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 populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> 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)
# merge dataframe back together
dataframe = resampled_merge(dataframe, dataframe_short)
dataframe = resampled_merge(dataframe, dataframe_long)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe.fillna(method='ffill', inplace=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 populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
tf_res = timeframe_to_minutes(self.timeframe) * 5
df_res = resample_to_interval(dataframe, tf_res)
df_res['sma'] = ta.SMA(df_res, 50, price='close')
dataframe = resampled_merge(dataframe, df_res, fill_na=True)
dataframe['resample_sma'] = dataframe[f'resample_{tf_res}_sma']
dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
dataframe['ema_close'] = ta.EMA(dataframe, timeperiod=5, price='close')
dataframe['ema_low'] = ta.EMA(dataframe, timeperiod=5, price='low')
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['adx'] = ta.ADX(dataframe)
dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['mfi'] = ta.MFI(dataframe)
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'],
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
return dataframe
def test_resampled_merge_contains_indicator(testdata_1m_btc):
resampled = resample_to_interval(testdata_1m_btc, 5)
resampled["cmf"] = chaikin_money_flow(resampled, 5)
merged = resampled_merge(testdata_1m_btc, resampled)
print(merged)
assert "resample_5_cmf" in merged
def test_resampled_merge(testdata_1m_btc):
resampled = resample_to_interval(testdata_1m_btc, 5)
merged = resampled_merge(testdata_1m_btc, resampled)
assert (len(merged) == len(testdata_1m_btc))
assert "resample_5_open" in merged
assert "resample_5_close" in merged
assert "resample_5_low" in merged
assert "resample_5_high" in merged
assert "resample_5_date" not in merged
assert "resample_5_volume" not in merged
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 populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['maShort'] = ta.EMA(dataframe, timeperiod=8)
dataframe['maMedium'] = ta.EMA(dataframe, timeperiod=21)
##################################################################################
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
self.resample_interval = timeframe_to_minutes(self.ticker_interval) * 12
dataframe_long = resample_to_interval(dataframe, self.resample_interval)
dataframe_long['sma'] = ta.SMA(dataframe_long, timeperiod=50, price='close')
dataframe = resampled_merge(dataframe, dataframe_long, fill_na=True)
return dataframe
def test_resampled_merge(testdata_1m_btc):
resampled = resample_to_interval(testdata_1m_btc, 5)
merged = resampled_merge(testdata_1m_btc, resampled)
assert len(merged) == len(testdata_1m_btc)
assert "resample_5_open" in merged
assert "resample_5_close" in merged
assert "resample_5_low" in merged
assert "resample_5_high" in merged
assert "resample_5_date" in merged
assert "resample_5_volume" in merged
# Verify the assignment goes to the correct candle
# If resampling to 5m, then the resampled value needs to be on the 5m candle.
date = pd.to_datetime("2017-11-14 22:45:00", utc=True)
assert merged.loc[merged["date"] == "2017-11-14 22:48:00",
"resample_5_date"].iloc[0] != date
# The 5m candle for 22:45 is available at 22:50,
# when both :49 1m and :45 5m candles close
assert merged.loc[merged["date"] == "2017-11-14 22:49:00",
"resample_5_date"].iloc[0] == date
assert merged.loc[merged["date"] == "2017-11-14 22:50:00",
"resample_5_date"].iloc[0] == date
assert merged.loc[merged["date"] == "2017-11-14 22:51:00",
"resample_5_date"].iloc[0] == date
assert merged.loc[merged["date"] == "2017-11-14 22:52:00",
"resample_5_date"].iloc[0] == date
assert merged.loc[merged["date"] == "2017-11-14 22:53:00",
"resample_5_date"].iloc[0] == date
# The 5m candle for 22:50 is available at 22:54,
# when both :54 1m and :50 5m candles close
date = pd.to_datetime("2017-11-14 22:50:00", utc=True)
assert merged.loc[merged["date"] == "2017-11-14 22:54:00",
"resample_5_date"].iloc[0] == date
assert merged.loc[merged["date"] == "2017-11-14 22:55:00",
"resample_5_date"].iloc[0] == date
assert merged.loc[merged["date"] == "2017-11-14 22:56:00",
"resample_5_date"].iloc[0] == date
assert merged.loc[merged["date"] == "2017-11-14 22:57:00",
"resample_5_date"].iloc[0] == date
assert merged.loc[merged["date"] == "2017-11-14 22:58:00",
"resample_5_date"].iloc[0] == date
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# longer RSI used for determining trend
resample_rsi_indicator = self.get_ticker_indicator() * 12
resample_rsi_key = 'resample_{}_rsi'.format(resample_rsi_indicator)
dataframe_long = resample_to_interval(dataframe, resample_rsi_indicator)
dataframe_long['rsi'] = ta.RSI(dataframe_long, timeperiod=14)
dataframe = resampled_merge(dataframe, dataframe_long)
dataframe[resample_rsi_key].fillna(method='ffill', inplace=True)
# bull used to select between two different sets of buy/sell threshold values
# based on long RSI
dataframe['bull'] = dataframe[resample_rsi_key].gt(60).astype('int')
# normal rsi acts mainly as the buy trigger
# used for sell as well, but more likely to ROI or stop out
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def test_resampled_merge(testdata_1m_btc):
resampled = resample_to_interval(testdata_1m_btc, 5)
merged = resampled_merge(testdata_1m_btc, resampled)
assert len(merged) == len(testdata_1m_btc)
assert "resample_5_open" in merged
assert "resample_5_close" in merged
assert "resample_5_low" in merged
assert "resample_5_high" in merged
assert "resample_5_date" not in merged
assert "resample_5_volume" not in merged
# Verify the assignment goes to the correct candle
# If resampling to 5m, then the resampled value needs to be on the 5m candle.
assert sum(merged.loc[merged["date"] == "2017-11-14 22:54:00",
"resample_5_close"].isna()) == 1
assert sum(merged.loc[merged["date"] == "2017-11-14 22:55:00",
"resample_5_close"].isna()) == 0
assert sum(merged.loc[merged["date"] == "2017-11-14 22:56:00",
"resample_5_close"].isna()) == 1
def test_resample_to_interval(testdata_1m_btc):
result = resample_to_interval(testdata_1m_btc, 5)
# should be roughly a factor 5
assert (len(testdata_1m_btc) / len(result) > 4.5)
assert (len(testdata_1m_btc) / len(result) < 5.5)
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
from technical.util import resample_to_interval
from technical.util import resampled_merge
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# EMA - Exponential Moving Average
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
# Stoch
stoch = ta.STOCH(dataframe,
fastk_period=5,
slowk_period=2,
slowk_matype=0,
slowd_period=2,
slowd_matype=0)
dataframe['slowd15'] = stoch['slowd']
dataframe['slowk15'] = stoch['slowk']
stoch = ta.STOCH(dataframe,
fastk_period=10,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=24)
dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=24)
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2,
nbdevdn=2)['lowerband']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
dataframe['sma20'] = ta.SMA(dataframe, timeperiod=20)
dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
dataframe['sma220'] = ta.SMA(dataframe, timeperiod=220)
dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
dataframe['willr'] = ta.WILLR(dataframe, timeperiod=28)
# resample our dataframes
dataframe_short = resample_to_interval(dataframe,
self.get_ticker_indicator() * 3)
dataframe_long = resample_to_interval(dataframe,
self.get_ticker_indicator() * 7)
# compute our RSI's
dataframe_short['rsi'] = ta.RSI(dataframe_short, timeperiod=14)
dataframe_long['rsi'] = ta.RSI(dataframe_long, timeperiod=14)
dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# merge dataframe back together
dataframe = resampled_merge(dataframe, dataframe_short)
dataframe = resampled_merge(dataframe, dataframe_long)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe.fillna(method='ffill', inplace=True)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo .gl/2JGGoy)
dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi'])
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
dataframe['resample_rsi_2'] = dataframe['resample_{}_rsi'.format(
self.get_ticker_indicator() * 3)]
dataframe['resample_rsi_8'] = dataframe['resample_{}_rsi'.format(
self.get_ticker_indicator() * 7)]
dataframe['average'] = (dataframe['close'] + dataframe['open'] +
dataframe['high'] + dataframe['low']) / 4
return dataframe
