async_get_candle_history(self, exchange: EXCHANGE_FIXTURE_TYPE):
exc, exchangename = exchange
# For some weired reason, this test returns random lengths for bittrex.
if not exchange._ft_has['ohlcv_has_history'] or exchangename in ('bittrex'):
if not exc._ft_has['ohlcv_has_history'] or exchangename in ('bittrex'):
return
pair = EXCHANGES[exchangename]['pair']
timeframe = EXCHANGES[exchangename]['timeframe']
self.ccxt__async_get_candle_history(
exchange, exchangename, pair, timeframe, CandleType.SPOT)
exc, exchangename, pair, timeframe, CandleType.SPOT)
def test_ccxt__async_get_candle_history_futures(self, exchange_futures):
def test_ccxt__async_get_candle_history_futures(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange_futures
if not exchange:
# exchange_futures only returns values for supported exchanges
@ -309,7 +452,7 @@ class TestCCXTExchange():
self.ccxt__async_get_candle_history(
exchange, exchangename, pair, timeframe, CandleType.FUTURES)
def test_ccxt_fetch_funding_rate_history(self, exchange_futures):
def test_ccxt_fetch_funding_rate_history(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange_futures
if not exchange:
# exchange_futures only returns values for supported exchanges
@ -347,7 +490,7 @@ class TestCCXTExchange():
(rate['open'].min() != rate['open'].max())
)
def test_ccxt_fetch_mark_price_history(self, exchange_futures):
def test_ccxt_fetch_mark_price_history(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange_futures
if not exchange:
# exchange_futures only returns values for supported exchanges
@ -371,7 +514,7 @@ class TestCCXTExchange():
assert mark_candles[mark_candles['date'] == prev_hour].iloc[0]['open'] != 0.0
assert mark_candles[mark_candles['date'] == this_hour].iloc[0]['open'] != 0.0
def test_ccxt__calculate_funding_fees(self, exchange_futures):
def test_ccxt__calculate_funding_fees(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
exchange, exchangename = exchange_futures
if not exchange:
# exchange_futures only returns values for supported exchanges
@ -387,16 +530,16 @@ class TestCCXTExchange():
# TODO: tests fetch_trades (?)
def test_ccxt_get_fee(self, exchange):
exchange, exchangename = exchange
def test_ccxt_get_fee(self, exchange: EXCHANGE_FIXTURE_TYPE):
exch, exchangename = exchange
pair = EXCHANGES[exchangename]['pair']
threshold = 0.01
assert 0 < exchange.get_fee(pair, 'limit', 'buy') < threshold
assert 0 < exchange.get_fee(pair, 'limit', 'sell') < threshold
assert 0 < exchange.get_fee(pair, 'market', 'buy') < threshold
assert 0 < exchange.get_fee(pair, 'market', 'sell') < threshold
assert 0 < exch.get_fee(pair, 'limit', 'buy') < threshold
assert 0 < exch.get_fee(pair, 'limit', 'sell') < threshold
assert 0 < exch.get_fee(pair, 'market', 'buy') < threshold
assert 0 < exch.get_fee(pair, 'market', 'sell') < threshold
def test_ccxt_get_max_leverage_spot(self, exchange):
def test_ccxt_get_max_leverage_spot(self, exchange: EXCHANGE_FIXTURE_TYPE):
spot, spot_name = exchange
if spot:
leverage_in_market_spot = EXCHANGES[spot_name].get('leverage_in_spot_market')
@ -406,7 +549,7 @@ class TestCCXTExchange():
assert (isinstance(spot_leverage, float) or isinstance(spot_leverage, int))
assert spot_leverage >= 1.0
def test_ccxt_get_max_leverage_futures(self, exchange_futures):
def test_ccxt_get_max_leverage_futures(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
futures, futures_name = exchange_futures
if futures:
leverage_tiers_public = EXCHANGES[futures_name].get('leverage_tiers_public')
@ -419,7 +562,7 @@ class TestCCXTExchange():
assert (isinstance(futures_leverage, float) or isinstance(futures_leverage, int))
assert futures_leverage >= 1.0
def test_ccxt_get_contract_size(self, exchange_futures):
def test_ccxt_get_contract_size(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
futures, futures_name = exchange_futures
if futures:
futures_pair = EXCHANGES[futures_name].get(
@ -430,7 +573,7 @@ class TestCCXTExchange():
assert (isinstance(contract_size, float) or isinstance(contract_size, int))
assert contract_size >= 0.0
def test_ccxt_load_leverage_tiers(self, exchange_futures):
def test_ccxt_load_leverage_tiers(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
futures, futures_name = exchange_futures
if futures and EXCHANGES[futures_name].get('leverage_tiers_public'):
leverage_tiers = futures.load_leverage_tiers()
@ -463,7 +606,7 @@ class TestCCXTExchange():
oldminNotional = tier['minNotional']
oldmaxNotional = tier['maxNotional']
def test_ccxt_dry_run_liquidation_price(self, exchange_futures):
def test_ccxt_dry_run_liquidation_price(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
futures, futures_name = exchange_futures
if futures and EXCHANGES[futures_name].get('leverage_tiers_public'):
@ -494,7 +637,7 @@ class TestCCXTExchange():
assert (isinstance(liquidation_price, float))
assert liquidation_price >= 0.0
def test_ccxt_get_max_pair_stake_amount(self, exchange_futures):
def test_ccxt_get_max_pair_stake_amount(self, exchange_futures: EXCHANGE_FIXTURE_TYPE):
futures, futures_name = exchange_futures
if futures:
futures_pair = EXCHANGES[futures_name].get(

104
tests/exchange/test_exchange.py
View File

@ -1955,7 +1955,7 @@ def test_get_historic_ohlcv(default_conf, mocker, caplog, exchange_name, candle_
pair = 'ETH/BTC'
async def mock_candle_hist(pair, timeframe, candle_type, since_ms):
return pair, timeframe, candle_type, ohlcv
return pair, timeframe, candle_type, ohlcv, True
exchange._async_get_candle_history = Mock(wraps=mock_candle_hist)
# one_call calculation * 1.8 should do 2 calls
@ -1988,62 +1988,6 @@ def test_get_historic_ohlcv(default_conf, mocker, caplog, exchange_name, candle_
assert log_has_re(r"Async code raised an exception: .*", caplog)
@pytest.mark.parametrize("exchange_name", EXCHANGES)
@pytest.mark.parametrize('candle_type', ['mark', ''])
def test_get_historic_ohlcv_as_df(default_conf, mocker, exchange_name, candle_type):
exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
ohlcv = [
[
arrow.utcnow().int_timestamp * 1000, # unix timestamp ms
1, # open
2, # high
3, # low
4, # close
5, # volume (in quote currency)
],
[
arrow.utcnow().shift(minutes=5).int_timestamp * 1000, # unix timestamp ms
1, # open
2, # high
3, # low
4, # close
5, # volume (in quote currency)
],
[
arrow.utcnow().shift(minutes=10).int_timestamp * 1000, # unix timestamp ms
1, # open
2, # high
3, # low
4, # close
5, # volume (in quote currency)
]
]
pair = 'ETH/BTC'
async def mock_candle_hist(pair, timeframe, candle_type, since_ms):
return pair, timeframe, candle_type, ohlcv
exchange._async_get_candle_history = Mock(wraps=mock_candle_hist)
# one_call calculation * 1.8 should do 2 calls
since = 5 * 60 * exchange.ohlcv_candle_limit('5m', CandleType.SPOT) * 1.8
ret = exchange.get_historic_ohlcv_as_df(
pair,
"5m",
int((arrow.utcnow().int_timestamp - since) * 1000),
candle_type=candle_type
)
assert exchange._async_get_candle_history.call_count == 2
# Returns twice the above OHLCV data
assert len(ret) == 2
assert isinstance(ret, DataFrame)
assert 'date' in ret.columns
assert 'open' in ret.columns
assert 'close' in ret.columns
assert 'high' in ret.columns
@pytest.mark.asyncio
@pytest.mark.parametrize("exchange_name", EXCHANGES)
@pytest.mark.parametrize('candle_type', [CandleType.MARK, CandleType.SPOT])
@ -2063,7 +2007,7 @@ async def test__async_get_historic_ohlcv(default_conf, mocker, caplog, exchange_
exchange._api_async.fetch_ohlcv = get_mock_coro(ohlcv)
pair = 'ETH/USDT'
respair, restf, _, res = await exchange._async_get_historic_ohlcv(
respair, restf, _, res, _ = await exchange._async_get_historic_ohlcv(
pair, "5m", 1500000000000, candle_type=candle_type, is_new_pair=False)
assert respair == pair
assert restf == '5m'
@ -2074,7 +2018,7 @@ async def test__async_get_historic_ohlcv(default_conf, mocker, caplog, exchange_
exchange._api_async.fetch_ohlcv.reset_mock()
end_ts = 1_500_500_000_000
start_ts = 1_500_000_000_000
respair, restf, _, res = await exchange._async_get_historic_ohlcv(
respair, restf, _, res, _ = await exchange._async_get_historic_ohlcv(
pair, "5m", since_ms=start_ts, candle_type=candle_type, is_new_pair=False,
until_ms=end_ts
)
@ -2306,7 +2250,7 @@ async def test__async_get_candle_history(default_conf, mocker, caplog, exchange_
pair = 'ETH/BTC'
res = await exchange._async_get_candle_history(pair, "5m", CandleType.SPOT)
assert type(res) is tuple
assert len(res) == 4
assert len(res) == 5
assert res[0] == pair
assert res[1] == "5m"
assert res[2] == CandleType.SPOT
@ -2393,7 +2337,7 @@ async def test__async_get_candle_history_empty(default_conf, mocker, caplog):
pair = 'ETH/BTC'
res = await exchange._async_get_candle_history(pair, "5m", CandleType.SPOT)
assert type(res) is tuple
assert len(res) == 4
assert len(res) == 5
assert res[0] == pair
assert res[1] == "5m"
assert res[2] == CandleType.SPOT
@ -4013,7 +3957,7 @@ def test_validate_trading_mode_and_margin_mode(
@pytest.mark.parametrize("exchange_name,trading_mode,ccxt_config", [
("binance", "spot", {}),
("binance", "margin", {"options": {"defaultType": "margin"}}),
("binance", "futures", {"options": {"defaultType": "future"}}),
("binance", "futures", {"options": {"defaultType": "swap"}}),
("bybit", "spot", {"options": {"defaultType": "spot"}}),
("bybit", "futures", {"options": {"defaultType": "linear"}}),
("gateio", "futures", {"options": {"defaultType": "swap"}}),
@ -4954,22 +4898,22 @@ def test_get_maintenance_ratio_and_amt_exceptions(mocker, default_conf, leverage
OperationalException,
match='nominal value can not be lower than 0',
):
exchange.get_maintenance_ratio_and_amt('1000SHIB/USDT', -1)
exchange.get_maintenance_ratio_and_amt('1000SHIB/USDT:USDT', -1)
exchange._leverage_tiers = {}
with pytest.raises(
InvalidOrderException,
match="Maintenance margin rate for 1000SHIB/USDT is unavailable for",
match="Maintenance margin rate for 1000SHIB/USDT:USDT is unavailable for",
):
exchange.get_maintenance_ratio_and_amt('1000SHIB/USDT', 10000)
exchange.get_maintenance_ratio_and_amt('1000SHIB/USDT:USDT', 10000)
@pytest.mark.parametrize('pair,value,mmr,maintAmt', [
('ADA/BUSD', 500, 0.025, 0.0),
('ADA/BUSD', 20000000, 0.5, 1527500.0),
('ZEC/USDT', 500, 0.01, 0.0),
('ZEC/USDT', 20000000, 0.5, 654500.0),
('ADA/BUSD:BUSD', 500, 0.025, 0.0),
('ADA/BUSD:BUSD', 20000000, 0.5, 1527500.0),
('ZEC/USDT:USDT', 500, 0.01, 0.0),
('ZEC/USDT:USDT', 20000000, 0.5, 654500.0),
])
def test_get_maintenance_ratio_and_amt(
mocker,
@ -5002,21 +4946,21 @@ def test_get_max_leverage_futures(default_conf, mocker, leverage_tiers):
exchange._leverage_tiers = leverage_tiers
assert exchange.get_max_leverage("BNB/BUSD", 1.0) == 20.0
assert exchange.get_max_leverage("BNB/USDT", 100.0) == 75.0
assert exchange.get_max_leverage("BTC/USDT", 170.30) == 125.0
assert pytest.approx(exchange.get_max_leverage("BNB/BUSD", 99999.9)) == 5.000005
assert pytest.approx(exchange.get_max_leverage("BNB/USDT", 1500)) == 33.333333333333333
assert exchange.get_max_leverage("BTC/USDT", 300000000) == 2.0
assert exchange.get_max_leverage("BTC/USDT", 600000000) == 1.0 # Last tier
assert exchange.get_max_leverage("BNB/BUSD:BUSD", 1.0) == 20.0
assert exchange.get_max_leverage("BNB/USDT:USDT", 100.0) == 75.0
assert exchange.get_max_leverage("BTC/USDT:USDT", 170.30) == 125.0
assert pytest.approx(exchange.get_max_leverage("BNB/BUSD:BUSD", 99999.9)) == 5.000005
assert pytest.approx(exchange.get_max_leverage("BNB/USDT:USDT", 1500)) == 33.333333333333333
assert exchange.get_max_leverage("BTC/USDT:USDT", 300000000) == 2.0
assert exchange.get_max_leverage("BTC/USDT:USDT", 600000000) == 1.0 # Last tier
assert exchange.get_max_leverage("SPONGE/USDT", 200) == 1.0 # Pair not in leverage_tiers
assert exchange.get_max_leverage("BTC/USDT", 0.0) == 125.0 # No stake amount
assert exchange.get_max_leverage("SPONGE/USDT:USDT", 200) == 1.0 # Pair not in leverage_tiers
assert exchange.get_max_leverage("BTC/USDT:USDT", 0.0) == 125.0 # No stake amount
with pytest.raises(
InvalidOrderException,
match=r'Amount 1000000000.01 too high for BTC/USDT'
match=r'Amount 1000000000.01 too high for BTC/USDT:USDT'
):
exchange.get_max_leverage("BTC/USDT", 1000000000.01)
exchange.get_max_leverage("BTC/USDT:USDT", 1000000000.01)
@pytest.mark.parametrize("exchange_name", ['bittrex', 'binance', 'kraken', 'gateio', 'okx'])

10
tests/exchange/test_okx.py
View File

@ -195,12 +195,12 @@ def test_get_max_pair_stake_amount_okx(default_conf, mocker, leverage_tiers):
exchange = get_patched_exchange(mocker, default_conf, id="okx")
exchange._leverage_tiers = leverage_tiers
assert exchange.get_max_pair_stake_amount('BNB/BUSD', 1.0) == 30000000
assert exchange.get_max_pair_stake_amount('BNB/USDT', 1.0) == 50000000
assert exchange.get_max_pair_stake_amount('BTC/USDT', 1.0) == 1000000000
assert exchange.get_max_pair_stake_amount('BTC/USDT', 1.0, 10.0) == 100000000
assert exchange.get_max_pair_stake_amount('BNB/BUSD:BUSD', 1.0) == 30000000
assert exchange.get_max_pair_stake_amount('BNB/USDT:USDT', 1.0) == 50000000
assert exchange.get_max_pair_stake_amount('BTC/USDT:USDT', 1.0) == 1000000000
assert exchange.get_max_pair_stake_amount('BTC/USDT:USDT', 1.0, 10.0) == 100000000
assert exchange.get_max_pair_stake_amount('TTT/USDT', 1.0) == float('inf') # Not in tiers
assert exchange.get_max_pair_stake_amount('TTT/USDT:USDT', 1.0) == float('inf') # Not in tiers
@pytest.mark.parametrize('mode,side,reduceonly,result', [

4
tests/freqai/test_freqai_datakitchen.py
View File

@ -82,7 +82,7 @@ def test_compute_distances(mocker, freqai_conf):
freqai = make_data_dictionary(mocker, freqai_conf)
freqai_conf['freqai']['feature_parameters'].update({"DI_threshold": 1})
avg_mean_dist = freqai.dk.compute_distances()
assert round(avg_mean_dist, 2) == 1.99
assert round(avg_mean_dist, 2) == 1.98
def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf, caplog):
@ -90,7 +90,7 @@ def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf,
freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 0.1})
freqai.dk.use_SVM_to_remove_outliers(predict=False)
assert log_has_re(
"SVM detected 7.36%",
"SVM detected 7.83%",
caplog,
)

39
tests/freqai/test_freqai_interface.py
View File

@ -222,6 +222,9 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
if 'test_4ac' in model:
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
{"indicator_periods_candles": [2]})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
@ -232,15 +235,14 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = base_df[freqai_conf["timeframe"]]
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
df = freqai.cache_corr_pairlist_dfs(df, freqai.dk)
for i in range(5):
df[f'%-constant_{i}'] = i
metadata = {"pair": "LTC/BTC"}
freqai.start_backtesting(df, metadata, freqai.dk)
freqai.start_backtesting(df, metadata, freqai.dk, strategy)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
assert len(model_folders) == num_files
@ -261,6 +263,8 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180120-20180124"})
freqai_conf.get("freqai", {}).update({"backtest_period_days": 0.5})
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
{"indicator_periods_candles": [2]})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
@ -271,12 +275,11 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = base_df[freqai_conf["timeframe"]]
metadata = {"pair": "LTC/BTC"}
freqai.start_backtesting(df, metadata, freqai.dk)
freqai.start_backtesting(df, metadata, freqai.dk, strategy)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
assert len(model_folders) == 9
@ -287,6 +290,8 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
freqai_conf.update({"timerange": "20180120-20180130"})
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
{"indicator_periods_candles": [2]})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
@ -296,15 +301,14 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
sub_timerange = TimeRange.parse_timerange("20180101-20180130")
_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = base_df[freqai_conf["timeframe"]]
pair = "ADA/BTC"
metadata = {"pair": pair}
freqai.dk.pair = pair
freqai.start_backtesting(df, metadata, freqai.dk)
freqai.start_backtesting(df, metadata, freqai.dk, strategy)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
assert len(model_folders) == 2
@ -322,14 +326,13 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = base_df[freqai_conf["timeframe"]]
pair = "ADA/BTC"
metadata = {"pair": pair}
freqai.dk.pair = pair
freqai.start_backtesting(df, metadata, freqai.dk)
freqai.start_backtesting(df, metadata, freqai.dk, strategy)
assert log_has_re(
"Found backtesting prediction file ",
@ -339,7 +342,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
pair = "ETH/BTC"
metadata = {"pair": pair}
freqai.dk.pair = pair
freqai.start_backtesting(df, metadata, freqai.dk)
freqai.start_backtesting(df, metadata, freqai.dk, strategy)
path = (freqai.dd.full_path / freqai.dk.backtest_predictions_folder)
prediction_files = [x for x in path.iterdir() if x.is_file()]

11
tests/leverage/test_interest.py
View File

@ -1,5 +1,6 @@
import pytest
from freqtrade.exceptions import OperationalException
from freqtrade.leverage import interest
from freqtrade.util import FtPrecise
@ -29,3 +30,13 @@ def test_interest(exchange, interest_rate, hours, expected):
rate=FtPrecise(interest_rate),
hours=hours
))) == expected
def test_interest_exception():
with pytest.raises(OperationalException, match=r"Leverage not available on .* with freqtrade"):
interest(
exchange_name='bitmex',
borrowed=FtPrecise(60.0),
rate=FtPrecise(0.0005),
hours=ten_mins
)

4
tests/optimize/conftest.py
View File

@ -48,8 +48,8 @@ def hyperopt_results():
return pd.DataFrame(
{
'pair': ['ETH/USDT', 'ETH/USDT', 'ETH/USDT', 'ETH/USDT'],
'profit_ratio': [-0.1, 0.2, -0.1, 0.3],
'profit_abs': [-0.2, 0.4, -0.2, 0.6],
'profit_ratio': [-0.1, 0.2, -0.12, 0.3],
'profit_abs': [-0.2, 0.4, -0.21, 0.6],
'trade_duration': [10, 30, 10, 10],
'amount': [0.1, 0.1, 0.1, 0.1],
'exit_reason': [ExitType.STOP_LOSS, ExitType.ROI, ExitType.STOP_LOSS, ExitType.ROI],

2
tests/optimize/test_backtest_detail.py
View File

@ -919,6 +919,7 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data: BTContainer)
default_conf["trailing_stop_positive"] = data.trailing_stop_positive
default_conf["trailing_stop_positive_offset"] = data.trailing_stop_positive_offset
default_conf["use_exit_signal"] = data.use_exit_signal
default_conf["max_open_trades"] = 10
mocker.patch("freqtrade.exchange.Exchange.get_fee", return_value=0.0)
mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
@ -951,7 +952,6 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data: BTContainer)
processed=data_processed,
start_date=min_date,
end_date=max_date,
max_open_trades=10,
)
results = result['results']

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