Merge branch 'develop' into backtest_live_models

This commit is contained in:
Wagner Costa Santos 2022-11-07 15:14:10 -03:00
commit 6559384286
18 changed files with 102 additions and 27 deletions

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@ -18,6 +18,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer. | `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
| `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`. | `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`.
| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`. | `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`.
| `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
| | **Feature parameters** | | **Feature parameters**
| `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. | `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_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).
@ -37,7 +38,6 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: `0`. | `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: `0`.
| `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`. | `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
| `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal). | `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
| `write_metrics_to_disk` | Collect train timings, inference timings and cpu usage in json file. <br> **Datatype:** Boolean. <br> Default: `False`
| | **Data split parameters** | | **Data split parameters**
| `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary. | `data_split_parameters` | Include any additional parameters available from Scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
| `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1. | `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.

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@ -1,6 +1,6 @@
markdown==3.3.7 markdown==3.3.7
mkdocs==1.4.1 mkdocs==1.4.2
mkdocs-material==8.5.7 mkdocs-material==8.5.8
mdx_truly_sane_lists==1.3 mdx_truly_sane_lists==1.3
pymdown-extensions==9.7 pymdown-extensions==9.7
jinja2==3.1.2 jinja2==3.1.2

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@ -542,7 +542,7 @@ CONF_SCHEMA = {
"keras": {"type": "boolean", "default": False}, "keras": {"type": "boolean", "default": False},
"write_metrics_to_disk": {"type": "boolean", "default": False}, "write_metrics_to_disk": {"type": "boolean", "default": False},
"purge_old_models": {"type": "boolean", "default": True}, "purge_old_models": {"type": "boolean", "default": True},
"conv_width": {"type": "integer", "default": 2}, "conv_width": {"type": "integer", "default": 1},
"train_period_days": {"type": "integer", "default": 0}, "train_period_days": {"type": "integer", "default": 0},
"backtest_period_days": {"type": "number", "default": 7}, "backtest_period_days": {"type": "number", "default": 7},
"identifier": {"type": "string", "default": "example"}, "identifier": {"type": "string", "default": "example"},

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@ -26,7 +26,7 @@ BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
'profit_ratio', 'profit_abs', 'exit_reason', 'profit_ratio', 'profit_abs', 'exit_reason',
'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs', 'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
'stop_loss_ratio', 'min_rate', 'max_rate', 'is_open', 'enter_tag', 'stop_loss_ratio', 'min_rate', 'max_rate', 'is_open', 'enter_tag',
'is_short', 'open_timestamp', 'close_timestamp', 'orders' 'leverage', 'is_short', 'open_timestamp', 'close_timestamp', 'orders'
] ]
@ -280,6 +280,8 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
# Compatibility support for pre short Columns # Compatibility support for pre short Columns
if 'is_short' not in df.columns: if 'is_short' not in df.columns:
df['is_short'] = 0 df['is_short'] = 0
if 'leverage' not in df.columns:
df['leverage'] = 1.0
if 'enter_tag' not in df.columns: if 'enter_tag' not in df.columns:
df['enter_tag'] = df['buy_tag'] df['enter_tag'] = df['buy_tag']
df = df.drop(['buy_tag'], axis=1) df = df.drop(['buy_tag'], axis=1)

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@ -102,6 +102,11 @@ class IDataHandler(ABC):
:return: (min, max) :return: (min, max)
""" """
data = self._ohlcv_load(pair, timeframe, None, candle_type) data = self._ohlcv_load(pair, timeframe, None, candle_type)
if data.empty:
return (
datetime.fromtimestamp(0, tz=timezone.utc),
datetime.fromtimestamp(0, tz=timezone.utc)
)
return data.iloc[0]['date'].to_pydatetime(), data.iloc[-1]['date'].to_pydatetime() return data.iloc[0]['date'].to_pydatetime(), data.iloc[-1]['date'].to_pydatetime()
@abstractmethod @abstractmethod

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@ -1689,6 +1689,17 @@ class Exchange:
@retrier @retrier
def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1, def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
price: float = 1, taker_or_maker: MakerTaker = 'maker') -> float: price: float = 1, taker_or_maker: MakerTaker = 'maker') -> float:
"""
Retrieve fee from exchange
:param symbol: Pair
:param type: Type of order (market, limit, ...)
:param side: Side of order (buy, sell)
:param amount: Amount of order
:param price: Price of order
:param taker_or_maker: 'maker' or 'taker' (ignored if "type" is provided)
"""
if type and type == 'market':
taker_or_maker = 'taker'
try: try:
if self._config['dry_run'] and self._config.get('fee', None) is not None: if self._config['dry_run'] and self._config.get('fee', None) is not None:
return self._config['fee'] return self._config['fee']

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@ -636,6 +636,8 @@ class FreqaiDataDrawer:
axis=0, axis=0,
) )
self.current_candle = history_data[dk.pair][self.config['timeframe']].iloc[-1]['date']
def load_all_pair_histories(self, timerange: TimeRange, dk: FreqaiDataKitchen) -> None: def load_all_pair_histories(self, timerange: TimeRange, dk: FreqaiDataKitchen) -> None:
""" """
Load pair histories for all whitelist and corr_pairlist pairs. Load pair histories for all whitelist and corr_pairlist pairs.

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@ -1177,9 +1177,11 @@ class FreqaiDataKitchen:
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", []) pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
for pair in pairs: for pair in pairs:
pair = pair.replace(':', '') # lightgbm doesnt like colons
valid_strs = [f"%-{pair}", f"%{pair}", f"%_{pair}"] valid_strs = [f"%-{pair}", f"%{pair}", f"%_{pair}"]
pair_cols = [col for col in dataframe.columns if pair_cols = [col for col in dataframe.columns if
any(substr in col for substr in valid_strs)] any(substr in col for substr in valid_strs)]
if pair_cols:
pair_cols.insert(0, 'date') pair_cols.insert(0, 'date')
corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1) corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
@ -1199,8 +1201,9 @@ class FreqaiDataKitchen:
ready for training ready for training
""" """
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", []) pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
current_pair = current_pair.replace(':', '')
for pair in pairs: for pair in pairs:
pair = pair.replace(':', '') # lightgbm doesnt work with colons
if current_pair != pair: if current_pair != pair:
dataframe = dataframe.merge(corr_dataframes[pair], how='left', on='date') dataframe = dataframe.merge(corr_dataframes[pair], how='left', on='date')
@ -1270,6 +1273,8 @@ class FreqaiDataKitchen:
self.get_unique_classes_from_labels(dataframe) self.get_unique_classes_from_labels(dataframe)
dataframe = self.remove_special_chars_from_feature_names(dataframe)
return dataframe return dataframe
def fit_labels(self) -> None: def fit_labels(self) -> None:
@ -1471,3 +1476,16 @@ class FreqaiDataKitchen:
assets_end_dates[asset].append(model_end_date) assets_end_dates[asset].append(model_end_date)
return assets_end_dates return assets_end_dates
def remove_special_chars_from_feature_names(self, dataframe: pd.DataFrame) -> pd.DataFrame:
"""
Remove all special characters from feature strings (:)
:param dataframe: the dataframe that just finished indicator population. (unfiltered)
:return: dataframe with cleaned featrue names
"""
spec_chars = [':']
for c in spec_chars:
dataframe.columns = dataframe.columns.str.replace(c, "")
return dataframe

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@ -68,6 +68,9 @@ class IFreqaiModel(ABC):
if self.save_backtest_models: if self.save_backtest_models:
logger.info('Backtesting module configured to save all models.') logger.info('Backtesting module configured to save all models.')
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode) self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
# set current candle to arbitrary historical date
self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
self.dd.current_candle = self.current_candle
self.scanning = False self.scanning = False
self.ft_params = self.freqai_info["feature_parameters"] self.ft_params = self.freqai_info["feature_parameters"]
self.corr_pairlist: List[str] = self.ft_params.get("include_corr_pairlist", []) self.corr_pairlist: List[str] = self.ft_params.get("include_corr_pairlist", [])
@ -75,7 +78,7 @@ class IFreqaiModel(ABC):
if self.keras and self.ft_params.get("DI_threshold", 0): if self.keras and self.ft_params.get("DI_threshold", 0):
self.ft_params["DI_threshold"] = 0 self.ft_params["DI_threshold"] = 0
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.") logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2) self.CONV_WIDTH = self.freqai_info.get('conv_width', 1)
if self.ft_params.get("inlier_metric_window", 0): if self.ft_params.get("inlier_metric_window", 0):
self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2 self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
self.pair_it = 0 self.pair_it = 0
@ -93,7 +96,6 @@ class IFreqaiModel(ABC):
# get_corr_dataframes is controlling the caching of corr_dataframes # get_corr_dataframes is controlling the caching of corr_dataframes
# for improved performance. Careful with this boolean. # for improved performance. Careful with this boolean.
self.get_corr_dataframes: bool = True self.get_corr_dataframes: bool = True
self._threads: List[threading.Thread] = [] self._threads: List[threading.Thread] = []
self._stop_event = threading.Event() self._stop_event = threading.Event()
@ -338,6 +340,7 @@ class IFreqaiModel(ABC):
if self.dd.historic_data: if self.dd.historic_data:
self.dd.update_historic_data(strategy, dk) self.dd.update_historic_data(strategy, dk)
logger.debug(f'Updating historic data on pair {metadata["pair"]}') logger.debug(f'Updating historic data on pair {metadata["pair"]}')
self.track_current_candle()
if not self.follow_mode: if not self.follow_mode:
@ -682,8 +685,6 @@ class IFreqaiModel(ABC):
" avoid blinding open trades and degrading performance.") " avoid blinding open trades and degrading performance.")
self.pair_it = 0 self.pair_it = 0
self.inference_time = 0 self.inference_time = 0
if self.corr_pairlist:
self.get_corr_dataframes = True
return return
def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''): def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
@ -759,12 +760,24 @@ class IFreqaiModel(ABC):
"is included in the column names when you are creating features " "is included in the column names when you are creating features "
"in `populate_any_indicators()`.") "in `populate_any_indicators()`.")
self.get_corr_dataframes = not bool(self.corr_dataframes) self.get_corr_dataframes = not bool(self.corr_dataframes)
else: elif self.corr_dataframes:
dataframe = dk.attach_corr_pair_columns( dataframe = dk.attach_corr_pair_columns(
dataframe, self.corr_dataframes, dk.pair) dataframe, self.corr_dataframes, dk.pair)
return dataframe return dataframe
def track_current_candle(self):
"""
Checks if the latest candle appended by the datadrawer is
equivalent to the latest candle seen by FreqAI. If not, it
asks to refresh the cached corr_dfs, and resets the pair
counter.
"""
if self.dd.current_candle > self.current_candle:
self.get_corr_dataframes = True
self.pair_it = 1
self.current_candle = self.dd.current_candle
def ensure_data_exists(self, dataframe_backtest: DataFrame, def ensure_data_exists(self, dataframe_backtest: DataFrame,
tr_backtest: TimeRange, pair: str) -> bool: tr_backtest: TimeRange, pair: str) -> bool:
""" """

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@ -10,7 +10,7 @@ flake8==5.0.4
flake8-tidy-imports==4.8.0 flake8-tidy-imports==4.8.0
mypy==0.982 mypy==0.982
pre-commit==2.20.0 pre-commit==2.20.0
pytest==7.1.3 pytest==7.2.0
pytest-asyncio==0.20.1 pytest-asyncio==0.20.1
pytest-cov==4.0.0 pytest-cov==4.0.0
pytest-mock==3.10.0 pytest-mock==3.10.0

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@ -6,4 +6,4 @@ scipy==1.9.3
scikit-learn==1.1.3 scikit-learn==1.1.3
scikit-optimize==0.9.0 scikit-optimize==0.9.0
filelock==3.8.0 filelock==3.8.0
progressbar2==4.1.1 progressbar2==4.2.0

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@ -2,17 +2,17 @@ numpy==1.23.4
pandas==1.5.1 pandas==1.5.1
pandas-ta==0.3.14b pandas-ta==0.3.14b
ccxt==2.0.96 ccxt==2.1.33
# Pin cryptography for now due to rust build errors with piwheels # Pin cryptography for now due to rust build errors with piwheels
cryptography==38.0.1 cryptography==38.0.1
aiohttp==3.8.3 aiohttp==3.8.3
SQLAlchemy==1.4.42 SQLAlchemy==1.4.43
python-telegram-bot==13.14 python-telegram-bot==13.14
arrow==1.2.3 arrow==1.2.3
cachetools==4.2.2 cachetools==4.2.2
requests==2.28.1 requests==2.28.1
urllib3==1.26.12 urllib3==1.26.12
jsonschema==4.16.0 jsonschema==4.17.0
TA-Lib==0.4.25 TA-Lib==0.4.25
technical==1.3.0 technical==1.3.0
tabulate==0.9.0 tabulate==0.9.0
@ -29,7 +29,7 @@ py_find_1st==1.1.5
# Load ticker files 30% faster # Load ticker files 30% faster
python-rapidjson==1.9 python-rapidjson==1.9
# Properly format api responses # Properly format api responses
orjson==3.8.0 orjson==3.8.1
# Notify systemd # Notify systemd
sdnotify==0.3.2 sdnotify==0.3.2
@ -46,7 +46,7 @@ psutil==5.9.3
colorama==0.4.6 colorama==0.4.6
# Building config files interactively # Building config files interactively
questionary==1.10.0 questionary==1.10.0
prompt-toolkit==3.0.31 prompt-toolkit==3.0.32
# Extensions to datetime library # Extensions to datetime library
python-dateutil==2.8.2 python-dateutil==2.8.2

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@ -1,6 +1,7 @@
# pragma pylint: disable=missing-docstring, protected-access, C0103 # pragma pylint: disable=missing-docstring, protected-access, C0103
import re import re
from datetime import datetime, timezone
from pathlib import Path from pathlib import Path
from unittest.mock import MagicMock from unittest.mock import MagicMock
@ -154,6 +155,23 @@ def test_jsondatahandler_ohlcv_load(testdatadir, caplog):
assert df.columns.equals(df1.columns) assert df.columns.equals(df1.columns)
def test_datahandler_ohlcv_data_min_max(testdatadir):
dh = JsonDataHandler(testdatadir)
min_max = dh.ohlcv_data_min_max('UNITTEST/BTC', '5m', 'spot')
assert len(min_max) == 2
# Empty pair
min_max = dh.ohlcv_data_min_max('UNITTEST/BTC', '8m', 'spot')
assert len(min_max) == 2
assert min_max[0] == datetime.fromtimestamp(0, tz=timezone.utc)
assert min_max[0] == min_max[1]
# Empty pair2
min_max = dh.ohlcv_data_min_max('NOPAIR/XXX', '4m', 'spot')
assert len(min_max) == 2
assert min_max[0] == datetime.fromtimestamp(0, tz=timezone.utc)
assert min_max[0] == min_max[1]
def test_datahandler__check_empty_df(testdatadir, caplog): def test_datahandler__check_empty_df(testdatadir, caplog):
dh = JsonDataHandler(testdatadir) dh = JsonDataHandler(testdatadir)
expected_text = r"Price jump in UNITTEST/USDT, 1h, spot between" expected_text = r"Price jump in UNITTEST/USDT, 1h, spot between"

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@ -22,6 +22,7 @@ def test_update_historic_data(mocker, freqai_conf):
historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"]) historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m")) dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m"))
candle_difference = dp_candles - historic_candles candle_difference = dp_candles - historic_candles
freqai.dk.pair = "ADA/BTC"
freqai.dd.update_historic_data(strategy, freqai.dk) freqai.dd.update_historic_data(strategy, freqai.dk)
updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"]) updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])

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@ -194,6 +194,7 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk) 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") 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): for i in range(5):
df[f'%-constant_{i}'] = i df[f'%-constant_{i}'] = i
# df.loc[:, f'%-constant_{i}'] = i # df.loc[:, f'%-constant_{i}'] = i
@ -339,6 +340,7 @@ def test_follow_mode(mocker, freqai_conf):
df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m') df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
freqai.dk.pair = "ADA/BTC"
freqai.start_live(df, metadata, strategy, freqai.dk) freqai.start_live(df, metadata, strategy, freqai.dk)
assert len(freqai.dk.return_dataframe.index) == 5702 assert len(freqai.dk.return_dataframe.index) == 5702

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@ -764,6 +764,7 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
'max_rate': [0.10501, 0.1038888], 'max_rate': [0.10501, 0.1038888],
'is_open': [False, False], 'is_open': [False, False],
'enter_tag': [None, None], 'enter_tag': [None, None],
"leverage": [1.0, 1.0],
"is_short": [False, False], "is_short": [False, False],
'open_timestamp': [1517251200000, 1517283000000], 'open_timestamp': [1517251200000, 1517283000000],
'close_timestamp': [1517265300000, 1517285400000], 'close_timestamp': [1517265300000, 1517285400000],
@ -788,13 +789,14 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
assert len(t['orders']) == 2 assert len(t['orders']) == 2
ln = data_pair.loc[data_pair["date"] == t["open_date"]] ln = data_pair.loc[data_pair["date"] == t["open_date"]]
# Check open trade rate alignes to open rate # Check open trade rate alignes to open rate
assert ln is not None assert not ln.empty
assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6) assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
# check close trade rate alignes to close rate or is between high and low # check close trade rate alignes to close rate or is between high and low
ln = data_pair.loc[data_pair["date"] == t["close_date"]] ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or assert not ln1.empty
round(ln.iloc[0]["low"], 6) < round( assert (round(ln1.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
t["close_rate"], 6) < round(ln.iloc[0]["high"], 6)) round(ln1.iloc[0]["low"], 6) < round(
t["close_rate"], 6) < round(ln1.iloc[0]["high"], 6))
def test_backtest_timedout_entry_orders(default_conf, fee, mocker, testdatadir) -> None: def test_backtest_timedout_entry_orders(default_conf, fee, mocker, testdatadir) -> None:

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@ -72,6 +72,7 @@ def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) ->
'max_rate': [0.10481985, 0.1038888], 'max_rate': [0.10481985, 0.1038888],
'is_open': [False, False], 'is_open': [False, False],
'enter_tag': [None, None], 'enter_tag': [None, None],
'leverage': [1.0, 1.0],
'is_short': [False, False], 'is_short': [False, False],
'open_timestamp': [1517251200000, 1517283000000], 'open_timestamp': [1517251200000, 1517283000000],
'close_timestamp': [1517265300000, 1517285400000], 'close_timestamp': [1517265300000, 1517285400000],

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@ -5305,7 +5305,7 @@ def test_get_valid_price(mocker, default_conf_usdt) -> None:
]) ])
def test_update_funding_fees_schedule(mocker, default_conf, trading_mode, calls, time_machine, def test_update_funding_fees_schedule(mocker, default_conf, trading_mode, calls, time_machine,
t1, t2): t1, t2):
time_machine.move_to(f"{t1} +00:00") time_machine.move_to(f"{t1} +00:00", tick=False)
patch_RPCManager(mocker) patch_RPCManager(mocker)
patch_exchange(mocker) patch_exchange(mocker)
@ -5314,7 +5314,7 @@ def test_update_funding_fees_schedule(mocker, default_conf, trading_mode, calls,
default_conf['margin_mode'] = 'isolated' default_conf['margin_mode'] = 'isolated'
freqtrade = get_patched_freqtradebot(mocker, default_conf) freqtrade = get_patched_freqtradebot(mocker, default_conf)
time_machine.move_to(f"{t2} +00:00") time_machine.move_to(f"{t2} +00:00", tick=False)
# Check schedule jobs in debugging with freqtrade._schedule.jobs # Check schedule jobs in debugging with freqtrade._schedule.jobs
freqtrade._schedule.run_pending() freqtrade._schedule.run_pending()