adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

add timeframe to strategy and hyperopt metadata

Browse Source
This commit is contained in:
hroff-1902 2019-06-08 07:09:23 +03:00
parent 92113ce1c9
commit 39230364fd
5 changed files with 12 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
freqtrade/edge/__init__.py
View File

@ -47,7 +47,6 @@ class Edge():
self.exchange = exchange
self.strategy = strategy
self.ticker_interval = self.strategy.ticker_interval
self.tickerdata_to_dataframe = self.strategy.tickerdata_to_dataframe
self.advise_sell = self.strategy.advise_sell
self.advise_buy = self.strategy.advise_buy
@ -112,7 +111,7 @@ class Edge():
logger.critical("No data found. Edge is stopped ...")
return False
preprocessed = self.tickerdata_to_dataframe(data)
preprocessed = self.strategy.tickerdata_to_dataframe(data, self.ticker_interval)
# Print timeframe
min_date, max_date = history.get_timeframe(preprocessed)
@ -130,8 +129,9 @@ class Edge():
pair_data = pair_data.sort_values(by=['date'])
pair_data = pair_data.reset_index(drop=True)
metadata = {'pair': pair, 'timeframe': self.ticker_interval}
ticker_data = self.advise_sell(
self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
self.advise_buy(pair_data, metadata), metadata)[headers].copy()
trades += self._find_trades_for_stoploss_range(ticker_data, pair, self._stoploss_range)

6
freqtrade/optimize/backtesting.py
View File

@ -94,7 +94,6 @@ class Backtesting(object):
self.ticker_interval = self.config.get('ticker_interval')
self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval)
self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe
self.advise_buy = strategy.advise_buy
self.advise_sell = strategy.advise_sell
# Set stoploss_on_exchange to false for backtesting,
@ -219,8 +218,9 @@ class Backtesting(object):
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
metadata = {'pair': pair, 'timeframe': self.ticker_interval}
ticker_data = self.advise_sell(
self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
self.advise_buy(pair_data, metadata), metadata)[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
@ -439,7 +439,7 @@ class Backtesting(object):
(max_date - min_date).days
)
# need to reprocess data every time to populate signals
preprocessed = self.strategy.tickerdata_to_dataframe(data)
preprocessed = self.strategy.tickerdata_to_dataframe(data, self.ticker_interval)
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest(

1
freqtrade/optimize/default_hyperopt.py
View File

@ -105,7 +105,6 @@ class DefaultHyperOpts(IHyperOpt):
"""
Sell strategy Hyperopt will build and use
"""
# print(params)
conditions = []
# GUARDS AND TRENDS
if 'sell-mfi-enabled' in params and params['sell-mfi-enabled']:

2
freqtrade/optimize/hyperopt.py
View File

@ -296,7 +296,7 @@ class Hyperopt(Backtesting):
self.strategy.advise_indicators = \
self.custom_hyperopt.populate_indicators # type: ignore
dump(self.strategy.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)
dump(self.strategy.tickerdata_to_dataframe(data, self.ticker_interval), TICKERDATA_PICKLE)
# We don't need exchange instance anymore while running hyperopt
self.exchange = None # type: ignore

8
freqtrade/strategy/interface.py
View File

@ -198,7 +198,7 @@ class IStrategy(ABC):
return False, False
try:
dataframe = self.analyze_ticker(dataframe, {'pair': pair})
dataframe = self.analyze_ticker(dataframe, {'pair': pair, 'timeframe': interval})
except ValueError as error:
logger.warning(
'Unable to analyze ticker for pair %s: %s',
@ -362,11 +362,13 @@ class IStrategy(ABC):
return False
def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
def tickerdata_to_dataframe(self, tickerdata: Dict[str, List],
ticker_interval: str) -> Dict[str, DataFrame]:
"""
Creates a dataframe and populates indicators for given ticker data
"""
return {pair: self.advise_indicators(pair_data, {'pair': pair})
return {pair: self.advise_indicators(pair_data,
{'pair': pair, 'timeframe': ticker_interval})
for pair, pair_data in tickerdata.items()}
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: