def get_momentum_indicators(self):
# https://mrjbq7.github.io/ta-lib/func_groups/momentum_indicators.html
if self.verbose:
print self.ticker, 'get_momentum_indicators'
_a = ['open', 'high', 'low', 'close', 'volume']
inputs = {_a[i]: self.data[_a[i]].values for i in range(len(_a))}
# average directional movement index
self.data['mi_adx_14'] = abstract.ADX(inputs, timeperiod=14)
# average directional movement index rating
self.data['mi_adxr_14'] = abstract.ADX(inputs, timeperiod=14)
# absolute price oscillator
self.data['mi_apo'] = abstract.APO(inputs,
fastperiod=12,
slowperiod=26,
matype=0)
# aroon
self.data['mi_aroon_d'], self.data['mi_aroon_u'] = abstract.AROON(
inputs, timeperiod=14)
# aroon oscillator
self.data['mi_aroonosc'] = abstract.AROONOSC(inputs, timeperiod=14)
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:
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=25)
dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=25)
dataframe['sar'] = ta.SAR(dataframe)
dataframe['mom'] = ta.MOM(dataframe, timeperiod=14)
dataframe['sell-adx'] = ta.ADX(dataframe, timeperiod=14)
dataframe['sell-plus_di'] = ta.PLUS_DI(dataframe, timeperiod=25)
dataframe['sell-minus_di'] = ta.MINUS_DI(dataframe, timeperiod=25)
dataframe['sell-sar'] = ta.SAR(dataframe)
dataframe['sell-mom'] = ta.MOM(dataframe, timeperiod=14)
return dataframe
def populate_indicators_and_buy_signal(dataframe):
dataframe['ema'] = ta.EMA(dataframe, timeperiod=33)
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.22)
dataframe['adx'] = ta.ADX(dataframe)
prev_sar = dataframe['sar'].shift(1)
prev_close = dataframe['close'].shift(1)
prev_sar2 = dataframe['sar'].shift(2)
prev_close2 = dataframe['close'].shift(2)
# wait for stable turn from bearish to bullish market
dataframe.loc[(dataframe['close'] > dataframe['sar']) &
(prev_close > prev_sar) & (prev_close2 < prev_sar2),
'swap'] = 1
# consider prices above ema to be in upswing
dataframe.loc[dataframe['ema'] <= dataframe['close'], 'upswing'] = 1
dataframe.loc[(dataframe['upswing'] == 1) & (dataframe['swap'] == 1) & (
dataframe['adx'] > 25), # adx over 25 tells there's enough momentum
'buy'] = 1
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
return dataframe, info_msg % (dataframe.iloc[-1]['ema'],
dataframe.iloc[-1]['sar'],
dataframe.iloc[-1]['adx'])
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe = self.resample(dataframe, self.ticker_interval,
self.resample_factor)
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, 5.0, 3.0, 0.0, 3.0, 0.0)
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 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 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:
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
dataframe['short'] = ta.SMA(dataframe, timeperiod=3)
dataframe['long'] = ta.SMA(dataframe, timeperiod=6)
return dataframe
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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, 5.0, 3.0, 0.0, 3.0, 0.0)
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']
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['cci'] = ta.CCI(dataframe)
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 populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = numpy.nan_to_num(ta.RSI(dataframe, timeperiod=5))
dataframe['emarsi'] = numpy.nan_to_num(ta.EMA(dataframe['rsi'], timeperiod=5))
dataframe['adx'] = numpy.nan_to_num(ta.ADX(dataframe))
dataframe['minusdi'] = numpy.nan_to_num(ta.MINUS_DI(dataframe))
dataframe['minusdiema'] = numpy.nan_to_num(ta.EMA(dataframe['minusdi'], timeperiod=25))
dataframe['plusdi'] = numpy.nan_to_num(ta.PLUS_DI(dataframe))
dataframe['plusdiema'] = numpy.nan_to_num(ta.EMA(dataframe['plusdi'], timeperiod=5))
dataframe['lowsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=60))
dataframe['highsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=120))
dataframe['fastsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=120))
dataframe['slowsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=240))
dataframe['bigup'] = dataframe['fastsma'].gt(dataframe['slowsma']) & ((dataframe['fastsma'] - dataframe['slowsma']) > dataframe['close'] / 300)
dataframe['bigdown'] = ~dataframe['bigup']
dataframe['trend'] = dataframe['fastsma'] - dataframe['slowsma']
dataframe['preparechangetrend'] = dataframe['trend'].gt(dataframe['trend'].shift())
dataframe['preparechangetrendconfirm'] = dataframe['preparechangetrend'] & dataframe['trend'].shift().gt(dataframe['trend'].shift(2))
dataframe['continueup'] = dataframe['slowsma'].gt(dataframe['slowsma'].shift()) & dataframe['slowsma'].shift().gt(dataframe['slowsma'].shift(2))
dataframe['delta'] = dataframe['fastsma'] - dataframe['fastsma'].shift()
dataframe['slowingdown'] = dataframe['delta'].lt(dataframe['delta'].shift())
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 __init__(self, equityDataFrame, tickerCode):
"""
Create the Average Directional-specific logic. Everything else lives in the Indicator Base Class.
:param equityDataFrame: A Pandas DataFrame from the Equity Class.
:param tickerCode: Ticker Code (String).
"""
tableName = "Indicator_ADX"
tickerCode = tickerCode
insertQuery = "insert or replace into %s (Date, Code, ADX, ADX_ROC) values (?,?,?,?)" % (
tableName)
# Create a new DataFrame with just Date and Code
indicatorDataFrame = equityDataFrame.ix[:, 'Code':]
# Stick the Indicator Values into the new DataFrame
indicatorDataFrame['ADX'] = abstract.ADX(
equityDataFrame, timeperiod=14, prices=['High', 'Low', 'Close'])
indicatorDataFrame['ADX_ROC'] = abstract.ROC(indicatorDataFrame,
timeperiod=5,
price='ADX')
Indicator.__init__(self, tableName, tickerCode, insertQuery,
equityDataFrame, indicatorDataFrame)
def ta_detect(symbol):
try:
data = exchange.fetch_ohlcv(symbol.symbol, TA_TIME_FRAME)
df = DataFrame(
data, columns=['time', 'open', 'high', 'low', 'close', 'volume'])
df.set_index('time', inplace=True, drop=True)
df['rsi'] = ta.RSI(df)
df['adx'] = ta.ADX(df)
df['plus_di'] = ta.PLUS_DI(df)
df['minus_di'] = ta.MINUS_DI(df)
df['fastd'] = ta.STOCHF(df)['fastd']
df.loc[((df['rsi'] < 35) & (df['fastd'] < 35) & (df['adx'] > 30) &
(df['plus_di'] > 0.5)) | ((df['adx'] > 65) &
(df['plus_di'] > 0.5)), 'buy'] = 1
df.loc[(((crossed_above(df['rsi'], 70)) |
(crossed_above(df['fastd'], 70))) & (df['adx'] > 10) &
(df['minus_di'] > 0)) | ((df['adx'] > 70) &
(df['minus_di'] > 0.5)), 'sell'] = 1
buy_signal, sell_signal = df.iloc[-1]['buy'], df.iloc[-1]['sell']
if buy_signal == 1:
log('{} TA BUY'.format(symbol.symbol))
rd.publish('ta_buy', symbol.symbol)
return True
elif sell_signal == 1:
log('{} TA SELL'.format(symbol.symbol))
rd.publish('ta_sell', symbol.symbol)
return True
return False
except Exception as e:
log('{} error: {}'.format(symbol.symbol, str(e)))
time.sleep(30)
return False
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['sar'] = ta.SAR(dataframe)
dataframe['adx'] = ta.ADX(dataframe)
stoch = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2,
nbdevdn=2)['lowerband']
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['cci'] = ta.CCI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['mom'] = ta.MOM(dataframe)
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)
dataframe['ao'] = awesome_oscillator(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=25)
dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=25)
dataframe['sar'] = ta.SAR(dataframe)
dataframe['mom'] = ta.MOM(dataframe, timeperiod=14)
#print(f"\"{metadata['pair']}\"")
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Populate/update the trade data if there is any, set trades to false if not live/dry
self.custom_trade_info[metadata['pair']] = self.populate_trades(
metadata['pair'])
dataframe['rsi'] = np.nan_to_num(ta.RSI(dataframe, timeperiod=5))
dataframe['emarsi'] = np.nan_to_num(
ta.EMA(dataframe['rsi'], timeperiod=5))
dataframe['adx'] = np.nan_to_num(ta.ADX(dataframe))
dataframe['minusdi'] = np.nan_to_num(ta.MINUS_DI(dataframe))
dataframe['minusdiema'] = np.nan_to_num(
ta.EMA(dataframe['minusdi'], timeperiod=25))
dataframe['plusdi'] = np.nan_to_num(ta.PLUS_DI(dataframe))
dataframe['plusdiema'] = np.nan_to_num(
ta.EMA(dataframe['plusdi'], timeperiod=5))
dataframe['lowsma'] = np.nan_to_num(ta.EMA(dataframe, timeperiod=60))
dataframe['highsma'] = np.nan_to_num(ta.EMA(dataframe, timeperiod=120))
dataframe['fastsma'] = np.nan_to_num(ta.SMA(dataframe, timeperiod=120))
dataframe['slowsma'] = np.nan_to_num(ta.SMA(dataframe, timeperiod=240))
dataframe['bigup'] = dataframe['fastsma'].gt(dataframe['slowsma']) & (
(dataframe['fastsma'] - dataframe['slowsma']) >
dataframe['close'] / 300)
dataframe['bigdown'] = ~dataframe['bigup']
dataframe['trend'] = dataframe['fastsma'] - dataframe['slowsma']
dataframe['preparechangetrend'] = dataframe['trend'].gt(
dataframe['trend'].shift())
dataframe['preparechangetrendconfirm'] = dataframe[
'preparechangetrend'] & dataframe['trend'].shift().gt(
dataframe['trend'].shift(2))
dataframe['continueup'] = dataframe['slowsma'].gt(
dataframe['slowsma'].shift()) & dataframe['slowsma'].shift().gt(
dataframe['slowsma'].shift(2))
dataframe['delta'] = dataframe['fastsma'] - dataframe['fastsma'].shift(
)
dataframe['slowingdown'] = dataframe['delta'].lt(
dataframe['delta'].shift())
dataframe['rmi-slow'] = RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = RMI(dataframe, length=8, mom=4)
dataframe['rmi-up'] = np.where(
dataframe['rmi-slow'] >= dataframe['rmi-slow'].shift(), 1, 0)
dataframe['rmi-dn'] = np.where(
dataframe['rmi-slow'] <= dataframe['rmi-slow'].shift(), 1, 0)
dataframe['rmi-up-trend'] = np.where(
dataframe['rmi-up'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
dataframe['rmi-dn-trend'] = np.where(
dataframe['rmi-dn'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
return dataframe
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
# Log the dataframe
dataframe.to_csv("dataframe.csv", sep='\t')
#Determine Trend
dataframe = indicators.TREND(dataframe)
#Exponential Moving Average
dataframe['ema'] = ta.EMA(dataframe, timeperiod=33)
#Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.22)
#Average Directional Movement Index
dataframe['adx'] = ta.ADX(dataframe)
#MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macds'] = macd['macdsignal']
dataframe['macdh'] = macd['macdhist']
#Relative Strength Index
dataframe['rsi'] = ta.RSI(dataframe, 14)
dataframe = indicators.RSI(dataframe)
#Absolute Price Oscillator
dataframe['apo'] = ta.APO(dataframe,
fastperiod=12,
slowperiod=26,
matype=0)
#Momentum
dataframe['mom'] = ta.MOM(dataframe, 10)
#Bollinger Bands
dataframe = indicators.BBANDS(20, dataframe, 2)
# Stochcastic
dataframe['K'] = indicators.STOK(dataframe, 14)
dataframe['D'] = indicators.STOD(dataframe, 14)
dataframe = indicators.STOCH(dataframe)
# if Counter.counter < 30:
# print(dataframe)
# print (macd)
# print (dataframe ['adx'])
# Counter.counter = Counter.counter + 1
# else:
# exit()
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 adx(self):
adx = tb.ADX(self.dataframe, timeperiod=14)
value = adx[len(adx) - 1]
if value > adxLimit:
return "strong"
elif value < adxLimit:
return "weak"
else:
return "neutral"
def apply_indicators(df: pd.DataFrame):
# ADX
df['adx'] = ta.ADX(df)
# EMA
df['ema_5'] = ta.EMA(df, 5)
df['ema_10'] = ta.EMA(df, 10)
df['ema_20'] = ta.EMA(df, 20)
df['ema_50'] = ta.EMA(df, 50)
df['ema_100'] = ta.EMA(df, 100)
df['ema_200'] = ta.EMA(df, 200)
# MACD
macd = ta.MACD(df)
df['macd'] = macd['macd']
df['macdsignal'] = macd['macdsignal']
df['macdhist'] = macd['macdhist']
# inverse Fisher rsi/ RSI
df['rsi'] = ta.RSI(df)
rsi = 0.1 - (df['rsi'] - 50)
df['i_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# Stoch fast
stoch_fast = ta.STOCHF(df)
df['fastd'] = stoch_fast['fastd']
df['fastk'] = stoch_fast['fastk']
# Stock slow
stoch_slow = ta.STOCH(df)
df['slowd'] = stoch_slow['slowd']
df['slowk'] = stoch_slow['slowk']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(df),
window=20,
stds=2)
df['bb_lowerband'] = bollinger['lower']
df['bb_middleband'] = bollinger['mid']
df['bb_upperband'] = bollinger['upper']
# ROC
df['roc'] = ta.ROC(df, 10)
# CCI
df['cci'] = ta.CCI(df, 14)
# on balance volume
df['obv'] = ta.OBV(df)
# Average True Range
df['atr'] = ta.ATR(df, 14)
df = ichimoku(df)
return df
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['ema'] = ta.EMA(dataframe, timeperiod=33)
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.22)
dataframe['adx'] = ta.ADX(dataframe)
return dataframe
def generate_indicators(dataset,
timeperiod=5,
generate_target=True,
reset_index=False):
# To avoid changes from original dataset
df = pd.DataFrame(dataset).copy()
# To prevent index from being Date
if (reset_index):
df = df.reset_index()
df.columns = df.columns.str.lower()
# check the given dataset has necessary_columns
__check_columns(df)
df = df[necessary_columns]
# df_indicators has all columns except date, this is for talib to produces other indicators
df_indicators = df.iloc[:, 1:]
# Produce other indicators
RSI = abstract.RSI(df_indicators.close, timeperiod)
RSI = pd.DataFrame({'RSI': RSI})
MOM = abstract.MOM(df_indicators.close, timeperiod)
MOM = pd.DataFrame({'MOM': MOM})
KD = abstract.STOCH(df_indicators)
KD = pd.DataFrame(KD) # KD has slowd and slowk
MACD = abstract.MACD(df_indicators)
MACD = pd.DataFrame(MACD) # MACD has its own column names
ADX = abstract.ADX(df_indicators)
ADX = pd.DataFrame({'ADX': ADX})
SMA = abstract.SMA(df_indicators.close)
SMA = pd.DataFrame({'SMA': SMA})
upper, middle, lower = talib.BBANDS(df_indicators.close, matype=MA_Type.T3)
bb_df = pd.DataFrame({ \
'upper_bb' : upper,
'middel_bb' : middle,
'lower_bb' : lower})
# Combine all metrix
frames = [df, RSI, MOM, KD, MACD, ADX, SMA, bb_df]
combined = pd.concat(frames, axis=1)
if (generate_target):
target_name = 'next_' + str(timeperiod) + 'day_trend'
combined[target_name] = np.where(
combined.close.shift(-timeperiod) > combined.close, 1, 0)
return combined
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=25)
dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=25)
dataframe["sar"] = ta.SAR(dataframe)
dataframe["mom"] = ta.MOM(dataframe, timeperiod=14)
return dataframe
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe, timeperiod=90) #90
aroon = ta.AROON(dataframe, timeperiod=60) #60
dataframe['aroon-down'] = aroon['aroondown']
dataframe['aroon-up'] = aroon['aroonup']
return dataframe
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
stoch_fast = ta.STOCHF(dataframe, 5.0, 3.0, 0.0, 3.0, 0.0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
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')
dataframe['adx'] = ta.ADX(dataframe)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe["fastd"] = stoch_fast["fastd"]
dataframe["fastk"] = stoch_fast["fastk"]
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")
dataframe["adx"] = ta.ADX(dataframe)
return dataframe
def ADX(self):
ADX = tb.ADX(self.dataframe, timeperiod=14)
value = ADX[len(ADX) - 1]
if (value > adxLimit):
#print("ADX: " + str(value) + " sell")
return "strong"
elif (value < adxLimit):
#print("ADX: " + str(value) + " buy")
return "weak"
else:
#print("ADX: " + str(value) + " neutral")
return "neutral"
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 (timeperiod is expressed in candles)
# -------------------
# ADX - Average Directional Index (The Trend Strength Indicator)
dataframe['adx'] = ta.ADX(
dataframe, timeperiod=14) # 14 timeperiods is usually used for ADX
# +DM (Positive Directional Indicator) = current high - previous high
dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=25)
# -DM (Negative Directional Indicator) = previous low - current low
dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=25)
# RSI - Relative Strength Index (Under bought / Over sold & Over bought / Under sold indicator Indicator)
dataframe['rsi'] = ta.RSI(dataframe)
# 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)
# Initialize total signal variables (should be 0 = false by default)
dataframe['total_buy_signal_strength'] = dataframe[
'total_sell_signal_strength'] = 0
#divergences
dataframe = self.divergences(dataframe)
#get trend
dataframe.loc[(dataframe['adx'] > 20) &
(dataframe['plus_di'] < dataframe['minus_di']),
'trend'] = 'downwards'
dataframe.loc[dataframe['adx'] < 20, 'trend'] = 'sideways'
dataframe.loc[(dataframe['adx'] > 20) &
(dataframe['plus_di'] > dataframe['minus_di']),
'trend'] = 'upwards'
return dataframe
