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(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 run(self):
print("Bot is running")
while True:
for coin in self.coins:
try:
klines = self.client.get_klines(symbol=coin, interval=Bot.interval)
array = np.array(klines, dtype='f8')
df = pd.DataFrame(data=array[:, 0:6], columns=["date", "open", "high", "low", "close", "volume"])
plus_di = tb.PLUS_DI(df, timeperiod=20)
minus_di = tb.MINUS_DI(df, timeperiod=20)
upperBB, lowerBB, old_upperBB, old_lowerBB = self.BBANDS(df, length=20, mult=1.75)
upperKC, lowerKC, old_upperKC, old_lowerKC = self.KELCH(df, length=20, mult=1.5)
plus = plus_di[len(plus_di) - 2]
old_plus = plus_di[len(plus_di) - 1]
minus = minus_di[len(minus_di) - 2]
old_minus = minus_di[len(minus_di) - 1]
print(coin, plus, minus, lowerKC, lowerBB, upperBB, upperKC)
if plus > minus and old_plus > old_minus and plus > 27 and old_plus > 27 and \
lowerBB < lowerKC and old_lowerBB < old_lowerKC and \
upperBB > upperKC and old_upperBB > old_upperKC:
self.buyCoin(coin, df)
if (plus < minus and old_plus < old_minus) or (plus < 20 and old_plus < 20):
self.sellCoin(coin, df)
except Exception as ex:
print("Exception", ex)
time.sleep(60.0)
pass
time.sleep(60 * 10)
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 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(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(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,
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 (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
def analyze(self, historical_data, period_count=14,
signal=['minus_di'], hot_thresh=None, cold_thresh=None):
dataframe = self.convert_to_dataframe(historical_data)
dmi_values = abstract.MINUS_DI(dataframe).to_frame()
dmi_values.dropna(how='all', inplace=True)
dmi_values.rename(columns={0: 'minus_di'}, inplace=True)
if dmi_values[signal[0]].shape[0]:
dmi_values['is_hot'] = dmi_values[signal[0]] < hot_thresh
dmi_values['is_cold'] = dmi_values[signal[0]] > cold_thresh
return dmi_values
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:
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# Minus Directional Indicator / Movement
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
return dataframe
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
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']
dataframe['sar'] = ta.SAR(dataframe)
return dataframe
def evaluate_adx(self,
period=14,
prefix="adx",
trend_line=25,
use_di=True,
impact_buy=1,
impact_sell=1):
"""
evaluates the adx (optionally use plus_di and minus_di to detect buy or sell)
:param dataframe:
:param period:
:param prefix:
:param trend_line: The ADX value at which we consider that a trend is present (Default: 25)
:param use_di: Enable/disable the usage of plus_di and minus_di (Default: Enabled)
:return:
"""
self._weights(impact_buy, impact_sell)
dataframe = self.dataframe
name = f"{prefix}_{period}"
dataframe[name] = ta.ADX(dataframe, timeperiod=period)
# We can use PLUS_DI and MINUS_DI to be able to detect if we should buy or sell
# See https://www.investopedia.com/articles/trading/07/adx-trend-indicator.asp
if use_di:
dataframe[f"{name}_plus_di"] = ta.PLUS_DI(dataframe,
timeperiod=period)
dataframe[f"{name}_minus_di"] = ta.MINUS_DI(dataframe,
timeperiod=period)
dataframe.loc[(
(dataframe[name] > trend_line)
&
(dataframe[f"{name}_plus_di"] > dataframe[f"{name}_minus_di"])
), f"buy_{name}", ] = (1 * impact_buy)
dataframe.loc[(
(dataframe[name] > trend_line)
&
(dataframe[f"{name}_plus_di"] < dataframe[f"{name}_minus_di"])
), f"sell_{name}", ] = (1 * impact_sell)
else:
dataframe.loc[((dataframe[name] > trend_line)),
f"buy_{name}"] = 1 * impact_buy
dataframe.loc[((dataframe[name] > trend_line)),
f"sell_{name}"] = 1 * impact_sell
def populateindicators(dataframe):
bardata = dataframe.assign(mfi=ta.MFI(dataframe['high'],
dataframe['low'],
dataframe['close'],
np.asarray(dataframe['volume'],
dtype='float'),
timeperiod=14),
ultosc=ta.ULTOSC(dataframe['high'],
dataframe['low'],
dataframe['close']),
minusdi=tab.MINUS_DI(dataframe, timeperiod=14),
plusdi=tab.PLUS_DI(dataframe, timeperiod=14),
rsi=tab.RSI(dataframe,
timeperiod=14,
price='close'),
adx=tab.ADX(dataframe))
return bardata
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# ADX
dataframe['adx'] = ta.ADX(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# Minus Directional Indicator / Movement
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
return dataframe
def buyCoin(self, coin, df):
self.refreshBalance()
if (coin not in self.available_currencies):
print("Buying coin attempt: " + coin)
print("Available currencies: " + str(self.available_currencies))
if float(self.BTC) < 0.0011:
print("Less than 0.001 BTC", self.BTC)
min_coin = ""
min_plus = 100
min_df = None
for curr_coin in self.available_currencies:
klines = self.client.get_klines(symbol=curr_coin, interval=Bot.interval)
array = np.array(klines, dtype='f8')
df = pd.DataFrame(data=array[:, 0:6], columns=["date", "open", "high", "low", "close", "volume"])
plus_di = tb.PLUS_DI(df, timeperiod=20)
minus_di = tb.MINUS_DI(df, timeperiod=20)
upperBB, lowerBB, old_upperBB, old_lowerBB = self.BBANDS(df, length=20, mult=1.75)
upperKC, lowerKC, old_upperKC, old_lowerKC = self.KELCH(df, length=20, mult=1.5)
plus = plus_di[len(plus_di) - 2]
if min_plus > plus and lowerBB > lowerKC and upperBB < upperKC:
min_coin = curr_coin
min_plus = plus
min_df = df
if min_plus < 25:
self.sellCoin(min_coin, min_df)
self.refreshBalance()
time.sleep(30.0)
else:
price = df["close"][len(df) - 1]
min_amount = int(Bot.min_amount_dict[coin])
amount = self.maxSpendInBTC / float(price)
amount = round(amount, min_amount)
while amount * price < 0.001:
amount += pow(10, -min_amount)
print("Buying", amount, coin, "at", price)
self.client.create_order(symbol=coin, side="BUY", type="MARKET", quantity=amount)
self.refreshBalance()
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.
"""
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
# Minus Directional Indicator / Movement
dataframe['minus_di'] = ta.MINUS_DI(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 fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Overlap Studies
# ------------------------------------
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
return dataframe
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class.
"""
dataframe['adx'] = ta.ADX(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
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']
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))
rsiframe = DataFrame(dataframe["rsi"]).rename(columns={"rsi": "close"})
dataframe["emarsi"] = numpy.nan_to_num(ta.EMA(rsiframe, timeperiod=5))
dataframe["adx"] = numpy.nan_to_num(ta.ADX(dataframe))
dataframe["minusdi"] = numpy.nan_to_num(ta.MINUS_DI(dataframe))
minusdiframe = DataFrame(
dataframe["minusdi"]).rename(columns={"minusdi": "close"})
dataframe["minusdiema"] = numpy.nan_to_num(
ta.EMA(minusdiframe, timeperiod=25))
dataframe["plusdi"] = numpy.nan_to_num(ta.PLUS_DI(dataframe))
plusdiframe = DataFrame(
dataframe["plusdi"]).rename(columns={"plusdi": "close"})
dataframe["plusdiema"] = numpy.nan_to_num(
ta.EMA(plusdiframe, 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:
# 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_buy_trend(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
conditions = []
# GUARDS
if self.buy_plusdi_enabled.value:
conditions.append(
ta.PLUS_DI(dataframe, timeperiod=int(self.buy_plusdi.value)) >
ta.MINUS_DI(dataframe, timeperiod=int(self.buy_minusdi.value)))
# TRIGGERS
try:
conditions.append(
ta.ADX(dataframe, timeperiod=int(self.buy_adx_timeframe.value))
> self.buy_adx.value)
except Exception:
pass
if conditions:
dataframe.loc[reduce(lambda x, y: x & y, conditions), 'buy'] = 1
return dataframe
def ta_signal(self, symbol, time_frame):
data = self.exchange.fetch_ohlcv(symbol, 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[(((self.crossed_above(df['rsi'], 70)) |
(self.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:
return 'BUY'
elif sell_signal == 1:
return 'SELL'
return 'neutral'
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
# MACD
# tadoc.org/indicator/MACD.htm
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
# MINUS DI
# tadoc.org/indicator/MINUS_DI.htm
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# RSI
# tadoc.org/indicator/RSI.htm
# tradingview.com/scripts/fishertransform/
# goo.gl/2JGGoy
dataframe['rsi'] = ta.RSI(dataframe)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (
np.exp(2 * rsi) + 1
) # Inverse Fisher transform on RSI, values [-1.0, 1.0]
dataframe['fisher_rsi_norma'] = 50 * (
dataframe['fisher_rsi'] + 1
) # Inverse Fisher transform on RSI normalized, value [0.0, 100.0]
# STOCH FAST
# tadoc.org/indicator/STOCHF.htm
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# SAR
dataframe['sar'] = ta.SAR(dataframe)
# SMA
dataframe['sma'] = ta.SMA(dataframe, timeperiod=50)
return dataframe
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 DI(self):
minus_di = abstract.MINUS_DI(self.company_stock, timeperiod=14)
plus_di = abstract.PLUS_DI(self.company_stock, timeperiod=14)
self.company_stock['MINUS_DI'] = minus_di
self.company_stock['PLUS_DI'] = plus_di
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
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.
"""
# 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)
dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi'])
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# 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
# ------------------------------------
# Previous Bollinger bands
# Because ta.BBANDS implementation is broken with small numbers, it actually
# returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
# and use middle band instead.
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2,
nbdevdn=2)['lowerband']
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
# 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']
return dataframe
def __countDMI(self): self.adx = ta.ADX(self.stock.inputs) self.plus_di = ta.PLUS_DI(self.stock.inputs) self.minus_di = ta.MINUS_DI(self.stock.inputs)
