Merge branch 'develop' into feat/short
This commit is contained in:
Matthias
commit
957551ea97
@ -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
|
||||
|
||||
|
||||
@ -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"
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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.
|
||||
|
||||
@ -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": {
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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.
|
||||
|
||||
@ -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")
|
||||
|
||||
```
|
||||
|
||||
@ -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
|
||||
|
||||
@ -191,6 +191,9 @@ CONF_SCHEMA = {
|
||||
},
|
||||
'required': ['price_side']
|
||||
},
|
||||
'custom_price_max_distance_ratio': {
|
||||
'type': 'number', 'minimum': 0.0
|
||||
},
|
||||
'order_types': {
|
||||
'type': 'object',
|
||||
'properties': {
|
||||
|
||||
@ -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']
|
||||
|
||||
@ -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)
|
||||
|
||||
@ -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
|
||||
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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,
|
||||
|
||||
23
freqtrade/exchange/gateio.py
Normal file
23
freqtrade/exchange/gateio.py
Normal 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,
|
||||
}
|
||||
@ -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)
|
||||
|
||||
@ -130,6 +130,9 @@ 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
|
||||
@ -215,7 +218,7 @@ 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', 'buy', 'open', 'close', 'sell', 'low', 'high']
|
||||
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag']
|
||||
data: Dict = {}
|
||||
self.progress.init_step(BacktestState.CONVERT, len(processed))
|
||||
|
||||
@ -223,12 +226,9 @@ class Backtesting:
|
||||
for pair, pair_data in processed.items():
|
||||
self.check_abort()
|
||||
self.progress.increment()
|
||||
has_buy_tag = 'buy_tag' in pair_data
|
||||
headers = headers + ['buy_tag'] if has_buy_tag else headers
|
||||
if not pair_data.empty:
|
||||
pair_data.loc[:, 'buy'] = 0 # cleanup if buy_signal is exist
|
||||
pair_data.loc[:, 'sell'] = 0 # cleanup if sell_signal is exist
|
||||
if has_buy_tag:
|
||||
pair_data.loc[:, 'buy_tag'] = None # cleanup if buy_tag is exist
|
||||
|
||||
df_analyzed = self.strategy.advise_sell(
|
||||
@ -242,14 +242,13 @@ class Backtesting:
|
||||
# from the previous candle
|
||||
df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
|
||||
df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
|
||||
if has_buy_tag:
|
||||
df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1)
|
||||
|
||||
df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
|
||||
|
||||
# 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()
|
||||
@ -322,14 +321,14 @@ class Backtesting:
|
||||
return sell_row[OPEN_IDX]
|
||||
|
||||
def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
|
||||
|
||||
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)
|
||||
@ -341,7 +340,7 @@ 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)
|
||||
@ -465,6 +464,8 @@ 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.
|
||||
@ -476,8 +477,8 @@ 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
|
||||
@ -501,7 +502,7 @@ 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
|
||||
@ -532,7 +533,8 @@ 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())
|
||||
@ -551,7 +553,7 @@ 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)
|
||||
|
||||
@ -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)
|
||||
|
||||
128
freqtrade/optimize/hyperopt_epoch_filters.py
Normal file
128
freqtrade/optimize/hyperopt_epoch_filters.py
Normal 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
|
||||
@ -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 ""
|
||||
)
|
||||
|
||||
@ -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:
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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]
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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')
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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():
|
||||
|
||||
@ -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,
|
||||
|
||||
@ -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())
|
||||
|
||||
@ -280,6 +280,43 @@ 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]]:
|
||||
"""
|
||||
@ -577,7 +614,7 @@ 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,
|
||||
@ -741,7 +778,7 @@ 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!
|
||||
|
||||
@ -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": [
|
||||
|
||||
@ -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
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
# Include all requirements to run the bot.
|
||||
-r requirements.txt
|
||||
|
||||
plotly==5.1.0
|
||||
plotly==5.2.1
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
2
setup.sh
2
setup.sh
@ -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") ]
|
||||
|
||||
@ -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')
|
||||
|
||||
|
||||
@ -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 = [
|
||||
|
||||
@ -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']
|
||||
|
||||
@ -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"
|
||||
|
||||
@ -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',
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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])
|
||||
|
||||
|
||||
@ -52,4 +52,6 @@ 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
|
||||
|
||||
@ -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,
|
||||
|
||||
@ -351,7 +351,7 @@ 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()
|
||||
@ -426,7 +426,7 @@ 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'})
|
||||
|
||||
@ -438,7 +438,7 @@ 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'})
|
||||
|
||||
@ -463,7 +463,7 @@ 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'})
|
||||
|
||||
@ -660,7 +660,7 @@ 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()
|
||||
@ -713,7 +713,7 @@ 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()
|
||||
@ -761,7 +761,7 @@ 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()
|
||||
@ -805,7 +805,7 @@ 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
|
||||
@ -844,7 +844,7 @@ 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
|
||||
@ -886,7 +886,7 @@ 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
|
||||
@ -940,7 +940,7 @@ 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
|
||||
@ -985,7 +985,7 @@ 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)
|
||||
|
||||
@ -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", [
|
||||
|
||||
@ -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)
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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)
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user