def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["rsi"] = ta.RSI(dataframe)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=1)
dataframe["bb1_lowerband"] = bollinger["lower"]
dataframe["bb1_middleband"] = bollinger["mid"]
dataframe["bb1_upperband"] = bollinger["upper"]
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe["bb2_lowerband"] = bollinger["lower"]
dataframe["bb2_middleband"] = bollinger["mid"]
dataframe["bb2_upperband"] = bollinger["upper"]
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=3)
dataframe["bb3_lowerband"] = bollinger["lower"]
dataframe["bb3_middleband"] = bollinger["mid"]
dataframe["bb3_upperband"] = bollinger["upper"]
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=4)
dataframe["bb4_lowerband"] = bollinger["lower"]
dataframe["bb4_middleband"] = bollinger["mid"]
dataframe["bb4_upperband"] = bollinger["upper"]
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# Bollinger bands
bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=1)
dataframe['bb1_lowerband'] = bollinger1['lower']
dataframe['bb1_middleband'] = bollinger1['mid']
dataframe['bb1_upperband'] = bollinger1['upper']
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb2_lowerband'] = bollinger2['lower']
dataframe['bb2_middleband'] = bollinger2['mid']
dataframe['bb2_upperband'] = bollinger2['upper']
bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=3)
dataframe['bb3_lowerband'] = bollinger3['lower']
dataframe['bb3_middleband'] = bollinger3['mid']
dataframe['bb3_upperband'] = bollinger3['upper']
bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=4)
dataframe['bb4_lowerband'] = bollinger4['lower']
dataframe['bb4_middleband'] = bollinger4['mid']
dataframe['bb4_upperband'] = bollinger4['upper']
return dataframe
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi-buy'] = ta.RSI(dataframe)
dataframe['rsi-sell'] = ta.RSI(dataframe)
# Bollinger bands
bollinger_1sd = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=20, stds=1)
dataframe['bb_lowerband_1sd'] = bollinger_1sd['lower']
dataframe['bb_middleband_1sd'] = bollinger_1sd['mid']
dataframe['bb_upperband_1sd'] = bollinger_1sd['upper']
bollinger_2sd = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband_2sd'] = bollinger_2sd['lower']
dataframe['bb_middleband_2sd'] = bollinger_2sd['mid']
dataframe['bb_upperband_2sd'] = bollinger_2sd['upper']
bollinger_3sd = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=20, stds=3)
dataframe['bb_lowerband_3sd'] = bollinger_3sd['lower']
dataframe['bb_middleband_3sd'] = bollinger_3sd['mid']
dataframe['bb_upperband_3sd'] = bollinger_3sd['upper']
bollinger_4sd = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=20, stds=4)
dataframe['bb_lowerband_4sd'] = bollinger_4sd['lower']
dataframe['bb_middleband_4sd'] = bollinger_4sd['mid']
dataframe['bb_upperband_4sd'] = bollinger_4sd['upper']
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
"""
# Momentum Indicators
# ------------------------------------
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['sell-rsi'] = ta.RSI(dataframe)
# Bollinger Bands 1 STD
bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=1)
dataframe['bb_lowerband1'] = bollinger1['lower']
dataframe['bb_middleband1'] = bollinger1['mid']
dataframe['bb_upperband1'] = bollinger1['upper']
# Bollinger Bands 4 STD
bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=4)
dataframe['bb_lowerband4'] = bollinger4['lower']
dataframe['bb_middleband4'] = bollinger4['mid']
dataframe['bb_upperband4'] = bollinger4['upper']
return dataframe
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['sell-rsi'] = ta.RSI(dataframe)
# Bollinger bands
bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1)
dataframe['bb_lowerband1'] = bollinger1['lower']
dataframe['bb_middleband1'] = bollinger1['mid']
dataframe['bb_upperband1'] = bollinger1['upper']
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband2'] = bollinger2['lower']
dataframe['bb_middleband2'] = bollinger2['mid']
dataframe['bb_upperband2'] = bollinger2['upper']
bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
dataframe['bb_lowerband3'] = bollinger3['lower']
dataframe['bb_middleband3'] = bollinger3['mid']
dataframe['bb_upperband3'] = bollinger3['upper']
bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4)
dataframe['bb_lowerband4'] = bollinger4['lower']
dataframe['bb_middleband4'] = bollinger4['mid']
dataframe['bb_upperband4'] = bollinger4['upper']
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Bollinger bands
bollinger_1sd = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1)
dataframe['bb_upperband_1sd'] = bollinger_1sd['upper']
dataframe['bb_lowerband_1sd'] = bollinger_1sd['lower']
bollinger_4sd = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4)
dataframe['bb_lowerband_4sd'] = bollinger_4sd['lower']
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Set Up Bollinger Bands
mid, lower = bollinger_bands(dataframe['close'],
window_size=40,
num_of_std=2)
dataframe['lower'] = lower
dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
dataframe['closedelta'] = (dataframe['close'] -
dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(
window=30).mean()
dataframe['rocr'] = ta.ROCR(dataframe, timeperiod=28)
inf_tf = '1h'
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=inf_tf)
informative['rocr'] = ta.ROCR(informative, timeperiod=168)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
inf_tf,
ffill=True)
return dataframe
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# strategy BinHV45
bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
dataframe['lower'] = bb_40['lower']
dataframe['mid'] = bb_40['mid']
dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs()
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
# strategy ClucMay72018
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['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
# EMA
dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
# SMA
dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Add several indicators needed for buy and sell strategies defined below.
"""
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
# Minus-DI
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
# SAR
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
def do_populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
"""
Adds multiple TA indicators to MoniGoMani's DataFrame per pair.
Should be called with 'informative_pair' (1h candles) during backtesting/hyperopting with TimeFrame-Zoom!
Performance Note: For the best performance be frugal on the number of indicators you are using.
Only add in indicators that you are using in your weighted signal configuration for MoniGoMani,
otherwise you will waste your memory and CPU usage.
:param dataframe: (DataFrame) DataFrame with data from the exchange
:param metadata: (dict) Additional information, like the currently traded pair
:return DataFrame: DataFrame for MoniGoMani with all mandatory indicator data populated
"""
# Momentum Indicators (timeperiod is expressed in candles)
# -------------------
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# Stochastic Slow
stoch = ta.STOCH(dataframe)
dataframe['slowk'] = stoch['slowk']
# MACD - Moving Average Convergence Divergence
macd = ta.MACD(dataframe)
dataframe['macd'] = macd[
'macd'] # MACD - Blue TradingView Line (Bullish if on top)
dataframe['macdsignal'] = macd[
'macdsignal'] # Signal - Orange TradingView Line (Bearish if on top)
# MFI - Money Flow Index (Under bought / Over sold & Over bought / Under sold / volume Indicator)
dataframe['mfi'] = ta.MFI(dataframe)
# Overlap Studies
# ---------------
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_middleband'] = bollinger['mid']
# SMA's & EMA's are trend following tools (Should not be used when line goes sideways)
# SMA - Simple Moving Average (Moves slower compared to EMA, price trend over X periods)
dataframe['sma9'] = ta.SMA(dataframe, timeperiod=9)
dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Volume Indicators
# -----------------
# Rolling VWAP - Volume Weighted Average Price
dataframe['rolling_vwap'] = qtpylib.rolling_vwap(dataframe)
return dataframe
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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['volume_mean_slow'] = dataframe['volume'].rolling(window=48).mean()
# EMA
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
# MACD
dataframe['macd'], dataframe['signal'], dataframe['hist'] = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
# SMA
dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# ------ ATR stuff
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
# Calculate all ma_sell values
for val in self.base_nb_candles_sell.range:
dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
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.
"""
dataframe['gap'] = dataframe['close'].shift(1) - (
(dataframe['high'].shift(1) - dataframe['low'].shift(1)) * 0.1)
dataframe['adx'] = ta.ADX(dataframe)
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Fast
stoch_fast = ta.STOCH(dataframe)
dataframe['slowd'] = stoch_fast['slowd']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
return dataframe
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
##################################################################################
# buy and sell indicators
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']
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# dataframe['cci'] = ta.CCI(dataframe)
# dataframe['mfi'] = ta.MFI(dataframe)
# dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
# dataframe['canbuy'] = np.NaN
# dataframe['canbuy2'] = np.NaN
# dataframe.loc[dataframe.close.rolling(49).min() <= 1.1 * dataframe.close, 'canbuy'] == 1
# dataframe.loc[dataframe.close.rolling(600).max() < 1.2 * dataframe.close, 'canbuy'] = 1
# dataframe.loc[dataframe.close.rolling(600).max() * 0.8 > dataframe.close, 'canbuy2'] = 1
##################################################################################
# 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 populate_indicators(self, dataframe: DataFrame) -> 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.
"""
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowk'] = stoch['slowk']
# 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)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
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
"""
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
for std in range(1, 5):
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=std)
dataframe[f'bb_lowerband{std}'] = bollinger['lower']
dataframe[f'bb_middleband{std}'] = bollinger['mid']
dataframe[f'bb_upperband{std}'] = bollinger['upper']
# TEMA - Triple Exponential Moving Average
dataframe[f'tema'] = ta.TEMA(dataframe, timeperiod=9)
"""
# 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 normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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['volume_mean_slow'] = dataframe['volume'].rolling(window=48).mean()
# EMA
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
# MACD
dataframe['macd'], dataframe['signal'], dataframe['hist'] = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
# SMA
dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Set Up Bollinger Bands
upper_bb1, mid_bb1, lower_bb1 = ta.BBANDS(dataframe['close'],
timeperiod=40)
upper_bb2, mid_bb2, lower_bb2 = ta.BBANDS(
qtpylib.typical_price(dataframe), timeperiod=20)
# only putting some bands into dataframe as the others are not used elsewhere in the strategy
dataframe['lower-bb1'] = lower_bb1
dataframe['lower-bb2'] = lower_bb2
dataframe['mid-bb2'] = mid_bb2
dataframe['bb1-delta'] = (mid_bb1 - dataframe['lower-bb1']).abs()
dataframe['closedelta'] = (dataframe['close'] -
dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
dataframe['ema_slow'] = ta.EMA(dataframe['close'], timeperiod=48)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(
window=24).mean()
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher-rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
dataframe['adx'] = ta.ADX(dataframe)
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
"""
length = 70
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=length,
stds=2)
dataframe['lower'] = bollinger['lower']
dataframe['middle'] = bollinger['mid']
dataframe['upper'] = bollinger['upper']
# EMA
dataframe['ema'] = ta.EMA(dataframe, timeperiod=length)
return dataframe
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
""" Adds several different TA indicators to the given DataFrame
"""
dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_SHORT_TERM
)
dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_MEDIUM_TERM
)
dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_LONG_TERM
)
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['min'] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# 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["bb_percent"] = (
(dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]))
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) /
dataframe["bb_middleband"])
# TEMA
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
return dataframe
def pivots_points(dataframe: pd.DataFrame,
timeperiod=1,
levels=4) -> pd.DataFrame:
"""
Pivots Points
https://www.tradingview.com/support/solutions/43000521824-pivot-points-standard/
Formula:
Pivot = (Previous High + Previous Low + Previous Close)/3
Resistance #1 = (2 x Pivot) - Previous Low
Support #1 = (2 x Pivot) - Previous High
Resistance #2 = (Pivot - Support #1) + Resistance #1
Support #2 = Pivot - (Resistance #1 - Support #1)
Resistance #3 = (Pivot - Support #2) + Resistance #2
Support #3 = Pivot - (Resistance #2 - Support #2)
...
:param dataframe:
:param timeperiod: Period to compare (in ticker)
:param levels: Num of support/resistance desired
:return: dataframe
"""
data = {}
low = qtpylib.rolling_mean(series=pd.Series(index=dataframe.index,
data=dataframe["low"]),
window=timeperiod)
high = qtpylib.rolling_mean(series=pd.Series(index=dataframe.index,
data=dataframe["high"]),
window=timeperiod)
# Pivot
data["pivot"] = qtpylib.rolling_mean(
series=qtpylib.typical_price(dataframe), window=timeperiod)
# Resistance #1
# data["r1"] = (2 * data["pivot"]) - low ... Standard
# R1 = PP + 0.382 * (HIGHprev - LOWprev) ... fibonacci
data["r1"] = data['pivot'] + 0.382 * (high - low)
data["rS1"] = data['pivot'] + 0.0955 * (high - low)
# Resistance #2
# data["s1"] = (2 * data["pivot"]) - high ... Standard
# S1 = PP - 0.382 * (HIGHprev - LOWprev) ... fibonacci
data["s1"] = data["pivot"] - 0.382 * (high - low)
# Calculate Resistances and Supports >1
for i in range(2, levels + 1):
prev_support = data["s" + str(i - 1)]
prev_resistance = data["r" + str(i - 1)]
# Resitance
data["r" + str(i)] = (data["pivot"] - prev_support) + prev_resistance
# Support
data["s" + str(i)] = data["pivot"] - (prev_resistance - prev_support)
return pd.DataFrame(index=dataframe.index, data=data)
def pivots_points(dataframe: pd.DataFrame,
timeperiod=30,
levels=3) -> pd.DataFrame:
"""
Pivots Points
https://www.tradingview.com/support/solutions/43000521824-pivot-points-standard/
Formula:
Pivot = (Previous High + Previous Low + Previous Close)/3
Resistance #1 = (2 x Pivot) - Previous Low
Support #1 = (2 x Pivot) - Previous High
Resistance #2 = (Pivot - Support #1) + Resistance #1
Support #2 = Pivot - (Resistance #1 - Support #1)
Resistance #3 = (Pivot - Support #2) + Resistance #2
Support #3 = Pivot - (Resistance #2 - Support #2)
...
:param dataframe:
:param timeperiod: Period to compare (in ticker)
:param levels: Num of support/resistance desired
:return: dataframe
"""
data = {}
low = qtpylib.rolling_mean(series=pd.Series(index=dataframe.index,
data=dataframe['low']),
window=timeperiod)
high = qtpylib.rolling_mean(series=pd.Series(index=dataframe.index,
data=dataframe['high']),
window=timeperiod)
# Pivot
data['pivot'] = qtpylib.rolling_mean(
series=qtpylib.typical_price(dataframe), window=timeperiod)
# Resistance #1
data['r1'] = (2 * data['pivot']) - low
# Resistance #2
data['s1'] = (2 * data['pivot']) - high
# Calculate Resistances and Supports >1
for i in range(2, levels + 1):
prev_support = data['s' + str(i - 1)]
prev_resistance = data['r' + str(i - 1)]
# Resitance
data['r' + str(i)] = (data['pivot'] - prev_support) + prev_resistance
# Support
data['s' + str(i)] = data['pivot'] - (prev_resistance - prev_support)
return pd.DataFrame(index=dataframe.index, data=data)
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Set Up Bollinger Bands
upper_bb1, mid_bb1, lower_bb1 = ta.BBANDS(dataframe['close'],
timeperiod=36)
upper_bb2, mid_bb2, lower_bb2 = ta.BBANDS(
qtpylib.typical_price(dataframe), timeperiod=12)
# Only putting some bands into dataframe as the others are not used elsewhere in the strategy
dataframe['lower-bb1'] = lower_bb1
dataframe['lower-bb2'] = lower_bb2
dataframe['mid-bb2'] = mid_bb2
dataframe['bb1-delta'] = (mid_bb1 - dataframe['lower-bb1']).abs()
dataframe['closedelta'] = (dataframe['close'] -
dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
# Additional indicators
dataframe['ema_fast'] = ta.EMA(dataframe['close'], timeperiod=6)
dataframe['ema_slow'] = ta.EMA(dataframe['close'], timeperiod=48)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(
window=24).mean()
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher-rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# Informative Pair Indicators
coin, stake = metadata['pair'].split('/')
fiat = self.fiat
stake_fiat = f"{stake}/{self.fiat}"
coin_fiat = f"{coin}/{self.fiat}"
coin_fiat_inf = self.dp.get_pair_dataframe(pair=f"{coin}/{fiat}",
timeframe=self.timeframe)
dataframe['coin-fiat-adx'] = ta.ADX(coin_fiat_inf, timeperiod=21)
coin_aroon = ta.AROON(coin_fiat_inf, timeperiod=25)
dataframe['coin-fiat-aroon-down'] = coin_aroon['aroondown']
dataframe['coin-fiat-aroon-up'] = coin_aroon['aroonup']
stake_fiat_inf = self.dp.get_pair_dataframe(pair=f"{stake}/{fiat}",
timeframe=self.timeframe)
dataframe['stake-fiat-adx'] = ta.ADX(stake_fiat_inf, timeperiod=21)
stake_aroon = ta.AROON(stake_fiat_inf, timeperiod=25)
dataframe['stake-fiat-aroon-down'] = stake_aroon['aroondown']
dataframe['stake-fiat-aroon-up'] = stake_aroon['aroonup']
# These indicators are used to persist a buy signal in live trading only
# They dramatically slow backtesting down
if self.config['runmode'].value in ('live', 'dry_run'):
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)
# Bollinger bands
bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=17,
stds=1)
dataframe['bb_lowerband'] = bollinger1['lower']
dataframe['bb_middleband'] = bollinger1['mid']
dataframe['bb_upperband'] = bollinger1['upper']
# Close delta
dataframe['closedelta'] = (dataframe['close'] -
dataframe['close'].shift()).abs()
# Dip Protection
dataframe['tpct_change_0'] = top_percent_change(dataframe, 0)
dataframe['tpct_change_1'] = top_percent_change(dataframe, 1)
dataframe['tpct_change_2'] = top_percent_change(dataframe, 2)
dataframe['tpct_change_4'] = top_percent_change(dataframe, 4)
dataframe['tpct_change_5'] = top_percent_change(dataframe, 5)
dataframe['tpct_change_9'] = top_percent_change(dataframe, 9)
# SMA
dataframe['sma_50'] = ta.SMA(dataframe['close'], timeperiod=50)
dataframe['sma_200'] = ta.SMA(dataframe['close'], timeperiod=200)
# CTI
dataframe['cti'] = pta.cti(dataframe["close"], length=20)
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# %R
dataframe['r_14'] = williams_r(dataframe, period=14)
dataframe['r_96'] = williams_r(dataframe, period=96)
# MAMA / FAMA
dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2
dataframe['mama'], dataframe['fama'] = ta.MAMA(dataframe['hl2'], 0.5,
0.05)
dataframe['mama_diff'] = ((dataframe['mama'] - dataframe['fama']) /
dataframe['hl2'])
# CRSI (3, 2, 100)
crsi_closechange = dataframe['close'] / dataframe['close'].shift(1)
crsi_updown = np.where(crsi_closechange.gt(1), 1.0,
np.where(crsi_closechange.lt(1), -1.0, 0.0))
dataframe['crsi'] = (ta.RSI(dataframe['close'], timeperiod=3) + ta.RSI(
crsi_updown, timeperiod=2) + ta.ROC(dataframe['close'], 100)) / 3
return dataframe
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
# 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['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Adding EMA's into the dataframe
dataframe["s1_ema_xs"] = ta.EMA(dataframe, timeperiod=self.s1_ema_xs)
dataframe["s1_ema_sm"] = ta.EMA(dataframe, timeperiod=self.s1_ema_sm)
dataframe["s1_ema_md"] = ta.EMA(dataframe, timeperiod=self.s1_ema_md)
dataframe["s1_ema_xl"] = ta.EMA(dataframe, timeperiod=self.s1_ema_xl)
dataframe["s1_ema_xxl"] = ta.EMA(dataframe, timeperiod=self.s1_ema_xxl)
s2_ema_value = ta.EMA(dataframe, timeperiod=self.s2_ema_input)
s2_ema_xxl_value = ta.EMA(dataframe, timeperiod=200)
dataframe[
"s2_ema"] = s2_ema_value - s2_ema_value * self.s2_ema_offset_input
dataframe[
"s2_ema_xxl_off"] = s2_ema_xxl_value - s2_ema_xxl_value * self.s2_fib_lower_value
dataframe["s2_ema_xxl"] = ta.EMA(dataframe, timeperiod=200)
s2_bb_sma_value = ta.SMA(dataframe, timeperiod=self.s2_bb_sma_length)
s2_bb_std_dev_value = ta.STDDEV(dataframe, self.s2_bb_std_dev_length)
dataframe["s2_bb_std_dev_value"] = s2_bb_std_dev_value
dataframe["s2_bb_lower_band"] = s2_bb_sma_value - (
s2_bb_std_dev_value * self.s2_bb_lower_offset)
s2_fib_atr_value = ta.ATR(dataframe, timeframe=self.s2_fib_atr_len)
s2_fib_sma_value = ta.SMA(dataframe, timeperiod=self.s2_fib_sma_len)
dataframe[
"s2_fib_lower_band"] = s2_fib_sma_value - s2_fib_atr_value * self.s2_fib_lower_value
s3_bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=20, stds=3)
dataframe["s3_bb_lowerband"] = s3_bollinger["lower"]
dataframe["s3_ema_long"] = ta.EMA(dataframe,
timeperiod=self.s3_ema_long)
dataframe["s3_ema_short"] = ta.EMA(dataframe,
timeperiod=self.s3_ema_short)
dataframe["s3_fast_ma"] = ta.EMA(
dataframe["volume"] * dataframe["close"],
self.s3_ma_fast) / ta.EMA(dataframe["volume"], self.s3_ma_fast)
dataframe["s3_slow_ma"] = ta.EMA(
dataframe["volume"] * dataframe["close"],
self.s3_ma_slow) / ta.EMA(dataframe["volume"], self.s3_ma_slow)
# Volume weighted MACD
dataframe["fastMA"] = ta.EMA(dataframe["volume"] * dataframe["close"],
12) / ta.EMA(dataframe["volume"], 12)
dataframe["slowMA"] = ta.EMA(dataframe["volume"] * dataframe["close"],
26) / ta.EMA(dataframe["volume"], 26)
dataframe["vwmacd"] = dataframe["fastMA"] - dataframe["slowMA"]
dataframe["signal"] = ta.EMA(dataframe["vwmacd"], 9)
dataframe["hist"] = dataframe["vwmacd"] - dataframe["signal"]
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=21,
stds=2.7)
dataframe['bblow'] = bollinger['lower']
bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=21,
stds=2.1)
dataframe['bbhi'] = bollinger3['upper']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# #RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# #StochRSI
period = 14
smoothD = 3
SmoothK = 3
stochrsi = (dataframe['rsi'] - dataframe['rsi'].rolling(period).min()
) / (dataframe['rsi'].rolling(period).max() -
dataframe['rsi'].rolling(period).min())
dataframe['srsi_k'] = stochrsi.rolling(SmoothK).mean() * 100
dataframe['srsi_d'] = dataframe['srsi_k'].rolling(smoothD).mean()
# dataframe_5m = resample_to_interval(dataframe, 5)
# dataframe_5m['rsi']=ta.RSI(dataframe_5m, timeperiod=14)
# stochrsi_5m = (dataframe_5m['rsi'] - dataframe_5m['rsi'].rolling(period).min()) / (dataframe_5m['rsi'].rolling(period).max() - dataframe_5m['rsi'].rolling(period).min())
# dataframe_5m['srsik']= stochrsi_5m.rolling(SmoothK).mean() * 100
# dataframe_5m['srsid'] = dataframe_5m['srsi_k'].rolling(smoothD).mean()
# dataframe = resampled_merge(dataframe, dataframe_5m, fill_na=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stochastic Slow
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Bollinger Bands
bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=1)
dataframe['bb_lowerband1'] = bollinger1['lower']
dataframe['bb_middleband1'] = bollinger1['mid']
dataframe['bb_upperband1'] = bollinger1['upper']
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband2'] = bollinger2['lower']
dataframe['bb_middleband2'] = bollinger2['mid']
dataframe['bb_upperband2'] = bollinger2['upper']
bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=3)
dataframe['bb_lowerband3'] = bollinger3['lower']
dataframe['bb_middleband3'] = bollinger3['mid']
dataframe['bb_upperband3'] = bollinger3['upper']
bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=4)
dataframe['bb_lowerband4'] = bollinger4['lower']
dataframe['bb_middleband4'] = bollinger4['mid']
dataframe['bb_upperband4'] = bollinger4['upper']
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
"""
# Momentum Indicator
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# Minus Directional Indicator / Movement
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# 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['ema10'] = ta.EMA(dataframe, timeperiod=10)
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
"""
# Momentum Indicators
# ------------------------------------
dataframe['rsi'] = ta.RSI(dataframe)
# # Stochastic RSI
stoch_rsi = ta.STOCHRSI(dataframe)
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50)
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
# dataframe['macdsignal'] = macd['macdsignal']
# dataframe['macdhist'] = macd['macdhist']
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# # ROC
# dataframe['roc'] = ta.ROC(dataframe)
# 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']
# Bollinger Bands - Weighted (EMA based instead of SMA)
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
# qtpylib.typical_price(dataframe), window=20, stds=2
# )
# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
# dataframe["wbb_percent"] = (
# (dataframe["close"] - dataframe["wbb_lowerband"]) /
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
# )
# dataframe["wbb_width"] = (
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]
# )
return dataframe