def evaluate_ichimoku(self,
prefix="ichimoku",
impact_buy=1,
impact_sell=1):
"""
evaluates the ichimoku
:param dataframe:
:param period:
:param prefix:
:return:
"""
from technical.indicators import ichimoku
self._weights(impact_buy, impact_sell)
dataframe = self.dataframe
name = '{}'.format(prefix)
ichimoku = ichimoku(dataframe)
dataframe['{}_tenkan_sen'.format(name)] = ichimoku['tenkan_sen']
dataframe['{}_kijun_sen'.format(name)] = ichimoku['kijun_sen']
dataframe['{}_senkou_span_a'.format(name)] = ichimoku['senkou_span_a']
dataframe['{}_senkou_span_b'.format(name)] = ichimoku['senkou_span_b']
dataframe['{}_chikou_span'.format(name)] = ichimoku['chikou_span']
# price is above the cloud
dataframe.loc[(
(dataframe['{}_senkou_span_a'.format(name)] > dataframe['open']) &
(dataframe['{}_senkou_span_b'.format(name)] > dataframe['open'])),
'buy_{}'.format(name)] = (1 * impact_buy)
# price is below the cloud
dataframe.loc[(
(dataframe['{}_senkou_span_a'.format(name)] < dataframe['open']) &
(dataframe['{}_senkou_span_b'.format(name)] < dataframe['open'])),
'sell_{}'.format(name)] = (1 * impact_sell)
def evaluate_ichimoku(self,
prefix="ichimoku",
impact_buy=1,
impact_sell=1):
"""
evaluates the ichimoku
:param dataframe:
:param period:
:param prefix:
:return:
"""
from technical.indicators import ichimoku
self._weights(impact_buy, impact_sell)
dataframe = self.dataframe
name = f"{prefix}"
ichimoku = ichimoku(dataframe)
dataframe[f"{name}_tenkan_sen"] = ichimoku["tenkan_sen"]
dataframe[f"{name}_kijun_sen"] = ichimoku["kijun_sen"]
dataframe[f"{name}_senkou_span_a"] = ichimoku["senkou_span_a"]
dataframe[f"{name}_senkou_span_b"] = ichimoku["senkou_span_b"]
dataframe[f"{name}_chikou_span"] = ichimoku["chikou_span"]
# price is above the cloud
dataframe.loc[(
(dataframe[f"{name}_senkou_span_a"] > dataframe["open"])
& (dataframe[f"{name}_senkou_span_b"] > dataframe["open"])),
f"buy_{name}", ] = impact_buy
# price is below the cloud
dataframe.loc[(
(dataframe[f"{name}_senkou_span_a"] < dataframe["open"])
& (dataframe[f"{name}_senkou_span_b"] < dataframe["open"])),
f"sell_{name}", ] = impact_sell
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# # Standard Settings
# displacement = 26
# ichimoku = ftt.ichimoku(dataframe,
# conversion_line_period=9,
# base_line_periods=26,
# laggin_span=52,
# displacement=displacement
# )
# Crypto Settings
displacement = 30
ichimoku = ftt.ichimoku(dataframe,
conversion_line_period=20,
base_line_periods=60,
laggin_span=120,
displacement=displacement)
dataframe['chikou_span'] = ichimoku['chikou_span']
# cross indicators
dataframe['tenkan_sen'] = ichimoku['tenkan_sen']
dataframe['kijun_sen'] = ichimoku['kijun_sen']
# cloud, green a > b, red a < b
dataframe['senkou_a'] = ichimoku['senkou_span_a']
dataframe['senkou_b'] = ichimoku['senkou_span_b']
dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a']
dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b']
dataframe['cloud_green'] = ichimoku['cloud_green'] * 1
dataframe['cloud_red'] = ichimoku['cloud_red'] * -1
# DANGER ZONE START
# NOTE: Not actually the future, present data that is normally shifted forward for display as the cloud
dataframe['future_green'] = (
dataframe['leading_senkou_span_a'] >
dataframe['leading_senkou_span_b']).astype('int') * 2
# The chikou_span is shifted into the past, so we need to be careful not to read the
# current value. But if we shift it forward again by displacement it should be safe to use.
# We're effectively "looking back" at where it normally appears on the chart.
dataframe['chikou_high'] = (
(dataframe['chikou_span'] > dataframe['senkou_a']) &
(dataframe['chikou_span'] > dataframe['senkou_b'])
).shift(displacement).fillna(0).astype('int')
# DANGER ZONE END
dataframe['go_long'] = (
(dataframe['tenkan_sen'] > dataframe['kijun_sen']) &
(dataframe['close'] > dataframe['senkou_a']) &
(dataframe['close'] > dataframe['senkou_b']) &
(dataframe['future_green'] > 0) &
(dataframe['chikou_high'] > 0)).astype('int') * 3
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
ichimoku = ftt.ichimoku(dataframe,
conversion_line_period=20,
base_line_periods=60,
laggin_span=120,
displacement=30
)
# cross indicators
dataframe['tenkan_sen'] = ichimoku['tenkan_sen']
dataframe['kijun_sen'] = ichimoku['kijun_sen']
# cloud, green a > b, red a < b
dataframe['senkou_a'] = ichimoku['senkou_span_a']
dataframe['senkou_b'] = ichimoku['senkou_span_b']
# dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a']
# dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b']
dataframe['cloud_green'] = ichimoku['cloud_green'] * 1
dataframe['cloud_red'] = ichimoku['cloud_red'] * -1
dataframe['cloud_green_strong'] = (
dataframe['cloud_green'] &
(dataframe['tenkan_sen'] > dataframe['kijun_sen']) &
(dataframe['kijun_sen'] > dataframe['senkou_a'])
).astype('int') * 2
dataframe['cloud_red_strong'] = (
dataframe['cloud_red'] &
(dataframe['tenkan_sen'] < dataframe['kijun_sen']) &
(dataframe['kijun_sen'] < dataframe['senkou_b'])
).astype('int') * -2
dataframe.loc[
qtpylib.crossed_above(dataframe['tenkan_sen'], dataframe['kijun_sen']),
'tk_cross_up'] = 3
dataframe['tk_cross_up'].fillna(method='ffill', inplace=True, limit=2)
dataframe['tk_cross_up'].fillna(value=0, inplace=True)
# dataframe['rsi_ok'] = (dataframe['rsi'] < 75).astype('int')
dataframe['ema35_ok'] = (
(dataframe['ema3'] > dataframe['ema5']) &
(dataframe['ema5'] > dataframe['ema10'])
).astype('int')
dataframe['spike'] = (
(dataframe['close'] > (dataframe['close'].shift(3) * (1 - self.stoploss * 0.9)))
).astype('int')
dataframe['recent_high'] = dataframe['high'].rolling(12).max()
return dataframe
def create_ichimoku(dataframe, conversion_line_period, displacement,
base_line_periods, laggin_span):
ichimoku = ftt.ichimoku(dataframe,
conversion_line_period=conversion_line_period,
base_line_periods=base_line_periods,
laggin_span=laggin_span,
displacement=displacement)
dataframe[f'tenkan_sen_{conversion_line_period}'] = ichimoku['tenkan_sen']
dataframe[f'kijun_sen_{conversion_line_period}'] = ichimoku['kijun_sen']
dataframe[f'senkou_a_{conversion_line_period}'] = ichimoku['senkou_span_a']
dataframe[f'senkou_b_{conversion_line_period}'] = ichimoku['senkou_span_b']
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
cloud = ichimoku(dataframe,
conversion_line_period=200,
base_line_periods=350,
laggin_span=150,
displacement=75)
dataframe['tenkan_sen'] = cloud['tenkan_sen']
dataframe['kijun_sen'] = cloud['kijun_sen']
dataframe['senkou_span_a'] = cloud['senkou_span_a']
dataframe['senkou_span_b'] = cloud['senkou_span_b']
dataframe['chikou_span'] = cloud['chikou_span']
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
ichi=ichimoku(dataframe)
dataframe['tenkan']=ichi['tenkan_sen']
dataframe['kijun']=ichi['kijun_sen']
dataframe['senkou_a']=ichi['senkou_span_a']
dataframe['senkou_b']=ichi['senkou_span_b']
dataframe['cloud_green']=ichi['cloud_green']
dataframe['cloud_red']=ichi['cloud_red']
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# ichis
ichi = ichimoku(dataframe)
dataframe['tenkan'] = ichi['tenkan_sen']
dataframe['kijun'] = ichi['kijun_sen']
dataframe['senkou_a'] = ichi['senkou_span_a']
dataframe['senkou_b'] = ichi['senkou_span_b']
dataframe['cloud_green'] = ichi['cloud_green']
dataframe['cloud_red'] = ichi['cloud_red']
# Momentum Indicator
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
"""
# Awesome oscillator
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# Minus Directional Indicator / Movement
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# ROC
dataframe['roc'] = ta.ROC(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stoch
stoch = ta.STOCH(dataframe)
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']
# Stoch RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
"""
# Overlap Studies
# ------------------------------------
# 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']
"""
# 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['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
"""
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
"""
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# Inverted Hammer: values [0, 100]
dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# Dragonfly Doji: values [0, 100]
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# Piercing Line: values [0, 100]
dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# Morningstar: values [0, 100]
dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# Three White Soldiers: values [0, 100]
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
"""
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
"""
# Hanging Man: values [0, 100]
dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# Shooting Star: values [0, 100]
dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# Gravestone Doji: values [0, 100]
dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# Dark Cloud Cover: values [0, 100]
dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# Evening Doji Star: values [0, 100]
dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# Evening Star: values [0, 100]
dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
"""
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
"""
# Three Line Strike: values [0, -100, 100]
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# Spinning Top: values [0, -100, 100]
dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# Engulfing: values [0, -100, 100]
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# Harami: values [0, -100, 100]
dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# Three Outside Up/Down: values [0, -100, 100]
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# Three Inside Up/Down: values [0, -100, 100]
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
"""
# Chart type
# ------------------------------------
"""
# Heikinashi stategy
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
"""
# Retrieve best bid and best ask
# ------------------------------------
"""
# first check if dataprovider is available
if self.dp:
if self.dp.runmode in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
"""
return dataframe
def slow_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
displacement = 30
ichimoku = ftt.ichimoku(dataframe,
conversion_line_period=20,
base_line_periods=60,
laggin_span=120,
displacement=displacement
)
dataframe['chikou_span'] = ichimoku['chikou_span']
# cross indicators
dataframe['tenkan_sen'] = ichimoku['tenkan_sen']
dataframe['kijun_sen'] = ichimoku['kijun_sen']
# cloud, green a > b, red a < b
dataframe['senkou_a'] = ichimoku['senkou_span_a']
dataframe['senkou_b'] = ichimoku['senkou_span_b']
dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a']
dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b']
dataframe['cloud_green'] = ichimoku['cloud_green'] * 1
dataframe['cloud_red'] = ichimoku['cloud_red'] * -1
dataframe.loc[:, 'cloud_top'] = dataframe.loc[:, ['senkou_a', 'senkou_b']].max(axis=1)
dataframe.loc[:, 'cloud_bottom'] = dataframe.loc[:, ['senkou_a', 'senkou_b']].min(axis=1)
# DANGER ZONE START
# NOTE: Not actually the future, present data that is normally shifted forward for display as the cloud
dataframe['future_green'] = (dataframe['leading_senkou_span_a'] > dataframe['leading_senkou_span_b']).astype('int') * 2
dataframe['future_red'] = (dataframe['leading_senkou_span_a'] < dataframe['leading_senkou_span_b']).astype('int') * 2
# The chikou_span is shifted into the past, so we need to be careful not to read the
# current value. But if we shift it forward again by displacement it should be safe to use.
# We're effectively "looking back" at where it normally appears on the chart.
dataframe['chikou_high'] = (
(dataframe['chikou_span'] > dataframe['cloud_top'])
).shift(displacement).fillna(0).astype('int')
dataframe['chikou_low'] = (
(dataframe['chikou_span'] < dataframe['cloud_bottom'])
).shift(displacement).fillna(0).astype('int')
# DANGER ZONE END
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
ssl_down, ssl_up = ssl_atr(dataframe, 10)
dataframe['ssl_down'] = ssl_down
dataframe['ssl_up'] = ssl_up
dataframe['ssl_ok'] = (
(ssl_up > ssl_down)
).astype('int') * 3
dataframe['ssl_bear'] = (
(ssl_up < ssl_down)
).astype('int') * 3
dataframe['ichimoku_ok'] = (
(dataframe['tenkan_sen'] > dataframe['kijun_sen'])
& (dataframe['close'] > dataframe['cloud_top'])
& (dataframe['future_green'] > 0)
& (dataframe['chikou_high'] > 0)
).astype('int') * 4
dataframe['ichimoku_bear'] = (
(dataframe['tenkan_sen'] < dataframe['kijun_sen'])
& (dataframe['close'] < dataframe['cloud_bottom'])
& (dataframe['future_red'] > 0)
& (dataframe['chikou_low'] > 0)
).astype('int') * 4
dataframe['ichimoku_valid'] = (
(dataframe['leading_senkou_span_b'] == dataframe['leading_senkou_span_b']) # not NaN
).astype('int') * 1
dataframe['trend_pulse'] = (
(dataframe['ichimoku_ok'] > 0)
& (dataframe['ssl_ok'] > 0)
).astype('int') * 2
dataframe['bear_trend_pulse'] = (
(dataframe['ichimoku_bear'] > 0)
& (dataframe['ssl_bear'] > 0)
).astype('int') * 2
dataframe['trend_over'] = (
(dataframe['ssl_ok'] == 0)
| (dataframe['close'] < dataframe['cloud_top'])
).astype('int') * 1
dataframe['bear_trend_over'] = (
(dataframe['ssl_bear'] == 0)
| (dataframe['close'] > dataframe['cloud_bottom'])
).astype('int') * 1
dataframe.loc[ (dataframe['trend_pulse'] > 0), 'trending'] = 3
dataframe.loc[ (dataframe['trend_over'] > 0) , 'trending'] = 0
dataframe['trending'].fillna(method='ffill', inplace=True)
dataframe.loc[ (dataframe['bear_trend_pulse'] > 0), 'bear_trending'] = 3
dataframe.loc[ (dataframe['bear_trend_over'] > 0) , 'bear_trending'] = 0
dataframe['bear_trending'].fillna(method='ffill', inplace=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
displacement = 30
ichimoku = ftt.ichimoku(dataframe,
conversion_line_period=20,
base_line_periods=60,
laggin_span=120,
displacement=displacement
)
#dataframe['chikou_span'] = ichimoku['chikou_span']
# cross indicators
dataframe['tenkan_sen'] = ichimoku['tenkan_sen']
dataframe['kijun_sen'] = ichimoku['kijun_sen']
# cloud, green a > b, red a < b
#dataframe['senkou_a'] = ichimoku['senkou_span_a']
#dataframe['senkou_b'] = ichimoku['senkou_span_b']
dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a']
dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b']
#dataframe['cloud_green'] = ichimoku['cloud_green'] * 1
#dataframe['cloud_red'] = ichimoku['cloud_red'] * -1
# DANGER ZONE START
# The cloud is normally shifted into the future visually, but it's based on present data.
# So in this case it should be ok to look at the "future" (which is actually the present)
# by shifting it back by displacement.
#dataframe['future_green'] = ichimoku['cloud_green'].shift(-displacement).fillna(0).astype('int') * 2
# The chikou_span is shifted into the past, so we need to be careful not to read the
# current value. But if we shift it forward again by displacement it should be safe to use.
# We're effectively "looking back" at where it normally appears on the chart.
#dataframe['chikou_high'] = (
# (dataframe['chikou_span'] > dataframe['senkou_a']) &
# (dataframe['chikou_span'] > dataframe['senkou_b'])
# ).shift(displacement).fillna(0).astype('int')
# DANGER ZONE END
dataframe['go_long'] = (
(dataframe['tenkan_sen'] > dataframe['kijun_sen']) &
(dataframe['close'] > dataframe['leading_senkou_span_a']) &
(dataframe['close'] > dataframe['leading_senkou_span_b'])
#&
#(dataframe['future_green'] > 0) &
#(dataframe['chikou_high'] > 0)
).astype('int') * 3
def SSLChannels(dataframe, length = 7, mode='sma'):
df = dataframe.copy()
df['ATR'] = ta.ATR(df, timeperiod=14)
df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN))
df['hlv'] = df['hlv'].ffill()
df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
return df['sslDown'], df['sslUp']
ssl = SSLChannels(dataframe, 10)
dataframe['sslDown'] = ssl[0]
dataframe['sslUp'] = ssl[1]
dataframe['max'] = dataframe['high'].rolling(3).max()
dataframe['min'] = dataframe['low'].rolling(6).min()
dataframe['upper'] = np.where(dataframe['max'] > dataframe['max'].shift(),1,0)
dataframe['lower'] = np.where(dataframe['min'] < dataframe['min'].shift(),1,0)
dataframe['up_trend'] = np.where(dataframe['upper'].rolling(5, min_periods=1).sum() != 0,1,0)
dataframe['dn_trend'] = np.where(dataframe['lower'].rolling(5, min_periods=1).sum() != 0,1,0)
return dataframe
def slow_tf_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
displacement = 88
ichimoku = ftt.ichimoku(dataframe,
conversion_line_period=20,
base_line_periods=88,
laggin_span=88,
displacement=displacement)
dataframe['chikou_span'] = ichimoku['chikou_span']
# cross indicators
dataframe['tenkan_sen'] = ichimoku['tenkan_sen']
dataframe['kijun_sen'] = ichimoku['kijun_sen']
# cloud, green a > b, red a < b
dataframe['senkou_a'] = ichimoku['senkou_span_a']
dataframe['senkou_b'] = ichimoku['senkou_span_b']
dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a']
dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b']
dataframe['cloud_green'] = ichimoku['cloud_green'] * 1
dataframe['cloud_red'] = ichimoku['cloud_red'] * -1
dataframe.loc[:, 'cloud_top'] = dataframe.loc[:,
['senkou_a', 'senkou_b'
]].max(axis=1)
dataframe.loc[:,
'cloud_bottom'] = dataframe.loc[:,
['senkou_a', 'senkou_b'
]].min(axis=1)
# DANGER ZONE START
# NOTE: Not actually the future, present data that is normally shifted forward for display as the cloud
dataframe['future_green'] = (
dataframe['leading_senkou_span_a'] >
dataframe['leading_senkou_span_b']).astype('int') * 2
# The chikou_span is shifted into the past, so we need to be careful not to read the
# current value. But if we shift it forward again by displacement it should be safe to use.
# We're effectively "looking back" at where it normally appears on the chart.
dataframe['chikou_high'] = (
(dataframe['chikou_span'] > dataframe['senkou_a']) &
(dataframe['chikou_span'] > dataframe['senkou_b'])
).shift(displacement).fillna(0).astype('int')
# DANGER ZONE END
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
dataframe['ema_ok'] = (
(dataframe['close'] > dataframe['ema50'])
& (dataframe['ema50'] > dataframe['ema200'])).astype('int') * 2
dataframe['efi_base'] = (
(dataframe['close'] - dataframe['close'].shift()) *
dataframe['volume'])
dataframe['efi'] = ta.EMA(dataframe['efi_base'], 13)
dataframe['efi_ok'] = (dataframe['efi'] > 0).astype('int')
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
ssl_down, ssl_up = ssl_atr(dataframe,
10) #TODO TEST THIS NUMBER WITH HYPEROPT
dataframe['ssl_down'] = ssl_down
dataframe['ssl_up'] = ssl_up
dataframe['ssl_ok'] = ((ssl_up > ssl_down)).astype('int') * 3
dataframe['ichimoku_ok'] = (
(dataframe['tenkan_sen'] > dataframe['kijun_sen'])
& (dataframe['close'] > dataframe['cloud_top'])
& (dataframe['future_green'] > 0)
& (dataframe['chikou_high'] > 0)).astype('int') * 4
dataframe['entry_ok'] = (
(dataframe['efi_ok'] > 0)
& (dataframe['open'] < dataframe['ssl_up'])
& (dataframe['close'] < dataframe['ssl_up'])).astype('int') * 1
dataframe['trend_pulse'] = (
(dataframe['ichimoku_ok'] > 0)
& (dataframe['ssl_ok'] > 0)
& (dataframe['ema_ok'] > 0)).astype('int') * 2
dataframe['trend_over'] = (
(dataframe['ssl_ok'] == 0)).astype('int') * 1
dataframe.loc[(dataframe['trend_pulse'] > 0), 'trending'] = 3
dataframe.loc[(dataframe['trend_over'] > 0), 'trending'] = 0
return dataframe
