def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
self.custom_trade_info[metadata['pair']] = self.populate_trades(
metadata['pair'])
dataframe['rmi-slow'] = RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = RMI(dataframe, length=8, mom=4)
dataframe['roc'] = ta.ROC(dataframe, timeperiod=6)
dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=6)
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)
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
informative['rsi'] = ta.RSI(informative, timeperiod=14)
informative['1d_high'] = informative['close'].rolling(24).max()
informative['3d_low'] = informative['close'].rolling(72).min()
informative['adr'] = informative['1d_high'] - informative['3d_low']
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
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 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['sar'] = ta.SAR(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# 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['ema3'] = ta.EMA(informative, timeperiod=3)
informative['ema12'] = ta.EMA(informative, timeperiod=12)
inf_macd = ta.MACD(informative)
informative['macd'] = inf_macd['macd']
informative['macdsignal'] = inf_macd['macdsignal']
informative['macdhist'] = inf_macd['macdhist']
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True)
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:
self.custom_trade_info[metadata['pair']] = self.populate_trades(
metadata['pair'])
# Relative Momentum Index
dataframe['rmi-slow'] = RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = RMI(dataframe, length=8, mom=4)
# Momentum Pinball
dataframe['roc'] = ta.ROC(dataframe, timeperiod=6)
dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=6)
# Trend Calculations
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)
# Informative for STAKE/FIAT and COIN/FIAT on default timeframe, only relevant if stake currency is BTC or ETH
if self.config['stake_currency'] in ('BTC', 'ETH'):
coin, stake = metadata['pair'].split('/')
fiat = self.custom_fiat
coin_fiat = f"{coin}/{fiat}"
stake_fiat = f"{stake}/{fiat}"
# COIN/FIAT (e.g. XLM/USD) - timeframe
coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat,
timeframe=self.timeframe)
dataframe[f"{fiat}_rsi"] = ta.RSI(coin_fiat_tf, timeperiod=14)
# STAKE/FIAT (e.g. BTC/USD) - inf_timeframe
stake_fiat_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.timeframe)
stake_fiat_inf_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.inf_timeframe)
dataframe[f"{stake}_rsi"] = ta.RSI(stake_fiat_tf, timeperiod=14)
dataframe[f"{stake}_rmi_{self.inf_timeframe}"] = RMI(
stake_fiat_inf_tf, length=21, mom=5)
# Informative indicators for current pair on inf_timeframe
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
informative['rsi'] = ta.RSI(informative, timeperiod=14)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Misc calculations regarding existing open positions
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:
trade_data['active_trade'] = True
trade_data['current_profit'] = active_trade[0].calc_profit_ratio(rate=self.get_current_price(metadata['pair']))
trade_data['peak_profit'] = active_trade[0].calc_profit_ratio(rate=active_trade[0].max_rate)
trade_data['current_peak_ratio'] = (trade_data['current_profit'] / trade_data['peak_profit'])
if other_trades:
trade_data['other_trades'] = True
total_other_profit = sum(trade.calc_profit_ratio(rate=self.get_current_price(trade.pair)) for trade in other_trades)
trade_data['avg_other_profit'] = total_other_profit / len(other_trades)
self.custom_trade_info[metadata['pair']] = trade_data
# 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['sar'] = ta.SAR(dataframe)
# 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)
# Consensus dashboard based on default timeframe and informative pair
# Example: https://www.tradingview.com/symbols/BTCUSD/technicals/
# Code: https://github.com/freqtrade/technical/blob/master/technical/tradingview/__init__.py
consensus = tv.SummaryConsensus(dataframe).score()
dataframe['tv-consensus-sell'] = consensus['sell_agreement']
dataframe['tv-consensus-buy'] = consensus['buy_agreement']
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_timeframe)
consensus_inf = tv.SummaryConsensus(informative).score()
informative['tv-consensus-sell'] = consensus_inf['sell_agreement']
informative['tv-consensus-buy'] = consensus_inf['buy_agreement']
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True)
# dataframe.to_csv('user_data/foo.csv')
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair'])
# Base timeframe indicators
dataframe['rmi-slow'] = RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = RMI(dataframe, length=8, mom=4)
# Momentum Pinball:
dataframe['roc'] = ta.ROC(dataframe, timeperiod=6)
dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=6)
# RMI Trends and Peaks
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)
dataframe['rmi-max'] = dataframe['rmi-slow'].rolling(10, min_periods=1).max()
# See: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L1059
# matype = vidya, src = hl2
dataframe = PMAX(dataframe, period=10, multiplier=3, length=10, MAtype=5, src=2)
# Other stake specific informative indicators
# TODO: Re-evaluate the value of these informative indicators
if self.config['stake_currency'] in ('BTC', 'ETH'):
coin, stake = metadata['pair'].split('/')
fiat = self.custom_fiat
coin_fiat = f"{coin}/{fiat}"
stake_fiat = f"{stake}/{fiat}"
coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat, timeframe=self.timeframe)
dataframe[f"{fiat}_rsi"] = ta.RSI(coin_fiat_tf, timeperiod=14)
stake_fiat_tf = self.dp.get_pair_dataframe(pair=stake_fiat, timeframe=self.timeframe)
stake_fiat_inf_tf = self.dp.get_pair_dataframe(pair=stake_fiat, timeframe=self.inf_timeframe)
dataframe[f"{stake}_rsi"] = ta.RSI(stake_fiat_tf, timeperiod=14)
dataframe[f"{stake}_rmi_{self.inf_timeframe}"] = RMI(stake_fiat_inf_tf, length=21, mom=5)
# Base pair informative timeframe indicators
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_timeframe)
informative['rsi'] = ta.RSI(informative, timeperiod=14)
# Get the "average day range" between the 1d high and 3d low to set up guards
informative['1d_high'] = informative['close'].rolling(24).max()
informative['3d_low'] = informative['close'].rolling(72).min()
informative['adr'] = informative['1d_high'] - informative['3d_low']
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True)
dataframe.to_csv('user_data/foo.csv')
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:
self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair'])
dataframe['rmi-slow'] = RMI(dataframe, length=21, mom=5)
dataframe['rmi-fast'] = RMI(dataframe, length=8, mom=4)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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)
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_timeframe)
informative['rsi'] = ta.RSI(informative, timeperiod=14)
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.inf_timeframe, ffill=True)
dataframe['bull'] = dataframe[f"rsi_{self.inf_timeframe}"].gt(60).astype('int') * 20
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['sar'] = ta.SAR(dataframe)
dataframe['rmi'] = RMI(dataframe)
dataframe['kama-3'] = ta.KAMA(dataframe, timeperiod=3)
dataframe['kama-21'] = ta.KAMA(dataframe, timeperiod=21)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['volume_ma'] = dataframe['volume'].rolling(window=24).mean()
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=24).mean()
dataframe['RMI'] = RMI(dataframe)
dataframe['sar'] = ta.SAR(dataframe)
dataframe['max'] = dataframe['high'].rolling(60).max()
dataframe['min'] = dataframe['low'].rolling(60).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(10, min_periods=1).sum() != 0,1,0)
dataframe['dn_trend'] = np.where(dataframe['lower'].rolling(10, min_periods=1).sum() != 0,1,0)
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'])
# Momentum Pinball
dataframe['roc'] = ta.ROC(dataframe, timeperiod=1)
dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=3)
# MA Streak
dataframe['mac'] = cta.macross(dataframe, 21, 55)
dataframe['streak'] = cta.mastreak(dataframe, period=4)
lookup_idxs = dataframe.index.values - (
abs(dataframe['streak'].values) + 1)
valid_lookups = lookup_idxs >= 0
dataframe['sbc'] = np.nan
dataframe.loc[valid_lookups, 'sbc'] = dataframe['close'].to_numpy()[
lookup_idxs[valid_lookups].astype(int)]
dataframe['streak-roc'] = 100 * (dataframe['close'] -
dataframe['sbc']) / dataframe['sbc']
# Percent Change Channel
upper, mid, lower = cta.pcc(dataframe, period=20, mult=2)
dataframe['pcc-lowerband'] = lower
dataframe['pcc-upperband'] = upper
# RMI Trend Strength
dataframe['rmi'] = RMI(dataframe, length=21, mom=5)
# RMI Trend Calculations
dataframe['rmi-up'] = np.where(
dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0)
dataframe['rmi-dn'] = np.where(
dataframe['rmi'] <= dataframe['rmi'].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:
# 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
upper_bb1, mid_bb1, lower_bb1 = ta.BBANDS(dataframe['ha_close'],
timeperiod=40)
upper_bb2, mid_bb2, lower_bb2 = ta.BBANDS(
qtpylib.typical_price(heikinashi), 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['ha_close'] -
dataframe['ha_close'].shift()).abs()
dataframe['tail'] = (dataframe['ha_close'] - dataframe['ha_low']).abs()
dataframe['ema_slow'] = ta.EMA(dataframe['ha_close'], timeperiod=48)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(
window=24).mean()
dataframe['rsi'] = ta.RSI(heikinashi, timeperiod=14)
dataframe['tema'] = ta.TEMA(heikinashi, timeperiod=9)
dataframe['adx'] = ta.ADX(heikinashi)
dataframe['rmi'] = RMI(heikinashi)
dataframe['sar'] = ta.SAR(heikinashi)
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 populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
# Misc calculations regarding existing open positions
if self.config['runmode'].value in ('live', 'dry_run'):
self.custom_trade_info[metadata['pair']] = trade_data = {}
trade_data['active_trade'] = trade_data['other_trades'] = False
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:
trade_data['active_trade'] = True
trade_data['current_profit'] = active_trade[
0].calc_profit_ratio(
rate=self.get_current_price(metadata['pair']))
trade_data['peak_profit'] = active_trade[0].calc_profit_ratio(
rate=active_trade[0].max_rate)
trade_data['current_peak_ratio'] = (
trade_data['current_profit'] / trade_data['peak_profit'])
if other_trades:
trade_data['other_trades'] = True
total_other_profit = sum(
trade.calc_profit_ratio(
rate=self.get_current_price(trade.pair))
for trade in other_trades)
trade_data['avg_other_profit'] = total_other_profit / len(
other_trades)
self.custom_trade_info[metadata['pair']] = trade_data
# 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['sar'] = ta.SAR(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# 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)
# Set Up Bollinger Bands
upper_bb, mid_bb, lower_bb = ta.BBANDS(dataframe['close'],
timeperiod=20)
dataframe['upper-bb'] = upper_bb
dataframe['lower-bb'] = lower_bb
dataframe['mid-bb'] = mid_bb
# did candle low break-out of bottom bband?
dataframe['breakout-low'] = np.where(
dataframe['low'] <= dataframe['lower-bb'], True, False)
last = np.nan
for i in range(len(dataframe)):
if dataframe.loc[i, 'breakout-low']:
last = i
dataframe.loc[i, 'breakout-low-last'] = i - last
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'])
# Momentum Pinball
dataframe['roc'] = ta.ROC(dataframe, timeperiod=1)
dataframe['mp'] = ta.RSI(dataframe['roc'], timeperiod=3)
# MA Streak
dataframe['mac'] = ci_mac(dataframe, 20, 50)
dataframe['streak'] = ci_mastreak(dataframe, period=4)
streak = abs(int(dataframe['streak'].iloc[-1]))
streak_back_close = dataframe['close'].shift(streak + 1)
dataframe['streak-roc'] = 100 * (dataframe['close'] -
streak_back_close) / streak_back_close
# Percent Change Channel
pcc = ci_pcc(dataframe, period=20, mult=2)
dataframe['pcc-lowerband'] = pcc.lowerband
dataframe['pcc-upperband'] = pcc.upperband
# RMI Trend Strength
dataframe['rmi'] = RMI(dataframe, length=21, mom=5)
# RMI Trend Calculations
dataframe['rmi-up'] = np.where(
dataframe['rmi'] >= dataframe['rmi'].shift(), 1, 0)
dataframe['rmi-dn'] = np.where(
dataframe['rmi'] <= dataframe['rmi'].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)
# Informative for STAKE/FIAT and COIN/FIAT on default timeframe, only relevant if stake currency is BTC or ETH
if self.config['stake_currency'] in ('BTC', 'ETH'):
coin, stake = metadata['pair'].split('/')
fiat = self.custom_fiat
coin_fiat = f"{coin}/{fiat}"
stake_fiat = f"{stake}/{fiat}"
# COIN/FIAT (e.g. XLM/USD) - timeframe
coin_fiat_tf = self.dp.get_pair_dataframe(pair=coin_fiat,
timeframe=self.timeframe)
dataframe[f"{fiat}_rsi"] = ta.RSI(coin_fiat_tf, timeperiod=14)
# STAKE/FIAT (e.g. BTC/USD) - inf_timeframe
stake_fiat_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.timeframe)
stake_fiat_inf_tf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.inf_timeframe)
dataframe[f"{stake}_rsi"] = ta.RSI(stake_fiat_tf, timeperiod=14)
dataframe[f"{stake}_rmi_{self.inf_timeframe}"] = RMI(
stake_fiat_inf_tf, length=21, mom=5)
# Informative indicators for current pair on inf_timeframe
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
informative['rsi'] = ta.RSI(informative, timeperiod=14)
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
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'])
# Set up primary indicators
dataframe['rmi-slow'] = RMI(dataframe, length=20, mom=5)
dataframe['rmi-fast'] = RMI(dataframe, length=9, mom=3)
dataframe['vidya'] = VIDYA(dataframe)
macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['kama-fast'] = ta.KAMA(dataframe, timeperiod=5)
dataframe['kama-slow'] = ta.KAMA(dataframe, timeperiod=13)
# Trend Calculations
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)
dataframe['vdy-up'] = np.where(
dataframe['vidya'] >= dataframe['vidya'].shift(), 1, 0)
dataframe['vdy-dn'] = np.where(
dataframe['vidya'] <= dataframe['vidya'].shift(), 1, 0)
dataframe['vdy-up-trend'] = np.where(
dataframe['vdy-up'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
dataframe['vdy-dn-trend'] = np.where(
dataframe['vdy-dn'].rolling(3, min_periods=1).sum() >= 2, 1, 0)
# Informative for STAKE/FIAT and COIN/FIAT on default timeframe, only relevant if stake currency is BTC or ETH
if self.config['stake_currency'] in ('BTC', 'ETH'):
coin, stake = metadata['pair'].split('/')
coin_fiat = f"{coin}/{self.custom_fiat}"
stake_fiat = f"{self.config['stake_currency']}/{self.custom_fiat}"
coin_fiat_inf = self.dp.get_pair_dataframe(
pair=coin_fiat, timeframe=self.timeframe)
stake_fiat_inf = self.dp.get_pair_dataframe(
pair=stake_fiat, timeframe=self.timeframe)
dataframe[f"{self.custom_fiat}_rmi-slow"] = RMI(coin_fiat_inf,
length=20,
mom=5)
dataframe[f"{self.config['stake_currency']}_rmi-slow"] = RMI(
stake_fiat_inf, length=20, mom=5)
# Informative for current pair on inf_timeframe
informative = self.dp.get_pair_dataframe(pair=metadata['pair'],
timeframe=self.inf_timeframe)
inf_macd = ta.MACD(informative,
fastperiod=12,
slowperiod=26,
signalperiod=9)
informative['macd'] = inf_macd['macd']
informative['macdsignal'] = inf_macd['macdsignal']
informative['macdhist'] = inf_macd['macdhist']
dataframe = merge_informative_pair(dataframe,
informative,
self.timeframe,
self.inf_timeframe,
ffill=True)
return dataframe