lychiyu/freqtrade_origin
1
0
Fork 0
mirror of https://github.com/freqtrade/freqtrade.git synced 2024-11-10 02:12:01 +00:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

Fix typos discovered by codespell

Browse Source
This commit is contained in:
Christian Clauss 2024-04-18 22:51:25 +02:00
parent 298f9b225d
commit 99b4767bf4
59 changed files with 93 additions and 93 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
build_helpers/pre_commit_update.py
View File

@ -1,4 +1,4 @@
# File used in CI to ensure pre-commit dependencies are kept uptodate.
# File used in CI to ensure pre-commit dependencies are kept up-to-date.
import sys
from pathlib import Path

2
docs/advanced-backtesting.md
View File

@ -36,7 +36,7 @@ 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
used to specify the various tabular outputs showing the profit fo each group or trade,
used to specify the various tabular outputs showing the profit of each group or trade,
ranging from the simplest (0) to the most detailed per pair, per buy and per sell tag (4):
* 0: overall winrate and profit summary by enter_tag

2
docs/backtesting.md
View File

@ -587,7 +587,7 @@ These precision values are based on current exchange limits (as described in the
## Improved backtest accuracy
One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or viceversa?).
One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or vice-versa?).
So assuming you run backtesting with a 1h timeframe, there will be 4 prices for that candle (Open, High, Low, Close).
While backtesting does take some assumptions (read above) about this - this can never be perfect, and will always be biased in one way or the other.

4
docs/faq.md
View File

@ -2,7 +2,7 @@
## Supported Markets
Freqtrade supports spot trading, as well as (isolated) futures trading for some selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an uptodate list of supported exchanges.
Freqtrade supports spot trading, as well as (isolated) futures trading for some selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an up-to-date list of supported exchanges.
### Can my bot open short positions?
@ -14,7 +14,7 @@ In spot markets, you can in some cases use leveraged spot tokens, which reflect
### Can my bot trade options or futures?
Futures trading is supported for selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an uptodate list of supported exchanges.
Futures trading is supported for selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an up-to-date list of supported exchanges.
## Beginner Tips & Tricks

2
docs/freqai-parameter-table.md
View File

@ -55,7 +55,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
| | **Data split parameters within the `freqai.data_split_parameters` sub 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.
| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Defaut: `False`.
| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Default: `False`.
### Model training parameters

2
docs/rest-api.md
View File

@ -454,7 +454,7 @@ To properly configure your reverse proxy (securely), please consult it's documen
- **Caddy**: Caddy v2 supports websockets out of the box, see the [documentation](https://caddyserver.com/docs/v2-upgrade#proxy)
!!! Tip "SSL certificates"
You can use tools like certbot to setup ssl certificates to access your bot's UI through encrypted connection by using any fo the above reverse proxies.
You can use tools like certbot to setup ssl certificates to access your bot's UI through encrypted connection by using any of the above reverse proxies.
While this will protect your data in transit, we do not recommend to run the freqtrade API outside of your private network (VPN, SSH tunnel).
### OpenAPI interface

2
docs/stoploss.md
View File

@ -240,7 +240,7 @@ When using leverage, the same principle is applied - with stoploss defining the
Therefore, a stoploss of 10% on a 10x trade would trigger on a 1% price move.
If your stake amount (own capital) was 100$ - this trade would be 1000$ at 10x (after leverage).
If price moves 1% - you've lost 10$ of your own capital - therfore stoploss will trigger in this case.
If price moves 1% - you've lost 10$ of your own capital - therefore stoploss will trigger in this case.
Make sure to be aware of this, and avoid using too tight stoploss (at 10x leverage, 10% risk may be too little to allow the trade to "breath" a little).

2
docs/strategy-advanced.md
View File

@ -326,4 +326,4 @@ for val in self.buy_ema_short.range:
dataframe = pd.concat(frames, axis=1)
```
Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existant.
Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existent.

2
docs/strategy-customization.md
View File

@ -776,7 +776,7 @@ The orderbook structure is aligned with the order structure from [ccxt](https://
Therefore, using `ob['bids'][0][0]` as demonstrated above will result in using the best bid price. `ob['bids'][0][1]` would look at the amount at this orderbook position.
!!! Warning "Warning about backtesting"
The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used, as the method will return uptodate values.
The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used, as the method will return up-to-date values.
### *ticker(pair)*

4
docs/trade-object.md
View File

@ -126,7 +126,7 @@ An `Order` object will always be tied to it's corresponding [`Trade`](#trade-obj
### Order - Available attributes
an Order object is typically attached to a trade.
Most properties here can be None as they are dependant on the exchange response.
Most properties here can be None as they are dependent on the exchange response.
| Attribute | DataType | Description |
|------------|-------------|-------------|
@ -141,7 +141,7 @@ Most properties here can be None as they are dependant on the exchange response.
`amount` | float | Amount in base currency
`filled` | float | Filled amount (in base currency)
`remaining` | float | Remaining amount
`cost` | float | Cost of the order - usually average * filled (*Exchange dependant on futures, may contain the cost with or without leverage and may be in contracts.*)
`cost` | float | Cost of the order - usually average * filled (*Exchange dependent on futures, may contain the cost with or without leverage and may be in contracts.*)
`stake_amount` | float | Stake amount used for this order. *Added in 2023.7.*
`order_date` | datetime | Order creation date **use `order_date_utc` instead**
`order_date_utc` | datetime | Order creation date (in UTC)

2
freqtrade/configuration/configuration.py
View File

@ -202,7 +202,7 @@ class Configuration:
if self.args.get('show_sensitive'):
logger.warning(
"Sensitive information will be shown in the upcomming output. "
"Sensitive information will be shown in the upcoming output. "
"Please make sure to never share this output without redacting "
"the information yourself.")

2
freqtrade/data/dataprovider.py
View File

@ -523,7 +523,7 @@ class DataProvider:
Send custom RPC Notifications from your bot.
Will not send any bot in modes other than Dry-run or Live.
:param message: Message to be sent. Must be below 4096.
:param always_send: If False, will send the message only once per candle, and surpress
:param always_send: If False, will send the message only once per candle, and suppress
identical messages.
Careful as this can end up spaming your chat.
Defaults to False

14
freqtrade/exchange/exchange.py
View File

@ -758,7 +758,7 @@ class Exchange:
def price_get_one_pip(self, pair: str, price: float) -> float:
"""
Get's the "1 pip" value for this pair.
Gets the "1 pip" value for this pair.
Used in PriceFilter to calculate the 1pip movements.
"""
precision = self.markets[pair]['precision']['price']
@ -2007,7 +2007,7 @@ class Exchange:
range(since_ms, until_ms or dt_ts(), one_call)]
data: List = []
# Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
# Chunk requests into batches of 100 to avoid overwhelming ccxt Throttling
for input_coro in chunks(input_coroutines, 100):
results = await asyncio.gather(*input_coro, return_exceptions=True)
@ -2124,7 +2124,7 @@ class Exchange:
Only used in the dataprovider.refresh() method.
:param pair_list: List of 2 element tuples containing pair, interval to refresh
:param since_ms: time since when to download, in milliseconds
:param cache: Assign result to _klines. Usefull for one-off downloads like for pairlists
:param cache: Assign result to _klines. Useful for one-off downloads like for pairlists
:param drop_incomplete: Control candle dropping.
Specifying None defaults to _ohlcv_partial_candle
:return: Dict of [{(pair, timeframe): Dataframe}]
@ -2135,7 +2135,7 @@ class Exchange:
input_coroutines, cached_pairs = self._build_ohlcv_dl_jobs(pair_list, since_ms, cache)
results_df = {}
# Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
# Chunk requests into batches of 100 to avoid overwhelming ccxt Throttling
for input_coro in chunks(input_coroutines, 100):
async def gather_stuff():
return await asyncio.gather(*input_coro, return_exceptions=True)
@ -2295,7 +2295,7 @@ class Exchange:
since: Optional[int] = None,
params: Optional[dict] = None) -> Tuple[List[List], Any]:
"""
Asyncronously gets trade history using fetch_trades.
Asynchronously gets trade history using fetch_trades.
Handles exchange errors, does one call to the exchange.
:param pair: Pair to fetch trade data for
:param since: Since as integer timestamp in milliseconds
@ -2352,7 +2352,7 @@ class Exchange:
since: Optional[int] = None,
from_id: Optional[str] = None) -> Tuple[str, List[List]]:
"""
Asyncronously gets trade history using fetch_trades
Asynchronously gets trade history using fetch_trades
use this when exchange uses id-based iteration (check `self._trades_pagination`)
:param pair: Pair to fetch trade data for
:param since: Since as integer timestamp in milliseconds
@ -2403,7 +2403,7 @@ class Exchange:
async def _async_get_trade_history_time(self, pair: str, until: int,
since: Optional[int] = None) -> Tuple[str, List[List]]:
"""
Asyncronously gets trade history using fetch_trades,
Asynchronously gets trade history using fetch_trades,
when the exchange uses time-based iteration (check `self._trades_pagination`)
:param pair: Pair to fetch trade data for
:param since: Since as integer timestamp in milliseconds

2
freqtrade/exchange/gate.py
View File

@ -79,7 +79,7 @@ class Gate(Exchange):
# As such, futures orders on gate will not contain a fee, which causes
# a repeated "update fee" cycle and wrong calculations.
# Therefore we patch the response with fees if it's not available.
# An alternative also contianing fees would be
# An alternative also containing fees would be
# privateFuturesGetSettleAccountBook({"settle": "usdt"})
pair_fees = self._trading_fees.get(pair, {})
if pair_fees:

2
freqtrade/exchange/okx.py
View File

@ -56,7 +56,7 @@ class Okx(Exchange):
"""
Exchange ohlcv candle limit
OKX has the following behaviour:
* 300 candles for uptodate data
* 300 candles for up-to-date data
* 100 candles for historic data
* 100 candles for additional candles (not futures or spot).
:param timeframe: Timeframe to check

2
freqtrade/freqai/RL/BaseEnvironment.py
View File

@ -222,7 +222,7 @@ class BaseEnvironment(gym.Env):
@abstractmethod
def step(self, action: int):
"""
Step depeneds on action types, this must be inherited.
Step depends on action types, this must be inherited.
"""
return

4
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@ -311,7 +311,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
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 '
logger.warning('Reinforcement learning module didn't find the correct raw prices '
'assigned in feature_engineering_standard(). '
'Please assign them with:\n'
'dataframe["%-raw_close"] = dataframe["close"]\n'
@ -458,7 +458,7 @@ def make_env(MyRLEnv: Type[BaseEnvironment], env_id: str, rank: int,
:param env_id: (str) the environment ID
:param num_env: (int) the number of environment you wish to have in subprocesses
:param seed: (int) the inital seed for RNG
:param seed: (int) the initial seed for RNG
:param rank: (int) index of the subprocess
:param env_info: (dict) all required arguments to instantiate the environment.
:return: (Callable)

2
freqtrade/freqai/data_drawer.py
View File

@ -280,7 +280,7 @@ class FreqaiDataDrawer:
new_pred = pred_df.copy()
# set new_pred values to nans (we want to signal to user that there was nothing
# historically made during downtime. The newest pred will get appeneded later in
# historically made during downtime. The newest pred will get appended later in
# append_model_predictions)
new_pred["date_pred"] = dataframe["date"]

4
freqtrade/freqai/data_kitchen.py
View File

@ -612,7 +612,7 @@ class FreqaiDataKitchen:
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
for pair in pairs:
pair = pair.replace(':', '') # lightgbm doesnt like colons
pair = pair.replace(':', '') # lightgbm does not like colons
pair_cols = [col for col in dataframe.columns if col.startswith("%")
and f"{pair}_" in col]
@ -638,7 +638,7 @@ class FreqaiDataKitchen:
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
current_pair = current_pair.replace(':', '')
for pair in pairs:
pair = pair.replace(':', '') # lightgbm doesnt work with colons
pair = pair.replace(':', '') # lightgbm does not work with colons
if current_pair != pair:
dataframe = dataframe.merge(corr_dataframes[pair], how='left', on='date')

2
freqtrade/freqai/freqai_interface.py
View File

@ -222,7 +222,7 @@ class IFreqaiModel(ABC):
time.sleep(1)
pair = self.train_queue[0]
# ensure pair is avaialble in dp
# ensure pair is available in dp
if pair not in strategy.dp.current_whitelist():
self.train_queue.popleft()
logger.warning(f'{pair} not in current whitelist, removing from train queue.')

2
freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
View File

@ -74,7 +74,7 @@ class PyTorchMLPClassifier(BasePyTorchClassifier):
model.to(self.device)
optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
criterion = torch.nn.CrossEntropyLoss()
# check if continual_learning is activated, and retreive the model to continue training
# check if continual_learning is activated, and retrieve the model to continue training
trainer = self.get_init_model(dk.pair)
if trainer is None:
trainer = PyTorchModelTrainer(

2
freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
View File

@ -69,7 +69,7 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
model.to(self.device)
optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
criterion = torch.nn.MSELoss()
# check if continual_learning is activated, and retreive the model to continue training
# check if continual_learning is activated, and retrieve the model to continue training
trainer = self.get_init_model(dk.pair)
if trainer is None:
trainer = PyTorchModelTrainer(

2
freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
View File

@ -80,7 +80,7 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
model.to(self.device)
optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
criterion = torch.nn.MSELoss()
# check if continual_learning is activated, and retreive the model to continue training
# check if continual_learning is activated, and retrieve the model to continue training
trainer = self.get_init_model(dk.pair)
if trainer is None:
trainer = PyTorchTransformerTrainer(

2
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@ -63,6 +63,6 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
is_masking_supported(self.eval_env)))
# TENSORBOARD CALLBACK DOES NOT RECOMMENDED TO USE WITH MULTIPLE ENVS,
# IT WILL RETURN FALSE INFORMATIONS, NEVERTHLESS NOT THREAD SAFE WITH SB3!!!
# IT WILL RETURN FALSE INFORMATION, NEVERTHELESS NOT THREAD SAFE WITH SB3!!!
actions = self.train_env.env_method("get_actions")[0]
self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)

2
freqtrade/freqai/torch/PyTorchModelTrainer.py
View File

@ -38,7 +38,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
:param init_model: A dictionary containing the initial model/optimizer
state_dict and model_meta_data saved by self.save() method.
:param model_meta_data: Additional metadata about the model (optional).
:param data_convertor: convertor from pd.DataFrame to torch.tensor.
:param data_convertor: converter from pd.DataFrame to torch.tensor.
:param n_steps: used to calculate n_epochs. The number of training iterations to run.
iteration here refers to the number of times optimizer.step() is called.
ignored if n_epochs is set.

2
freqtrade/freqai/utils.py
View File

@ -178,7 +178,7 @@ def record_params(config: Dict[str, Any], full_path: Path) -> None:
def get_timerange_backtest_live_models(config: Config) -> str:
"""
Returns a formated timerange for backtest live/ready models
Returns a formatted timerange for backtest live/ready models
:param config: Configuration dictionary
:return: a string timerange (format example: '20220801-20220822')

10
freqtrade/freqtradebot.py
View File

@ -64,7 +64,7 @@ class FreqtradeBot(LoggingMixin):
# Init objects
self.config = config
exchange_config: ExchangeConfig = deepcopy(config['exchange'])
# Remove credentials from original exchange config to avoid accidental credentail exposure
# Remove credentials from original exchange config to avoid accidental credential exposure
remove_exchange_credentials(config['exchange'], True)
self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
@ -176,7 +176,7 @@ class FreqtradeBot(LoggingMixin):
try:
Trade.commit()
except Exception:
# Exeptions here will be happening if the db disappeared.
# Exceptions here will be happening if the db disappeared.
# At which point we can no longer commit anyway.
pass
@ -227,7 +227,7 @@ class FreqtradeBot(LoggingMixin):
self.strategy.analyze(self.active_pair_whitelist)
with self._exit_lock:
# Check for exchange cancelations, timeouts and user requested replace
# Check for exchange cancellations, timeouts and user requested replace
self.manage_open_orders()
# Protect from collisions with force_exit.
@ -1291,12 +1291,12 @@ class FreqtradeBot(LoggingMixin):
def manage_trade_stoploss_orders(self, trade: Trade, stoploss_orders: List[Dict]):
"""
Perform required actions acording to existing stoploss orders of trade
Perform required actions according to existing stoploss orders of trade
:param trade: Corresponding Trade
:param stoploss_orders: Current on exchange stoploss orders
:return: None
"""
# If all stoploss orderd are canceled for some reason we add it again
# If all stoploss ordered are canceled for some reason we add it again
canceled_sl_orders = [o for o in stoploss_orders
if o['status'] in ('canceled', 'cancelled')]
if (

4
freqtrade/optimize/backtesting.py
View File

@ -297,7 +297,7 @@ class Backtesting:
candle_type=CandleType.FUNDING_RATE
)
# For simplicity, assign to CandleType.Mark (might contian index candles!)
# For simplicity, assign to CandleType.Mark (might contain index candles!)
mark_rates_dict = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
@ -1217,7 +1217,7 @@ class Backtesting:
:return: DataFrame with trades (results of backtesting)
"""
self.prepare_backtest(self.enable_protections)
# Ensure wallets are uptodate (important for --strategy-list)
# Ensure wallets are up-to-date (important for --strategy-list)
self.wallets.update()
# Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames)

2
freqtrade/persistence/custom_data.py
View File

@ -18,7 +18,7 @@ class _CustomData(ModelBase):
"""
CustomData database model
Keeps records of metadata as key/value store
for trades or global persistant values
for trades or global persistent values
One to many relationship with Trades:
- One trade can have many metadata entries
- One metadata entry can only be associated with one Trade

6
freqtrade/persistence/trade_model.py
View File

@ -847,7 +847,7 @@ class LocalTrade:
isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC)
or (not recalculating and order.safe_amount_after_fee > amount_tr)
):
# When recalculating a trade, only comming out to 0 can force a close
# When recalculating a trade, only coming out to 0 can force a close
self.close(order.safe_price)
else:
self.recalc_trade_from_orders()
@ -1125,7 +1125,7 @@ class LocalTrade:
prof = self.calculate_profit(exit_rate, exit_amount, float(avg_price))
close_profit_abs += prof.profit_abs
if total_stake > 0:
# This needs to be calculated based on the last occuring exit to be aligned
# This needs to be calculated based on the last occurring exit to be aligned
# with realized_profit.
close_profit = (close_profit_abs / total_stake) * self.leverage
else:
@ -1502,7 +1502,7 @@ class Trade(ModelBase, LocalTrade):
id: Mapped[int] = mapped_column(Integer, primary_key=True) # type: ignore
orders: Mapped[List[Order]] = relationship(
"Order", order_by="Order.id", cascade="all, delete-orphan", lazy="selectin",
"Order", order_by="Order.id", cascade="all, delete-orphan", lazy="selection",
innerjoin=True) # type: ignore
custom_data: Mapped[List[_CustomData]] = relationship(
"_CustomData", cascade="all, delete-orphan",

4
freqtrade/plot/plotting.py
View File

@ -445,7 +445,7 @@ def generate_candlestick_graph(
) -> 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
Volume will always be plotted in row2, so Row 1 and 3 are to our disposal for custom indicators
:param pair: Pair to Display on the graph
:param data: OHLCV DataFrame containing indicators and entry/exit signals
:param trades: All trades created
@ -673,7 +673,7 @@ def plot_profit(config: Config) -> None:
"""
Plots the total profit for all pairs.
Note, the profit calculation isn't realistic.
But should be somewhat proportional, and therefor useful
But should be somewhat proportional, and therefore useful
in helping out to find a good algorithm.
"""
if 'timeframe' not in config:

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

@ -116,7 +116,7 @@ class RemotePairList(IPairList):
"default": "filter",
"options": ["filter", "append"],
"description": "Processing mode",
"help": "Append pairs to incomming pairlist or filter them?",
"help": "Append pairs to incoming pairlist or filter them?",
},
**IPairList.refresh_period_parameter(),
"keep_pairlist_on_failure": {

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

@ -65,7 +65,7 @@ class VolumePairList(IPairList):
self._tf_in_min = timeframe_to_minutes(self._lookback_timeframe)
_tf_in_sec = self._tf_in_min * 60
# wether to use range lookback or not
# whether to use range lookback or not
self._use_range = (self._tf_in_min > 0) & (self._lookback_period > 0)
if self._use_range & (self._refresh_period < _tf_in_sec):

2
freqtrade/plugins/protections/iprotection.py
View File

@ -110,7 +110,7 @@ class IProtection(LoggingMixin, ABC):
Get lock end time
"""
max_date: datetime = max([trade.close_date for trade in trades if trade.close_date])
# comming from Database, tzinfo is not set.
# coming from Database, tzinfo is not set.
if max_date.tzinfo is None:
max_date = max_date.replace(tzinfo=timezone.utc)

2
freqtrade/resolvers/iresolver.py
View File

@ -163,7 +163,7 @@ class IResolver:
def load_object(cls, object_name: str, config: Config, *, kwargs: dict,
extra_dir: Optional[str] = None) -> Any:
"""
Search and loads the specified object as configured in hte child class.
Search and loads the specified object as configured in the child class.
:param object_name: name of the module to import
:param config: configuration dictionary
:param extra_dir: additional directory to search for the given pairlist

2
freqtrade/rpc/external_message_consumer.py
View File

@ -237,7 +237,7 @@ class ExternalMessageConsumer:
continue
except Exception as e:
# An unforseen error has occurred, log and continue
# An unforeseen error has occurred, log and continue
logger.error("Unexpected error has occurred:")
logger.exception(e)
await asyncio.sleep(self.sleep_time)

2
freqtrade/rpc/rpc.py
View File

@ -301,7 +301,7 @@ class RPC:
for oo in trade.open_orders
]
# exemple: '*.**.**' trying to enter, exit and exit with 3 different orders
# example: '*.**.**' trying to enter, exit and exit with 3 different orders
active_attempt_side_symbols_str = '.'.join(active_attempt_side_symbols)
detail_trade = [

2
freqtrade/rpc/telegram.py
View File

@ -488,7 +488,7 @@ class Telegram(RPCHandler):
elif msg['type'] == RPCMessageType.WARNING:
message = f"\N{WARNING SIGN} *Warning:* `{msg['status']}`"
elif msg['type'] == RPCMessageType.EXCEPTION:
# Errors will contain exceptions, which are wrapped in tripple ticks.
# Errors will contain exceptions, which are wrapped in triple ticks.
message = f"\N{WARNING SIGN} *ERROR:* \n {msg['status']}"
elif msg['type'] == RPCMessageType.STARTUP:

2
freqtrade/strategy/strategy_helper.py
View File

@ -78,7 +78,7 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
# all indicators on the informative sample MUST be calculated before this point
if ffill:
# https://pandas.pydata.org/docs/user_guide/merging.html#timeseries-friendly-merging
# merge_ordered - ffill method is 2.5x faster than seperate ffill()
# merge_ordered - ffill method is 2.5x faster than separate ffill()
dataframe = pd.merge_ordered(dataframe, informative, fill_method="ffill", left_on='date',
right_on=date_merge, how='left')
else:

2
freqtrade/templates/strategy_subtemplates/strategy_methods_advanced.j2
View File

@ -3,7 +3,7 @@ def bot_loop_start(self, current_time: datetime, **kwargs) -> None:
"""
Called at the start of the bot iteration (one loop).
Might be used to perform pair-independent tasks
(e.g. gather some remote ressource for comparison)
(e.g. gather some remote resource for comparison)
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/

2
freqtrade/wallets.py
View File

@ -306,7 +306,7 @@ class Wallets:
:raise: DependencyException if the available stake amount is too low
"""
stake_amount: float
# Ensure wallets are uptodate.
# Ensure wallets are up-to-date.
if update:
self.update()
val_tied_up = Trade.total_open_trades_stakes()

2
freqtrade/worker.py
View File

@ -137,7 +137,7 @@ class Worker:
Throttles the given callable that it
takes at least `min_secs` to finish execution.
:param func: Any callable
:param throttle_secs: throttling interation execution time limit in seconds
:param throttle_secs: throttling iteration execution time limit in seconds
:param timeframe: ensure iteration is executed at the beginning of the next candle.
:param timeframe_offset: offset in seconds to apply to the next candle time.
:return: Any (result of execution of func)

4
scripts/ws_client.py
View File

@ -191,7 +191,7 @@ class ClientProtocol:
self.logger.info("Empty DataFrame")
async def _handle_default(self, name, type, data):
self.logger.info("Unkown message of type {type} received...")
self.logger.info("Unknown message of type {type} received...")
self.logger.info(data)
@ -277,7 +277,7 @@ async def create_client(
continue
except Exception as e:
# An unforseen error has occurred, log and try reconnecting again
# An unforeseen error has occurred, log and try reconnecting again
logger.error("Unexpected error has occurred:")
logger.exception(e)

2
tests/commands/test_commands.py
View File

@ -1609,4 +1609,4 @@ def test_start_show_config(capsys, caplog):
assert "Your combined configuration is:" in captured.out
assert '"max_open_trades":' in captured.out
assert '"secret": "REDACTED"' not in captured.out
assert log_has_re(r'Sensitive information will be shown in the upcomming output.*', caplog)
assert log_has_re(r'Sensitive information will be shown in the upcoming output.*', caplog)

2
tests/conftest.py
View File

@ -49,7 +49,7 @@ def pytest_addoption(parser):
def pytest_configure(config):
config.addinivalue_line(
"markers", "longrun: mark test that is running slowly and should not be run regularily"
"markers", "longrun: mark test that is running slowly and should not be run regularly"
)
if not config.option.longrun:
setattr(config.option, 'markexpr', 'not longrun')

2
tests/data/test_datahandler.py
View File

@ -251,7 +251,7 @@ def test_datahandler__check_empty_df(testdatadir, caplog):
# @pytest.mark.parametrize('datahandler', [])
@pytest.mark.skip("All datahandlers currently support trades data.")
def test_datahandler_trades_not_supported(datahandler, testdatadir, ):
# Currently disabled. Reenable should a new provider not support trades data.
# Currently disabled. Re-enable should a new provider not support trades data.
dh = get_datahandler(testdatadir, datahandler)
with pytest.raises(NotImplementedError):
dh.trades_load('UNITTEST/ETH')

6
tests/exchange/test_exchange.py
View File

@ -3830,7 +3830,7 @@ def test_ohlcv_candle_limit(default_conf, mocker, exchange_name):
[
("BTC/USDT", 'BTC', 'USDT', "binance", True, False, False, 'spot', {}, True),
("USDT/BTC", 'USDT', 'BTC', "binance", True, False, False, 'spot', {}, True),
# No seperating /
# No separating /
("BTCUSDT", 'BTC', 'USDT', "binance", True, False, False, 'spot', {}, True),
("BTCUSDT", None, "USDT", "binance", True, False, False, 'spot', {}, False),
("USDT/BTC", "BTC", None, "binance", True, False, False, 'spot', {}, False),
@ -4346,7 +4346,7 @@ def test_combine_funding_and_mark(
('binance', 0, 2, "2021-09-01 00:00:01", "2021-09-01 08:00:00", 30.0, -0.00091409999),
('binance', 0, 2, "2021-08-31 23:58:00", "2021-09-01 08:00:00", 30.0, -0.00091409999),
('binance', 0, 2, "2021-09-01 00:10:01", "2021-09-01 08:00:00", 30.0, -0.0002493),
# TODO: Uncoment once _calculate_funding_fees can pas time_in_ratio to exchange._get_funding_fee
# TODO: Uncomment once _calculate_funding_fees can pas time_in_ratio to exchange._get_funding_fee
# ('kraken', "2021-09-01 00:00:00", "2021-09-01 08:00:00", 30.0, -0.0014937),
# ('kraken', "2021-09-01 00:00:15", "2021-09-01 08:00:00", 30.0, -0.0008289),
# ('kraken', "2021-09-01 01:00:14", "2021-09-01 08:00:00", 30.0, -0.0008289),
@ -4358,7 +4358,7 @@ def test_combine_funding_and_mark(
('gate', 0, 2, "2021-09-01 00:00:00", "2021-09-01 12:00:00", 30.0, -0.0009140999),
('gate', 1, 2, "2021-09-01 00:00:01", "2021-09-01 08:00:00", 30.0, -0.0002493),
('binance', 0, 2, "2021-09-01 00:00:00", "2021-09-01 08:00:00", 50.0, -0.0015235),
# TODO: Uncoment once _calculate_funding_fees can pas time_in_ratio to exchange._get_funding_fee
# TODO: Uncomment once _calculate_funding_fees can pas time_in_ratio to exchange._get_funding_fee
# ('kraken', "2021-09-01 00:00:00", "2021-09-01 08:00:00", 50.0, -0.0024895),
])
def test__fetch_and_calculate_funding_fees(

2
tests/freqai/test_freqai_datadrawer.py
View File

@ -143,7 +143,7 @@ def test_get_timerange_from_backtesting_live_df_pred_not_found(mocker, freqai_co
def test_set_initial_return_values(mocker, freqai_conf):
"""
Simple test of the set initial return values that ensures
we are concatening and ffilling values properly.
we are concatenating and ffilling values properly.
"""
strategy = get_patched_freqai_strategy(mocker, freqai_conf)

12
tests/freqtradebot/test_freqtradebot.py
View File

@ -285,7 +285,7 @@ def test_edge_overrides_stoploss(limit_order, fee, caplog, mocker,
'last': enter_price * buy_price_mult,
})
# stoploss shoud be hit
# stoploss should be hit
assert freqtrade.handle_trade(trade) is not ignore_strat_sl
if not ignore_strat_sl:
assert log_has_re('Exit for NEO/BTC detected. Reason: stop_loss.*', caplog)
@ -1398,7 +1398,7 @@ def test_update_trade_state_sell(
assert order.status == 'open'
freqtrade.update_trade_state(trade, trade.open_orders_ids[-1], l_order)
assert trade.amount == l_order['amount']
# Wallet needs to be updated after closing a limit-sell order to reenable buying
# Wallet needs to be updated after closing a limit-sell order to re-enable buying
assert wallet_mock.call_count == 1
assert not trade.is_open
# Order is updated by update_trade_state
@ -3240,7 +3240,7 @@ def test_locked_pairs(default_conf_usdt, ticker_usdt, fee,
)
trade.close(ticker_usdt_sell_down()['bid'])
assert freqtrade.strategy.is_pair_locked(trade.pair, side='*')
# Boths sides are locked
# Both sides are locked
assert freqtrade.strategy.is_pair_locked(trade.pair, side='long')
assert freqtrade.strategy.is_pair_locked(trade.pair, side='short')
@ -4829,7 +4829,7 @@ def test_update_funding_fees(
freqtrade.execute_entry('ETH/USDT', 123, is_short=is_short)
freqtrade.execute_entry('LTC/USDT', 2.0, is_short=is_short)
freqtrade.execute_entry('XRP/USDT', 123, is_short=is_short)
multipl = 1 if is_short else -1
multiple = 1 if is_short else -1
trades = Trade.get_open_trades()
assert len(trades) == 3
for trade in trades:
@ -4847,7 +4847,7 @@ def test_update_funding_fees(
assert trade.funding_fees == pytest.approx(sum(
trade.amount *
mark_prices[trade.pair].iloc[1:2]['open'] *
funding_rates[trade.pair].iloc[1:2]['open'] * multipl
funding_rates[trade.pair].iloc[1:2]['open'] * multiple
))
else:
@ -4859,7 +4859,7 @@ def test_update_funding_fees(
trade.amount *
mark_prices[trade.pair].iloc[1:2]['open'] *
funding_rates[trade.pair].iloc[1:2]['open'] *
multipl
multiple
))

4
tests/optimize/test_backtest_detail.py
View File

@ -107,7 +107,7 @@ tc5 = BTContainer(data=[
trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=3)]
)
# Test 6: Drops 3% / Recovers 6% Positive / Closes 1% positve, Stop-Loss triggers 2% Loss
# Test 6: Drops 3% / Recovers 6% Positive / Closes 1% positive, Stop-Loss triggers 2% Loss
# stop-loss: 2% ROI: 5%
tc6 = BTContainer(data=[
# D O H L C V EL XL ES Xs BT
@ -121,7 +121,7 @@ tc6 = BTContainer(data=[
trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=2)]
)
# Test 7: 6% Positive / 1% Negative / Close 1% Positve, ROI Triggers 3% Gain
# Test 7: 6% Positive / 1% Negative / Close 1% Positive, ROI Triggers 3% Gain
# stop-loss: 2% ROI: 3%
tc7 = BTContainer(data=[
# D O H L C V EL XL ES Xs BT

4
tests/optimize/test_backtesting_adjust_position.py
View File

@ -87,9 +87,9 @@ def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) ->
for _, t in results.iterrows():
ln = data_pair.loc[data_pair["date"] == t["open_date"]]
# Check open trade rate alignes to open rate
# Check open trade rate aligns to open rate
assert ln is not None
# check close trade rate alignes to close rate or is between high and low
# check close trade rate aligns to close rate or is between high and low
ln = data_pair.loc[data_pair["date"] == t["close_date"]]
assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
round(ln.iloc[0]["low"], 6) < round(

2
tests/optimize/test_lookahead_analysis.py
View File

@ -152,7 +152,7 @@ def test_lookahead_helper_text_table_lookahead_analysis_instances(lookahead_conf
assert data[0][2].__contains__('too few trades')
assert len(data[0]) == 3
# now check for an error which occured after enough trades
# now check for an error which occurred after enough trades
analysis.total_signals = 12
analysis.false_entry_signals = 11
analysis.false_exit_signals = 10

2
tests/optimize/test_optimize_reports.py
View File

@ -129,7 +129,7 @@ def test_generate_backtest_stats(default_conf, testdatadir, tmp_path):
assert strat_stats['backtest_start'] == min_date.strftime(DATETIME_PRINT_FORMAT)
assert strat_stats['backtest_end'] == max_date.strftime(DATETIME_PRINT_FORMAT)
assert strat_stats['total_trades'] == len(results['DefStrat']['results'])
# Above sample had no loosing trade
# Above sample had no losing trade
assert strat_stats['max_drawdown_account'] == 0.0
# Retry with losing trade

2
tests/persistence/test_persistence.py
View File

@ -1871,7 +1871,7 @@ def test_get_trades__query(fee, is_short):
# without orders there should be no join issued.
query1 = Trade.get_trades_query([], include_orders=False)
# Empty "with-options -> default - selectin"
# Empty "with-options -> default - selection"
assert query._with_options == ()
assert query1._with_options != ()

4
tests/plugins/test_pairlist.py
View File

@ -605,7 +605,7 @@ def test_VolumePairList_whitelist_gen(mocker, whitelist_conf, shitcoinmarkets, t
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
"lookback_days": 1}],
"BTC", "binance", "default_refresh_too_short"), # OperationalException expected
# ambigous configuration with lookback days and period
# ambiguous configuration with lookback days and period
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
"lookback_days": 1, "lookback_period": 1}],
"BTC", "binance", "lookback_days_and_period"), # OperationalException expected
@ -617,7 +617,7 @@ def test_VolumePairList_whitelist_gen(mocker, whitelist_conf, shitcoinmarkets, t
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
"lookback_timeframe": "1m", "lookback_period": 2000, "refresh_period": 3600}],
"BTC", "binance", "lookback_exceeds_exchange_request_size"), # OperationalException expected
# expecing pairs as given
# expecting pairs as given
([{"method": "VolumePairList", "number_assets": 5, "sort_key": "quoteVolume",
"lookback_timeframe": "1d", "lookback_period": 1, "refresh_period": 86400}],
"BTC", "binance", ['LTC/BTC', 'ETH/BTC', 'TKN/BTC', 'XRP/BTC', 'HOT/BTC']),

2
tests/rpc/test_rpc_apiserver.py
View File

@ -1619,7 +1619,7 @@ def test_api_pair_history(botclient, mocker):
assert 'data' in result
data = result['data']
assert len(data) == 289
# analyed DF has 30 columns
# analyzed DF has 30 columns
assert len(result['columns']) == 30
assert len(data[0]) == 30
date_col_idx = [idx for idx, c in enumerate(result['columns']) if c == 'date'][0]

2
tests/strategy/test_interface.py
View File

@ -81,7 +81,7 @@ def test_returns_latest_signal(ohlcv_history):
assert _STRATEGY.get_entry_signal('ETH/BTC', '5m', mocked_history) == (None, None)
_STRATEGY.config['trading_mode'] = 'futures'
# Short signal get's ignored as can_short is not set.
# Short signal gets ignored as can_short is not set.
assert _STRATEGY.get_entry_signal('ETH/BTC', '5m', mocked_history) == (None, None)
_STRATEGY.can_short = True

4
tests/test_plotting.py
View File

@ -125,7 +125,7 @@ def test_add_areas(default_conf, testdatadir, caplog):
assert fig == fig2
assert log_has_re(r'Indicator "no_indicator" ignored\..*', caplog)
# everythin given in plot config, row 3
# everything given in plot config, row 3
fig3 = add_areas(fig, 3, data, indicators)
figure = fig3.layout.figure
fill_macd = find_trace_in_fig_data(figure.data, "MACD Fill")
@ -495,7 +495,7 @@ def test_plot_profit(default_conf, mocker, testdatadir):
# no main_plot, adds empty main_plot
([], [], {'subplots': {'RSI': {'rsi': {'color': 'red'}}}},
{'main_plot': {}, 'subplots': {'RSI': {'rsi': {'color': 'red'}}}}),
# indicator 1 / 2 should have prevelance
# indicator 1 / 2 should have prevalence
(['sma', 'ema3'], ['macd'],
{'main_plot': {'sma': {}}, 'subplots': {'RSI': {'rsi': {'color': 'red'}}}},
{'main_plot': {'sma': {}, 'ema3': {}}, 'subplots': {'Other': {'macd': {}}}}