def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
# resample our dataframes
dataframe_short = resample_to_interval(dataframe, self.get_ticker_indicator() * 2)
dataframe_long = resample_to_interval(dataframe, self.get_ticker_indicator() * 8)
# compute our RSI's
dataframe_short['rsi'] = ta.RSI(dataframe_short, timeperiod=14)
dataframe_long['rsi'] = ta.RSI(dataframe_long, timeperiod=14)
dataframe_short['cmf'] = cmf(dataframe_short, 14)
dataframe_long['cmf'] = cmf(dataframe_long, 14)
dataframe_short['osc'] = osc(dataframe_short, 14)
dataframe_long['osc'] = osc(dataframe_long, 14)
# merge dataframe back together
dataframe = resampled_merge(dataframe, dataframe_short)
dataframe = resampled_merge(dataframe, dataframe_long)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# fill NA values with previes
dataframe.fillna(method='ffill', inplace=True)
# Volume Flow Index: Add VFI, VFIMA, Histogram to DF
dataframe['vfi'], dataframe['vfima'], dataframe['vfi_hist'] = \
vfi(dataframe, length=130, coef=0.2, vcoef=2.5, signalLength=5, smoothVFI=True)
return dataframe
def test_resampled_merge_contains_indicator(testdata_1m_btc):
resampled = resample_to_interval(testdata_1m_btc, 5)
resampled['cmf'] = cmf(resampled, 5)
merged = resampled_merge(testdata_1m_btc, resampled)
print(merged)
assert "resample_5_cmf" in merged
def test_cmf(testdata_1m_btc):
from technical.indicators import cmf
result = cmf(testdata_1m_btc, 14)
#drop nan, they are exspected, based on the period
result = result[~numpy.isnan(result)]
assert result.min() >= -1
assert result.max() <= 1
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
# otherwise freqshow import won't work
# since lambda is too large with all the dependencies
from technical.util import resample_to_interval
from technical.util import resampled_merge
from technical.indicators import cmf
from technical.indicators import osc
dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
# resample our dataframes
dataframe_short = resample_to_interval(dataframe,
self.get_ticker_indicator() * 2)
dataframe_long = resample_to_interval(dataframe,
self.get_ticker_indicator() * 8)
# compute our RSI's
dataframe_short['rsi'] = ta.RSI(dataframe_short, timeperiod=14)
dataframe_long['rsi'] = ta.RSI(dataframe_long, timeperiod=14)
dataframe_short['cmf'] = cmf(dataframe_short, 14)
dataframe_long['cmf'] = cmf(dataframe_long, 14)
dataframe_short['osc'] = osc(dataframe_short, 14)
dataframe_long['osc'] = osc(dataframe_long, 14)
# merge dataframe back together
dataframe = resampled_merge(dataframe, dataframe_short)
dataframe = resampled_merge(dataframe, dataframe_long)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# fill NA values with previes
dataframe.fillna(method='ffill', inplace=True)
return dataframe
def evaluate_cmf(self,
period=12,
prefix="cmf",
impact_buy=1,
impact_sell=1):
"""
evaluates the osc
:param dataframe:
:param period:
:param prefix:
:return:
"""
from technical.indicators import cmf
self._weights(impact_buy, impact_sell)
dataframe = self.dataframe
name = '{}_{}'.format(prefix, period)
dataframe[name] = cmf(dataframe, period)
dataframe.loc[((dataframe[name] > 0.5)),
'buy_{}'.format(name)] = (1 * impact_buy)
dataframe.loc[((dataframe[name] < -0.5)),
'sell_{}'.format(name)] = (1 * impact_sell)
def evaluate_cmf(self,
period=12,
prefix="cmf",
impact_buy=1,
impact_sell=1):
"""
evaluates the cmf
:param dataframe:
:param period:
:param prefix:
:return:
"""
from technical.indicators import cmf
self._weights(impact_buy, impact_sell)
dataframe = self.dataframe
name = f"{prefix}_{period}"
dataframe[name] = cmf(dataframe, period)
dataframe.loc[((dataframe[name] > 0.5)),
f"buy_{name}"] = 1 * impact_buy
dataframe.loc[((dataframe[name] < -0.5)),
f"sell_{name}"] = 1 * impact_sell
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe['cmf'] = cmf(dataframe, 21)
return dataframe