def STOCHRSI(self):
STOCHRSI = tb.STOCHRSI(self.dataframe,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
fastk = STOCHRSI["fastk"][len(STOCHRSI) - 1] # main
fastd = STOCHRSI["fastd"][len(STOCHRSI) - 1] # signal
oldFastk = STOCHRSI["fastk"][len(STOCHRSI) - 2] # main
oldFastd = STOCHRSI["fastd"][len(STOCHRSI) - 2] # signal
# sell when main line > upper band (80) and main line crosses the signal line from above-down
# buy when main line < lower band (20) and main line crosses the signal line from bottom-up
if (fastk > 80):
#print("RSI: " + str(value) + " overbought")
return "overbought"
elif (fastk > 60):
#print("RSI: " + str(value) + " buy")
return "buy"
elif (fastk < 20):
#print("RSI: " + str(value) + " oversold")
return "oversold"
elif (fastk < 40):
#print("RSI: " + str(value) + " sell")
return "sell"
else:
#print("RSI: " + str(value) + " neutral")
return "neutral"
def analyze(self,
historical_data,
period_count=14,
hot_thresh=None,
cold_thresh=None,
all_data=False):
"""Performs a Stochastic RSI analysis on the historical data
Args:
historical_data (list): A matrix of historical OHCLV data.
period_count (int, optional): Defaults to 14. The number of data points to consider for
our simple moving average.
hot_thresh (float, optional): Defaults to None. The threshold at which this might be
good to purchase.
cold_thresh (float, optional): Defaults to None. The threshold at which this might be
good to sell.
all_data (bool, optional): Defaults to False. If True, we return the Stochastic RSI
associated with each data point in our historical dataset. Otherwise just return
the last one.
Returns:
dict: A dictionary containing a tuple of indicator values and booleans for buy / sell
indication.
"""
dataframe = self.convert_to_dataframe(historical_data)
rsi_values = abstract.STOCHRSI(dataframe, period_count).iloc[:, 0]
rsi_result_data = []
for rsi_value in rsi_values:
if math.isnan(rsi_value):
continue
is_hot = False
if hot_thresh is not None:
is_hot = rsi_value < hot_thresh
is_cold = False
if cold_thresh is not None:
is_cold = rsi_value > cold_thresh
data_point_result = {
'values': (rsi_value, ),
'is_cold': is_cold,
'is_hot': is_hot
}
rsi_result_data.append(data_point_result)
if all_data:
return rsi_result_data
else:
try:
return rsi_result_data[-1]
except IndexError:
return rsi_result_data
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
bollinger = qtpylib.bollinger_bands(dataframe['close'],
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_width'] = (
(dataframe['bb_upperband'] - dataframe['bb_lowerband']) /
dataframe['bb_middleband'])
dataframe['bb_bottom_cross'] = qtpylib.crossed_below(
dataframe['close'], dataframe['bb_lowerband']).astype('int')
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=10)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
dataframe['cci'] = ta.CCI(dataframe, 30)
dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
dataframe['cmf'] = chaikin_mf(dataframe)
dataframe['rmi'] = RMI(dataframe, length=8, mom=4)
stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2)
dataframe['srsi_fk'] = stoch['fastk']
dataframe['srsi_fd'] = stoch['fastd']
dataframe['fastEMA'] = ta.EMA(dataframe['volume'], timeperiod=12)
dataframe['slowEMA'] = ta.EMA(dataframe['volume'], timeperiod=26)
dataframe['pvo'] = ((dataframe['fastEMA'] - dataframe['slowEMA']) /
dataframe['slowEMA']) * 100
dataframe['is_dip'] = ((dataframe['rmi'] < 20)
& (dataframe['cci'] <= -150)
& (dataframe['srsi_fk'] < 20)
# Maybe comment mfi and cmf to make more trades
& (dataframe['mfi'] < 25)
& (dataframe['cmf'] <= -0.1)).astype('int')
dataframe['is_break'] = (
(dataframe['bb_width'] > 0.025)
& (dataframe['bb_bottom_cross'].rolling(10).sum() > 1)
& (dataframe['close'] < 0.99 * dataframe['bb_lowerband'])
).astype('int')
dataframe['buy_signal'] = ((dataframe['is_dip'] > 0)
& (dataframe['is_break'] > 0)).astype('int')
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
def get_ticker_dataframe(pair: str) -> DataFrame:
"""
Analyses the trend for the given pair
:param pair: pair as str in format BTC_ETH or BTC-ETH
:return: DataFrame
"""
minimum_date = arrow.now() - timedelta(hours=6)
url = 'https://bittrex.com/Api/v2.0/pub/market/GetTicks'
headers = {
'User-Agent':
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36',
}
params = {
'marketName': pair.replace('_', '-'),
'tickInterval': 'OneMin',
'_': minimum_date.timestamp * 1000
}
data = requests.get(url, params=params, headers=headers).json()
if not data['success']:
raise RuntimeError('BITTREX: {}'.format(data['message']))
data = [{
'close': t['C'],
'volume': t['V'],
'open': t['O'],
'high': t['H'],
'low': t['L'],
'date': t['T'],
} for t in sorted(data['result'], key=lambda k: k['T'])
if arrow.get(t['T']) > minimum_date]
dataframe = DataFrame(json_normalize(data))
dataframe['close_30_ema'] = ta.EMA(dataframe, timeperiod=30)
dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90)
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.2)
# calculate StochRSI
stochrsi = ta.STOCHRSI(dataframe)
dataframe['stochrsi'] = stochrsi['fastd'] # values between 0-100, not 0-1
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macds'] = macd['macdsignal']
dataframe['macdh'] = macd['macdhist']
return dataframe
def stochrsi(self):
stochrsi = tb.STOCHRSI(self.dataframe,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
fastk = stochrsi["fastk"][len(stochrsi) - 1]
if fastk > 80:
return "overbought"
elif fastk > 60:
return "buy"
elif fastk < 20:
return "oversold"
elif fastk < 40:
return "sell"
else:
return "neutral"
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['close_30_ema'] = ta.EMA(dataframe, timeperiod=30)
dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90)
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.2)
# calculate StochRSI
stochrsi = ta.STOCHRSI(dataframe)
dataframe['stochrsi'] = stochrsi['fastd'] # values between 0-100, not 0-1
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macds'] = macd['macdsignal']
dataframe['macdh'] = macd['macdhist']
return dataframe
def populate_indicators_and_buy_signal(dataframe):
dataframe['close_30_ema'] = ta.EMA(dataframe, timeperiod=30)
dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90)
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.2)
stochrsi = ta.STOCHRSI(dataframe)
dataframe['stochrsi'] = stochrsi['fastd'] # values between 0-100, not 0-1
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macds'] = macd['macdsignal']
dataframe['macdh'] = macd['macdhist']
dataframe.loc[(dataframe['stochrsi'] < 20)
& (dataframe['macd'] > dataframe['macds'])
& (dataframe['close'] > dataframe['sar']), 'buy'] = 1
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
return dataframe, info_msg % (dataframe.iloc[-1]['stochrsi'])
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
# ------------------------------------
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe, 5, 3, 3)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# # Stochastic RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
# MACD
macd = ta.MACD(dataframe, 12, 26, 1)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# # EMA - Exponential Moving Average
dataframe['ema5c'] = ta.EMA(dataframe['close'], timeperiod=5)
dataframe['ema5o'] = ta.EMA(dataframe['open'], timeperiod=5)
return dataframe
def analyze(self,
historical_data,
period_count=14,
hot_thresh=None,
cold_thresh=None,
all_data=False):
"""Performs a Stochastic RSI analysis on the historical data
Args:
historical_data (list): A matrix of historical OHCLV data.
period_count (int, optional): Defaults to 14. The number of data points to consider for
our simple moving average.
hot_thresh (float, optional): Defaults to None. The threshold at which this might be
good to purchase.
cold_thresh (float, optional): Defaults to None. The threshold at which this might be
good to sell.
all_data (bool, optional): Defaults to False. If True, we return the Stochastic RSI
associated with each data point in our historical dataset. Otherwise just return
the last one.
Returns:
dict: A dictionary containing a tuple of indicator values and booleans for buy / sell
indication.
"""
dataframe = self.convert_to_dataframe(historical_data)
rsi_values = abstract.STOCHRSI(dataframe, period_count).iloc[:, 0]
analyzed_data = [(value, ) for value in rsi_values]
return self.analyze_results(analyzed_data,
is_hot=lambda v: v < hot_thresh
if hot_thresh else False,
is_cold=lambda v: v > cold_thresh
if cold_thresh else False,
all_data=all_data)
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Momentum Indicators
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# # Minus Directional Indicator / Movement
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Aroon, Aroon Oscillator
aroon = ta.AROON(dataframe)
dataframe['aroonup'] = aroon['aroonup']
dataframe['aroondown'] = aroon['aroondown']
dataframe['aroonosc'] = ta.AROONOSC(dataframe)
# Awesome Oscillator
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# # Keltner Channel
# keltner = qtpylib.keltner_channel(dataframe)
# dataframe["kc_upperband"] = keltner["upper"]
# dataframe["kc_lowerband"] = keltner["lower"]
# dataframe["kc_middleband"] = keltner["mid"]
# dataframe["kc_percent"] = (
# (dataframe["close"] - dataframe["kc_lowerband"]) /
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
# )
# dataframe["kc_width"] = (
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
# )
# Ultimate Oscillator
dataframe['uo'] = ta.ULTOSC(dataframe)
# Commodity Channel Index: values [Oversold:-100, Overbought:100]
dataframe['cci'] = ta.CCI(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'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stochastic Slow
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Stochastic RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(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']
# 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"]
# )
# # 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"]
# )
# # 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['ema21'] = ta.EMA(dataframe, timeperiod=21)
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# # SMA - Simple Moving Average
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
# Parabolic SAR
# dataframe['sar'] = ta.SAR(dataframe)
# 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
# # ------------------------------------
# # Heikin Ashi Strategy
# 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 from the orderbook
# ------------------------------------
"""
# 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 populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['adx'] = ta.ADX(dataframe)
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
dataframe['cci'] = ta.CCI(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['roc'] = ta.ROC(dataframe)
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']
# 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 Parabolic
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)
# 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']
return dataframe
