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Merge pull request #4922 from rokups/rk/fix-docs

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Docs update regarding dataframe access
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Matthias 2021-05-12 19:58:11 +02:00 committed by GitHub
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@ -40,6 +40,41 @@ class AwesomeStrategy(IStrategy):
!!! Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
## Dataframe access
You may access dataframe in various strategy functions by querying it from dataprovider.
``` python
from freqtrade.exchange import timeframe_to_prev_date
class AwesomeStrategy(IStrategy):
def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str,
current_time: 'datetime', **kwargs) -> bool:
# Obtain pair dataframe.
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
# Obtain last available candle. Do not use current_time to look up latest candle, because
# current_time points to curret incomplete candle whose data is not available.
last_candle = dataframe.iloc[-1].squeeze()
# <...>
# In dry/live runs trade open date will not match candle open date therefore it must be
# rounded.
trade_date = timeframe_to_prev_date(trade.open_date_utc)
# Look up trade candle.
trade_candle = dataframe.loc[dataframe['date'] == trade_date]
# trade_candle may be None for trades that just opened as it is still incomplete.
if trade_candle is not None:
# <...>
```
!!! Warning "Using .iloc[-1]"
You can use `.iloc[-1]` here because `get_analyzed_dataframe()` only returns candles that backtesting is allowed to see.
This will not work in `populate_*` methods, so make sure to not use `.iloc[]` in that area.
Also, this will only work starting with version 2021.5.
***
## Custom sell signal
@ -62,19 +97,16 @@ class AwesomeStrategy(IStrategy):
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
# Get the row at trade open
trade_open_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
trade_open_row = dataframe.loc[dataframe['date'] == trade_open_date].squeeze()
last_candle = dataframe.iloc[-1].squeeze()
# Above 20% profit, sell when rsi < 80
if current_profit > 0.2:
if trade_open_row['rsi'] < 80:
if last_candle['rsi'] < 80:
return 'rsi_below_80'
# Between 2% and 10%, sell if EMA-long above EMA-short
if 0.02 < current_profit < 0.1:
if trade_open_row['emalong'] > trade_open_row['emashort']:
if last_candle['emalong'] > last_candle['emashort']:
return 'ema_long_below_80'
# Sell any positions at a loss if they are held for more than one day.
@ -82,7 +114,7 @@ class AwesomeStrategy(IStrategy):
return 'unclog'
```
See [Custom stoploss using an indicator from dataframe example](#custom-stoploss-using-an-indicator-from-dataframe-example) for explanation on how to use `dataframe` parameter.
See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
## Custom stoploss
@ -265,57 +297,35 @@ class AwesomeStrategy(IStrategy):
#### Custom stoploss using an indicator from dataframe example
Imagine you want to use `custom_stoploss()` to use a trailing indicator like e.g. "ATR"
!!! 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
an error. Use `date = timeframe_to_prev_date(self.timeframe, date)` to round a date to the candle's open date
before using it to access `dataframe`.
Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
``` python
from freqtrade.exchange import timeframe_to_prev_date
from freqtrade.persistence import Trade
from freqtrade.state import RunMode
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# <...>
dataframe['sar'] = ta.SAR(dataframe)
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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.
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
if 'atr' in current_candle:
# new stoploss relative to 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
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
return result
# Use parabolic sar as absolute stoploss price
stoploss_price = last_candle['sar']
# Convert absolute price to percentage relative to current_rate
if stoploss_price < current_rate:
return (stoploss_price / current_rate) - 1
# return maximum stoploss value, keeping current stoploss price unchanged
return 1
```
!!! Warning "Using .iloc[-1]"
You can use `.iloc[-1]` here because `get_analyzed_dataframe()` only returns candles that backtesting is allowed to see.
This will not work in `populate_*` methods, so make sure to not use `.iloc[]` in that area.
Also, this will only work starting with version 2021.5.
See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
---