Merge branch 'feat/short' into pr/samgermain/5378

This commit is contained in:
Matthias 2021-08-24 06:28:16 +02:00
commit 7a977a8eaf
57 changed files with 1076 additions and 811 deletions

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@ -37,7 +37,7 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
Exchanges confirmed working by the community:
- [X] [Bitvavo](https://bitvavo.com/)
- [X] [Kukoin](https://www.kucoin.com/)
- [X] [Kucoin](https://www.kucoin.com/)
## Documentation

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@ -74,7 +74,5 @@ fi
docker images
if [ $? -ne 0 ]; then
echo "failed building image"
return 1
fi
# Cleanup old images from arm64 node.
docker image prune -a --force --filter "until=24h"

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@ -35,12 +35,13 @@ By default, loop runs every few seconds (`internals.process_throttle_secs`) and
* Calls `check_buy_timeout()` strategy callback for open buy orders.
* Calls `check_sell_timeout()` strategy callback for open sell orders.
* Verifies existing positions and eventually places sell orders.
* Considers stoploss, ROI and sell-signal.
* Determine sell-price based on `ask_strategy` configuration setting.
* Considers stoploss, ROI and sell-signal, `custom_sell()` and `custom_stoploss()`.
* Determine sell-price based on `ask_strategy` configuration setting or by using the `custom_exit_price()` callback.
* Before a sell order is placed, `confirm_trade_exit()` strategy callback is called.
* Check if trade-slots are still available (if `max_open_trades` is reached).
* Verifies buy signal trying to enter new positions.
* Determine buy-price based on `bid_strategy` configuration setting.
* Determine buy-price based on `bid_strategy` configuration setting, or by using the `custom_entry_price()` callback.
* Determine stake size by calling the `custom_stake_amount()` callback.
* Before a buy order is placed, `confirm_trade_entry()` strategy callback is called.
This loop will be repeated again and again until the bot is stopped.
@ -52,9 +53,10 @@ This loop will be repeated again and again until the bot is stopped.
* Load historic data for configured pairlist.
* Calls `bot_loop_start()` once.
* Calculate indicators (calls `populate_indicators()` once per pair).
* Calculate buy / sell signals (calls `populate_buy_trend()` and `populate_sell_trend()` once per pair)
* Confirm trade buy / sell (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy)
* Calculate buy / sell signals (calls `populate_buy_trend()` and `populate_sell_trend()` once per pair).
* Loops per candle simulating entry and exit points.
* Confirm trade buy / sell (calls `confirm_trade_entry()` and `confirm_trade_exit()` if implemented in the strategy).
* Call `custom_stoploss()` and `custom_sell()` to find custom exit points.
* Generate backtest report output
!!! Note

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@ -105,11 +105,12 @@ Mandatory parameters are marked as **Required**, which means that they are requi
| `ask_strategy.order_book_top` | Bot will use the top N rate in Order Book "price_side" to sell. I.e. a value of 2 will allow the bot to pick the 2nd ask rate in [Order Book Asks](#sell-price-with-orderbook-enabled)<br>*Defaults to `1`.* <br> **Datatype:** Positive Integer
| `use_sell_signal` | Use sell signals produced by the strategy in addition to the `minimal_roi`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `true`.* <br> **Datatype:** Boolean
| `sell_profit_only` | Wait until the bot reaches `sell_profit_offset` before taking a sell decision. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `sell_profit_offset` | Sell-signal is only active above this value. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0`.* <br> **Datatype:** Float (as ratio)
| `sell_profit_offset` | Sell-signal is only active above this value. Only active in combination with `sell_profit_only=True`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0`.* <br> **Datatype:** Float (as ratio)
| `ignore_roi_if_buy_signal` | Do not sell if the buy signal is still active. This setting takes preference over `minimal_roi` and `use_sell_signal`. [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.* <br> **Datatype:** Boolean
| `ignore_buying_expired_candle_after` | Specifies the number of seconds until a buy signal is no longer used. <br> **Datatype:** Integer
| `order_types` | Configure order-types depending on the action (`"buy"`, `"sell"`, `"stoploss"`, `"stoploss_on_exchange"`). [More information below](#understand-order_types). [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Dict
| `order_time_in_force` | Configure time in force for buy and sell orders. [More information below](#understand-order_time_in_force). [Strategy Override](#parameters-in-the-strategy). <br> **Datatype:** Dict
| `custom_price_max_distance_ratio` | Configure maximum distance ratio between current and custom entry or exit price. <br>*Defaults to `0.02` 2%).*<br> **Datatype:** Positive float
| `exchange.name` | **Required.** Name of the exchange class to use. [List below](#user-content-what-values-for-exchangename). <br> **Datatype:** String
| `exchange.sandbox` | Use the 'sandbox' version of the exchange, where the exchange provides a sandbox for risk-free integration. See [here](sandbox-testing.md) in more details.<br> **Datatype:** Boolean
| `exchange.key` | API key to use for the exchange. Only required when you are in production mode.<br>**Keep it in secret, do not disclose publicly.** <br> **Datatype:** String

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@ -240,11 +240,18 @@ The `IProtection` parent class provides a helper method for this in `calculate_l
!!! Note
This section is a Work in Progress and is not a complete guide on how to test a new exchange with Freqtrade.
!!! Note
Make sure to use an up-to-date version of CCXT before running any of the below tests.
You can get the latest version of ccxt by running `pip install -U ccxt` with activated virtual environment.
Native docker is not supported for these tests, however the available dev-container will support all required actions and eventually necessary changes.
Most exchanges supported by CCXT should work out of the box.
To quickly test the public endpoints of an exchange, add a configuration for your exchange to `test_ccxt_compat.py` and run these tests with `pytest --longrun tests/exchange/test_ccxt_compat.py`.
Completing these tests successfully a good basis point (it's a requirement, actually), however these won't guarantee correct exchange functioning, as this only tests public endpoints, but no private endpoint (like generate order or similar).
Also try to use `freqtrade download-data` for an extended timerange and verify that the data downloaded correctly (no holes, the specified timerange was actually downloaded).
### Stoploss On Exchange
Check if the new exchange supports Stoploss on Exchange orders through their API.

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@ -105,7 +105,7 @@ To use subaccounts with FTX, you need to edit the configuration and add the foll
## Kucoin
Kucoin requries a passphrase for each api key, you will therefore need to add this key into the configuration so your exchange section looks as follows:
Kucoin requires a passphrase for each api key, you will therefore need to add this key into the configuration so your exchange section looks as follows:
```json
"exchange": {

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@ -58,7 +58,7 @@ This option must be configured along with `exchange.skip_pair_validation` in the
When used in the chain of Pairlist Handlers in a non-leading position (after StaticPairList and other Pairlist Filters), `VolumePairList` considers outputs of previous Pairlist Handlers, adding its sorting/selection of the pairs by the trading volume.
When used on the leading position of the chain of Pairlist Handlers, it does not consider `pair_whitelist` configuration setting, but selects the top assets from all available markets (with matching stake-currency) on the exchange.
When used in the leading position of the chain of Pairlist Handlers, the `pair_whitelist` configuration setting is ignored. Instead, `VolumePairList` selects the top assets from all available markets with matching stake-currency on the exchange.
The `refresh_period` setting allows to define the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
The pairlist cache (`refresh_period`) on `VolumePairList` is only applicable to generating pairlists.
@ -74,11 +74,14 @@ Filtering instances (not the first position in the list) will not apply any cach
"method": "VolumePairList",
"number_assets": 20,
"sort_key": "quoteVolume",
"min_value": 0,
"refresh_period": 1800
}
],
```
You can define a minimum volume with `min_value` - which will filter out pairs with a volume lower than the specified value in the specified timerange.
`VolumePairList` can also operate in an advanced mode to build volume over a given timerange of specified candle size. It utilizes exchange historical candle data, builds a typical price (calculated by (open+high+low)/3) and multiplies the typical price with every candle's volume. The sum is the `quoteVolume` over the given range. This allows different scenarios, for a more smoothened volume, when using longer ranges with larger candle sizes, or the opposite when using a short range with small candles.
For convenience `lookback_days` can be specified, which will imply that 1d candles will be used for the lookback. In the example below the pairlist would be created based on the last 7 days:
@ -89,6 +92,7 @@ For convenience `lookback_days` can be specified, which will imply that 1d candl
"method": "VolumePairList",
"number_assets": 20,
"sort_key": "quoteVolume",
"min_value": 0,
"refresh_period": 86400,
"lookback_days": 7
}
@ -109,6 +113,7 @@ More sophisticated approach can be used, by using `lookback_timeframe` for candl
"method": "VolumePairList",
"number_assets": 20,
"sort_key": "quoteVolume",
"min_value": 0,
"refresh_period": 3600,
"lookback_timeframe": "1h",
"lookback_period": 72

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@ -47,7 +47,7 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
Exchanges confirmed working by the community:
- [X] [Bitvavo](https://bitvavo.com/)
- [X] [Kukoin](https://www.kucoin.com/)
- [X] [Kucoin](https://www.kucoin.com/)
## Requirements

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@ -1,4 +1,4 @@
mkdocs==1.2.2
mkdocs-material==7.2.2
mkdocs-material==7.2.4
mdx_truly_sane_lists==1.2
pymdown-extensions==8.2

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@ -357,6 +357,55 @@ See [Dataframe access](#dataframe-access) for more information about dataframe u
---
## Custom order price rules
By default, freqtrade use the orderbook to automatically set an order price([Relevant documentation](configuration.md#prices-used-for-orders)), you also have the option to create custom order prices based on your strategy.
You can use this feature by creating a `custom_entry_price()` function in your strategy file to customize entry prices and `custom_exit_price()` for exits.
!!! Note
If your custom pricing function return None or an invalid value, price will fall back to `proposed_rate`, which is based on the regular pricing configuration.
### Custom order entry and exit price example
``` python
from datetime import datetime, timedelta, timezone
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def custom_entry_price(self, pair: str, current_time: datetime,
proposed_rate, **kwargs) -> float:
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
timeframe=self.timeframe)
new_entryprice = dataframe['bollinger_10_lowerband'].iat[-1]
return new_entryprice
def custom_exit_price(self, pair: str, trade: Trade,
current_time: datetime, proposed_rate: float,
current_profit: float, **kwargs) -> float:
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
timeframe=self.timeframe)
new_exitprice = dataframe['bollinger_10_upperband'].iat[-1]
return new_exitprice
```
!!! Warning
Modifying entry and exit prices will only work for limit orders. Depending on the price chosen, this can result in a lot of unfilled orders. By default the maximum allowed distance between the current price and the custom price is 2%, this value can be changed in config with the `custom_price_max_distance_ratio` parameter.
!!! Example
If the new_entryprice is 97, the proposed_rate is 100 and the `custom_price_max_distance_ratio` is set to 2%, The retained valid custom entry price will be 98.
!!! Warning "No backtesting support"
Custom entry-prices are currently not supported during backtesting.
## Custom order timeout rules
Simple, time-based order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.

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@ -228,7 +228,7 @@ graph = generate_candlestick_graph(pair=pair,
# Show graph inline
# graph.show()
# Render graph in a seperate window
# Render graph in a separate window
graph.show(renderer="browser")
```

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@ -1,6 +1,6 @@
import logging
from operator import itemgetter
from typing import Any, Dict, List
from typing import Any, Dict
from colorama import init as colorama_init
@ -28,30 +28,12 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
no_details = config.get('hyperopt_list_no_details', False)
no_header = False
filteroptions = {
'only_best': config.get('hyperopt_list_best', False),
'only_profitable': config.get('hyperopt_list_profitable', False),
'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
'filter_min_objective': config.get('hyperopt_list_min_objective', None),
'filter_max_objective': config.get('hyperopt_list_max_objective', None),
}
results_file = get_latest_hyperopt_file(
config['user_data_dir'] / 'hyperopt_results',
config.get('hyperoptexportfilename'))
# Previous evaluations
epochs = HyperoptTools.load_previous_results(results_file)
total_epochs = len(epochs)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
epochs, total_epochs = HyperoptTools.load_filtered_results(results_file, config)
if print_colorized:
colorama_init(autoreset=True)
@ -59,7 +41,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
if not export_csv:
try:
print(HyperoptTools.get_result_table(config, epochs, total_epochs,
not filteroptions['only_best'],
not config.get('hyperopt_list_best', False),
print_colorized, 0))
except KeyboardInterrupt:
print('User interrupted..')
@ -71,7 +53,7 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
if epochs and export_csv:
HyperoptTools.export_csv_file(
config, epochs, total_epochs, not filteroptions['only_best'], export_csv
config, epochs, total_epochs, not config.get('hyperopt_list_best', False), export_csv
)
@ -91,26 +73,9 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
n = config.get('hyperopt_show_index', -1)
filteroptions = {
'only_best': config.get('hyperopt_list_best', False),
'only_profitable': config.get('hyperopt_list_profitable', False),
'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
'filter_min_objective': config.get('hyperopt_list_min_objective', None),
'filter_max_objective': config.get('hyperopt_list_max_objective', None)
}
# Previous evaluations
epochs = HyperoptTools.load_previous_results(results_file)
total_epochs = len(epochs)
epochs, total_epochs = HyperoptTools.load_filtered_results(results_file, config)
epochs = hyperopt_filter_epochs(epochs, filteroptions)
filtered_epochs = len(epochs)
if n > filtered_epochs:
@ -137,138 +102,3 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
HyperoptTools.show_epoch_details(val, total_epochs, print_json, no_header,
header_str="Epoch details")
def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
"""
Filter our items from the list of hyperopt results
TODO: after 2021.5 remove all "legacy" mode queries.
"""
if filteroptions['only_best']:
epochs = [x for x in epochs if x['is_best']]
if filteroptions['only_profitable']:
epochs = [x for x in epochs if x['results_metrics'].get(
'profit', x['results_metrics'].get('profit_total', 0)) > 0]
epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
epochs = _hyperopt_filter_epochs_duration(epochs, filteroptions)
epochs = _hyperopt_filter_epochs_profit(epochs, filteroptions)
epochs = _hyperopt_filter_epochs_objective(epochs, filteroptions)
logger.info(f"{len(epochs)} " +
("best " if filteroptions['only_best'] else "") +
("profitable " if filteroptions['only_profitable'] else "") +
"epochs found.")
return epochs
def _hyperopt_filter_epochs_trade(epochs: List, trade_count: int):
"""
Filter epochs with trade-counts > trades
"""
return [
x for x in epochs
if x['results_metrics'].get(
'trade_count', x['results_metrics'].get('total_trades', 0)
) > trade_count
]
def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_trades'] > 0:
epochs = _hyperopt_filter_epochs_trade(epochs, filteroptions['filter_min_trades'])
if filteroptions['filter_max_trades'] > 0:
epochs = [
x for x in epochs
if x['results_metrics'].get(
'trade_count', x['results_metrics'].get('total_trades')
) < filteroptions['filter_max_trades']
]
return epochs
def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
def get_duration_value(x):
# Duration in minutes ...
if 'duration' in x['results_metrics']:
return x['results_metrics']['duration']
else:
# New mode
if 'holding_avg_s' in x['results_metrics']:
avg = x['results_metrics']['holding_avg_s']
return avg // 60
raise OperationalException(
"Holding-average not available. Please omit the filter on average time, "
"or rerun hyperopt with this version")
if filteroptions['filter_min_avg_time'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if get_duration_value(x) > filteroptions['filter_min_avg_time']
]
if filteroptions['filter_max_avg_time'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if get_duration_value(x) < filteroptions['filter_max_avg_time']
]
return epochs
def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_avg_profit'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics'].get(
'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
) > filteroptions['filter_min_avg_profit']
]
if filteroptions['filter_max_avg_profit'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics'].get(
'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
) < filteroptions['filter_max_avg_profit']
]
if filteroptions['filter_min_total_profit'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics'].get(
'profit', x['results_metrics'].get('profit_total_abs', 0)
) > filteroptions['filter_min_total_profit']
]
if filteroptions['filter_max_total_profit'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics'].get(
'profit', x['results_metrics'].get('profit_total_abs', 0)
) < filteroptions['filter_max_total_profit']
]
return epochs
def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_objective'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']]
if filteroptions['filter_max_objective'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]
return epochs

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@ -191,6 +191,9 @@ CONF_SCHEMA = {
},
'required': ['price_side']
},
'custom_price_max_distance_ratio': {
'type': 'number', 'minimum': 0.0
},
'order_types': {
'type': 'object',
'properties': {

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@ -19,7 +19,7 @@ logger = logging.getLogger(__name__)
BT_DATA_COLUMNS_OLD = ["pair", "profit_percent", "open_date", "close_date", "index",
"trade_duration", "open_rate", "close_rate", "open_at_end", "sell_reason"]
# Mid-term format, crated by BacktestResult Named Tuple
# Mid-term format, created by BacktestResult Named Tuple
BT_DATA_COLUMNS_MID = ['pair', 'profit_percent', 'open_date', 'close_date', 'trade_duration',
'open_rate', 'close_rate', 'open_at_end', 'sell_reason', 'fee_open',
'fee_close', 'amount', 'profit_abs', 'profit_ratio']

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@ -242,7 +242,7 @@ def convert_trades_format(config: Dict[str, Any], convert_from: str, convert_to:
:param config: Config dictionary
:param convert_from: Source format
:param convert_to: Target format
:param erase: Erase souce data (does not apply if source and target format are identical)
:param erase: Erase source data (does not apply if source and target format are identical)
"""
from freqtrade.data.history.idatahandler import get_datahandler
src = get_datahandler(config['datadir'], convert_from)
@ -267,7 +267,7 @@ def convert_ohlcv_format(config: Dict[str, Any], convert_from: str, convert_to:
:param config: Config dictionary
:param convert_from: Source format
:param convert_to: Target format
:param erase: Erase souce data (does not apply if source and target format are identical)
:param erase: Erase source data (does not apply if source and target format are identical)
"""
from freqtrade.data.history.idatahandler import get_datahandler
src = get_datahandler(config['datadir'], convert_from)

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@ -117,10 +117,11 @@ def refresh_data(datadir: Path,
:param timerange: Limit data to be loaded to this timerange
"""
data_handler = get_datahandler(datadir, data_format)
for pair in pairs:
_download_pair_history(pair=pair, timeframe=timeframe,
datadir=datadir, timerange=timerange,
exchange=exchange, data_handler=data_handler)
for idx, pair in enumerate(pairs):
process = f'{idx}/{len(pairs)}'
_download_pair_history(pair=pair, process=process,
timeframe=timeframe, datadir=datadir,
timerange=timerange, exchange=exchange, data_handler=data_handler)
def _load_cached_data_for_updating(pair: str, timeframe: str, timerange: Optional[TimeRange],
@ -153,13 +154,14 @@ def _load_cached_data_for_updating(pair: str, timeframe: str, timerange: Optiona
return data, start_ms
def _download_pair_history(datadir: Path,
def _download_pair_history(pair: str, *,
datadir: Path,
exchange: Exchange,
pair: str, *,
new_pairs_days: int = 30,
timeframe: str = '5m',
timerange: Optional[TimeRange] = None,
data_handler: IDataHandler = None) -> bool:
process: str = '',
new_pairs_days: int = 30,
data_handler: IDataHandler = None,
timerange: Optional[TimeRange] = None) -> bool:
"""
Download latest candles from the exchange for the pair and timeframe passed in parameters
The data is downloaded starting from the last correct data that
@ -177,7 +179,7 @@ def _download_pair_history(datadir: Path,
try:
logger.info(
f'Download history data for pair: "{pair}", timeframe: {timeframe} '
f'Download history data for pair: "{pair}" ({process}), timeframe: {timeframe} '
f'and store in {datadir}.'
)
@ -234,7 +236,7 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
"""
pairs_not_available = []
data_handler = get_datahandler(datadir, data_format)
for pair in pairs:
for idx, pair in enumerate(pairs, start=1):
if pair not in exchange.markets:
pairs_not_available.append(pair)
logger.info(f"Skipping pair {pair}...")
@ -247,10 +249,11 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
f'Deleting existing data for pair {pair}, interval {timeframe}.')
logger.info(f'Downloading pair {pair}, interval {timeframe}.')
_download_pair_history(datadir=datadir, exchange=exchange,
pair=pair, timeframe=str(timeframe),
new_pairs_days=new_pairs_days,
timerange=timerange, data_handler=data_handler)
process = f'{idx}/{len(pairs)}'
_download_pair_history(pair=pair, process=process,
datadir=datadir, exchange=exchange,
timerange=timerange, data_handler=data_handler,
timeframe=str(timeframe), new_pairs_days=new_pairs_days)
return pairs_not_available

View File

@ -151,7 +151,7 @@ class Edge:
# Fake run-mode to Edge
prior_rm = self.config['runmode']
self.config['runmode'] = RunMode.EDGE
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
preprocessed = self.strategy.advise_all_indicators(data)
self.config['runmode'] = prior_rm
# Print timeframe

View File

@ -15,6 +15,7 @@ from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges,
timeframe_to_seconds, validate_exchange,
validate_exchanges)
from freqtrade.exchange.ftx import Ftx
from freqtrade.exchange.gateio import Gateio
from freqtrade.exchange.hitbtc import Hitbtc
from freqtrade.exchange.kraken import Kraken
from freqtrade.exchange.kucoin import Kucoin

View File

@ -618,6 +618,8 @@ class Exchange:
if self.exchange_has('fetchL2OrderBook'):
ob = self.fetch_l2_order_book(pair, 20)
ob_type = 'asks' if side == 'buy' else 'bids'
slippage = 0.05
max_slippage_val = rate * ((1 + slippage) if side == 'buy' else (1 - slippage))
remaining_amount = amount
filled_amount = 0
@ -626,7 +628,9 @@ class Exchange:
book_entry_coin_volume = book_entry[1]
if remaining_amount > 0:
if remaining_amount < book_entry_coin_volume:
# Orderbook at this slot bigger than remaining amount
filled_amount += remaining_amount * book_entry_price
break
else:
filled_amount += book_entry_coin_volume * book_entry_price
remaining_amount -= book_entry_coin_volume
@ -635,7 +639,14 @@ class Exchange:
else:
# If remaining_amount wasn't consumed completely (break was not called)
filled_amount += remaining_amount * book_entry_price
forecast_avg_filled_price = filled_amount / amount
forecast_avg_filled_price = max(filled_amount, 0) / amount
# Limit max. slippage to specified value
if side == 'buy':
forecast_avg_filled_price = min(forecast_avg_filled_price, max_slippage_val)
else:
forecast_avg_filled_price = max(forecast_avg_filled_price, max_slippage_val)
return self.price_to_precision(pair, forecast_avg_filled_price)
return rate
@ -1242,7 +1253,7 @@ class Exchange:
logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
input_coroutines = []
cached_pairs = []
# Gather coroutines to run
for pair, timeframe in set(pair_list):
if (((pair, timeframe) not in self._klines)
@ -1254,6 +1265,7 @@ class Exchange:
"Using cached candle (OHLCV) data for pair %s, timeframe %s ...",
pair, timeframe
)
cached_pairs.append((pair, timeframe))
results = asyncio.get_event_loop().run_until_complete(
asyncio.gather(*input_coroutines, return_exceptions=True))
@ -1276,6 +1288,10 @@ class Exchange:
results_df[(pair, timeframe)] = ohlcv_df
if cache:
self._klines[(pair, timeframe)] = ohlcv_df
# Return cached klines
for pair, timeframe in cached_pairs:
results_df[(pair, timeframe)] = self.klines((pair, timeframe), copy=False)
return results_df
def _now_is_time_to_refresh(self, pair: str, timeframe: str) -> bool:
@ -1486,7 +1502,7 @@ class Exchange:
:returns List of trade data
"""
if not self.exchange_has("fetchTrades"):
raise OperationalException("This exchange does not suport downloading Trades.")
raise OperationalException("This exchange does not support downloading Trades.")
return asyncio.get_event_loop().run_until_complete(
self._async_get_trade_history(pair=pair, since=since,

View File

@ -0,0 +1,23 @@
""" Gate.io exchange subclass """
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Gateio(Exchange):
"""
Gate.io exchange class. Contains adjustments needed for Freqtrade to work
with this exchange.
Please note that this exchange is not included in the list of exchanges
officially supported by the Freqtrade development team. So some features
may still not work as expected.
"""
_ft_has: Dict = {
"ohlcv_candle_limit": 1000,
}

View File

@ -479,7 +479,13 @@ class FreqtradeBot(LoggingMixin):
buy_limit_requested = price
else:
# Calculate price
buy_limit_requested = self.exchange.get_rate(pair, refresh=True, side="buy")
proposed_buy_rate = self.exchange.get_rate(pair, refresh=True, side="buy")
custom_entry_price = strategy_safe_wrapper(self.strategy.custom_entry_price,
default_retval=proposed_buy_rate)(
pair=pair, current_time=datetime.now(timezone.utc),
proposed_rate=proposed_buy_rate)
buy_limit_requested = self.get_valid_price(custom_entry_price, proposed_buy_rate)
if not buy_limit_requested:
raise PricingError('Could not determine buy price.')
@ -977,7 +983,7 @@ class FreqtradeBot(LoggingMixin):
# if trade is partially complete, edit the stake details for the trade
# and close the order
# cancel_order may not contain the full order dict, so we need to fallback
# to the order dict aquired before cancelling.
# to the order dict acquired before cancelling.
# we need to fall back to the values from order if corder does not contain these keys.
trade.amount = filled_amount
trade.stake_amount = trade.amount * trade.open_rate
@ -1076,6 +1082,17 @@ class FreqtradeBot(LoggingMixin):
and self.strategy.order_types['stoploss_on_exchange']:
limit = trade.stop_loss
# set custom_exit_price if available
proposed_limit_rate = limit
current_profit = trade.calc_profit_ratio(limit)
custom_exit_price = strategy_safe_wrapper(self.strategy.custom_exit_price,
default_retval=proposed_limit_rate)(
pair=trade.pair, trade=trade,
current_time=datetime.now(timezone.utc),
proposed_rate=proposed_limit_rate, current_profit=current_profit)
limit = self.get_valid_price(custom_exit_price, proposed_limit_rate)
# First cancelling stoploss on exchange ...
if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
try:
@ -1364,6 +1381,8 @@ class FreqtradeBot(LoggingMixin):
if fee_currency:
# fee_rate should use mean
fee_rate = sum(fee_rate_array) / float(len(fee_rate_array)) if fee_rate_array else None
if fee_rate is not None and fee_rate < 0.02:
# Only update if fee-rate is < 2%
trade.update_fee(fee_cost, fee_currency, fee_rate, order.get('side', ''))
if not isclose(amount, order_amount, abs_tol=constants.MATH_CLOSE_PREC):
@ -1375,3 +1394,26 @@ class FreqtradeBot(LoggingMixin):
amount=amount, fee_abs=fee_abs)
else:
return amount
def get_valid_price(self, custom_price: float, proposed_price: float) -> float:
"""
Return the valid price.
Check if the custom price is of the good type if not return proposed_price
:return: valid price for the order
"""
if custom_price:
try:
valid_custom_price = float(custom_price)
except ValueError:
valid_custom_price = proposed_price
else:
valid_custom_price = proposed_price
cust_p_max_dist_r = self.config.get('custom_price_max_distance_ratio', 0.02)
min_custom_price_allowed = proposed_price - (proposed_price * cust_p_max_dist_r)
max_custom_price_allowed = proposed_price + (proposed_price * cust_p_max_dist_r)
# Bracket between min_custom_price_allowed and max_custom_price_allowed
return max(
min(valid_custom_price, max_custom_price_allowed),
min_custom_price_allowed)

View File

@ -133,6 +133,9 @@ class Backtesting:
self.abort = False
def __del__(self):
self.cleanup()
def cleanup(self):
LoggingMixin.show_output = True
PairLocks.use_db = True
Trade.use_db = True
@ -219,7 +222,7 @@ class Backtesting:
# Every change to this headers list must evaluate further usages of the resulting tuple
# and eventually change the constants for indexes at the top
headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
'enter_short', 'exit_short']
'enter_short', 'exit_short', 'long_tag', 'short_tag']
data: Dict = {}
self.progress.init_step(BacktestState.CONVERT, len(processed))
@ -227,20 +230,14 @@ class Backtesting:
for pair, pair_data in processed.items():
self.check_abort()
self.progress.increment()
has_buy_tag = 'long_tag' in pair_data
has_short_tag = 'short_tag' in pair_data
headers = headers + ['long_tag'] if has_buy_tag else headers
headers = headers + ['short_tag'] if has_short_tag else headers
if not pair_data.empty:
# Cleanup from prior runs
pair_data.loc[:, 'buy'] = 0 # TODO: Should be renamed to enter_long
pair_data.loc[:, 'enter_short'] = 0
pair_data.loc[:, 'sell'] = 0 # TODO: should be renamed to exit_long
pair_data.loc[:, 'exit_short'] = 0
# pair_data.loc[:, 'sell'] = 0
if has_buy_tag:
pair_data.loc[:, 'long_tag'] = None # cleanup if buy_tag is exist
if has_short_tag:
pair_data.loc[:, 'short_tag'] = None # cleanup if short_tag is exist
df_analyzed = self.strategy.advise_sell(
@ -256,7 +253,6 @@ class Backtesting:
df_analyzed.loc[:, 'enter_short'] = df_analyzed.loc[:, 'enter_short'].shift(1)
df_analyzed.loc[:, 'exit_long'] = df_analyzed.loc[:, 'exit_long'].shift(1)
df_analyzed.loc[:, 'exit_short'] = df_analyzed.loc[:, 'exit_short'].shift(1)
if has_buy_tag:
df_analyzed.loc[:, 'long_tag'] = df_analyzed.loc[:, 'long_tag'].shift(1)
df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
@ -264,6 +260,8 @@ class Backtesting:
# Update dataprovider cache
self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
data[pair] = df_analyzed[headers].values.tolist()
@ -337,13 +335,14 @@ class Backtesting:
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
# TODO: short exits
sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], # type: ignore
sell_row[DATE_IDX].to_pydatetime(), sell_row[BUY_IDX],
sell_candle_time, sell_row[BUY_IDX],
sell_row[SELL_IDX],
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade.close_date = sell_row[DATE_IDX].to_pydatetime()
trade.close_date = sell_candle_time
trade.sell_reason = sell.sell_reason
trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
@ -355,7 +354,7 @@ class Backtesting:
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_reason,
current_time=sell_row[DATE_IDX].to_pydatetime()):
current_time=sell_candle_time):
return None
trade.close(closerate, show_msg=False)
@ -494,6 +493,8 @@ class Backtesting:
for i, pair in enumerate(data):
row_index = indexes[pair]
try:
# Row is treated as "current incomplete candle".
# Buy / sell signals are shifted by 1 to compensate for this.
row = data[pair][row_index]
except IndexError:
# missing Data for one pair at the end.
@ -505,8 +506,8 @@ class Backtesting:
continue
row_index += 1
self.dataprovider._set_dataframe_max_index(row_index)
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
@ -530,7 +531,7 @@ class Backtesting:
open_trades[pair].append(trade)
LocalTrade.add_bt_trade(trade)
for trade in open_trades[pair]:
for trade in list(open_trades[pair]):
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(trade, row)
# Sell occurred
@ -561,7 +562,8 @@ class Backtesting:
'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
}
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, DataFrame],
timerange: TimeRange):
self.progress.init_step(BacktestState.ANALYZE, 0)
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
@ -580,7 +582,7 @@ class Backtesting:
max_open_trades = 0
# need to reprocess data every time to populate signals
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
preprocessed = self.strategy.advise_all_indicators(data)
# Trim startup period from analyzed dataframe
preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup)

View File

@ -394,7 +394,7 @@ class Hyperopt:
data, timerange = self.backtesting.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
preprocessed = self.backtesting.strategy.advise_all_indicators(data)
# Trim startup period from analyzed dataframe to get correct dates for output.
processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)

View File

@ -0,0 +1,128 @@
import logging
from typing import List
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
def hyperopt_filter_epochs(epochs: List, filteroptions: dict, log: bool = True) -> List:
"""
Filter our items from the list of hyperopt results
"""
if filteroptions['only_best']:
epochs = [x for x in epochs if x['is_best']]
if filteroptions['only_profitable']:
epochs = [x for x in epochs
if x['results_metrics'].get('profit_total', 0) > 0]
epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
epochs = _hyperopt_filter_epochs_duration(epochs, filteroptions)
epochs = _hyperopt_filter_epochs_profit(epochs, filteroptions)
epochs = _hyperopt_filter_epochs_objective(epochs, filteroptions)
if log:
logger.info(f"{len(epochs)} " +
("best " if filteroptions['only_best'] else "") +
("profitable " if filteroptions['only_profitable'] else "") +
"epochs found.")
return epochs
def _hyperopt_filter_epochs_trade(epochs: List, trade_count: int):
"""
Filter epochs with trade-counts > trades
"""
return [
x for x in epochs if x['results_metrics'].get('total_trades', 0) > trade_count
]
def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_trades'] > 0:
epochs = _hyperopt_filter_epochs_trade(epochs, filteroptions['filter_min_trades'])
if filteroptions['filter_max_trades'] > 0:
epochs = [
x for x in epochs
if x['results_metrics'].get('total_trades') < filteroptions['filter_max_trades']
]
return epochs
def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
def get_duration_value(x):
# Duration in minutes ...
if 'holding_avg_s' in x['results_metrics']:
avg = x['results_metrics']['holding_avg_s']
return avg // 60
raise OperationalException(
"Holding-average not available. Please omit the filter on average time, "
"or rerun hyperopt with this version")
if filteroptions['filter_min_avg_time'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if get_duration_value(x) > filteroptions['filter_min_avg_time']
]
if filteroptions['filter_max_avg_time'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if get_duration_value(x) < filteroptions['filter_max_avg_time']
]
return epochs
def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_avg_profit'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics'].get('profit_mean', 0) * 100
> filteroptions['filter_min_avg_profit']
]
if filteroptions['filter_max_avg_profit'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics'].get('profit_mean', 0) * 100
< filteroptions['filter_max_avg_profit']
]
if filteroptions['filter_min_total_profit'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics'].get('profit_total_abs', 0)
> filteroptions['filter_min_total_profit']
]
if filteroptions['filter_max_total_profit'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [
x for x in epochs
if x['results_metrics'].get('profit_total_abs', 0)
< filteroptions['filter_max_total_profit']
]
return epochs
def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List:
if filteroptions['filter_min_objective'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']]
if filteroptions['filter_max_objective'] is not None:
epochs = _hyperopt_filter_epochs_trade(epochs, 0)
epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]
return epochs

View File

@ -4,7 +4,7 @@ import logging
from copy import deepcopy
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional
from typing import Any, Dict, Iterator, List, Optional, Tuple
import numpy as np
import rapidjson
@ -15,6 +15,7 @@ from pandas import isna, json_normalize
from freqtrade.constants import FTHYPT_FILEVERSION, USERPATH_STRATEGIES
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts, round_coin_value, round_dict, safe_value_fallback2
from freqtrade.optimize.hyperopt_epoch_filters import hyperopt_filter_epochs
logger = logging.getLogger(__name__)
@ -89,46 +90,70 @@ class HyperoptTools():
return any(s in config['spaces'] for s in [space, 'all', 'default'])
@staticmethod
def _read_results_pickle(results_file: Path) -> List:
def _read_results(results_file: Path, batch_size: int = 10) -> Iterator[List[Any]]:
"""
Read hyperopt results from pickle file
LEGACY method - new files are written as json and cannot be read with this method.
"""
from joblib import load
logger.info(f"Reading pickled epochs from '{results_file}'")
data = load(results_file)
return data
@staticmethod
def _read_results(results_file: Path) -> List:
"""
Read hyperopt results from file
Stream hyperopt results from file
"""
import rapidjson
logger.info(f"Reading epochs from '{results_file}'")
with results_file.open('r') as f:
data = [rapidjson.loads(line) for line in f]
return data
data = []
for line in f:
data += [rapidjson.loads(line)]
if len(data) >= batch_size:
yield data
data = []
yield data
@staticmethod
def load_previous_results(results_file: Path) -> List:
"""
Load data for epochs from the file if we have one
"""
epochs: List = []
def _test_hyperopt_results_exist(results_file) -> bool:
if results_file.is_file() and results_file.stat().st_size > 0:
if results_file.suffix == '.pickle':
epochs = HyperoptTools._read_results_pickle(results_file)
raise OperationalException(
"Legacy hyperopt results are no longer supported."
"Please rerun hyperopt or use an older version to load this file."
)
return True
else:
epochs = HyperoptTools._read_results(results_file)
# Detection of some old format, without 'is_best' field saved
if epochs[0].get('is_best') is None:
# No file found.
return False
@staticmethod
def load_filtered_results(results_file: Path, config: Dict[str, Any]) -> Tuple[List, int]:
filteroptions = {
'only_best': config.get('hyperopt_list_best', False),
'only_profitable': config.get('hyperopt_list_profitable', False),
'filter_min_trades': config.get('hyperopt_list_min_trades', 0),
'filter_max_trades': config.get('hyperopt_list_max_trades', 0),
'filter_min_avg_time': config.get('hyperopt_list_min_avg_time', None),
'filter_max_avg_time': config.get('hyperopt_list_max_avg_time', None),
'filter_min_avg_profit': config.get('hyperopt_list_min_avg_profit', None),
'filter_max_avg_profit': config.get('hyperopt_list_max_avg_profit', None),
'filter_min_total_profit': config.get('hyperopt_list_min_total_profit', None),
'filter_max_total_profit': config.get('hyperopt_list_max_total_profit', None),
'filter_min_objective': config.get('hyperopt_list_min_objective', None),
'filter_max_objective': config.get('hyperopt_list_max_objective', None),
}
if not HyperoptTools._test_hyperopt_results_exist(results_file):
# No file found.
return [], 0
epochs = []
total_epochs = 0
for epochs_tmp in HyperoptTools._read_results(results_file):
if total_epochs == 0 and epochs_tmp[0].get('is_best') is None:
raise OperationalException(
"The file with HyperoptTools results is incompatible with this version "
"of Freqtrade and cannot be loaded.")
logger.info(f"Loaded {len(epochs)} previous evaluations from disk.")
return epochs
total_epochs += len(epochs_tmp)
epochs += hyperopt_filter_epochs(epochs_tmp, filteroptions, log=False)
logger.info(f"Loaded {total_epochs} previous evaluations from disk.")
# Final filter run ...
epochs = hyperopt_filter_epochs(epochs, filteroptions, log=True)
return epochs, total_epochs
@staticmethod
def show_epoch_details(results, total_epochs: int, print_json: bool,
@ -433,7 +458,6 @@ class HyperoptTools():
trials['Best'] = ''
trials['Stake currency'] = config['stake_currency']
if 'results_metrics.total_trades' in trials:
base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
'results_metrics.profit_mean', 'results_metrics.profit_median',
'results_metrics.profit_total',
@ -441,13 +465,7 @@ class HyperoptTools():
'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
'loss', 'is_initial_point', 'is_best']
perc_multi = 100
else:
perc_multi = 1
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
'results_metrics.avg_profit', 'results_metrics.median_profit',
'results_metrics.total_profit',
'Stake currency', 'results_metrics.profit', 'results_metrics.duration',
'loss', 'is_initial_point', 'is_best']
param_metrics = [("params_dict."+param) for param in results[0]['params_dict'].keys()]
trials = trials[base_metrics + param_metrics]
@ -475,11 +493,6 @@ class HyperoptTools():
trials['Avg profit'] = trials['Avg profit'].apply(
lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
)
if perc_multi == 1:
trials['Avg duration'] = trials['Avg duration'].apply(
lambda x: f'{x:,.1f} m' if isinstance(
x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else ""
)
trials['Objective'] = trials['Objective'].apply(
lambda x: f'{x:,.5f}' if x != 100000 else ""
)

View File

@ -166,7 +166,7 @@ class Order(_DECL_BASE):
self.ft_is_open = True
if self.status in ('closed', 'canceled', 'cancelled'):
self.ft_is_open = False
if order.get('filled', 0) > 0:
if (order.get('filled', 0.0) or 0.0) > 0:
self.order_filled_date = datetime.now(timezone.utc)
self.order_update_date = datetime.now(timezone.utc)
@ -451,12 +451,12 @@ class LocalTrade():
LocalTrade.trades_open = []
LocalTrade.total_profit = 0
def adjust_min_max_rates(self, current_price: float) -> None:
def adjust_min_max_rates(self, current_price: float, current_price_low: float) -> None:
"""
Adjust the max_rate and min_rate.
"""
self.max_rate = max(current_price, self.max_rate or self.open_rate)
self.min_rate = min(current_price, self.min_rate or self.open_rate)
self.min_rate = min(current_price_low, self.min_rate or self.open_rate)
def adjust_stop_loss(self, current_price: float, stoploss: float,
initial: bool = False) -> None:

View File

@ -538,7 +538,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
- Initializes plot-script
- Get candle (OHLCV) data
- Generate Dafaframes populated with indicators and signals based on configured strategy
- Load trades excecuted during the selected period
- Load trades executed during the selected period
- Generate Plotly plot objects
- Generate plot files
:return: None

View File

@ -150,18 +150,20 @@ class IPairList(LoggingMixin, ABC):
for pair in pairlist:
# pair is not in the generated dynamic market or has the wrong stake currency
if pair not in markets:
logger.warning(f"Pair {pair} is not compatible with exchange "
f"{self._exchange.name}. Removing it from whitelist..")
self.log_once(f"Pair {pair} is not compatible with exchange "
f"{self._exchange.name}. Removing it from whitelist..",
logger.warning)
continue
if not self._exchange.market_is_tradable(markets[pair]):
logger.warning(f"Pair {pair} is not tradable with Freqtrade."
"Removing it from whitelist..")
self.log_once(f"Pair {pair} is not tradable with Freqtrade."
"Removing it from whitelist..", logger.warning)
continue
if self._exchange.get_pair_quote_currency(pair) != self._config['stake_currency']:
logger.warning(f"Pair {pair} is not compatible with your stake currency "
f"{self._config['stake_currency']}. Removing it from whitelist..")
self.log_once(f"Pair {pair} is not compatible with your stake currency "
f"{self._config['stake_currency']}. Removing it from whitelist..",
logger.warning)
continue
# Check if market is active

View File

@ -4,6 +4,7 @@ Volume PairList provider
Provides dynamic pair list based on trade volumes
"""
import logging
from functools import partial
from typing import Any, Dict, List
import arrow
@ -115,7 +116,7 @@ class VolumePairList(IPairList):
pairlist = self._pair_cache.get('pairlist')
if pairlist:
# Item found - no refresh necessary
return pairlist
return pairlist.copy()
else:
# Use fresh pairlist
# Check if pair quote currency equals to the stake currency.
@ -126,7 +127,7 @@ class VolumePairList(IPairList):
pairlist = [s['symbol'] for s in filtered_tickers]
pairlist = self.filter_pairlist(pairlist, tickers)
self._pair_cache['pairlist'] = pairlist
self._pair_cache['pairlist'] = pairlist.copy()
return pairlist
@ -203,7 +204,7 @@ class VolumePairList(IPairList):
# Validate whitelist to only have active market pairs
pairs = self._whitelist_for_active_markets([s['symbol'] for s in sorted_tickers])
pairs = self.verify_blacklist(pairs, logger.info)
pairs = self.verify_blacklist(pairs, partial(self.log_once, logmethod=logger.info))
# Limit pairlist to the requested number of pairs
pairs = pairs[:self._number_pairs]

View File

@ -120,5 +120,6 @@ class RangeStabilityFilter(IPairList):
logger.info)
result = False
self._pair_cache[pair] = result
else:
self.log_once(f"Removed {pair} from whitelist, no candles found.", logger.info)
return result

View File

@ -223,11 +223,11 @@ def list_strategies(config=Depends(get_config)):
@router.get('/strategy/{strategy}', response_model=StrategyResponse, tags=['strategy'])
def get_strategy(strategy: str, config=Depends(get_config)):
config = deepcopy(config)
config_ = deepcopy(config)
from freqtrade.resolvers.strategy_resolver import StrategyResolver
try:
strategy_obj = StrategyResolver._load_strategy(strategy, config,
extra_dir=config.get('strategy_path'))
strategy_obj = StrategyResolver._load_strategy(strategy, config_,
extra_dir=config_.get('strategy_path'))
except OperationalException:
raise HTTPException(status_code=404, detail='Strategy not found')

View File

@ -32,8 +32,11 @@ class UvicornServer(uvicorn.Server):
asyncio_setup()
else:
asyncio.set_event_loop(uvloop.new_event_loop())
try:
loop = asyncio.get_event_loop()
except RuntimeError:
# When running in a thread, we'll not have an eventloop yet.
loop = asyncio.new_event_loop()
loop.run_until_complete(self.serve(sockets=sockets))
@contextlib.contextmanager

View File

@ -29,6 +29,16 @@ async def ui_version():
}
def is_relative_to(path, base) -> bool:
# Helper function simulating behaviour of is_relative_to, which was only added in python 3.9
try:
path.relative_to(base)
return True
except ValueError:
pass
return False
@router_ui.get('/{rest_of_path:path}', include_in_schema=False)
async def index_html(rest_of_path: str):
"""
@ -37,8 +47,11 @@ async def index_html(rest_of_path: str):
if rest_of_path.startswith('api') or rest_of_path.startswith('.'):
raise HTTPException(status_code=404, detail="Not Found")
uibase = Path(__file__).parent / 'ui/installed/'
if (uibase / rest_of_path).is_file():
return FileResponse(str(uibase / rest_of_path))
filename = uibase / rest_of_path
# It's security relevant to check "relative_to".
# Without this, Directory-traversal is possible.
if filename.is_file() and is_relative_to(filename, uibase):
return FileResponse(str(filename))
index_file = uibase / 'index.html'
if not index_file.is_file():

View File

@ -5,7 +5,7 @@ e.g BTC to USD
import datetime
import logging
from typing import Dict
from typing import Dict, List
from cachetools.ttl import TTLCache
from pycoingecko import CoinGeckoAPI
@ -25,8 +25,7 @@ class CryptoToFiatConverter:
"""
__instance = None
_coingekko: CoinGeckoAPI = None
_cryptomap: Dict = {}
_coinlistings: List[Dict] = []
_backoff: float = 0.0
def __new__(cls):
@ -49,9 +48,8 @@ class CryptoToFiatConverter:
def _load_cryptomap(self) -> None:
try:
coinlistings = self._coingekko.get_coins_list()
# Create mapping table from symbol to coingekko_id
self._cryptomap = {x['symbol']: x['id'] for x in coinlistings}
# Use list-comprehension to ensure we get a list.
self._coinlistings = [x for x in self._coingekko.get_coins_list()]
except RequestException as request_exception:
if "429" in str(request_exception):
logger.warning(
@ -69,6 +67,24 @@ class CryptoToFiatConverter:
logger.error(
f"Could not load FIAT Cryptocurrency map for the following problem: {exception}")
def _get_gekko_id(self, crypto_symbol):
if not self._coinlistings:
if self._backoff <= datetime.datetime.now().timestamp():
self._load_cryptomap()
# Still not loaded.
if not self._coinlistings:
return None
else:
return None
found = [x for x in self._coinlistings if x['symbol'] == crypto_symbol]
if len(found) == 1:
return found[0]['id']
if len(found) > 0:
# Wrong!
logger.warning(f"Found multiple mappings in goingekko for {crypto_symbol}.")
return None
def convert_amount(self, crypto_amount: float, crypto_symbol: str, fiat_symbol: str) -> float:
"""
Convert an amount of crypto-currency to fiat
@ -143,22 +159,14 @@ class CryptoToFiatConverter:
if crypto_symbol == fiat_symbol:
return 1.0
if self._cryptomap == {}:
if self._backoff <= datetime.datetime.now().timestamp():
self._load_cryptomap()
# return 0.0 if we still don't have data to check, no reason to proceed
if self._cryptomap == {}:
return 0.0
else:
return 0.0
_gekko_id = self._get_gekko_id(crypto_symbol)
if crypto_symbol not in self._cryptomap:
if not _gekko_id:
# return 0 for unsupported stake currencies (fiat-convert should not break the bot)
logger.warning("unsupported crypto-symbol %s - returning 0.0", crypto_symbol)
return 0.0
try:
_gekko_id = self._cryptomap[crypto_symbol]
return float(
self._coingekko.get_price(
ids=_gekko_id,

View File

@ -776,7 +776,7 @@ class RPC:
if has_content:
dataframe.loc[:, '__date_ts'] = dataframe.loc[:, 'date'].view(int64) // 1000 // 1000
# Move open to seperate column when signal for easy plotting
# Move open to separate column when signal for easy plotting
if 'buy' in dataframe.columns:
buy_mask = (dataframe['buy'] == 1)
buy_signals = int(buy_mask.sum())

View File

@ -281,6 +281,43 @@ class IStrategy(ABC, HyperStrategyMixin):
"""
return self.stoploss
def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
**kwargs) -> float:
"""
Custom entry price logic, returning the new entry price.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns None, orderbook is used to set entry price
:param pair: Pair that's currently analyzed
:param current_time: datetime object, containing the current datetime
:param proposed_rate: Rate, calculated based on pricing settings in ask_strategy.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New entry price value if provided
"""
return proposed_rate
def custom_exit_price(self, pair: str, trade: Trade,
current_time: datetime, proposed_rate: float,
current_profit: float, **kwargs) -> float:
"""
Custom exit price logic, returning the new exit price.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns None, orderbook is used to set exit price
:param pair: Pair that's currently analyzed
:param trade: trade object.
:param current_time: datetime object, containing the current datetime
:param proposed_rate: Rate, calculated based on pricing settings in ask_strategy.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New exit price value if provided
"""
return proposed_rate
def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
"""
@ -591,7 +628,7 @@ class IStrategy(ABC, HyperStrategyMixin):
current_rate = rate
current_profit = trade.calc_profit_ratio(current_rate)
trade.adjust_min_max_rates(high or current_rate)
trade.adjust_min_max_rates(high or current_rate, low or current_rate)
stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
current_time=date, current_profit=current_profit,
@ -761,7 +798,7 @@ class IStrategy(ABC, HyperStrategyMixin):
else:
return current_profit > roi
def ohlcvdata_to_dataframe(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
def advise_all_indicators(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
"""
Populates indicators for given candle (OHLCV) data (for multiple pairs)
Does not run advise_buy or advise_sell!

View File

@ -25,7 +25,7 @@
"ask_strategy": {
"price_side": "ask",
"use_order_book": true,
"order_book_top": 1,
"order_book_top": 1
},
{{ exchange | indent(4) }},
"pairlists": [

View File

@ -6,7 +6,7 @@
coveralls==3.2.0
flake8==3.9.2
flake8-type-annotations==0.1.0
flake8-tidy-imports==4.3.0
flake8-tidy-imports==4.4.1
mypy==0.910
pytest==6.2.4
pytest-asyncio==0.15.1
@ -19,7 +19,7 @@ isort==5.9.3
nbconvert==6.1.0
# mypy types
types-cachetools==0.1.9
types-filelock==0.1.4
types-requests==2.25.1
types-tabulate==0.1.1
types-cachetools==4.2.0
types-filelock==0.1.5
types-requests==2.25.6
types-tabulate==0.8.2

View File

@ -1,5 +1,5 @@
# Include all requirements to run the bot.
-r requirements.txt
plotly==5.1.0
plotly==5.2.1

View File

@ -1,11 +1,11 @@
numpy==1.21.1
pandas==1.3.1
numpy==1.21.2
pandas==1.3.2
ccxt==1.54.24
ccxt==1.55.28
# Pin cryptography for now due to rust build errors with piwheels
cryptography==3.4.7
aiohttp==3.7.4.post0
SQLAlchemy==1.4.22
SQLAlchemy==1.4.23
python-telegram-bot==13.7
arrow==1.1.1
cachetools==4.2.2
@ -32,7 +32,7 @@ sdnotify==0.3.2
# API Server
fastapi==0.68.0
uvicorn==0.14.0
uvicorn==0.15.0
pyjwt==2.1.0
aiofiles==0.7.0
@ -40,4 +40,4 @@ aiofiles==0.7.0
colorama==0.4.4
# Building config files interactively
questionary==1.10.0
prompt-toolkit==3.0.19
prompt-toolkit==3.0.20

View File

@ -163,7 +163,7 @@ function update() {
# Reset Develop or Stable branch
function reset() {
echo "----------------------------"
echo "Reseting branch and virtual env"
echo "Resetting branch and virtual env"
echo "----------------------------"
if [ "1" == $(git branch -vv |grep -cE "\* develop|\* stable") ]

View File

@ -938,13 +938,19 @@ def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):
pytest.fail(f'Expected well formed JSON, but failed to parse: {captured.out}')
def test_hyperopt_list(mocker, capsys, caplog, saved_hyperopt_results,
saved_hyperopt_results_legacy, tmpdir):
def test_hyperopt_list(mocker, capsys, caplog, saved_hyperopt_results, tmpdir):
csv_file = Path(tmpdir) / "test.csv"
for res in (saved_hyperopt_results, saved_hyperopt_results_legacy):
mocker.patch(
'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
MagicMock(return_value=res)
'freqtrade.optimize.hyperopt_tools.HyperoptTools._test_hyperopt_results_exist',
return_value=True
)
def fake_iterator(*args, **kwargs):
yield from [saved_hyperopt_results]
mocker.patch(
'freqtrade.optimize.hyperopt_tools.HyperoptTools._read_results',
side_effect=fake_iterator
)
args = [
@ -1177,8 +1183,16 @@ def test_hyperopt_list(mocker, capsys, caplog, saved_hyperopt_results,
def test_hyperopt_show(mocker, capsys, saved_hyperopt_results):
mocker.patch(
'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
MagicMock(return_value=saved_hyperopt_results)
'freqtrade.optimize.hyperopt_tools.HyperoptTools._test_hyperopt_results_exist',
return_value=True
)
def fake_iterator(*args, **kwargs):
yield from [saved_hyperopt_results]
mocker.patch(
'freqtrade.optimize.hyperopt_tools.HyperoptTools._read_results',
side_effect=fake_iterator
)
mocker.patch('freqtrade.commands.hyperopt_commands.show_backtest_result')

View File

@ -1851,138 +1851,6 @@ def open_trade():
)
@pytest.fixture
def saved_hyperopt_results_legacy():
return [
{
'loss': 0.4366182531161519,
'params_dict': {
'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1190, 'roi_t2': 541, 'roi_t3': 408, 'roi_p1': 0.026035863879169705, 'roi_p2': 0.12508730043628782, 'roi_p3': 0.27766427921605896, 'stoploss': -0.2562930402099556}, # noqa: E501
'params_details': {'buy': {'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4287874435315165, 408: 0.15112316431545753, 949: 0.026035863879169705, 2139: 0}, 'stoploss': {'stoploss': -0.2562930402099556}}, # noqa: E501
'results_metrics': {'trade_count': 2, 'avg_profit': -1.254995, 'median_profit': -1.2222, 'total_profit': -0.00125625, 'profit': -2.50999, 'duration': 3930.0}, # noqa: E501
'results_explanation': ' 2 trades. Avg profit -1.25%. Total profit -0.00125625 BTC ( -2.51Σ%). Avg duration 3930.0 min.', # noqa: E501
'total_profit': -0.00125625,
'current_epoch': 1,
'is_initial_point': True,
'is_best': True
}, {
'loss': 20.0,
'params_dict': {
'mfi-value': 17, 'fastd-value': 38, 'adx-value': 48, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 334, 'roi_t2': 683, 'roi_t3': 140, 'roi_p1': 0.06403981740598495, 'roi_p2': 0.055519840060645045, 'roi_p3': 0.3253712811342459, 'stoploss': -0.338070047333259}, # noqa: E501
'params_details': {
'buy': {'mfi-value': 17, 'fastd-value': 38, 'adx-value': 48, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, # noqa: E501
'sell': {'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, # noqa: E501
'roi': {0: 0.4449309386008759, 140: 0.11955965746663, 823: 0.06403981740598495, 1157: 0}, # noqa: E501
'stoploss': {'stoploss': -0.338070047333259}},
'results_metrics': {'trade_count': 1, 'avg_profit': 0.12357, 'median_profit': -1.2222, 'total_profit': 6.185e-05, 'profit': 0.12357, 'duration': 1200.0}, # noqa: E501
'results_explanation': ' 1 trades. Avg profit 0.12%. Total profit 0.00006185 BTC ( 0.12Σ%). Avg duration 1200.0 min.', # noqa: E501
'total_profit': 6.185e-05,
'current_epoch': 2,
'is_initial_point': True,
'is_best': False
}, {
'loss': 14.241196856510731,
'params_dict': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 889, 'roi_t2': 533, 'roi_t3': 263, 'roi_p1': 0.04759065393663096, 'roi_p2': 0.1488819964638463, 'roi_p3': 0.4102801822104605, 'stoploss': -0.05394588767607611}, # noqa: E501
'params_details': {'buy': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.6067528326109377, 263: 0.19647265040047726, 796: 0.04759065393663096, 1685: 0}, 'stoploss': {'stoploss': -0.05394588767607611}}, # noqa: E501
'results_metrics': {'trade_count': 621, 'avg_profit': -0.43883302093397747, 'median_profit': -1.2222, 'total_profit': -0.13639474, 'profit': -272.515306, 'duration': 1691.207729468599}, # noqa: E501
'results_explanation': ' 621 trades. Avg profit -0.44%. Total profit -0.13639474 BTC (-272.52Σ%). Avg duration 1691.2 min.', # noqa: E501
'total_profit': -0.13639474,
'current_epoch': 3,
'is_initial_point': True,
'is_best': False
}, {
'loss': 100000,
'params_dict': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1402, 'roi_t2': 676, 'roi_t3': 215, 'roi_p1': 0.06264755784937427, 'roi_p2': 0.14258587851894644, 'roi_p3': 0.20671291201040828, 'stoploss': -0.11818343570194478}, # noqa: E501
'params_details': {'buy': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.411946348378729, 215: 0.2052334363683207, 891: 0.06264755784937427, 2293: 0}, 'stoploss': {'stoploss': -0.11818343570194478}}, # noqa: E501
'results_metrics': {'trade_count': 0, 'avg_profit': None, 'median_profit': None, 'total_profit': 0, 'profit': 0.0, 'duration': None}, # noqa: E501
'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_best': False
}, {
'loss': 0.22195522184191518,
'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014}, # noqa: E501
'params_details': {'buy': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3077646493708299, 444: 0.16227697603830155, 1045: 0.07280999507931168, 2314: 0}, 'stoploss': {'stoploss': -0.18181041180901014}}, # noqa: E501
'results_metrics': {'trade_count': 14, 'avg_profit': -0.3539515, 'median_profit': -1.2222, 'total_profit': -0.002480140000000001, 'profit': -4.955321, 'duration': 3402.8571428571427}, # noqa: E501
'results_explanation': ' 14 trades. Avg profit -0.35%. Total profit -0.00248014 BTC ( -4.96Σ%). Avg duration 3402.9 min.', # noqa: E501
'total_profit': -0.002480140000000001,
'current_epoch': 5,
'is_initial_point': True,
'is_best': True
}, {
'loss': 0.545315889154162,
'params_dict': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower', 'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 319, 'roi_t2': 556, 'roi_t3': 216, 'roi_p1': 0.06251955472249589, 'roi_p2': 0.11659519602202795, 'roi_p3': 0.0953744132197762, 'stoploss': -0.024551752215582423}, # noqa: E501
'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.2744891639643, 216: 0.17911475074452382, 772: 0.06251955472249589, 1091: 0}, 'stoploss': {'stoploss': -0.024551752215582423}}, # noqa: E501
'results_metrics': {'trade_count': 39, 'avg_profit': -0.21400679487179478, 'median_profit': -1.2222, 'total_profit': -0.0041773, 'profit': -8.346264999999997, 'duration': 636.9230769230769}, # noqa: E501
'results_explanation': ' 39 trades. Avg profit -0.21%. Total profit -0.00417730 BTC ( -8.35Σ%). Avg duration 636.9 min.', # noqa: E501
'total_profit': -0.0041773,
'current_epoch': 6,
'is_initial_point': True,
'is_best': False
}, {
'loss': 4.713497421432944,
'params_dict': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 771, 'roi_t2': 620, 'roi_t3': 145, 'roi_p1': 0.0586919200378493, 'roi_p2': 0.04984118697312542, 'roi_p3': 0.37521058680247044, 'stoploss': -0.14613268022709905}, # noqa: E501
'params_details': {
'buy': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.4837436938134452, 145: 0.10853310701097472, 765: 0.0586919200378493, 1536: 0}, # noqa: E501
'stoploss': {'stoploss': -0.14613268022709905}}, # noqa: E501
'results_metrics': {'trade_count': 318, 'avg_profit': -0.39833954716981146, 'median_profit': -1.2222, 'total_profit': -0.06339929, 'profit': -126.67197600000004, 'duration': 3140.377358490566}, # noqa: E501
'results_explanation': ' 318 trades. Avg profit -0.40%. Total profit -0.06339929 BTC (-126.67Σ%). Avg duration 3140.4 min.', # noqa: E501
'total_profit': -0.06339929,
'current_epoch': 7,
'is_initial_point': True,
'is_best': False
}, {
'loss': 20.0, # noqa: E501
'params_dict': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal', 'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 1149, 'roi_t2': 375, 'roi_t3': 289, 'roi_p1': 0.05571820757172588, 'roi_p2': 0.0606240398618907, 'roi_p3': 0.1729012220156157, 'stoploss': -0.1588514289110401}, # noqa: E501
'params_details': {'buy': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.2892434694492323, 289: 0.11634224743361658, 664: 0.05571820757172588, 1813: 0}, 'stoploss': {'stoploss': -0.1588514289110401}}, # noqa: E501
'results_metrics': {'trade_count': 1, 'avg_profit': 0.0, 'median_profit': 0.0, 'total_profit': 0.0, 'profit': 0.0, 'duration': 5340.0}, # noqa: E501
'results_explanation': ' 1 trades. Avg profit 0.00%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration 5340.0 min.', # noqa: E501
'total_profit': 0.0,
'current_epoch': 8,
'is_initial_point': True,
'is_best': False
}, {
'loss': 2.4731817780991223,
'params_dict': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1012, 'roi_t2': 584, 'roi_t3': 422, 'roi_p1': 0.036764323603472565, 'roi_p2': 0.10335480573205287, 'roi_p3': 0.10322347377503042, 'stoploss': -0.2780610808108503}, # noqa: E501
'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.2433426031105559, 422: 0.14011912933552545, 1006: 0.036764323603472565, 2018: 0}, 'stoploss': {'stoploss': -0.2780610808108503}}, # noqa: E501
'results_metrics': {'trade_count': 229, 'avg_profit': -0.38433433624454144, 'median_profit': -1.2222, 'total_profit': -0.044050070000000004, 'profit': -88.01256299999999, 'duration': 6505.676855895196}, # noqa: E501
'results_explanation': ' 229 trades. Avg profit -0.38%. Total profit -0.04405007 BTC ( -88.01Σ%). Avg duration 6505.7 min.', # noqa: E501
'total_profit': -0.044050070000000004, # noqa: E501
'current_epoch': 9,
'is_initial_point': True,
'is_best': False
}, {
'loss': -0.2604606005845212, # noqa: E501
'params_dict': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 792, 'roi_t2': 464, 'roi_t3': 215, 'roi_p1': 0.04594053535385903, 'roi_p2': 0.09623192684243963, 'roi_p3': 0.04428219070850663, 'stoploss': -0.16992287161634415}, # noqa: E501
'params_details': {'buy': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.18645465290480528, 215: 0.14217246219629864, 679: 0.04594053535385903, 1471: 0}, 'stoploss': {'stoploss': -0.16992287161634415}}, # noqa: E501
'results_metrics': {'trade_count': 4, 'avg_profit': 0.1080385, 'median_profit': -1.2222, 'total_profit': 0.00021629, 'profit': 0.432154, 'duration': 2850.0}, # noqa: E501
'results_explanation': ' 4 trades. Avg profit 0.11%. Total profit 0.00021629 BTC ( 0.43Σ%). Avg duration 2850.0 min.', # noqa: E501
'total_profit': 0.00021629,
'current_epoch': 10,
'is_initial_point': True,
'is_best': True
}, {
'loss': 4.876465945994304, # noqa: E501
'params_dict': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 579, 'roi_t2': 614, 'roi_t3': 273, 'roi_p1': 0.05307643172744114, 'roi_p2': 0.1352282078262871, 'roi_p3': 0.1913307406325751, 'stoploss': -0.25728526022513887}, # noqa: E501
'params_details': {'buy': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3796353801863034, 273: 0.18830463955372825, 887: 0.05307643172744114, 1466: 0}, 'stoploss': {'stoploss': -0.25728526022513887}}, # noqa: E501
'results_metrics': {'trade_count': 117, 'avg_profit': -1.2698609145299145, 'median_profit': -1.2222, 'total_profit': -0.07436117, 'profit': -148.573727, 'duration': 4282.5641025641025}, # noqa: E501
'results_explanation': ' 117 trades. Avg profit -1.27%. Total profit -0.07436117 BTC (-148.57Σ%). Avg duration 4282.6 min.', # noqa: E501
'total_profit': -0.07436117,
'current_epoch': 11,
'is_initial_point': True,
'is_best': False
}, {
'loss': 100000,
'params_dict': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1156, 'roi_t2': 581, 'roi_t3': 408, 'roi_p1': 0.06860454019988212, 'roi_p2': 0.12473718444931989, 'roi_p3': 0.2896360635226823, 'stoploss': -0.30889015124682806}, # noqa: E501
'params_details': {'buy': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4829777881718843, 408: 0.19334172464920202, 989: 0.06860454019988212, 2145: 0}, 'stoploss': {'stoploss': -0.30889015124682806}}, # noqa: E501
'results_metrics': {'trade_count': 0, 'avg_profit': None, 'median_profit': None, 'total_profit': 0, 'profit': 0.0, 'duration': None}, # noqa: E501
'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
'total_profit': 0,
'current_epoch': 12,
'is_initial_point': True,
'is_best': False
}
]
@pytest.fixture
def saved_hyperopt_results():
hyperopt_res = [

View File

@ -119,7 +119,7 @@ def test_ohlcv_fill_up_missing_data2(caplog):
# 3rd candle has been filled
row = data2.loc[2, :]
assert row['volume'] == 0
# close shoult match close of previous candle
# close should match close of previous candle
assert row['close'] == data.loc[1, 'close']
assert row['open'] == row['close']
assert row['high'] == row['close']

View File

@ -66,7 +66,7 @@ def test_historic_ohlcv_dataformat(mocker, default_conf, ohlcv_history):
hdf5loadmock.assert_not_called()
jsonloadmock.assert_called_once()
# Swiching to dataformat hdf5
# Switching to dataformat hdf5
hdf5loadmock.reset_mock()
jsonloadmock.reset_mock()
default_conf["dataformat_ohlcv"] = "hdf5"

View File

@ -133,8 +133,8 @@ def test_load_data_with_new_pair_1min(ohlcv_history_list, mocker, caplog,
load_pair_history(datadir=tmpdir1, timeframe='1m', pair='MEME/BTC')
assert file.is_file()
assert log_has_re(
'Download history data for pair: "MEME/BTC", timeframe: 1m '
'and store in .*', caplog
r'Download history data for pair: "MEME/BTC" \(0/1\), timeframe: 1m '
r'and store in .*', caplog
)
@ -200,15 +200,15 @@ def test_load_cached_data_for_updating(mocker, testdatadir) -> None:
assert start_ts == test_data[0][0] - 1000
# timeframe starts in the center of the cached data
# should return the chached data w/o the last item
# should return the cached data w/o the last item
timerange = TimeRange('date', None, test_data[0][0] / 1000 + 1, 0)
data, start_ts = _load_cached_data_for_updating('UNITTEST/BTC', '1m', timerange, data_handler)
assert_frame_equal(data, test_data_df.iloc[:-1])
assert test_data[-2][0] <= start_ts < test_data[-1][0]
# timeframe starts after the chached data
# should return the chached data w/o the last item
# timeframe starts after the cached data
# should return the cached data w/o the last item
timerange = TimeRange('date', None, test_data[-1][0] / 1000 + 100, 0)
data, start_ts = _load_cached_data_for_updating('UNITTEST/BTC', '1m', timerange, data_handler)
assert_frame_equal(data, test_data_df.iloc[:-1])
@ -278,8 +278,10 @@ def test_download_pair_history2(mocker, default_conf, testdatadir) -> None:
return_value=None)
mocker.patch('freqtrade.exchange.Exchange.get_historic_ohlcv', return_value=tick)
exchange = get_patched_exchange(mocker, default_conf)
_download_pair_history(testdatadir, exchange, pair="UNITTEST/BTC", timeframe='1m')
_download_pair_history(testdatadir, exchange, pair="UNITTEST/BTC", timeframe='3m')
_download_pair_history(datadir=testdatadir, exchange=exchange, pair="UNITTEST/BTC",
timeframe='1m')
_download_pair_history(datadir=testdatadir, exchange=exchange, pair="UNITTEST/BTC",
timeframe='3m')
assert json_dump_mock.call_count == 2
@ -381,7 +383,7 @@ def test_get_timerange(default_conf, mocker, testdatadir) -> None:
default_conf.update({'strategy': 'DefaultStrategy'})
strategy = StrategyResolver.load_strategy(default_conf)
data = strategy.ohlcvdata_to_dataframe(
data = strategy.advise_all_indicators(
load_data(
datadir=testdatadir,
timeframe='1m',
@ -399,7 +401,7 @@ def test_validate_backtest_data_warn(default_conf, mocker, caplog, testdatadir)
default_conf.update({'strategy': 'DefaultStrategy'})
strategy = StrategyResolver.load_strategy(default_conf)
data = strategy.ohlcvdata_to_dataframe(
data = strategy.advise_all_indicators(
load_data(
datadir=testdatadir,
timeframe='1m',
@ -424,7 +426,7 @@ def test_validate_backtest_data(default_conf, mocker, caplog, testdatadir) -> No
strategy = StrategyResolver.load_strategy(default_conf)
timerange = TimeRange('index', 'index', 200, 250)
data = strategy.ohlcvdata_to_dataframe(
data = strategy.advise_all_indicators(
load_data(
datadir=testdatadir,
timeframe='5m',

View File

@ -42,6 +42,11 @@ EXCHANGES = {
'hasQuoteVolume': True,
'timeframe': '5m',
},
'gateio': {
'pair': 'BTC/USDT',
'hasQuoteVolume': True,
'timeframe': '5m',
},
}
@ -142,8 +147,8 @@ class TestCCXTExchange():
def test_ccxt_get_fee(self, exchange):
exchange, exchangename = exchange
pair = EXCHANGES[exchangename]['pair']
assert 0 < exchange.get_fee(pair, 'limit', 'buy') < 1
assert 0 < exchange.get_fee(pair, 'limit', 'sell') < 1
assert 0 < exchange.get_fee(pair, 'market', 'buy') < 1
assert 0 < exchange.get_fee(pair, 'market', 'sell') < 1
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

View File

@ -984,16 +984,21 @@ def test_create_dry_run_order_limit_fill(default_conf, mocker, side, startprice,
assert order['fee']
@pytest.mark.parametrize("side,amount,endprice", [
("buy", 1, 25.566),
("buy", 100, 25.5672), # Requires interpolation
("buy", 1000, 25.575), # More than orderbook return
("sell", 1, 25.563),
("sell", 100, 25.5625), # Requires interpolation
("sell", 1000, 25.5555), # More than orderbook return
@pytest.mark.parametrize("side,rate,amount,endprice", [
# spread is 25.263-25.266
("buy", 25.564, 1, 25.566),
("buy", 25.564, 100, 25.5672), # Requires interpolation
("buy", 25.590, 100, 25.5672), # Price above spread ... average is lower
("buy", 25.564, 1000, 25.575), # More than orderbook return
("buy", 24.000, 100000, 25.200), # Run into max_slippage of 5%
("sell", 25.564, 1, 25.563),
("sell", 25.564, 100, 25.5625), # Requires interpolation
("sell", 25.510, 100, 25.5625), # price below spread - average is higher
("sell", 25.564, 1000, 25.5555), # More than orderbook return
("sell", 27, 10000, 25.65), # max-slippage 5%
])
@pytest.mark.parametrize("exchange_name", EXCHANGES)
def test_create_dry_run_order_market_fill(default_conf, mocker, side, amount, endprice,
def test_create_dry_run_order_market_fill(default_conf, mocker, side, rate, amount, endprice,
exchange_name, order_book_l2_usd):
default_conf['dry_run'] = True
exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
@ -1003,7 +1008,7 @@ def test_create_dry_run_order_market_fill(default_conf, mocker, side, amount, en
)
order = exchange.create_dry_run_order(
pair='LTC/USDT', ordertype='market', side=side, amount=amount, rate=25.5)
pair='LTC/USDT', ordertype='market', side=side, amount=amount, rate=rate)
assert 'id' in order
assert f'dry_run_{side}_' in order["id"]
assert order["side"] == side
@ -1559,13 +1564,16 @@ def test_refresh_latest_ohlcv(mocker, default_conf, caplog) -> None:
pairs = [('IOTA/ETH', '5m'), ('XRP/ETH', '5m')]
# empty dicts
assert not exchange._klines
exchange.refresh_latest_ohlcv(pairs, cache=False)
res = exchange.refresh_latest_ohlcv(pairs, cache=False)
# No caching
assert not exchange._klines
assert len(res) == len(pairs)
assert exchange._api_async.fetch_ohlcv.call_count == 2
exchange._api_async.fetch_ohlcv.reset_mock()
exchange.refresh_latest_ohlcv(pairs)
res = exchange.refresh_latest_ohlcv(pairs)
assert len(res) == len(pairs)
assert log_has(f'Refreshing candle (OHLCV) data for {len(pairs)} pairs', caplog)
assert exchange._klines
@ -1582,12 +1590,16 @@ def test_refresh_latest_ohlcv(mocker, default_conf, caplog) -> None:
assert exchange.klines(pair, copy=False) is exchange.klines(pair, copy=False)
# test caching
exchange.refresh_latest_ohlcv([('IOTA/ETH', '5m'), ('XRP/ETH', '5m')])
res = exchange.refresh_latest_ohlcv([('IOTA/ETH', '5m'), ('XRP/ETH', '5m')])
assert len(res) == len(pairs)
assert exchange._api_async.fetch_ohlcv.call_count == 2
assert log_has(f"Using cached candle (OHLCV) data for pair {pairs[0][0]}, "
f"timeframe {pairs[0][1]} ...",
caplog)
res = exchange.refresh_latest_ohlcv([('IOTA/ETH', '5m'), ('XRP/ETH', '5m'), ('XRP/ETH', '1d')],
cache=False)
assert len(res) == 3
@pytest.mark.asyncio
@ -2177,7 +2189,7 @@ def test_get_historic_trades_notsupported(default_conf, mocker, caplog, exchange
pair = 'ETH/BTC'
with pytest.raises(OperationalException,
match="This exchange does not suport downloading Trades."):
match="This exchange does not support downloading Trades."):
exchange.get_historic_trades(pair, since=trades_history[0][0],
until=trades_history[-1][0])

View File

@ -56,4 +56,6 @@ def _build_backtest_dataframe(data):
# Ensure floats are in place
for column in ['open', 'high', 'low', 'close', 'volume']:
frame[column] = frame[column].astype('float64')
if 'buy_tag' not in columns:
frame['buy_tag'] = None
return frame

View File

@ -1,6 +1,7 @@
# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
import random
from datetime import timedelta
from pathlib import Path
from unittest.mock import MagicMock, PropertyMock
@ -85,7 +86,7 @@ def simple_backtest(config, contour, mocker, testdatadir) -> None:
backtesting._set_strategy(backtesting.strategylist[0])
data = load_data_test(contour, testdatadir)
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
processed = backtesting.strategy.advise_all_indicators(data)
min_date, max_date = get_timerange(processed)
assert isinstance(processed, dict)
results = backtesting.backtest(
@ -107,7 +108,7 @@ def _make_backtest_conf(mocker, datadir, conf=None, pair='UNITTEST/BTC'):
patch_exchange(mocker)
backtesting = Backtesting(conf)
backtesting._set_strategy(backtesting.strategylist[0])
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
processed = backtesting.strategy.advise_all_indicators(data)
min_date, max_date = get_timerange(processed)
return {
'processed': processed,
@ -289,7 +290,7 @@ def test_backtesting_init(mocker, default_conf, order_types) -> None:
backtesting._set_strategy(backtesting.strategylist[0])
assert backtesting.config == default_conf
assert backtesting.timeframe == '5m'
assert callable(backtesting.strategy.ohlcvdata_to_dataframe)
assert callable(backtesting.strategy.advise_all_indicators)
assert callable(backtesting.strategy.advise_buy)
assert callable(backtesting.strategy.advise_sell)
assert isinstance(backtesting.strategy.dp, DataProvider)
@ -335,14 +336,14 @@ def test_data_to_dataframe_bt(default_conf, mocker, testdatadir) -> None:
fill_up_missing=True)
backtesting = Backtesting(default_conf)
backtesting._set_strategy(backtesting.strategylist[0])
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
processed = backtesting.strategy.advise_all_indicators(data)
assert len(processed['UNITTEST/BTC']) == 102
# Load strategy to compare the result between Backtesting function and strategy are the same
default_conf.update({'strategy': 'DefaultStrategy'})
strategy = StrategyResolver.load_strategy(default_conf)
processed2 = strategy.ohlcvdata_to_dataframe(data)
processed2 = strategy.advise_all_indicators(data)
assert processed['UNITTEST/BTC'].equals(processed2['UNITTEST/BTC'])
@ -535,6 +536,8 @@ def test_backtest__enter_trade(default_conf, fee, mocker) -> None:
trade = backtesting._enter_trade(pair, row=row)
assert trade is None
backtesting.cleanup()
def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
default_conf['use_sell_signal'] = False
@ -547,7 +550,7 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
timerange = TimeRange('date', None, 1517227800, 0)
data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
timerange=timerange)
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
processed = backtesting.strategy.advise_all_indicators(data)
min_date, max_date = get_timerange(processed)
result = backtesting.backtest(
processed=processed,
@ -581,7 +584,7 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
'initial_stop_loss_ratio': [-0.1, -0.1],
'stop_loss_abs': [0.0940005, 0.09272236],
'stop_loss_ratio': [-0.1, -0.1],
'min_rate': [0.1038, 0.10302485],
'min_rate': [0.10370188, 0.10300000000000001],
'max_rate': [0.10501, 0.1038888],
'is_open': [False, False],
'buy_tag': [None, None],
@ -612,7 +615,7 @@ def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None
timerange = TimeRange.parse_timerange('1510688220-1510700340')
data = history.load_data(datadir=testdatadir, timeframe='1m', pairs=['UNITTEST/BTC'],
timerange=timerange)
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
processed = backtesting.strategy.advise_all_indicators(data)
min_date, max_date = get_timerange(processed)
results = backtesting.backtest(
processed=processed,
@ -631,7 +634,7 @@ def test_processed(default_conf, mocker, testdatadir) -> None:
backtesting._set_strategy(backtesting.strategylist[0])
dict_of_tickerrows = load_data_test('raise', testdatadir)
dataframes = backtesting.strategy.ohlcvdata_to_dataframe(dict_of_tickerrows)
dataframes = backtesting.strategy.advise_all_indicators(dict_of_tickerrows)
dataframe = dataframes['UNITTEST/BTC']
cols = dataframe.columns
# assert the dataframe got some of the indicator columns
@ -739,8 +742,13 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
# 100 buys signals
results = result['results']
assert len(results) == 100
# Cached data should be 200 (no change since required_startup is 0)
assert len(backtesting.dataprovider.get_analyzed_dataframe('UNITTEST/BTC', '1m')[0]) == 200
# Cached data should be 200
analyzed_df = backtesting.dataprovider.get_analyzed_dataframe('UNITTEST/BTC', '1m')[0]
assert len(analyzed_df) == 200
# Expect last candle to be 1 below end date (as the last candle is assumed as "incomplete"
# during backtesting)
expected_last_candle_date = backtest_conf['end_date'] - timedelta(minutes=1)
assert analyzed_df.iloc[-1]['date'].to_pydatetime() == expected_last_candle_date
# One trade was force-closed at the end
assert len(results.loc[results['is_open']]) == 0
@ -772,6 +780,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
data = trim_dictlist(data, -500)
# Remove data for one pair from the beginning of the data
if tres > 0:
data[pair] = data[pair][tres:].reset_index()
default_conf['timeframe'] = '5m'
@ -780,7 +789,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
backtesting.strategy.advise_buy = _trend_alternate_hold # Override
backtesting.strategy.advise_sell = _trend_alternate_hold # Override
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
processed = backtesting.strategy.advise_all_indicators(data)
min_date, max_date = get_timerange(processed)
backtest_conf = {
'processed': processed,
@ -798,8 +807,11 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
assert len(evaluate_result_multi(results['results'], '5m', 3)) == 0
# Cached data correctly removed amounts
removed_candles = len(data[pair]) - 1 - backtesting.strategy.startup_candle_count
offset = 1 if tres == 0 else 0
removed_candles = len(data[pair]) - offset - backtesting.strategy.startup_candle_count
assert len(backtesting.dataprovider.get_analyzed_dataframe(pair, '5m')[0]) == removed_candles
assert len(backtesting.dataprovider.get_analyzed_dataframe(
'NXT/BTC', '5m')[0]) == len(data['NXT/BTC']) - 1 - backtesting.strategy.startup_candle_count
backtest_conf = {
'processed': processed,

View File

@ -354,7 +354,7 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
del hyperopt_conf['timeframe']
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
hyperopt.start()
@ -429,7 +429,7 @@ def test_hyperopt_format_results(hyperopt):
def test_populate_indicators(hyperopt, testdatadir) -> None:
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
dataframes = hyperopt.backtesting.strategy.ohlcvdata_to_dataframe(data)
dataframes = hyperopt.backtesting.strategy.advise_all_indicators(data)
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
@ -441,7 +441,7 @@ def test_populate_indicators(hyperopt, testdatadir) -> None:
def test_buy_strategy_generator(hyperopt, testdatadir) -> None:
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
dataframes = hyperopt.backtesting.strategy.ohlcvdata_to_dataframe(data)
dataframes = hyperopt.backtesting.strategy.advise_all_indicators(data)
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
@ -470,7 +470,7 @@ def test_buy_strategy_generator(hyperopt, testdatadir) -> None:
def test_sell_strategy_generator(hyperopt, testdatadir) -> None:
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], fill_up_missing=True)
dataframes = hyperopt.backtesting.strategy.ohlcvdata_to_dataframe(data)
dataframes = hyperopt.backtesting.strategy.advise_all_indicators(data)
dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
{'pair': 'UNITTEST/BTC'})
@ -671,7 +671,7 @@ def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
})
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
hyperopt.start()
@ -724,7 +724,7 @@ def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
hyperopt_conf.update({'print_json': True})
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
hyperopt.start()
@ -772,7 +772,7 @@ def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
})
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
hyperopt.start()
@ -816,7 +816,7 @@ def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> Non
hyperopt_conf.update({'spaces': 'roi stoploss'})
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
@ -855,7 +855,7 @@ def test_simplified_interface_all_failed(mocker, hyperopt_conf) -> None:
hyperopt_conf.update({'spaces': 'all', })
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
del hyperopt.custom_hyperopt.__class__.buy_strategy_generator
@ -897,7 +897,7 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
hyperopt_conf.update({'spaces': 'buy'})
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
# TODO: sell_strategy_generator() is actually not called because
@ -951,7 +951,7 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
hyperopt_conf.update({'spaces': 'sell', })
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
# TODO: buy_strategy_generator() is actually not called because
@ -996,7 +996,7 @@ def test_simplified_interface_failed(mocker, hyperopt_conf, method, space) -> No
hyperopt_conf.update({'spaces': space})
hyperopt = Hyperopt(hyperopt_conf)
hyperopt.backtesting.strategy.ohlcvdata_to_dataframe = MagicMock()
hyperopt.backtesting.strategy.advise_all_indicators = MagicMock()
hyperopt.custom_hyperopt.generate_roi_table = MagicMock(return_value={})
delattr(hyperopt.custom_hyperopt.__class__, method)

View File

@ -10,7 +10,7 @@ import rapidjson
from freqtrade.constants import FTHYPT_FILEVERSION
from freqtrade.exceptions import OperationalException
from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
from tests.conftest import log_has, log_has_re
from tests.conftest import log_has
# Functions for recurrent object patching
@ -20,9 +20,14 @@ def create_results() -> List[Dict]:
def test_save_results_saves_epochs(hyperopt, tmpdir, caplog) -> None:
hyperopt.results_file = Path(tmpdir / 'ut_results.fthypt')
hyperopt_epochs = HyperoptTools.load_filtered_results(hyperopt.results_file, {})
assert hyperopt_epochs == ([], 0)
# Test writing to temp dir and reading again
epochs = create_results()
hyperopt.results_file = Path(tmpdir / 'ut_results.fthypt')
caplog.set_level(logging.DEBUG)
@ -33,36 +38,28 @@ def test_save_results_saves_epochs(hyperopt, tmpdir, caplog) -> None:
hyperopt._save_result(epochs[0])
assert log_has(f"2 epochs saved to '{hyperopt.results_file}'.", caplog)
hyperopt_epochs = HyperoptTools.load_previous_results(hyperopt.results_file)
hyperopt_epochs = HyperoptTools.load_filtered_results(hyperopt.results_file, {})
assert len(hyperopt_epochs) == 2
assert hyperopt_epochs[1] == 2
assert len(hyperopt_epochs[0]) == 2
def test_load_previous_results(testdatadir, caplog) -> None:
results_file = testdatadir / 'hyperopt_results_SampleStrategy.pickle'
hyperopt_epochs = HyperoptTools.load_previous_results(results_file)
assert len(hyperopt_epochs) == 5
assert log_has_re(r"Reading pickled epochs from .*", caplog)
caplog.clear()
# Modern version
results_file = testdatadir / 'strategy_SampleStrategy.fthypt'
hyperopt_epochs = HyperoptTools.load_previous_results(results_file)
assert len(hyperopt_epochs) == 5
assert log_has_re(r"Reading epochs from .*", caplog)
result_gen = HyperoptTools._read_results(hyperopt.results_file, 1)
epoch = next(result_gen)
assert len(epoch) == 1
assert epoch[0] == epochs[0]
epoch = next(result_gen)
assert len(epoch) == 1
epoch = next(result_gen)
assert len(epoch) == 0
with pytest.raises(StopIteration):
next(result_gen)
def test_load_previous_results2(mocker, testdatadir, caplog) -> None:
mocker.patch('freqtrade.optimize.hyperopt_tools.HyperoptTools._read_results_pickle',
return_value=[{'asdf': '222'}])
results_file = testdatadir / 'hyperopt_results_SampleStrategy.pickle'
with pytest.raises(OperationalException, match=r"The file .* incompatible.*"):
HyperoptTools.load_previous_results(results_file)
with pytest.raises(OperationalException,
match=r"Legacy hyperopt results are no longer supported.*"):
HyperoptTools.load_filtered_results(results_file, {})
@pytest.mark.parametrize("spaces, expected_results", [

View File

@ -22,7 +22,7 @@ def test_fiat_convert_is_supported(mocker):
def test_fiat_convert_find_price(mocker):
fiat_convert = CryptoToFiatConverter()
fiat_convert._cryptomap = {}
fiat_convert._coinlistings = {}
fiat_convert._backoff = 0
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._load_cryptomap',
return_value=None)
@ -44,7 +44,7 @@ def test_fiat_convert_find_price(mocker):
def test_fiat_convert_unsupported_crypto(mocker, caplog):
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._cryptomap', return_value=[])
mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._coinlistings', return_value=[])
fiat_convert = CryptoToFiatConverter()
assert fiat_convert._find_price(crypto_symbol='CRYPTO_123', fiat_symbol='EUR') == 0.0
assert log_has('unsupported crypto-symbol CRYPTO_123 - returning 0.0', caplog)
@ -88,9 +88,9 @@ def test_fiat_convert_two_FIAT(mocker):
def test_loadcryptomap(mocker):
fiat_convert = CryptoToFiatConverter()
assert len(fiat_convert._cryptomap) == 2
assert len(fiat_convert._coinlistings) == 2
assert fiat_convert._cryptomap["btc"] == "bitcoin"
assert fiat_convert._get_gekko_id("btc") == "bitcoin"
def test_fiat_init_network_exception(mocker):
@ -102,11 +102,10 @@ def test_fiat_init_network_exception(mocker):
)
# with pytest.raises(RequestEsxception):
fiat_convert = CryptoToFiatConverter()
fiat_convert._cryptomap = {}
fiat_convert._coinlistings = {}
fiat_convert._load_cryptomap()
length_cryptomap = len(fiat_convert._cryptomap)
assert length_cryptomap == 0
assert len(fiat_convert._coinlistings) == 0
def test_fiat_convert_without_network(mocker):
@ -132,11 +131,10 @@ def test_fiat_too_many_requests_response(mocker, caplog):
)
# with pytest.raises(RequestEsxception):
fiat_convert = CryptoToFiatConverter()
fiat_convert._cryptomap = {}
fiat_convert._coinlistings = {}
fiat_convert._load_cryptomap()
length_cryptomap = len(fiat_convert._cryptomap)
assert length_cryptomap == 0
assert len(fiat_convert._coinlistings) == 0
assert fiat_convert._backoff > datetime.datetime.now().timestamp()
assert log_has(
'Too many requests for Coingecko API, backing off and trying again later.',
@ -144,20 +142,33 @@ def test_fiat_too_many_requests_response(mocker, caplog):
)
def test_fiat_multiple_coins(mocker, caplog):
fiat_convert = CryptoToFiatConverter()
fiat_convert._coinlistings = [
{'id': 'helium', 'symbol': 'hnt', 'name': 'Helium'},
{'id': 'hymnode', 'symbol': 'hnt', 'name': 'Hymnode'},
{'id': 'bitcoin', 'symbol': 'btc', 'name': 'Bitcoin'},
]
assert fiat_convert._get_gekko_id('btc') == 'bitcoin'
assert fiat_convert._get_gekko_id('hnt') is None
assert log_has('Found multiple mappings in goingekko for hnt.', caplog)
def test_fiat_invalid_response(mocker, caplog):
# Because CryptoToFiatConverter is a Singleton we reset the listings
listmock = MagicMock(return_value="{'novalidjson':DEADBEEFf}")
listmock = MagicMock(return_value=None)
mocker.patch.multiple(
'freqtrade.rpc.fiat_convert.CoinGeckoAPI',
get_coins_list=listmock,
)
# with pytest.raises(RequestEsxception):
fiat_convert = CryptoToFiatConverter()
fiat_convert._cryptomap = {}
fiat_convert._coinlistings = []
fiat_convert._load_cryptomap()
length_cryptomap = len(fiat_convert._cryptomap)
assert length_cryptomap == 0
assert len(fiat_convert._coinlistings) == 0
assert log_has_re('Could not load FIAT Cryptocurrency map for the following problem: .*',
caplog)

View File

@ -109,6 +109,11 @@ def test_api_ui_fallback(botclient):
rc = client_get(client, "/something")
assert rc.status_code == 200
# Test directory traversal
rc = client_get(client, '%2F%2F%2Fetc/passwd')
assert rc.status_code == 200
assert '`freqtrade install-ui`' in rc.text
def test_api_ui_version(botclient, mocker):
ftbot, client = botclient

View File

@ -232,25 +232,25 @@ def test_assert_df(ohlcv_history, caplog):
_STRATEGY.disable_dataframe_checks = False
def test_ohlcvdata_to_dataframe(default_conf, testdatadir) -> None:
def test_advise_all_indicators(default_conf, testdatadir) -> None:
default_conf.update({'strategy': 'DefaultStrategy'})
strategy = StrategyResolver.load_strategy(default_conf)
timerange = TimeRange.parse_timerange('1510694220-1510700340')
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
fill_up_missing=True)
processed = strategy.ohlcvdata_to_dataframe(data)
processed = strategy.advise_all_indicators(data)
assert len(processed['UNITTEST/BTC']) == 102 # partial candle was removed
def test_ohlcvdata_to_dataframe_copy(mocker, default_conf, testdatadir) -> None:
def test_advise_all_indicators_copy(mocker, default_conf, testdatadir) -> None:
default_conf.update({'strategy': 'DefaultStrategy'})
strategy = StrategyResolver.load_strategy(default_conf)
aimock = mocker.patch('freqtrade.strategy.interface.IStrategy.advise_indicators')
timerange = TimeRange.parse_timerange('1510694220-1510700340')
data = load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
fill_up_missing=True)
strategy.ohlcvdata_to_dataframe(data)
strategy.advise_all_indicators(data)
assert aimock.call_count == 1
# Ensure that a copy of the dataframe is passed to advice_indicators
assert aimock.call_args_list[0][0][0] is not data
@ -402,7 +402,7 @@ def test_stop_loss_reached(default_conf, fee, profit, adjusted, expected, traili
exchange='binance',
open_rate=1,
)
trade.adjust_min_max_rates(trade.open_rate)
trade.adjust_min_max_rates(trade.open_rate, trade.open_rate)
strategy.trailing_stop = trailing
strategy.trailing_stop_positive = -0.05
strategy.use_custom_stoploss = custom
@ -556,6 +556,7 @@ def test__analyze_ticker_internal_skip_analyze(ohlcv_history, mocker, caplog) ->
def test_is_pair_locked(default_conf):
default_conf.update({'strategy': 'DefaultStrategy'})
PairLocks.timeframe = default_conf['timeframe']
PairLocks.use_db = True
strategy = StrategyResolver.load_strategy(default_conf)
# No lock should be present
assert len(PairLocks.get_pair_locks(None)) == 0
@ -633,7 +634,7 @@ def test_strategy_safe_wrapper_error(caplog, error):
assert ret
caplog.clear()
# Test supressing error
# Test suppressing error
ret = strategy_safe_wrapper(failing_method, message='DeadBeef', supress_error=True)()
assert log_has_re(r'DeadBeef.*', caplog)

View File

@ -904,6 +904,40 @@ def test_execute_buy(mocker, default_conf, fee, limit_buy_order, limit_buy_order
with pytest.raises(PricingError, match="Could not determine buy price."):
freqtrade.execute_buy(pair, stake_amount)
# In case of custom entry price
mocker.patch('freqtrade.exchange.Exchange.get_rate', return_value=0.50)
limit_buy_order['status'] = 'open'
limit_buy_order['id'] = '5566'
freqtrade.strategy.custom_entry_price = lambda **kwargs: 0.508
assert freqtrade.execute_buy(pair, stake_amount)
trade = Trade.query.all()[6]
assert trade
assert trade.open_rate_requested == 0.508
# In case of custom entry price set to None
limit_buy_order['status'] = 'open'
limit_buy_order['id'] = '5567'
freqtrade.strategy.custom_entry_price = lambda **kwargs: None
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_rate=MagicMock(return_value=10),
)
assert freqtrade.execute_buy(pair, stake_amount)
trade = Trade.query.all()[7]
assert trade
assert trade.open_rate_requested == 10
# In case of custom entry price not float type
limit_buy_order['status'] = 'open'
limit_buy_order['id'] = '5568'
freqtrade.strategy.custom_entry_price = lambda **kwargs: "string price"
assert freqtrade.execute_buy(pair, stake_amount)
trade = Trade.query.all()[8]
assert trade
assert trade.open_rate_requested == 10
def test_execute_buy_confirm_error(mocker, default_conf, fee, limit_buy_order) -> None:
freqtrade = get_patched_freqtradebot(mocker, default_conf)
@ -2716,6 +2750,70 @@ def test_execute_sell_down(default_conf, ticker, fee, ticker_sell_down, mocker)
} == last_msg
def test_execute_sell_custom_exit_price(default_conf, ticker, fee, ticker_sell_up, mocker) -> None:
rpc_mock = patch_RPCManager(mocker)
patch_exchange(mocker)
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker,
get_fee=fee,
_is_dry_limit_order_filled=MagicMock(return_value=False),
)
patch_whitelist(mocker, default_conf)
freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade)
freqtrade.strategy.confirm_trade_exit = MagicMock(return_value=False)
# Create some test data
freqtrade.enter_positions()
rpc_mock.reset_mock()
trade = Trade.query.first()
assert trade
assert freqtrade.strategy.confirm_trade_exit.call_count == 0
# Increase the price and sell it
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
fetch_ticker=ticker_sell_up
)
freqtrade.strategy.confirm_trade_exit = MagicMock(return_value=True)
# Set a custom exit price
freqtrade.strategy.custom_exit_price = lambda **kwargs: 1.170e-05
freqtrade.execute_sell(trade=trade, limit=ticker_sell_up()['bid'],
sell_reason=SellCheckTuple(sell_type=SellType.SELL_SIGNAL))
# Sell price must be different to default bid price
assert freqtrade.strategy.confirm_trade_exit.call_count == 1
assert rpc_mock.call_count == 1
last_msg = rpc_mock.call_args_list[-1][0][0]
assert {
'trade_id': 1,
'type': RPCMessageType.SELL,
'exchange': 'Binance',
'pair': 'ETH/BTC',
'gain': 'profit',
'limit': 1.170e-05,
'amount': 91.07468123,
'order_type': 'limit',
'open_rate': 1.098e-05,
'current_rate': 1.173e-05,
'profit_amount': 6.041e-05,
'profit_ratio': 0.06025919,
'stake_currency': 'BTC',
'fiat_currency': 'USD',
'sell_reason': SellType.SELL_SIGNAL.value,
'open_date': ANY,
'close_date': ANY,
'close_rate': ANY,
} == last_msg
def test_execute_sell_down_stoploss_on_exchange_dry_run(default_conf, ticker, fee,
ticker_sell_down, mocker) -> None:
rpc_mock = patch_RPCManager(mocker)
@ -4503,3 +4601,43 @@ def test_refind_lost_order(mocker, default_conf, fee, caplog):
freqtrade.refind_lost_order(trades[4])
assert log_has(f"Error updating {order['id']}.", caplog)
def test_get_valid_price(mocker, default_conf) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
freqtrade = FreqtradeBot(default_conf)
freqtrade.config['custom_price_max_distance_ratio'] = 0.02
custom_price_string = "10"
custom_price_badstring = "10abc"
custom_price_float = 10.0
custom_price_int = 10
custom_price_over_max_alwd = 11.0
custom_price_under_min_alwd = 9.0
proposed_price = 10.1
valid_price_from_string = freqtrade.get_valid_price(custom_price_string, proposed_price)
valid_price_from_badstring = freqtrade.get_valid_price(custom_price_badstring, proposed_price)
valid_price_from_int = freqtrade.get_valid_price(custom_price_int, proposed_price)
valid_price_from_float = freqtrade.get_valid_price(custom_price_float, proposed_price)
valid_price_at_max_alwd = freqtrade.get_valid_price(custom_price_over_max_alwd, proposed_price)
valid_price_at_min_alwd = freqtrade.get_valid_price(custom_price_under_min_alwd, proposed_price)
assert isinstance(valid_price_from_string, float)
assert isinstance(valid_price_from_badstring, float)
assert isinstance(valid_price_from_int, float)
assert isinstance(valid_price_from_float, float)
assert valid_price_from_string == custom_price_float
assert valid_price_from_badstring == proposed_price
assert valid_price_from_int == custom_price_int
assert valid_price_from_float == custom_price_float
assert valid_price_at_max_alwd < custom_price_over_max_alwd
assert valid_price_at_max_alwd > proposed_price
assert valid_price_at_min_alwd > custom_price_under_min_alwd
assert valid_price_at_min_alwd < proposed_price

View File

@ -1587,25 +1587,30 @@ def test_adjust_min_max_rates(fee):
open_rate=1,
)
trade.adjust_min_max_rates(trade.open_rate)
trade.adjust_min_max_rates(trade.open_rate, trade.open_rate)
assert trade.max_rate == 1
assert trade.min_rate == 1
# check min adjusted, max remained
trade.adjust_min_max_rates(0.96)
trade.adjust_min_max_rates(0.96, 0.96)
assert trade.max_rate == 1
assert trade.min_rate == 0.96
# check max adjusted, min remains
trade.adjust_min_max_rates(1.05)
trade.adjust_min_max_rates(1.05, 1.05)
assert trade.max_rate == 1.05
assert trade.min_rate == 0.96
# current rate "in the middle" - no adjustment
trade.adjust_min_max_rates(1.03)
trade.adjust_min_max_rates(1.03, 1.03)
assert trade.max_rate == 1.05
assert trade.min_rate == 0.96
# current rate "in the middle" - no adjustment
trade.adjust_min_max_rates(1.10, 0.91)
assert trade.max_rate == 1.10
assert trade.min_rate == 0.91
@pytest.mark.usefixtures("init_persistence")
@pytest.mark.parametrize('use_db', [True, False])
@ -2099,6 +2104,11 @@ def test_update_order_from_ccxt(caplog):
assert o.ft_is_open
assert o.order_filled_date is None
# Order is unfilled, "filled" not set
# https://github.com/freqtrade/freqtrade/issues/5404
ccxt_order.update({'filled': None, 'remaining': 20.0, 'status': 'canceled'})
o.update_from_ccxt_object(ccxt_order)
# Order has been closed
ccxt_order.update({'filled': 20.0, 'remaining': 0.0, 'status': 'closed'})
o.update_from_ccxt_object(ccxt_order)