def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Set Up Bollinger Bands
mid, lower = bollinger_bands(dataframe['close'],
window_size=40,
num_of_std=2)
dataframe['lower'] = lower
dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
dataframe['closedelta'] = (dataframe['close'] -
dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(
window=30).mean()
dataframe['rocr'] = ta.ROCR(dataframe, timeperiod=28)
inf_tf = '1h'
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=inf_tf)
informative['rocr'] = ta.ROCR(informative, timeperiod=168)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
inf_tf,
ffill=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe['rocr'] = ta.ROCR(dataframe, period=499)
dataframe['rocr_200'] = ta.ROCR(dataframe, period=200)
dataframe['rocr_100'] = ta.ROCR(dataframe, period=100)
dataframe['rocr_20'] = ta.ROCR(dataframe, period=20)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# # Heikin Ashi Candles
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
# Set Up Bollinger Bands
mid, lower = bollinger_bands(ha_typical_price(dataframe),
window_size=40,
num_of_std=2)
dataframe['lower'] = lower
dataframe['mid'] = mid
dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
dataframe['closedelta'] = (dataframe['ha_close'] -
dataframe['ha_close'].shift()).abs()
dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs()
dataframe['bb_lowerband'] = dataframe['lower']
dataframe['bb_middleband'] = dataframe['mid']
dataframe['ema_fast'] = ta.EMA(dataframe['ha_close'], timeperiod=3)
dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=50)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(
window=30).mean()
dataframe['rocr'] = ta.ROCR(dataframe['ha_close'], timeperiod=28)
rsi = ta.RSI(dataframe)
dataframe["rsi"] = rsi
rsi = 0.1 * (rsi - 50)
dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
inf_tf = '1h'
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=inf_tf)
inf_heikinashi = qtpylib.heikinashi(informative)
informative['ha_close'] = inf_heikinashi['close']
informative['rocr'] = ta.ROCR(informative['ha_close'], timeperiod=168)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
inf_tf,
ffill=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe['rocr_499'] = ta.ROCR(dataframe, timeperiod=499)
dataframe['rocr_200'] = ta.ROCR(dataframe, timeperiod=200)
dataframe['rocr_100'] = ta.ROCR(dataframe, timeperiod=100)
dataframe['rocr_50'] = ta.ROCR(dataframe, timeperiod=50)
dataframe['rocr_10'] = ta.ROCR(dataframe, timeperiod=10)
dataframe['rocr_5'] = ta.ROCR(dataframe, timeperiod=5)
dataframe['rocr_2'] = ta.ROCR(dataframe, timeperiod=2)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Misc. calculations regarding existing open positions (reset on every loop iteration)
self.custom_trade_info[metadata['pair']] = trade_data = {}
trade_data['active_trade'] = trade_data['other_trades'] = False
if self.config['runmode'].value in ('live', 'dry_run'):
active_trade = Trade.get_trades([
Trade.pair == metadata['pair'],
Trade.is_open.is_(True),
]).all()
other_trades = Trade.get_trades([
Trade.pair != metadata['pair'],
Trade.is_open.is_(True),
]).all()
if active_trade:
current_rate = self.get_current_price(metadata['pair'])
active_trade[0].adjust_min_max_rates(current_rate)
trade_data['active_trade'] = True
trade_data['current_profit'] = active_trade[
0].calc_profit_ratio(current_rate)
trade_data['peak_profit'] = active_trade[0].calc_profit_ratio(
active_trade[0].max_rate)
if other_trades:
trade_data['other_trades'] = True
total_other_profit = tuple(
trade.calc_profit_ratio(self.get_current_price(trade.pair))
for trade in other_trades)
trade_data['avg_other_profit'] = mean(total_other_profit)
self.custom_trade_info[metadata['pair']] = trade_data
# Set up Bollinger Bands
mid, lower = bollinger_bands(dataframe['close'],
window_size=40,
num_of_std=2)
dataframe['lower'] = lower
dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
dataframe['closedelta'] = (dataframe['close'] -
dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe),
window=20,
stds=2)
dataframe['bb-lowerband'] = bollinger['lower']
dataframe['bb-middleband'] = bollinger['mid']
# Set up other indicators
dataframe['volume-mean-slow'] = dataframe['volume'].rolling(
window=24).mean()
dataframe['rmi-slow'] = RMI(dataframe, length=20, mom=5)
dataframe['rmi-fast'] = RMI(dataframe, length=9, mom=3)
dataframe['rocr'] = ta.ROCR(dataframe, timeperiod=28)
dataframe['ema-slow'] = ta.EMA(dataframe, timeperiod=50)
# Trend Calculations
dataframe['max'] = dataframe['high'].rolling(12).max()
dataframe['min'] = dataframe['low'].rolling(12).min()
dataframe['upper'] = np.where(
dataframe['max'] > dataframe['max'].shift(), 1, 0)
dataframe['lower'] = np.where(
dataframe['min'] < dataframe['min'].shift(), 1, 0)
dataframe['up_trend'] = np.where(
dataframe['upper'].rolling(3, min_periods=1).sum() != 0, 1, 0)
dataframe['dn_trend'] = np.where(
dataframe['lower'].rolling(3, min_periods=1).sum() != 0, 1, 0)
# Informative Pair Indicators
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
informative['rocr'] = ta.ROCR(informative, timeperiod=168)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
return dataframe
def __call__(self, data: np.ndarray):
return (ta.ROCR(data[OHLCVT.close], timeperiod=self.period) - 1) * 100
def do_indicators(self, dataframe: DataFrame, metadata: dict):
# # Standard Settings
# displacement = 26
# ichimoku = ftt.ichimoku(dataframe,
# conversion_line_period=9,
# base_line_periods=26,
# laggin_span=52,
# displacement=displacement
# )
# Crypto Settings
displacement = 30
ichimoku = ftt.ichimoku(dataframe,
conversion_line_period=20,
base_line_periods=60,
laggin_span=120,
displacement=displacement)
dataframe['chikou_span'] = ichimoku['chikou_span']
# cross indicators
dataframe['tenkan_sen'] = ichimoku['tenkan_sen']
dataframe['kijun_sen'] = ichimoku['kijun_sen']
# cloud, green a > b, red a < b
dataframe['senkou_a'] = ichimoku['senkou_span_a']
dataframe['senkou_b'] = ichimoku['senkou_span_b']
dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a']
dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b']
dataframe['cloud_green'] = ichimoku['cloud_green'] * 1
dataframe['cloud_red'] = ichimoku['cloud_red'] * -1
# DANGER ZONE START
# NOTE: Not actually the future, present data that is normally shifted forward for display as the cloud
dataframe['future_green'] = (
dataframe['leading_senkou_span_a'] >
dataframe['leading_senkou_span_b']).astype('int') * 2
# The chikou_span is shifted into the past, so we need to be careful not to read the
# current value. But if we shift it forward again by displacement it should be safe to use.
# We're effectively "looking back" at where it normally appears on the chart.
dataframe['chikou_high'] = (
(dataframe['chikou_span'] > dataframe['senkou_a']) &
(dataframe['chikou_span'] > dataframe['senkou_b'])
).shift(displacement).fillna(0).astype('int')
# DANGER ZONE END
ssl_down, ssl_up = SSLChannels(dataframe, 10)
dataframe['ssl_down'] = ssl_down
dataframe['ssl_up'] = ssl_up
dataframe['ssl_high'] = (ssl_up > ssl_down).astype('int') * 3
dataframe['rocr'] = ta.ROCR(dataframe, timeperiod=28)
dataframe['rmi-fast'] = ftt.RMI(dataframe, length=9, mom=3)
dataframe['go_long'] = (
(dataframe['tenkan_sen'] > dataframe['kijun_sen']) &
(dataframe['close'] > dataframe['senkou_a']) &
(dataframe['close'] > dataframe['senkou_b']) &
(dataframe['future_green'] > 0) & (dataframe['chikou_high'] > 0) &
(dataframe['ssl_high'] > 0) &
(dataframe['rocr'] > dataframe['rocr'].shift()) &
(dataframe['rmi-fast'] > dataframe['rmi-fast'].shift(2))
).astype('int') * 4
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
dataframe['rocr'] = ta.ROCR(dataframe, timeperiod=5)
dataframe['rocr_2'] = ta.ROCR(dataframe, timeperiod=2)
return dataframe