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Merge pull request #4851 from rokups/rk/backtest-dataprovider

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Data provider support in backtesting
This commit is contained in:
Matthias
2021-05-10 19:11:05 +02:00
committed by GitHub
parent 91fd367287 d495ea3693
commit b81f24d9c6
11 changed files with 237 additions and 151 deletions
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45
docs/strategy-advanced.md
View File

@@ -60,7 +60,8 @@ from freqtrade.strategy import IStrategy, timeframe_to_prev_date
class AwesomeStrategy(IStrategy):
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, dataframe: DataFrame, **kwargs):
current_profit: float, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
trade_row = dataframe.loc[dataframe['date'] == trade_open_date].squeeze()
@@ -105,8 +106,7 @@ class AwesomeStrategy(IStrategy):
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame,
**kwargs) -> float:
current_rate: float, current_profit: float, **kwargs) -> float:
"""
Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
e.g. returning -0.05 would create a stoploss 5% below current_rate.
@@ -156,8 +156,7 @@ class AwesomeStrategy(IStrategy):
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame,
**kwargs) -> float:
current_rate: float, current_profit: float, **kwargs) -> float:
# Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
if current_time - timedelta(minutes=120) > trade.open_date_utc:
@@ -183,8 +182,7 @@ class AwesomeStrategy(IStrategy):
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame,
**kwargs) -> float:
current_rate: float, current_profit: float, **kwargs) -> float:
if pair in ('ETH/BTC', 'XRP/BTC'):
return -0.10
@@ -210,8 +208,7 @@ class AwesomeStrategy(IStrategy):
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame,
**kwargs) -> float:
current_rate: float, current_profit: float, **kwargs) -> float:
if current_profit < 0.04:
return -1 # return a value bigger than the inital stoploss to keep using the inital stoploss
@@ -250,8 +247,7 @@ class AwesomeStrategy(IStrategy):
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame,
**kwargs) -> float:
current_rate: float, current_profit: float, **kwargs) -> float:
# evaluate highest to lowest, so that highest possible stop is used
if current_profit > 0.40:
@@ -269,12 +265,6 @@ class AwesomeStrategy(IStrategy):
Imagine you want to use `custom_stoploss()` to use a trailing indicator like e.g. "ATR"
!!! Warning
Only use `dataframe` values up until and including `current_time` value. Reading past
`current_time` you will look into the future, which will produce incorrect backtesting results
and throw an exception in dry/live runs.
see [Common mistakes when developing strategies](strategy-customization.md#common-mistakes-when-developing-strategies) for more info.
!!! Note
`dataframe['date']` contains the candle's open date. During dry/live runs `current_time` and
`trade.open_date_utc` will not match the candle date precisely and using them directly will throw
@@ -293,20 +283,27 @@ class AwesomeStrategy(IStrategy):
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame,
**kwargs) -> float:
current_rate: float, current_profit: float, **kwargs) -> float:
# Default return value
result = 1
if trade:
# Using current_time directly would only work in backtesting. Live/dry runs need time to
# be rounded to previous candle to be used as dataframe index. Rounding must also be
# applied to `trade.open_date(_utc)` if it is used for `dataframe` indexing.
current_time = timeframe_to_prev_date(self.timeframe, current_time)
current_row = dataframe.loc[dataframe['date'] == current_time].squeeze()
if 'atr' in current_row:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
current_candle = dataframe.loc[-1].squeeze()
if 'atr' in current_candle:
# new stoploss relative to current_rate
new_stoploss = (current_rate - current_row['atr']) / current_rate
new_stoploss = (current_rate - current_candle['atr']) / current_rate
# Round trade date to it's candle time.
trade_date = timeframe_to_prev_date(trade.open_date_utc)
trade_candle = dataframe.loc[dataframe['date'] == trade_date]
# Just opened trades do not have their candle complete yet therefore trade_candle may be None
if trade_candle is not None:
trade_candle = trade_candle.squeeze()
trade_stoploss = (current_rate - trade_candle['atr']) / current_rate
new_stoploss = max(new_stoploss, trade_stoploss)
# turn into relative negative offset required by `custom_stoploss` return implementation
result = new_stoploss - 1

7
docs/strategy-customization.md
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@@ -422,10 +422,6 @@ if self.dp:
Returns an empty dataframe if the requested pair was not cached.
This should not happen when using whitelisted pairs.
!!! Warning "Warning about backtesting"
This method will return an empty dataframe during backtesting.
### *orderbook(pair, maximum)*
``` python
@@ -631,8 +627,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, dataframe: DataFrame,
**kwargs) -> float:
current_rate: float, current_profit: float, **kwargs) -> float:
# once the profit has risen above 10%, keep the stoploss at 7% above the open price
if current_profit > 0.10: