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Author SHA1 Message Date
Matthias
ecdb466887
Merge pull request #7560 from smarmau/patch-2
Update freqai-spice-rack.md
2022-10-11 06:26:52 +02:00
smarmau
011759d1b7
Update freqai-spice-rack.md
Instructs newer users to place the code calling spice_rack in populate_indicators
2022-10-10 11:59:43 +11:00
robcaulk
7cdd510cf9 update spice-rack doc 2022-10-09 14:38:56 +02:00
robcaulk
1e5df9611b improve wording, move warning 2022-10-08 13:31:52 +02:00
robcaulk
f3dcbb9736 merge remote in to spice-rack 2022-10-08 12:50:09 +02:00
robcaulk
06f4f2db0a improve performance and documentation of spice-rack. 2022-10-08 12:45:49 +02:00
robcaulk
d362332527 Merge remote-tracking branch 'origin/develop' into spice-rack 2022-10-08 12:25:46 +02:00
Robert Caulk
760f3f157d
Merge branch 'develop' into add-spice-rack 2022-09-25 22:48:05 +02:00
robcaulk
c31f322349 reduce complexity of start_download_data() for flake8 2022-09-25 21:34:58 +02:00
robcaulk
aca03e38f6 Merge branch 'develop' into spice-rack 2022-09-25 11:37:38 +02:00
robcaulk
8b1e5daf22 revert remove_training_from_backtesting()` 2022-09-18 22:12:53 +02:00
robcaulk
7b390b8edb ensure spice_rack is backtestable. Ensure download-data knows about the spice_rack informative pair requirements 2022-09-18 18:40:03 +02:00
robcaulk
91e2a05aff remove test config now that spice_rack adapts to any config 2022-09-18 13:05:13 +02:00
robcaulk
793c54db9d improve spice rack test, remove spice rack test strat 2022-09-18 13:04:04 +02:00
Robert Caulk
b1e92933f4
Merge branch 'develop' into add-spice-rack 2022-09-17 17:56:08 +02:00
robcaulk
12a9fda885 fix spice-rack test 2022-09-17 17:36:48 +02:00
robcaulk
a7312dec03 add automatic change to process_only_new_candles, fix flake8 2022-09-17 16:37:39 +02:00
robcaulk
ff300d5c85 Add function to search exchange for closest matching pairs/tfs 2022-09-17 15:05:50 +02:00
robcaulk
4d93a6b757 add spice_rack strat to rpc test 2022-09-16 01:25:35 +02:00
robcaulk
dac07c5609 ensure pytest passes 2022-09-16 01:15:19 +02:00
robcaulk
fb2d190865 add tests for spice_rack 2022-09-16 00:46:55 +02:00
robcaulk
b209490009 add spice_rack to FreqAI 2022-09-15 23:26:43 +02:00
12 changed files with 387 additions and 40 deletions

71
docs/freqai-spice-rack.md Normal file
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@ -0,0 +1,71 @@
# Using the `spice_rack`
!!! Note:
`spice_rack` indicators should not be used exclusively for entries and exits, the following example is just a demonstration of syntax. `spice_rack` indicators should **always** be used to support existing strategies.
The `spice_rack` is aimed at users who do not wish to deal with setting up `FreqAI` confgs, but instead prefer to interact with `FreqAI` similar to a `talib` indicator. In this case, the user can instead simply add two keys to their config:
```json
"freqai_spice_rack": true,
"freqai_identifier": "spicey-id",
```
Which tells `FreqAI` to set up a pre-set `FreqAI` instance automatically under the hood with preset parameters. Now the user can access a suite of custom `FreqAI` supercharged indicators inside their strategy by placing the following code into `populate_indicators`:
```python
dataframe['dissimilarity_index'] = self.freqai.spice_rack(
'DI_values', dataframe, metadata, self)
dataframe['extrema'] = self.freqai.spice_rack(
'&s-extrema', dataframe, metadata, self)
self.freqai.close_spice_rack() # user must close the spicerack
```
Users can then use these columns in concert with all their own additional indicators added to `populate_indicators` in their entry/exit criteria and strategy callback methods the same way as any typical indicator. For example:
```python
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
df.loc[
(
(df['dissimilarity_index'] < 1) &
(df['extrema'] < -0.1)
),
'enter_long'] = 1
df.loc[
(
(df['dissimilarity_index'] < 1) &
(df['extrema'] > 0.1)
),
'enter_short'] = 1
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
df.loc[
(
(df['dissimilarity_index'] < 1) &
(df['extrema'] > 0.1)
),
'exit_long'] = 1
df.loc[
(
(df['dissimilarity_index'] < 1) &
(df['extrema'] < -0.1)
),
'exit_short'] = 1
return df
```
## Available indicators
| Parameter | Description |
|------------|-------------|
| `DI_values` | **Required.** <br> The dissimilarity index of the current candle to the recent candles. More information available [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di) <br> **Datatype:** Floats.
| `extrema` | **Required.** <br> A continuous prediction from FreqAI which aims to help predict if the current candle is a maxima or a minma. FreqAI aims for 1 to be a maxima and -1 to be a minima - but the values should typically hover between -0.2 and 0.2. <br> **Datatype:** Floats.

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@ -11,7 +11,8 @@ from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_oh
refresh_backtest_trades_data)
from freqtrade.enums import CandleType, RunMode, TradingMode
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import market_is_active, timeframe_to_minutes
from freqtrade.exchange import Exchange, market_is_active, timeframe_to_minutes
from freqtrade.freqai.utils import setup_freqai_spice_rack
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist, expand_pairlist
from freqtrade.resolvers import ExchangeResolver
@ -48,6 +49,10 @@ def start_download_data(args: Dict[str, Any]) -> None:
# Init exchange
exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config, validate=False)
if config.get('freqai_spice_rack', False):
config = setup_freqai_spice_rack(config, exchange)
markets = [p for p, m in exchange.markets.items() if market_is_active(m)
or config.get('include_inactive')]
@ -63,37 +68,7 @@ def start_download_data(args: Dict[str, Any]) -> None:
exchange.validate_timeframes(timeframe)
try:
if config.get('download_trades'):
if config.get('trading_mode') == 'futures':
raise OperationalException("Trade download not supported for futures.")
pairs_not_available = refresh_backtest_trades_data(
exchange, pairs=expanded_pairs, datadir=config['datadir'],
timerange=timerange, new_pairs_days=config['new_pairs_days'],
erase=bool(config.get('erase')), data_format=config['dataformat_trades'])
# Convert downloaded trade data to different timeframes
convert_trades_to_ohlcv(
pairs=expanded_pairs, timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
data_format_ohlcv=config['dataformat_ohlcv'],
data_format_trades=config['dataformat_trades'],
)
else:
if not exchange.get_option('ohlcv_has_history', True):
raise OperationalException(
f"Historic klines not available for {exchange.name}. "
"Please use `--dl-trades` instead for this exchange "
"(will unfortunately take a long time)."
)
pairs_not_available = refresh_backtest_ohlcv_data(
exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange,
new_pairs_days=config['new_pairs_days'],
erase=bool(config.get('erase')), data_format=config['dataformat_ohlcv'],
trading_mode=config.get('trading_mode', 'spot'),
prepend=config.get('prepend_data', False)
)
pairs_not_available = download_trades(exchange, expanded_pairs, config, timerange)
except KeyboardInterrupt:
sys.exit("SIGINT received, aborting ...")
@ -104,6 +79,42 @@ def start_download_data(args: Dict[str, Any]) -> None:
f"on exchange {exchange.name}.")
def download_trades(exchange: Exchange, expanded_pairs: list,
config: Dict[str, Any], timerange: TimeRange) -> list:
if config.get('download_trades'):
if config.get('trading_mode') == 'futures':
raise OperationalException("Trade download not supported for futures.")
pairs_not_available = refresh_backtest_trades_data(
exchange, pairs=expanded_pairs, datadir=config['datadir'],
timerange=timerange, new_pairs_days=config['new_pairs_days'],
erase=bool(config.get('erase')), data_format=config['dataformat_trades'])
# Convert downloaded trade data to different timeframes
convert_trades_to_ohlcv(
pairs=expanded_pairs, timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange, erase=bool(config.get('erase')),
data_format_ohlcv=config['dataformat_ohlcv'],
data_format_trades=config['dataformat_trades'],
)
else:
if not exchange.get_option('ohlcv_has_history', True):
raise OperationalException(
f"Historic klines not available for {exchange.name}. "
"Please use `--dl-trades` instead for this exchange "
"(will unfortunately take a long time)."
)
pairs_not_available = refresh_backtest_ohlcv_data(
exchange, pairs=expanded_pairs, timeframes=config['timeframes'],
datadir=config['datadir'], timerange=timerange,
new_pairs_days=config['new_pairs_days'],
erase=bool(config.get('erase')), data_format=config['dataformat_ohlcv'],
trading_mode=config.get('trading_mode', 'spot'),
prepend=config.get('prepend_data', False)
)
return pairs_not_available
def start_convert_trades(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)

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@ -520,7 +520,7 @@ class FreqaiDataDrawer:
f"Unable to load model, ensure model exists at " f"{dk.data_path} "
)
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
if self.config["freqai"]["feature_parameters"].get("principal_component_analysis", False):
dk.pca = cloudpickle.load(
open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
)
@ -616,7 +616,6 @@ class FreqaiDataDrawer:
pairs = self.freqai_info["feature_parameters"].get(
"include_corr_pairlist", []
)
for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
base_dataframes[tf] = dk.slice_dataframe(
timerange, historic_data[pair][tf]).reset_index(drop=True)

View File

@ -99,6 +99,7 @@ class FreqaiDataKitchen:
self.train_dates: DataFrame = pd.DataFrame()
self.unique_classes: Dict[str, list] = {}
self.unique_class_list: list = []
self.spice_dataframe: DataFrame = None
def set_paths(
self,
@ -1259,3 +1260,11 @@ class FreqaiDataKitchen:
f"Could not find backtesting prediction file at {path_to_predictionfile}"
)
return file_exists
def spice_extractor(self, indicator: str, dataframe: DataFrame) -> npt.NDArray:
if indicator in dataframe.columns:
return np.array(dataframe[indicator])
else:
logger.warning(f'User asked spice_rack for {indicator}, '
f'but it is not available. Returning 0s')
return np.zeros(len(dataframe.index))

View File

@ -93,7 +93,7 @@ class IFreqaiModel(ABC):
self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
self.continual_learning = self.freqai_info.get('continual_learning', False)
self.plot_features = self.ft_params.get("plot_feature_importances", 0)
self.spice_rack_open: bool = False
self._threads: List[threading.Thread] = []
self._stop_event = threading.Event()
@ -142,7 +142,7 @@ class IFreqaiModel(ABC):
dk = self.start_backtesting(dataframe, metadata, self.dk)
dataframe = dk.remove_features_from_df(dk.return_dataframe)
self.clean_up()
# self.clean_up()
if self.live:
self.inference_timer('stop')
return dataframe
@ -732,6 +732,18 @@ class IFreqaiModel(ABC):
f'Best approximation queue: {best_queue}')
return best_queue
def spice_rack(self, indicator: str, dataframe: DataFrame,
metadata: dict, strategy: IStrategy) -> NDArray:
if not self.spice_rack_open:
dataframe = self.start(dataframe, metadata, strategy)
self.dk.spice_dataframe = dataframe
self.spice_rack_open = True
return self.dk.spice_extractor(indicator, dataframe)
else:
return self.dk.spice_extractor(indicator, self.dk.spice_dataframe)
def close_spice_rack(self):
self.spice_rack_open = False
# Following methods which are overridden by user made prediction models.
# See freqai/prediction_models/CatboostPredictionModel.py for an example.

View File

@ -0,0 +1,37 @@
{
"freqai": {
"enabled": true,
"purge_old_models": true,
"train_period_days": 4,
"backtest_period_days": 1,
"identifier": "spicy-id",
"feature_parameters": {
"include_timeframes": [
"30m",
"1h",
"4h"
],
"include_corr_pairlist": [
"BTC/USD",
"ETH/USD"
],
"label_period_candles": 20,
"include_shifted_candles": 2,
"DI_threshold": 0.9,
"weight_factor": 0.9,
"principal_component_analysis": true,
"indicator_periods_candles": [
10,
20
]
},
"data_split_parameters": {
"test_size": 0,
"random_state": 1
},
"model_training_parameters": {
"n_estimators": 800
}
}
}

View File

@ -1,19 +1,24 @@
import logging
from datetime import datetime, timezone
from typing import Any
from typing import Any, Dict, Optional
import numpy as np
# for spice rack
import pandas as pd
import talib.abstract as ta
from scipy.signal import argrelextrema
from technical import qtpylib
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
from freqtrade.data.dataprovider import DataProvider
from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_seconds
from freqtrade.exchange import Exchange, timeframe_to_seconds
from freqtrade.exchange.exchange import market_is_active
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
from freqtrade.strategy import merge_informative_pair
logger = logging.getLogger(__name__)
@ -89,6 +94,136 @@ def get_required_data_timerange(config: Config) -> TimeRange:
return data_load_timerange
def auto_populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
"""
This is a premade `populate_any_indicators()` function which is set in
the user strategy is they enable `freqai_spice_rack: true` in their
configuration file.
"""
coin = pair.split('/')[0]
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"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=t, stds=2.2
)
informative[f"{coin}bb_lowerband-period_{t}"] = bollinger["lower"]
informative[f"{coin}bb_middleband-period_{t}"] = bollinger["mid"]
informative[f"{coin}bb_upperband-period_{t}"] = bollinger["upper"]
informative[f"%-{coin}bb_width-period_{t}"] = (
informative[f"{coin}bb_upperband-period_{t}"]
- informative[f"{coin}bb_lowerband-period_{t}"]
) / informative[f"{coin}bb_middleband-period_{t}"]
informative[f"%-{coin}close-bb_lower-period_{t}"] = (
informative["close"] / informative[f"{coin}bb_lowerband-period_{t}"]
)
informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
informative[f"%-{coin}relative_volume-period_{t}"] = (
informative["volume"] / informative["volume"].rolling(t).mean()
)
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
informative[f"%-{coin}raw_price"] = informative["close"]
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)
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
df["&s-extrema"] = 0
min_peaks = argrelextrema(df["close"].values, np.less, order=80)
max_peaks = argrelextrema(df["close"].values, np.greater, order=80)
for mp in min_peaks[0]:
df.at[mp, "&s-extrema"] = -1
for mp in max_peaks[0]:
df.at[mp, "&s-extrema"] = 1
return df
def setup_freqai_spice_rack(config: dict, exchange: Optional[Exchange]) -> Dict[str, Any]:
import difflib
import json
from pathlib import Path
auto_config = config.get('freqai_config', 'lightgbm_config.json')
with open(Path(__file__).parent / Path('spice_rack') / auto_config) as json_file:
freqai_config = json.load(json_file)
config['freqai'] = freqai_config['freqai']
config['freqai']['identifier'] = config['freqai_identifier']
corr_pairs = config['freqai']['feature_parameters']['include_corr_pairlist']
timeframes = config['freqai']['feature_parameters']['include_timeframes']
new_corr_pairs = []
new_tfs = []
if not exchange:
logger.warning('No dataprovider available.')
config['freqai']['enabled'] = False
return config
# find the closest pairs to what the default config wants
for pair in corr_pairs:
closest_pair = difflib.get_close_matches(
pair,
exchange.markets
)
if not closest_pair:
logger.warning(f'Could not find {pair} in markets, removing from '
f'corr_pairlist.')
else:
closest_pair = closest_pair[0]
new_corr_pairs.append(closest_pair)
logger.info(f'Spice rack will use {closest_pair} as informative in FreqAI model.')
# find the closest matching timeframes to what the default config wants
if timeframe_to_seconds(config['timeframe']) > timeframe_to_seconds('15m'):
logger.warning('Default spice rack is designed for lower base timeframes (e.g. > '
f'15m). But user passed {config["timeframe"]}.')
new_tfs.append(config['timeframe'])
list_tfs = [timeframe_to_seconds(tf) for tf
in exchange.timeframes]
for tf in timeframes:
tf_secs = timeframe_to_seconds(tf)
closest_index = min(range(len(list_tfs)), key=lambda i: abs(list_tfs[i] - tf_secs))
closest_tf = exchange.timeframes[closest_index]
logger.info(f'Spice rack will use {closest_tf} as informative tf in FreqAI model.')
new_tfs.append(closest_tf)
config['freqai']['feature_parameters'].update({'include_timeframes': new_tfs})
config['freqai']['feature_parameters'].update({'include_corr_pairlist': new_corr_pairs})
config.update({"freqaimodel": 'LightGBMRegressor'})
return config
# Keep below for when we wish to download heterogeneously lengthed data for FreqAI.
# def download_all_data_for_training(dp: DataProvider, config: Config) -> None:
# """

View File

@ -89,6 +89,10 @@ class Backtesting:
self._exchange_name, self.config, load_leverage_tiers=True)
self.dataprovider = DataProvider(self.config, self.exchange)
if config.get('freqai_spice_rack', False):
from freqtrade.freqai.utils import setup_freqai_spice_rack
self.config = setup_freqai_spice_rack(self.config, self.exchange)
if self.config.get('strategy_list'):
if self.config.get('freqai', {}).get('enabled', False):
logger.warning("Using --strategy-list with FreqAI REQUIRES all strategies "

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@ -146,12 +146,28 @@ class IStrategy(ABC, HyperStrategyMixin):
self._ft_informative.append((informative_data, cls_method))
def load_freqAI_model(self) -> None:
if self.config.get('freqai', {}).get('enabled', False):
spice_rack = self.config.get('freqai_spice_rack', False)
if self.config.get('freqai', {}).get('enabled', False) or spice_rack:
if spice_rack:
from freqtrade.freqai.utils import setup_freqai_spice_rack
self.config = setup_freqai_spice_rack(self.config, self.dp._exchange)
# Import here to avoid importing this if freqAI is disabled
from freqtrade.freqai.utils import download_all_data_for_training
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
self.freqai = FreqaiModelResolver.load_freqaimodel(self.config)
self.freqai_info = self.config["freqai"]
if not self.process_only_new_candles:
logger.warning('User set process_only_new_candles to false, '
'FreqAI requires true. Changing to true.')
self.process_only_new_candles = True
if spice_rack:
import types
from freqtrade.freqai.utils import auto_populate_any_indicators
self.populate_any_indicators = types.MethodType( # type: ignore
auto_populate_any_indicators, self)
self.freqai_info = self.config["freqai"]
# download the desired data in dry/live
if self.config.get('runmode') in (RunMode.DRY_RUN, RunMode.LIVE):
@ -161,6 +177,7 @@ class IStrategy(ABC, HyperStrategyMixin):
"already on disk."
)
download_all_data_for_training(self.dp, self.config)
else:
# Gracious failures if freqAI is disabled but "start" is called.
class DummyClass():

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@ -29,6 +29,7 @@ nav:
- Parameter table: freqai-parameter-table.md
- Feature engineering: freqai-feature-engineering.md
- Running FreqAI: freqai-running.md
- Spice Rack: freqai-spice-rack.md
- Developer guide: freqai-developers.md
- Short / Leverage: leverage.md
- Utility Sub-commands: utils.md

View File

@ -158,3 +158,28 @@ def test_make_train_test_datasets(mocker, freqai_conf):
assert data_dictionary
assert len(data_dictionary) == 7
assert len(data_dictionary['train_features'].index) == 1916
@pytest.mark.parametrize('indicator', [
'%-ADArsi-period_10_5m',
'doesnt_exist',
])
def test_spice_extractor(mocker, freqai_conf, indicator, caplog):
freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
freqai.dk.find_features(unfiltered_dataframe)
features_filtered, labels_filtered = freqai.dk.filter_features(
unfiltered_dataframe,
freqai.dk.training_features_list,
freqai.dk.label_list,
training_filter=True,
)
vec = freqai.dk.spice_extractor(indicator, features_filtered)
if 'doesnt_exist' in indicator:
assert log_has_re(
"User asked spice_rack for",
caplog,
)
else:
assert len(vec) == 2860

View File

@ -1,3 +1,4 @@
import copy
import platform
import shutil
from pathlib import Path
@ -382,6 +383,31 @@ def test_plot_feature_importance(mocker, freqai_conf):
shutil.rmtree(Path(freqai.dk.full_path))
def test_spice_rack(mocker, default_conf, tmpdir, caplog):
strategy = get_patched_freqai_strategy(mocker, default_conf)
exchange = get_patched_exchange(mocker, default_conf)
strategy.dp = DataProvider(default_conf, exchange)
default_conf.update({"freqai_spice_rack": "true"})
default_conf.update({"freqai_identifier": "spicy-id"})
default_conf["config_files"] = [Path('config_examples', 'config_freqai.example.json')]
default_conf["timerange"] = "20180110-20180115"
default_conf["datadir"] = Path(default_conf["datadir"])
default_conf['exchange'].update({'pair_whitelist':
['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC']})
default_conf["user_data_dir"] = Path(tmpdir)
freqai_conf = copy.deepcopy(default_conf)
strategy.config = freqai_conf
strategy.load_freqAI_model()
assert log_has_re("Spice rack will use LTC/USD", caplog)
assert log_has_re("Spice rack will use 15m", caplog)
assert 'freqai' in freqai_conf
assert strategy.freqai
@pytest.mark.parametrize('timeframes,corr_pairs', [
(['5m'], ['ADA/BTC', 'DASH/BTC']),
(['5m'], ['ADA/BTC', 'DASH/BTC', 'ETH/USDT']),