def analyze_ticker(self, ticker_history: List[Dict],
metadata: dict) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
"""
dataframe = parse_ticker_dataframe(ticker_history)
pair = str(metadata.get('pair'))
# Test if seen this pair and last candle before.
# always run if process_only_new_candles is set to true
if (not self.process_only_new_candles
or self._last_candle_seen_per_pair.get(
pair, None) != dataframe.iloc[-1]['date']):
# Defs that only make change on new candle data.
logging.debug("TA Analysis Launched")
dataframe = self.advise_indicators(dataframe, metadata)
dataframe = self.advise_buy(dataframe, metadata)
dataframe = self.advise_sell(dataframe, metadata)
self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
else:
logging.debug("Skippinig TA Analysis for already analyzed candle")
dataframe['buy'] = 0
dataframe['sell'] = 0
# Other Defs in strategy that want to be called every loop here
# twitter_sell = self.watch_twitter_feed(dataframe, metadata)
logging.debug("Loop Analysis Launched")
return dataframe
def tickerdata_to_dataframe(
self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
"""
Creates a dataframe and populates indicators for given ticker data
"""
return {
pair: self.advise_indicators(parse_ticker_dataframe(pair_data),
{'pair': pair})
for pair, pair_data in tickerdata.items()
}
def analyze_ticker(self, ticker_history: List[Dict],
metadata: dict) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
"""
dataframe = parse_ticker_dataframe(ticker_history)
dataframe = self.advise_indicators(dataframe, metadata)
dataframe = self.advise_buy(dataframe, metadata)
dataframe = self.advise_sell(dataframe, metadata)
return dataframe
def result():
with open('freqtrade/tests/testdata/ETH_BTC-1m.json') as data_file:
return parse_ticker_dataframe(json.load(data_file))
def test_dataframe_correct_length(result):
dataframe = parse_ticker_dataframe(result)
assert len(result.index) - 1 == len(
dataframe.index) # last partial candle removed
def test_parse_ticker_dataframe(ticker_history):
columns = ['date', 'open', 'high', 'low', 'close', 'volume']
# Test file with BV data
dataframe = parse_ticker_dataframe(ticker_history)
assert dataframe.columns.tolist() == columns